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This book describes the development and design of a unique combined data and power management infrastructure for small satellites. This new edition became necessary because in the frame of the system's impressive evolution from an academic prototype to one of today's most advanced core avionics, many elements were upgraded to their next technology generation and diverse new components complement the upgraded design. All elements are presented in updated respectively new chapters. This modular infrastructure was selected by the Swiss start-up ClearSpace SA for ESA's first mission ClearSpace-1 to remove space debris. Furthermore it is the baseline for the Thai national satellite development program and is used by an increasing number of universities worldwide for research studies.
This book represents the Flight Operations Manual for a reusable microsatellite platform – the “Future Low-cost Platform” (FLP), developed at the University of Stuttgart, Germany. It provides a basic insight on the onboard software functions, the core data handling system and on the power, communications, attitude control and thermal subsystem of the platform. Onboard failure detection, isolation and recovery functions are treated in detail. The platform is suited for satellites in the 50-150 kg class and is baseline of the microsatellite “Flying Laptop” from the University. The book covers the essential information for ground operators to controls an FLP-based satellite applying international command and control standards (CCSDS and ECSS PUS). Furthermore it provides an overview on the Flight Control Center in Stuttgart and on the link to the German Space Agency DLR Ground Station which is used for early mission phases. Flight procedure and mission planning chapters complement the book.
This book describes the development and design of a unique combined data and power management infrastructure The use in small satellites gives some particular requirements to the systems like potential hardware failure robustness and handling of different types of external analog and digital interfaces. These requirements lead to a functional merge between On Board Computer and the satellite's Power Control and Distribution Unit, which results in a very innovative design and even a patent affiliation. This book provides system engineers and university students with the technical knowledge as mix between technical brochure and a user guide.
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Infrastructure Asset Management with Power System Applications is about infrastructure asset management, which can be expressed as the combination of management, financial, economic, and engineering, applied to physical assets with the objective of providing the required level of service in the most cost-effective manner. It includes management of the whole lifecycle of a physical asset from design, construction, commission, operation, maintenance, modification, decommissioning, and disposal. It covers budget issues and focuses on asset management of an infrastructure for energy—i.e., the electric power system. Features Offers a comprehensive reference book providing definitions, terminology, and basic theories as well as a comprehensive set of examples from a wide range of applications for the electric power system and its components. Spans a wide range of applications for the electric power system area, including real data and pictures. Contains results from recently published research and application studies. Includes a wide range of application examples for the electric power systems area from hydro, nuclear, and wind, plus shows future trends. Contributes to the overall goals of developing a sustainable energy system by providing methods and tools for a resource efficient use of physical assets in the electric power system area.
This book looks at various application and data demand drivers, along with data infrastructure options from legacy on premise, public cloud, hybrid, software-defined data center (SDDC), software data infrastructure (SDI), container as well as serverless along with infrastructure as a Service (IaaS), IT as a Service (ITaaS) along with related technology, trends, tools, techniques and strategies. Filled with example scenarios, tips and strategy considerations, the book covers frequently asked questions and answers to aid strategy as well as decision-making.
Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. About the technology Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the book Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's inside Handle compute and orchestration in the cloud Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem Architect complex applications that require large datasets and models, and a team of data scientists About the reader For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the author At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Table of Contents 1 Introducing data science infrastructure 2 The toolchain of data science 3 Introducing Metaflow 4 Scaling with the compute layer 5 Practicing scalability and performance 6 Going to production 7 Processing data 8 Using and operating models 9 Machine learning with the full stack
This book explains how water, electricity/power, roads and other infrastructure services are linked together within the general basket of development and how to obtain the optimum use of resources. The emphasis, nowadays, is on multipurpose activities, optimum use of resources, environmental approach, minimum use of energy. This book tries to integrate all of these, by showing the links between the different components of infrastructure and trying to model them. A well articulated, socially attractive and desirable project may fail during the implementation or operation stage, not only from bad design, but also due to inadequate attention paid to the human aspects required for its operation. This book is intended for graduates and practising professionals who are involved in the general development planning of their country/region. It enables better understanding, collaboration and communication with other professionals in relation to their own or different disciplines.
Doctoral Thesis / Dissertation from the year 2017 in the subject Electrotechnology, grade: PhD, , course: Doctor of Philosophy, language: English, abstract: Wireless Sensor Networks (WSNs) is fast emerging as prominent study area that attracting considerable research attention globally. The field has seen tremendous development in design and development of application related interfaces with sensor networks. Sensor network finds applications in several domains such as medical, military, home networks, space and so on. Many researchers strongly believe that WSNs can become as important as the internet in the near future. Just as the internet allows access to digital information anywhere, WSNs could easily provide remote interaction with the physical world. It is going to be the backbone of Ubiquitous Computing (UBICOMP).Through local collaboration among sensors, elimination of duplicate data, participation of relevant nodes in the given task etc. can produce a significant difference in energy conservation, thereby increasing the life time of the sensor network. As the number of nodes increases, data security becomes the most challenging part of the network. The intruders can hack the data any time during processing, transmission or at the receiver end. So, as a popular approach data encryption is the most commendable approach in today’s network. Asymmetric key encryption consumes more energy in processing and so not recommended for WSNs. Symmetric key encryption gives better performance with respect to asymmetric key encryption in WSN applications. It uses less computational power due to relatively effortless mathematical operations, and eventually spends less power. This thesis also proposes a symmetric data encryption through Tabulation method of Boolean function reductionfor the WSNs for secure data transmission. It also suggests a new secure approach, SEEMd, Security Enabled Energy Efficient Middleware algorithmfor the critical data sensing and gives a second chance to the nodes before it falls into to sleep mode for energy management. WSNs are designed for applications which range from small-size healthcare surveillance systems to large-scale agricultural monitoring or environmental monitoring. Thus, any WSN deployment, data aggregation, processing and communication have to assure minimum Quality of Service (QoS) in the network from application to application. In this circumstances, the proposed algorithms in this thesis proved to be efficient and reliable in energy saving and life time enhancement.
Learn in-demand cloud computing skills from industry experts Deploying and Managing a Cloud Infrastructure is an excellent resource for IT professionals seeking to tap into the demand for cloud administrators. This book helps prepare candidates for the CompTIA Cloud+ Certification (CV0-001) cloud computing certification exam. Designed for IT professionals with 2-3 years of networking experience, this certification provides validation of your cloud infrastructure knowledge. With over 30 years of combined experience in cloud computing, the author team provides the latest expert perspectives on enterprise-level mobile computing, and covers the most essential topics for building and maintaining cloud-based systems, including: Understanding basic cloud-related computing concepts, terminology, and characteristics Identifying cloud delivery solutions and deploying new infrastructure Managing cloud technologies, services, and networks Monitoring hardware and software performance Featuring real-world examples and interactive exercises, Deploying and Managing Cloud Infrastructure delivers practical knowledge you can apply immediately. And, in addition, you also get access to a full set of electronic study tools including: Interactive Test Environment Electronic Flashcards Glossary of Key Terms Now is the time to learn the cloud computing skills you need to take that next step in your IT career.