Tabla de Contenidos: “…First Adiabatic Invariant -- 4.2 Particle Transport -- 4.2.1 Particle Transport Equations -- 4.2.2 Focused Transport -- 4.2.3 Diffusive Transport -- 4.3 Application: Description of Solar Energetic Particle Events -- 4.3.1 Numerical Techniques -- 4.3.2 Observations -- 4.3.3 Inferring Transport Conditions -- 4.4 Concluding Remarks -- References -- 5 Cosmic Ray Particle Transport in the Earth's Magnetosphere -- 5.1 Introduction -- 5.2 Motion of Charged Particles in a Magnetic Field: Lorentz Force -- 5.3 Earth's Magnetic Field -- 5.3.1 The Magnetic Field of the Earth as a Dipole Field -- 5.3.2 Magnetic Field Model Due to Internal Sources: IGRF -- 5.3.3 Contributions to the Earth's Magnetic Field by Magnetospheric Electric Currents -- 5.3.4 Magnetic Field Models of the External Sources -- 5.4 Computation of the Propagation of Cosmic Ray Particles in the Earth's Magnetosphere -- 5.5 The Concept of Cutoff Rigidities and Asymptotic Directions -- References -- 6 Ground-Based Measurements of Energetic Particles by Neutron Monitors -- 6.1 Introduction -- 6.2 History -- 6.3 Transport of Cosmic Ray Particles in the Earth's Atmosphere -- 6.3.1 Model of the Earth's Atmosphere -- 6.3.2 Particle Cascade in the Atmosphere -- 6.4 Neutron Monitor Detector -- 6.4.1 Components of a Neutron Monitor -- 6.4.2 Neutron Monitor Yield Function -- 6.4.3 Atmospheric Effects -- 6.5 Worldwide Network of Neutron Monitor Stations as a Giant Spectrometer -- 6.6 Neutron Monitor Database: NMDB -- References -- 7 HESPERIA Forecasting Tools: Real-Time and Post-Event -- 7.1 Introduction -- 7.2 Predicting SEP Event Onsets from Historical Microwave Data by Using the UMASEP Scheme -- 7.3 Predicting SEP Energy Spectra from Historical Microwave Data -- 7.4 Predicting 30-50 MeV SEP Events by Using the RELeASE Scheme…”

Accés lliure

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Packt is Searching for Authors Like You -- Table of Contents -- Preface -- Chapter 1: Giving Computers the Ability to Learn from Data -- Building intelligent machines to transform data into knowledge -- The three different types of machine learning -- Making predictions about the future with supervised learning -- Classification for predicting class labels -- Regression for predicting continuous outcomes -- Solving interactive problems with reinforcement learning -- Discovering hidden structures with unsupervised learning -- Finding subgroups with clustering -- Dimensionality reduction for data compression -- Introduction to the basic terminology and notations -- A roadmap for building machine learning systems -- Preprocessing - getting data into shape -- Training and selecting a predictive model -- Evaluating models and predicting unseen data instances -- Using Python for machine learning -- Installing Python and packages from the Python Package Index -- Using the Anaconda Python distribution and package manager -- Packages for scientific computing, data science, and machine learning -- Summary -- Chapter 2: Training Simple Machine Learning Algorithms for Classification -- Artificial neurons - a brief glimpse into the early history of machine learning -- The formal definition of an artificial neuron -- The perceptron learning rule -- Implementing a perceptron learning algorithm in Python -- An object-oriented perceptron API -- Training a perceptron model on the Iris dataset -- Adaptive linear neurons and the convergence of learning -- Minimizing cost functions with gradient descent -- Implementing Adaline in Python -- Improving gradient descent through feature scaling -- Large-scale machine learning and stochastic gradient descent -- Summary…”

