趣味机器人 带有Discord.js v12和discord-image-generation的简单易用的图像和有趣的bot 拳头： 转到config.json文件，然后更改-“令牌”和“前缀”- 第二： 安装所有可以在package.json中知道的npm软件包 :party_popper: 能源 :party_popper:

rowhammer 防御

EPC Global 发布的超高频RFID（800/900Mhz）协议标准，后来演变为18000-6C

Matlab实现Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame论文的方法，具体网站https://blog.csdn.net/caokaifa/article/details/108015374

该代码文件是一个完整的航班人员调度分配模型及求解. 模型说明为英文材料, 用百度等翻译一下, 即可顺利阅读. 包含了问题说明、数据、详细的gurobi列生成算法求解代码，是一份完整的航班人员调度分配、列生成算法、gurobi求解器的绝佳学习资料。所有代码均有详细注释，已经经过反复调试，可以直接运行。

下一代 soa ，现代面向服务计算的现实世界指南

The Waveform Generation Language (WGL) is a data description language. It is used to convey an editable ASCII representation of the data contained in a Waveform DataBase (WDB), allowing you to use your system’s text editor to fully customize the database

Mining Entity Synonyms with Efficient Neural Set Generation.pdf

5G NR _ the next generation wireless access technology-Academic Press (2018).pdf

Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive