09_Chap7(MWE9807)

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chapter07

[echo@echo echo]$nohup dir & [1] 613 [echo@echo echo]$nohup: appending output to 'nohup.out'
[1]+ Done nohup.dir [echo@echo echo]$ [echo@echo echo]$ ls #*mail*#1565tBp# Desktop Graphic KDE1.gif RMAIL bin dustbin lsecho nohup.out notmal work [echo@echo echo]$ cat nohup.out #*mail*#1565tBp# Graphic RMAIL Desktop KDE1.git bin nohup.out 7-10 RedHatLinux9基础教程(第2版)
7.3进程控制命令
监视进程运行状态在用户退出后让进程继续运行更改进程的优先级在进程有问题的时候杀死进程
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RedHatLinux9基础教程(第2版)
7.3.1监视进程
报告系统当前的进程状态:ps
# ps [选项]
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RedHatLinux9基础教程(第2版)
ps命令输出字段含义
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RedHatLinux9基础教程(第2版)
小结&习题
思考题 (1)什么是进程? (2)如何显示进程? (3)进程之间具有什么样的关系? (4)什么是多进程和多任务? 上机题 (1)查看系统运行后台进程. (2)如何启动多个进程? (3)如何调整进程的优先级?
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RedHatLinux9基础教程(第2版)
Ctrl+c:终止前台程序 kill :送一个结束进程的信号到某个当前运行的特定进程,从而结束进程

09_Chap8_1(MWE9808)

1
100 1.0 frequency (f/fo)
Pozar, Microwave Engineering, by Prof. JT Kuo, Dept. CM, NCTU, Fall 2009.
100
8.3 The Insertion Loss Method
Design methods: (1) Image Parameter Method A cascade of simple two-port filter sections to provide the desired cutoff frequencies and attenuation characteristics, but not allow the spec of a frequency response over the complete operating range. (2) Insertion Loss Method Network synthesis technique to complete the whole frequency response. Filter types: (1) Minimum insertion loss: Butterworth/binomial response (2) Sharpest cutoff: Chebyshev or elliptic response (3) Better phase response: linear-phase filter
1 M (ω 2 ) PLR = = 1+ 2 1− | Γ(ω ) | N (ω 2 )
Pozar, Microwave Engineering, by Prof. JT , Dept. CM, NCTU, Fall 2009.

海康雷达区间测速卡口方案

高清雷达测速卡口解决方案（IS-3013VR）目录第1 章概述 (1)1.1 应用背景 (1)1.2 设计原则 (1)1.3 设计依据 (4)第2 章系统总体设计 (7)2.1 设计思想 (7)2.1.1坚持两个原则 (7)2.1.2遵循三个模式 (7)2.1.3保持四个一致 (7)2.2 技术路线 (8)2.2.1卡口系统前端设备技术路线 (8)2.2.2卡口系统中心管理平台技术路线 (8)2.3 系统结构 (9)2.4 系统组成 (10)2.5 功能描述 (11)2.5.1车辆捕获功能 (11)2.5.2车辆速度检测功能 (11)2.5.3车辆图像记录功能 (11)2.5.4超速抓拍功能 (12)2.5.5智能补光功能 (12)2.5.6车辆牌照自动识别功能 (13)2.5.7车身颜色识别功能 (14)2.5.8车型判别功能 (15)2.5.9车标识别功能 (15)2.5.10车辆子品牌识别功能 (15)2.5.11未系安全带检测功能 (15)2.5.12接打电话检测功能 (15)2.5.13人脸特征抠图 (15)2.5.14打开遮阳板检测 (16)2.5.15前端备份存储功能 (16)2.5.16数据断点续传功能 (16)2.5.17图像防篡改功能 (16)2.5.18网络远程维护功能 (16)2.5.19全景高清录像功能（选配） (16)2.5.20平台功能 (17)2.6 系统性能指标 (17)第3 章前端子系统设计 (20)3.1 前端子系统组成 (20)3.1.1前端子系统组成 (20)3.1.2车辆测速单元 (21)3.1.3图像采集识别处理单元 (21)3.1.4前端数据处理及上传单元 (22)3.1.5网络传输单元 (22)3.1.6视频监控单元（选配） (22)3.2 系统现场布局 (22)3.2.1现场布局俯视图 (23)3.2.2现场布局侧视图 (23)3.3 硬件设备配置原则 (23)3.4 前端系统主要设备选型 (24)3.4.1 300万卡口抓拍单元 (24)3.4.2雷达 (26)3.4.3补光灯 (27)3.4.4终端服务器 (28)第4 章网络传输子系统设计 (30)第5 章中心存储子系统设计 (31)5.1 存储方案 (31)5.1.1存储需求 (31)5.1.2存储技术对比 (31)5.1.3存储方案选择 (33)5.2 数据存储设计 (33)5.3 图片存储设计 (34)5.4 视频存储设计（选配） (34)第6 章中心管理平台子系统设计 (36)6.1 平台概述 (36)6.1.1平台整体架构 (36)6.1.2平台功能模块 (38)6.1.3平台业务支撑 (39)6.2 运行环境要求 (40)6.2.1硬件环境 (40)6.2.2软件环境 (41)6.2.3网络环境 (42)6.3 配置推荐原则 (42)6.4 平台功能设计 (51)6.4.1平台基础应用 (51)6.4.2平台增值应用 (72)6.4.3平台新技术应用 (90)第7 章系统特点 (99)7.1 一套卡口抓拍单元覆盖2/3个车道 (99)7.2 摄像机高密度集成技术应用提升卡口前端系统稳定性 (99)7.3 车牌前端识别技术 (99)7.4 视频检测模式保障系统工作稳定性 (100)7.5 雷达测速模式保障速度的准确性 (100)7.6 系统运维成本低 (101)7.7 前端系统结构简单稳定 (101)第8 章系统拍摄效果 (102)8.1 300万雷达卡口抓拍效果 (102)8.1.1白天抓拍效果 (102)8.1.2夜间抓拍效果 (104)。

Nature of largest cluster size distribution at the percolation threshold

a r X i v :c o n d -m a t /0109229v 1 [c o n d -m a t .d i s -n n ] 13 S e p 2001Nature of largest cluster size distributionat the percolation thresholdParongama SenDepartment of Physics,University of Calcutta,92A.P.C.Road,Calcutta 700009,India.e-mail paro@cucc.ernet.inTwo distinct distribution functions P sp (m )and P ns (m )of the scaled largest cluster sizes m are obtained at the percolation threshold by numerical simulations,depending on the condition whether the lattice is actually spanned or not.With R (p c )the spanning probability,the total distribution of the largest cluster is given by P tot (m )=R (p c )P sp (m )+(1−R (p c ))P ns (m ).The three distributions apparently have similar forms in three and four dimensions while in two dimensions,P tot (m )does not follow a familiar form.By studying the ﬁrst and second cumulants of the distribution functions,the diﬀerent behaviour of P tot (m )in diﬀerent dimensions may be quantiﬁed.Much has been investigated regarding the distribution of cluster sizes as far as percolating clusters are concerned [1–3].There have been some recent studies on the largest cluster size distribution below criticality and also distri-bution of smaller clusters at the percolation threshold [4–7].The largest cluster size distributions,an example of extreme value statistics,is relevant for several physical phenomena like fracture and breakdown.One can obtain distribution functions for the spanning cluster as well as for smaller clusters though the form of the distribution,especially that of the percolating clusters is not simple.In general,it is a non-Gaussian function.Let p be the probability that a site is occupied in a lat-tice.At the percolation threshold p c ,there may or may not exist a spanning cluster.The spanning probability depends on many factors like the kind of percolation (site or bond),type of lattice,boundary conditions etc.[8].Spanning will occur with a certain probability less than one,and in the study of spanning or percolating clusters at p c ,only those cases where the lattice spans are taken into account.On the other hand,for the largest cluster size distribution,the calculations will include all the con-ﬁgurations whether the lattice spans or not.The largest cluster,in fact,enjoys a double role in the sense that it may or may not happen to be the spanning cluster.The probability of the smaller clusters being the span-ning cluster is relatively much smaller.Even when the lattice is spanned,the largest cluster may or may not be the spanning one.The event of the lattice being spanned or not cannot be predicted a-priori in random percolation.However,the consequence of the lattice being spanned or not would di-rectly be reﬂected by the nature of the distribution of the largest cluster which assumes diﬀerent roles for the two cases.We ﬁnd that the two distributions of the scaled mass or size m of the largest cluster,denoted by P sp (m )and P ns (m )for the spanning case and the non-spanning case respectively,are indeed diﬀerent.The scaled size m =M/L D ,where the mass of the largest cluster is M in a lattice of linear dimension L and D is the fractal dimension.The latter is related to the exponents of per-colation and is same for all clusters when they are ranked [9,7].The total distribution,which can be independently computed,is actually the weighted sum of the two dis-tributions:P tot (m )=R (p c )P sp (m )+(1−R (p c ))P ns (m ),(1)where R p c is the spanning probability at p c .We in-vestigate the nature of the distributions for hypercubic lattices in two,three and four dimensions,where the Hoshen-Kopelman algorithm [10]is used.Free boundary condition has been used in all dimensions,except that in two dimensions we have also used helical boundary con-ditions for comparison.In all cases,spanning has been considered from top to bottom.The largest lattice sizes considered are L =1600,L =98and L =27in two,three and four dimensions respectively with typically 106and 105conﬁgurations generated for the smallest and largest lattice sizes.We ﬁrst compare the distribution of the largest and the spanning cluster sizes in percolating two-dimensional lattices (Fig.1)and ﬁnd that they are numerically indis-tinguishable almost always except for some cases where the size of the clusters are very small.One can ignore that diﬀerence and assume that at least for large values,the spanning cluster and the largest clusters are identi-cal.However,for consistency,we will consider the largest cluster in the spanning case strictly and not the spanning cluster if they are diﬀerent.This also takes care of the fact that we will avoid the ambiguity arising due to the existence of more than one spanning cluster,which may happen in very few cases.Figures 2-4show the three distributions for two to four dimensions with free boundary conditions.For clarity,we have shown the distribution for a single lattice size in three and four dimensions,which represents the scaling distribution.Certain features are clear from the ﬁgures:the total distribution in two dimensions is quite diﬀerent in shape compared to those in three and four dimensions.The distribution when the lattice is not spanning,P ns ,is much more sharply peaked in the higher dimensions while the width of P sp ,the distribution for the spanning case,is of the same order in diﬀerent dimensions.These features will be conﬁrmed quantitatively later on.The forms of the two distributions P sp and P ns are clearlydiﬀerent and it should be noted that it is not possible to get a collapse by any trivial scale transformation.The distribution function for the percolating clusters has beenﬁt to the following forms[1–3]:a power law-exponential functionf(x)=ax b exp(−cx d),(2) or a double exponential functionf(x)=a exp(−bx−c)exp(−dx e).(3) What is important is the appearance of a number of parameters in both the functional forms and estimates from numerical results may turn messy and involve large errors.The focus of the present study is,however,not to obtain the precise form of the function but to compare the gross features of the distributions.Even without a detailed study,it is obvious that the total distribution P tot in two dimensions has a form very diﬀerent from that of the individual distributions.For three or four dimensions,although the distributions for the spanning and non-spanning cases are distinct,the total distribu-tion does not carry any signature that it was generated from these two.That the behaviour of P tot in two dimensions is diﬀer-ent becomes all the more apparent when one attempts to ﬁt the distributions to familiar forms.We do this without emphasis on the accuracy of the estimated parameters. In two dimensions as well as in three and four,the two distributions P sp and P nsﬁt quite well to the form(3) with diﬀerent values of the parameters a,b,c,d,e.For example,in three dimensions c∼2.8and e∼3.5for P sp and c∼2.2and e∼1.5for P ns.The total distribu-tion,however,is of the same form only in three and four dimensions.The values of the exponents e.g.,in three dimensions are c∼2.5and e∼1.5for P tot.Although in general the parameters are diﬀerent,we notice that among the exponents,the values of c are quite close for the three distributions and e is perhaps same for P tot and P ns.In four dimensions also the values of c are compara-ble for the three distributions:c lies between1.9and2.0 but the values of e are quite diﬀerent.In two dimensions, however,both c and e are widely diﬀerent for P ns and P sp:c∼2.5and∼1.45,e∼3.0and∼10.8for the two respectively.Since the boundary condition plays an important role in percolation problems,we also evaluate the distribu-tions with a diﬀerent boundary condition,namely,helical boundary conditions,in two dimensions.The results are shown in Fig.5.Here also,there are two distinct distri-butions P sp and P ns.The cluster sizes will obviously be larger for helical boundary condition(HBC)compared to the free boundary case(FBC).The shift in the proba-bility distributions can be explained by this but what is remarkable is that the total distribution shows a hump on the left side,showing that the form of the total distri-bution is again not conventional.The distribution,with the hump on the left side has a less pronounced form of the plateau-like region compared to the free boundary case.Such a hump has also been observed for small sizes using a renormalisation group scheme[11]and periodic boundary conditions.Hence one can conclude that in general the total distribution for the largest cluster size is not in a familiar form,the eﬀect being strongest in open boundary case.In order to understand the diﬀerence in the behaviour of the total distribution in diﬀerent dimensions we note the following points.The reason for the characteristic structure of P tot in two dimensions must be traced back to the features of the two independent distributions from which P tot is generated.P tot typically has a plateau like region(FBC)or a weak two peaked structure(HBC). This could be due to two reasons:either P sp and P ns, which are both peaked,have negligible overlap;or the width of the distributions are comparable.As the distri-butions are normalised,this would imply the heights of the peaks are comparable.The value of R(p c)should not play any part as it is not particularly diﬀerent in diﬀerent dimensions.The overlap between the distributions are not negligi-ble as one can observe from theﬁgures.In order to com-pare the behaviour in two,three and four dimensions,we evaluate the ratios r1=m(1)sp/m(1)ns and r2=m(2)sp/m(2)ns where m(r)is the r th cumulant of the distributions.The subscripts on m denote the spanning and non-spanning events as usual.Theﬁrst measure,if high,will indicate that the peaks of the distribution are far apart as r1is a measure of the mean scaled cluster size and lies close to the peak.The second ratio is roughly a ratio of the widths of the distributions which in turn is an estimate of the ratio of the height of the peaks.We notice very interesting behaviour of the two ratios deﬁned above and shown in Fig. 6.Theﬁrst ratio r1 varies between1.6and1.7with some weak dependence on dimensionality.r2on the other hand,shows strong dimensional dependence.The values of r1and r2in two dimensions with HBC and FBC indicate that both de-pend on boundary conditions.In two dimensions,r2is close to1,indicating that the peaks of the distributions lie at comparable heights.In higher dimensions,r2in-creases and diﬀers signiﬁcantly from unity.The latter may therefore be exclusively responsible for the diﬀerent behaviour of the total distributions in diﬀerent dimen-sions.The observation that there exist two separate distri-butions for the largest cluster sizes may not appear very surprising.However,it has not been noted in any previ-ous study although distribution functions for percolation is a much studied problem.Also,usually in percolation, quantities and their distributions have the same qualita-tive behaviour in dimensions below the upper critical di-mension.The spanning cluster distributions for example, in diﬀerent dimensions,could beﬁt to the same form[3]. The smaller clusters had distribution functions which had familiar form,very small clusters following a Gaussiandistribution presumably in two dimensions[7].This is perhaps theﬁrst time a novel feature is seen to be present in two dimensions and absent in higher dimensions as far as the total distribution for largest clusters are con-cerned.Even if one argues that P sp and P ns should be considered as the more fundamental distributions,rough estimates of the exponents for the distributions for these two showed that in two dimensions,they are diﬀerent by a much larger margin than in higher dimensions,indicat-ing that the two dimensional case is markedly diﬀerent regarding distributions of largest cluster sizes.This is an eﬀect independent of boundary conditions although the eﬀect may vary for diﬀerent cases.Since the largest and the spanning clusters coincide in most cases,P sp may be regarded as a previously known distribution.Hence P ns is the new distribution obtained from the study.We have made a brief comparison of P ns with the distribution of largest cluster sizes below p c,as in both cases the lattice does not span.The cu-mulative distribution Q cum(x)of the distribution Q(m l) of the largest cluster size m l below p c is well-known and has the form[4–6]Q cum(x)=Q(m l<x)∼exp(−exp(−λ1x+λ2))(4) such that ln(−ln(Q cum(x))is a straightline when plot-ted versus x.We recover this behaviour butﬁnd that for P ns,the behaviour of the corresponding cumulative distribution is much more complicated.Hence one can conclude that P ns is a completely independent distribu-tion and diﬀerent from any previously known distribution in percolation.We make one last remark:the existence of two distinct distributions at p c is not a unique feature of the largest cluster only,it is true for any cluster which does not span the lattice.But the total distribution will behave diﬀer-ently depending on the rank of the cluster.I thank Martin Bazant for the numerous discussions which inspired the present work and also for sending un-published results.I am also grateful to Dietrich Stauﬀer and an anonymous referee for comments.The computa-tions were done on a Origin200at the Calcutta Univer-sity Computer Center.FIG.1.The comparison of the distributions for the largest cluster and the spanning cluster sizes are shown in two dimensions for L=600with open boundary conditions.FIG.2.The three distributions P sp,P ns and P tot for the scaled mass in two dimensions are shown for two diﬀerent system sizes L=400(represented by×,△,and⋆respectively)and L=1000.The bestﬁt lines of the form(3)are also shown for P sp and P ns.FIG.3.The three distributions P sp,P ns and P tot for the scaled mass in three dimensions are shown for L=60along with the bestﬁt lines of the form(3).FIG.4.The three distributions P sp,P ns and P tot for the scaled mass in four dimensions are shown for L=21alongwith the bestﬁt lines of the form(3).FIG.5.The three distributions P sp,P ns and P tot for the scaled mass in two dimensions are shown for L=600with helical boundary conditions.FIG.6.The ratios r1and r2deﬁned in the text are shown in two,three and four dimensions(FBC and HBC indicate free and helical boundary conditions respectively).。

