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2023 华为Datacom-HCIE 真题题库单项选择题1.[试题编号：190585] (单选题)华为SD-WAN解决方案中，当CPE位于NAT设备后的私网时，特别是两个站点的CPE同时位于NAT设备后的私网时，CPE之间需要使用NAT穿越技术。

华为SD-WAN解决方案中使用以下哪一项技术帮助CPE之间实现NAT穿越?A、NAT ALGB、NAT serverC、IPsec VPND、STUN答案：D解析：华为SD-WAN解决方案是一种通过网络控制器集中管理CPE设备、零配置开局的解决方案，可以帮助企业应对云服务带来的挑战，做到业务随需而变。

当CPE 位于NAT设备后的私网时，特别是两个站点的CPE同时位于NAT设备后的私网时，CPE之间需要使用NAT穿越技术，才能实现业务流量的互通。

华为SD-WAN 解决方案中使用STUN技术帮助CPE之间实现NAT穿越。

下面我来分析一下各个选项：A项：NAT ALG。

这个描述是错误的，因为NAT ALG是一种应用层网关技术，用于修改应用层报文中的IP地址和端口信息，以适应NAT转换后的地址变化，而不是用于实现NAT穿越。

B项：NAT server。

这个描述也是错误的，因为NAT server是一种NAT设备上的功能，用于将公网IP地址和端口映射到私网IP地址和端口，以提供对外服务，而不是用于实现NAT穿越。

C项：IPsec VPN。

这个描述同样是错误的，因为IPsec VPN是一种安全隧道技术，用于在不安全的网络中建立加密和认证的通道，以保护数据传输的安全性，而不是用于实现NAT穿越。

D项：STUN。

这个描述是正确的，因为STUN是一种NAT会话穿越应用程序，用于检测网络中是否存在NAT设备，并获取两个通信端点经NAT设备分配的IP 地址和端口号，在两个通信端点之间建立一条可穿越NAT的P2P链接2。

2.[试题编号：190584] (单选题)如图所示，在虚拟化园区网络中部署业务随行，其中PC1属于Sales安全组，PC2属于R&D安全组，PC3属于Market安全组。

语⾳质量评估语⾳质量评估，就是通过⼈类或⾃动化的⽅法评价语⾳质量。

在实践中，有很多主观和客观的⽅法评价语⾳质量。

主观⽅法就是通过⼈类对语⾳进⾏打分，⽐如MOS、CMOS和ABX Test。

客观⽅法即是通过算法评测语⾳质量，在实时语⾳通话领域，这⼀问题研究较多，出现了诸如如PESQ和P.563这样的有参考和⽆参考的语⾳质量评价标准。

在语⾳合成领域，研究的⽐较少，论⽂中常常通过展⽰频谱细节，计算MCD(mel cepstral distortion)等⽅法作为客观评价。

所谓有参考和⽆参考质量评估，取决于该⽅法是否需要标准信号。

有参考除了待评测信号，还需要⼀个⾳质优异的，没有损伤的参考信号；⽽⽆参考则不需要，直接根据待评估信号，给出质量评分。

近些年也出现了MOSNet等基于深度⽹络的⾃动语⾳质量评估⽅法。

语⾳质量评测⽅法以下简单总结常⽤的语⾳质量评测⽅法。

主观评价：MOS[1], CMOS, ABX Test客观评价有参考质量评估(intrusive method)：ITU-T P.861(MNB), ITU-T P.862(PESQ)[2], ITU-T P.863(POLQA)[3], STOI[4], BSSEval[5]⽆参考质量评估(non-intrusive method)传统⽅法基于信号：ITU-T P.563[6], ANIQUE+[7]基于参数：ITU-T G.107(E-Model)[8]基于深度学习的⽅法：AutoMOS[9], QualityNet[10], NISQA[11], MOSNet[12]此外，有部分的⽅法，其代码已开源：：该仓库包括MOSNet, SRMR, BSSEval, PESQ, STOI的开源实现和对应的源仓库地址。

ITU组织已公布⾃⼰实现的P.563: 。

GitHub上⾯的微⼩修改版使其能够在Mac上编译。

在语⾳合成中会⽤到的计算MCD：此外，有⼀本书⽤来具体叙述评价语⾳质量：Quality of Synthetic Speech: Perceptual Dimensions, Influencing Factors, and Instrumental Assessment (T-Labs Series in Telecommunication Services)[13]。

MANUAL CAMBIO DE CLAVEWIFIEn este manual se intenta explicar cómo cambiar la clave del router WIFI que los clientes tienen en su domicilio. Aprovechar para comentar a los clientes que si no disponen de un router WIFI nuestro recomendamos que nos lo compren por varios motivos:-Todos los modelos de routers que nosotros vendemos soportan clave WPA o cualquier variación de la misma. La cual es imposible de descifrar a día de hoy, así nos evitaremos que cualquier usuario pueda“piratear” la clave de los routers que la mayoría de operadores habituales distribuyen, los cuales llevanuna clave WEP que es muy poco segura.-Nuestros routers disponen de 2 canales de transmisión (son MIMO), lo cual una mayor velocidad y cobertura en la casa de los clientes. Además llevan un radio más potente que los routers de losoperadores habituales, los cuales además solo llevan un canal de transmisión (no son MIMO).-Nuestros routers llevan un firmware especial programado a medida por nuestros técnicos, el cual asegura en todo momento una mejor estabilidad del servicio, además están preparados para queactualizarse de forma automática si detectamos que hay firmware que presente mejoras.-No se resetean automáticamente cuando hay problemas con el suministro eléctrico como muchos de los routers que ofrecen otros operadores de bajo coste, si se reseteasen dejan de funcionar con nuestroservicio porque se quedaría configurados para funcionar correctamente con su antiguo operador y esaconfiguración no será validad para mi conexión.CLIENTES QUE DISPONEN DE UN ROUTER WIFI QUE NO ES NUESTRO (LO COMPRARON POR SU CUENTA O LO APROVECHARON DE SU OPERADOR ANTERIOR):Sobre estos routers no damos soporte y si el cliente dispone de uno lo más recomendable es que lo cambia lo antes posible tal y como he comentado anteriormente.Es importante recalcar si detectamos que no tenemos servicio mediante WIFI y disponemos de uno de estos routers es importante no resetearlos en ningún caso (la mayoría de los routers se resetean con algún objeto punzante pulsándole por la parte de atrás en un botón durante un tiempo o a algunos modelos llevan un botón “normal” el cual hay que tener pulsado un tiempo también). Si reseteamos el router este volverá a los valores de fábrica con la configuración de su antiguo operador y dejara de funcionar con nuestra conexión de internet.A estos routers siempre le dejamos por defecto la IP del rango correspondiente del cliente terminada en 100, si tuviera más routers el siguiente que tenga se dejaría con la IP del rango correspondiente del cliente terminada en 99 y así sucesivamente…La clave también se la dejamos por defecto a no ser que el cliente nos diga lo contrario, en la mayoría de modelos la clave está en la etiqueta que el router suele llevar donde aparece el modelo que es y toda su información, si la clave se la hemos cambiado por otra se la ponemos con una pegatina de dimo por esta zona también.Para la mayoría de clientes el rango de red interno que dejamos a no ser que tengamos que cambiarlo por algún motivo en especial es el 192.168.1.0/24. Esto quiere decir que para acceder a la configuración del router tendremos que poner en nuestro explorador de internet la siguiente dirección: http://192.168.1.100, de la siguiente forma: -1º: Abriremos nuestro explorador de internet (cualquiera es válido: Internet Explorer, Mozilla Firefox, Google Chrome…) y escribiremos la dirección http://192.168.1.100:Una vez que tengamos la dirección pulsaremos la tecla INTRO.-2º: Si la dirección del router es la correcta nos pedirá un usuario y contraseña, por defecto, y siempre que nadie se lo haya cambiado (nosotros nunca lo hacemos) los usuarios y contraseñas más utilizadosson los siguientes:o Usuario: 1234 y contraseña: 1234o Usuario: admin y contraseña: admino Usuario: admin y contraseña: 1234o Usuario: admin y contraseña: “en blanco”.Si no es ninguno de estos posiblemente vendrá en la etiqueta del modelo del router, si no viene aquí también podéis buscar el usuario y contraseña que lleva el router especifico que tengáis por Internet, aunque los usuarios y contraseñas por defecto suelen ser las que he indicado antes.-3º: Una vez que metamos el usuario y la contraseña correctamente entraremos en la configuración del router. Depende de la marca y el modelo del router las opciones que nos aparecerán cambiaran, pero lamayoría de los routers llevan un apartado donde pone “Wireless” y dentro de ese apartado algo asícomo “Security”, entrando aquí y dependiendo la clave que utilice el router puede que ponga “WEP”, o“WPA” o al go por el estilo, aquí escribiremos la nueva clave y aplicaremos los cambios para que seguarde. Automáticamente después de hacer esto todos los dispositivos que tengamos conectados porWIFI se desconectaran y tendremos que meterles la clave nueva para que puedan volver a reconectar.CLIENTES QUE DISPONEN DE UN ROUTER WIFI NUESTRO:La clave por defecto de estos routers viene en la etiqueta que el router lleva por la parte de abajo:Salvo que el cliente nos lo pida expresamente siempre le dejamos esta clave, si el cliente nos pide que se la cambiemos y se la ponemos pegada con una dimo al lado de esta etiqueta original del router. Si el cambio de clave se hace remotamente (desde nuestras oficinas) la clave se le comenta verbalmente al cliente. De todas formas seguidamente comentare como ver y poder cambiar la clave al router.Para poder cambiarle la clave al router tenemos que acceder a él mediante web, pare ello tendremos que poner la dirección IP del router en el explorador, para seguir un patrón y poder acceder correctamente a los routers de los clientes de una forma rápida siempre le dejamos por defecto la IP del rango correspondiente del cliente terminada en 100, si tuviera más routers el siguiente que tenga se dejaría con la IP del rango correspondiente del cliente terminada en 99 y así sucesivamente…Para la mayoría de clientes el rango de red interno que dejamos a no ser que tengamos que cambiarlo por algún motivo en especial es el 192.168.1.0/24. Esto quiere decir que para acceder a la configuración del router tendremos que poner en nuestro explorador de internet la siguiente dirección: http://192.168.1.100, de la siguiente forma: -1º: Abriremos nuestro explorador de internet (cualquiera es válido: Internet Explorer, Mozilla Firefox, Google Chrome…) y escribiremos la dirección http://192.168.1.100:Una vez que tengamos la dirección pulsaremos la tecla INTRO.Si llegado este punto no podemos acceder al router será porque lleva otro tipo de configuración que noes la adecuada, se ruega que os pongáis en contacto con nuestro servicio técnico para que os loreprograme correctamente.-2º: Si la dirección del router es la correcta nos pedirá un usuario y contraseña, por defecto, y siempre que nadie se lo haya cambiado (nosotros nunca lo hacemos) el usuario y la contraseña son los siguientes: o Usuario: admin y contraseña: adminLlegado este punto nos podemos encontrar con 2 posibles apariencias al entrar en el router,depende de si ese router lleva el firmware personalizado nuestro o no lo lleva ya la ventana deautenticación del usuario y contraseña cambia, por lo tanto sigan el manual de la interfazcorrespondiente en cada caso:INTERFAZ DE FIRMWARE MODIFICADO:Pone mos el usuario y la contraseña y pulsamos el botón “Iniciar Sesión”.-3º: Una vez que entremos a la configuración del router, nos iremos a “Red” y luego a “Wifi”:-4º: Una vez que estemos dentro de “Redes Inalámbricas” pulsaremos en el botón “Editar”:-5º: Si queremos cambiar el nombre de la red nos iremos “ESSID” y escribiremos el nombre de red nuevo, y luego pulsaremos el botón de “Guardar y Aplicar”.No se recomienda cambiar el ESSID ya que con los 4 que lleva este nombre detrás de “INFORMATICA_FUENTEABILLA” podemos saber el tiempo que tiene el router y más datos de interés que nos pueden ser útiles de cara a posibles averías.-6º: Si queremos cambiar la clave nos iremos a “Seguridad inalámbrica” y luego a “Clave”: si queremos ver la clave actual podemos pulsar sobre las fechas: para que deje de ocultárnosla y si queremos cambiarla solo hay que escribir la clave nueva y luego puls aremos el botón de “Guardar y Aplicar”.INTERFAZ DE FIRMWARE ORIGINAL:Ponemos el usuario y la contraseña y pulsamos el botón “Aceptar”.-3º: Una vez que entremos a la configuración del router, nos iremos a “Wireless”:-5º: Si queremos cambiar el nombre de la red nos iremos “Wireless Network Name” y escribiremos el nombre de red nuevo, y luego pulsaremo s el botón de “Save”. No se recomienda cambiar el Wireless Network Name ya que con los 6 dígitos que lleva este nombre detrás d e “TP-LINK_” podemos saber el tiempo que tiene el router y más datos de interés que nos pueden ser útiles de cara a posibles averías.-Dependiendo de la versión del router puede aparecer un mensaje diciéndonos que para aplicar los cambios debemos de reiniciar el router, aquí aparecerá un link donde pone “clic k here for reboot” o algo así pulsaremos aquí para que reinicie ya que si no no aplicara los cambios.-6º: Si queremos cambiar la clave nos iremos al apartado “Wireless Security”, aquí tenemos va rias opciones dependiendo el tipo de clave que le queramos poner al router o bien la opción de “Disable Security”, con la cual lo dejaríamos sin clave. Se recomienda utilizar la clave WPA/WPA2 (dejar losparámetros de “Version” y de “Encryption” como vienen por defecto y poner la clave donde viene “PSK Password”. Después de hacer cualquier cambio pulsaremos el botón de “Save”.Dependiendo de la versión del router puede aparecer un mensaje diciéndonos que para aplicar los cambios debemos de reiniciar el r outer, aquí aparecerá un link donde pone “clic k here for reboot” o algo así pulsaremos aquí para que reinicie ya que si no no aplicara los cambios.。

