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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Onboard Science Data Analysis: Opportunities, Benefits, and Effects on Mission Design

Much of the initial focus for spacecraft autonomy has been on developing new software and systems concepts to automate engineering functions of the spacecraft: guidance, navigation and control, fault protection, and resources management. However, the ultimate objectives of NASA missions are science objectives, which implies that we need a new framework for perfoming science data evaluation and observation planning autonomously onboard spacecraft.

spacecraft autonomy observation planning science d↗
NASA NTRS1975

Command and data handling of science signals on Spacelab

The Orbiter Avionics and the Spacelab Command and Data Management System (CDMS) combine to provide a relatively complete command, control, and data handling service to the instrument complement during a Shuttle Sortie Mission. The Spacelab CDMS services the instruments and the Orbiter in turn services the Spacelab. The CDMS computer system includes three computers, two I/O units, a mass memory, and a variable number of remote acquisition units. Attention is given to the CDMS high rate multiplexer, CDMS tape recorders, closed circuit television for the visual monitoring of payload bay and cabin area activities, methods of science data acquisition, questions of transmission and recording, CDMS experiment computer usage, and experiment electronics.

Mccain, H. G.↗
NASA NTRS2022

NASA's Next Generation of Atmospheric Data Science

The Multi-Angle Imager for Aerosols (MAIA) and the Tropospheric Emission: Monitoring of Pollution(TEMPO) are NASA’s next-generation satellite missions for air quality monitoring. These missions will produce high-quality, high-resolution air quality data to support cross-displinary research. The MAIA mission is collaborating with health science researchers and epidemiologists to study the impacts of air quality on health outcomes. TEMPO aims to improve our understanding of tropospheric air pollution chemistry and our ability to make predictions about air quality and climate forcing. TEMPO will offer hourly measurements of tropospheric ozone, aerosols, and clouds focused on North America at high-spatial resolution, while MAIA will produce high-resolution measurements of speciated particulate matter targeting densely populated cities around the globe. Data from these missions will help improve our understanding of the sources, types, and interactions among the aerosols and trace gases that are polluting Earth’s atmosphere, as well as our understanding of the impact of air pollution on pollution on a wide range of important areas including human health, agriculture, weather, and climate change. The challenges of cross-disciplinary research, computationally expensive multi-variate analyses, and high-resolution data at both local and global scales are driving substantial changes across all of NASA’s Distributed Active Archive Centers (DAACs). High resolution data at scales such these requires a new approach to data ingest, archive, and publication. Like other NASA DAACs, the Atmospheric Science Data Center (ASDC), the DAAC that will be responsible for publishing MAIA and TEMPO data products has historically archived and distributed data on premise. DAACs of the future will archive and distribute data in the cloud, enabling them to remake themselves as research-focused data centers that will support on-demand, data-intensive computations for highly accurate retrospective analyses and predictions. Under the new paradigm, data formats and metadata must support on-demand spatial and temporal sub-setting, as well as other data transformation services such as re-gridding and re-sampling. This presentation will discuss work being done to address data formatting and metadata requirements in this dynamic new environment. In addition to the changes in data stewardship practices at the ASDC, the increased focus on supporting scientific research is driving changes in the relationship between DAACs and researchers. While the ASDC will continue to provide first rate data management and stewardship, it is increasingly focused on serving as a partner not only to the science teams that gather and produce the data it publishes, but to the researchers that use that data.

Beth Huffer↗
NASA NTRSDate not supplied

Distributed Lunar Data Platform with Advanced Machine Learning Capabilities in Support of Lunar Science and Exploration

The United States 2020 Space Policy directive declares that NASA, in cooperation with private industry, will “extend human economic activity into deep space by establishing a permanent human presence on the Moon”. This goal will require advanced data management, as well as analysis, modeling and representation of lunar information in order to prepare for Artemis human missions, lunar science investigations and exploration. To meet this requirement, we conceptualize and present an implementation strategy for a distributed platform for lunar data retrieval, inferencing and analysis, which will be based on federated learning and the NASA Celestial Mapping System (CMS). In addition to demonstrating the imperative of enabling lunar-borne data to remain in-situ but still accessible, this presentation will also include examples of how third parties could contribute both datasets and new functionality into this platform using an AI-based data import pipeline and a plug-in architecture respectively.