UTRANCELL集团导入

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Summary

NASA Technical Memorandum4435 Hypersonic Lateral and Directional Stability Characteristics of Aeroassist Flight Experiment Conﬁguration in Air and CF4John R.Micol and William L.WellsMAY1993NASA Technical Memorandum4435 Hypersonic Lateral and Directional Stability Characteristics of Aeroassist Flight Experiment Conﬁguration in Air and CF4John R.Micol and William L.WellsLangley Research CenterHampton,VirginiaSummaryThe proposed Aeroassist Flight Experiment (AFE)utilized a14-ft-diameter raked and blunted elliptical cone to demonstrate the ight character-istics of space transfer vehicles(STV's).The AFE was to be carried to orbit by and launched from the Space Shuttle orbiter,where instrumentation for 10on-board experiments would have obtained aero-dynamic and aerothermodynamic data for velocities near32000ft/sec at altitudes above245000ft.A pre ight ground-based test program was initiated to assess the aerodynamic and aerothermodynamic characteristics of the baseline concept and to pro-vide benchmark data for calibration of computational uid dynamics codes to be used in ight predictions. The data reported herein are results from one phase of this ground-based study.Static lateral and di-rectional stability characteristics were obtained for the AFE con guration at angles of attack from010 to10 .Tests were conducted in air at Mach num-bers of6and10and in tetra uoromethane(CF4) at Mach6to examine the e ects of Mach number, Reynolds number,and normal-shock density ratio.Changes in Mach number from6to10in air or in Reynolds number by a factor of4at Mach6had a negligible e ect on the lateral and directional sta-bility characteristics of the baseline AFE con gura-tion.Variations in density ratio across the normal portion of the bow shock from approximately5(air) to12(CF4)had a measurable e ect on lateral and di-rectional aerodynamic coe cients,but no signi cant e ect on lateral and directional stability character-istics.The tests in air and CF4indicated that the con guration was laterally and directionally stable through the test range of angle of attack.Unfortunately,the AFE program was cancelled in late1991.The realization of an AFE ight in the future is possible but uncertain.Thus,this paper documents the lateral and directional aerodynamic characteristics of the baseline AFE vehicle for use in the design of future aeroassist space transfer vehicles. IntroductionAmong the space transportation systems pro-posed for the future are space transfer vehicles (STV's),which are designed to ferry cargo between higher Earth orbits(for example,geosynchronous and lunar orbits)and lower Earth orbit where the Space Shuttle and Space Station Freedom will op-erate.(This class of vehicle was formerly referred to as orbital transfer vehicles or OTV's.)Upon re-turn of the vehicle from high Earth orbit,its velocity must be greatly reduced to attain a nearly circular low Earth orbit.This decrease in velocity can be achieved either by using retrorockets or by guiding the vehicle through a portion of the atmosphere and allowing aerodynamic drag forces to slow the vehi-cle.Studies have shown that lower propellant loads would be required for the aeroassist method(ref.1); thus,payloads could be increased.Future STV's that will be designed to use Earth atmosphere for deceleration are generally referred to as aeroassisted space transfer vehicles or ASTV's (formerly AOTV's).These vehicles will have high drag and a relatively low lift-to-drag ratio and will y at very high altitudes and velocities throughout the atmospheric portion of the trajectory.Before the actual ight vehicle can be designed with optimal aerodynamic and aerothermodynamic characteris-tics,additional information about very high-altitude, high-velocity ight is required.To obtain such in-formation,a subscale ight was proposed whereby a14-ft-diameter ASTV con guration with10on-board experiments would be launched from the Space Shuttle and accelerated back into the atmosphere with a rocket.This Aeroassist Flight Experiment (AFE)would make a sweep through the atmosphere to an altitude of about245000ft with a velocity of nearly32000ft/sec to gain aerodynamic and aero-thermal information and return to low Earth orbit for retrieval by the Space Shuttle.The on-board in-strumentation would measure and record the aero-dynamic characteristics and aerothermodynamic en-vironment of this entry trajectory,and the data would be used to validate computational uid dy-namics(CFD)computer codes and ground-to- ight extrapolation of experimental data for use in future ASTV designs.This ight experiment was proposed because the high-velocity,low-density ow environ-ment cannot be duplicated or simulated in present test facilities,nor can it be predicted with certainty by existing techniques.Naturally,the AFE would require an extensive aerodynamic and aerothermodynamic experimental and computational data base for its design and suc-cessful ight.Present test facilities,in conjunction with the best CFD codes,would provide this infor-mation.For this reason,a pre ight test program in ground-based hypersonic facilities(ref.2)was initiated to develop the required aerodynamic and aerothermodynamic data base.This data base will be used to perform the rst phase of CFD computer code calibration.The experimental results presented herein are part of an extensive ground-based test program performed at the Langley Research Center. Previous results are presented in references3{6.The details of the rationale for the ight experiment areoutlined in reference7,and the set of experiments to be performed is described in reference8.A primary concern for the AFE vehicle is the aerothermal heating on the fore-and aftbody thermal protection system(TPS).Because of these aerother-mal concerns,low values of sideslip angles are desir-able to minimize heating to the aftbody or payload and to prevent large thermal uctuations on the heat shield.Thus,an accurate knowledge of the lateral and directional stability characteristics of the AFE is required.(Lateral and directional stability require-ments for a low lift-to-drag aeromaneuvering vehicle are discussed in ref.9.)CFD codes are not generally used to provide aero-dynamic information for vehicles at sideslip angles. Computed lateral and directional stability charac-teristics for the AFE would require calculations of the entire body at various sideslip angles,thus in-creasing computational time,complexity,and cost. Hence,determination of these stability characteris-tics for the ight vehicle must rely on experimental data obtained in ground-based facilities.This paper addresses the e ects of Mach number, Reynolds number,and normal-shock density ratio(a \real gas"simulation parameter)on lateral and direc-tional aerodynamic characteristics measured on the baseline AFE con guration.Tests were conducted at Mach6and10in air and at Mach6in tetra- uoromethane(CF4)through a range of angle of at-tack and sideslip.During the continuum- ow portion of the ight, the AFE vehicle is expected to undergo normal-shock density ratios of about18,whereas conventional hy-personic wind tunnels that use air or nitrogen as the test gas only produce ratios of5to7.In ight,this large density ratio results from dissociation of air as it passes into the high-temperature shock layer.This real-gas e ect may have a signi cant impact on shock detachment distance,distributions of heating and pressure,and aerodynamic characteristics(ref.10).For blunt bodies at hypersonic speeds,the pri-mary factor that governs the shock stand-o distance and inviscid forebody ow is the normal-shock den-sity ratio.(See ref.10.)Certain aspects of a real gas can be simulated by the selection of a test gas that has a low ratio of speci c heats and provides large values of density ratio.These conditions can be obtained in the Langley Hypersonic CF4Tun-nel,which provides a simulation of this phenomenon by producing a density ratio of about12across the shock.This tunnel,in conjunction with the Lang-ley20-Inch Mach6Tunnel,provides the capability to test a given model at the same free-stream Mach number and Reynolds number,but at two values of density ratio(5.25in air and12.0in CF4).Thus, data for code calibration are provided that include the e ects of normal-shock density ratio.Tests were performed in air at Mach10and through a range of Reynolds numbers at Mach6to verify that aerody-namic characteristics were independent of signi cant changes in Mach numbers and Reynolds numbers for the blunt AFE con guration in hypersonic contin-uum ow.However,the AFE program cancellation ended the research e orts on this con guration.Thus, this paper documents the lateral and directional characteristics of the baseline AFE vehicle for use in the design of future aeroassist space transfer vehicles. SymbolsC l rolling-moment coe cient,Rolling momentq1dSC l=1C l=1 ;per degC n yawing-moment coe cient,Yawing momentq1dSC n =1C n=1 ,per degC y side-force coe cient,Side forceq1SC y=1C y=1 ,per degd model length in symmetry plane,in.M Mach numberp pressure,psiaq dynamic pressure,psiaRe1unit free-stream Reynoldsnumber,ft01Re2;d postshock Reynolds numberbased on dS reference area,model base area,in2(10.604in2when d=3.67in.and4.936in2when d=2.50in.)T temperature, RU velocity,ft/secX moment transfer distance in axialdirection( g.4),in.(1.673in.when d=3.67in.and1.559in.when d=2.50in.)x;y;z axial,lateral,and vertical coordi-nates for AFE( g.4)2Z moment transfer distance innormal direction( g.4),in.(0.129in.when d=3.67in.and0.0979in.when d=2.50in.)angle of attack,degangle of sideslip,degratio of speci c heats of the testgasdensity of the test gas,lbm/in3 Subscripts:t total conditions1free-stream conditions2conditions behind the normalshockAFE Con gurationThe AFE ight vehicle would consist of a14-ft-diameter drag brake,an instrument carrier at the base,a solid-rocket propulsion motor,and small control motors.A sketch of the vehicle is shown in gure1.The drag brake( g.2),which is the forebody con guration,is derived from a blunted 60 half-angle elliptical cone that is raked at73 to the cone centerline to produce a circular raked plane.A skirt with an arc radius equal to one-tenth the rake-plane diameter and with an arc length corresponding to60 has been attached to the rake plane to reduce aerodynamic heating around the base periphery.The blunt nose is an ellipsoid with an ellipticity equal to2.0in the symmetry plane.The ellipsoid nose and the skirt are at a tangent at their respective intersections to the elliptical cone surface.A detailed description of the forebody analytical shape is presented in reference11.Apparatus and TestsFacilitiesLangley31-Inch Mach10Tunnel.The Langley31-Inch Mach10Tunnel(formerly the Lang-ley Continuous Flow Hypersonic Tunnel)expands dry air through a three-dimensional contoured nozzle to a31-in-square test section to achieve a nominal Mach number of10.The air is heated to approxi-mately1850 R by an electrical resistance heater,and the maximum reservoir pressure is approximately 1500psia.The tunnel operates in the blowdown mode with run times of approximately60sec.Force and moment data can be obtained through a range of angle of attack or sideslip during one run by uti-lization of the pitch-pause capability of the model support system.This tunnel is described in more detail in reference12.Langley20-Inch Mach6Tunnel.The20-Inch Mach6Tunnel is a blowdown wind tunnel that uses dry air as the test gas.The air may be heated to a maximum temperature of approximately1100 R by an electrical resistance heater;the maximum reser-voir pressure is525psia.A xed-geometry,two-dimensional,contoured nozzle with parallel side walls expands the ow to a Mach number of6at the20-in-square test section.The model injection mechanism allows changes in angle of attack and sideslip during a run.Run durations are usually60to120sec,al-though longer times can be attained by connection to auxiliary vacuum storage.A description of this facility and the calibration results are presented in reference13.Langley20-Inch Mach6CF4Tunnel.The 20-Inch Mach6CF4Tunnel is a blowdown wind tunnel that uses CF4as the test gas.The CF4 can be heated to a maximum temperature of1530 R by two molten lead bath heat exchangers connected in parallel.The maximum pressure in the tunnel reservoir is2600psia.Flow is expanded through an axisymmetric,contoured nozzle designed to generate a Mach number of6at the20-in-diameter exit.This facility has an open-jet test section.Run duration can be as long as30sec,but10sec is su cient for most tests because the model injection system is not presently capable of changing angle of attack or sideslip during a run.A detailed description of the20-Inch Mach6CF4tunnel is presented in reference14.Just before the present test series,the tunnel was modi ed extensively.Included in those modi cations were a new nozzle,a new test section and model in-jection system,a new di user,and improvements in wiring of the controls and of the data acquisition system.The new nozzle was designed to improve ow quality along the centerline and to more closely match the Mach number in the Mach6air tunnel that is often used to produce data for comparison with the CF4data.Calibration results(ref.15)that were obtained after the new nozzle was installed indi-cate greatly improved ow uniformity near the nozzle centerline.For the present test series,the model was tested on the tunnel centerline.Previously,models were tested o centerline to avoid ow disturbances. (See ref.14.)3ModelsTwo aerodynamic models were fabricated and tested.The models were identical except for size;the base heights(d in g.2)at the symmetry plane were 3.67in.(2.2percent scale)as shown in gure3(a)and 2.50in.(1.5percent scale)as shown in gure3(b). The3.67-in-diameter model is made in three parts| a stainless steel forebody(aerobrake),an aluminum aftbody(instrument carrier and propulsion motor), and a stainless steel balance holder.The2.50-in-diameter model,shown mounted in the Langley 20-Inch Mach6CF4Tunnel in gure3(c),is fabri-cated of aluminum and does not include the circu-lar or hexagonally shaped aftbody and the simulated propulsion motor of previous models that were tested (ref.16).A cylinder protrudes from the base to ac-cept the balance.The acute angle between the bal-ance and cylinder axis and the base in the symmetry plane is73 .The2.50-in-diameter model was fabri-cated to provide an air gap between the end of the balance and the end of the cavity in the forebody; its purpose was to reduce conductive heating.For both models,shrouds were built to shield the bal-ance from base- ow closure.The shrouds attach to the sting,and clearance was provided to avoid in-terference with the balance during model movement when forces and moments were applied.The fore-bodies were machined to the design size and shape within a tolerance of60.003in.Angle of attack(see g.2)and sideslip(see g.4)in this paper are refer-enced to the axis of the original elliptical cone.InstrumentationAerodynamic force and moment data were mea-sured with sting-supported,six-component,water-cooled,internal strain gauge balances.Two ther-mocouples were installed in the water jacket that surrounds the measuring elements to monitor inter-nal balance temperatures.The load rating for each component of the two balances(one for each model size)is presented in table I.The calibration accuracy is0.5percent of the maximum load rating for each component.Test ConditionsThe tests were conducted at nominal free-stream Mach numbers of6and10in air and at Mach6 in CF4.(Nominal test conditions are presented in table II.)The angles of attack for Mach6in air were 0 and65 with nominal sideslip angles of0 ,02 , and04 .Tests at Mach6in CF4were at angles of attack of0 ,65 ,and610 with nominal sideslip angles of0 ,62.5 ,and65 ;at Mach10(except for =02:5 ,where only a negative sweep was performed),the angles of attack were0 ,62.5 ,65 , and610 with nominal sideslip angles of0 ,62 ,and 64 .Test ProceduresBlunt models are conducive to heat conduction through the forebody face during a run,which gener-ally produces a gradual increase in temperature gra-dients along the balance even though the balance is water cooled.Because temperature gradients were not accounted for in the laboratory calibration of the balance,e orts were made to minimize these gradi-ents by limiting the test times.In the20-Inch Mach6 CF4Tunnel,the model was mounted at the desired angle of attack and sideslip before the run.After the test-stream ow was established,the model was in-jected to the test-stream centerline.Data were gath-ered for approximately5sec,then the model was re-tracted.In the air tunnels,the model was mounted at = =0 before the run.After test-stream ow was established,the model was injected to the stream centerline,then pitched to the next angle of attack(or sideslip angle)by the pitch-pause mech-anism.Data were taken while the model was sta-tionary at each position.The balance thermocouples were monitored during each run to assure that the temperature gradient within the balance remained within an acceptable limit.Typical run times for a set of and sweeps in the air facilities were about 15sec.Data Reduction and UncertaintyEach of the three test facilities has a dedicated stand-alone data system.Output signals from the balances were sampled and digitized by an analog-to-digital converter,then stored and processed by a computer.The analog signals were sampled at a rate of50per second in the Mach6CF4and Mach10air tunnels and at20per second in the Mach6air tunnel.A single value of data reported herein represents an average of values measured for 2sec in the Mach6CF4and Mach6air tunnels and for0.5sec in the Mach10air tunnel.Corrections were made for model tare weights at each angle of attack and for interactions between di erent elements of the balances.Corrections were not made for base pressures.Balance-related calculated uncertainties in the measured static aerodynamic coe cients are given in table III.These uncertainties are based on balance output signals related to forces and moments by a laboratory calibration that is accurate to60.5per-cent of the rated load for each component.(See ta-ble I.)For the AFE,the moment reference center is4located at the center of the rake plane.(See g.4.) Thus,moments reduced about the model rake-plane center and reported herein have greater uncertainties than those measured at the balance moment center. The yawing and rolling moments at the balance have an uncertainty of only60.5percent of the rated load, whereas the moment at the rake-plane center also in-cludes uncertainties associated with the forces in the transfer equation.The transfer equation isYawing moment RP=Yawing moment B0(X)(Side force)andRolling moment RP=Rolling moment B0(Z)(Side force)where the subscripts RP and B denote the rake-plane center and the balance moment center,respectively. The transfer distances X and Z are de ned in g-ure4.In coe cient form,the uncertainty1related to the balance calibration for the side force is1C y=6(0:005)(Force rating)q1SThe uncertainty for the yawing moment is1C n;B=6(0:005)(Moment rating)q1dSand an identical equation applies for the rolling mo-ment.These balance uncertainties are su cient for measurements at the balance moment center.How-ever,at the rake-plane center,the yawing-moment uncertainty is1C n;RP=62401C n;B12+1C y X!2350:5and the rolling-moment uncertainty is1C l;RP=62401C l;B12+1C y Zd!2350:5Note that all the terms include the free-stream dy-namic pressure in the denominator so that the un-certainties are less at test conditions where q1is large|that is,at a higher Reynolds number rather than at a lower Reynolds number.The uncertainty in dynamic pressure is63percent.The ow condi-tions for which the present uncertainties have been calculated are presented in table II.Results and DiscussionsThe aerodynamic data from the Mach10air tests are tabulated in table IV.The Mach6results are presented in tables V and VI for air and in table VII for CF4.The test Reynolds number and model diameter are indicated in each table title.The aerodynamic coe cients C y,C n,and C l are plotted for an angle-of-sideslip range at various an-gles of attack in each facility and presented in g-ures5{7for Mach10in air,Mach6in air,and Mach6 in CF4,respectively.Data obtained at Mach6in air( g.6)indicated no e ect of Reynolds number on measured lateral and directional coe cients for a factor-of-4increase in postshock Reynolds num-ber.(Similar trends with respect to Reynolds num-ber were also observed for AFE longitudinal aero-dynamic characteristics presented in ref.16in which a negligible e ect of Reynolds number was noted for Mach6and10in air and at Mach6in CF4.) Therefore,the assumption is made that the e ect of Reynolds number on measured lateral and direc-tional data at Mach10in air and Mach6in CF4 is also negligible.The data are amenable to linear curve ts as shown in gures5{7,for which the ordi-nate scale is quite sensitive.These curves would be expected to go through the origin because the model was symmetrical about the pitch plane.However,as observed in gures5{7,an o set exists.This o set may be attributed to model misalignment or to any small stray signal in the data system that could cause a constant data o set because of the very small val-ues being measured relative to the load range of the balance.For example,if a slight misalignment of the model in the roll direction were introduced during model setup or if the balance location within the model were slightly misaligned,thereby producing a small o set in the center of gravity location(that is,within a few thousandths of an inch)in the side plane(y di-rection in g.4),then the e ect of the large axial-force component on this small moment arm may pro-duce a continuous bias in the measured quantities. For instance,from reference16at = =0 , Re1=0:462106/ft,and Mach6in CF4,the axial-force coe cient is1.382.The yawing-moment coe -cient,from table VII for similar conditions,is0.004. In much the same way as the change in the cen-ter of pressure in longitudinal aerodynamics is lo-cated,forming the ratio of yawing-moment coe -cient to axial-force coe cient yields the moment arm in the y direction,which for this case is approxi-mately0.003in.and thus within acceptable fabri-cation tolerances.A second linear curve,parallel to the data-faired curve,is drawn through the origin in5each part of gures 5{7.Values from measurements and the curve through the origin of gures 5{7are presented in tables IV{e of the slopes of these parallel curves through the origin to represent the lateral and directional stability derivatives should be valid because the data curves are linear through the test sideslip range.The lateral and directional stability derivatives are presented in gure 8and table VIII through the range of angle of attack for which tests were per-formed in each facility.For all test conditions,the con guration was laterally and directionally stable,as indicated by the positive values of C n and nega-tive values of C l .A comparison of lateral and direc-tional stability derivatives obtained at Mach num-bers of 6and 10in air illustrates no signi cant e ect of Mach number on stability characteristics ;a comparison of these stability derivatives with those obtained at Mach 6in CF 4indicates a small but measurable e ect of normal-shock density ratio on lateral and directional stability characteristics.Al-though the numerical values for air and CF 4are not greatly di erent,the data trends in air and CF 4ap-pear to be opposite.(Similar trends were observed in the longitudinal aerodynamic characteristics dis-cussed in ref.16.)This trend is most obvious for C l ,wherein the small numerical values require an expanded scale on the graph.The wind tunnel re-sults in CF 4are believed to be a better simulation of ight data than those in air because the shock de-tachment distance for CF 4is closer to the distance predicted for the actual ight case.(For example,see refs.6and 16.)Concluding RemarksStatic lateral and directional stability character-istics were obtained for the Aeroassist Flight Exper-iment (AFE)con guration through a range of angle of attack from 010 to 10 .Tests were conducted on two di erent-sized models at Mach numbers of 6and 10in air and at a Mach number of 6in tetra- uoromethane (CF 4).The e ects of Mach number,Reynolds number,and normal-shock density ratio on lateral and directional stability characteristics were examined.Changes in Mach number from 6to 10in air or in Reynolds number by a factor of 4at Mach 6had a negligible e ect on the lateral and directional sta-bility characteristics of the baseline AFE con gura-tion.Variations in density ratio across the normal portion of the bow shock from approximately 5(air)to 12(CF 4)had a measurable e ect on lateral and directional aerodynamic coe cients,but no signi -cant e ect on lateral and directional stability char-acteristics.The tests in air and CF 4indicated that the con guration is laterally and directionally stable through the test range of angle of attack as indicated by the positive values of C n and negative values of C l (positive e ective dihedral).In late 1991,the AFE program was cancelled and thus ended research e orts on this con guration.The realization of an AFE ight in the future is possible but uncertain.Hence,this paper documents the lateral and directional aerodynamic characteristics of the baseline AFE vehicle for use in the design of future aeroassist space transfer vehicles.NASA Langley Research Center Hampton,VA 23681-0001March 25,1993References1.Walberg,Gerald D.:A Review of Aeroassisted Orbit Transfer.AIAA-82-1378,Aug.1982.2.Wells,William L.:Wind-Tunnel Pre ight Test Program for Aeroassist Flight Experiment.Technical Papers|AIAA Atmospheric Flight Mechanics Conference ,Aug.1987,pp.151{163.(Available as AIAA-87-2367.)3.Wells,William L.:Free-Shear-Layer Turning Angle in Wake of Aeroassist Flight Experiment (AFE)Vehicle at Incidence in M =10Air and M =6CF4.NASA TM-100479,1988.4.Micol,John R.:Experimentaland Predicted Pressure and Heating Distributions for Aeroassist Flight Experiment Vehicle.J.Thermophys.&Heat Transf.,July{Sept.1991,pp.301{307.5.Wells,WilliamL.:SurfaceFlow and HeatingDistributions on a Cylinder in Near Wake of Aeroassist Flight Experi-ment (AFE)Con guration at Incidence in Mach 10Air.NASA TP-2954,1990.6.Micol,John R.:Simulation of Real-Gas E ects on Pres-sure Distributions for Aeroassist Flight Experiment Vehi-cle and Comparison With Prediction.NASA TP-3157,1992.7.Jones,Jim J.:The Rationale for an Aeroassist Flight Experiment.AIAA-87-1508,June 1987.8.Walberg,G.D.;Siemers,P.M.,III;Calloway,R.L.;and Jones,J.J.:The Aeroassist Flight Experiment.IAF Paper 87-197,Oct.1987.9.Gamble,Joe D.;Spratlin,Kenneth M.;and Skalecki,Lisa M.:Lateral Directional Requirements for a Low L/D Aeromaneuvering Orbital Transfer Vehicle.A Collection of Technical Papers|AIAA Atmospheric Flight Mechan-ics Conference,Aug.1984,pp.402{413.(Available as AIAA-84-2123.)610.Jones,Robert A.;and Hunt,James L.(appendix Aby James L.Hunt,Kathryn A.Smith,and Robert B.Reynolds and appendix B by James L.Hunt and Lillian R.Boney):Use of Tetra uoromethane To Simulate Real-Gas E ects on the Hypersonic Aero dynamics of Blunt Vehicles.NASA TR R-312,1969.11.Cheatwood,F.McNeil;DeJarnette,Fred R.;and Hamil-ton,H.Harris,II:Geometrical Description for a Pro-posed AeroassistedFlight ExperimentVehicle.NASA TM-87714,1986.ler, C.G.:Langley Hypersonic Aerodynamic/Aerothermodynamic Testing Capabilities|Present and Future.AIAA-90-1376,ler,Charles G.,III;and Gno o,Peter A.:PressureDistributions and Shock Shapes for12.84 /7 On-Axis and Bent-Nose Biconics in Air at Mach6.NASA TM-83222,1981.14.Midden,Raymond E.;and Miller,Charles G.,III:De-scription and Calibration of the Langley Hypersonic CF4 Tunnel|A Facility for Simulating Low Flow as Occurs for a Real Gas.NASA TP-2384,1985.15.Micol,John R.;Midden,Raymond E.;and Miller,CharlesG.,III:Langley20-Inch Hypersonic CF4Tunnel:A Facil-ity for Simulating Real-Gas E ects.AIAA-92-3939,July 1992.16.Wells,William L.:Measured and Predicted AerodynamicCoe cients and Shock Shapes for AeroassistFlight Exper-iment(AFE)Con guration.NASA TP-2956,1990.7。

