Experiments and Data Requirements to Validate Grid Energy Storage Systems at Different Stages of Development
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Description of magnetic head designs, tape transport features, encoding techniques, bit error rates, and data rate capabilities of tape recorders used on board satellites for temporary storage of sensor data. Helical- and transverse-scan rotary head mechanisms used for wideband video signals from imaging systems are explained along with associated electronic circuitry. Decoding and decision procedures employed in multiple-track recorders are outlined, and deskewing electronics are discussed.
Data Dictionary is defined to be the set of all data attributes, which describe data objects in terms of their intrinsic attributes, such as name, type, size, format and definition. It is recognized as the data base for the Information Resource Management, to facilitate understanding and communication about the relationship between systems applications and systems data usage and to help assist in achieving data independence by permitting systems applications to access data knowledge of the location or storage characteristics of the data in the system. A research and development effort to use Ada has produced a data dictionary with data base design capabilities. This project supports data specification and analysis and offers a choice of the relational, network, and hierarchical model for logical data based design. It provides a highly integrated set of analysis and design transformation tools which range from templates for data element definition, spreadsheet for defining functional dependencies, normalization, to logical design generator.
A data migration mechanism is proposed that allows an explicit and controlled mapping of data to memory. While read or write copies of each data element can be assigned to any processor's memory, longer term storage of each data element is assigned to a specific location in the memory of a particular processor. The proposed integration of a data migration scheme with a compiler is able to eliminate the migration of unneeded data that can occur in multiprocessor paging or caching. The overhead of adjudicating multiple concurrent writes to the same page or cache line is also eliminated. Data is presented that suggests that the scheme may be a pratical method for efficiently supporting data migration.
A data migration mechanism is proposed that allows an explicit and controlled mapping of data to memory. While read or write copies of each data element can be assigned to any processor's memory, longer term storage of each data element is assigned to a specific location in the memory of a particular processor. Data is presented that suggests that the scheme may be a practical method for efficiently supporting data migration.
The design and implementation of the real-time data acquisition and processing system employed in the AMPERES project is described, including effective data structures for efficient storage and flexible manipulation of the data by the knowledge-based system (KBS), the interprocess communication mechanism required between the data acquisition system and the KBS, and the appropriate data acquisition protocols for collecting data from the sensors. Sensor data are categorized as critical or noncritical data on the basis of the inherent frequencies of the signals and the diagnostic requirements reflected in their values. The critical data set contains 30 analog values and 42 digital values and is collected every 10 ms. The noncritical data set contains 240 analog values and is collected every second. The collected critical and noncritical data are stored in separate circular buffers. Buffers are created in shared memory to enable other processes, i.e., the fault monitoring and diagnosis process and the user interface process, to freely access the data sets.
This paper describes the implementation and potential applications of a workstation-based orbit determination system developed by Storm Integration, Inc. called the Precision Orbit Determination System (PODS). PODS is offered as a layered product to the commercially-available Satellite Tool Kit (STK) produced by Analytical Graphics, Inc. PODS also incorporates the Workstation/Precision Orbit Determination (WS/POD) product offered by Van Martin System, Inc. The STK graphical user interface is used to access and invoke the PODS capabilities and to display the results. WS/POD is used to compute a best-fit solution to user-supplied tracking data. PODS provides the capability to simultaneously estimate the orbits of up to 99 satellites based on a wide variety of observation types including angles, range, range rate, and Global Positioning System (GPS) data. PODS can also estimate ground facility locations, Earth geopotential model coefficients, solar pressure and atmospheric drag parameters, and observation data biases. All determined data is automatically incorporated into the STK data base, which allows storage, manipulation and export of the data to other applications. PODS is offered in three levels: Standard, Basic GPS and Extended GPS. Standard allows processing of non-GPS observation types for any number of vehicles and facilities. Basic GPS adds processing of GPS pseudo-ranging data to the Standard capabilities. Extended GPS adds the ability to process GPS carrier phase data.
The National Space Science Data Center (NSSDC) is making a growing fraction of its most customer-desirable data electronically accessible via both the local and wide area networks. NSSDC is witnessing a great increase in its data dissemination owing to this network accessibility. To provide its customers the best data accessibility, the NSSDC makes data available from a nearline, mass storage system, the NSSDC Data Archive and Dissemination Service (NDADS). The NDADS, the initial version was made available in January 1992, is a customized system of hardware and software that provides users access to the nearline data via ANONYMOUS FTP, an e-mail interface (ARMS), and a C-based software library. In January 1992, the NDADS registered 416 requests for 1,957 files. By December of 1994, NDADS had been populated with 800 gigabytes of electronically accessible data and had registered 1458 requests for 20,887 files. In this report we describe the NDADS system, both hardware and software. Later in the report, we discuss some of the lessons that were learned as a result of operating NDADS, particularly in the area of ingest and dissemination.