FORTINAC AND THE FORTINET SECURITY FABRIC EXECUTIVE SUMMARYOutdated endpoint access security solutions leave mobile and Internet of Things (IoT) devices vulnerable to targeted attacks that can put the entire network at risk.To protect valuable data, organizations need next-generation network access control (NAC). As part of the Fortinet Security Fabric, FortiNAC provides comprehensive device visibility, enforces dynamic controls, and orchestrates automated threat responses that reduce containment time from days to seconds. It enables policy-based network segmentation for controlling access to sensitive information.THE NEED FOR THIRD-GENERA TION NACEnterprise networks are undergoing dramatic change through the widespread adoptionof bring-your-own-device (BYOD) policies, loT, and multi-cloud technologies. When thisis coupled with a highly mobile workforce and geographically dispersed data centers,the security challenges multiply. With endpoint devices remaining a top attack target, organizations must address the outdated access controls that leave their networks exposed to undue risk.The first generation of NAC solutions authenticated and authorized endpoints (primarily managed PCs) using simple scan-and-block technologies. Second-generation NAC products solved the emerging demand for guest network access—visitors, contractors, and partners.But securing dynamic and distributed environments now requires security and networking that share intelligence and collaborate to detect and respond to threats. As part of the Fortinet Security Fabric architecture, FortiNAC offers a third-generation NAC solutionthat leverages the built-in commands of network switches, routers, and access points to establish a live inventory of network connections and enforce control over network access. FortiNAC identifies, validates, and controls every connection before granting access. COMPREHENSIVE DEVICE AND USER VISIBILITYAs a result of BYOD and IoT proliferation, security teams must now protect countless devices that aren’t owned, managed, or updated by corporate IT. FortiNAC addresses this challenge in a couple of different ways. First, it enables detailed profiling of even headless devices using multiple information and behavior sources to accurately identify everythingon the network. Comprehensive agentless scanning automatically discovers endpoints, classifies them by type, and determines if the device is corporate-issued or employee-owned. Second, the user is also identified in order to apply additional role-based policies. HIGHLIGHTSnn Comprehensive network visibilitynn Profiles and classifies all devices and usersnn Provides policy-based access controlsnn Extends dynamic segmentationto third-party devicesnn Orchestrates automated threat responsesnn Contains potential threats in seconds nn Simplifies guest access and onboardingnn Low TCO—maximizes existing security investmentsSOLUTION BRIEF: FORTINAC AND THE FORTINET SECURITY FABRICMacintosh HD:Users:bhoulihan:Documents:_Projects:Solution Brief:Solution Brief - FortiNAC:sb-fortiNAC:sb-fortiNACCopyright © 2018 Fortinet, Inc. All rights reserved. Fortinet , FortiGate , FortiCare and FortiGuard , and certain other marks are registered trademarks of Fortinet, Inc., and other Fortinet names herein may also be registered and/or common law trademarks of Fortinet. All other product or company names may be trademarks of their respective owners. Performance and other metrics contained herein were attained in internal lab tests under ideal conditions, and actual performance and other results may vary. Network variables, different network environments and other conditions may affect performance results. Nothing herein represents any binding commitment by Fortinet, and Fortinet disclaims all warranties, whether express or implied, except to the extent Fortinet enters a binding written contract, signed by Fortinet’s General Counsel, with a purchaser that expressly warrants that the identified product will perform according to certain expressly-identified performance metrics and, in such event, only the specific performance metrics expressly identified in such binding written contract shall be binding on Fortinet. For absolute clarity, any such warranty will be limited to performance in the same ideal conditions as in Fortinet’s internal lab tests. Fortinet disclaims in full any covenants, representations, and guarantees pursuant hereto, whether express or implied. Fortinet reserves the right to change, modify, transfer, or otherwise revise this publication without notice, and the most current version of the publication shall be applicable. Fortinet disclaims in full any covenants, representations, and guarantees pursuant hereto, whether express or implied. Fortinet reserves the right to change, modify, transfer, or otherwise revise this publication without notice, and the most current version of the publication shall be applicable.GLOBAL HEADQUARTERS Fortinet Inc.899 Kifer RoadSunnyvale, CA 94086United StatesTel: +/salesEMEA SALES OFFICE 905 rue Albert Einstein 06560 Valbonne FranceTel: +33.4.8987.0500APAC SALES OFFICE8 Temasek Boulevard #12-01Suntec Tower Three Singapore 038988Tel: +65-6395-7899Fax: +65-6295-0015LATIN AMERICA HEADQUARTERS Sawgrass Lakes Center13450 W. Sunrise Blvd., Suite 430Sunrise, FL 33323Tel: +1.954.368.9990August 31, 2018 12:32 PMDYNAMIC NETWORK CONTROLOnce devices and users are identified, FortiNAC assigns the appropriate level of access while restricting use of non-relatedcontent. This dynamic, role-based system logically creates detailed network segments by grouping applications and like data together to limit access to specific groups of users. In this manner, if a device is compromised, its ability to travel in the network and attack other assets will be limited. Security Fabric integration allows FortiNAC to implement segmentation policies and change configurations on switches and wireless products, including solutions from more than 70 different vendors.FortiNAC also streamlines the secure registration process of guest users while keeping them safely away from any parts of the network containing sensitive data. When appropriate, users can self-register their own devices (laptops, tablets, or smartphones), shifting the workload away from IT staff.AUTOMA TED RESPONSIVENESSAutomation is the “holy grail” of an integrated security architecture. Policy-based automated security actions help Security Fabric solutions share real-time intelligence to contain potential threats before they can spread. FortiNAC offers a broad and customizable set of automation policies that can instantly trigger containment settings in other Security Fabric elements such as FortiGate, FortiSwitch, or FortiAP when a targeted behavior is observed. This extends to all Fabric-integrated products, including third-party solutions.Potential threats are contained by isolating suspect users and vulnerable devices, or by enforcing a range of responsive actions. This in turn reduces containment times from days to seconds—while helping to maintain compliance with increasingly strict standards, regulations, and privacy laws.HOW IT WORKSAs an integrated Security Fabric solution, FortiNAC helps to provide additional layers of protection against device-borne threats. For example, if a customer is using FortiSIEM, FortiNAC providescomplete visibility and policy-based control for network, mobile, and IoT devices, while FortiSIEM provides the security intelligence. FortiNAC offers complete visibility into all of these devices, gathers the alerts, and provides the contextual information—the who, what, where, and when for the events. This increases the fidelity of the alerts and enables accurate triage.FortiNAC sends the event to FortiSIEM to ingest the alert, then FortiSIEM directs FortiNAC to restrict or quarantine the device if necessary. FortiSIEM and FortiNAC communicate back and forth to compile all relevant information and deliver it to a security analyst.。

DIRECTIVE NUMBER: CPL 02-00-150 EFFECTIVE DATE: April 22, 2011 SUBJECT: Field Operations Manual (FOM)ABSTRACTPurpose: This instruction cancels and replaces OSHA Instruction CPL 02-00-148,Field Operations Manual (FOM), issued November 9, 2009, whichreplaced the September 26, 1994 Instruction that implemented the FieldInspection Reference Manual (FIRM). The FOM is a revision of OSHA’senforcement policies and procedures manual that provides the field officesa reference document for identifying the responsibilities associated withthe majority of their inspection duties. This Instruction also cancels OSHAInstruction FAP 01-00-003 Federal Agency Safety and Health Programs,May 17, 1996 and Chapter 13 of OSHA Instruction CPL 02-00-045,Revised Field Operations Manual, June 15, 1989.Scope: OSHA-wide.References: Title 29 Code of Federal Regulations §1903.6, Advance Notice ofInspections; 29 Code of Federal Regulations §1903.14, Policy RegardingEmployee Rescue Activities; 29 Code of Federal Regulations §1903.19,Abatement Verification; 29 Code of Federal Regulations §1904.39,Reporting Fatalities and Multiple Hospitalizations to OSHA; and Housingfor Agricultural Workers: Final Rule, Federal Register, March 4, 1980 (45FR 14180).Cancellations: OSHA Instruction CPL 02-00-148, Field Operations Manual, November9, 2009.OSHA Instruction FAP 01-00-003, Federal Agency Safety and HealthPrograms, May 17, 1996.Chapter 13 of OSHA Instruction CPL 02-00-045, Revised FieldOperations Manual, June 15, 1989.State Impact: Notice of Intent and Adoption required. See paragraph VI.Action Offices: National, Regional, and Area OfficesOriginating Office: Directorate of Enforcement Programs Contact: Directorate of Enforcement ProgramsOffice of General Industry Enforcement200 Constitution Avenue, NW, N3 119Washington, DC 20210202-693-1850By and Under the Authority ofDavid Michaels, PhD, MPHAssistant SecretaryExecutive SummaryThis instruction cancels and replaces OSHA Instruction CPL 02-00-148, Field Operations Manual (FOM), issued November 9, 2009. The one remaining part of the prior Field Operations Manual, the chapter on Disclosure, will be added at a later date. This Instruction also cancels OSHA Instruction FAP 01-00-003 Federal Agency Safety and Health Programs, May 17, 1996 and Chapter 13 of OSHA Instruction CPL 02-00-045, Revised Field Operations Manual, June 15, 1989. This Instruction constitutes OSHA’s general enforcement policies and procedures manual for use by the field offices in conducting inspections, issuing citations and proposing penalties.Significant Changes∙A new Table of Contents for the entire FOM is added.∙ A new References section for the entire FOM is added∙ A new Cancellations section for the entire FOM is added.∙Adds a Maritime Industry Sector to Section III of Chapter 10, Industry Sectors.∙Revises sections referring to the Enhanced Enforcement Program (EEP) replacing the information with the Severe Violator Enforcement Program (SVEP).∙Adds Chapter 13, Federal Agency Field Activities.∙Cancels OSHA Instruction FAP 01-00-003, Federal Agency Safety and Health Programs, May 17, 1996.DisclaimerThis manual is intended to provide instruction regarding some of the internal operations of the Occupational Safety and Health Administration (OSHA), and is solely for the benefit of the Government. No duties, rights, or benefits, substantive or procedural, are created or implied by this manual. The contents of this manual are not enforceable by any person or entity against the Department of Labor or the United States. Statements which reflect current Occupational Safety and Health Review Commission or court precedents do not necessarily indicate acquiescence with those precedents.Table of ContentsCHAPTER 1INTRODUCTIONI.PURPOSE. ........................................................................................................... 1-1 II.SCOPE. ................................................................................................................ 1-1 III.REFERENCES .................................................................................................... 1-1 IV.CANCELLATIONS............................................................................................. 1-8 V. ACTION INFORMATION ................................................................................. 1-8A.R ESPONSIBLE O FFICE.......................................................................................................................................... 1-8B.A CTION O FFICES. .................................................................................................................... 1-8C. I NFORMATION O FFICES............................................................................................................ 1-8 VI. STATE IMPACT. ................................................................................................ 1-8 VII.SIGNIFICANT CHANGES. ............................................................................... 1-9 VIII.BACKGROUND. ................................................................................................. 1-9 IX. DEFINITIONS AND TERMINOLOGY. ........................................................ 1-10A.T HE A CT................................................................................................................................................................. 1-10B. C OMPLIANCE S AFETY AND H EALTH O FFICER (CSHO). ...........................................................1-10B.H E/S HE AND H IS/H ERS ..................................................................................................................................... 1-10C.P ROFESSIONAL J UDGMENT............................................................................................................................... 1-10E. W ORKPLACE AND W ORKSITE ......................................................................................................................... 1-10CHAPTER 2PROGRAM PLANNINGI.INTRODUCTION ............................................................................................... 2-1 II.AREA OFFICE RESPONSIBILITIES. .............................................................. 2-1A.P ROVIDING A SSISTANCE TO S MALL E MPLOYERS. ...................................................................................... 2-1B.A REA O FFICE O UTREACH P ROGRAM. ............................................................................................................. 2-1C. R ESPONDING TO R EQUESTS FOR A SSISTANCE. ............................................................................................ 2-2 III. OSHA COOPERATIVE PROGRAMS OVERVIEW. ...................................... 2-2A.V OLUNTARY P ROTECTION P ROGRAM (VPP). ........................................................................... 2-2B.O NSITE C ONSULTATION P ROGRAM. ................................................................................................................ 2-2C.S TRATEGIC P ARTNERSHIPS................................................................................................................................. 2-3D.A LLIANCE P ROGRAM ........................................................................................................................................... 2-3 IV. ENFORCEMENT PROGRAM SCHEDULING. ................................................ 2-4A.G ENERAL ................................................................................................................................................................. 2-4B.I NSPECTION P RIORITY C RITERIA. ..................................................................................................................... 2-4C.E FFECT OF C ONTEST ............................................................................................................................................ 2-5D.E NFORCEMENT E XEMPTIONS AND L IMITATIONS. ....................................................................................... 2-6E.P REEMPTION BY A NOTHER F EDERAL A GENCY ........................................................................................... 2-6F.U NITED S TATES P OSTAL S ERVICE. .................................................................................................................. 2-7G.H OME-B ASED W ORKSITES. ................................................................................................................................ 2-8H.I NSPECTION/I NVESTIGATION T YPES. ............................................................................................................... 2-8 V.UNPROGRAMMED ACTIVITY – HAZARD EVALUATION AND INSPECTION SCHEDULING ............................................................................ 2-9 VI.PROGRAMMED INSPECTIONS. ................................................................... 2-10A.S ITE-S PECIFIC T ARGETING (SST) P ROGRAM. ............................................................................................. 2-10B.S CHEDULING FOR C ONSTRUCTION I NSPECTIONS. ..................................................................................... 2-10C.S CHEDULING FOR M ARITIME I NSPECTIONS. ............................................................................. 2-11D.S PECIAL E MPHASIS P ROGRAMS (SEP S). ................................................................................... 2-12E.N ATIONAL E MPHASIS P ROGRAMS (NEP S) ............................................................................... 2-13F.L OCAL E MPHASIS P ROGRAMS (LEP S) AND R EGIONAL E MPHASIS P ROGRAMS (REP S) ............ 2-13G.O THER S PECIAL P ROGRAMS. ............................................................................................................................ 2-13H.I NSPECTION S CHEDULING AND I NTERFACE WITH C OOPERATIVE P ROGRAM P ARTICIPANTS ....... 2-13CHAPTER 3INSPECTION PROCEDURESI.INSPECTION PREPARATION. .......................................................................... 3-1 II.INSPECTION PLANNING. .................................................................................. 3-1A.R EVIEW OF I NSPECTION H ISTORY .................................................................................................................... 3-1B.R EVIEW OF C OOPERATIVE P ROGRAM P ARTICIPATION .............................................................................. 3-1C.OSHA D ATA I NITIATIVE (ODI) D ATA R EVIEW .......................................................................................... 3-2D.S AFETY AND H EALTH I SSUES R ELATING TO CSHO S.................................................................. 3-2E.A DVANCE N OTICE. ................................................................................................................................................ 3-3F.P RE-I NSPECTION C OMPULSORY P ROCESS ...................................................................................................... 3-5G.P ERSONAL S ECURITY C LEARANCE. ................................................................................................................. 3-5H.E XPERT A SSISTANCE. ........................................................................................................................................... 3-5 III. INSPECTION SCOPE. ......................................................................................... 3-6A.C OMPREHENSIVE ................................................................................................................................................... 3-6B.P ARTIAL. ................................................................................................................................................................... 3-6 IV. CONDUCT OF INSPECTION .............................................................................. 3-6A.T IME OF I NSPECTION............................................................................................................................................. 3-6B.P RESENTING C REDENTIALS. ............................................................................................................................... 3-6C.R EFUSAL TO P ERMIT I NSPECTION AND I NTERFERENCE ............................................................................. 3-7D.E MPLOYEE P ARTICIPATION. ............................................................................................................................... 3-9E.R ELEASE FOR E NTRY ............................................................................................................................................ 3-9F.B ANKRUPT OR O UT OF B USINESS. .................................................................................................................... 3-9G.E MPLOYEE R ESPONSIBILITIES. ................................................................................................. 3-10H.S TRIKE OR L ABOR D ISPUTE ............................................................................................................................. 3-10I. V ARIANCES. .......................................................................................................................................................... 3-11 V. OPENING CONFERENCE. ................................................................................ 3-11A.G ENERAL ................................................................................................................................................................ 3-11B.R EVIEW OF A PPROPRIATION A CT E XEMPTIONS AND L IMITATION. ..................................................... 3-13C.R EVIEW S CREENING FOR P ROCESS S AFETY M ANAGEMENT (PSM) C OVERAGE............................. 3-13D.R EVIEW OF V OLUNTARY C OMPLIANCE P ROGRAMS. ................................................................................ 3-14E.D ISRUPTIVE C ONDUCT. ...................................................................................................................................... 3-15F.C LASSIFIED A REAS ............................................................................................................................................. 3-16VI. REVIEW OF RECORDS. ................................................................................... 3-16A.I NJURY AND I LLNESS R ECORDS...................................................................................................................... 3-16B.R ECORDING C RITERIA. ...................................................................................................................................... 3-18C. R ECORDKEEPING D EFICIENCIES. .................................................................................................................. 3-18 VII. WALKAROUND INSPECTION. ....................................................................... 3-19A.W ALKAROUND R EPRESENTATIVES ............................................................................................................... 3-19B.E VALUATION OF S AFETY AND H EALTH M ANAGEMENT S YSTEM. ....................................................... 3-20C.R ECORD A LL F ACTS P ERTINENT TO A V IOLATION. ................................................................................. 3-20D.T ESTIFYING IN H EARINGS ................................................................................................................................ 3-21E.T RADE S ECRETS. ................................................................................................................................................. 3-21F.C OLLECTING S AMPLES. ..................................................................................................................................... 3-22G.P HOTOGRAPHS AND V IDEOTAPES.................................................................................................................. 3-22H.V IOLATIONS OF O THER L AWS. ....................................................................................................................... 3-23I.I NTERVIEWS OF N ON-M ANAGERIAL E MPLOYEES .................................................................................... 3-23J.M ULTI-E MPLOYER W ORKSITES ..................................................................................................................... 3-27 K.A DMINISTRATIVE S UBPOENA.......................................................................................................................... 3-27 L.E MPLOYER A BATEMENT A SSISTANCE. ........................................................................................................ 3-27 VIII. CLOSING CONFERENCE. .............................................................................. 3-28A.P ARTICIPANTS. ..................................................................................................................................................... 3-28B.D ISCUSSION I TEMS. ............................................................................................................................................ 3-28C.A DVICE TO A TTENDEES .................................................................................................................................... 3-29D.P ENALTIES............................................................................................................................................................. 3-30E.F EASIBLE A DMINISTRATIVE, W ORK P RACTICE AND E NGINEERING C ONTROLS. ............................ 3-30F.R EDUCING E MPLOYEE E XPOSURE. ................................................................................................................ 3-32G.A BATEMENT V ERIFICATION. ........................................................................................................................... 3-32H.E MPLOYEE D ISCRIMINATION .......................................................................................................................... 3-33 IX. SPECIAL INSPECTION PROCEDURES. ...................................................... 3-33A.F OLLOW-UP AND M ONITORING I NSPECTIONS............................................................................................ 3-33B.C ONSTRUCTION I NSPECTIONS ......................................................................................................................... 3-34C. F EDERAL A GENCY I NSPECTIONS. ................................................................................................................. 3-35CHAPTER 4VIOLATIONSI. BASIS OF VIOLATIONS ..................................................................................... 4-1A.S TANDARDS AND R EGULATIONS. .................................................................................................................... 4-1B.E MPLOYEE E XPOSURE. ........................................................................................................................................ 4-3C.R EGULATORY R EQUIREMENTS. ........................................................................................................................ 4-6D.H AZARD C OMMUNICATION. .............................................................................................................................. 4-6E. E MPLOYER/E MPLOYEE R ESPONSIBILITIES ................................................................................................... 4-6 II. SERIOUS VIOLATIONS. .................................................................................... 4-8A.S ECTION 17(K). ......................................................................................................................... 4-8B.E STABLISHING S ERIOUS V IOLATIONS ............................................................................................................ 4-8C. F OUR S TEPS TO BE D OCUMENTED. ................................................................................................................... 4-8 III. GENERAL DUTY REQUIREMENTS ............................................................. 4-14A.E VALUATION OF G ENERAL D UTY R EQUIREMENTS ................................................................................. 4-14B.E LEMENTS OF A G ENERAL D UTY R EQUIREMENT V IOLATION.............................................................. 4-14C. U SE OF THE G ENERAL D UTY C LAUSE ........................................................................................................ 4-23D.L IMITATIONS OF U SE OF THE G ENERAL D UTY C LAUSE. ..............................................................E.C LASSIFICATION OF V IOLATIONS C ITED U NDER THE G ENERAL D UTY C LAUSE. ..................F. P ROCEDURES FOR I MPLEMENTATION OF S ECTION 5(A)(1) E NFORCEMENT ............................ 4-25 4-27 4-27IV.OTHER-THAN-SERIOUS VIOLATIONS ............................................... 4-28 V.WILLFUL VIOLATIONS. ......................................................................... 4-28A.I NTENTIONAL D ISREGARD V IOLATIONS. ..........................................................................................4-28B.P LAIN I NDIFFERENCE V IOLATIONS. ...................................................................................................4-29 VI. CRIMINAL/WILLFUL VIOLATIONS. ................................................... 4-30A.A REA D IRECTOR C OORDINATION ....................................................................................................... 4-31B.C RITERIA FOR I NVESTIGATING P OSSIBLE C RIMINAL/W ILLFUL V IOLATIONS ........................ 4-31C. W ILLFUL V IOLATIONS R ELATED TO A F ATALITY .......................................................................... 4-32 VII. REPEATED VIOLATIONS. ...................................................................... 4-32A.F EDERAL AND S TATE P LAN V IOLATIONS. ........................................................................................4-32B.I DENTICAL S TANDARDS. .......................................................................................................................4-32C.D IFFERENT S TANDARDS. .......................................................................................................................4-33D.O BTAINING I NSPECTION H ISTORY. .....................................................................................................4-33E.T IME L IMITATIONS..................................................................................................................................4-34F.R EPEATED V. F AILURE TO A BATE....................................................................................................... 4-34G. A REA D IRECTOR R ESPONSIBILITIES. .............................................................................. 4-35 VIII. DE MINIMIS CONDITIONS. ................................................................... 4-36A.C RITERIA ................................................................................................................................................... 4-36B.P ROFESSIONAL J UDGMENT. ..................................................................................................................4-37C. A REA D IRECTOR R ESPONSIBILITIES. .............................................................................. 4-37 IX. CITING IN THE ALTERNATIVE ............................................................ 4-37 X. COMBINING AND GROUPING VIOLATIONS. ................................... 4-37A.C OMBINING. ..............................................................................................................................................4-37B.G ROUPING. ................................................................................................................................................4-38C. W HEN N OT TO G ROUP OR C OMBINE. ................................................................................................4-38 XI. HEALTH STANDARD VIOLATIONS ....................................................... 4-39A.C ITATION OF V ENTILATION S TANDARDS ......................................................................................... 4-39B.V IOLATIONS OF THE N OISE S TANDARD. ...........................................................................................4-40 XII. VIOLATIONS OF THE RESPIRATORY PROTECTION STANDARD(§1910.134). ....................................................................................................... XIII. VIOLATIONS OF AIR CONTAMINANT STANDARDS (§1910.1000) ... 4-43 4-43A.R EQUIREMENTS UNDER THE STANDARD: .................................................................................................. 4-43B.C LASSIFICATION OF V IOLATIONS OF A IR C ONTAMINANT S TANDARDS. ......................................... 4-43 XIV. CITING IMPROPER PERSONAL HYGIENE PRACTICES. ................... 4-45A.I NGESTION H AZARDS. .................................................................................................................................... 4-45B.A BSORPTION H AZARDS. ................................................................................................................................ 4-46C.W IPE S AMPLING. ............................................................................................................................................. 4-46D.C ITATION P OLICY ............................................................................................................................................ 4-46 XV. BIOLOGICAL MONITORING. ...................................................................... 4-47CHAPTER 5CASE FILE PREPARATION AND DOCUMENTATIONI.INTRODUCTION ............................................................................................... 5-1 II.INSPECTION CONDUCTED, CITATIONS BEING ISSUED. .................... 5-1A.OSHA-1 ................................................................................................................................... 5-1B.OSHA-1A. ............................................................................................................................... 5-1C. OSHA-1B. ................................................................................................................................ 5-2 III.INSPECTION CONDUCTED BUT NO CITATIONS ISSUED .................... 5-5 IV.NO INSPECTION ............................................................................................... 5-5 V. HEALTH INSPECTIONS. ................................................................................. 5-6A.D OCUMENT P OTENTIAL E XPOSURE. ............................................................................................................... 5-6B.E MPLOYER’S O CCUPATIONAL S AFETY AND H EALTH S YSTEM. ............................................................. 5-6 VI. AFFIRMATIVE DEFENSES............................................................................. 5-8A.B URDEN OF P ROOF. .............................................................................................................................................. 5-8B.E XPLANATIONS. ..................................................................................................................................................... 5-8 VII. INTERVIEW STATEMENTS. ........................................................................ 5-10A.G ENERALLY. ......................................................................................................................................................... 5-10B.CSHO S SHALL OBTAIN WRITTEN STATEMENTS WHEN: .......................................................................... 5-10C.L ANGUAGE AND W ORDING OF S TATEMENT. ............................................................................................. 5-11D.R EFUSAL TO S IGN S TATEMENT ...................................................................................................................... 5-11E.V IDEO AND A UDIOTAPED S TATEMENTS. ..................................................................................................... 5-11F.A DMINISTRATIVE D EPOSITIONS. .............................................................................................5-11 VIII. PAPERWORK AND WRITTEN PROGRAM REQUIREMENTS. .......... 5-12 IX.GUIDELINES FOR CASE FILE DOCUMENTATION FOR USE WITH VIDEOTAPES AND AUDIOTAPES .............................................................. 5-12 X.CASE FILE ACTIVITY DIARY SHEET. ..................................................... 5-12 XI. CITATIONS. ..................................................................................................... 5-12A.S TATUTE OF L IMITATIONS. .............................................................................................................................. 5-13B.I SSUING C ITATIONS. ........................................................................................................................................... 5-13C.A MENDING/W ITHDRAWING C ITATIONS AND N OTIFICATION OF P ENALTIES. .................................. 5-13D.P ROCEDURES FOR A MENDING OR W ITHDRAWING C ITATIONS ............................................................ 5-14 XII. INSPECTION RECORDS. ............................................................................... 5-15A.G ENERALLY. ......................................................................................................................................................... 5-15B.R ELEASE OF I NSPECTION I NFORMATION ..................................................................................................... 5-15C. C LASSIFIED AND T RADE S ECRET I NFORMATION ...................................................................................... 5-16。