Artificial Intelligence↗
NASA NTRS2017

Transforming Science Data for GIS: How to Find and Use NASA Earth Observation Data Without Being a Rocket Scientist

NASAs Earth Observing System Data Information System (EOSDIS) manages Earth Observation satellites and the Distributed Active Archive Centers (DAACs), where the data is stored and processed. The challenge is that Earth Observation data is complicated. There is plenty of data available, however, the science teams have had a top-down approach: define what it is you are trying to study -select a set of satellite(s) and sensor(s), and drill down for the data.Our alternative is to take a bottom-up approach using eight environmental fields of interest as defined by the Group on Earth Observations (GEO) called Societal Benefit Areas (SBAs): Disaster Resilience (DR) Public Health Surveillance (PHS) Energy and Mineral Resource Management (EMRM) Water Resources Management (WRM) Infrastructure and Transport Management (ITM) Sustainable Urban Development (SUD) Food Security and Sustainable Agriculture (FSSA) Biodiversity and Ecosystems Sustainability (BES).

DAAC↗
NASA NTRS2012

The Organization and Management of the Virtual Astronomical Observatory

The U.S. Virtual Astronomical Observatory (VAO; http://www.us-vao.org/) has been in operation since May 2010. Its goal is to enable new science through efficient integration of distributed multi-wavelength data. This paper describes the management and organization of the VAO, and emphasizes the techniques used to ensure efficiency in a distributed organization. Management methods include using an annual program plan as the basis for establishing contracts with member organizations, regular communication, and monitoring of processes.

management↗
NASA NTRS1991

Information management challenges of the EOS Data and Information System

An overview of the current information management concepts that are embodied in the plans for the Earth Observing System Data and Information System (EOSDIS) is presented, and some of the technology development and application areas that are envisioned to be particularly challenging are introduced. The Information Management System (IMS) is the EOSDIS element that provides the primary interface between the science users and the data products and services of EOSDIS. The goals of IMS are to define a clear and complete set of functional requirements and to apply innovative methods and technologies to satisfy them. The information management functions are described in detail, and some applicable technolgies are discussed. Some of the general issues affecting the successful development and operation of the information management element are addressed.

Mcdonald, Kenneth R.↗
NASA NTRS2002

1999 NCCS Highlights

The NASA Center for Computational Sciences (NCCS) is a high-performance scientific computing facility operated, maintained and managed by the Earth and Space Data Computing Division (ESDCD) of NASA Goddard Space Flight Center's (GSFC) Earth Sciences Directorate. The mission of the NCCS is to advance leading-edge science by providing the best people, computers, and data storage systems to NASA's Earth and space sciences programs and those of other U.S. Government agencies, universities, and private institutions. Among the many computationally demanding Earth science research efforts supported by the NCCS in Fiscal Year 1999 (FY99) are the NASA Seasonal-to-Interannual Prediction Project, the NASA Search and Rescue Mission, Earth gravitational model development efforts, the National Weather Service's North American Observing System program, Data Assimilation Office studies, a NASA-sponsored project at the Center for Ocean-Land-Atmosphere Studies, a NASA-sponsored microgravity project conducted by researchers at the City University of New York and the University of Pennsylvania, the completion of a satellite-derived global climate data set, simulations of a new geodynamo model, and studies of Earth's torque. This document presents highlights of these research efforts and an overview of the NCCS, its facilities, and its people.

Bennett, Jerome↗
NASA NTRS2016

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.↗
NASA NTRSDate not supplied

Transitioning a Flexible and Scalable Satellite Ground Station Observation Network (GSON) Framework to an Operational Environment

Obtaining accurate and timely satellite observations is of paramount importance in fields like disaster management, weather diagnoses/forecasting, and Earth Sciences remote sensing. Stored mission data (SMD), from low Earth orbiting (LEO) satellite sensors, provides important observations for these fields and applications, however data access to SMD can be delayed from one and half hours to three hours from the time the observations were made. This data latency poses a significant impact on data product optimal use. We developed a Ground Station Observation Network (GSON) that utilizes commercial ground station as a service (GSaaS) providers to acquire low latency direct broadcast (DB) data from AQUA, SNPP, and JPSS-1 satellites using antennas located in strategic locations around the world. We will discuss techniques to improve the deployment efficiency and code reliability and quality of the GSON framework. Topics include right-sizing and containerization of the code to facilitate integration and adaptation with continuous delivery (CD) pipeline, locating non-code assets in referenceable repositories separated from code, adaptation of pipelines as code and simplification of CD, intersecting with code quality tests and checks as part of the pipeline execution and deployment, and establishing distributed repositories, registries, and system identities in a way that mitigates compromise to the CD pipeline. These techniques enable deployment of processing systems that are both highly specific but also dynamically modifiable. This new class of system allows for a flexible and scalable deployment while avoiding the “black box” issues that can plague large system deployments.

cluster↗
NASA NTRSDate not supplied

NASA Power: Global Solar Insolation, Meteorological Parameter Data, and Web Services to Support Sustainable Building Design and Operations

The buildings industry is currently striving to adopt green solutions to make infrastructure more energy-efficient in order to meet the 2050 net-zero climate goals. This planning requires reliable environmental datasets that are crucial in designing, building, and maintaining our world’s-built environment, as well as other energy-related processes and investments. This webinar for the National Institute of Building Sciences provides an overview of NASA’s Prediction Of Worldwide Energy Resources (POWER) Project that informs decision-making and development for sustainable building design and operations by enabling public open discovery, efficient access, and convenient distribution of NASA’s Earth Observations and global atmospheric model datasets. POWER’s datastore is comprised of solar radiation and surface meteorology parameters, spanning nearly 40 years of hourly data, that are easily accessible via several access methods and tools to support three focus areas: 1) renewable energy deployment and management, 2) sustainable infrastructure, and 3) agroclimatology applications. POWER and NASA Earth Science both plan future data parameters, updated tools, and improved observations that could directly support U.S. and international sustainable development goals, climate strategies, and building information modeling. To this end, solar data from several NASA projects and meteorological data from NASA assimilation models have already been reformatted and disseminated to the public via a user-friendly web GIS-enabled based data portal through the POWER Project. POWER data is analysis-ready and accessible through an Application Programming Interface (API), ArcGIS Image Services, and the project’s Data Access Viewer enhanced (DAVe), an interactive online tool. The POWER DAVe also features data consistent with ASHRAE Climate Design Conditions and has developed web image services showing Building Climate Zones and their variability. Through those tools, the data can be downloaded into multiple formats that support the infrastructure community, including CSV and Energy Plus Weather (EPW). POWER’s entire data product catalog is available through Amazon Web Services (AWS) Open Data Registry (ODR) via a free and publicly accessible Simple Storage Service (S3). This webinar provides a full overview of the NASA POWER Project's data and services developed in collaboration with the sustainable infrastructure community. Examples of how the renewable energy and building communities have utilized POWER data products to make decisions and a preview of future data product expansion, including climate projections, and web services will also be provided. Additionally, use case stories from our broad community of users will be presented.

Paul W. Stackhouse↗
NASA NTRS2007

Earth Observations to Assess Impact of Hurricane Katrina on John C. Stennis Space Center

The peril from hurricanes to Space Operations Centers is real and is forecast to continue; Katrina, Rita, and Wilma of 2005 and Charley, Frances, Ivan, and Jeanne of 2004 are sufficient motivation for NASA to develop a multi-Center plan for preparedness and response. As was demonstrated at SSC (Stennis Space Center) in response to Hurricane Katrina, NASA Centers are efficiently activated as local command centers, playing host to Federal and State agencies and first responders to coordinate and provide evacuation, relocation, response, and recovery activities. Remote sensing decision support provides critical insight for managing NASA infrastructure and for assisting Center decision makers. Managers require geospatial information to manage the federal city. Immediately following Katrina, SSC s power and network connections were disabled, hardware was inoperative, technical staff was displaced and/or out of contact, and graphical decision support tools were non-existent or less than fully effective. Despite this circumstance, SSC EOC (Emergency Operations Center) implemented response operations to assess damage and to activate recovery plans. To assist Center Managers, the NASA ASP (Applied Sciences Program) made its archive of high-resolution data over the site available. In the weeks and months after the immediate crisis, NASA supplemented this data with high-resolution, post-Katrina imagery over SSC and much of the affected coastal areas. Much of the high-resolution imagery was made available through the Department of Defense Clear View contract and was distributed through U.S. Geological Survey Center for Earth Resources Observation and Science "Hurricane Katrina Disaster Response" Web site. By integrating multiple image data types with other information sources, ASP applied an all-source solutions approach to develop decision support tools that enabled managers to respond to critical issues, such as expedient access to infrastructure and deployment of resources, provision of temporary shelter, logistical control of critical supplies, and the mobilization and coordination of assets from ground crews to aircraft/airspace management. Furthermore, ASP developed information products that illustrate risks to SSC's infrastructure from surge, inundation, and flood. Current plans include developing wind-risk prototype products for refinement and adoption into EOC plans.

