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Earth resources: A continuing bibliography with indexes (issue 62)

This bibliography lists 544 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis.

Source record↗

Earth Resources: a Continuing Bibliography with Indexes (Issue 63)

This bibliography lists 449 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1 and September 31, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, and instrumentation and sensors.

Source record↗

Reflight of the First Microgravity Science Laboratory: Quick Turnaround of a Space Shuttle Mission

Due to the short flight of Space Shuttle Columbia, STS-83, in April 1997, NASA chose to refly the same crew, shuttle, and payload on STS-94 in July 1997. This was the first reflight of an entire mission complement. The reflight of the First Microgravity Science Laboratory (MSL-1) on STS-94 required an innovative approach to Space Shuttle payload ground processing. Ground processing time for the Spacelab Module, which served as the laboratory for MSL-1 experiments, was reduced by seventy-five percent. The Spacelab Module is a pressurized facility with avionics and thermal cooling and heating accommodations. Boeing-Huntsville, formerly McDonnell Douglas Aerospace, has been the Spacelab Integration Contractor since 1977. The first Spacelab Module flight was in 1983. An experienced team determined what was required to refurbish the Spacelab Module for reflight. Team members had diverse knowledge, skills, and background. An engineering assessment of subsystems, including mechanical, electrical power distribution, command and data management, and environmental control and life support, was performed. Recommendations for resolution of STS-83 Spacelab in-flight anomalies were provided. Inspections and tests that must be done on critical Spacelab components were identified. This assessment contributed to the successful reflight of MSL-1, the fifteenth Spacelab Module mission.

Simms, Yvonne↗

CCSDS Spacecraft Monitor and Control Mission Operations Interoperability Prototype

We are entering a new era in space exploration. Reduced operating budgets require innovative solutions to leverage existing systems to implement the capabilities of future missions. Custom solutions to fulfill mission objectives are no longer viable. Can NASA adopt international standards to reduce costs and increase interoperability with other space agencies? Can legacy systems be leveraged in a service oriented architecture (SOA) to further reduce operations costs? The Operations Technology Facility (OTF) at the Johnson Space Center (JSC) is collaborating with Deutsches Zentrum fur Luft- und Raumfahrt (DLR) to answer these very questions. The Mission Operations and Information Management Services Area (MOIMS) Spacecraft Monitor and Control (SM&C) Working Group within the Consultative Committee for Space Data Systems (CCSDS) is developing the Mission Operations standards to address this problem space. The set of proposed standards presents a service oriented architecture to increase the level of interoperability among space agencies. The OTF and DLR are developing independent implementations of the standards as part of an interoperability prototype. This prototype will address three key components: validation of the SM&C Mission Operations protocol, exploration of the Object Management Group (OMG) Data Distribution Service (DDS), and the incorporation of legacy systems in a SOA. The OTF will implement the service providers described in the SM&C Mission Operation standards to create a portal for interaction with a spacecraft simulator. DLR will implement the service consumers to perform the monitor and control of the spacecraft. The specifications insulate the applications from the underlying transport layer. We will gain experience with a DDS transport layer as we delegate responsibility to the middleware and explore transport bridges to connect disparate middleware products. A SOA facilitates the reuse of software components. The prototype will leverage the capabilities of existing legacy systems. Various custom applications and middleware solutions will be combined into one system providing the illusion of a set of homogenous services. This paper will document our journey as we implement the interoperability prototype. The team consists of software engineers with experience on the current command, telemetry and messaging systems that support the International Space Station (ISS) and Space Shuttle programs. Emphasis will be on the objectives, results and potential cost saving benefits.

Lucord, Steve↗

Knowledge-based assistance for science visualization and analysis using large distributed databases

Within this decade, the growth in complexity of exploratory data analysis and the sheer volume of space data require new and innovative approaches to support science investigators in achieving their research objectives. To date, there have been numerous efforts addressing the individual issues involved in inter-disciplinary, multi-instrument investigations. However, while successful in small scale, these efforts have not proven to be open and scalable. This proposal addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded with this proposal is the integration of three automation technologies, namely, knowledge-based expert systems, science visualization and science data management. This integration is based on concept called the DataHub. With the DataHub concept, NASA will be able to apply a more complete solution to all nodes of a distributed system. Both computation nodes and interactive nodes will be able to effectively and efficiently use the data services (address, retrieval, update, etc.) with a distributed, interdisciplinary information system in a uniform and standard way. This will allow the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis will be on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to publishable scientific results. In addition, the proposed work includes all the required end-to-end components and interfaces to demonstrate the completed concept.