. Seguirequesta procedura:a. Eseguire la scansione di un codiceQR o cercare NETGEAR Orbinell'Apple App Store o Google Play Store.b. Scaricare e avviare l'app NETGEAROrbi sul dispositivo mobile e seguire le istruzioni visualizzate.• Browser Web . Seguire questaprocedura:a. Scollegare il modem, quindirimuovere e reinserire la batteria di backup se in uso.b. Ricollegare il modem.c. Utilizzare il cavo Ethernet indotazione per collegare il modem alla porta Internet gialla situata sul router.Nota: se si desidera collegare il router a un gateway esistente, si consiglia di disattivare il WiFi sul gateway esistente.d. Collegare il router.Il LED di alimentazione sul pannello posteriore del router si illumina in verde. Se il LED di alimentazione non si accende, premere il pulsante di alimentazione .e. Attendere che il LED circolare delrouter sia bianco.f. Posizionare il satellite, collegarloalla presa e attendere che il LED circolare del satellite diventi blu o arancione.Se il LED circolare del satellite si accende di colore magenta, spostare il satellite più vicino al router.Per ulteriori informazioni, consultare Colori LED sincronizzazione satellite .g. Connettere il proprio computer odispositivo mobile al router o al satellite tramite una connessione Ethernet o WiFi:• Ethernet . Utilizzare un cavo Ethernet per collegare il computer al router. • WiFi . Utilizzare il nome di rete WiFi (SSID) e lapassword predefiniti riportati sull'etichetta del router o del satellite per stabilire la connessione alla rete WiFi Orbi.h. Avviare un browser Web, quindivisitare il sito Web e seguire le istruzioni.Se viene visualizzata una finestra per l'accesso, immettere il nome utente e la password. Il nome utente è admin e la password predefinita è password .BluLa connessione tra il satellite e il router è buona. ArancioneLa connessione tra il router e il satellite è discreta. Avvicinare il satellite al router.MagentaIl satellite non è riuscito aconnettersi al router. Avvicinare il satellite al router.Nota: se il LED circolare è ancora magenta dopo circa un minuto, premere il pulsante Sync (Sincronizza) sul router e sulsatellite. Se il satellite si sincronizza correttamente con il router, il LED circolare del satellite si accende di colore bianco. Successivamente, il LED circolare diventa blu a indicare che la connessione è buona, quindi si spegne.Dopo aver acceso il satellite, il LED circolare del satellite si accende dicolore bianco mentre il satellite tenta di eseguire la sincronizzazione con il router. Successivamente, il LED circolare cambia in uno dei seguenti colori per circa 3 minuti, quindi si spegne:Colori LEDsincronizzazione satelliteNETGEAR INTL LTDBuilding 3, University Technology Centre Curraheen Road, Cork, IrlandaNETGEAR, Inc.350 East Plumeria DriveSan Jose, CA 95134, Stati Uniti© NETGEAR, Inc. NETGEAR e il logo NETGEAR sono marchi di NETGEAR, Inc. Qualsiasi marchio non NETGEAR è utilizzato solo come riferimento.Giugno 2017LED circolare (non mostrato nell'immagine)Pulsante Sync (Sincronizza) (utilizzato anche per la connessione WPS)Porte Internet (il satellite Orbi non include una porta Internet)Porte EthernetSupportoGrazie per aver acquistato questo prodotto NETGEAR. Visitare il sito Web/support per registrare il prodotto, ricevere assistenza, accedere ai download e ai manuali per l'utente più recenti e partecipare alla nostra community. Consigliamo di utilizzare solo risorse di assistenza NETGEAR ufficiali.Per consultare la Dichiarazione diconformità UE attuale, visitare la pagina: /app/answers/detail/a_id/11621/.Per informazioni sulla conformità alle normative, visita il sito Web all'indirizzo/about/regulatory/.Prima di collegare l'alimentazione, consultare il documento relativo alla conformità normativa.Panoramica sul router OrbiRouter Orbi (modello RBR50)Satellite Orbi (modello RBS50)Cavo EthernetAlimentatori (2)(varia in base alla regione)Panoramica sul satellite OrbiContenuto della confezione 2134128218Porta USBPulsante e LED di alimentazione Connettore di alimentazione CC Pulsante di ripristino5678。