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________________________________________________ Session S3C MINORITY ENGINEERING PROGRAM C

________________________________________________ 1Joseph E. Urban, Arizona State University, Department of Computer Science and Engineering, P.O. Box 875406, Tempe, Arizona, 85287-5406, joseph.urban@ 2Maria A. Reyes, Arizona State University, College of Engineering and Applied Sciences, Po Box 874521, Tempe, Arizona 852189-955, maria@ 3Mary R. Anderson-Rowland, Arizona State University, College of Engineering and Applied Sciences, P.O. Box 875506, Tempe, Arizona 85287-5506, mary.Anderson@MINORITY ENGINEERING PROGRAM COMPUTER BASICS WITH AVISIONJoseph E. Urban 1, Maria A. Reyes 2, and Mary R. Anderson-Rowland 3Abstract - Basic computer skills are necessary for success in an undergraduate engineering degree program. Students who lack basic computer skills are immediately at risk when entering the university campus. This paper describes a one semester, one unit course that provided basic computer skills to minority engineering students during the Fall semester of 2001. Computer applications and software development were the primary topics covered in the course that are discussed in this paper. In addition, there is a description of the manner in which the course was conducted. The paper concludes with an evaluation of the effort and future directions.Index Terms - Minority, Freshmen, Computer SkillsI NTRODUCTIONEntering engineering freshmen are assumed to have basic computer skills. These skills include, at a minimum, word processing, sending and receiving emails, using spreadsheets, and accessing and searching the Internet. Some entering freshmen, however, have had little or no experience with computers. Their home did not have a computer and access to a computer at their school may have been very limited. Many of these students are underrepresented minority students. This situation provided the basis for the development of a unique course for minority engineering students. The pilot course described here represents a work in progress that helped enough of the students that there is a basis to continue to improve the course.It is well known that, in general, enrollment, retention, and graduation rates for underrepresented minority engineering students are lower than for others in engineering, computer science, and construction management. For this reason the Office of Minority Engineering Programs (OMEP, which includes the Minority Engineering Program (MEP) and the outreach program Mathematics, Engineering, Science Achievement (MESA)) in the College of Engineering and Applied Sciences (CEAS) at Arizona State University (ASU) was reestablished in 1993to increase the enrollment, retention, and graduation of these underrepresented minority students. Undergraduate underrepresented minority enrollment has increased from 400 students in Fall 1992 to 752 students in Fall 2001 [1]. Retention has also increased during this time, largely due to a highly successful Minority Engineering Bridge Program conducted for two weeks during the summer before matriculation to the college [2] - [4]. These Bridge students were further supported with a two-unit Academic Success class during their first semester. This class included study skills, time management, and concept building for their mathematics class [5]. The underrepresented minority students in the CEAS were also supported through student chapters of the American Indian Science and Engineering Society (AISES), the National Society of Black Engineers (NSBE), and the Society of Hispanic Professional Engineers (SHPE). The students received additional support from a model collaboration within the minority engineering student societies (CEMS) and later expanded to CEMS/SWE with the addition of the student chapter of the Society of Women Engineers (SWE) [6]. However, one problem still persisted: many of these same students found that they were lacking in the basic computer skills expected of them in the Introduction to Engineering course, as well as introductory computer science courses.Therefore, during the Fall 2001 Semester an MEP Computer Basics pilot course was offered. Nineteen underrepresented students took this one-unit course conducted weekly. Most of the students were also in the two-unit Academic Success class. The students, taught by a Computer Science professor, learned computer basics, including the sending and receiving of email, word processing, spreadsheets, sending files, algorithm development, design reviews, group communication, and web page development. The students were also given a vision of advanced computer science courses and engineering and of computing careers.An evaluation of the course was conducted through a short evaluation done by each of five teams at the end of each class, as well as the end of semester student evaluations of the course and the instructor. This paper describes theclass, the students, the course activities, and an assessment of the short-term overall success of the effort.M INORITY E NGINEERING P ROGRAMSThe OMEP works actively to recruit, to retain, and to graduate historically underrepresented students in the college. This is done through targeted programs in the K-12 system and at the university level [7], [8]. The retention aspects of the program are delivered through the Minority Engineering Program (MEP), which has a dedicated program coordinator. Although the focus of the retention initiatives is centered on the disciplines in engineering, the MEP works with retention initiatives and programs campus wide.The student’s efforts to work across disciplines and collaborate with other culturally based organizations give them the opportunity to work with their peers. At ASU the result was the creation of culturally based coalitions. Some of these coalitions include the American Indian Council, El Concilio – a coalition of Hispanic student organizations, and the Black & African Coalition. The students’ efforts are significant because they are mirrored at the program/staff level. As a result, significant collaboration of programs that serve minority students occurs bringing continuity to the students.It is through a collaboration effort that the MEP works closely with other campus programs that serve minority students such as: Math/Science Honors Program, Hispanic Mother/Daughter Program, Native American Achievement Program, Phoenix Union High School District Partnership Program, and the American Indian Institute. In particular, the MEP office had a focus on the retention and success of the Native American students in the College. This was due in large part to the outreach efforts of the OMEP, which are channeled through the MESA Program. The ASU MESA Program works very closely with constituents on the Navajo Nation and the San Carlos Apache Indian Reservation. It was through the MESA Program and working with the other campus support programs that the CEAS began investigating the success of the Native American students in the College. It was a discovery process that was not very positive. Through a cohort investigation that was initiated by the Associate Dean of Student Affairs, it was found that the retention rate of the Native American students in the CEAS was significantly lower than the rate of other minority populations within the College.In the spring of 2000, the OMEP and the CEAS Associate Dean of Student Affairs called a meeting with other Native American support programs from across the campus. In attendance were representatives from the American Indian Institute, the Native American Achievement Program, the Math/Science Honors Program, the Assistant Dean of Student Life, who works with the student coalitions, and the Counselor to the ASU President on American Indian Affairs, Peterson Zah. It was throughthis dialogue that many issues surrounding student success and retention were discussed. Although the issues andconcerns of each participant were very serious, the positiveeffect of the collaboration should be mentioned and noted. One of the many issues discussed was a general reality that ahigh number of Native American students were c oming to the university with minimal exposure to technology. Even through the efforts in the MESA program to expose studentsto technology and related careers, in most cases the schoolsin their local areas either lacked connectivity or basic hardware. In other cases, where students had availability to technology, they lacked teachers with the skills to help them in their endeavors to learn about it. Some students were entering the university with the intention to purse degrees in the Science, Technology, Engineering, and Mathematics (STEM) areas, but were ill prepared in the skills to utilize technology as a tool. This was particularly disturbing in the areas of Computer Science and Computer Systems Engineering where the basic entry-level course expected students to have a general knowledge of computers and applications. The result was evident in the cohort study. Students were failing the entry-level courses of CSE 100 (Principals of Programming with C++) or CSE 110 (Principals of Programming with Java) and CSE 200 (Concepts of Computer Science) that has the equivalent of CSE 100 or CSE 110 as a prerequisite. The students were also reporting difficulty with ECE 100, (Introduction to Engineering Design) due to a lack of assumed computer skills. During the discussion, it became evident that assistance in the area of technology skill development would be of significance to some students in CEAS.The MEP had been offering a seminar course inAcademic Success – ASE 194. This two-credit coursecovered topics in study skills, personal development, academic culture issues and professional development. The course was targeted to historically underrepresented minority students who were in the CEAS [3]. It was proposed by the MEP and the Associate Dean of Student Affairs to add a one-credit option to the ASE 194 course that would focus entirely on preparing students in the use of technology.A C OMPUTERB ASICSC OURSEThe course, ASE 194 – MEP Computer Basics, was offered during the Fall 2001 semester as a one-unit class that met on Friday afternoons from 3:40 pm to 4:30 pm. The course was originally intended for entering computer science students who had little or no background using computer applications or developing computer programs. However, enrollment was open to non-computer science students who subsequently took advantage of the opportunity. The course was offered in a computer-mediated classroom, which meantthat lectures, in- class activities, and examinations could all be administered on comp uters.During course development prior to the start of the semester, the faculty member did some analysis of existing courses at other universities that are used by students to assimilate computing technology. In addition, he did a review of the comp uter applications that were expected of the students in the courses found in most freshman engineering programs.The weekly class meetings consisted of lectures, group quizzes, accessing computer applications, and group activities. The lectures covered hardware, software, and system topics with an emphasis on software development [9]. The primary goals of the course were twofold. Firstly, the students needed to achieve a familiarity with using the computer applications that would be expected in the freshman engineering courses. Secondly, the students were to get a vision of the type of activities that would be expected during the upper division courses in computer science and computer systems engineering and later in the computer industry.Initially, there were twenty-two students in the course, which consisted of sixteen freshmen, five sophomores, and one junior. One student, a nursing freshman, withdrew early on and never attended the course. Of the remaining twenty-one students, there were seven students who had no degree program preference; of which six students now are declared in engineering degree programs and the seventh student remains undecided. The degree programs of the twenty-one students after completion of the course are ten in the computing degree programs with four in computer science and six in computer systems engineering. The remaining nine students includes one student in social work, one student is not decided, and the rest are widely distributed over the College with two students in the civil engineering program and one student each in bioengineering, electrical engineering, industrial engineering, material science & engineering, and mechanical engineering.These student degree program demographics presented a challenge to maintain interest for the non-computing degree program students when covering the software development topics. Conversely, the computer science and computer systems engineering students needed motivation when covering applications. This balance was maintained for the most part by developing an understanding that each could help the other in the long run by working together.The computer applications covered during the semester included e-mail, word processing, web searching, and spreadsheets. The original plan included the use of databases, but that was not possible due to the time limitation of one hour per week. The software development aspects included discussion of software requirements through specification, design, coding, and testing. The emphasis was on algorithm development and design review. The course grade was composed of twenty-five percent each for homework, class participation, midterm examination, and final examination. An example of a homework assignment involved searching the web in a manner that was more complex than a simple search. In order to submit the assignment, each student just had to send an email message to the faculty member with the information requested below. The email message must be sent from a student email address so that a reply can be sent by email. Included in the body of the email message was to be an answer for each item below and the URLs that were used for determining each answer: expected high temperature in Centigrade on September 6, 2001 for Lafayette, LA; conversion of one US Dollar to Peruvian Nuevo Sols and then those converted Peruvian Nuevo Sols to Polish Zlotys and then those converted Polish Zlotys to US Dollars; birth date and birth place of the current US Secretary of State; between now and Thursday, September 6, 2001 at 5:00 pm the expected and actual arrival times for any US domestic flight that is not departing or arriving to Phoenix, AZ; and your favorite web site and why the web site is your favorite. With the exception of the favorite web site, each item required either multiple sites or multiple levels to search. The identification of the favorite web site was introduced for comparison purposes later in the semester.The midterm and final examinations were composed of problems that built on the in-class and homework activities. Both examinations required the use of computers in the classroom. The submission of a completed examination was much like the homework assignments as an e-mail message with attachments. This approach of electronic submission worked well for reinforcing the use of computers for course deliverables, date / time stamping of completed activities, and a means for delivering graded results. The current technology leaves much to be desired for marking up a document in the traditional sense of hand grading an assignment or examination. However, the students and faculty member worked well with this form of response. More importantly, a major problem occurred after the completion of the final examination. One of the students, through an accident, submitted the executable part of a browser as an attachment, which brought the e-mail system to such a degraded state that grading was impossible until the problem was corrected. An ftp drop box would be simple solution in order to avoid this type of accident in the future until another solution is found for the e-mail system.In order to get students to work together on various aspects of the course, there was a group quiz and assignment component that was added about midway through the course. The group activities did not count towards the final grade, however the students were promised an award for the group that scored the highest number of points.There were two group quizzes on algorithm development and one out-of-class group assignment. The assignment was a group effort in website development. This assignment involved the development of a website that instructs. The conceptual functionality the group selected for theassignment was to be described in a one-page typed double spaced written report by November 9, 2001. During the November 30, 2001 class, each group presented to the rest of the class a prototype of what the website would look like to the end user. The reports and prototypes were subject to approval and/or refinement. Group members were expected to perform at approximately an equal amount of effort. There were five groups with four members in four groups and three members in one group that were randomly determined in class. Each group had one or more students in the computer science or computer systems engineering degree programs.The three group activities were graded on a basis of one million points. This amount of points was interesting from the standpoint of understanding relative value. There was one group elated over earning 600,000 points on the first quiz until the group found out that was the lowest score. In searching for the group award, the faculty member sought a computer circuit board in order to retrieve chips for each member of the best group. During the search, a staff member pointed out another staff member who salvages computers for the College. This second staff member obtained defective parts for each student in the class. The result was that each m ember of the highest scoring group received a motherboard, in other words, most of the internals that form a complete PC. All the other students received central processing units. Although these “awards” were defective parts, the students viewed these items as display artifacts that could be kept throughout their careers.C OURSE E VALUATIONOn a weekly basis, there were small assessments that were made about the progress of the course. One student was selected from each team to answer three questions about the activities of the day: “What was the most important topic covered today?”, “What topic covered was the ‘muddiest’?”, and “About what topic would you like to know more?”, as well as the opportunity to provide “Other comments.” Typically, the muddiest topic was the one introduced at the end of a class period and to be later elaborated on in the next class. By collecting these evaluation each class period, the instructor was able to keep a pulse on the class, to answer questions, to elaborate on areas considered “muddy” by the students, and to discuss, as time allowed, topics about which the students wished to know more.The overall course evaluation was quite good. Nineteen of the 21 students completed a course evaluation. A five-point scale w as used to evaluate aspects of the course and the instructor. An A was “very good,” a B was “good,” a C was “fair,” a D was “poor,” and an E was “not applicable.” The mean ranking was 4.35 on the course. An average ranking of 4.57, the highest for the s even criteria on the course in general, was for “Testbook/ supplementary material in support of the course.” The “Definition and application of criteria for grading” received the next highest marks in the course category with an average of 4.44. The lowest evaluation of the seven criteria for the course was a 4.17 for “Value of assigned homework in support of the course topics.”The mean student ranking of the instructor was 4.47. Of the nine criteria for the instructor, the highest ranking of 4.89 was “The instructor exhibited enthusiasm for and interest in the subject.” Given the nature and purpose of this course, this is a very meaningful measure of the success of the course. “The instructor was well prepared” was also judged high with a mean rank of 4.67. Two other important aspects of this course, “The instructor’s approach stimulated student thinking” and “The instructor related course material to its application” were ranked at 4.56 and 4.50, respectively. The lowest average rank of 4.11 was for “The instructor or assistants were available for outside assistance.” The instructor keep posted office hours, but there was not an assistant for the course.The “Overall quality of the course and instruction” received an average rank of 4.39 and “How do you rate yourself as a student in this course?” received an average rank of 4.35. Only a few of the students responded to the number of hours per week that they studies for the course. All of the students reported attending at least 70% of the time and 75% of the students said that they attended over 90% of the time. The students’ estimate seemed to be accurate.A common comment from the student evaluations was that “the professor was a fun teacher, made class fun, and explained everything well.” A common complaint was that the class was taught late (3:40 to 4:30) on a Friday. Some students judged the class to be an easy class that taught some basics about computers; other students did not think that there was enough time to cover all o f the topics. These opposite reactions make sense when we recall that the students were a broad mix of degree programs and of basic computer abilities. Similarly, some students liked that the class projects “were not overwhelming,” while other students thought that there was too little time to learn too much and too much work was required for a one credit class. Several students expressed that they wished the course could have been longer because they wanted to learn more about the general topics in the course. The instructor was judged to be a good role model by the students. This matched the pleasure that the instructor had with this class. He thoroughly enjoyed working with the students.A SSESSMENTS A ND C ONCLUSIONSNear the end of the Spring 2002 semester, a follow-up survey that consisted of three questions was sent to the students from the Fall 2001 semester computer basics course. These questions were: “Which CSE course(s) wereyou enrolled in this semester?; How did ASE 194 - Computer Basi cs help you in your coursework this semester?; and What else should be covered that we did not cover in the course?”. There were eight students who responded to the follow-up survey. Only one of these eight students had enrolled in a CSE course. There was consistency that the computer basics course helped in terms of being able to use computer applications in courses, as well as understanding concepts of computing. Many of the students asked for shortcuts in using the word processing and spreadsheet applications. A more detailed analysis of the survey results will be used for enhancements to the next offering of the computer basics course. During the Spring 2002 semester, there was another set of eight students from the Fall 2001 semester computer basi cs course who enrolled in one on the next possible computer science courses mentioned earlier, CSE 110 or CSE 200. The grade distribution among these students was one grade of A, four grades of B, two withdrawals, and one grade of D. The two withdrawals appear to be consistent with concerns in the other courses. The one grade of D was unique in that the student was enrolled in a CSE course concurrently with the computer basics course, contrary to the advice of the MEP program. Those students who were not enrolled in a computer science course during the Spring 2002 semester will be tracked through the future semesters. The results of the follow-up survey and computer science course grade analysis will provide a foundation for enhancements to the computer basics course that is planned to be offered again during the Fall 2002 semester.S UMMARY A ND F UTURE D IRECTIONSThis paper described a computer basics course. In general, the course was considered to be a success. The true evaluation of this course will be measured as we do follow-up studies of these students to determine how they fare in subsequent courses that require basic computer skills. Future offerings of the course are expected to address non-standard computing devices, such as robots as a means to inspire the students to excel in the computing field.R EFERENCES[1] Office of Institutional Analysis, Arizona State UniversityEnro llment Summary, Fall Semester , 1992-2001, Tempe,Arizona.[2] Reyes, Maria A., Gotes, Maria Amparo, McNeill, Barry,Anderson-Rowland, Mary R., “MEP Summer Bridge Program: A Model Curriculum Project,” 1999 Proceedings, American Society for Engineering Education, Charlotte, North Carolina, June 1999, CD-ROM, 8 pages.[3] Reyes, Maria A., Anderson-Rowland, Mary R., andMcCartney, Mary Ann, “Learning from our MinorityEngineering Students: Improving Retention,” 2000Proceedings, American Society for Engineering Education,St. Louis, Missouri, June 2000, Session 2470, CD-ROM, 10pages.[4] Adair, Jennifer K,, Reyes, Maria A., Anderson-Rowland,Mary R., McNeill, Barry W., “An Education/BusinessPartnership: ASU’s Minority Engineering Program and theTempe Chamber of Commerce,” 2001 Proceeding, AmericanSociety for Engineering Education, Albuquerque, NewMexico, June 2001, CD-ROM, 9 pages.[5] Adair, Jennifer K., Reyes, Maria A., Anderson-Rowland,Mary R., Kouris, Demitris A., “Workshops vs. Tutoring:How ASU’s Minority Engineering Program is Changing theWay Engineering Students Learn, “ Frontiers in Education’01 Conference Proceedings, Reno, Nevada, October 2001,CD-ROM, pp. T4G-7 – T4G-11.[6] Reyes, Maria A., Anderson-Rowland, Mary R., Fletcher,Shawna L., and McCartney, Mary Ann, “ModelCollaboration within Minority Engineering StudentSocieties,” 2000 Proceedings, American Society forEngineering Education, St. Louis, Missouri, June 2000, CD-ROM, 8 pages.[7] Anderson-Rowland, Mary R., Blaisdell, Stephanie L.,Fletcher, Shawna, Fussell, Peggy A., Jordan, Cathryne,McCartney, Mary Ann, Reyes, Maria A., and White, Mary,“A Comprehensive Programmatic Approach to Recruitmentand Retention in the College of Engineering and AppliedSciences,” Frontiers in Education ’99 ConferenceProceedings, San Juan, Puerto Rico, November 1999, CD-ROM, pp. 12a7-6 – 12a7-13.[8] Anderson-Rowland, Mary R., Blaisdell, Stephanie L.,Fletcher, Shawna L., Fussell, Peggy A., McCartney, MaryAnn, Reyes, Maria A., and White, Mary Aleta, “ACollaborative Effort to Recruit and Retain UnderrepresentedEngineering Students,” Journal of Women and Minorities inScience and Engineering, vol.5, pp. 323-349, 1999.[9] Pfleeger, S. L., Software Engineering: Theory and Practice,Prentice-Hall, Inc., Upper Saddle River, NJ, 1998.。

09_Chap8_2(MWE9808)

38
Circuit Realization in Planar Microstrip
RS = 1 λ/8 Zo = 4.35 λ/8 Zo = 4.35 RL = 1
Zo = 1.299
Zo = 1.405
Zo = 1.299
Microstrip realization:
Attenuation (dB) 50 Ω λ/8 64.9 Ω 217.5 Ω λ/8 λ/8 λ/8 50 Ω
Example 8.6 Low-Pass Filter Design
Design a microstrip low-pass filter, fc = 4 GHz, N = 3, Zo = 50 Ω, 3-dB equal-ripple response. Sol: Low-pass prototype (TABLE 8.4): g1 = L1 = 3.3487 g2 = C2 = 0.7117 g3 = L3 = 3.3487 L → Zo = L, l = λ/8 at ω = ωc C → Zo = C−1, l = λ/8 L1 and L3 are series elements which are impractical in microstrip form. Series stub → Shunt stub (how?) o.c. Zo = 1.408
Zo = L1 Zo = L 1
s.c.
s.c.
λ/8 λ/8 Zo = 1 o.c.
λ/8 λ/8 Zo = 1 Zo = 1.408
RL = 1
The major difficulties include the realization of series stubs and no space among the three stubs. Next, apply the Kuroda identities to transform the circuit to a form realizable by the planar microstrip technology.

