EN 61000-4系列标准是中国和国际上广泛采用的一套电磁兼容（EMC）测试标准，主要用于确保电子设备在各种电磁环境中能够正常工作。这套标准由多个部分组成，涵盖了电磁环境中的不同骚扰类型。在这个"EN 61000-4标准2-6全套"压缩包中，我们重点关注的是第二部分（EN 61000-4-2）和第六部分（EN 61000-4-6）的内容，它们分别涉及静电放电抗扰度和传导骚扰抗扰度。 **EN 61000-4-2 静电放电抗扰度** 这部分标准定义了如何评估设备对静电放电的耐受能力。静电放电通常发生在人体与设备接触时，或者物体之间的摩擦产生静电。这种现象可能导致电子设备的瞬间故障或永久损坏。EN 61000-4-2规定了测试方法，包括放电电压等级、放电模式以及测试距离等参数，以确保设备在实际环境中能抵御静电放电的影响。测试过程中，会模拟不同级别和类型的静电放电，以检查设备的稳定性。 **EN 61000-4-6 传导骚扰抗扰度** 传导骚扰抗扰度测试则关注的是通过电源线或其他连接线引入的电磁骚扰。这些骚扰可能来自电网、其他设备，或者是设备自身的谐波电流。EN 61000-4-6定义了评估设备承受传导骚扰的能力，包括测试频率范围、骚扰电平以及测试条件。测试目的是确保设备在受到这些骚扰时仍能保持正常功能。 EMC测试的重要性在于，它确保了电子设备不仅自身不会产生过量的电磁骚扰，而且还能抵抗外部电磁环境的干扰，从而保证设备在整个生命周期内的可靠性和安全性。尤其对于医疗设备、航空航天、汽车电子等领域，EMC测试是产品认证不可或缺的一部分。 在实际应用中，EN 61000-4标准通常与其他相关标准（如IEC 61000系列、GB/T 17626系列等）一起参考，以形成全面的EMC合规性评估。这些文档的使用者可能包括产品设计工程师、质量保证人员、实验室测试技术人员，以及法规合规部门等，他们需要理解并执行这些标准，以满足不同国家和地区的市场准入要求。 综上所述，"EN 61000-4标准2-6全套"包含的测试规范对电子设备的电磁兼容性能提供了严谨的评估框架，是保障产品质量和用户安全的重要工具。通过详细阅读和理解这些文档，相关人员可以有效地进行产品的EMC设计优化和测试验证，确保产品能够在复杂多变的电磁环境中稳定运行。