Tabla de Contenidos: “….) -- 5.4.1 COMPARACIÓN DE FOUS Y VARS EN PLANTAS DE SEGUNDO ORDEN -- 5.4.2 COMPARACIÓN OPERADORES AND/OR EN PLANTAS DE SEGUNDO ORDEN -- 5.4.3 COMPARACIÓN ENTRE NUEVO ASSCD Y PID EN PLANTAS DE SEGUNDO ORDEN -- 5.5 RESUMEN DE LAS PRUEBAS -- 6 EFECTO DE LOS OPERADORES AND/OR EN PLANTAS CON INCERTIDUMBRE -- 6.1 EFECTOS DE LOS OPERADORES LÓGICOS AND Y OR -- 6.2 EFECTOS DE LOS OPERADORES LÓGICOS AND Y OR EN SISTEMAS CON (...) -- CONCLUSIONES -- GLOSARIO -- APÉNDICE -- RUTINAS PRINCIPALES -- ASSCD3.M -- CALCULAPID.M -- CURVA.M -- DATOS.M -- EXPERIMENTO.M -- IMPR.M -- RANGOS.M -- SELPLAN.M -- SOLUCION.M -- RUTINAS AUXILIARES -- CHR.M -- GETFOD.M -- REGCEN.FIS -- REGCENOR.FIS -- SELMOD.M -- TMPOLEV.M -- TMPOSBL.M -- VTUN2.M -- VTUN4.M -- ZIEGLER.M -- DIAGRAMAS DE SIMULINK -- ASSCD2.MDL -- ASSCD2P.MDL -- ASSCD2P_SR.MDL -- ASSCD2_SR.MDL -- ASSCD_CONICO.MDL -- ASSCD_CONICO_PULSO.MDL -- ASSCD_NL.MDL -- CURVA_REACCION.MDL -- CURVA_REACCION_NL.MDL -- RANGOS_CL.MDL -- RANGOS_OL.MDL -- RANGOS_OL_CONICO.MDL -- RANGOS_OL_NL.MDL -- REFERENCIAS…”

Tabla de Contenidos: “…Relationship between different concepts of hours worked -- Measuring hours worked -- Hours worked data in the OECD Productivity Statistics (database) (PDB) -- References -- Annex C. Capital input measures at the OECD -- Introduction -- Definitions -- Productive capital stock (and capital services) -- Net and gross (wealth) capital stocks -- Measuring capital input -- Capital measures in OECD statistics -- Table C.1. …”

Tabla de Contenidos: “…Saving and loading in Three.js JSON format -- Importing from 3D file formats -- The OBJ and MTL formats -- Loading a gLTF model -- Showing complete LEGO models -- Loading voxel-based models -- Showing proteins from PDB -- Loading a point cloud from a PLY model -- Other loaders -- Summary -- Chapter 9: Animation and Moving the Camera -- Basic animations -- Simple animations -- Selecting and moving objects -- Animating with Tween.js -- Working with the camera -- ArcballControls -- TrackBallControls -- FlyControls -- FirstPersonControls -- OrbitControls -- Morphing and skeleton animation -- Animation with morph targets -- Animation with a mixer and morph targets -- Animation using bones and skinning -- Creating animations using external models -- Using gltfLoader -- Visualizing motions captured models using fbxLoader -- Loading an animation from a Quake model -- Loading an animation from a COLLADA model -- Visualizing a skeleton with BVHLoader -- Summary -- Chapter 10: Loading and Working with Textures -- Using textures in materials -- Loading a texture and applying it to a mesh -- Using a bump map to provide extra details to a mesh -- Achieving more detailed bumps and wrinkles with a normal map -- Using a displacement map to alter the position of vertices -- Adding subtle shadows with an ambient occlusion map -- Creating fake lighting using a lightmap -- Metalness and roughness maps -- Using an alpha map to create transparent models -- Using an emissive map for models that glow -- Using a specular map to determine shininess -- Creating fake reflections using an environment map -- Repeat wrapping -- Rendering to a canvas and using it as a texture -- Using the canvas as a color map -- Using the canvas as a bump map -- Using the output from a video as a texture -- Summary -- Part 4: Post-Processing, Physics, and Sounds…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Speed It Up! …”

Tabla de Contenidos: “…Linux Performance Data Collection and Analysis Tools -- VMSTAT -- XOSVIEW -- TOP -- IOSTAT &amp -- SAR -- BMC PATROL -- RMF PM with support for Linux enterprise server -- ESALPS -- Monitoring requirements -- Standard interface -- Performance Database (PDB) -- Appendix E. …”