The partition function of 2d string theory

a rXiv:h ep-th/92831v111A ug1992IASSNS-HEP-92/48YCTP-P22-92hepth@xxx.9208031The Partition Function of 2D String Theory Robbert Dijkgraaf 1School of Natural Sciences Institute for Advanced Study Princeton,NJ 08540Gregory Moore and Ronen Plesser Department of Physics Yale University New Haven,CT 06511-8167We derive a compact and explicit expression for the generating functional of all correla-tion functions of tachyon operators in 2D string theory.This expression makes manifest relations of the c =1system to KP ﬂow and W 1+∞constraints.Moreover we derive aKontsevich-Penner integral representation of this generating functional.August 11,19921.IntroductionOne of the beautiful aspects of the matrix-model formulation of c<1string theory is that it gives a natural and mathematically precise formulation of the partition function of strings moving in diﬀerent backgrounds.This result began with Kazakov’s fundamental discovery of the appearance of matterﬁelds in the one-matrix model[1]and culminated in the discovery of the generalized KdVﬂow equations and the associated W N constraints in the c<1matrix models coupled to gravity[2–6].Recently these results have been further deepened through the use of a Kontsevich matrix model representation for the tau functions relevant to theseﬂows[7],see also[8,9].Analogous results in the c=1model have been strangely absent,and this paper is aﬁrst step in an attempt to change that situation. Using recently developed techniques for calculating tachyon correlators in the c=1model we derive a simple and compact expression(equation(3.10))for the generating functional of tachyon correlators,or equivalently the string partition function in an arbitrary tachyon background,valid to all orders in string perturbation theory.In Euclidean space this quantity can be interpreted as the partition function of a nonlinear sigma model as a function of an inﬁnite set of coupling constants t k,¯t k for a set of marginal operators. Upon appropriate analytic continuation to Minkowski space the partition function may be interpreted as the string S-matrix in a coherent state basis.One immediate consequence of our result(3.10)is that the partition function is natu-rally represented as a tau function of the Toda hierarchy.From this result we obtain W∞ﬂow equations(equation4.10)when the c=1coordinate X is compactiﬁed at the self-dual radius.Moreover,this expression can be used to derive a Kontsevich-Penner representa-tion of the partition function as a matrix integral,as described in sectionﬁve below.In section six we discuss how time-independent changes in the matrix model backgroundﬁt into our formalism,and in section seven we discuss some open problems and the relation of this work to other recent papers on c=1and W∞.2.Deﬁning correlation functionsIn a particular background,string propagation in a two-dimensional spacetime is described on the string worldsheet by the conformalﬁeld theory of a massless scalar X coupled to a c=25Liouville theoryφwith worldsheet action(excluding ghosts)A= 12R(2)φ+µe√Via its dual interpretation as the conformal gauge action for the coupling of X to two-dimensional gravity,(2.1)isexpressible asthecontinuumlimitofa sumover discretized surfaces.The discrete sum,as is by now well known,is generated by a matrix integral.In the double scaling limit which leads to the continuum theory this is in turn equivalent to a theory of free nonrelativistic fermions with actionS = ∞−∞dxdλˆψ† i ddλ2−V (λ) ˆψ.(2.2)The potential V (λ)in (2.2)is required to approach −1Γ(−|q |) Σe iqX/√2(1−12φso that the bounding circles C have lengths ℓ= C e φ/√sinπ|p |µ−|p |/2I |p |(2√r 2−p 2µ−r/2I r (2√2The behavior of V (λ)for negative λis irrelevant to all orders of perturbation theory in 1/µ.Indeed,the results of this paper should be interpreted in this perturbative sense.Many results are true in the nonperturbative context and we will indicate this in the appropriate places.Where we mention nonperturbative results we will refer to potentials which grow suﬃciently rapidly for large negative λ.In [10]these were termed “type I”models.whereˆB r,p are redundant operators for p/∈Z Z.We may thus extract tachyon correlators from macroscopic loop amplitudes asni=1W(ℓi,q i) =n i=1Γ(−|q i|)ℓ|q i|i n i=1T q i +O(ℓ2i) +analytic inℓi.(2.5)The matrix model formulation of the theory leads to a simple computation of the appro-priate limits of loop amplitudes.In the matrix model the macroscopic loop is related by a Laplace transform to the eigenvalue densityˆρ(λ,x)=ˆψ†ˆψ(λ,x):W(ℓ,x)= ∞0e−ℓλˆρ(λ,x)dλW(ℓ,q)= ∞−∞e iqx W(ℓ,x)dx.(2.6) DeﬁningˆW(z,x)= ∞0e−zℓW(ℓ,x)dℓ(2.7) we recoverˆρ(λ,x)=−i2)/βwith m∈Z Z.The eigenvalue correlator can be written as:ˆρ(λi ,q i ) =∞ m =−∞ σ∈Σn n k =1I (Q σk ,λσ(k ),λσ(k +1))(3.1)where Σn is the set of permutations of n objects,Q σk ≡p m +q σ(1)+···+q σ(k ).The λdependence of the correlator is determined by the function I (q,λ1,λ2)=(I (−q,λ1,λ2))∗=λ1|1√4|λ21−λ22|R [q,λ1,λ2]=R q exp i (µ+iq )log (λ1λ2)−i 4λ2with an inﬁnite wallat λ=0it is given byR q =i 1−ie −π(µ+iq ) 2−iµ+q )2+iµ−q ).(3.3)Inserting(3.2)in the expression (3.1)leads to a sum of terms.The calculation of tachyon correlators requires the extraction of those terms in the sum with the correct asymptotic dependence on λi .For each permutation σ,at most a ﬁnite number of terms in the sum over the loop momentum p m contribute to the result.A graphical procedure for performing this extraction was developed in [10]and used to derive an explicit expression for arbitrary tachyon correlators.We divide the tachyon insertions into “incoming”(q <0)and ‘outgoing”(q >0)particles.As in a Feynman diagram there is a vertex in the (x,λ)half-space corresponding to each operator ˆρ(x,λ).While the ﬁnal result will of course be independent of the order in which the λi are increased to inﬁnity,in intermediate steps we will choose some order and locate the vertices accordingly.Points are connected by line segments,representing the integral I ,to form a one-loop graph.Since the expression for I in (3.2)has two terms we have both direct and reﬂected propagators as in ﬁg.1.Each line segment carries a momentum and an arrow.Note that in ﬁg.1the reﬂected propagator,which we call simply a “bounce,”is composedof two segments with opposite arrows and momenta.These line segments are joined to form a one-loop graph according to the following rules:RH1.Lines with positive(negative)momenta slope upwards to the right(left).RH2.At any vertex arrows are conserved and momentum is conserved as time ﬂows upwards.In particular momentum q i is inserted at the vertex inﬁg.2.RH3.Outgoing vertices at(x out,λout)all have later times than incoming vertices (x in,λin):x out>x in.Diagrams drawn according to these rules correspond to possible physical processes in real time and were hence termed“real histories”.The connected tachyon correlation function is found by summing the terms in(3.1)corresponding to all real histories,and reads schematicallyni=1T q i =(−i)n RH± m bounces R Q(−R Q)∗.(3.4)The graphical rules allow one to convert(3.4)into an explicit formula for the amplitude [10].In the next subsection we will show that this result may be written quite simply in terms of free fermionicﬁelds,representing a fermionized version of the free relativistic bosonicﬁeld which describes the asymptotic behavior of the tachyon.3.2.Free Energy in terms of free oscillatorsOne of the central results of[10]is that the graphical rules described above are equiv-alent to the composition of three transformations on the scattering states:fermionization, free fermion scattering,and bosonization:i f→b◦S ff◦i b→f as inﬁg.3.The various real his-tories correspond to the possible contractions among the incoming and outgoing fermions, and the fermion scattering matrix describes a simple one-body process,given essentially by the phase shift in the nonrelativistic problem.It should be noted that this does not imply the(false)statement that bosonization is exact for the nonrelativistic fermion prob-lem.Rather,it is a statement about the asymptotics of certain correlators in the theory for a particular class of potentials.Here we will rewrite the tachyon amplitude using this formulation as a matrix element of a certain operator in the conformalﬁeld theory of a free Weyl fermion.It is convenient to deﬁne rescaled tachyon vertex operators V q =µ1−|q |/2T q and two free scalar ﬁelds ∂φin/out = n αin/out n z −n −1,such thatn i =1V n i /βm j =1V −n ′j /β =−(iµ)n2with expansionsψ(z )=m ∈Z Z ψm +12z −m −1{ψr ,¯ψs }=δr +s,0.(3.6)Now the result of [10]states that (3.5)is equivalent toψin−(m +12)¯ψin −(m +12).(3.7)Unitarity of the tachyon S -matrix is equivalent to the identityR q R ∗−q =1(3.8)on the reﬂection factors.3Using this,we can rewrite (3.7)as a unitary transformationψin (z )=Sψout (z )S −1¯ψin (z )=S ¯ψout (z )S −1S =:exp m ∈Z Z log R p m ψout−(m +12 :.(3.9)Thus we may write the full generating functional for connected Green’s functions in terms of a single free boson with modes αn :µ2F ≡ e n ≥1t n V n/β+ n ≥1¯t n V −n/β c=−1µ2F 1+···.This formula is anenormous simpliﬁcation over previous expressions for c =1amplitudes.The generating function for all amplitudes isZ =e µ2F .(3.11)4.W 1+∞constraintsIn correlation functions of tachyons with integer (Euclidean)momentum,the bounce factors R q of(3.3)simplify due to the following identityR ∗ξR n −ξ=(−iµ)−n1µ∂Zw n dzz )m 0|eiµ n ≥1t n αn ψ(z )¯ψ(w )Se iµ n ≥1¯t n α−n |0(4.2)At the self-dual radius β=1,where all tachyon momenta are integral,we may simplify the sum on m using (4.1)(−iµ)−n (−iµ+z ∂z )m (4.3)the latter sum acting like a delta function.Now integrate by parts and use the identity(−iµ−z ∂∂z)n z iµ.(4.4)It is convenient to bosonizeψ(z)=eφ(z),¯ψ(z)=e−φ(z)and shift the zero mode:˜φ(z)=φ(z)+iµlog z.(4.5)Taking the operator product of the two exponentials inφ,and using the delta function and charge conservation weﬁnd the operator:dw(iµ)−n1∂¯t n=Z−1 dw(iµ)−(n+1)w+ n>0nt n w n−1−1∂t n w−n−1.(4.8) The genus zero result of[18]is easily obtained from this as the leading term at largeµ. (Note that this was obtained atβ=∞but genus zero correlators are independent ofβ[17].)The operators P(n)(z)=:e−˜φ(z)∂n e˜φ(z):and their derivatives generate the algebra W1+∞[19].The standard generators are related to these byW(n)(z)=n−1l=0(−1)l(2n−2)l∂l P(n−l)(z).(4.9)The rescaling of the scalarﬁeld required to obtain(4.8)is simply a change of basis eﬀected by the operator:e log(iµ)πφ˜φ:whereπφis the momentum conjugate to˜φ.Inserting this we can rewrite(4.7)as∂Z5.Tau-functions and the Kontsevich-Penner matrix integralIn this section we will point out that the above reformulation of the generating func-tional of the c=1string represents mathematically aτ-function of the Toda Lattice hierarchy.The Toda Lattice naturally contains the KP and KdV hierarchies,and thus the c=1results are closely related to the expressions obtained for c<1.We will also show how to rewrite the partition function(at the self-dual radius)as a matrix integral, generalizing expressions previously considered by Kontsevich[7]and Penner[20].5.1.Grassmannians and tau-functionsLet usﬁrst brieﬂy explain the notion of a tau-function and its relation with the universal Grassmannian.For more details see e.g.[21]and[22].We will focus here on the relation with conformalﬁeld theory instead of the Lax pair formulation.Consider a two-dimensional free chiral scalarﬁeldϕ(z),with the usual mode expansion∂ϕ(z)= nαn z−n−1.(5.1)The reader is encouraged to think about this scalarﬁeld as the target space tachyonﬁeld at spatial inﬁnity with a periodic Euclidean time coordinate.We have a Hilbert space H built on the vacuum|0 ,and as in the case of a harmonic oscillator one can consider coherent states,∞ n=1it nα−n(5.2)|t =expand their Hermitian conjugates∞ n=1−it nαn(5.3)t|= 0|exp(The parameters t n are considered to be real here.)Now to any state|W in the Hilbert space H we can associate a coherent state wavefunctionτW(t)by considering the inner productτW(t)= t|W .(5.4)This function is a tau-function of the KP hierarchy if and only if the state|W lies in the so-called Grassmannian.To explain the concept of the Grassmannian we have to turn to the alternative de-scription of this chiral conformalﬁeld theory in terms of chiral Weyl fermionsψ(z),¯ψ(z) by means of the well-known bosonization formulas4i∂ϕ=¯ψψ,ψ=e iϕ,¯ψ=e−iϕ.(5.5)Loosely speaking,the Grassmannian can be deﬁned as the collection of all fermionic Bogo-liobov transforms of the vacuum|0 .That is,the state|W belongs to the Grassmannian if it is annihilated by particular linear combinations of the fermionic creation and annihilation operators.)|W =0,n≥0,(5.6) (ψn+12or equivalently,|W =S·|0 ,S=exp n,m A nm¯ψ−n−12.(5.7)Note that the operator S can be considered as an element of the inﬁnite-dimensional linear group,S∈GL(∞,4Since we do not wish toﬂaunt tradition we change conventions for bosonization in this section relative to the previous sections.with|¯t and t|the coherent states(5.2)and(5.3)and S a general GL(∞,z−n i.(5.12)nWith this choice of parameterization,and after taking into account a normal ordering contribution,the tau-function can be written asdet v j−1(z i)τ(t)=The techniques of the double-scaled matrix models leads to two important results.First, the partition functionτ(t)is a tau-function of the KP hierarchy,that is,it can be written asτ(t)= t|W = t|S|0 ,(5.16) for some state|W and matrix S∈GL(∞,ipz p−1z p+1/λ· ∞−∞dy·y n·e i(z p y−y p+1p+1)/λ.(5.19)p+1Here Y and Z are both N×N Hermitian matrices,and the parameterization of the KP times t k in terms of the matrix Z isλt k=∆(V′(z))2·det V′′(Z)·5.3.The Kontsevich-Penner integralWe have seen that the c=1partition function can be succinctly written as a tau-function of the Toda Lattice hierarchyτ(t,¯t)= t|S|¯t .(5.23)Forﬁxed¯t k we recover a tau-function of the KP hierarchy,which we can study with the techniques of the previous subsection.Indeed the operators O n of the minimal models should now be compared to the outgoing tachyons of the c=1model.We want to determine in more detail the element W(¯t)in the Grassmannian that parametrizes this particular orbit of the KPﬂows.To this end we have to consider the state|W(¯t) =S·U(¯t)·|0 ,U(¯t)=exp∞ n=1iµ¯t nα−n.(5.24)We will describe|W(¯t) by giving a basis v k(z;¯t),k≥0,of one-particle wave-functions. First we observe that the operator U(¯t)acts on the wave-functions z n by simple multipli-cationU(¯t):z n→exp iµ¯t k z−k ·z n.(5.25) Similarly we have for the action of S a multiplicationS:z n→R p n·z n.(5.26)We have already seen that the reﬂection factors R pncontain all the relevant information of the c=1matrix model.At radiusβthey can be chosen to beR pn =(−iµ)−n+1βΓ(12Γ(1with a normalization constant c k such that v k(z;0)=z k.(This corresponds to the normal ordering of the S-matrix in(3.9).)Since the reﬂection factor is basically a gamma function, the result can be expressed as a Laplace transformv k(z;¯t)=c′(z)· ∞0dy·y k·y−iµβ+(β−1)/2e iµ(y/z)βexp iµ¯t k y−k (5.29) Here the constant c′(z)is given byc′(z)=β(−iµ/zβ)1√2−iµ).(5.30)These integral representations are of Kontsevich type if and only ifβ=1,that is,only at the self-dual radius.Indeed in that case we havev k(z;¯t)=c′(z)· ∞0dy·y k·exp iµ y/z−log y+ ¯t k y−k (5.31) Therefore,following the procedure in[8,9],we can write the following matrix integral representation for the generating functional.Deﬁne the integralσ(Z,¯t)= dY e iµT r[Y Z−1+V(Y)],(5.32) whereV(Y)=−log Y+ ¯t k Y−k,(5.33) and we integrate over positive deﬁnite matrices Y.Then we haveτ(t,¯t)=σ(Z,¯t)nT rZ−n.(5.35) Note that with this normalizationτ(t,0)=1,which is appropriate since we consider normalized correlation functions.In order to write down the result(5.34)we had to treat the incoming and outgoing tachyons very diﬀerently,parametrizing the outgoing states through(5.35),whereas the coupling coeﬃcients to the incoming states enter the matrix integral in a much more straightforward fashion.Equation(5.34)should be considered as an asymptotic expansion inµ−1,but,for small enough t k,¯t k the expansion in these variables will be convergent.In some cases,(e.g.the sine-Gordon case considered in[26]) the expansion has aﬁnite radius of convergence,and as we increase|t k|beyond the radius of convergence we can have phase transitions.5.4.The partition functionMatrix integrals of the above type have appeared in the work of the mathematicians Harer and Zagier[27]and Penner[20]in their investigations of the Euler characteristic of the moduli space M g,s of Riemann surfaces with g handles and s punctures.(See[28]for more details on these wonderful calculations.)The double scaling limit of this so-called Penner integral was considered by Distler and Vafa[29]who also speculated on the relation with c=1string theory.Their work has been followed by a number of papers concerned with double scaling limits and multi-critical behaviour of matrix models with logarithmic potentials[25].All these papers considered essentially the case Z=1and¯t n=0,in the notation of(5.32).Distler and Vafa noticed that—after a double scaling limit and an analytic contin-uation—the Penner matrix integral could reproduce the c=1partition function at the self-dual radiusβ=1.Recall that the free energy at that radius is given by[30]∂2Fxe−iµx x/22µ2logµ−12g(2g−2)µ2−2g.(5.37)(Up to analytic terms inµ.)This makes one wonder whether our result(5.34)can be sharpened to give the unnormalized correlation functions.To this end let us put the incoming coupling constants¯t k to zero(and thereby also t k=0)and take a closer look at the integralσ(Z)= dY e iµT r[Y Z−1−log Y].(5.38) First of all it has a trivial Z-dependenceσ(Z)=(det Z)N−iµ·σ(1).(5.39) Actually,it is convenient to work with the quantity F deﬁned bye F=(πi/µ)−N2As an asymptotic expansion in 1/µit has the representatione F = dY ·e iµ ∞k =21dY·eiµ1µ)−N/2 (−iµ)iµΓ(−iµ) N N−1p =1(1−p/iµ)N −p(5.44)from which one may obtain the formulae:F g,s =(−1)s B 2g2g (2g −2)1−(1−x )2−2g ,g ≥2,F 1(x )=−12(1−x )2log(1−x )+32x .(5.47)The double-scaling limit considered by Distler and Vafa in[29]keeps N−iµﬁxed,while sending N,µ→∞(and x→1in(5.47).).This is clearly only possible for imaginary µ,which is precisely the case they study.However,here we want to consider a simpler limit in whichµis keptﬁxed,but N tends to inﬁnity.We already mentioned that the parameterization(5.35)only makes sense in this limit.Indeed,the absence of a double scaling limit is very much in the spirit of Kontsevich integrals.The contribution for genus 2or higher have a smooth limit,as is evident from(5.47).(Recall,we send x→∞.) However,we have to worry about the genus zero and one pieces,which have to be corrected by hand.(This is by the way also true for the double scaling limit.)Combining all ingredients we obtain the followingﬁnal result for the unnormalized generating functional for the c=1string theoryτ(t,¯t)=c(Z)· dY exp iµT r Y Z−1−log Y+ ¯t k Y−k .(5.48) where the normalization constant is given byc(Z)=e−iµN(2πi/µ)N2/2(det Z)iµ−N(5.49)(1+iN/µ)112µ112e32N/µ.The expression(5.48)has a smooth large N limit.6.Other BackgroundsThe results of the previous sections comprise in principle a calculation of the partition function in arbitrary tachyon backgrounds(subject to the equations of motion).The full space of classical backgrounds in the theory includes in addition to these excitations of the“discrete states”corresponding to global modes like the radius of the1D universe and generalizations thereof.Of these,the ones best understood in terms of the matrix model are the zero-momentum excitations which are thought to be represented by variations in the double-scaled potential.In this section we study the dependence of the amplitudes on these extra parameters.We note that in principle the formulation of section three applies in arbitrary potentials.What we add here is a study of the variation of the reﬂection factor R q,hence of the partition function,under variations of the potential.6.1.Dependence on βThe most obvious parameter is β,the radius at which we compactify the scalar ﬁeld X .The formulas of section four are valid for arbitrary β,however as pointed out in [17],correlation functions at diﬀerent radii are related.The relation is most simply written in terms of rescaled couplings t n .DeﬁningˆF [t n ,¯t n ;β;µ]≡µ2F [µn 2β−1¯t n ;β;µ](6.1)so that derivatives of ˆFyield correlation functions of T q ,we have ˆF [t n ,¯t n ;β;µ]=1∂µ2β∂2β−1t (n/β),µn∂t n ∂¯t n =µnn −1 m =0R ∗p mR n −p m =i n n −1 m =0(−iµ−m )n .(6.3)Inverting the operator in (6.2)assin(∂∂2−iµ−x )n (6.5)in agreement with the result of [10].6.2.Other zero-momentum modesThe matrix model naturally suggests candidate representatives of the special states at zero X momentum.Operators with the appropriate quantum numbers may be introduced as generating variations in the double-scaled potential V (λ).Their correlators may thus be studied by analysis of the variation of the partition function Z computed above under these changes in V .From the deﬁnition of I (q,λ1,λ2)we can obtain directly constraints on thevariation of R q.Essentially these follow upon integration by parts from the linear Gelfand-Dikii equation satisﬁed by a product of Sturm-Liouville eigenfunctions[31].Explicitly,we haveL q,k R q=0k≥−1L q,k=−k(k2−1)∂∂s+2 p≥0s p(2k+p+2)∂k5In[33]proposals for c=1ﬂow equations were made by taking the N→∞limit of the W N constraints of the c<1models.It should be noted that,although our equations have some similarities to the proposals of[33],they are not equivalent.From the relation of these results to a Kontsevich-type matrix model it appears that we have taken a step closer to a uniﬁed description of all the c≤1models along the lines proposed by[8,9].Moreover,the description(5.48)of the partition function is a strong hint that the c=1correlators have a description in terms of a topologicalﬁeld theory.If this is so then the present results provide a direct bridge between a topologicalﬁeld theory at the self-dual radius and the local physics of the c=1tachyon in the uncompactiﬁed theory.There have been many discussions of W∞symmetry in the c=1system.Our con-straints are related to the results of[15,34–36].The other modes of the W∞currents appearing in equation(4.10)deﬁne a set of operatorsσn(T q)whose correlation functions are determined by the subleading terms proportional toλ−|q|−2n in the largeλasymp-totics of the eigenvalue correlators.6These“operators”exist at any radius for X and have free fermion representations as fermion bilinears.Their correlators are also given by a Toda tau function generalizing that in(3.10).Note that these operators appear at any momentum q and are related to fractional powers ofℓ(or,equivalently,ofλ).Therefore, at generic q they cannot be the special state operators but rather are related to contact terms associated to singular geometries created by intersecting macroscopic loops[15].At integer q the distinction between special states and theσn(T q)is less clear.We hope to return to the subtleties of these contact terms in a future publication.The W∞symmetry we have discussed might also be related to the W∞Ward identities of[37–43].In these references the Liouvilleﬁeld is treated as a freeﬁeld,in other words, one works atµ=0.One should be cautious about identifying these W∞symmetries with those of the matrix model.As we have emphasized,the W∞modes of the matrix model σn(T q)are constructed from the tachyon degrees of freedom in distinction to the W∞currents of[37–43].Moreover,our Ward identities are highly nonlinear when expressed in terms of the correlation functions7in contrast to the quadratic identities of[39–43]. Finally the ghost sector of the theory is crucial in[39–43],leading to many more“special state operators”at given X,φmomenta than are considered in[15,34–36].Clearly there is a certain amount of tension between these two approaches and further work is needed to see if these diﬀerences are superﬁcial or essential.We must emphasize that at c =1the W ∞-constraints are actually somewhat sec-ondary,since we have an explicit solution of the appropriate Toda tau function given by (3.10).Analogous representations for thec <1tau functions (at nontopological points)replace the simple operator S by complicated and uncomputable objects like the “star operators”of [44].This is why the Virasoro constraints at c <1are essential to the actual computation of amplitudes.It would be interesting to investigate further the physical properties of these diﬀerent time-dependent backgrounds.In [18,26]some results along these lines were discussed.Our result (3.10)should allow a much more complete analysis of the space of time-dependent backgrounds in 2D string theory and the various phase transitions occurring as one in-creases the coordinates t k .What is needed for further progress is a more eﬀective way to compute the tau function (perhaps from the Kontsevich representation)or a deeper understanding of the inﬁnite dimensional geometry of the associated Grassmannian.AcknowledgementsWe would like to thank T.Banks,E.Martinec,N.Seiberg,C.Vafa,and H.Verlinde for discussions.This work is supported by DOE grant DE-AC02-76ER03075and by a Presidential Young Investigator Award (G.M.,R.P.)and by the W.M.Keck foundation (R.D.).G.M would like to thank the Isaac Newton Institute for Mathematical Sciences for hospitality.Appendix A.Dependence on the potentialIn this appendix we will derive constraints on the dependence of the free energy upon the double-scaled matrix model potential V (λ).We restrict attention to variations of the potential which preserve the asymptotics V (λ)∼−1H −µ−iq |λ2 =−∞−∞dλδV (λ)I (q,λ1,λ)I (q,λ,λ2).(A.1)We now recall the calculation of I from[10](see appendix A of this work for a detailed calculation for particular potentials).For simplicity let q>0.We will make use of the eigenfunctions of H=d22∓iz e±iλ2.(A.2) In terms of these we can write the resolvent quite easily by imposing the boundary condi-tions and the deﬁning property(H−z)I(q,λ1,λ2)=δ(λ1−λ2)as in[10]I(q,λ1,λ2)=−iθ(λ1−λ2) χ−(z,λ1)χ+(z,λ2)+R qχ−(z,λ1)χ−(z,λ2) +(λ1↔λ2).(A.3)The reﬂection factor R q contains all the eﬀects of the potential,and for the standard V is given by(3.3).Inserting this into(A.1)and neglecting terms of orderδV(λ1,2)for large λi,weﬁnd that a variation of V yieldsδR q=−i ∞−∞dλδV(λ)ψ(z,λ)2(A.4) whereψ=χ++R qχ−is the solution satisfying the boundary conditions at smallλ.The integrand F(z,λ)=ψ(z,λ)2in(A.4)satisﬁes a diﬀerential equation[31]following from that satisﬁed byψF′′′−4(V(λ)+z)F′−2V′F=0(A.5) where primes denoteλdiﬀerentiation.Let us choose as a convenient set of variations of the potentialδV(λ)=ǫe−ℓλ.Inserting this in(A.4)and integrating by parts weﬁnd8[ℓ3−4zℓ−4ℓV(−d dℓ)]δR q=0.(A.6) The integration by parts is justiﬁed by the limiting conditions we have imposed uponψandδV.Formally expanding V= n≥0s nλn the bounce factor becomes a function of the s j: R q=R q[s1,s2,...].RewritingδV as a motion in s j and inserting the resulting expression forδR q in(A.6)we obtain(after shifting s0)L q,k R q=0k≥−1+2 p≥0s p(2k+p+2)∂L q,k=−k(k2−1)∂∂sk8The similarity of this to the WdW equation of[16]is no coincidence;setting z=µand λ1=λ2weﬁnd that(A.1)is essentially the WdW wavefunction of the cosmological constant.。