Handley, Thomas H., Jr.↗

MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training

Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning. In-memory distributed storage is preferred for its speed, enabling rapid, random, and frequent data access required by stochastic optimizers. Processes use one-sided or collective communication to fetch remote data, with optimal performance depending on (i) dataset characteristics, (ii) training scale, and (iii) interconnection network. Empirical analysis shows collective communication excels with larger mini-batch sizes and/or fewer processes, whereas one-sided communication outperforms at larger scales. We propose MDLoader, a hybrid in-memory data loader for distributed graph neural network training. MDLoader features a model-driven performance estimator that dynamically selects between one-sided and collective communication at the beginning of training using Tree of Parzen Estimators (TPE). Evaluations on NERSC Perlmutter and OLCF Summit show MDLoader outperforms single-backend loaders by up to 2.83 × and predicts the suitable communication method with 96.3% (Perlmutter) and 94.3% (Summit) success rate.

Bae, Jonghyun↗

The application of connectionism to query planning/scheduling in intelligent user interfaces

In the mid nineties, the Earth Observing System (EOS) will generate an estimated 10 terabytes of data per day. This enormous amount of data will require the use of sophisticated technologies from real time distributed Artificial Intelligence (AI) and data management. Without regard to the overall problems in distributed AI, efficient models were developed for doing query planning and/or scheduling in intelligent user interfaces that reside in a network environment. Before intelligent query/planning can be done, a model for real time AI planning and/or scheduling must be developed. As Connectionist Models (CM) have shown promise in increasing run times, a connectionist approach to AI planning and/or scheduling is proposed. The solution involves merging a CM rule based system to a general spreading activation model for the generation and selection of plans. The system was implemented in the Rochester Connectionist Simulator and runs on a Sun 3/260.

Short, Nicholas, Jr.↗

DataHub - Science data management in support of interactive exploratory analysis

DataHub addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, viz. knowledge-based expert systems, science visualization, and science data management. This integration is based on a concept called the DataHub. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system. Both computational nodes and interactive nodes are able to effectively and efficiently use the data services (access, retrieval, update, etc.) in a distributed, interdisciplinary information system in a uniform and standard way. This allows the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis is on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to information. The DataHub includes all the required end-to-end components and interfaces to demonstrate the complete concept.

Handley, Thomas H., Jr.↗

DataHub: Science data management in support of interactive exploratory analysis

The DataHub addresses four areas of significant needs: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, viz. knowledge-based expert systems, science visualization, and science data management. This integration is based on a concept called the DataHub. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system. Both computational nodes and interactives nodes are able to effectively and efficiently use the data services (access, retrieval, update, etc), in a distributed, interdisciplinary information system in a uniform and standard way. This allows the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to information. The DataHub includes all the required end-to-end components and interfaces to demonstrate the complete concept.

Handley, Thomas H., Jr.↗

Managing and Servicing Physical Oceanographic Data at a NASA Distributed Active Archive Center