Workshop Program:07:00-08:30 Registration/Breakfast08:30-08:40 Welcome Address08:40-09:40 Invited Talks (Session Chair: Milica Stojanovic, MIT/WHOI) • Acoustic Propagation Considerations for Underwater Acoustic Communications Network DevelopmentJames Preisig (WHOI)• Modulation and Demodulation Techniques for Underwater Acoustic Communications John Proakis (UCSD)• State-of-the-Art in Protocol Research for Underwater Sensor NetworksIan Akyildiz (Georgia Institute of Technology)09:40-10:40 Session 1: Systems (Session Chair: Jun-Hong Cui, UCONN) • A Survey of Practical Issues in Underwater NetworksJim Partan (UMass-Amherst/WHOI), Jim Kurose (UMass-Amherst), Brian Levine(UMass-Amherst) • Wide Area Ocean Networks: Architecture and System Design ConsiderationsSumit Roy, Payman Arabshahi, Dan Rouseff, Warren Fox (University of Washington)• Localization in Underwater Sensor Networks -- Survey and ChallengesVijay Chandrasekhar, Winston KG Seah (Institute for Infocomm Research , Singapore), Yoo Sang Choo, How Voon Ee (National University of Singapore)10:40-11:00 Coffee Break11:00-12:00 Session 2: Analysis and Algorithms (Session Chair: Urbashi Mitra, USC)• On the relationship between capacity and distance in an underwater acoustic communication channelMilica Stojanovic (MIT/WHOI)• Deployment Analysis in Underwater Acoustic Wireless Sensor NetworksDario Pompili, Tommaso Melodia, Ian F. Akyildiz (Georgia Institute of Technology) • An Optimization Framework for Joint Sensor Deployment, Link Scheduling and Routing in Underwater Sensor NetworksLeonardo Badia (IMT Lucca Institute for Advanced Studies, Italy), Michele Mastrogiovanni, Chiara Petrioli, Stamatis Stefanakos (University of Rome, Italy), Michele Zorzi (University of Padova, Italy) 12:00-14:20 Poster/Demo Session (including short papers, work-in-progress posters, and demos) and LunchShort Papers (presented as posters)• Sensor Networks of Freely Drifting Autonomous Underwater ExplorersJules S. Jaffe (Scripps Institution of Oceanography/UCSD), Curt Schurgers (UCSD)• Cooperative Multihop Communication for Underwater Acoustic NetworksCecilia Carbonell (Qualcomm)i, Urbashi Mitra (USC)• Reconfigurable Acoustic Modem for Underwater Sensor NetworksEthem M. Sozer, Milica Stojanovic (MIT)• Why Underwater Acoustic Nodes Should Sleep With One Eye Open: Idle-time Power Management In Underwater Sensor NetworksAlbert F. Harris III (University of Padova , Italy), Milica Stojanovic (MIT, USA), Michele Zorzi(University of Padova , Italy)• On Applying Network Coding to Underwater Sensor NetworksZheng Guo, Peng Xie, Jun-Hong Cui, Bing Wang (UCONN)• A MAC Protocol for Ad-Hoc Underwater Acoustic Sensor NetworksBorja Peleato, Milica Stojanovic (MIT)Work-in-Progress Posters• Medium Access for Underwater Acoustic Sensor NetworksAffan A. Syed, Wei Ye, John Heidemann (USC)<Extended Abstract>• Underwater Sensor Network Deployment for Water Quality MonitoringNadjib Achir, Khaled Boussetta (University Paris 13, France), Nadjib Aitsaadi, Guy Pujolle(University Paris 6, France)<Extended Abstract>• Analyzing the Effect of 3D Geometric Parameters on Localization Accuracy for Underwater Sensor NetworksDiba Mirza and Curt Schurgers (UCSD)<Extended Abstract>• Non-geographical Underwater Routing with No Proactiveness and Negligible On-demand Floods Uichin Lee, Jiejun Kong, Eugenio Magistretti, Luiz Filipe M. Vieira, Mario Gerla (UCLA)<Extended Abstract>• Analysis of Aloha Protocols for Underwater Acoustic Sensor NetworksLuiz Filipe M. Vieira, Jiejun Kong, Uichin Lee, Mario Gerla (UCLA)<Extended Abstract>• Localization for Large-Scale Underwater Sensor NetworksZhong Zhou, Jun-Hong Cui, Shengli Zhou (UCONN)<Extended Abstract>• R-MAC: A Reservation-Based MAC Protocol for Underwater Sensor NetworksPeng Xie, Zhong Zhou, Jun-Hong Cui (UCONN)<Extended Abstract>• A Lab Underwater Sensor Network (UWSN) Testbed Using Micro-Modem Zheng Peng, Jun-Hong Cui (UCONN), Lee Freitag (WHOI)<Presentation Slides>Demos (Click Here for Detailed Information)Institution Demo Participants MIT r-Modem Ethem Sozer, Milica StojanovicMIT iV-Modem Iuliu Vasilescu, Carrick Detweiller, DanielaRusUCSB Mooring Modem Ryan Kastner, Bridget BensonUSC Robotic AlgaeDetector<Pic1> <Pic2>Gaurav SukhatmeWHOI Micro-Modem Jim Partan14:20-15:50 Panel Discussion: Underwater Networks: Methods, Systems, and Applications• Chris Clark (Bioacoustics Research Program, Cornell Lab of Ornithology)• Scott Gallagher (Biology, WHOI)• John Heidemann (ISI/USC)• Daniel Nagle (NUWC Newport)• Richard Nielsen (Boeing)• Jason Redi (Mobile Networking Systems, BBN Technologies)• Thomas Torgersen (Marine Sciences, UCONN)15:50-16:10 Coffee Break16:10-17:30 Session 3: System Design (Session Chair: Kevin Fall, Intel Research)• Electromagnetic Communications within Swarms of Autonomous Underwater Vehicles Michael R. Frater, Michael J. Ryan, Robin M. Dunbar (The University of New South Wales,Australia)• Design of a Low-cost Acoustic Modem for Moored Oceanographic ApplicationsBridget Benson, Grace Chang, Derek Manov, Brian Graham, Ryan Kastner (UCSB) • Low-Power Acoustic Modem for Dense Underwater Sensor NetworksJack Wills, Wei Ye, John Heidemann (ISI/USC)• Status Packet Deprecation and Store-Forward Routing in AUSNetMatthew Haag, Emmanuel Agu (Worcester Polytechnic Institute), Rick Komerska, Steven G.Chappell (Autonomous Undersea Systems Institute), Radim Bartos (University of New Hampshire) 17:30-17:40 Wrap-Up and End of Workshop。

英语四级考试题型样卷Part I Listening ComprehensionSection ADirections: In Section A, you will hear ten short conversations between two speakers. At the end of each conversation, a question will be asked about what was said. The conversations and the questions will be spoken only once. After you hear a conversation and the question about it, read the four possible answers on your paper, and decide which one is the best answer to the question you have heard.1. A. Visiting the doctor.B. Traveling to another country.C. Taking a vacation.D. Going to a concert.2. A. In a bookshop.B. In a restaurant.C. In a drugstore.D. In a supermarket.3. A. The woman is uninterested in the pictures.B. The woman doesn't have money to buy the pictures.C. The woman thinks the pictures are too expensive.D. The woman will buy the pictures later.4. A. At 1:30.B. At 2:00.C. At 2:30.D. At 3:00.5. A. He doesn't like his brother's gift.B. He has already received a gift from his brother.C. He is happy with his brother's gift.D. He hates receiving gifts from his brother.6. A. They want to go sightseeing in the city.B. They need directions to the nearest bus stop.C. They are going to walk to the tourist attraction.D. They have no idea how to get to their destination.7. A. The woman should go to the theater earlier.B. The woman can't get two tickets for the show.C. The woman has an extra ticket for the show.D. The woman doesn't want to see the show.8. A. She forgot about the appointment.B. She wants to reschedule the appointment.C. She can't make the appointment.D. She already made the appointment.9. A. She has never tasted curry before.B. She doesn't like spicy food.C. She loves curry.D. She often cooks curry for dinner.10. A. They have traveled to New York City before.B. They have never taken a subway in their hometown.C. They always take the subway to get around.D. They prefer taking a taxi over the subway.Section BDirections: In Section B, you will hear two short passages, and you will be asked three questions on each of the passages. The passages will be read twice, but the questions will be spoken only once. When you hear a question, read the four possible answers on your paper and decide which one would be the best answer to the question you have heard.Passage One11. A. Get a job.B. Find a place to live.C. Earn a degree.D. Make friends.12. A. To find a roommate.B. To discuss their favorite subjects.C. To participate in extracurricular activities.D. To get familiar with the campus.13. A. In a library.B. In a bookstore.C. In a café.D. In a classroom.Passage Two14. A. Earthquake preparation.B. Life preservation skills.C. Emergency response.D. Natural disaster prevention.15. A. Monitor warning systems.B. Prepare an emergency kit.C. Evacuate promptly.D. Stay indoors during a disaster.16. A. It provides information about disaster readiness.B. It is a reliable source for weather forecasts.C. It offers services during natural disasters.D. It provides training for emergency responders.Section CDirections: In Section C, you will hear two longer conversations. The conversations will be read twice. After you hear each conversation, you are required to fill in the numbered blanks with the information you have heard. Write your answers on your answer sheet.Conversation OneM: Hi, Lisa! What are your plans for the summer vacation?W: I'm going to take a summer course in (17) _______________ because I need to complete some additional credits for graduation.M: That's great! How long will the course last?W: It will run for (18) _______________ from June 1st to July 12th.M: Where will you live during the course?W: I'm planning to (19) _______________ with my cousin who lives in the city.M: How will you get to the campus every day?W: I'll take the (20) _______________ as it is more convenient for me.Conversation TwoW: Hey, John! How was your job interview yesterday?M: It went well, I think. The company is one of the leading (21)_______________ in the technology industry.W: That's impressive! Do they offer any benefits for employees?M: Yes. They provide (22) _______________ to cover medical expenses and a retirement plan.W: That's great! What about working hours?M: They have flexible working hours, which is excellent for (23)_______________ like me.W: Did they mention anything about the salary?M: Yes, they mentioned the starting salary would be around (24)_______________ per month.Part II Reading ComprehensionDirections: There are four reading passages in this part. Each passage is followed by some questions or unfinished statements. For each of them, there are four choices marked A, B, C, and D. You should decide on the best choice and mark the corresponding letter on your answer sheet.Passage OneQuestions 25 to 28 are based on the following passage.25. What does the passage mainly discuss?A. The importance of teamwork in the workplace.B. Leadership strategies in different industries.C. The relationship between leaders and team members.D. The challenges of working in a team.26. According to the passage, what is one of the key characteristics of a successful team leader?A. Setting strict rules for team members.B. Avoiding direct communication with team members.C. Providing support and guidance to team members.D. Taking full credit for the team's achievements.27. What does the author suggest regarding team members' contributions?A. Individual achievements should always be praised by the team leader.B. Team leaders should take all the credit for the team's success.C. Team members should be provided with fair recognition for their efforts.D. Team leaders should prioritize their own goals over the goals of the team.28. What can be inferred about the importance of teamwork in companies?A. Teamwork is not crucial for the success of a company.B. Companies with effective teamwork tend to have higher productivity.C. Teamwork is only beneficial for individual team members.D. Companies should focus on developing individual talents instead of teamwork.Passage TwoQuestions 29 to 32 are based on the following passage.29. According to the passage, why did the writer start cooking?A. To lower food expenses.B. To eat healthier meals.C. To impress friends and family.D. To enhance culinary skills.30. What does the writer recommend for beginner cooks?A. Trying complicated recipes to challenge oneself.B. Experimenting with different ingredients and flavors.C. Following step-by-step recipes to improve cooking skills.D. Taking cooking classes to learn from professional chefs.31. How does the writer suggest dealing with cooking failures?A. Throwing away the failed dish and ordering takeout.B. Analyzing the mistakes and learning from them.C. Giving up on cooking and relying on ready-to-eat meals.D. Blaming the recipe for the cooking failure.32. What does the writer suggest in terms of cooking equipment?A. Investing in high-end cooking appliances for better results.B. Using basic kitchen tools and utensils for simple meals.C. Avoiding specialty equipment to save money.D. Trying out different cooking techniques with various equipment. Passage ThreeQuestions 33 to 35 are based on the following passage.33. According to the passage, what are microaggressions?A. Aggressive behaviors during social interactions.B. Accidental slips of the tongue during conversations.C. Subtle and everyday discriminatory actions or comments.D. Intentional acts of discrimination and prejudice.34. How do microaggressions affect those who experience them?A. They can cause deep emotional harm and psychological distress.B. They have no long-term effects on individuals' well-being.C. They lead to open conflicts and confrontations.D. They are easily dismissed and forgotten by individuals.35. What does the author suggest regarding the impact of microaggressions?A. Individuals should confront and address microaggressions immediately.B. Microaggressions should be ignored to maintain social harmony.C. Society needs to raise awareness about the harmful effects of microaggressions.D. Individuals should respond to microaggressions with aggressive behavior.Passage FourQuestions 36 to 40 are based on the following passage.36. What is the main topic of the passage?A. The history of film festivals in different countries.B. The significance of film festivals in the film industry.C. The types of films showcased at international film festivals.D. The selection process for films at a film festival.37. According to the passage, why are film festivals important for filmmakers?A. They provide a platform for filmmakers to showcase their work.B. They offer financial support for the production of films.C. They guarantee worldwide distribution for selected films.D. They determine the box office success of a film.38. What is the purpose of award ceremonies at film festivals?A. To provide recognition and honor to outstanding films and individuals.B. To generate financial revenue for the film festival organizers.C. To showcase the latest trends and technologies in filmmaking.D. To attract famous actors and directors to the festival.39. According to the passage, what are the criteria for selecting films at a festival?A. Popularity and box office success.B. Positive reviews from film critics.C. Creative and innovative storytelling.D. Approval from government organizations.40. What can be inferred about international film festivals from the passage?A. They cater exclusively to mainstream Hollywood films.B. They are primarily attended by famous actors and directors.C. They play a significant role in promoting cultural exchange and understanding.D. They have limited impact on the film industry.Part III Vocabulary and StructureDirections: There are 40 incomplete sentences in this part. For each sentence, there are four choices marked A, B, C, and D. Choose the one that best completes the sentence and mark the corresponding letter on your answer sheet.41. They have traveled to many countries, _________ those in Europe and Asia.A. includingB. includesC. includedD. includable42. It is __________ important to take regular breaks during work to avoid burnout.A. highlyB. moreC. mostD. almost43. I tried to contact her, but her phone was __________.A. engagedB. bouncyC. confinedD. shared44. The weather forecast indicates that it will ________ tomorrow.A. showerB. ringC. stretchD. drizzle45. Mark failed the exam again __________ his hard work.A. althoughB. withoutC. despiteD. besides46. The new shopping mall will be ________ in the city center.A. locatedB. locationC. localityD. locating47. I always find it difficult to ________ up early in the morning.B. bringC. setD. catch48. The online course offers participants the opportunity to learn at their ________ pace.A. individualB. singleC. uniqueD. particular49. The students are required to ________ their assignments by the end of the week.A. submitB. commitC. omitD. transmit50. My parents allowed me to go on the trip ________ I completed all my homework.A. untilB. unlessD. oncePart IV Translation51.看到那位老人脸上的笑容，我们顿时感到温馨和幸福。