Tabla de Contenidos: “…封 -- 版权声明 -- 内容提 -- 作 简介 -- 前 -- 审稿人简介 -- 目录 -- 第一 分 导 --熟悉实时分析 -- 第1章 实时分析简介 -- 1.1 大数据的定义 -- 1.2 大数据的基础 施 -- 1.3 实时分析--神 与现实 -- 1.4 实时 决方案--可用的架构 -- 1.4.1 NRT的Storm 决方案 -- 1.4.2 NRT的Spark 决方案 -- 1.5 Lambda 架构--分析可 性 -- 1.6 物 网--想法与可 性 -- 缘分析 -- 1.7 云-- NRT 和物 网 -- 1.8 小结 -- 第2章 实时应用的基本组件 -- 2.1 NRT 系统及其构建模块 -- 2.1.1 数据 -- 2.1.2 流处理 -- 2.1.3 分析层-服务终端用户 -- 2.2 NRT 的 级系统 图 -- 2.3 NRT 的技术 图 -- 2.3.1 事件生产 -- 2.3.2 数据收 -- 2.3.3 代理 -- 2.3.4 换和处理 -- 2.3.5 存储 -- 2.4 小结 -- 第二 分 搭建基础 施 -- 第3章 了 和 数据流 -- 3.1 了 数据流 -- 3.2 为数据提取安 基础 施 -- 3.2.1 Apache Kafka -- 3.2.2 Apache NiFi -- 3.2.3 Logstash -- 3.2.4 Fluentd -- 3.2.5 Flume -- 3.3 将数据从源填到处理器--期望和注意事 -- 3.4 比 与 择 合用例的最佳实 -- 3.5 小 牛刀 -- 置Elasticsearch -- 3.6 小结 -- 第4章 安 和 置Storm -- 4.1 Storm 概 -- 4.2 Storm 架构和组件 -- 4.2.1 特征 -- 4.2.2 组件 -- 4.2.3 流分组 -- 4.3 安 和 置 Storm -- 4.3.1 安 ZooKeeper -- 4.3.2 置Apache Storm -- 4.4 在 Storm 上实时处理任务 -- 任务 -- 4.5 小结 -- 第5章 置Apache Spark和Flink -- 5.1 安 并快 Spark -- 5.1.1 源码构建 -- 5.1.2 下 Spark 安 包 -- 5.1.3 示例 -- 5.2 安 并快 Flink -- 5.2.1 使用源码构建Flink -- 5.2.2 下 Flink -- 5.2.3 示例 -- 5.3 安 并快 Apache Beam -- 5.3.1 Beam模型 -- 5.3.2 示例 -- 5.3.3 MinimalWordCount 示例 -- 5.4 Apache Beam 中的平 -- 5.5 小结 -- 第三 分 Storm 实时 算 -- 第6章 成Storm 与数据源 -- 6.1 RabbitMQ 有效的消息传 -- 6.2 RabbitMQ 交换器 -- 6.2.1 直接交换器 -- 6.2.2 RabbitMQ安 置 -- 6.2.3 RabbitMQ的发布和 -- 6.3 RabbitMQ 与 Storm 成 -- AMQPSpout -- 6.4 PubNub 数据流发布 -- 6.5 将Storm 和RMQ_PubNub 传感器数据拓扑串在一 -- 6.6 小结 -- 第7章 从Storm 到Sink -- 7.1 安 并 置 Cassandra -- 7.1.1 安 Cassandra -- 7.1.2 置Cassandra -- 7.2 Storm 和 Cassandra 拓扑 -- 7.3 Storm 和 IMDB 成处理维度数据 -- 7.4 成 示层与 Storm -- 使用Elasticsearch插件 置Grafana -- 7.5 小 牛刀 -- 7.6 小结 -- 第8章 Storm Trident -- 8.1 状态保持和 Trident -- 8.1.1 事务性spout -- 8.1.2 不 明事务性spout -- 8.2 基本 Storm Trident 拓扑 -- 8.3 Trident 内 实现…”

Tabla de Contenidos: “…-- APIs versus SDKs -- Dependency management -- Android -- iOS -- Available APIs -- An iOS app proving our hypotheses, MoviUber -- Hypothesis -- Validating the idea through customer interviews -- Let's build an app -- Movie locations -- Uber -- IMDB -- Displaying locations on a map -- Uber integration -- Enriching the data -- Look! …”

Tabla de Contenidos: “…-- Part 6 The Part of Tens -- Chapter 17 Ten Appetizing Apps -- Shazam -- Action Movie FX -- Halide Mark II Pro Camera -- Evernote -- Words with Friends 2 -- IMDb -- Libby -- Waze -- BeCasso -- Clubhouse -- Chapter 18 Ten Helpful Hints, Tips, and Shortcuts -- Use QuickPath for Speed and Accuracy -- Autocorrect Is Your Friend -- Assault on Batteries -- Tricks with Links and Phone Numbers -- Share the Love . . . and the Links -- Choose a Home Page for Safari -- Like a Time Machine for iPhone Backups -- Create Ringtones for Free in GarageBand -- Getting Apps Out of the Multitasking Screen…”