The NASA Earth Science Data Information Systems Project funds and operates 12 Distributed Active Archive Center(s) (DAAC) throughout the United States. Of these 12 centers, the Physical Oceanography DAAC (PO.DAAC) is committed to providing long term archival, distribution and stewardship for NASA physical oceanographic data, primarily derived from space-born satellite systems, but also including a growing set of recent and future in situ observations from the SPURS-1 and SPURS-2 campaigns. Notable NASA missions supported include: Seasat, TOPEX/Poseidon, NSCAT, QuikSCAT, ISS-RapidScat, Jason-1, Jason-2/OSTM, GRACE, Aquarius, GHRSST, and MODIS. The following interagency and international missions are also supported by PO.DAAC: AVHRR, Coriolis, DMSP, MetOp-A, MetOp-B, Oceansat-2. The PO.DAAC currently holds 525 datasets in public distribution, spanning the following observational parameters: sea surface temperature, sea surface salinity, ocean color, ocean surface currents, ocean surface wind speed, ocean surface wind direction, sea surface height, significant wave height, ocean water mass/thickness, and sea ice age. A hundred of these datasets are available in near-real-time. Datasets are distributed through a variety of open-source access protocols including FTP, OPeNDAP, and THREDDS. FTP will soon be phased out in favor of a recently introduced HTTPS PO.DAAC Drive interface that supports WebDAV and interoperable machine-to-machine communication. OPeNDAP supports remote data/metadata query, subset, and download. THREDDS provides the features of OPeNDAP with the additional feature of temporal aggregation. PO.DAAC also offers proprietary tools and services to further enhance the data discovery, visualization and analysis experience, including but not limited to: State of the Ocean, Web Services (data/metadata discovery and extraction), HiTIDE Level-2 subsetter, Live Access Server (LAS), Webification (w10nsci), and Rich Site Summary (RSS) Datacasting. To assist with provenance of datasets, PO.DAAC has implemented DOIs for the data it distributes so that they can be properly cited. There is a user forum and helpdesk that contains data recipes and via which users can get guidance. In summary, this presentation aims to provide a general overview of PO.DAAC’s web portal and data holdings along with a set of illustrative examples leading prospective data users into the practical utility of its tools and services.

Moroni, David F.↗

Performance issues in management of the Space Station Information System

The onboard segment of the Space Station Information System (SSIS), called the Data Management System (DMS), will consist of a Fiber Distributed Data Interface (FDDI) token-ring network. The performance of the DMS in scenarios involving two kinds of network management is analyzed. In the first scenario, how the transmission of routine management messages impacts performance of the DMS is examined. In the second scenario, techniques for ensuring low latency of real-time control messages in an emergency are examined.

Johnson, Marjory J.↗

Description of the PMAD systems test bed facility and data system

The power management and distribution (PMAD) systems test bed facility, including the power sources and loads available, is discussed, and the PMAD data system (PDS) is described. The PDS controls the test-bed facility hardware, and monitors and records the electric power system control data bus and external data. The PDS architecture is discussed, and each of the subsystems is described.

Trase, Larry↗

Overview of the distributed image processing infrastructure to produce the Legacy Survey of Space and Time

The Vera C. Rubin Observatory is preparing to execute the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently the final phase of construction is under way in the Chilean Andes, with the Observatory’s ten-year science mission scheduled to begin in 2025. Rubin’s 8.4-meter telescope will nightly scan the southern hemisphere collecting imagery in the wavelength range 320–1050 nm covering the entire observable sky every 4 nights using a 3.2 gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties. In this article we present an overview of the system currently being constructed to perform data distribution as well as the annual campaigns which reprocess the entire image dataset collected since the beginning of the survey. These processing campaigns will utilize computing and storage resources provided by three Rubin data facilities (one in the US and two in Europe). Each year a Data Release will be produced and disseminated to science collaborations for use in studies comprising four main science pillars: probing dark matter and dark energy, taking inventory of solar system objects, exploring the transient optical sky and mapping the Milky Way. Also presented is the method by which we leverage some of the common tools and best practices used for management of large-scale distributed data processing projects in the high energy physics and astronomy communities. We also demonstrate how these tools and practices are utilized within the Rubin project in order to overcome the specific challenges faced by the Observatory.

79 ASTRONOMY AND ASTROPHYSICS↗

An overview of the EOSDIS V0 information management system: Lessons learned from the implementation of a distributed data system

The EOSDIS Version 0 system, released in July, 1994, is a working prototype of a distributed data system. One of the purposes of the V0 project is to take several existing data systems and coordinate them into one system while maintaining the independent nature of the original systems. The project is a learning experience and the lessons are being passed on to the architects of the system which will distribute the data received from the planned EOS satellites. In the V0 system, the data resides on heterogeneous systems across the globe but users are presented with a single, integrated interface. This interface allows users to query the participating data centers based on a wide set of criteria. Because this system is a prototype, we used many novel approaches in trying to connect a diverse group of users with the huge amount of available data. Some of these methods worked and others did not. Now that V0 has been released to the public, we can look back at the design and implementation of the system and also consider some possible future directions for the next generation of EOSDIS.