Graham, William D.↗
NASA NTRS1991

The Land Analysis System (LAS)

The Land Analysis System (LAS) is an interactive software system, available in the public domain, for the analysis, display, and management of multispectral and other digital image data. The system was developed to support earth sciences research and development activities. LAS provides over 240 applications functions and utilities, a flexible user interface, complete on-line and hardcopy documentation, extensive image data file management, reformatting, and conversion utilities, and high level device independent access to image display hardware. The capabilities are summarized of the latest release of the system (version 5). Emphasis is given to the system portability and the isolation of hardware and software dependencies in this release.

Lu, Yun-Chi↗
NASA NTRS2019

The Value of Being a Trustworthy Repository

Today, NASA's Earth Observing System Data and Information System (EOSDIS), a system ofactive archives is attaching the CoreTrustSeal to its websites signifying that it merits theconfidence of its user community. But what value does being a trustworthy repository impart to auser? What does it mean to the owners and operators of repositories? What will it mean in thefuture? EOSDIS was started in the 1990s based on a framework of discipline-oriented, geographicallydistributed centers of expertise, named Distributed Active Archive Centers (DAACs). The functionof EOSDIS is to collect Earth Science data sensor measurements (principally those created andneeded by NASA) and manage the data and many derived digital products. EOSDIS providesmany services, including processing, curating, documenting, disseminating, and enabling datadiscovery as well as efficient use of the data. The EOSDIS has been operational over 25 years andmany lessons have been learned relative to the TRUST principles. During the tenure of EOSDIS,many changes have occurred as we have increased the size of the collection from gigabytes totens of petabytes and the distribution of the data to millions of users. We have had severalstages of system evolution that have improved EOSDIS in order to meet both stakeholder andcustomer expectations. This type of evolution is an on-going process to ensure that ourrepositories remain trustworthy. It is also important that our own community of data managersand system engineers add value in being trustworthy. This paper will discuss approaches to change within a large system of Earth Science data and services, while remaining a trustworthyrepository.

Behnke, Jeanne↗
NASA NTRS1999

The Navajo Learning Network and the NASA Life Sciences/AFOSR Infrastructure Development Project

The NSF-funded Navajo Learning Network project, with help from NASA Life Sciences and AFOSR, enabled Dine College to take a giant leap forward technologically - in a way that could never had been possible had these projects been managed separately. The combination of these and other efforts created a network of over 500 computers located at ten sites across the Navajo reservation. Additionally, the college was able to install a modern telephone system which shares network data, and purchase a new higher education management system. The NASA Life Sciences funds further allowed the college library system to go online and become available to the entire campus community. NSF, NASA and AFOSR are committed to improving minority access to higher education opportunities and promoting faculty development and undergraduate research through infrastructure support and development. This project has begun to address critical inequalities in access to science, mathematics, engineering and technology for Navajo students and educators. As a result, Navajo K-12 education has been bolstered and Dine College will therefore better prepare students to transfer successfully to four-year institutions. Due to the integration of the NSF and NASA/AFOSR components of the project, a unified project report is appropriate.

Source record↗
NASA NTRSDate not supplied

Automated Collection of Scientific Publications Linked to NASA Earth Science Datasets

NASA's Earth Observing System Data and Information System (EOSDIS) began dataset Digital Object Identifier (DOI) registration in 2012. The number of dataset DOIs registered as of January of 2023 exceeds 11,000. As the research community becomes aware of the importance of sharing data through Open Science and optimizing data reuse through Findability, Accessibility, Interoperability, and Reuse (FAIR) data management principles, datasets are increasingly being cited in scientific publications. When datasets are cited explicitly by DOI within published works, automated methods can be developed for collecting these published works from a variety of bibliometric sources. The coverage of the sources varies, so each source can collect citations that are only available within it. Using major citation databases such as Scopus and Web of Science, the Google Scholar search engine, the CrossRef Open Citation Index, and the dataset DOI registry DataCite, we present an automated workflow for dataset citation collection. By harvesting citations automatically, a citation library is created explicitly linking EOSDIS datasets to publications that cite them. Using Zotero, a free and open-source citation manager, we demonstrate how to access and browse this library by the tags indicating bibliometric sources, dataset DOI, and the dataset archive center. We also demonstrate temporary trends in the number of publications harvested from bibliometric sources.

Infometrics↗