A Survey of Clustering Data Mining TechniquesPavel BerkhinYahoo!,Inc.pberkhin@Summary.Clustering is the division of data into groups of similar objects.It dis-regards some details in exchange for data simplifirmally,clustering can be viewed as data modeling concisely summarizing the data,and,therefore,it re-lates to many disciplines from statistics to numerical analysis.Clustering plays an important role in a broad range of applications,from information retrieval to CRM. Such applications usually deal with large datasets and many attributes.Exploration of such data is a subject of data mining.This survey concentrates on clustering algorithms from a data mining perspective.1IntroductionThe goal of this survey is to provide a comprehensive review of different clus-tering techniques in data mining.Clustering is a division of data into groups of similar objects.Each group,called a cluster,consists of objects that are similar to one another and dissimilar to objects of other groups.When repre-senting data with fewer clusters necessarily loses certainfine details(akin to lossy data compression),but achieves simplification.It represents many data objects by few clusters,and hence,it models data by its clusters.Data mod-eling puts clustering in a historical perspective rooted in mathematics,sta-tistics,and numerical analysis.From a machine learning perspective clusters correspond to hidden patterns,the search for clusters is unsupervised learn-ing,and the resulting system represents a data concept.Therefore,clustering is unsupervised learning of a hidden data concept.Data mining applications add to a general picture three complications:(a)large databases,(b)many attributes,(c)attributes of different types.This imposes on a data analysis se-vere computational requirements.Data mining applications include scientific data exploration,information retrieval,text mining,spatial databases,Web analysis,CRM,marketing,medical diagnostics,computational biology,and many others.They present real challenges to classic clustering algorithms. These challenges led to the emergence of powerful broadly applicable data2Pavel Berkhinmining clustering methods developed on the foundation of classic techniques.They are subject of this survey.1.1NotationsTo fix the context and clarify terminology,consider a dataset X consisting of data points (i.e.,objects ,instances ,cases ,patterns ,tuples ,transactions )x i =(x i 1,···,x id ),i =1:N ,in attribute space A ,where each component x il ∈A l ,l =1:d ,is a numerical or nominal categorical attribute (i.e.,feature ,variable ,dimension ,component ,field ).For a discussion of attribute data types see [106].Such point-by-attribute data format conceptually corresponds to a N ×d matrix and is used by a majority of algorithms reviewed below.However,data of other formats,such as variable length sequences and heterogeneous data,are not uncommon.The simplest subset in an attribute space is a direct Cartesian product of sub-ranges C = C l ⊂A ,C l ⊂A l ,called a segment (i.e.,cube ,cell ,region ).A unit is an elementary segment whose sub-ranges consist of a single category value,or of a small numerical bin.Describing the numbers of data points per every unit represents an extreme case of clustering,a histogram .This is a very expensive representation,and not a very revealing er driven segmentation is another commonly used practice in data exploration that utilizes expert knowledge regarding the importance of certain sub-domains.Unlike segmentation,clustering is assumed to be automatic,and so it is a machine learning technique.The ultimate goal of clustering is to assign points to a finite system of k subsets (clusters).Usually (but not always)subsets do not intersect,and their union is equal to a full dataset with the possible exception of outliersX =C 1 ··· C k C outliers ,C i C j =0,i =j.1.2Clustering Bibliography at GlanceGeneral references regarding clustering include [110],[205],[116],[131],[63],[72],[165],[119],[75],[141],[107],[91].A very good introduction to contem-porary data mining clustering techniques can be found in the textbook [106].There is a close relationship between clustering and many other fields.Clustering has always been used in statistics [10]and science [158].The clas-sic introduction into pattern recognition framework is given in [64].Typical applications include speech and character recognition.Machine learning clus-tering algorithms were applied to image segmentation and computer vision[117].For statistical approaches to pattern recognition see [56]and [85].Clus-tering can be viewed as a density estimation problem.This is the subject of traditional multivariate statistical estimation [197].Clustering is also widelyA Survey of Clustering Data Mining Techniques3 used for data compression in image processing,which is also known as vec-tor quantization[89].Datafitting in numerical analysis provides still another venue in data modeling[53].This survey’s emphasis is on clustering in data mining.Such clustering is characterized by large datasets with many attributes of different types. Though we do not even try to review particular applications,many important ideas are related to the specificfields.Clustering in data mining was brought to life by intense developments in information retrieval and text mining[52], [206],[58],spatial database applications,for example,GIS or astronomical data,[223],[189],[68],sequence and heterogeneous data analysis[43],Web applications[48],[111],[81],DNA analysis in computational biology[23],and many others.They resulted in a large amount of application-specific devel-opments,but also in some general techniques.These techniques and classic clustering algorithms that relate to them are surveyed below.1.3Plan of Further PresentationClassification of clustering algorithms is neither straightforward,nor canoni-cal.In reality,different classes of algorithms overlap.Traditionally clustering techniques are broadly divided in hierarchical and partitioning.Hierarchical clustering is further subdivided into agglomerative and divisive.The basics of hierarchical clustering include Lance-Williams formula,idea of conceptual clustering,now classic algorithms SLINK,COBWEB,as well as newer algo-rithms CURE and CHAMELEON.We survey these algorithms in the section Hierarchical Clustering.While hierarchical algorithms gradually(dis)assemble points into clusters (as crystals grow),partitioning algorithms learn clusters directly.In doing so they try to discover clusters either by iteratively relocating points between subsets,or by identifying areas heavily populated with data.Algorithms of thefirst kind are called Partitioning Relocation Clustering. They are further classified into probabilistic clustering(EM framework,al-gorithms SNOB,AUTOCLASS,MCLUST),k-medoids methods(algorithms PAM,CLARA,CLARANS,and its extension),and k-means methods(differ-ent schemes,initialization,optimization,harmonic means,extensions).Such methods concentrate on how well pointsfit into their clusters and tend to build clusters of proper convex shapes.Partitioning algorithms of the second type are surveyed in the section Density-Based Partitioning.They attempt to discover dense connected com-ponents of data,which areflexible in terms of their shape.Density-based connectivity is used in the algorithms DBSCAN,OPTICS,DBCLASD,while the algorithm DENCLUE exploits space density functions.These algorithms are less sensitive to outliers and can discover clusters of irregular shape.They usually work with low-dimensional numerical data,known as spatial data. Spatial objects could include not only points,but also geometrically extended objects(algorithm GDBSCAN).4Pavel BerkhinSome algorithms work with data indirectly by constructing summaries of data over the attribute space subsets.They perform space segmentation and then aggregate appropriate segments.We discuss them in the section Grid-Based Methods.They frequently use hierarchical agglomeration as one phase of processing.Algorithms BANG,STING,WaveCluster,and FC are discussed in this section.Grid-based methods are fast and handle outliers well.Grid-based methodology is also used as an intermediate step in many other algorithms (for example,CLIQUE,MAFIA).Categorical data is intimately connected with transactional databases.The concept of a similarity alone is not sufficient for clustering such data.The idea of categorical data co-occurrence comes to the rescue.The algorithms ROCK,SNN,and CACTUS are surveyed in the section Co-Occurrence of Categorical Data.The situation gets even more aggravated with the growth of the number of items involved.To help with this problem the effort is shifted from data clustering to pre-clustering of items or categorical attribute values. Development based on hyper-graph partitioning and the algorithm STIRR exemplify this approach.Many other clustering techniques are developed,primarily in machine learning,that either have theoretical significance,are used traditionally out-side the data mining community,or do notfit in previously outlined categories. The boundary is blurred.In the section Other Developments we discuss the emerging direction of constraint-based clustering,the important researchfield of graph partitioning,and the relationship of clustering to supervised learning, gradient descent,artificial neural networks,and evolutionary methods.Data Mining primarily works with large databases.Clustering large datasets presents scalability problems reviewed in the section Scalability and VLDB Extensions.Here we talk about algorithms like DIGNET,about BIRCH and other data squashing techniques,and about Hoffding or Chernoffbounds.Another trait of real-life data is high dimensionality.Corresponding de-velopments are surveyed in the section Clustering High Dimensional Data. The trouble comes from a decrease in metric separation when the dimension grows.One approach to dimensionality reduction uses attributes transforma-tions(DFT,PCA,wavelets).Another way to address the problem is through subspace clustering(algorithms CLIQUE,MAFIA,ENCLUS,OPTIGRID, PROCLUS,ORCLUS).Still another approach clusters attributes in groups and uses their derived proxies to cluster objects.This double clustering is known as co-clustering.Issues common to different clustering methods are overviewed in the sec-tion General Algorithmic Issues.We talk about assessment of results,de-termination of appropriate number of clusters to build,data preprocessing, proximity measures,and handling of outliers.For reader’s convenience we provide a classification of clustering algorithms closely followed by this survey:•Hierarchical MethodsA Survey of Clustering Data Mining Techniques5Agglomerative AlgorithmsDivisive Algorithms•Partitioning Relocation MethodsProbabilistic ClusteringK-medoids MethodsK-means Methods•Density-Based Partitioning MethodsDensity-Based Connectivity ClusteringDensity Functions Clustering•Grid-Based Methods•Methods Based on Co-Occurrence of Categorical Data•Other Clustering TechniquesConstraint-Based ClusteringGraph PartitioningClustering Algorithms and Supervised LearningClustering Algorithms in Machine Learning•Scalable Clustering Algorithms•Algorithms For High Dimensional DataSubspace ClusteringCo-Clustering Techniques1.4Important IssuesThe properties of clustering algorithms we are primarily concerned with in data mining include:•Type of attributes algorithm can handle•Scalability to large datasets•Ability to work with high dimensional data•Ability tofind clusters of irregular shape•Handling outliers•Time complexity(we frequently simply use the term complexity)•Data order dependency•Labeling or assignment(hard or strict vs.soft or fuzzy)•Reliance on a priori knowledge and user defined parameters •Interpretability of resultsRealistically,with every algorithm we discuss only some of these properties. The list is in no way exhaustive.For example,as appropriate,we also discuss algorithms ability to work in pre-defined memory buffer,to restart,and to provide an intermediate solution.6Pavel Berkhin2Hierarchical ClusteringHierarchical clustering builds a cluster hierarchy or a tree of clusters,also known as a dendrogram.Every cluster node contains child clusters;sibling clusters partition the points covered by their common parent.Such an ap-proach allows exploring data on different levels of granularity.Hierarchical clustering methods are categorized into agglomerative(bottom-up)and divi-sive(top-down)[116],[131].An agglomerative clustering starts with one-point (singleton)clusters and recursively merges two or more of the most similar clusters.A divisive clustering starts with a single cluster containing all data points and recursively splits the most appropriate cluster.The process contin-ues until a stopping criterion(frequently,the requested number k of clusters) is achieved.Advantages of hierarchical clustering include:•Flexibility regarding the level of granularity•Ease of handling any form of similarity or distance•Applicability to any attribute typesDisadvantages of hierarchical clustering are related to:•Vagueness of termination criteria•Most hierarchical algorithms do not revisit(intermediate)clusters once constructed.The classic approaches to hierarchical clustering are presented in the sub-section Linkage Metrics.Hierarchical clustering based on linkage metrics re-sults in clusters of proper(convex)shapes.Active contemporary efforts to build cluster systems that incorporate our intuitive concept of clusters as con-nected components of arbitrary shape,including the algorithms CURE and CHAMELEON,are surveyed in the subsection Hierarchical Clusters of Arbi-trary Shapes.Divisive techniques based on binary taxonomies are presented in the subsection Binary Divisive Partitioning.The subsection Other Devel-opments contains information related to incremental learning,model-based clustering,and cluster refinement.In hierarchical clustering our regular point-by-attribute data representa-tion frequently is of secondary importance.Instead,hierarchical clustering frequently deals with the N×N matrix of distances(dissimilarities)or sim-ilarities between training points sometimes called a connectivity matrix.So-called linkage metrics are constructed from elements of this matrix.The re-quirement of keeping a connectivity matrix in memory is unrealistic.To relax this limitation different techniques are used to sparsify(introduce zeros into) the connectivity matrix.This can be done by omitting entries smaller than a certain threshold,by using only a certain subset of data representatives,or by keeping with each point only a certain number of its nearest neighbors(for nearest neighbor chains see[177]).Notice that the way we process the original (dis)similarity matrix and construct a linkage metric reflects our a priori ideas about the data model.A Survey of Clustering Data Mining Techniques7With the(sparsified)connectivity matrix we can associate the weighted connectivity graph G(X,E)whose vertices X are data points,and edges E and their weights are defined by the connectivity matrix.This establishes a connection between hierarchical clustering and graph partitioning.One of the most striking developments in hierarchical clustering is the algorithm BIRCH.It is discussed in the section Scalable VLDB Extensions.Hierarchical clustering initializes a cluster system as a set of singleton clusters(agglomerative case)or a single cluster of all points(divisive case) and proceeds iteratively merging or splitting the most appropriate cluster(s) until the stopping criterion is achieved.The appropriateness of a cluster(s) for merging or splitting depends on the(dis)similarity of cluster(s)elements. This reflects a general presumption that clusters consist of similar points.An important example of dissimilarity between two points is the distance between them.To merge or split subsets of points rather than individual points,the dis-tance between individual points has to be generalized to the distance between subsets.Such a derived proximity measure is called a linkage metric.The type of a linkage metric significantly affects hierarchical algorithms,because it re-flects a particular concept of closeness and connectivity.Major inter-cluster linkage metrics[171],[177]include single link,average link,and complete link. The underlying dissimilarity measure(usually,distance)is computed for every pair of nodes with one node in thefirst set and another node in the second set.A specific operation such as minimum(single link),average(average link),or maximum(complete link)is applied to pair-wise dissimilarity measures:d(C1,C2)=Op{d(x,y),x∈C1,y∈C2}Early examples include the algorithm SLINK[199],which implements single link(Op=min),Voorhees’method[215],which implements average link (Op=Avr),and the algorithm CLINK[55],which implements complete link (Op=max).It is related to the problem offinding the Euclidean minimal spanning tree[224]and has O(N2)complexity.The methods using inter-cluster distances defined in terms of pairs of nodes(one in each respective cluster)are called graph methods.They do not use any cluster representation other than a set of points.This name naturally relates to the connectivity graph G(X,E)introduced above,because every data partition corresponds to a graph partition.Such methods can be augmented by so-called geometric methods in which a cluster is represented by its central point.Under the assumption of numerical attributes,the center point is defined as a centroid or an average of two cluster centroids subject to agglomeration.It results in centroid,median,and minimum variance linkage metrics.All of the above linkage metrics can be derived from the Lance-Williams updating formula[145],d(C iC j,C k)=a(i)d(C i,C k)+a(j)d(C j,C k)+b·d(C i,C j)+c|d(C i,C k)−d(C j,C k)|.8Pavel BerkhinHere a,b,c are coefficients corresponding to a particular linkage.This formula expresses a linkage metric between a union of the two clusters and the third cluster in terms of underlying nodes.The Lance-Williams formula is crucial to making the dis(similarity)computations feasible.Surveys of linkage metrics can be found in [170][54].When distance is used as a base measure,linkage metrics capture inter-cluster proximity.However,a similarity-based view that results in intra-cluster connectivity considerations is also used,for example,in the original average link agglomeration (Group-Average Method)[116].Under reasonable assumptions,such as reducibility condition (graph meth-ods satisfy this condition),linkage metrics methods suffer from O N 2 time complexity [177].Despite the unfavorable time complexity,these algorithms are widely used.As an example,the algorithm AGNES (AGlomerative NESt-ing)[131]is used in S-Plus.When the connectivity N ×N matrix is sparsified,graph methods directly dealing with the connectivity graph G can be used.In particular,hierarchical divisive MST (Minimum Spanning Tree)algorithm is based on graph parti-tioning [116].2.1Hierarchical Clusters of Arbitrary ShapesFor spatial data,linkage metrics based on Euclidean distance naturally gener-ate clusters of convex shapes.Meanwhile,visual inspection of spatial images frequently discovers clusters with curvy appearance.Guha et al.[99]introduced the hierarchical agglomerative clustering algo-rithm CURE (Clustering Using REpresentatives).This algorithm has a num-ber of novel features of general importance.It takes special steps to handle outliers and to provide labeling in assignment stage.It also uses two techniques to achieve scalability:data sampling (section 8),and data partitioning.CURE creates p partitions,so that fine granularity clusters are constructed in parti-tions first.A major feature of CURE is that it represents a cluster by a fixed number,c ,of points scattered around it.The distance between two clusters used in the agglomerative process is the minimum of distances between two scattered representatives.Therefore,CURE takes a middle approach between the graph (all-points)methods and the geometric (one centroid)methods.Single and average link closeness are replaced by representatives’aggregate closeness.Selecting representatives scattered around a cluster makes it pos-sible to cover non-spherical shapes.As before,agglomeration continues until the requested number k of clusters is achieved.CURE employs one additional trick:originally selected scattered points are shrunk to the geometric centroid of the cluster by a user-specified factor α.Shrinkage suppresses the affect of outliers;outliers happen to be located further from the cluster centroid than the other scattered representatives.CURE is capable of finding clusters of different shapes and sizes,and it is insensitive to outliers.Because CURE uses sampling,estimation of its complexity is not straightforward.For low-dimensional data authors provide a complexity estimate of O (N 2sample )definedA Survey of Clustering Data Mining Techniques9 in terms of a sample size.More exact bounds depend on input parameters: shrink factorα,number of representative points c,number of partitions p,and a sample size.Figure1(a)illustrates agglomeration in CURE.Three clusters, each with three representatives,are shown before and after the merge and shrinkage.Two closest representatives are connected.While the algorithm CURE works with numerical attributes(particularly low dimensional spatial data),the algorithm ROCK developed by the same researchers[100]targets hierarchical agglomerative clustering for categorical attributes.It is reviewed in the section Co-Occurrence of Categorical Data.The hierarchical agglomerative algorithm CHAMELEON[127]uses the connectivity graph G corresponding to the K-nearest neighbor model spar-sification of the connectivity matrix:the edges of K most similar points to any given point are preserved,the rest are pruned.CHAMELEON has two stages.In thefirst stage small tight clusters are built to ignite the second stage.This involves a graph partitioning[129].In the second stage agglomer-ative process is performed.It utilizes measures of relative inter-connectivity RI(C i,C j)and relative closeness RC(C i,C j);both are locally normalized by internal interconnectivity and closeness of clusters C i and C j.In this sense the modeling is dynamic:it depends on data locally.Normalization involves certain non-obvious graph operations[129].CHAMELEON relies heavily on graph partitioning implemented in the library HMETIS(see the section6). Agglomerative process depends on user provided thresholds.A decision to merge is made based on the combinationRI(C i,C j)·RC(C i,C j)αof local measures.The algorithm does not depend on assumptions about the data model.It has been proven tofind clusters of different shapes,densities, and sizes in2D(two-dimensional)space.It has a complexity of O(Nm+ Nlog(N)+m2log(m),where m is the number of sub-clusters built during the first initialization phase.Figure1(b)(analogous to the one in[127])clarifies the difference with CURE.It presents a choice of four clusters(a)-(d)for a merge.While CURE would merge clusters(a)and(b),CHAMELEON makes intuitively better choice of merging(c)and(d).2.2Binary Divisive PartitioningIn linguistics,information retrieval,and document clustering applications bi-nary taxonomies are very useful.Linear algebra methods,based on singular value decomposition(SVD)are used for this purpose in collaborativefilter-ing and information retrieval[26].Application of SVD to hierarchical divisive clustering of document collections resulted in the PDDP(Principal Direction Divisive Partitioning)algorithm[31].In our notations,object x is a docu-ment,l th attribute corresponds to a word(index term),and a matrix X entry x il is a measure(e.g.TF-IDF)of l-term frequency in a document x.PDDP constructs SVD decomposition of the matrix10Pavel Berkhin(a)Algorithm CURE (b)Algorithm CHAMELEONFig.1.Agglomeration in Clusters of Arbitrary Shapes(X −e ¯x ),¯x =1Ni =1:N x i ,e =(1,...,1)T .This algorithm bisects data in Euclidean space by a hyperplane that passes through data centroid orthogonal to the eigenvector with the largest singular value.A k -way split is also possible if the k largest singular values are consid-ered.Bisecting is a good way to categorize documents and it yields a binary tree.When k -means (2-means)is used for bisecting,the dividing hyperplane is orthogonal to the line connecting the two centroids.The comparative study of SVD vs.k -means approaches [191]can be used for further references.Hier-archical divisive bisecting k -means was proven [206]to be preferable to PDDP for document clustering.While PDDP or 2-means are concerned with how to split a cluster,the problem of which cluster to split is also important.Simple strategies are:(1)split each node at a given level,(2)split the cluster with highest cardinality,and,(3)split the cluster with the largest intra-cluster variance.All three strategies have problems.For a more detailed analysis of this subject and better strategies,see [192].2.3Other DevelopmentsOne of early agglomerative clustering algorithms,Ward’s method [222],is based not on linkage metric,but on an objective function used in k -means.The merger decision is viewed in terms of its effect on the objective function.The popular hierarchical clustering algorithm for categorical data COB-WEB [77]has two very important qualities.First,it utilizes incremental learn-ing.Instead of following divisive or agglomerative approaches,it dynamically builds a dendrogram by processing one data point at a time.Second,COB-WEB is an example of conceptual or model-based learning.This means that each cluster is considered as a model that can be described intrinsically,rather than as a collection of points assigned to it.COBWEB’s dendrogram is calleda classification tree.Each tree node(cluster)C is associated with the condi-tional probabilities for categorical attribute-values pairs,P r(x l=νlp|C),l=1:d,p=1:|A l|.This easily can be recognized as a C-specific Na¨ıve Bayes classifier.During the classification tree construction,every new point is descended along the tree and the tree is potentially updated(by an insert/split/merge/create op-eration).Decisions are based on the category utility[49]CU{C1,...,C k}=1j=1:kCU(C j)CU(C j)=l,p(P r(x l=νlp|C j)2−(P r(x l=νlp)2.Category utility is similar to the GINI index.It rewards clusters C j for in-creases in predictability of the categorical attribute valuesνlp.Being incre-mental,COBWEB is fast with a complexity of O(tN),though it depends non-linearly on tree characteristics packed into a constant t.There is a similar incremental hierarchical algorithm for all numerical attributes called CLAS-SIT[88].CLASSIT associates normal distributions with cluster nodes.Both algorithms can result in highly unbalanced trees.Chiu et al.[47]proposed another conceptual or model-based approach to hierarchical clustering.This development contains several different use-ful features,such as the extension of scalability preprocessing to categori-cal attributes,outliers handling,and a two-step strategy for monitoring the number of clusters including BIC(defined below).A model associated with a cluster covers both numerical and categorical attributes and constitutes a blend of Gaussian and multinomial models.Denote corresponding multivari-ate parameters byθ.With every cluster C we associate a logarithm of its (classification)likelihoodl C=x i∈Clog(p(x i|θ))The algorithm uses maximum likelihood estimates for parameterθ.The dis-tance between two clusters is defined(instead of linkage metric)as a decrease in log-likelihoodd(C1,C2)=l C1+l C2−l C1∪C2caused by merging of the two clusters under consideration.The agglomerative process continues until the stopping criterion is satisfied.As such,determina-tion of the best k is automatic.This algorithm has the commercial implemen-tation(in SPSS Clementine).The complexity of the algorithm is linear in N for the summarization phase.Traditional hierarchical clustering does not change points membership in once assigned clusters due to its greedy approach:after a merge or a split is selected it is not refined.Though COBWEB does reconsider its decisions,its。