HSTSTIS spectroscopy of the exposed white dwarf in the short-period dwarf nova EK TrA

a r X i v :a s t r o -p h /0106023v 1 1 J u n 2001Astronomy &Astrophysics manuscript no.(will be inserted by hand later)HST/STIS spectroscopy of the exposed white dwarf in theshort-period dwarf nova EK TrA ⋆B.T.G¨a nsicke 1,P.Szkody 2,E.M.Sion 3,D.W.Hoard 4,S.Howell 5,F.H.Cheng 3,and I.Hubeny 61Universit¨a ts-Sternwarte,Geismarlandstr.11,37083G¨o ttingen,Germany2Department of Astronomy,Box 351580,University of Washington,Seattle,WA 98195,USA 3Department of Astronomy &Astrophysics,Villanova University,Villanova,PA 19085,USA,4Cerro Tololo Inter-American Observatory,Casilla 603,La Serena,Chile,5Astrophysics Group,Planetary Science Institute,620North 6th Avenue,Tucson,AZ 85705.6Laboratory for Astronomy and Solar Physics,NASA/GSFC,Greenbelt,MD 20711,USAReceivedAbstract.We present high resolution Hubble Space Telescope ultraviolet spectroscopy of the dwarf nova EK TrA obtained in deep quiescence.The Space Telescope Imaging Spectrograph data reveal the broad Ly αabsorption proﬁle typical of a moderately cool white dwarf,overlayed by numerous broad emission lines of He,C,N,and Si and by a number of narrow absorption lines,mainly of C I and Si II .Assuming a white dwarf mass in the range 0.3−1.4M ⊙we derive T eﬀ=17500−23400K for the primary in EK TrA;T eﬀ=18800K for a canonical mass of 0.6M ⊙.From the narrow photospheric absorption lines,we measure the white dwarf rotational velocity,v sin i =200±100km s −1.Even though the strong contamination of the photospheric white dwarf absorption spectrum by the emission lines prevents a detailed quantitative analysis of the chemical abundances of the atmosphere,the available data suggest slightly sub-solar abundances.The high time resolution of the STIS data allows us to associate the observed ultraviolet ﬂickering with the emission lines,possibly originating in a hot optically thin corona above the cold accretion disk.Key words.Accretion,accretion disks –Stars:individual:EK TrA –novae,cataclysmic variables –white dwarfs –Ultraviolet:stars1.IntroductionPhotospheric emission from the accreting white dwarfs in non-magnetic cataclysmic variables (CVs)was unmistak-ably identiﬁed for the ﬁrst time in the quiescent ultraviolet spectra of the two dwarf novae U Gem and VW Hyi (Panek &Holm 1984;Mateo &Szkody 1984),obtained with the International Ultraviolet Explorer (IUE).While relatively good estimates for the eﬀective temperatures of these stars could be derived from the IUE data,a full analysis of the properties of accreting white dwarfs in CVs –such as photospheric abundances,mass and rotation rate –had to await the availability of high resolution and high signal-to-noise ratio ultraviolet spectroscopy.A few CVs have been well studied with the Hubble Space Telescope throughout2G¨a nsicke et al.:HST/STIS spectroscopy of the exposed white dwarf in the short-period dwarf nova EK TrA Table1.Line identiﬁcations in the STIS spectrum ofEK TrA.e=emission line,p=photospheric absorptionline,i=interstellar absorption line,:=uncertain detec-tion.C III1176∗)e Si II1309∗)pSi III1207∗)e C II1324∗)pN V1240∗)e C II1335∗)e,p,iSi II1260e:,p,i Si IV1400∗)eSi II1265∗)e:,p Si II1527e,p,iC I1277∗)p Si II1533e,pC I1280∗)p C IV1550∗)eSi III1294–1303∗)e He II1640∗)eO I1302i Al II1671+)pSi II1305∗)p,iG¨a nsicke et al.:HST/STIS spectroscopy of the exposed white dwarf in the short-period dwarf nova EK TrA3 Fig.3.Quasi-simultaneous ultraviolet(left),optical,and near-infrared photometry(right,ﬁlled symbols:V,open symbols:I)of EK TrA.The STIS data have been binned in120s.The light curve of the comparison star used in the reduction of the ground-based data is shown as small dots(shifted down by1.9mag).Fig.4.The HST/STIS spectrum of EK TrA(gray line)along with a(T eﬀ=18800K,log g=8.0)white dwarf+Gaussian emission line model.The contribution of theGaussian emission lines is shown for clarity also by dottedlines.curve.The images were reduced in the standard fashionwith IRAF tasks utilizing zero and skyﬂatﬁeld imagesobtained on the same night.We measured instrumentalmagnitudes using the IRAF task qphot with a10pixel(4′′)radius aperture(≈4×the seeing FWHM),and thencalibrated them using the standard star data.The meanvalues and1σuncertainties of the nine BV RI observa-tions obtained for EK TrA are:B=16.261±0.121,V=16.267±0.135,R=15.854±0.076,and I=15.590±0.071.The uncertainties of these mean magnitudes include bothrandom(detection noise andﬂickering)and systematic(calibration)eﬀects.Typical single measurement system-atic uncertainties areσB=0.035mag,σV=0.019mag,σR=0.014mag,andσI=0.014mag;typical detec-tion noise uncertainties for the V and I light curves areσV=σI<∼0.02mag.3.AnalysisG¨a nsicke et al.(1997)analysed an IUE spectrum ofEK TrA obtained during the late decline from a su-peroutburst with a composite accretion disk plus whitedwarf model.They found that a white dwarf with T eﬀ≈16000−20000contributes∼25%to the ultravioletﬂux.Considering that theﬁrst HST observation of EK TrA wasobtained a long time after its last outburst,and that theultravioletﬂux corresponds quite well to theﬂux level ofthe white dwarf predicted by G¨a nsicke et al.,we modelledthe observed ultraviolet spectrum with a set of white dwarfmodel spectra,and neglect the possible continuum contri-bution of the accretion disk.The likely contribution of thedisk is discussed below.3.1.White dwarf eﬀective temperature and surfacegravityWe have computed a grid of solar abundance model spec-tra covering T eﬀ=16000−20000K in200K steps andlog g=7.25−8.50in0.25steps for the analysis of the pho-tospheric white dwarf emission.This spectral library wasgenerated with the codes TLUSTY195and SYNSPEC45(Hubeny1988;Hubeny&Lanz1995).Weﬁtted the modelspectra to the STIS data,allowing for Gaussian emissionof He II,C II,III,N V,and Si II,III(see Table1),and we ex-cluded from theﬁt a20˚A broad region centered on thegeocoronal Lyαemission.In order to achieve a physicallymeaningfulﬁt,we had to constrain the components of theSi IIλ1260,65doublet to have the same FWHM,and thecomponents of Si IIλ1527,33to have the same FWHM andﬂux.It is,in principle,possible to derive from such aﬁtboth the eﬀective temperature and the surface gravityof the white dwarf,as both parameters determine thedetailed shape of the photospheric Lyαabsorption pro-ﬁle.Unfortunately,in our observation of EK TrA theLyαproﬁle is strongly contaminated by various emissionlines.In addition,the pressure-sensitive H+2transition at1400˚A,which is formed in a white dwarf photospherewith T eﬀ<∼20000K,is totally covered up by emission ofSi IVλ1394,1403.The best-ﬁt parameters(T eﬀ,log g)areapproximately linearly correlated,T eﬀ≈2360×log g−954G¨a nsicke et al.:HST/STIS spectroscopy of the exposed white dwarf in the short-period dwarf nova EK TrA with insigniﬁcant variations inχ2.As a result,it is notpossible to derive an estimate for the surface gravity,and,hence,for the mass of the white dwarf.For the rangeof possible white dwarf masses,0.3−1.4M⊙,theﬁt tothe STIS data constrains the white dwarf temperature toT eﬀ≈17500−23400K,which conﬁrms the results ob-tained by G¨a nsicke et al.(1997).The scaling factor of themodel spectrum provides an estimate of the distance toEK TrA which depends,however,on the assumed whitedwarf mass.For a typical0.6M⊙white dwarf,d=200pc,for the extreme limits M wd=0.3(1.4)M⊙,d=300(34)pc.Considering that G¨a nsicke et al.(1997)deriveda lower limit on the distance of∼180pc from the non-detection of the donor star–in agreement with the200pcestimated by Warner(1987)from the disk brightness–a massive white dwarf(M wd>∼1.0M⊙)can probably beexcluded.A modelﬁt with(T eﬀ=18800K,log g=8.0),corresponding to a white dwarf mass of∼0.6M⊙,isshown in Fig.4.The observedﬂux exceeds the modelﬂuxby∼10−15%at the red end of the STIS spectrum(λ>∼1550˚A).In the optical,the model shown in Fig.4hasV=17.4,which is well below the quasi-simultaneous op-tical magnitude of our CTIO photometry(Sect2.2).Theopticalﬂux excess over the white dwarf contribution hasbeen modelled by G¨a nsicke et al.(1997)with emissionfrom an optically thin accretion disk,providing a quitegoodﬁt to the Balmer emission lines.It is very likely thattheﬂux excess at ultraviolet wavelengths is related to thesame source.Indeed,theﬂux contribution of an opticallythin disk is dominated in the near-ultraviolet by bound-free and its emission increases towards longer wavelengths,reaching a maximum at the Balmer jump.In view of theshort available wavelength range where the disk notice-ably contributes(∼100˚A),we refrained from a quanti-tative analysis of the disk contribution.Nevertheless,asa conservative estimate,the contribution of the accretiondisk to the continuumﬂux at wavelengths shorter than1550˚A is certainly much lower than10%.3.2.Photospheric abundances and rotation rateA number of narrow photospheric absorption lines areobserved that clearly have an origin in the white dwarfphotosphere1(Table1).Unfortunately most of these ab-sorption lines are contaminated by optically thin radiationfrom the accretion disk.For instance,the STIS spectrumreveals weak emission of Si IIλλ1527,33.Consequently,even though no obvious Si IIλλ1260,65emission is ob-served,we can not assume that the photospheric absorp-tion lines of this Si II resonance doublet are uncontami-nated.It is therefore clear that a quantitative analysis ofG¨a nsicke et al.:HST/STIS spectroscopy of the exposed white dwarf in the short-period dwarf nova EK TrA5Fig.5.Two regions of the STIS spectrum which contain narrow metal absorption lines (see Table 1).The STIS dataand the model spectra are sampled in 0.3˚A steps,which resolves well the observed photospheric absorption lines.Plotted as thick lines is a (T eﬀ=18800K,log g =8.0,abundances =0.5×solar)white dwarf model,broadened for rotational velocities of 100,300,500km s −1(from top to bottom).Note the narrow interstellar absorption features at Si II λ1260,O I λ1302,Si II λ1304,C II λλ1334,35,and Si II λrger amplitude.For comparison,the standard deviation from the mean count rate is σ≈30%in the C IV light curve vs.σ≈9%in the total light curve.The varia-tion in the continuum count rate extracted from the line-free region is σ≈6%,which is comparable to the errors due to photon statistics.This conﬁrms the assumption that the continuum is mainly made up of photospheric emission from the white dwarf.Considering that ∼25%of the total ultraviolet ﬂux observed with STIS is con-tained in the various emission lines,we conclude that the ﬂickering is primarily associated with an optically thin re-gion,possibly some kind of corona above a cold accretion disk.For a discussion of the possible excitation mecha-nisms causing the line emission,see Mauche et al.(1997).Assuming a distance of 180pc,the luminosity of the ultra-violet line emission is ∼6.6×1030erg s −1,about ∼20%of the sum of the optical disk luminosity and the X-ray luminosity (G¨a nsicke et al.1997).Including the ultravi-olet disk emission in the energy balance of the system does,therefore,not noticeably change the conclusion of G¨a nsicke et al.(1997)that the accretion rate in EK TrA is a factor ∼5lower than in the prototypical SU UMa dwarf nova VW Hyi.It is interesting to compare the short-term ultravio-let variability of EK TrA with that of a long-period dwarf nova.Hoard et al.(1997)analysed fast HST/FOS spec-troscopy of IP Peg and found a strong contribution ofTable 2.White dwarf rotation rates measured from Doppler-broadened absorption lines,and eﬀective tem-peratures measured in deep quiescence.WZ Sge DN/WZ 81.61200149001EK TrA DN/SU 90.5200188002VW Hyi DN/SU 106.9400190003,4U Gem DN/UG 254.7≤100320005,6RX And DN/UG 302.21503500076G¨a nsicke et al.:HST/STIS spectroscopy of the exposed white dwarf in the short-period dwarf nova EK TrA4.Discussion&ConclusionsEK TrA was selected as a target for our HST program as it appears to be very similar to the well-studied SU UMa dwarf nova VW Hyi(G¨a nsicke et al.1997).Our analysis of the STIS data conﬁrms this suggestion:weﬁnd a white dwarf temperature and rotation rate that are very close to the values derived for VW Hyi.EK TrA is only theﬁfth CV white dwarf whose rota-tion rate could be accurately measured from the Doppler-broadened metal lines in high-resolution ultraviolet spec-tra,and it is only the second typical SU UMa-type sys-tem(Table2).The other four stars include the ultra-short period large-outburst amplitude dwarf nova WZ Sge,the prototypical SU UMa dwarf nova VW Hyi,and the two long-period U Gem-type dwarf novae U Gem and RX And. If we consider the membership to one of these three groups as a measure of the evolutionary stage of a system,with the U Gem type stars above the orbital period gap be-ing the youngest systems,the SU UMa stars below the gap being signiﬁcantly older,and the WZ Sge stars being the oldest–possibly containing already degenerate donors and evolving to longer orbital periods–then the presently known white dwarf rotation rates are not in disagreement with an increase of the rotation rate with increasing age. Such a trend is indeed expected,as the angular momen-tum of accreted matter spins the white dwarf up(King et al.1991),even though the long-term angular momen-tum evolution of accreting white dwarfs–taking into ac-count nova explosions–is not yet well understood(Livio &Pringle1998).Acknowledgements.We thank the CTIO Director’s Oﬃce for the allocation of discretionary time used for this project.CTIO is operated by AURA,Inc.,under cooperative agreement with the United States National Science Foundation.BTG was sup-ported by the DLR under grant50OR99036.PS,EMS,and SBH acknowledge partial support of this research from HST grant GO-08103.03-97A.ReferencesCheng,F.H.,Sion,E.M.,Szkody,P.,&Huang,M.1997,ApJ Lett.,484,L149G¨a nsicke,B.T.2000,Reviews of Modern Astronomy,13,151 G¨a nsicke,B.T.&Beuermann,K.1996,A&A,309,L47G¨a nsicke, B.T.,Beuermann,K.,&Thomas,H. C.1997, MNRAS,289,388Hassall,B.J.M.1985,MNRAS,216,335Hoard,D.W.,Baptista,R.,Eracleous,M.Horne,K.,Misselt, K. 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ACM-GIS%202006-A%20Peer-to-Peer%20Spatial%20Cloaking%20Algorithm%20for%20Anonymous%20Location-based%