Tabla de Contenidos: “…GPT-4 creates an application to display IMDb reviews -- 5. GPT-4 creates an application to display a newsfeed -- 6. …”

Tabla de Contenidos: “…-- Introduction to LLMs -- Origins of LLMs -- Early LLMs -- GPT lineage -- BERT -- LaMDA -- LLaMA's family tree -- Exploring modern LLMs -- GPT-4 -- LLaMA-2 -- Gemini (formerly Bard) -- Amazon Olympus -- How Transformers work -- How an LLM processes a piece of text -- ChatGPT uses reinforcement learning from human feedback -- LLMs are expensive -- A note on the mathematics of LLMs -- Applications of LLMs -- Summary -- Bibliography -- Chapter 2: Unleashing the Power of LLMs for Coding: A Paradigm Shift -- Technical requirements -- Unveiling the advantages of coding with LLMs -- The short version -- The longer version -- Planning your LLM-powered coding -- 1. …”

Tabla de Contenidos: “…Application development -- 7.1 MVC development using the Struts framework -- 7.1.1 Creating a Web diagram -- 7.1.2 Coding Struts elements -- 7.2 Developing a message-driven bean with WebSphere Studio -- 7.2.1 Message-driven bean implementation -- 7.2.2 Life cycle of a message-driven bean -- 7.2.3 Creating an MDB using WebSphere Studio -- 7.2.4 Coding the message-driven bean -- 7.3 XML and XSLT development -- 7.3.1 XML as data transfer technology -- 7.3.2 Guidelines for creating an XML message -- 7.3.3 Performing XML transformations -- 7.3.4 Working with XSLTC -- 7.3.5 WebSphere Studio XML support -- 7.3.6 Using XML JavaBeans -- Chapter 8. …”

Tabla de Contenidos: “…Web Access configuration parameters -- Updating the configuration file -- Debug option directive -- Data set name directives -- General control directives -- User ID and privilege class directives -- Directives that control the Information Management API -- UNIX System Services path and file reference directives -- Server side include (SSI) directives -- Business logic exit routine directives -- User profile directives -- Record type directives (used for all record types) -- Generic database search directives -- HTML generation directives -- S-words to left-zero pad and create hyperlinks -- Related publications -- IBM Redbooks -- Other resources -- How to get IBM Redbooks -- IBM Redbooks collections -- Index -- Back cover…”

Tabla de Contenidos: “…8.4.1 R&D Activities from Industry and Government Agencies 217 -- 8.4.2 Academic Activities 218 -- 8.4.3 3GPP Activities in General 219 -- 8.5 3GPP Enhancement on Supporting Drones 220 -- 8.5.1 3GPP Drone Study Item and Work Item in RAN1 220 -- 8.5.2 3GPP Drone Study Item and Work Item in RAN2 223 -- 8.6 5G Challenges, Solutions, and Further Studies 225 -- 8.6.1 Challenges, New Emerging Usages, and Requirements for 5G 225 -- 8.6.2 3GPP Features Addressing These New Requirements 227 -- 8.6.2.1 eMBB 227 -- 8.6.2.2 URLLC 228 -- 8.6.2.3 Massive Machine-Type Communications 228 -- 8.6.2.4 V2X 228 -- 8.6.2.5 Next Gen Core Network 229 -- 8.6.2.6 Positioning 229 -- 8.6.3 Further Study Needed for Aerial Vehicles in 5G 229 -- Acronyms 231 -- References 231 -- 9 Connected Automobiles 235 /Murali Narasimha and Ana Lucia Pinheiro -- 9.1 Introduction 235 -- 9.2 Levels of Vehicle Automation 238 -- 9.3 Multi-Access Edge Computing in 5G 239 -- 9.4 Platoon-Based Driving Use Case 240 -- 9.4.1 Platoon Model 242 -- 9.4.2 Edge Computing for Platooning 249 -- 9.5 High Definition Maps Use Case 251 -- 9.5.1 HD Maps Procedures 253 -- 9.5.1.1 Map Download Procedure 253 -- 9.5.1.2 Map Update Procedure 254 -- 9.5.2 Edge Computing for HD Maps 257 -- 9.6 Summary 259 -- Acknowledgment 261 -- References 261 -- 10 Connected Factory 263 /Amanda Xiang and Anthony C.K. …”