2656小型微型计算机系统2020 年in collaborative filtering recommender systems [ J ]. Knowledge- Based Systems,2016,100( 10) ：74-88.[19] Zhou W,W en J,Xiong Q,et al. SVM-TIA a shilling attack detec­tion method based on SVM and target item analysis in recommen­der systems [ J ]. Neurocomputing, 2016,210 (40) ： 197 -205.[20] Tong C, Yin X,Li J,et al. A shilling attack detector based on conv­olutional neural network for collaborative recommender system in social aware network [ J ]. The Computer Journal ,2018,61 ( 7 )：949-958.[21 ] Xu Y,Zhang F. Detecting shilling attacks in social recommendersystems based on time series analysis and trust features[ J]. Knowl­edge-Based Systems,2019,178(16) ：25-47.[22] Wu Z,G ao J,M ao B,et al. Semi-SAD： applying semi-supervisedlearning to shilling attack detection [ C ]//Proceedings of the 5th ACM Conference on Recommender Systems, ( RecSys),ACM, 2011：289-292.[23] Wu Z,W u J,Cao J,et al. HySAD：a semi-supervised hybrid shillingattack detector for trustworthy product recommendation[ C]//P ro­ceedings of the ACM SIGKDD Conference on Knowledge Discov­ery and Data Mining(KDD) ,ACM.2012：985-993.[24] Nie F,Wang Z, Wang R. Adaptive local linear discriminant analysis[J ]. ACM Transactions on Knowledge Discovery from Data, 2020,14(1) ：1-19.[25] Abdi H,Williams L J. Principal component analysis[ J]. Wiley In­terdisciplinary Reviews：Computational Statistics,2010,2(4) ：433-459.[26] Zhong S,Wen Q,Ge Z. Semi-supervised Fisher discriminant analysismodel for fault classification in industrial processes [ J ]. Chemomet-rics and Intelligent Laboratory Systems,2014,138(9) ：203-211. [27] Wu Fei,Pei Yuan,Wu Xiang-qian. Android malware traffic featureanalysis technique based on improved Bayesian mcxlel [ J ]. Journalof Chinese Computer Systems,2018,39(2) ：230-234.[28] Chen Zhi,Guo Wu. Text classification based on depth learning onunbalanced data[ J]. Journal of Chinese Computer Systems,2020, 41(l)：l-5.附中文参考文献：[1]陈海蚊，努尔布力.协同推荐研究前沿与发展趋势的知识图谱分析[J].小型微型计算机系统，2018,39(4) :814名19.[9]李聪，骆志刚，石金龙.一种探测推荐系统托攻击的无监督算法[J].自动化学报，2011，37(2):160-167.[15]伍之昂，庄毅，王有权，等.基于特征选择的推荐系统托攻击检测算法[J].电子学报，2012,40(8): 1687-1693.[17]李文涛，高旻，李华，等.一种基于流行度分类特征的托攻击检测算法[J].自动化学报，2015，41 (9) :1563-1576.[27]吴非，裴源，吴向前.一种改进贝叶斯模型的Android恶意软件流量特征分析技术[J] •小型微型计算机系统，2〇18,39(2) ：230-234.[28]陈志，郭武.不平衡训练数据下的基于深度学习的文本分类[J].小型微型计算机系统,2020，41 (1): 1 -5.《小型微型计算机系统》期刊简介《小型微型计算机系统》创刊于1980年，由中国科学院主管、中国科学院沈阳计算技术研究所主办,为中国计算机 学会会刊.创刊40年来，该刊主要面向国内从事计算机研究和教学的科研人员与大专院校的教师，始终致力于传播我国计算 机研究领域最新科研和应用成果，发表高水平的学术文章和高质量的应用文章，坚持严谨的办刊风格，因而受到计算机 业界的普遍欢迎.《小型微型计算机系统》所刊登的内容涵盖了计算机学科的各个领域，包括计算机科学理论、体系结构、软件、数据 库理论、网络（含传感器网络）、人工智能与算法、服务计算、计算机图形与图像等.在收录与检索方面，在国内入选为:《中文核心期刊要目总览》、《中国学术期刊文摘（中英文版）》、《中国科学引文 数据库》（CSCD)、《中国科技论文统计源期刊》、《中国科技论文统计与分析》（RCCSE)，并被中国科技论文与引文数据 库、中国期刊全文数据库、中国科技期刊精品数据库、中国学术期刊综合评价数据库、中国核心期刊（遴选）数据库等收 录.还被英国《科学文摘》（INSPEC)、俄罗斯《文摘杂志》（AJ)、美国《剑桥科学文摘》（C S A(N S)和CSA(T))、美国《乌利希期刊指南》（UPD)、日本《日本科学技术振兴机构中国文献数据库》（JS T)和波兰《哥白尼索弓|》（IC)收录.。