A Peer-to-Peer Spatial Cloaking Algorithm for AnonymousLocation-based Services∗Chi-Yin Chow Department of Computer Science and Engineering University of Minnesota Minneapolis,MN cchow@ Mohamed F.MokbelDepartment of ComputerScience and EngineeringUniversity of MinnesotaMinneapolis,MNmokbel@Xuan LiuIBM Thomas J.WatsonResearch CenterHawthorne,NYxuanliu@ABSTRACTThis paper tackles a major privacy threat in current location-based services where users have to report their ex-act locations to the database server in order to obtain their desired services.For example,a mobile user asking about her nearest restaurant has to report her exact location.With untrusted service providers,reporting private location in-formation may lead to several privacy threats.In this pa-per,we present a peer-to-peer(P2P)spatial cloaking algo-rithm in which mobile and stationary users can entertain location-based services without revealing their exact loca-tion information.The main idea is that before requesting any location-based service,the mobile user will form a group from her peers via single-hop communication and/or multi-hop routing.Then,the spatial cloaked area is computed as the region that covers the entire group of peers.Two modes of operations are supported within the proposed P2P spa-tial cloaking algorithm,namely,the on-demand mode and the proactive mode.Experimental results show that the P2P spatial cloaking algorithm operated in the on-demand mode has lower communication cost and better quality of services than the proactive mode,but the on-demand incurs longer response time.Categories and Subject Descriptors:H.2.8[Database Applications]:Spatial databases and GISGeneral Terms:Algorithms and Experimentation. Keywords:Mobile computing,location-based services,lo-cation privacy and spatial cloaking.1.INTRODUCTIONThe emergence of state-of-the-art location-detection de-vices,e.g.,cellular phones,global positioning system(GPS) devices,and radio-frequency identiﬁcation(RFID)chips re-sults in a location-dependent information access paradigm,∗This work is supported in part by the Grants-in-Aid of Re-search,Artistry,and Scholarship,University of Minnesota. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proﬁt or commercial advantage and that copies bear this notice and the full citation on theﬁrst page.To copy otherwise,to republish,to post on servers or to redistribute to lists,requires prior speciﬁc permission and/or a fee.ACM-GIS’06,November10-11,2006,Arlington,Virginia,USA. Copyright2006ACM1-59593-529-0/06/0011...$5.00.known as location-based services(LBS)[30].In LBS,mobile users have the ability to issue location-based queries to the location-based database server.Examples of such queries include“where is my nearest gas station”,“what are the restaurants within one mile of my location”,and“what is the traﬃc condition within ten minutes of my route”.To get the precise answer of these queries,the user has to pro-vide her exact location information to the database server. With untrustworthy servers,adversaries may access sensi-tive information about speciﬁc individuals based on their location information and issued queries.For example,an adversary may check a user’s habit and interest by knowing the places she visits and the time of each visit,or someone can track the locations of his ex-friends.In fact,in many cases,GPS devices have been used in stalking personal lo-cations[12,39].To tackle this major privacy concern,three centralized privacy-preserving frameworks are proposed for LBS[13,14,31],in which a trusted third party is used as a middleware to blur user locations into spatial regions to achieve k-anonymity,i.e.,a user is indistinguishable among other k−1users.The centralized privacy-preserving frame-work possesses the following shortcomings:1)The central-ized trusted third party could be the system bottleneck or single point of failure.2)Since the centralized third party has the complete knowledge of the location information and queries of all users,it may pose a serious privacy threat when the third party is attacked by adversaries.In this paper,we propose a peer-to-peer(P2P)spatial cloaking algorithm.Mobile users adopting the P2P spatial cloaking algorithm can protect their privacy without seeking help from any centralized third party.Other than the short-comings of the centralized approach,our work is also moti-vated by the following facts:1)The computation power and storage capacity of most mobile devices have been improv-ing at a fast pace.2)P2P communication technologies,such as IEEE802.11and Bluetooth,have been widely deployed.3)Many new applications based on P2P information shar-ing have rapidly taken shape,e.g.,cooperative information access[9,32]and P2P spatio-temporal query processing[20, 24].Figure1gives an illustrative example of P2P spatial cloak-ing.The mobile user A wants toﬁnd her nearest gas station while beingﬁve anonymous,i.e.,the user is indistinguish-able amongﬁve users.Thus,the mobile user A has to look around andﬁnd other four peers to collaborate as a group. In this example,the four peers are B,C,D,and E.Then, the mobile user A cloaks her exact location into a spatialA B CDEBase Stationregion that covers the entire group of mobile users A ,B ,C ,D ,and E .The mobile user A randomly selects one of the mobile users within the group as an agent .In the ex-ample given in Figure 1,the mobile user D is selected as an agent.Then,the mobile user A sends her query (i.e.,what is the nearest gas station)along with her cloaked spa-tial region to the agent.The agent forwards the query to the location-based database server through a base station.Since the location-based database server processes the query based on the cloaked spatial region,it can only give a list of candidate answers that includes the actual answers and some false positives.After the agent receives the candidate answers,it forwards the candidate answers to the mobile user A .Finally,the mobile user A gets the actual answer by ﬁltering out all the false positives.The proposed P2P spatial cloaking algorithm can operate in two modes:on-demand and proactive .In the on-demand mode,mobile clients execute the cloaking algorithm when they need to access information from the location-based database server.On the other side,in the proactive mode,mobile clients periodically look around to ﬁnd the desired number of peers.Thus,they can cloak their exact locations into spatial regions whenever they want to retrieve informa-tion from the location-based database server.In general,the contributions of this paper can be summarized as follows:1.We introduce a distributed system architecture for pro-viding anonymous location-based services (LBS)for mobile users.2.We propose the ﬁrst P2P spatial cloaking algorithm for mobile users to entertain high quality location-based services without compromising their privacy.3.We provide experimental evidence that our proposed algorithm is eﬃcient in terms of the response time,is scalable to large numbers of mobile clients,and is eﬀective as it provides high-quality services for mobile clients without the need of exact location information.The rest of this paper is organized as follows.Section 2highlights the related work.The system model of the P2P spatial cloaking algorithm is presented in Section 3.The P2P spatial cloaking algorithm is described in Section 4.Section 5discusses the integration of the P2P spatial cloak-ing algorithm with privacy-aware location-based database servers.Section 6depicts the experimental evaluation of the P2P spatial cloaking algorithm.Finally,Section 7con-cludes this paper.2.RELATED WORKThe k -anonymity model [37,38]has been widely used in maintaining privacy in databases [5,26,27,28].The main idea is to have each tuple in the table as k -anonymous,i.e.,indistinguishable among other k −1tuples.Although we aim for the similar k -anonymity model for the P2P spatial cloaking algorithm,none of these techniques can be applied to protect user privacy for LBS,mainly for the following four reasons:1)These techniques preserve the privacy of the stored data.In our model,we aim not to store the data at all.Instead,we store perturbed versions of the data.Thus,data privacy is managed before storing the data.2)These approaches protect the data not the queries.In anonymous LBS,we aim to protect the user who issues the query to the location-based database server.For example,a mobile user who wants to ask about her nearest gas station needs to pro-tect her location while the location information of the gas station is not protected.3)These approaches guarantee the k -anonymity for a snapshot of the database.In LBS,the user location is continuously changing.Such dynamic be-havior calls for continuous maintenance of the k -anonymity model.(4)These approaches assume a uniﬁed k -anonymity requirement for all the stored records.In our P2P spatial cloaking algorithm,k -anonymity is a user-speciﬁed privacy requirement which may have a diﬀerent value for each user.Motivated by the privacy threats of location-detection de-vices [1,4,6,40],several research eﬀorts are dedicated to protect the locations of mobile users (e.g.,false dummies [23],landmark objects [18],and location perturbation [10,13,14]).The most closed approaches to ours are two centralized spatial cloaking algorithms,namely,the spatio-temporal cloaking [14]and the CliqueCloak algorithm [13],and one decentralized privacy-preserving algorithm [23].The spatio-temporal cloaking algorithm [14]assumes that all users have the same k -anonymity requirements.Furthermore,it lacks the scalability because it deals with each single request of each user individually.The CliqueCloak algorithm [13]as-sumes a diﬀerent k -anonymity requirement for each user.However,since it has large computation overhead,it is lim-ited to a small k -anonymity requirement,i.e.,k is from 5to 10.A decentralized privacy-preserving algorithm is proposed for LBS [23].The main idea is that the mobile client sends a set of false locations,called dummies ,along with its true location to the location-based database server.However,the disadvantages of using dummies are threefold.First,the user has to generate realistic dummies to pre-vent the adversary from guessing its true location.Second,the location-based database server wastes a lot of resources to process the dummies.Finally,the adversary may esti-mate the user location by using cellular positioning tech-niques [34],e.g.,the time-of-arrival (TOA),the time diﬀer-ence of arrival (TDOA)and the direction of arrival (DOA).Although several existing distributed group formation al-gorithms can be used to ﬁnd peers in a mobile environment,they are not designed for privacy preserving in LBS.Some algorithms are limited to only ﬁnding the neighboring peers,e.g.,lowest-ID [11],largest-connectivity (degree)[33]and mobility-based clustering algorithms [2,25].When a mo-bile user with a strict privacy requirement,i.e.,the value of k −1is larger than the number of neighboring peers,it has to enlist other peers for help via multi-hop routing.Other algorithms do not have this limitation,but they are designed for grouping stable mobile clients together to facil-Location-based Database ServerDatabase ServerDatabase ServerFigure 2:The system architectureitate eﬃcient data replica allocation,e.g.,dynamic connec-tivity based group algorithm [16]and mobility-based clus-tering algorithm,called DRAM [19].Our work is diﬀerent from these approaches in that we propose a P2P spatial cloaking algorithm that is dedicated for mobile users to dis-cover other k −1peers via single-hop communication and/or via multi-hop routing,in order to preserve user privacy in LBS.3.SYSTEM MODELFigure 2depicts the system architecture for the pro-posed P2P spatial cloaking algorithm which contains two main components:mobile clients and location-based data-base server .Each mobile client has its own privacy proﬁle that speciﬁes its desired level of privacy.A privacy proﬁle includes two parameters,k and A min ,k indicates that the user wants to be k -anonymous,i.e.,indistinguishable among k users,while A min speciﬁes the minimum resolution of the cloaked spatial region.The larger the value of k and A min ,the more strict privacy requirements a user needs.Mobile users have the ability to change their privacy proﬁle at any time.Our employed privacy proﬁle matches the privacy re-quirements of mobiles users as depicted by several social science studies (e.g.,see [4,15,17,22,29]).In this architecture,each mobile user is equipped with two wireless network interface cards;one of them is dedicated to communicate with the location-based database server through the base station,while the other one is devoted to the communication with other peers.A similar multi-interface technique has been used to implement IP multi-homing for stream control transmission protocol (SCTP),in which a machine is installed with multiple network in-terface cards,and each assigned a diﬀerent IP address [36].Similarly,in mobile P2P cooperation environment,mobile users have a network connection to access information from the server,e.g.,through a wireless modem or a base station,and the mobile users also have the ability to communicate with other peers via a wireless LAN,e.g.,IEEE 802.11or Bluetooth [9,24,32].Furthermore,each mobile client is equipped with a positioning device, e.g.,GPS or sensor-based local positioning systems,to determine its current lo-cation information.4.P2P SPATIAL CLOAKINGIn this section,we present the data structure and the P2P spatial cloaking algorithm.Then,we describe two operation modes of the algorithm:on-demand and proactive .4.1Data StructureThe entire system area is divided into grid.The mobile client communicates with each other to discover other k −1peers,in order to achieve the k -anonymity requirement.TheAlgorithm 1P2P Spatial Cloaking:Request Originator m 1:Function P2PCloaking-Originator (h ,k )2://Phase 1:Peer searching phase 3:The hop distance h is set to h4:The set of discovered peers T is set to {∅},and the number ofdiscovered peers k =|T |=05:while k <k −1do6:Broadcast a FORM GROUP request with the parameter h (Al-gorithm 2gives the response of each peer p that receives this request)7:T is the set of peers that respond back to m by executingAlgorithm 28:k =|T |;9:if k <k −1then 10:if T =T then 11:Suspend the request 12:end if 13:h ←h +1;14:T ←T ;15:end if 16:end while17://Phase 2:Location adjustment phase 18:for all T i ∈T do19:|mT i .p |←the greatest possible distance between m and T i .pby considering the timestamp of T i .p ’s reply and maximum speed20:end for21://Phase 3:Spatial cloaking phase22:Form a group with k −1peers having the smallest |mp |23:h ←the largest hop distance h p of the selected k −1peers 24:Determine a grid area A that covers the entire group 25:if A <A min then26:Extend the area of A till it covers A min 27:end if28:Randomly select a mobile client of the group as an agent 29:Forward the query and A to the agentmobile client can thus blur its exact location into a cloaked spatial region that is the minimum grid area covering the k −1peers and itself,and satisﬁes A min as well.The grid area is represented by the ID of the left-bottom and right-top cells,i.e.,(l,b )and (r,t ).In addition,each mobile client maintains a parameter h that is the required hop distance of the last peer searching.The initial value of h is equal to one.4.2AlgorithmFigure 3gives a running example for the P2P spatial cloaking algorithm.There are 15mobile clients,m 1to m 15,represented as solid circles.m 8is the request originator,other black circles represent the mobile clients received the request from m 8.The dotted circles represent the commu-nication range of the mobile client,and the arrow represents the movement direction.Algorithms 1and 2give the pseudo code for the request originator (denoted as m )and the re-quest receivers (denoted as p ),respectively.In general,the algorithm consists of the following three phases:Phase 1:Peer searching phase .The request origina-tor m wants to retrieve information from the location-based database server.m ﬁrst sets h to h ,a set of discovered peers T to {∅}and the number of discovered peers k to zero,i.e.,|T |.(Lines 3to 4in Algorithm 1).Then,m broadcasts a FORM GROUP request along with a message sequence ID and the hop distance h to its neighboring peers (Line 6in Algorithm 1).m listens to the network and waits for the reply from its neighboring peers.Algorithm 2describes how a peer p responds to the FORM GROUP request along with a hop distance h and aFigure3:P2P spatial cloaking algorithm.Algorithm2P2P Spatial Cloaking:Request Receiver p1:Function P2PCloaking-Receiver(h)2://Let r be the request forwarder3:if the request is duplicate then4:Reply r with an ACK message5:return;6:end if7:h p←1;8:if h=1then9:Send the tuple T=<p,(x p,y p),v maxp ,t p,h p>to r10:else11:h←h−1;12:Broadcast a FORM GROUP request with the parameter h 13:T p is the set of peers that respond back to p14:for all T i∈T p do15:T i.h p←T i.h p+1;16:end for17:T p←T p∪{<p,(x p,y p),v maxp ,t p,h p>};18:Send T p back to r19:end ifmessage sequence ID from another peer(denoted as r)that is either the request originator or the forwarder of the re-quest.First,p checks if it is a duplicate request based on the message sequence ID.If it is a duplicate request,it sim-ply replies r with an ACK message without processing the request.Otherwise,p processes the request based on the value of h:Case1:h= 1.p turns in a tuple that contains its ID,current location,maximum movement speed,a timestamp and a hop distance(it is set to one),i.e.,< p,(x p,y p),v max p,t p,h p>,to r(Line9in Algorithm2). Case2:h> 1.p decrements h and broadcasts the FORM GROUP request with the updated h and the origi-nal message sequence ID to its neighboring peers.p keeps listening to the network,until it collects the replies from all its neighboring peers.After that,p increments the h p of each collected tuple,and then it appends its own tuple to the collected tuples T p.Finally,it sends T p back to r (Lines11to18in Algorithm2).After m collects the tuples T from its neighboring peers, if m cannotﬁnd other k−1peers with a hop distance of h,it increments h and re-broadcasts the FORM GROUP request along with a new message sequence ID and h.m repeatedly increments h till itﬁnds other k−1peers(Lines6to14in Algorithm1).However,if mﬁnds the same set of peers in two consecutive broadcasts,i.e.,with hop distances h and h+1,there are not enough connected peers for m.Thus, m has to relax its privacy proﬁle,i.e.,use a smaller value of k,or to be suspended for a period of time(Line11in Algorithm1).Figures3(a)and3(b)depict single-hop and multi-hop peer searching in our running example,respectively.In Fig-ure3(a),the request originator,m8,(e.g.,k=5)canﬁnd k−1peers via single-hop communication,so m8sets h=1. Since h=1,its neighboring peers,m5,m6,m7,m9,m10, and m11,will not further broadcast the FORM GROUP re-quest.On the other hand,in Figure3(b),m8does not connect to k−1peers directly,so it has to set h>1.Thus, its neighboring peers,m7,m10,and m11,will broadcast the FORM GROUP request along with a decremented hop dis-tance,i.e.,h=h−1,and the original message sequence ID to their neighboring peers.Phase2:Location adjustment phase.Since the peer keeps moving,we have to capture the movement between the time when the peer sends its tuple and the current time. For each received tuple from a peer p,the request originator, m,determines the greatest possible distance between them by an equation,|mp |=|mp|+(t c−t p)×v max p,where |mp|is the Euclidean distance between m and p at time t p,i.e.,|mp|=(x m−x p)2+(y m−y p)2,t c is the currenttime,t p is the timestamp of the tuple and v maxpis the maximum speed of p(Lines18to20in Algorithm1).In this paper,a conservative approach is used to determine the distance,because we assume that the peer will move with the maximum speed in any direction.If p gives its movement direction,m has the ability to determine a more precise distance between them.Figure3(c)illustrates that,for each discovered peer,the circle represents the largest region where the peer can lo-cate at time t c.The greatest possible distance between the request originator m8and its discovered peer,m5,m6,m7, m9,m10,or m11is represented by a dotted line.For exam-ple,the distance of the line m8m 11is the greatest possible distance between m8and m11at time t c,i.e.,|m8m 11|. Phase3:Spatial cloaking phase.In this phase,the request originator,m,forms a virtual group with the k−1 nearest peers,based on the greatest possible distance be-tween them(Line22in Algorithm1).To adapt to the dynamic network topology and k-anonymity requirement, m sets h to the largest value of h p of the selected k−1 peers(Line15in Algorithm1).Then,m determines the minimum grid area A covering the entire group(Line24in Algorithm1).If the area of A is less than A min,m extends A,until it satisﬁes A min(Lines25to27in Algorithm1). Figure3(c)gives the k−1nearest peers,m6,m7,m10,and m11to the request originator,m8.For example,the privacy proﬁle of m8is(k=5,A min=20cells),and the required cloaked spatial region of m8is represented by a bold rectan-gle,as depicted in Figure3(d).To issue the query to the location-based database server anonymously,m randomly selects a mobile client in the group as an agent(Line28in Algorithm1).Then,m sendsthe query along with the cloaked spatial region,i.e.,A,to the agent(Line29in Algorithm1).The agent forwards thequery to the location-based database server.After the serverprocesses the query with respect to the cloaked spatial re-gion,it sends a list of candidate answers back to the agent.The agent forwards the candidate answer to m,and then mﬁlters out the false positives from the candidate answers. 4.3Modes of OperationsThe P2P spatial cloaking algorithm can operate in twomodes,on-demand and proactive.The on-demand mode:The mobile client only executesthe algorithm when it needs to retrieve information from the location-based database server.The algorithm operatedin the on-demand mode generally incurs less communica-tion overhead than the proactive mode,because the mobileclient only executes the algorithm when necessary.However,it suﬀers from a longer response time than the algorithm op-erated in the proactive mode.The proactive mode:The mobile client adopting theproactive mode periodically executes the algorithm in back-ground.The mobile client can cloak its location into a spa-tial region immediately,once it wants to communicate withthe location-based database server.The proactive mode pro-vides a better response time than the on-demand mode,but it generally incurs higher communication overhead and giveslower quality of service than the on-demand mode.5.ANONYMOUS LOCATION-BASEDSERVICESHaving the spatial cloaked region as an output form Algo-rithm1,the mobile user m sends her request to the location-based server through an agent p that is randomly selected.Existing location-based database servers can support onlyexact point locations rather than cloaked regions.In or-der to be able to work with a spatial region,location-basedservers need to be equipped with a privacy-aware queryprocessor(e.g.,see[29,31]).The main idea of the privacy-aware query processor is to return a list of candidate answerrather than the exact query answer.Then,the mobile user m willﬁlter the candidate list to eliminate its false positives andﬁnd its exact answer.The tighter the spatial cloaked re-gion,the lower is the size of the candidate answer,and hencethe better is the performance of the privacy-aware query processor.However,tight cloaked regions may represent re-laxed privacy constrained.Thus,a trade-oﬀbetween the user privacy and the quality of service can be achieved[31]. Figure4(a)depicts such scenario by showing the data stored at the server side.There are32target objects,i.e., gas stations,T1to T32represented as black circles,the shaded area represents the spatial cloaked area of the mo-bile client who issued the query.For clariﬁcation,the actual mobile client location is plotted in Figure4(a)as a black square inside the cloaked area.However,such information is neither stored at the server side nor revealed to the server. The privacy-aware query processor determines a range that includes all target objects that are possibly contributing to the answer given that the actual location of the mobile client could be anywhere within the shaded area.The range is rep-resented as a bold rectangle,as depicted in Figure4(b).The server sends a list of candidate answers,i.e.,T8,T12,T13, T16,T17,T21,and T22,back to the agent.The agent next for-(a)Server Side(b)Client SideFigure4:Anonymous location-based services wards the candidate answers to the requesting mobile client either through single-hop communication or through multi-hop routing.Finally,the mobile client can get the actualanswer,i.e.,T13,byﬁltering out the false positives from thecandidate answers.The algorithmic details of the privacy-aware query proces-sor is beyond the scope of this paper.Interested readers are referred to[31]for more details.6.EXPERIMENTAL RESULTSIn this section,we evaluate and compare the scalabilityand eﬃciency of the P2P spatial cloaking algorithm in boththe on-demand and proactive modes with respect to the av-erage response time per query,the average number of mes-sages per query,and the size of the returned candidate an-swers from the location-based database server.The queryresponse time in the on-demand mode is deﬁned as the timeelapsed between a mobile client starting to search k−1peersand receiving the candidate answers from the agent.On theother hand,the query response time in the proactive mode is deﬁned as the time elapsed between a mobile client startingto forward its query along with the cloaked spatial regionto the agent and receiving the candidate answers from theagent.The simulation model is implemented in C++usingCSIM[35].In all the experiments in this section,we consider an in-dividual random walk model that is based on“random way-point”model[7,8].At the beginning,the mobile clientsare randomly distributed in a spatial space of1,000×1,000square meters,in which a uniform grid structure of100×100cells is constructed.Each mobile client randomly chooses itsown destination in the space with a randomly determined speed s from a uniform distribution U(v min,v max).When the mobile client reaches the destination,it comes to a stand-still for one second to determine its next destination.Afterthat,the mobile client moves towards its new destinationwith another speed.All the mobile clients repeat this move-ment behavior during the simulation.The time interval be-tween two consecutive queries generated by a mobile client follows an exponential distribution with a mean of ten sec-onds.All the experiments consider one half-duplex wirelesschannel for a mobile client to communicate with its peers with a total bandwidth of2Mbps and a transmission range of250meters.When a mobile client wants to communicate with other peers or the location-based database server,it has to wait if the requested channel is busy.In the simulated mobile environment,there is a centralized location-based database server,and one wireless communication channel between the location-based database server and the mobile。