Ryan, Patrick M.↗

Space station - Technology development

The NASA manned space station program's systems technology effort involves the development of novel techniques that will reduce the scope of tasks neeeded for design, development, testing and evaluation of the hardware. Operations technology efforts encompass analyses that will define those techniques best able to improve the efficiency and reduce the costs of space station functions. The technology objective for data management calls for a fault-tolerant, distributed, expandable and adaptable, as well as repairable and user-friendly, flight data management system that employs state-of-the-art hardware and software. The space station's power system includes the largest element, a 'solar blanket', and the heaviest component, the batteries, of all the subsystems. A thermal management system for the power system is of paramount importance. Attention is also given to the exacting demands of attitude control and stabilization and a regenerative life support system of the requisite capacity and reliability.

Carlisle, R. F.↗

Countermeasure Evaluation and Validation Project (CEVP) Database Requirement Documentation

The initial focus of the project by the JSC laboratories will be to develop, test and implement a standardized complement of integrated physiological test (Integrated Testing Regimen, ITR) that will examine both system and intersystem function, and will be used to validate and certify candidate countermeasures. The ITR will consist of medical requirements (MRs) and non-MR core ITR tests, and countermeasure-specific testing. Non-MR and countermeasure-specific test data will be archived in a database specific to the CEVP. Development of a CEVP Database will be critical to documenting the progress of candidate countermeasures. The goal of this work is a fully functional software system that will integrate computer-based data collection and storage with secure, efficient, and practical distribution of that data over the Internet. This system will provide the foundation of a new level of interagency and international cooperation for scientific experimentation and research, providing intramural, international, and extramural collaboration through management and distribution of the CEVP data. The research performed this summer includes the first phase of the project. The first phase of the project is a requirements analysis. This analysis will identify the expected behavior of the system under normal conditions and abnormal conditions; that could affect the system's ability to produce this behavior; and the internal features in the system needed to reduce the risk of unexpected or unwanted behaviors. The second phase of this project have also performed in this summer. The second phase of project is the design of data entry screen and data retrieval screen for a working model of the Ground Data Database. The final report provided the requirements for the CEVP system in a variety of ways, so that both the development team and JSC technical management have a thorough understanding of how the system is expected to behave.

Shin, Sung Y.↗

EOS ground data systems: A description and interface overview

The Earth Observing System (EOS) is planned as a space-based measurement system, earth-science research program, and data and information system (EOSDIS). It will consist of several high data rate spacecraft with multiple earth sensing instruments which provide investigators with a thorough, longterm view of the earth's environment. Up to seven spacecraft may be supported at once, either in operational, checkout, or testing phases; and the average data rate from the EOS satellites in orbit at any one time is expected to be from 18 to 60 Mbps. Providing the data processing and flight operations support for EOS will be the EOSDIS Core System (ECS). The ECS will command and control the spacecraft; process and store the EOS data; provide access to the data for years; and support researchers. The data processing aspects of the ECS consist of a collection of Distributed Active Archive Centers (DAAC's) which perform the product generation, data archive and distribution, and information management services. Flight operations aspects will be provided by the EOS Operations Center, by instrument control centers, and by widely distributed instrument support terminals. The communications and system management aspects will be provided by the EOSDIS Science Network and the System Management Center. In addition to the EOS satellite data, other data sets from earlier earth science missions are also to be added to designated DAAC's. Other ground data systems which will provide support to EOS for acquiring, transporting, processing, and distributing the transformed spacecraft data are currently being defined or are being upgraded for the EOS era. These systems include the Space Network consisting of the Tracking and Data Relay Satellite System (TDRSS), the TDRSS Ground Terminals, and the Network Control Center as well as the night Dynamics Facility, the EOS Data and Operations System, and EOS Communications. This paper briefly describes data handling by the ECS, the support data systems, their interfaces, and their roles.

Smith, Gene↗

Achieving cost reductions in EOSDIS operations through technology evolution

The earth observing system (EOS) data information system (EOSDIS) mission includes the cost-effective management and distribution of large amounts of data to the earth science community. The effect of the introduction of new information system technologies on the evolution of EOSDIS is considered. One of the steps taken by NASA to enable the introduction of new information system technologies into the EOSDIS is the funding of technology development through prototyping. Recent and ongoing prototyping efforts and their potential impact on the performance and cost-effectiveness of the EOSDIS are discussed. The technology evolution process as it related to the effective operation of EOSDIS is described, and methods are identified for the support of the transfer of relevant technology to EOSDIS components.

Newsome, Penny↗