NVIDIA® Spectrum™ SN4000 series switches are the 4th generation of Spectrumswitches, purpose-built for leaf/spine/super-spine datacenter applications. Allowing maximum flexibility, SN4000 series provides port speeds spanning from 1GbE to 400GbE, with a port density that enables full rack connectivity to any server at any speed. In addition, the uplink ports allow a variety of blocking ratios to suit any application requirement.The SN4000 series is ideal for building wire-speed and cloud-scale layer-2 and layer-3 networks. The SN4000 platforms deliver high performance, consistent low latency along with support for advanced software defined networking features, making it the ideal choice for web scale IT, cloud, hyperconverged storage and data analytics applications. Network Disaggregation: NVIDIA Open EthernetOpen Ethernet breaks the paradigm of traditional switch systems, eliminating vendor lock-in. Instead of forcing network operators to use the specific software that is provided by the switch vendor, Open Ethernet offers the flexibility to use a choiceof operating systems on top of Ethernet switches, thereby re-gaining control of the network, and optimizing utilization, efficiency and overall return on investment. Open Ethernet adopts the same principles as standard open solutions for servers and storage, and applies them to the world of networking infrastructure. It encourages an ecosystem of open source, standard network solutions.These solutions can then be easily deployed into the modern data center across network equipment that eases management and ensures full interoperability. With a range of system form factors, and a rich software ecosystem, NVIDIA SN4000 series allows you to pick and choose the right components for your data center.NVIDIA SN4000 SeriesSN4000 series platforms are based on the high-performance Spectrum-3 ASIC with a bidirectional switching capacity of 12.8Tbps. SN4000 platforms are available in a range of configurations, each delivering high performance combined with feature-rich layer2 and layer3 forwarding, ideally suited for both top-of-rack leaf and fixed configuration spines. SN4000 series provides full wire speed, cut through-mode latency, on-chip fully-shared 64MB packet buffering, and flexible port use in addition to advanced capabilities. Combining a wide range of innovations in the area of programmability, telemetry, and tunneling with industry leading performance, NVIDIA SN4000 series is capable of addressing today’s data center’s complex networking requirements.VISIBILITY>What Just Happened?® (WJH) telemetry dramatically reduces mean time to issue resolution by providing answers to: When, What, Who, Where and Why>Hardware-accelerated histograms track and summarize queue depthsat sub-microsecond granularity>Inband network telemetry(INT)-ready hardware>Streaming Telemetry>512K on-chip flow counters PERFORMANCE>Fully shared packet buffer provides a fair, predictable and high bandwidth data path >Consistent and low cut-through latency >Intelligent hardware-accelerated data movement, congestion management and load balancing for RoCE and Machine learning applications that leverage GPUDirect®>Best-in-class VXLAN scale-10X more tunnels and tunnel endpoints>512K shared forwarding entriesflexibly shared across ACL, LPM routes, host routes, MAC, ECMPand tunnel applications>Up to 1M IPv4 route entriesAGILITY>Comprehensive Layer-2, Layer-3and RoCE>Advanced network virtualization with high performance single pass VXLAN routing and IPv6 segment routing>Cloud Scale NAT – 100K+ sessions>Programmable pipeline that can programmatically parse, processand edit packets>Deep Packet Inspection – 512B deepNVIDIA SpECTRUMSN4000 SERIES SWITCHES for accelerated data centers DATASHEETSN4700The SN4700 spine/super-spine offers 32 ports of 400GbE in a compact 1U form factor . It enables connectivity to endpoints at varying speeds and carries a throughput of 12.8 Tb/s, with a landmark 8.4Bpps processing capacity. As an ideal spine solution, the SN4700 allows maximum flexibility, with port speeds spanning from 1 to 400GbE per port.SN4600SN4600 is a 2U 64-port 200GbE spine that can also be used as a high density leaf, fully splittable to up to 128X 10/25/50GbE ports when used with splitter cables. SN4600 allows for maximum flexibility, with ports spanning from 1 to 200GbE and port density that enables full rack connectivity to any server at any speed, and a variety of blocking ratios.SN4600CSN4600C is a 64-port 100GbE switch system that is ideal for spine/super-spine applications. With a landmark 8.4Bpps processing capacity and 6.4Tb/s throughput in a dense 2U form factor, SN4600C offers diverse connectivity in combinations of 10/25/40/50/100GbE. The SN4600C is well-suited to answer the challenging needs of large virtualized data centers and cloud environments.SN4410SN4410 is a 48-port 100GbE (24x QSFP28-DD) + 8x 400GbE (8x QSFP56-DD) leaf/spine switch system. The SN4410 is ideal for interconnecting 100GbE servers and networks to 400GbE infrastructure. With a landmark 8.4Bpps processing capacity and 8.0Tb/s throughput in a dense 1U form factor, SN4410 offers diverse connectivity in combinations of 10/25/40/50/100/200/400GbE.SN4800SN4800 is a modular switch platform ideally-suited for large virtualized data centers and cloud environments, allowing flexibility and customization with up to 8 line cards and a single management card. Demonstrating a landmark 8.4B pps processing capacity and up to 12.8Tb/s throughput in a versatile 4U form factor . The SN4800 offers 10/25/40/50/100GbE connectivity with a 16 x 100GbE (QSFP28) line card.Linux Switch†††††* Future Optionplatform Software OptionsSN4000 series platforms are available out of the factory in three different flavors:>Pre-installed with NVIDIA Cumulus Linux, a revolutionary operating system, taking the Linux user experience from servers to switches and providing a rich routing functionality for large scale applications.>Pre-installed with NVIDIA Onyx™, a home-grown operating system utilizingcommon networking user experiences and an industry standard CLI.>Bare metal including ONIE image, installable with any ONIE-mounted OS.ONIE-based platforms utilize the advantages of Open Networking and theSpectrum-3 ASIC capabilities.High AvailabilitySN4000 series switches are designed with the following software and hardware features for high availability:>1+1 hot-swappable power supplies and N+1 hot-swappable fans>Color-coded PSUs and fans>Up to 128X 100/50/25/10/1GbE, 64X 200GbE or 32X 400GbE>Multi-chassis LAG for active/active L2 multipathing>128-way ECMP routing for load balancing and redundancySN4000 Series: A Rich Software EcosystemNVIDIA Cumulus-LinuxNVIDIA Cumulus Linux is a powerful open network operating system enabling advanced automation, customization and scalability using web-scale principles like the world’s largest data centers. It accelerates networking functions and provides choice from an extensive list of supported switch models including Spectrum based switches. Cumulus Linux was built for automation, scalability and flexibility, allowing you to build data center and campus networks that ideally suits your business needs. Cumulus Linux is the only open network OS that allows you to build affordable and efficient network operations like the world’s largest data center operators, unlocking web-scale networking for businesses of all sizes.SONiCSONiC was designed for cloud networking scenarios, where simplicity and managing at scale are the highest priority. NVIDIA fully supports the Pure Open Source SONiC from the SONiC community site on all of the SN4000 series switch platforms. With advanced monitoring and diagnostic capabilities, SONiC is a perfect fit for the NVIDIA SN4000 series. Among other innovations, SONiC on SN4000 series enables fine-grained failure recovery and in-service upgrades (ISSU), with zero downtime.Linux Switch and DentLinux Switch enables users to natively install and use any standard Linux distributionas the switch operating system, such as DENT, a Linux-based networking OS stackthat is suitable for campus and remote networking. Linux Switch is based on a Linux kernel driver model for Ethernet switches (Switchdev). It breaks the dependency of using vendor-specific, closed-source software development kits. The open-source Linux driver is developed and maintained in the Linux kernel, replacing proprietary APIs with standard Linux kernel interfaces to control the switch hardware. This allows off-the-shelf Linux-based networking applications to operate on Spectrum-based switches for L2 switching and L3 routing, including open source routing protocol stacks, such as FRR (Quagga), Bird and XORP, OpenFlow applications, or user-specific implementations.NVIDIA OnyxOnyx is a high performance switch operating system, with a classic CLI interface. Whether building a robust storage fabric, cloud, financial or media and entertainment fabric, customers can leverage the flexibility of Onyx to tailor their network platform to their environment. With built-in workflow automation, monitoring and visibility tools, enhanced high availability mechanisms, and more, Onyx simplifies network processes and workflows, increasing efficiencies and reducing operating expenses and time-to-service. Onyx leverages capabilities of the SN4000 series to provide greater magnitudes of scale, state-of-the-art telemetry, enhanced QoS, exceptional programmability that enables a flexible pipeline supporting both new and legacy protocols, a larger fully-shared buffer, and more**.NVIDIA NetQNVIDIA NetQ is a highly-scalable, modern, network operations tool set that provides visibility, troubleshooting and lifecycle management of your open networks inreal time. NetQ delivers actionable insights and operational intelligence about the health of your data center and campus networks — from the container or host, all the way to the switch and port, enabling a NetDevOps approach. NetQ is the leading network operations tool that utilizes telemetry for deep troubleshooting, visibility and automated workflows from a single GUI interface, reducing maintenance and network downtimes. With the addition of full lifecycle management functionality, NetQ now combines the ability to easily upgrade, configure and deploy network elements with a full suite of operations capabilities, such as visibility, troubleshooting, validation, trace and comparative look-back functionality.ONIEThe open network install environment (ONIE) is an open compute project open source initiative driven by a community to define an open “install environment” for bare metal network switches, such as the NVIDIA SN4000 series. ONIE enables a bare metal network switch ecosystem where end users have a choice of different network operating systems.Docker ContainersNVIDIA fully supports the running of third party containerized applications on the switch system itself. The third party application has complete access to the bare-metal switch via its direct access to the SDK. The switch has tight controls over the amount of memory and CPU cycles each container is allowed to use, along with fine grained monitoring of those resources.Docker Containers SupportNVIDIA Spectrum-3: Build your cloud without compromise Groundbreaking PerformancePacket buffer architecture has a major impact on overall switch performance.The Spectrum-3 packet buffer is monolithic and fully shared across all ports, supporting cut-through line rate traffic from all ports, without compromising scale or features. With its fast packet buffer, Spectrum-3 is able to provide a high-performance fair and bottleneck-free data path for mission-critical applications.Pervasive VisibilitySpectrum-3 provides deep and contextual network visibility, which enables network operators to proactively manage issues and reduce mean time to recovery/innocence. The WJH feature leverages the underlying silicon and software capability to provide granular and event-triggered information about infrastructure issues. In addition, the rich telemetry information from Spectrum-3 is readily available via open APIs that are integratable with third party software tools and workflow engines. Unprecedented AgilityFor modern data center infrastructure to be software defined and agile, both its compute and network building blocks need to be agile. Spectrum-3 features a unique feature rich and efficient packet processing pipeline that offers rich data center network virtualization features without compromising on performance or scale. Spectrum-3 has a programmable pipeline and a deep packet parser/editor that can process payloads up to the first 512B. Spectrum-3 supports single pass VXLAN routing as well as bridging. Additionally, Spectrum-3 supports advanced virtualization features such as IPv6 segment routing, and Network Address Translation (NAT). Massive ScaleThe number of endpoints in the data center is increasing exponentially. With the current shift from virtual machine-based architectures to container-based architectures, the high-scale forwarding tables required by modern data centers and mega-clouds increase by up to an order of magnitude or more. To answer these needs for scalability and flexibility, Spectrum-3 uses intelligent algorithms and efficient resource sharing, and supports unprecedented forwarding table, counters and policy scale.>Fine-grained resource allocation to fit all specific needs, allowing up to 512Kentries to be dynamically shared across MAC, ARP, IPv4/IPv6 routes, ACLs,ECMP, and Tunnels.>An innovative algorithmic TCAM optimized for data centers and cloudenvironments, which can scale the number of rules to up to half a million rules.End-to-End SolutionThe SN4000 series is part of the NVIDIA complete end-to-end solution which provides 1GbE through 400GbE interconnectivity within the data center. Other devices in this solution include ConnectX®-based network interface cards and LinkX® copper or fiber cabling.specificationsSupported Transceivers and CablesOrdering informationComplianceAccessories and Replacement partsNVIDIA SN4000 series switches come with a one-year limited hardware return-and-repair warranty, with a 14 business day turnaround after the unit is received. For more information, please visit the NVIDIA Technical Support User Guide .Additional InformationSupport services including next business day and 4-hour technician dispatch are available. For more information, please visit the NVIDIA Technical Support User Guide . NVIDIA offers installation, configuration, troubleshooting and monitoring services, available on-site or remotely delivered. For more information, please visit the NVIDIA Global Services website .Ordering InformationFor ordering information, please contact *************。