centrecom fs980m系列快速以太网管理型交换机command参考手册（alliedwar

Centre COM ®FS980M Series FAST ETHERNET MANAGED ACCESS SWITCHES Command Reference for AlliedWare Plus™ Version 5.4.6-2.x FS980M/9FS980M/9PSFS980M/18FS980M/18PS FS980M/28FS980M/28PS FS980M/52FS980M/52PSAcknowledgmentsThis product includes software developed by the University of California, Berkeley and its contributors.Copyright©1982, 1986, 1990, 1991, 1993 The Regents of the University of California.All rights reserved.This product includes software developed by the OpenSSL Project for use in the OpenSSL Toolkit. For information about this see /Copyright©1998-2008 The OpenSSL Project. All rights reserved.This product includes software licensed under v2 and v3 of the GNU General Public License, available from: /licenses/gpl2.html and /licenses/gpl.html respectively.Source code for all GPL licensed software in this product can be obtained from the Allied Telesis GPL Code Download Center at:/support/Allied Telesis is committed to meeting the requirements of the open source licenses including the GNU General Public License (GPL) and will make all required source code available.If you would like a copy of the GPL source code contained in Allied Telesis products, please send us a request by registered mail including a check for US$15 to cover production and shipping costs and a CD with the GPL code will be mailed to you.GPL Code RequestAllied Telesis Labs (Ltd)PO Box 8011ChristchurchNew ZealandAllied Telesis, AlliedWare Plus, Allied Telesis Management Framework, EPSRing, SwitchBlade, VCStack, and VCStack Plus are trademarks or registered trademarks in the United States and elsewhere of Allied Telesis, Inc.Microsoft and Internet Explorer are registered trademarks of Microsoft Corporation. All other product names, company names, logos or other designations mentioned herein may be trademarks or registered trademarks of their respective owners.© 2017 Allied Telesis, Inc.All rights reserved. No part of this publication may be reproduced without prior written permission from Allied Telesis, Inc.Allied Telesis, Inc. reserves the right to make changes in specifications and other information contained in this document without prior written notice. The information provided herein is subject to change without notice. In no event shall Allied Telesis, Inc. be liable for any incidental, special, indirect, or consequential damages whatsoever, including but not limited to lost profits, arising out of or related to this manual or the information contained herein, even if Allied Telesis, Inc. has been advised of, known, or should have known, the possibility of such damages.ContentsPART 1:Setup and Troubleshooting . . . . . . . . . . . . . . . . . . . . . . . . . . . .58Chapter 1:CLI Navigation Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59configure terminal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .60disable (Privileged Exec mode) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61do . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62enable (Privileged Exec mode) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .63end . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65exit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .66help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67logout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .68show history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .69Chapter 2:File and Configuration Management Commands . . . . . . . . . . . . . . .70 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70autoboot enable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .74boot config-file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75boot config-file backup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77boot system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .78boot system backup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .80cd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .81copy (filename) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .82copy current-software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .84copy debug . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .85copy running-config . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .86copy startup-config . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87copy zmodem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .88create autoboot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .89delete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .90delete debug . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .91dir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .92edit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .94edit (filename) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .95erase startup-config . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .96ip tftp source-interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .97ipv6 tftp source-interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .98mkdir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .99move . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .100move debug . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .101pwd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .102rmdir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .103show autoboot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .104show boot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .105show file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .107show file systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .108show running-config . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .110show running-config interface . . . . . . . . . . . . . . . . . . . . . . . . . . . .114show startup-config . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .116show version . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .117write file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .118write memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .119write terminal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .120Chapter 3:User Access Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .121clear line console . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .123clear line vty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .124enable password . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .125enable secret . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .128exec-timeout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .131flowcontrol hardware (asyn/console) . . . . . . . . . . . . . . . . . . . . . . . .133length (asyn) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .135line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .136privilege level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .138security-password history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .139security-password forced-change . . . . . . . . . . . . . . . . . . . . . . . . . .140security-password lifetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .141security-password minimum-categories . . . . . . . . . . . . . . . . . . . . . .142security-password minimum-length . . . . . . . . . . . . . . . . . . . . . . . . .143security-password reject-expired-pwd . . . . . . . . . . . . . . . . . . . . . . .144security-password warning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .145service advanced-vty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .146service password-encryption . . . . . . . . . . . . . . . . . . . . . . . . . . . . .147service telnet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .148show privilege . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .149show security-password configuration . . . . . . . . . . . . . . . . . . . . . . .150show security-password user . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151show telnet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .152show users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .153telnet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .154telnet server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .155terminal length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .156terminal resize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .157username . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .158Chapter 4:GUI Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .160atmf topology-gui enable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .161gui-timeout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .162log event-host . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .164service http . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .165show http . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .166Chapter 5:System Configuration and Monitoring Commands . . . . . . . . . . . . 167 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .167banner exec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .169banner login (system) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .171banner motd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .173clock set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .175clock summer-time date . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .176clock summer-time recurring . . . . . . . . . . . . . . . . . . . . . . . . . . . . .178clock timezone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .180ecofriendly led . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .181findme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .182findme trigger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .184hostname . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .185no debug all . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .187reboot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .188reload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .189show clock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .190show cpu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .192show cpu history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .195show debugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .198show ecofriendly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .199show interface memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .200show memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .202show memory allocations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .204show memory history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .206show memory pools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .208show memory shared . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .209show process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .210show reboot history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .213show router-id . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .214show system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .215show system environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .216show system interrupts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .217show system mac . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .218show system serialnumber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .219show tech-support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .220speed (asyn) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .222terminal monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .224undebug all . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .225Chapter 6:Pluggables and Cabling Commands . . . . . . . . . . . . . . . . . . . . . . 226 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .226clear test cable-diagnostics tdr . . . . . . . . . . . . . . . . . . . . . . . . . . . .227show system pluggable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .228show system pluggable detail . . . . . . . . . . . . . . . . . . . . . . . . . . . . .230show system pluggable diagnostics . . . . . . . . . . . . . . . . . . . . . . . . .233show test cable-diagnostics tdr . . . . . . . . . . . . . . . . . . . . . . . . . . . .235test cable-diagnostics tdr interface . . . . . . . . . . . . . . . . . . . . . . . . . .236Chapter 7:Logging Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .237clear exception log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .239clear log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .240clear log buffered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .241clear log permanent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .242default log buffered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .243default log console . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .244default log email . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .245default log host . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .246default log monitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .247default log permanent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .248log buffered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .249log buffered (filter) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .250log buffered exclude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .253log buffered size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .256log console . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .257log console (filter) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .258log console exclude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .261log email . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .264log email (filter) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .265log email exclude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .268log email time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .271log facility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .273log host . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .275log host (filter) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .277log host exclude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .280log host source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .283log host time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .284log monitor (filter) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .286log monitor exclude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .289log permanent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .292log permanent (filter) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .293log permanent exclude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .296log permanent size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .299log-rate-limit nsm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .300log trustpoint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .302show counter log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .303show exception log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .304show log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .305show log config . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .307show log permanent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .309show running-config log . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .311Chapter 8:Scripting Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .312activate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .313wait . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .315Chapter 9:Interface Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .316description (interface) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .317interface (to configure) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .318mtu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .320show interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .322show interface brief . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .325show interface memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .326show interface status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .328shutdown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .330Chapter 10:Port Mirroring Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .331mirror interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .332show mirror . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .334show mirror interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .335Chapter 11:Interface Testing Commands . . . . . . . . . . . . . . . . . . . . . . . . . . 336 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .336clear test interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .337service test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .338test interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .339 PART 2:Layer Two Switching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341Chapter 12:Switching Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .342backpressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .344clear loop-protection counters . . . . . . . . . . . . . . . . . . . . . . . . . . . .346clear mac address-table dynamic . . . . . . . . . . . . . . . . . . . . . . . . . . .347clear mac address-table static . . . . . . . . . . . . . . . . . . . . . . . . . . . . .349clear port counter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .350clear port-security intrusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .351debug loopprot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .354debug platform packet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .355duplex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .357flowcontrol (switch port) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .359linkflap action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .361loop-protection loop-detect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .362loop-protection action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .363loop-protection action-delay-time . . . . . . . . . . . . . . . . . . . . . . . . . .364loop-protection timeout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .365mac address-table acquire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .366mac address-table ageing-time . . . . . . . . . . . . . . . . . . . . . . . . . . . .367mac address-table static . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .368mac address-table thrash-limit . . . . . . . . . . . . . . . . . . . . . . . . . . . .369platform jumboframe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .370platform stop-unreg-mc-flooding . . . . . . . . . . . . . . . . . . . . . . . . . .371show debugging loopprot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .374show debugging platform packet . . . . . . . . . . . . . . . . . . . . . . . . . .375show flowcontrol interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .376show interface err-disabled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .377show interface switchport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .378show loop-protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .379show mac address-table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .381show mac address-table thrash-limit . . . . . . . . . . . . . . . . . . . . . . . .383show platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .384show platform classifier statistics utilization brief . . . . . . . . . . . . . . . . .385show platform port . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .386show port-security interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .391show port-security intrusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .392show storm-control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .393speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .394storm-control level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .396switchport port-security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .397switchport port-security aging . . . . . . . . . . . . . . . . . . . . . . . . . . . .398switchport port-security maximum . . . . . . . . . . . . . . . . . . . . . . . . .399switchport port-security violation . . . . . . . . . . . . . . . . . . . . . . . . . .400thrash-limiting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .401undebug loopprot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .402undebug platform packet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .403Chapter 13:VLAN Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .404private-vlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .406private-vlan association . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .407show vlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .408show vlan classifier group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .409show vlan classifier group interface . . . . . . . . . . . . . . . . . . . . . . . . .410show vlan classifier interface group . . . . . . . . . . . . . . . . . . . . . . . . .411show vlan classifier rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .412show vlan private-vlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .413switchport access vlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .414switchport enable vlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .415switchport mode access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .416switchport mode private-vlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . .417switchport mode private-vlan trunk promiscuous . . . . . . . . . . . . . . . .418switchport mode private-vlan trunk secondary . . . . . . . . . . . . . . . . . .420switchport mode trunk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .422switchport private-vlan host-association . . . . . . . . . . . . . . . . . . . . . .423switchport private-vlan mapping . . . . . . . . . . . . . . . . . . . . . . . . . . .424switchport trunk allowed vlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . .425switchport trunk native vlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .428switchport voice dscp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .429switchport voice vlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .430switchport voice vlan priority . . . . . . . . . . . . . . . . . . . . . . . . . . . . .432vlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .433vlan classifier activate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .434vlan classifier group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .435vlan classifier rule ipv4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .436。