In the control center, raw telemetry data is received for storage, display, and analysis. This raw data must be combined and manipulated in various ways by mathematical computations to facilitate analysis, provide diversified fault detection mechanisms, and enhance display readability. A development tool called the Graphical Computation Builder (GCB) has been implemented which provides flight controllers with the capability to implement computations for use in the control center. The GCB provides a language that contains both general programming constructs and language elements specifically tailored for the control center environment. The GCB concept allows staff who are not skilled in computer programming to author and maintain computer programs. The GCB user is isolated from the details of external subsystem interfaces and has access to high-level functions such as matrix operators, trigonometric functions, and unit conversion macros. The GCB provides a high level of feedback during computation development that improves upon the often cryptic errors produced by computer language compilers. An equivalent need can be identified in the industrial data acquisition and process control domain: that of an integrated graphical development tool tailored to the application to hide the operating system, computer language, and data acquisition interface details. The GCB features a modular design which makes it suitable for technology transfer without significant rework. Control center-specific language elements can be replaced by elements specific to industrial process control.
The Mission Operations Laboratory (MOL) at Marshall Space Flight Center (MSFC) is responsible for Engineering Support capability for NASA s Ares launch system development. In pursuit of this, MOL is building the Ares Engineering and Operations Network (AEON), a web-based portal intended to provide a seamless interface to support and simplify two critical activities: a) Access and analyze Ares manufacturing, test, and flight performance data, with access to Shuttle data for comparison. b) Provide archive storage for engineering instrumentation data to support engineering design, development, and test. A mix of NASA-written and COTS software provides engineering analysis tools. A by-product of using a data portal to access and display data is access to collaborative tools inherent in a Web 2.0 environment. This paper discusses how Web 2.0 techniques, particularly social media, might be applied to the traditionally conservative and formal engineering support arena. A related paper by the author [1] considers use
Although the use of UASs in military and public service operations is proliferating, civilian use of UASs remains limited in the United States today. With efforts underway to accommodate and integrate UASs into the NAS, a proactive understanding of safety issues, i.e., the unique hazards and the corresponding risks that UASs pose not only through their operations for commercial purposes, but also to existing operations in the NAS, is especially important so as to (a) support the development of a sound regulatory basis, (b) regulate, design and properly equip UASs, and (c) effectively mitigate the risks posed. Data, especially about system and component failures, incidents, and accidents, provides valuable insight into how performance and operational capabilities/limitations contribute to hazards. Since the majority of UAS operations today take place in a context that is significantly different from the norm in civil aviation, i.e., with different operational goals and standards, identifying that which constitutes useful and sufficient data on UASs and their operations is a substantial research challenge.
HDF5 (Hierarchical Data Format 5) is open-source, high-performance software that consists of an abstract data model, library, and fileformat used for storing and managing extremely large and/or complex data collections. NASA Earth Observing System (EOS) Data and Information Systems use HDF5 as an archival format to store remote sensing data from EOS satellites. HDF5 is also used to store other types of Geoscience and Strophysical data, e.g., seismic data and data from Low-Frequency Array (LOFAR) radio telescopes. Data stored in HDF5 has reached tens of petabytes and is growing at an accelerated rate.With the growing amout of HDF5 Earth Science data to analyze and process, scientists need to adopt big data technologies including new storage paradigms such as cloud and object storage. To run models and perform data analysis they also need to utilizied efficient and diverse ways to access data, from high-performance computing's (HPC) Message Passing Interface (MPI) I/O and deep memory hierarchies (DMH) to non-HPC frameworks such as Apache Hadoop, Spark, and Drill. The HDF Group continually works to enable usage of big data technologies in HDF software.
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Abstract Not Provided
Abstract not provided.
We describe our work on the use of HPC data formats and storage technologies to efficiently describe, store and access High Energy Physics (HEP) data. In the HEP community, important data sets are large, currently at the scale of tens of PiB, and are expected to grow dramatically in the next decade. These data require different data representations—ranging from complex C++ objects to tabular data—that are used at the many varied stages of data analysis. Traditionally, these data have been stored in large collections of files at each stage. This approach does not scale well on HPC resources. To obtain good scalability, without requiring that analysts have parallel programming expertise, our work stresses the use of HPCoptimized tools and implicit data-parallelism. We describe data organization and the performance and scaling of data processing using examples from three HEP experiments (LArIAT, NOvA and DUNE), and for three different stages of analysis. We compare the performance of our HPC-oriented solutions to the traditional HEP approaches.
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