华为5G中级考试(试卷编号261)1.[单选题]在做NR网络的下行峰值调测时，PDSCHDMRS类型应该配置为以下哪一项？A)Type3B)Type2C)Type1D)Type4答案:B解析:2.[单选题]以下关于预调度的描述，错误的是哪一项?A)采用基本预调度时，无论是否有业务请求，只要调度资源有剩余，基站就会持续进行调度B)预调度功能也需要终端侧的支持C)预调度只用于上行调度D)该算法用于降低初始调度时答案:B解析:3.[单选题]N.SA网络中,在以下哪条消息之后标示着UE完全接入5G网络?A)SgNodeB:Addi tion Comlete之后B)SgNodeBAdditionRequest之后C)UE完成在SgNodeB上的RA之后D)RRCConnection Reconfi guration之后答案:C解析:4.[单选题]以下哪种SRS的资源仅用于高频组网?A)Non code bookB)Beam managementC)Code bookD)Antenna switching答案:B解析:5.[单选题]遵照协议段内连续CA场景，n41频段载波1（带宽为60MHZ）和载波2（带宽为100MHZ)的载波中心频段间隔最大为多少?A)89.8MHZB)99.8MHZC)69.8MHZ解析:6.[单选题]以下关于5G 小区存在的寻呼方式的描述，错误的是哪一项？A)RAN 使用 I-RNTI 在 RNA 寻呼B)使用 PDSCH 信道发送寻呼消息C)核心网使用 I-RNT I 在 RNA 寻呼D)核心网使用 5G-S-TMSI 在 TAL寻呼答案:D解析:7.[单选题]5G系统中一共定义了多少种CQI的映射关系表？A)1种B)2种C)3种D)4种答案:C解析:8.[单选题]NR系统中1个CCE包含了多少个REG?A)8B)6C)4D)2答案:B解析:9.[单选题]以下哪一个协议层完成基站侧资源调度？A)PDCPB)RLCC)MACD)SDAP答案:C解析:10.[单选题]华为RAN3.132TRX的AAU最大支持多少层PDCCH?A)16层B)8层C)4层D)2层答案:C11.[单选题]SA网络NR小区重选过程中同频邻区偏置Qoffset在哪条消息中发送?A)SIB4B)SIB1C)SIB3D)SIB2答案:C解析:12.[单选题]5GCPE接收机的Noise Figure（NF）典型值为哪项？A)5dbB)3dbC)1dbD)7db答案:B解析:13.[单选题]NR下行带宽100Mhz使用SCS为30KHz时，每个RBG包含多少个PRB?A)16B)8C)2D)4答案:A解析:14.[单选题]排查SCS=30KHz的NR小区干扰时， PRB级别正常的干扰底噪有多大?A)-90dBmB)-110dBmC)-115dBmD)-125dBm答案:C解析:15.[单选题]为应对大规模连接,5G适应mMTC物联网场景时，推荐采用的SCS子载波间隔为多少?A)60KhzB)30KhzC)15khzD)120Khz答案:C解析:B)A1门限需要大于A2C)需要打开频率优先级异频切换开关D)A4门J限需大于目标小区设置的基于覆盖的A2[门限答案:A解析:17.[单选题]当前华为基站低频最大支持频段内多少载波聚合?A)4B)2C)3D)5答案:B解析:18.[单选题]以下关于NSA场景下UE在5C侧接入的描述，错误的是哪一项?A)UE和基站之间没有RRC信令B)优先采用基于非竞争的随机接入流程C)UE无需进行小区选择判决D)UE通过sync raster进行扫描找到SSB的位置答案:D解析:19.[单选题]在使用Pobe进行NsA网络的测试中，发现LTE上行的MAC层速率20Mbps,，但是PDCP层速率为0，以下理解正确的是哪项?A)误码太高，数据为重传导致B)LTE的数据汇聚到NR的PDCP层C)工具统计异常D)PDCP层故障答案:B解析:20.[单选题]NR小区SSB波束采用默认模式，天线挂高35米，机械下倾角为3°，数字下倾配置为0°，则此小区主覆盖波瓣的下沿(近点)距离基站大约是多少米?A)1200 米B)150 米C)330 米D)670 米答案:D解析:A)6312B)513000C)504990D)2104答案:B解析:22.[单选题]在NSA组网中，如果只有5G发送了掉话，那么终端收到的空口消息是以下哪条?A)RRC ReleaseB)RRC ReconfigurationC)SCG Failue InfoD)RRC Reestablishment答案:C解析:23.[单选题]EN-DC中，使用哪种承载模式由哪个网元决定？A)MNB)PSCellC)SCellD)SN答案:A解析:24.[单选题]以下哪一项不属于业务类指标？A)最大用户数B)平均用户数C)PRB利用率D)上/下行数据业务量答案:C解析:25.[单选题]在下行调度计算用户的RB数量时以下哪个因素是不涉及的了A)RI上报B)MCSC)PMI上报D)缓存数据量答案:C解析:26.[单选题]PRACH时域位置由"PRACH Configuration Index'"确定，不包含以下哪一项？C)和SSB Index的关系D)前导格式答案:C解析:27.[单选题]NSA组网上行TDM功控时， TDM-Pattern信息通过哪条消息发送？A)SgNB Change RequiredB)SgNB Addition RequestC)SgNB Modification RequiredD)SgNB Modi fication Request答案:C解析:28.[单选题]以下关于5GQoS带宽XBR参数的描述，错误的是哪一项?A)gfbr是QoS Flow 的承诺速率B)e-MbR是UE所有Non-GBR QoS Flow 速率之和的上限C)Session -AMBRPDU 会话中所有QoS Flow速率之和的下限D)MFBR是QoS Flow 的最大速率答案:C解析:29.[单选题]"在线用户数"是统计以下哪个UE状态的数量？A)RRC连接B)Inactive?状态C)缓存中有数据的RRC用户D)缓存中没有数据的RRC用户答案:A解析:30.[单选题]以下不属于UPF功能的是哪一项?A)生成QoS策略B)下行缓存数据通知C)QOS策略执行D)分组路由和转发答案:A解析:31.[单选题]5G PUSCH支持CP-OFDM和DFT-S-OFDM两种波形，以下描述错误的是哪一项?A)DFT-S-OFDM优点:可使用不连续的频域资源B)CP-OFDM优点:可以使用不连续的频域资源32.[单选题]G频点栅格定义了NR小区的中心频点，Sub3G的频点号计算公式中△FG1oba1是多少?A)60kHzB)5kHzC)15kHzD)30kHz答案:B解析:33.[单选题]以下哪个测量对象可以反映特定两小区的切换性能，统计系统内特定两两小区之间的切换指标?A)NRCELLtoECELLB)NRDUCELLC)NRDUCELLTRPD)NRCellRelation答案:D解析:34.[单选题]5G 网络开启Inactive态功能后，以下描述错误的是哪一项？A)RRC 建立次数减少B)X2 口消息可能增加C)Uu 口寻呼消息可能增如D)RRC 重配次数增加答案:D解析:35.[单选题]5G协议规定了gNodeB支持多少个逻辑信道分组？A)8B)4C)16D)12答案:A解析:36.[单选题]华为RAN3.1的FR内频段内下行CA要求，PCe11和SCe11的帧偏置差的绝对值要小于等于多少？A)3075TsB)1024Ts37.[单选题]当5G小区的SCS=30KHz时，一个TA（timingadvance）有多大？A)39mB)48mC)96mD)78m答案:A解析:38.[单选题]以下关于广播波束的缺省覆盖场景的描述,错误的是哪一项?A)垂直3dB波宽6B)默认采用7+1波束扫描~C)倾角可调范围0°-9°%D)水平3dB波宽105°答案:C解析:39.[单选题]5G小区下吧按照DCI信息接收PDSCH上的数据包后，使用哪个参数决定下行HARQ传输时刻A)K0B)K2C)K1D)K3答案:C解析:40.[单选题]以下哪一种场景优先采用非竞争随机接入？A)初始RRC连接建立B)切换C)RRC重建D)上行数据发送答案:B解析:41.[单选题]5G信道栅格用于指示小区的实际频点位置，以N28为例，其NREF的步长必须是多少?A)20B)3C)242.[单选题]华为RAN3.1中，以下哪一项原因值会导致N.NsaDc.SgNB.AbnormRel指标增加？A)SCG MobilityB)Resource OptimisationC)TDCoverall ExpiryD)Cell not Available答案:C解析:43.[单选题]NSA终端在eNodeB侧接入过程中，如果eNdoeB收到ME的消息，里面携带了nRestriciontion的指示，以下 哪个是问题的原因？（ ）A)终端不支持NSA功能B)用户5G未开户C)用户开户速率过低D)EPC相关网元不支持NSA答案:B解析:44.[单选题]RRC Inactive恢复到RRC Connected时，使用到哪一个UE ID?A)I-RNTIB)SUPIC)C-RNTID)5G-GUTI答案:A解析:45.[单选题]以下关于5G小区存在的寻呼方式的描述，错误的是哪一项?A)核心网使用I-RNTI在RNA寻呼B)RAN使用I-RNTI在RNA寻呼C)使用PDSCH信道发送寻呼消息D)核心网使用5G-S-TMSI在TAL寻呼答案:A解析:46.[单选题]某小区下行带宽为34RB,RBG大小为2，假设给某UE调度的RBG按照bitmap为"01110001011010001，则调度的RB数为多少A)16B)18C)1447.[单选题]SA小区的系统内同频切换不存在以下哪一个环节？A)测量环节B)触发环节C)判决环节D)切换环节答案:B解析:48.[单选题]做5G的C波段上行链路估算时，UE的发射功率一般为多少?A)26dBmB)30dBmC)33dBmD)23dBm答案:D解析:49.[单选题]以下关于 SSB 物理周期的描述，错误的是哪一项？A)物理周期可以灵活配置B)sSB 的周期和 MIB 周期可以设置不一样C)每个周期内采用扫描的方式发送所有的 SSB 波束D)周期越大 UE 搜索小区更快答案:D解析:50.[单选题]在NSA组网的gNodeB添加流程，以下哪个指标只能在gNodeB侧统计?A)随机接入成功次数B)gNodeB添加成功次数C)gNodeB添加尝试次数D)gNodeB添加拒绝次数答案:D解析:51.[单选题]参数 PRACH Configuration Index中不包含哪一项信息？A)Preamble格式B)slot编号C)无线帧D)PRB位置答案:A52.[单选题]以下关于上行波束训练的描述，错误的是哪一项？A)上行波束可使用SRS的BM功能确定B)上行波束可以通过QCL确定C)SRs发送的波束信息通过RRC配置D)终端同时只能发送一个SRS上行波束答案:B解析:53.[单选题]在gnodeb的配置里，以下哪个协议层的参数无需和QCI进行映射?A)RLCB)MACC)PHYD)PDCP答案:D解析:54.[单选题]在低频场景下，UE是如何获取当前SSB的波束ID?A)通过MIB消息获取B)通过PBCH物理层编码信息获取C)通过SIB1消息获取D)通过PBCH DMRS获取答案:D解析:55.[单选题]对比QoS和切片SLA，以下描述错误的是哪一项?A)Qos是切片SA的基础B)切片SLA在较长时间范围内调整资源满足业务C)切片支持数最大可到254个D)切片SL A对准用户体验、系统性能、部署运维等全方位需求答案:C解析:56.[单选题]相同小区带宽下，以下哪一种时隙配比的上行速率最高?A)上行下行时隙配比2:3B)上行下行时隙配比8:2C)上行下行时隙配比7:3D)上行下行时隙配比4:;1答案:A解析:B)ce11IDC)PCID)sI-RNII答案:C解析:58.[单选题]NSA网络中,在以下哪条消息之后标示着UE完全接入5G网络?A)SgNodeB: Addi tion Comlete之后B)SgNodeB Addi tion Request之后C)UE完成在 SgNodeB上的RA之后D)RRC Connection Reconfi guration之后答案:C解析:59.[单选题]5G 控制信道采用预定义的权值会生成以下哪种波束？A)半静态波束B)静态波束C)宽波束D)动态波束答案:B解析:60.[单选题]NR2.6GHzSCS=30KHz小区和LTE-TDD2.6GHz共同组网场景，当LTE小区采用DSUDD，SSP7，NR小区采用8：2配比，SS54时，还需要设多少偏置才能保证帧结构对齐？A)4msB)3msC)1msD)2ms答案:B解析:61.[单选题]以下哪一项原因不会导致IBLER升高？A)SINR陡降B)天线不平衡C)强干扰D)上行TA异常答案:B解析:62.[单选题]NSA锚点切换流程中使用的是以下哪种事件报告?解析:63.[单选题]5G 中上行一共定义多少个逻辑信道组?A)2B)4C)8D)16答案:C解析:64.[单选题]华为基站的下行信道配置中，最大的功率偏置为多少?A)6dBB)12dBC)9dBD)15dB答案:D解析:65.[单选题]以下关于PUCCH资源分配协议约束的描述,错误是哪一项?A)ACK/NACK支持半静态调度PUCCH资源B)周期CSI支持半静态调度PUCCH资源C)周期CSI支持RRC信令配置PUCCH资源D)SR只能通过RRC信令配置PUCCH资源答案:D解析:66.[单选题]NSA组网上行TDM功控时，TDM-Pattern信息通过哪条消息发送？A)SgNB Change RequiredB)SgNB Addition RequestC)SgNB Modification RequiredD)SgNB Modi fication Request答案:D解析:67.[单选题]空口质量是影响下行峰值速率体验的最关键因素，以下哪一个不属于空口质量因素？A)低RANKB)高IBLER68.[单选题]以下业务中，哪类业务默认的优先级是最高的?A)5QI=9B)5QI=2C)5QI=5D)5QI=1答案:C解析:69.[单选题]当PDSCH采用type2的dmrs时，最大支持的端口数是多少?A)8B)6C)4D)12答案:D解析:70.[单选题]在SA组网中做下载业务时，因服务小区信号差而启动切换判决阶段的事件是哪项?A)A1B)A2C)A3D)A4答案:B解析:71.[单选题]华为基站基于覆盖的异频切换使用以下哪个事件进行触发?A)A5B)A3C)A6D)A4答案:A解析:72.[单选题]N.R下行带宽100Mhz使用SCS为30KHz时,每个RBG包含多少个PRB?A)16B)8C)2D)473.[单选题]以下关于CU/DU划分描述错误的是哪项?A)CU与DU之间承载称为回传B)5G时延苛刻，如果按BBU/RRU架构，C-RAN范围小， 部署受限C)5G阶段，BBU裂化为CU和DU，对时延处理要求严格的功能放到DU,DU需要尽量靠近AAUD)时延不敏感处理部分放到cU,这样CU可以放到适当高的网络位置，提升基站之间协同的能力和资源共享答案:A解析:74.[单选题]在上行调度过程中,每个non-GBR逻辑信道的“逻辑信道保障速率”固定为多少?A)16kbpsB)32kbpsC)8kbpsD)4kbps答案:C解析:75.[单选题]NR系统中1个CCE包含了多少个REG?A)6B)8C)4D)2答案:A解析:76.[单选题]系统可以根据测量到的 SRS SINR确定下行MCSA)当PDSCH存在大量padding时可以选择B)低阶MCS，提升传输可靠性C)系统主要根据UE上报的CQI选择MCSD)调度器根据下行IBLER目标值计算MCS调整量答案:A解析:77.[单选题]RRC连接建立流程中，RRC Setup Complete消息对应的逻辑信道是哪一项?A)CCCHB)DCCHC)DTCHD)BCCH答案:A78.[单选题]华为基站的ChMeas .MCS .主要是用来测量以下哪一类指标A)小区业务量B)移动性能C)网络负载D)信道质量答案:D解析:79.[单选题]N.SA组网中,要CPE1.0达到下行1000Mbps峰值,以下哪一项为小区下行速率的最低要求?A)900MbpsB)860MbpsC)800MbpsD)700Mbps答案:B解析:80.[单选题]以下关于华为基站侧PDCCH时域位置的描述，错误的是哪一项?A)低频场景符号数支持固定和自适应配置B)高频场景符号数支持固定和自适应配置C)通过RRC Reconfiguration将符号数发给用户D)支持根据CCE需求自适应调整符号数答案:B解析:81.[单选题]以下关于5G用户身份安全的描述，错误的是哪项？A)SUCI在AUSF侧解密B)SUCI包含不加密部分C)SUPI在UE侧加密D)SUPI类型包括IMSI或NAI答案:A解析:82.[单选题]以下关于预调度的描述，错误的是哪一项?A)预调度只用于上行调度B)该算法用于降低初始调度时延C)采用基本预调度时，无论UE是否有业务请求，只要调度资源有剩余，基站就会持续进行调度D)预调度功能也需要终端侧的支持答案:D解析:答案:C解析:84.[单选题]影响RANK的因素很多，以下描述错误的是哪一项?A)RF调整构造折射/反射提升RANKB)一般离基站越近RANK更高C)减少机械 下倾角来提升RANKD)SU MIMO多流开关需打开答案:C解析:85.[单选题]对于5G NR来说，一个无线帧占用多少毫秒？A)1B)5C)10D)不固定，和SCS有关答案:C解析:86.[单选题]5GRAN3.1AAU可调电下倾角的调整粒度为以下哪一项？A)0.1°B)0.5°C)1°D)2°答案:C解析:87.[单选题]超过多少时间没有任何业务且服务gNodeB没有改变，UE从RRC INACTIVE迁移到 RRC_ IDLE?A)1500sB)1000sC)1800sD)1200s答案:B解析:88.[单选题]在NR组网下，为了用户能获得接近上行最高速率，其MCS值最低要求应该是多少?解析:89.[单选题]以下关于EN-DC双连接组网的描述，错误的是哪一项？A)只能支持4G和5G的双连接B)UE在空可以和两个基站建立信令面C)UE在空只能和一个基站建立信令面D)OUE在空可以和两个基站建立用户面答案:B解析:90.[单选题]Option3x架构下，上行分流的控制参数是在以下哪一个网元里配置的?A)gNodeBB)UEC)SGWD)eNodeB答案:A解析:91.[单选题]N.SA组网上行TDM功控时,TDM-Pattern信息通过哪条消息发送?A)SgNB ChangeRequiredB)SgNB Addition RequestC)SgNB Modification RequiredD)SgNB Modi fication Request答案:C解析:92.[单选题]5G同步栅格指示UE搜索小区SSB，终端开机对Sub3G颇段的搜索步长是多少?A)17.28MHzB)100kHzC)1.44MHzD)1.2MHz答案:D解析:93.[单选题]在PUSCH功率算法中，路损是通过以下哪个信道的测量计算出来的？A)SSBB)PDSCH94.[单选题]5G SA组网中,以下哪种RRC状态转换流程是不支持的?A)RC空闲到RRC连接B)RRC去激活到RRC空闲C)RRC空闲到RRC去激活D)RRC去激活到RRC连接答案:C解析:95.[单选题]在NSA组网中，如果在eNodeb例配置的5GSSB频点和实际的不一致，会出现以下哪个问题A)gnodeb拒地添加请求B)enodeb无法下发NR的测量配置C)UE随机接入失败D)UE无法上报5G测量结果答案:D解析:96.[单选题]以下哪条消息用于指示完成站内RNA更新？A)RRCSetupB)RRCReleaseC)RRCResumeRequest1D)RRCReconfiguration答案:C解析:97.[单选题]5G SA C-Band小区下,终端开机搜索SSB的步长是多大?A)1.44MHzB)30KHzC)1.2MHzD)17.28HHz答案:A解析:98.[单选题]在RAM3.0版本中的下行信道配置中，最大的功率偏置为多少?A)15dBB)9dBC)6dB99.[单选题]5G 小区系统带宽用哪个参数来表征A)PRBB)CRBC)DRBD)PintA答案:A解析:100.[单选题]以下哪个参数确定了PRACH信道的时域位置?A)PRACH config indexB)NcsC)Logic root sequenceD)Frequency offset答案:A解析:101.[单选题]以下场景化波束中，哪一项不适用于高层楼宇覆盖？A)H65V25B)H110V6C)H25V25D)H110V25答案:B解析:102.[单选题]以下哪一项不是PUCCH信道管理的过程?A)UE自行决定资源发送UCIB)UE根据分配的资源发送UCIC)C gNodeB为UE分配PUCCH资源D)小区级PUCCH配置答案:A解析:103.[单选题]NR小区峰值测试中，UE支持256QAM时MCS最高能达到多少阶？A)27阶B)26阶C)28阶D)29阶答案:A104.[单选题]M.assive MIMO的哪一项增益不能改善系统覆盖性能?A)阵列增益B)空间复用增益C)干扰抑制增益D)空间分集增益答案:B解析:105.[单选题]盲切换相对于普通切换不包含哪一个环节A)目标小区判决环节B)测量环节C)切换环节D)触发环节答案:B解析:106.[单选题]以下关于预调度的描述，错误的是哪一项A)预调度只用于上行调度B)该算法用于降低初始调度时延C)采用基本预调度时，无论UE是否有业务请求，只要调度资源有剩余，基站就会持续进行调度D)预调度功能也需要终端侧的支持答案:C解析:107.[单选题]当5G小区实际SSB中心频点为3449.28MHz时，小区测量配置中下发的频点信息是什么?A)629952B)7811C)636666D)3449答案:B解析:108.[单选题]以下哪个是DTF-s-OFD1波形特有的物理层处理步骤?A)Layer mappingB)ModulationC)Transformer PrecodingD)Scrbling答案:C解析:B)INT-RNTIC)P-RNTID)MCS-C-RNTI答案:BD解析:110.[多选题]上下行解耦特性开通后的增益有哪些?A)口NR小区中心用户下行吞吐率提升B)口NR小区边缘用户体验得到改善C)口N小区边缘上行吞吐率提升D)口NR小区用户数增加答案:BCD解析:111.[多选题]NR网络出现重叠覆盖会对网络产生什么影响?A)口掉话率较高B)口切换次数增加C)口网络质量变好D)口切换成功率较低答案:ABD解析:112.[多选题]以下关于5G 上下行共享信道DMRS的描述，正确的是哪些项？A)Additional DMRS 符号数和前置 DMRS 相同B)必须配置前置 DMRS 符号C)前置 DMRS 可以占用第一个 OFDM 符号D)最大支持 12 个端口答案:ABCD解析:113.[多选题]BWP包含哪些分类A)dedicated BWPB)default BWPC)Initial BWP DActive BWPD)Active BWP答案:ABCD解析:114.[多选题]SgNBMolificationRquest消息可以用于以下哪些场景？A)MeNB触发的带SgNB的站间切换D)MeNB触发的SgNB承载修改答案:BCD解析:115.[多选题]关于30KHZ SCS，以下哪些时隙配比（下行，上行）可以支持PRACH的长格式?A)7:03B)8:02C)3:01D)4:01答案:AB解析:116.[多选题]以下哪些操作可以减少PSCe11辅站乒乓变更概率？A)增加邻小区同频A3偏置B)增加A3时间迟滞C)增加A3幅度迟滞D)增加本服务小区偏置答案:BCD解析:117.[多选题]SA组网中，以下哪些信令中包含掉话原因值？A)UE_CONTEXT_REL_REQB)RRC ReconfigurationC)SgNodeBBReleaseRequestD)UE_CONTEXT_REL_CMD答案:AC解析:118.[多选题]华为E-UTRAN至NG-RAN的小区重选优先级，通过以下哪些参数决定？A)SIB5中携带的cel1ReselectionSubPriority信元B)SIB5中携带的cellResclectionpriority信元C)SIB24中携带的cel1Reselectionpriority-r15信元D)SIB24中携带的cel1ReselectionSubPriority-r15信元答案:CD解析:119.[多选题]以下哪些是5G gNB RLC层的功能？A)纠错B)级联C)分段和重组解析:120.[多选题]以下哪些是导致某段路测SSB RSRP覆盖率低的因素？A)基站设备故障B)AAU方位角不合理C)站点高度不合理D)切换参数不合理答案:ABCD解析:121.[多选题]以下关于最小速率保障的描述，错误的是哪项?A)如果当前业务平均速率高于最小保障速率，基站会降低调度优先级B)如果当前业务平均速率低于最小保障速率，基站会提升调度优先级C)该参数不是3GPP规范的标准参数D)该参数是用于 non-GBR业务答案:ACD解析:122.[多选题]PUCCH的UCI中包括以下哪些信息?A)上行调度请求B)PUSCH调度C)ACK/NAK反馈D)CSI反馈答案:ACD解析:123.[多选题]对比4G，以下哪些是NR中新增的RNTI类型?A)□INT-RNTIB)□SFI-RNTIC)□sI-RNIID)□ P-RNTI答案:AB解析:124.[多选题]以下关于NSA组网下上行TDM功控的描述，正确的是哪些()A)TDM- Pattem, 只能从LTE TDD的SA0-SA6中选取B)由LTE侧决定使用的TDM- PattermC)仅适用于LTE FDD和NR TDD组合场景D)需要终端支持TDM功控答案:ABD125.[多选题]NSA的接入流程中，如果UE在eNodeB发生了随机换人响应相时的问题。