The Hadoop Distributed File System

The Hadoop Distributed File System Konstantin Shvachko, Hairong Kuang, Sanjay Radia, Robert ChanslerYahoo!Sunnyvale, California USA{Shv, Hairong, SRadia, Chansler}@Abstract—The Hadoop Distributed File System (HDFS) isdesigned to store very large data sets reliably, and to streamthose data sets at high bandwidth to user applications. In a largecluster, thousands of servers both host directly attached storageand execute user application tasks. By distributing storage andcomputation across many servers, the resource can grow withdemand while remaining economical at every size. We describethe architecture of HDFS and report on experience using HDFSto manage 25 petabytes of enterprise data at Yahoo!.Keywords: Hadoop, HDFS, distributed file systemI.I NTRODUCTION AND R ELATED W ORKHadoop [1][16][19] provides a distributed file system and aframework for the analysis and transformation of very largedata sets using the MapReduce [3] paradigm. An importantcharacteristic of Hadoop is the partitioning of data and compu-tation across many (thousands) of hosts, and executing applica-tion computations in parallel close to their data. A Hadoopcluster scales computation capacity, storage capacity and IObandwidth by simply adding commodity servers. Hadoop clus-ters at Yahoo! span 25 000 servers, and store 25 petabytes ofapplication data, with the largest cluster being 3500 servers.One hundred other organizations worldwide report usingHadoop.HDFS Distributed file system Subject of this paper!MapReduce Distributed computation framework HBase Column-oriented table servicePig Dataflow language and parallel execution frameworkHive Data warehouse infrastructureZooKeeper Distributed coordination serviceChukwa System for collecting management dataAvro Data serialization systemTable 1. Hadoop project componentsHadoop is an Apache project; all components are available via the Apache open source license. Yahoo! has developed and contributed to 80% of the core of Hadoop (HDFS and MapRe-duce). HBase was originally developed at Powerset, now a department at Microsoft. Hive [15] was originated and devel-developed at Facebook. Pig [4], ZooKeeper [6], and Chukwa were originated and developed at Yahoo! Avro was originated at Yahoo! and is being co-developed with Cloudera.HDFS is the file system component of Hadoop. While the interface to HDFS is patterned after the UNIX file system, faithfulness to standards was sacrificed in favor of improved performance for the applications at hand.HDFS stores file system metadata and application data separately. As in other distributed file systems, like PVFS [2][14], Lustre [7] and GFS [5][8], HDFS stores metadata on a dedicated server, called the NameNode. Application data are stored on other servers called DataNodes. All servers are fully connected and communicate with each other using TCP-based protocols.Unlike Lustre and PVFS, the DataNodes in HDFS do not use data protection mechanisms such as RAID to make the data durable. Instead, like GFS, the file content is replicated on mul-tiple DataNodes for reliability. While ensuring data durability, this strategy has the added advantage that data transfer band-width is multiplied, and there are more opportunities for locat-ing computation near the needed data.Several distributed file systems have or are exploring truly distributed implementations of the namespace. Ceph [17] has a cluster of namespace servers (MDS) and uses a dynamic sub-tree partitioning algorithm in order to map the namespace tree to MDSs evenly. GFS is also evolving into a distributed name-space implementation [8]. The new GFS will have hundreds of namespace servers (masters) with 100 million files per master. Lustre [7] has an implementation of clustered namespace on its roadmap for Lustre 2.2 release. The intent is to stripe a direc-tory over multiple metadata servers (MDS), each of which con-tains a disjoint portion of the namespace. A file is assigned to a particular MDS using a hash function on the file name.II.A RCHITECTURENodeThe HDFS namespace is a hierarchy of files and directo-ries. Files and directories are represented on the NameNode by inodes, which record attributes like permissions, modification and access times, namespace and disk space quotas. The file content is split into large blocks (typically 128 megabytes, but user selectable file-by-file) and each block of the file is inde-pendently replicated at multiple DataNodes (typically three, but user selectable file-by-file). The NameNode maintains the namespace tree and the mapping of file blocks to DataNodes(the physical location of file data). An HDFS client wanting to read a file first contacts the NameNode for the locations of data blocks comprising the file and then reads block contents from the DataNode closest to the client. When writing data, the cli-ent requests the NameNode to nominate a suite of three DataNodes to host the block replicas. The client then writes data to the DataNodes in a pipeline fashion. The current design has a single NameNode for each cluster. The cluster can have thousands of DataNodes and tens of thousands of HDFS clients per cluster, as each DataNode may execute multiple application tasks concurrently.HDFS keeps the entire namespace in RAM. The inode data and the list of blocks belonging to each file comprise the meta-data of the name system called the image. The persistent recordof the image stored in the local host’s native files system is called a checkpoint.The NameNode also stores the modifica-tion log of the image called the journal in the local host’s na-tive file system. For improved durability, redundant copies of the checkpoint and journal can be made at other servers. Dur-ing restarts the NameNode restores the namespace by reading the namespace and replaying the journal. The locations of block replicas may change over time and are not part of the persistent checkpoint.B.DataNodesEach block replica on a DataNode is represented by two files in the local host’s native file system. The first file contains the data itself and the second file is block’s metadata including checksums for the block data and the block’s generation stamp. The size of the data file equals the actual length of the block and does not require extra space to round it up to the nominal block size as in traditional file systems. Thus, if a block is half full it needs only half of the space of the full block on the local drive.During startup each DataNode connects to the NameNode and performs a handshake. The purpose of the handshake is to verify the namespace ID and the software version of the DataNode. If either does not match that of the NameNode the DataNode automatically shuts down.The namespace ID is assigned to the file system instance when it is formatted. The namespace ID is persistently storedon all nodes of the cluster. Nodes with a different namespaceID will not be able to join the cluster, thus preserving the integ-rity of the file system.The consistency of software versions is important because incompatible version may cause data corruption or loss, and on large clusters of thousands of machines it is easy to overlook nodes that did not shut down properly prior to the software upgrade or were not available during the upgrade.A DataNode that is newly initialized and without any namespace ID is permitted to join the cluster and receive the cluster’s namespace ID.After the handshake the DataNode registers with the NameNode. DataNodes persistently store their unique storage IDs. The storage ID is an internal identifier of the DataNode, which makes it recognizable even if it is restarted with a differ-ent IP address or port. The storage ID is assigned to the DataNode when it registers with the NameNode for the first time and never changes after that.A DataNode identifies block replicas in its possession to the NameNode by sending a block report. A block report contains the block id, the generation stamp and the length for each block replica the server hosts. The first block report is sent immedi-ately after the DataNode registration. Subsequent block reports are sent every hour and provide the NameNode with an up-to-date view of where block replicas are located on the cluster.During normal operation DataNodes send heartbeats to the NameNode to confirm that the DataNode is operating and the block replicas it hosts are available. The default heartbeat in-terval is three seconds. If the NameNode does not receive a heartbeat from a DataNode in ten minutes the NameNode con-siders the DataNode to be out of service and the block replicas hosted by that DataNode to be unavailable. The NameNode then schedules creation of new replicas of those blocks on other DataNodes.Heartbeats from a DataNode also carry information about total storage capacity, fraction of storage in use, and the num-ber of data transfers currently in progress. These statistics are used for the NameNode’s space allocation and load balancing decisions.The NameNode does not directly call DataNodes. It uses replies to heartbeats to send instructions to the DataNodes. The instructions include commands to:•replicate blocks to other nodes;•remove local block replicas;•re-register or to shut down the node;•send an immediate block report.These commands are important for maintaining the overall system integrity and therefore it is critical to keep heartbeats frequent even on big clusters. The NameNode can process thousands of heartbeats per second without affecting other NameNode operations.C.HDFS ClientUser applications access the file system using the HDFS client, a code library that exports the HDFS file system inter-face.Similar to most conventional file systems, HDFS supports operations to read, write and delete files, and operations to cre-ate and delete directories. The user references files and directo-ries by paths in the namespace. The user application generally does not need to know that file system metadata and storage are on different servers, or that blocks have multiple replicas.When an application reads a file, the HDFS client first asks the NameNode for the list of DataNodes that host replicas of the blocks of the file. It then contacts a DataNode directly and requests the transfer of the desired block. When a client writes, it first asks the NameNode to choose DataNodes to host repli-cas of the first block of the file. The client organizes a pipeline from node-to-node and sends the data. When the first block is filled, the client requests new DataNodes to be chosen to host replicas of the next block. A new pipeline is organized, and theFigure 1. An HDFS client creates a new file by giving its path to the NameNode. For each block of the file, the NameNode returns a list of DataNodes to host its replicas. The client then pipelines data to the chosen DataNodes, which eventually confirm thecreation of the block replicas to the NameNode.client sends the further bytes of the file. Each choice of DataNodes is likely to be different. The interactions among the client, the NameNode and the DataNodes are illustrated inFig. 1.Unlike conventional file systems, HDFS provides an API that exposes the locations of a file blocks. This allows applica-tions like the MapReduce framework to schedule a task to where the data are located, thus improving the read perform-ance. It also allows an application to set the replication factor of a file. By default a file’s replication factor is three. For criti-cal files or files which are accessed very often, having a higher replication factor improves their tolerance against faults and increase their read bandwidth.D.Image and JournalThe namespace image is the file system metadata that de-scribes the organization of application data as directories and files. A persistent record of the image written to disk is called a checkpoint. The journal is a write-ahead commit log for changes to the file system that must be persistent. For each client-initiated transaction, the change is recorded in the jour-nal, and the journal file is flushed and synched before the change is committed to the HDFS client. The checkpoint file is never changed by the NameNode; it is replaced in its entirety when a new checkpoint is created during restart, when re-quested by the administrator, or by the CheckpointNode de-scribed in the next section. During startup the NameNode ini-tializes the namespace image from the checkpoint, and then replays changes from the journal until the image is up-to-date with the last state of the file system. A new checkpoint and empty journal are written back to the storage directories before the NameNode starts serving clients.If either the checkpoint or the journal is missing, or be-comes corrupt, the namespace information will be lost partly or entirely. In order to preserve this critical information HDFS can be configured to store the checkpoint and journal in multiplestorage directories. Recommended practice is to place the di-rectories on different volumes, and for one storage directory tobe on a remote NFS server. The first choice prevents loss from single volume failures, and the second choice protects against failure of the entire node. If the NameNode encounters an error writing the journal to one of the storage directories it automati-cally excludes that directory from the list of storage directories. The NameNode automatically shuts itself down if no storage directory is available.The NameNode is a multithreaded system and processesrequests simultaneously from multiple clients. Saving a trans-action to disk becomes a bottleneck since all other threads need to wait until the synchronous flush-and-sync procedure initi-ated by one of them is complete. In order to optimize this process the NameNode batches multiple transactions initiated by different clients. When one of the NameNode’s threads ini-tiates a flush-and-sync operation, all transactions batched at that time are committed together. Remaining threads only need to check that their transactions have been saved and do not need to initiate a flush-and-sync operation.E.CheckpointNodeThe NameNode in HDFS, in addition to its primary role serving client requests, can alternatively execute either of two other roles, either a CheckpointNode or a BackupNode. The role is specified at the node startup.The CheckpointNode periodically combines the existing checkpoint and journal to create a new checkpoint and an empty journal. The CheckpointNode usually runs on a different host from the NameNode since it has the same memory re-quirements as the NameNode. It downloads the current check-point and journal files from the NameNode, merges them lo-cally, and returns the new checkpoint back to the NameNode.Creating periodic checkpoints is one way to protect the file system metadata. The system can start from the most recent checkpoint if all other persistent copies of the namespace im-age or journal are unavailable.Creating a checkpoint lets the NameNode truncate the tail of the journal when the new checkpoint is uploaded to the NameNode. HDFS clusters run for prolonged periods of time without restarts during which the journal constantly grows. If the journal grows very large, the probability of loss or corrup-tion of the journal file increases. Also, a very large journal ex-tends the time required to restart the NameNode. For a large cluster, it takes an hour to process a week-long journal. Good practice is to create a daily checkpoint.F.BackupNodeA recently introduced feature of HDFS is the BackupNode. Like a CheckpointNode, the BackupNode is capable of creating periodic checkpoints, but in addition it maintains an in-memory, up-to-date image of the file system namespace that is always synchronized with the state of the NameNode.The BackupNode accepts the journal stream of namespace transactions from the active NameNode, saves them to its own storage directories, and applies these transactions to its own namespace image in memory. The NameNode treats the BackupNode as a journal store the same as it treats journal files in its storage directories. If the NameNode fails, the BackupNode’s image in memory and the checkpoint on disk is a record of the latest namespace state.The BackupNode can create a checkpoint without down-loading checkpoint and journal files from the active NameNode, since it already has an up-to-date namespace im-age in its memory. This makes the checkpoint process on the BackupNode more efficient as it only needs to save the name-space into its local storage directories.The BackupNode can be viewed as a read-only NameNode. It contains all file system metadata information except for block locations. It can perform all operations of the regular NameNode that do not involve modification of the namespace or knowledge of block locations. Use of a BackupNode pro-vides the option of running the NameNode without persistent storage, delegating responsibility for the namespace state per-sisting to the BackupNode.G.Upgrades, File System SnapshotsDuring software upgrades the possibility of corrupting the system due to software bugs or human mistakes increases. The purpose of creating snapshots in HDFS is to minimize potential damage to the data stored in the system during upgrades.The snapshot mechanism lets administrators persistently save the current state of the file system, so that if the upgrade results in data loss or corruption it is possible to rollback the upgrade and return HDFS to the namespace and storage state as they were at the time of the snapshot.The snapshot (only one can exist) is created at the cluster administrator’s option whenever the system is started. If a snapshot is requested, the NameNode first reads the checkpoint and journal files and merges them in memory. Then it writes the new checkpoint and the empty journal to a new location, so that the old checkpoint and journal remain unchanged.During handshake the NameNode instructs DataNodes whether to create a local snapshot. The local snapshot on the DataNode cannot be created by replicating the data files direc-tories as this will require doubling the storage capacity of every DataNode on the cluster. Instead each DataNode creates a copy of the storage directory and hard links existing block files into it. When the DataNode removes a block it removes only the hard link, and block modifications during appends use the copy-on-write technique. Thus old block replicas remain un-touched in their old directories.The cluster administrator can choose to roll back HDFS to the snapshot state when restarting the system. The NameNode recovers the checkpoint saved when the snapshot was created. DataNodes restore the previously renamed directories and initi-ate a background process to delete block replicas created after the snapshot was made. Having chosen to roll back, there is no provision to roll forward. The cluster administrator can recover the storage occupied by the snapshot by commanding the sys-tem to abandon the snapshot, thus finalizing the software up-grade.System evolution may lead to a change in the format of the NameNode’s checkpoint and journal files, or in the data repre-sentation of block replica files on DataNodes. The layout ver-sion identifies the data representation formats, and is persis-tently stored in the NameNode’s and the DataNodes’ storage directories. During startup each node compares the layout ver-sion of the current software with the version stored in its stor-age directories and automatically converts data from older for-mats to the newer ones. The conversion requires the mandatory creation of a snapshot when the system restarts with the new software layout version.HDFS does not separate layout versions for the NameNode and DataNodes because snapshot creation must be an all-cluster effort rather than a node-selective event. If an upgraded NameNode due to a software bug purges its image then back-ing up only the namespace state still results in total data loss, as the NameNode will not recognize the blocks reported by DataNodes, and will order their deletion. Rolling back in this case will recover the metadata, but the data itself will be lost. A coordinated snapshot is required to avoid a cataclysmic de-struction.III.F ILE I/O O PERATIONS AND R EPLICA MANGEMENT A.File Read and WriteAn application adds data to HDFS by creating a new file and writing the data to it. After the file is closed, the bytes writ-ten cannot be altered or removed except that new data can be added to the file by reopening the file for append. HDFS im-plements a single-writer, multiple-reader model.The HDFS client that opens a file for writing is granted a lease for the file; no other client can write to the file. The writ-ing client periodically renews the lease by sending a heartbeat to the NameNode. When the file is closed, the lease is revoked.The lease duration is bound by a soft limit and a hard limit. Until the soft limit expires, the writer is certain of exclusive access to the file. If the soft limit expires and the client fails to close the file or renew the lease, another client can preempt the lease. If after the hard limit expires (one hour) and the client has failed to renew the lease, HDFS assumes that the client has quit and will automatically close the file on behalf of the writer, and recover the lease. The writer's lease does not prevent other clients from reading the file; a file may have many concurrent readers.An HDFS file consists of blocks. When there is a need for a new block, the NameNode allocates a block with a unique block ID and determines a list of DataNodes to host replicas of the block. The DataNodes form a pipeline, the order of which minimizes the total network distance from the client to the last DataNode. Bytes are pushed to the pipeline as a sequence of packets. The bytes that an application writes first buffer at the client side. After a packet buffer is filled (typically 64 KB), the data are pushed to the pipeline. The next packet can be pushed to the pipeline before receiving the acknowledgement for the previous packets. The number of outstanding packets is limited by the outstanding packets window size of the client.After data are written to an HDFS file, HDFS does not pro-vide any guarantee that data are visible to a new reader until the file is closed. If a user application needs the visibility guaran-tee, it can explicitly call the hflush operation. Then the current packet is immediately pushed to the pipeline, and the hflush operation will wait until all DataNodes in the pipeline ac-knowledge the successful transmission of the packet. All data written before the hflush operation are then certain to be visibleto readers.Figure 2. Data pipeline during block construction If no error occurs, block construction goes through three stages as shown in Fig. 2 illustrating a pipeline of three DataNodes (DN) and a block of five packets. In the picture, bold lines represent data packets, dashed lines represent ac-knowledgment messages, and thin lines represent control mes-sages to setup and close the pipeline. Vertical lines represent activity at the client and the three DataNodes where time pro-ceeds from top to bottom. From t0to t1is the pipeline setup stage. The interval t1 to t2 is the data streaming stage, where t1 is the time when the first data packet gets sent and t2 is the time that the acknowledgment to the last packet gets received. Here an hflush operation transmits the second packet. The hflush indication travels with the packet data and is not a separate operation. The final interval t2 to t3 is the pipeline close stage for this block.In a cluster of thousands of nodes, failures of a node (most commonly storage faults) are daily occurrences. A replica stored on a DataNode may become corrupted because of faults in memory, disk, or network. HDFS generates and stores checksums for each data block of an HDFS file. Checksums are verified by the HDFS client while reading to help detect any corruption caused either by client, DataNodes, or network. When a client creates an HDFS file, it computes the checksum sequence for each block and sends it to a DataNode along with the data. A DataNode stores checksums in a metadata file sepa-rate from the block’s data file. When HDFS reads a file, each block’s data and checksums are shipped to the client. The client computes the checksum for the received data and verifies that the newly computed checksums matches the checksums it re-ceived. If not, the client notifies the NameNode of the corrupt replica and then fetches a different replica of the block from another DataNode.When a client opens a file to read, it fetches the list of blocks and the locations of each block replica from the NameNode. The locations of each block are ordered by their distance from the reader. When reading the content of a block, the client tries the closest replica first. If the read attempt fails, the client tries the next replica in sequence. A read may fail if the target DataNode is unavailable, the node no longer hosts a replica of the block, or the replica is found to be corrupt when checksums are tested.HDFS permits a client to read a file that is open for writing. When reading a file open for writing, the length of the last block still being written is unknown to the NameNode. In this case, the client asks one of the replicas for the latest length be-fore starting to read its content.The design of HDFS I/O is particularly optimized for batch processing systems, like MapReduce, which require high throughput for sequential reads and writes. However, many efforts have been put to improve its read/write response time in order to support applications like Scribe that provide real-time data streaming to HDFS, or HBase that provides random, real-time access to large tables.B.Block PlacementFor a large cluster, it may not be practical to connect all nodes in a flat topology. A common practice is to spread the nodes across multiple racks. Nodes of a rack share a switch, and rack switches are connected by one or more core switches. Communication between two nodes in different racks has to go through multiple switches. In most cases, network bandwidthbetween nodes in the same rack is greater than network band-width between nodes in different racks. Fig. 3 describes a clus-ter with two racks, each of which contains three nodes.Figure 3. Cluster topology exampleHDFS estimates the network bandwidth between two nodes by their distance. The distance from a node to its parent node is assumed to be one. A distance between two nodes can be cal-culated by summing up their distances to their closest common ancestor. A shorter distance between two nodes means that the greater bandwidth they can utilize to transfer data.HDFS allows an administrator to configure a script that re-turns a node’s rack identification given a node’s address. The NameNode is the central place that resolves the rack location of each DataNode. When a DataNode registers with the NameNode, the NameNode runs a configured script to decide which rack the node belongs to. If no such a script is config-ured, the NameNode assumes that all the nodes belong to a default single rack.The placement of replicas is critical to HDFS data reliabil-ity and read/write performance. A good replica placement pol-icy should improve data reliability, availability, and network bandwidth utilization. Currently HDFS provides a configurable block placement policy interface so that the users and research-ers can experiment and test any policy that’s optimal for their applications.The default HDFS block placement policy provides a tradeoff between minimizing the write cost, and maximizing data reliability, availability and aggregate read bandwidth. When a new block is created, HDFS places the first replica on the node where the writer is located, the second and the third replicas on two different nodes in a different rack, and the rest are placed on random nodes with restrictions that no more than one replica is placed at one node and no more than two replicas are placed in the same rack when the number of replicas is less than twice the number of racks. The choice to place the second and third replicas on a different rack better distributes the block replicas for a single file across the cluster. If the first two repli-cas were placed on the same rack, for any file, two-thirds of its block replicas would be on the same rack.After all target nodes are selected, nodes are organized as a pipeline in the order of their proximity to the first replica. Data are pushed to nodes in this order. For reading, the NameNode first checks if the client’s host is located in the cluster. If yes, block locations are returned to the client in the order of its closeness to the reader. The block is read from DataNodes in this preference order. (It is usual for MapReduce applications to run on cluster nodes, but as long as a host can connect to the NameNode and DataNodes, it can execute the HDFS client.) This policy reduces the inter-rack and inter-node write traf-fic and generally improves write performance. Because the chance of a rack failure is far less than that of a node failure, this policy does not impact data reliability and availability guarantees. In the usual case of three replicas, it can reduce the aggregate network bandwidth used when reading data since a block is placed in only two unique racks rather than three.The default HDFS replica placement policy can be summa-rized as follows:1.No Datanode contains more than one replica ofany block.2.No rack contains more than two replicas of thesame block, provided there are sufficient racks onthe cluster.C.Replication managementThe NameNode endeavors to ensure that each block always has the intended number of replicas. The NameNode detects that a block has become under- or over-replicated when a block report from a DataNode arrives. When a block becomes over replicated, the NameNode chooses a replica to remove. The NameNode will prefer not to reduce the number of racks that host replicas, and secondly prefer to remove a replica from the DataNode with the least amount of available disk space. The goal is to balance storage utilization across DataNodes without reducing the block’s availability.When a block becomes under-replicated, it is put in the rep-lication priority queue. A block with only one replica has the highest priority, while a block with a number of replicas that is greater than two thirds of its replication factor has the lowest priority. A background thread periodically scans the head of the replication queue to decide where to place new replicas. Block replication follows a similar policy as that of the new block placement. If the number of existing replicas is one, HDFS places the next replica on a different rack. In case that the block has two existing replicas, if the two existing replicas are on the same rack, the third replica is placed on a different rack; other-wise, the third replica is placed on a different node in the same rack as an existing replica. Here the goal is to reduce the cost of creating new replicas.The NameNode also makes sure that not all replicas of a block are located on one rack. If the NameNode detects that a block’s replicas end up at one rack, the NameNode treats the block as under-replicated and replicates the block to a different rack using the same block placement policy described above. After the NameNode receives the notification that the replica is created, the block becomes over-replicated. The NameNode then will decides to remove an old replica because the over-replication policy prefers not to reduce the number of racks.D.BalancerHDFS block placement strategy does not take into account DataNode disk space utilization. This is to avoid placing new—more likely to be referenced—data at a small subset of。

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S21 = e jθ I S12 = e jθ S33 = e jφ II
III
Pozar, Microwave Engineering, by Prof. JT Kuo, Dept. CM, NCTU, Fall 2009.
6
Four-Port Networks (Directional Couplers)
Pozar, Microwave Engineering, by Prof. JT Kuo, Dept. CM, NCTU, Fall 2009.
4
The Two Circulators
III II II III
I
⎡0 0 1 ⎤ [S ] = ⎢1 0 0⎥ ⎢ ⎥ ⎢0 1 0 ⎥ ⎣ ⎦
Clockwise Circulator
If only two ports are matched, a lossless and reciprocal three-port can be realized. ⎡ 0 S12 S13 ⎤ (1) Port 1 & 2 are matched, S = ⎢ S12 0 S 23 ⎥ ⎢ ⎥ ⎢ S13 S 23 0 ⎥ ⎣ ⎦ (2) Lossless → S matrix is unitary S13*S23 = 0 S12*S13 + S23*S33 = 0 S23*S12 + S33*S13 = 0 |S12|2 + |S13|2 =1 |S12|2 + |S23|2 = 1 |S13|2 + |S32|2 + |S33|2 = 1 S13 = S23 = 0 and |S12| = |S33| = 1.
Hybrid Coupler: C = 3 dB oler quadrature hybrid θ = φ = 90o
anti-symmetric coupler magic-T hybrid or rat-race hybrid θ = 0o, φ = 180o
If S14 = S23 = 0, |S12|2 + |S13|2 = 1 |S12|2 + |S24|2 = 1 |S13|2 + |S34|2 = 1 |S24|2 + |S34|2 = 1 ⇒ |S13| = |S24|, |S12| = |S34| Choose S12 = S34 = α, S13 = β e jθ, S24 = β e jφ
⎡0 1 j 0 ⎤ ⎢ ⎥ 1 ⎢1 0 0 j ⎥ [S ] = 2 ⎢ j 0 0 1⎥ ⎢ ⎥ 0 j 1 0⎦ ⎣
⎡0 1 ⎢ 1 ⎢1 0 [S ] = 2 ⎢1 0 ⎢ ⎣0 − 1
0⎤ 0 − 1⎥ ⎥ 0 1⎥ ⎥ 1 0⎦ 1
Pozar, Microwave Engineering, by Prof. JT Kuo, Dept. CM, NCTU, Fall 2009.
Input |S41|2 = 0 Isolated Through |S21|2 = 1–β 2 = α 2 Coupled |S31|2 = β 2
Input |S41|2 = 0 Isolated
Through |S21|2 = 1−β 2 = α 2 Coupled |S31|2 = β 2
Coupling: C = 10 log (P1/P3) = −20 log|β| dB Directivity: D = 10 log (P3/P4) = −20 log|S41/β| dB Isolation: I = 10 log (P1/P4) = −20 log|S41| dB Note that (1) I = C + D (dB) (2) C, D, I > 1, C (dB), D (dB), I (dB) > 0 (3) D → ∞ and I → ∞ for an ideal coupler.
⎡0 ⎢α [S ] = ⎢ ⎢ jβ ⎢ ⎣0
⎡0 ⎢S S = ⎢ 12 ⎢ S13 ⎢ X ⎣ S14
S12 0 S 23 X S 24
S13 S 23 X 0 S34
S14 ⎤ X S 24 ⎥ ⎥ S34 ⎥ ⎥ 0⎦
(2) Anti-symmetric coupler: θ = 0, φ = π.
Pozar, Microwave Engineering, by Prof. JT Kuo, Dept. CM, NCTU, Fall 2009.
7
Four-Port Networks (Directional Couplers)
If S14 = S23 = 0, |S12|2 ⇒ + |S13|2 = 1, |S12|2 + |S24|2 =1 |S13|2 + |S34|2 = 1, |S24|2 + |S34|2 = 1 |S13| = |S24|, |S12| = |S34| Choose S12 = S34 = α, S13 = β e jθ, S24 = β e jφ S12*S13 + S24*S34 = 0 θ + φ = π ± 2nπ (1) Symmetric coupler: θ = φ = π/2
1
Single-Balanced MESFET Mixer with Coupler and Power Combiner
fRF
Branch-Line Coupler fLO , Local Osc.
Input Matching Network Input Matching Network
Output Matching Network Output Matching Network 180o Phase Shifter
Chapter 7
Power Dividers and Directional Couplers
P1 Divider Coupler Power divider P2 P3 P2 = α P1 P3 = (1–α )P1
P1 = P2 + P3
Divider Coupler Power combiner
For both divider and combiners, • Three-port network: T-junction • Four-port network: Directional coupler, hybrid
Pozar, Microwave Engineering, by Prof. JT Kuo, Dept. CM, NCTU, Fall 2009.
S12 = S23 = S31 = 0 & |S21| = |S32| = |S13| = 1; S21 = S32 = S13 = 0 & |S12| = |S23| = |S31| = 1.
The results show that Sij ≠ Sji, for i ≠ j, which implies that the device must be nonreciprocal. The two possible solutions are the following two circulators.
Wilkinson Power Divider
fIF
Pozar, Microwave Engineering, by Prof. JT Kuo, Dept. CM, NCTU, Fall 2009.
2
7.1 Basic Properties
Three-port network: X ⎡ S11 S = ⎢ S 21 ⎢ ⎢ S31 ⎣
I
⎡0 1 0 ⎤ [S ] = ⎢0 0 1⎥ ⎢ ⎥ ⎢1 0 0⎥ ⎣ ⎦
Counterclockwise Circulator
Pozar, Microwave Engineering, by Prof. JT Kuo, Dept. CM, NCTU, Fall 2009.
5
Three-Port Network
At least two of (S12, S13, S23) must be zero ⇒ inconsistent! A three-port network cannot be lossless, reciprocal, and matched at all ports.
Pozar, Microwave Engineering, by Prof. JT Kuo, Dept. CM, NCTU, Fall 2009.
3
Matched Lossless Three-port Nonreciprocal Network
Matched at all ports: (nonreciprocal)
⎡0 S = ⎢ S 21 ⎢ ⎢ S31 ⎣
S12 0 S32
S13 ⎤ S 23 ⎥ ⎥ 0⎥ ⎦
Lossless, [S] is unitary: |S12|2 + |S13|2 = 1, |S21|2 + |S23|2 = 1, |S31|2 + |S32|2 = 1, or S31*S32 = 0; S21*S23 = 0; S12*S13 = 0.
(1) Reciprocal and matched at all ports (2) Lossless, [S] is unitary
⎡0 ⎢S S = ⎢ 12 ⎢ S13 ⎢ ⎣ S14 S12 0 S 23 S 24 S13 S 23 0 S34 S14 ⎤ S 24 ⎥ ⎥ S34 ⎥ ⎥ 0⎦
S12 S 22 X S32
S13 ⎤ S 23 ⎥ ⎥ S33 ⎥ X ⎦
2 1 3-port 3
If no anisotropic material: Sij = Sji or [S] is symmetric. If all ports are matched, Sii = 0. If all ports are matched and the network is lossless, [S] is unitary, i.e., |S21|2 + |S31|2 = |S12|2 + |S13|2 = 1, S31*S32 = 0; |S12|2 + |S32|2 = |S21|2 + |S23|2 = 1, |S13|2 + |S23|2 = |S31|2 + |S32|2 = 1, S12*S13 = 0; S21*S23 = 0.