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greenplum-db-6.2.1-rhel7-x86_64.rpm Pivotal Greenplum 6.2 Release Notes This document contains pertinent release information about Pivotal Greenplum Database 6.2 releases. For previous versions of the release notes for Greenplum Database, go to Pivotal Greenplum Database Documentation. For information about Greenplum Database end of life, see Pivotal Greenplum Database end of life policy. Pivotal Greenplum 6 software is available for download from the Pivotal Greenplum page on Pivotal Network. Pivotal Greenplum 6 is based on the open source Greenplum Database project code. Important: Pivotal Support does not provide support for open source versions of Greenplum Database. Only Pivotal Greenplum Database is supported by Pivotal Support. Release 6.2.1 Release Date: 2019-12-12 Pivotal Greenplum 6.2.1 is a minor release that includes new features and resolves several issues. New Features Greenplum Database 6.2.1 includes these new features: Greenplum Database supports materialized views. Materialized views are similar to views. A materialized view enables you to save a frequently used or complex query, then access the query results in a SELECT statement as if they were a table. Materialized views persist the query results in a table-like form. Materialized view data cannot be directly updated. To refresh the materialized view data, use the REFRESH MATERIALIZED VIEW command. See Creating and Managing Materialized Views. Note: Known Issues and Limitations describes a limitation of materialized view support in Greenplum 6.2.1. The gpinitsystem utility supports the --ignore-warnings option. The option controls the value returned by gpinitsystem when warnings or an error occurs. If you specify this option, gpinitsystem returns 0 if warnings occurred during system initialization, and returns a non-zero value if a fatal error occurs. If this option is not specified, gpinitsystem returns 1 if initialization completes with warnings, and returns value of 2 or greater if a fatal error occurs. PXF version 5.10.0 is included, which introduces several new and changed features and bug fixes. See PXF Version 5.10.0 below. PXF Version 5.10.0 PXF 5.10.0 includes the following new and changed features: PXF has improved its performance when reading a large number of files from HDFS or an object store. PXF bundles newer tomcat and jackson libraries. The PXF JDBC Connector now supports pushdown of OR and NOT logical filter operators when specified in a JDBC named query or in an external table query filter condition. PXF supports writing Avro-format data to Hadoop and object stores. Refer to Reading and Writing HDFS Avro Data for more information about this feature. PXF is now certified with Hadoop 2.x and 3.1.x and Hive Server 2.x and 3.1, and bundles new and upgraded Hadoop libraries to support these versions. PXF supports Kerberos authentication to Hive Server 2.x and 3.1.x. PXF supports per-server user impersonation configuration. PXF supports concurrent access to multiple Kerberized Hadoop clusters. In previous releases of Greenplum Database, PXF supported accessing a single Hadoop cluster secured with Kerberos, and this Hadoop cluster must have been configured as the default PXF server. PXF introduces a new template file, pxf-site.xml, to specify the Kerberos and impersonation property settings for a Hadoop or JDBC server configuration. Refer to About Kerberos and User Impersonation Configuration (pxf-site.xml) for more information about this file. PXF now supports connecting to Hadoop with a configurable Hadoop user identity. PXF previously supported only proxy access to Hadoop via the gpadmin Greenplum user. PXF version 5.10.0 deprecates the following configuration properties. Note: These property settings continue to work. The PXF_USER_IMPERSONATION, PXF_PRINCIPAL, and PXF_KEYTAB settings in the pxf-env.sh file. You can use the pxf-site.xml file to configure Kerberos and impersonation settings for your new Hadoop server configurations. The pxf.impersonation.jdbc property setting in the jdbc-site.xml file. You can use the pxf.service.user.impersonation property to configure user impersonation for a new JDBC server configuration. Note: If you have previously configured a PXF JDBC server to access Kerberos-secured Hive, you must upgrade the server definition. See Upgrading PXF in Greenplum 6.x for more information. Changed Features Greenplum Database 6.2.1 includes these changed features: Greenplum Stream Server version 1.3.1 is included in the Greenplum distribution. Resolved Issues Pivotal Greenplum 6.2.1 is a minor release that resolves these issues: 29454 - gpstart During Greenplum Database start up, the gpstart utility did not report when a segment instance failed to start. The utility always displayed 0 skipped segment starts. This issue has been resolved. gpstart output was also enhanced to provide additional warnings and summary information about the number of skipped segments. For example: [WARNING]:-********

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内容概要：该资源介绍了使用机器学习方法对毒蘑菇进行分类的实现。主要包含了逻辑回归、高斯朴素贝叶斯、支持向量机、随机森林、决策树和人工神经网络等六种监督学习模型的应用。 适用人群：对机器学习和分类算法感兴趣的学习者、数据科学家、机器学习工程师等。 使用场景及目标：本资源可用于学习如何使用不同的监督学习模型对毒蘑菇进行分类，帮助用户理解各种模型的原理和应用场景，并能够根据实际需求选择合适的模型进行分类任务。 其他说明：资源中提供了详细的代码示例和实验结果，以及对比不同模型在毒蘑菇分类任务上的性能评估，帮助用户深入理解各个模型的优缺点和适用范围。

从VMware Online软件仓库创建ESXi安装ISO或Offline Bundle（标准模式） 从本地ESXi脱机捆绑包（-izip模式）创建ESXi安装ISO或脱机捆绑包 使用来自VMware Online仓库的ESXi补丁包更新本地ESXi脱机捆绑包（-izip -update模式）

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