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NASA NTRS2021

Marshall Space Flight Center Faculty Fellowship Program

The 2020 Marshall Aerospace Fellowship Program involved 11 faculty in the laboratories and departments at Marshall Space Flight Center and one faculty researcher working from Colorado. These faculty engineers and scientists worked with NASA collaborators on NASA projects, bringing new perspectives and solutions to bear. This Technical Memorandum is a compilation of nine research reports of the 2020 Marshall Aerospace Fellowship program, along with the Program Announcement (Appendix A) and the Program Description (Appendix B). The research affected the following five areas: (1) Materials (2) Spacecraft systems (3) Human-System Interactions (4) Vehicle systems (5) Space science Additive manufacturing was the single materials investigation during the summer of 2020. Spacecraft systems tasks dominated the fellows’ explorations: covering camera sensors for lunar surface mobility, a real-time system for navigation and mapping, flexible sensor modeling, and transmitter design for high data rate telemetry. One project concentrated on Human-System Interactions. Another fellow designed a model to predict heat leak into a cryogen tank, as in most vehicle systems. One of the scientists worked on a proposed space-based gravitational wave interferometer that will detect Galactic white dwarf binaries. Another space science fellow used a mature astrophysical code to synthesize galactic stellar populations. Our goal is to continue the Marshall Aerospace Program funded by Center project offices.

N. F. Six↗
NASA NTRS2022

Event Report for The Ethical Artificial Intelligence Quantification Workshop

Artificial Intelligence (AI) is a powerful emerging technology area which requires special attention to using it ethically. AI ethics is still an emerging field, and the partners for this workshop and report seek to move AI ethics discussion ahead by experimenting with ways to measure AI ethics criteria. The following document describes the outcomes and learnings from The Ethical Artificial Intelligence Quantification Workshop held at the National Institute for Aerospace (NIA), Hampton, Virginia on May 12th, 2022. The purpose of the workshop was for participants to evaluate and experiment-with the methodology and process presented by AIEthics.World in cooperation with Intel Corporation. The meeting participants learned about the Ethical AI Certification and Maturity Model™ and applied the methodology to selected notional AI systems. The workshop facilitated the evaluation of the maturity of the AI system according to ethical considerations relevant to NASA, NIA and other participants. The workshop consisted of three main phases. The first phase focused on understanding and summarizing NASA’s ethical approaches, mission and values based on published documentation, discussions and individual insights & opinions of participants. This information was prioritized, weighted, ordered, and quantified in phase two, to formulate an alignment between human values (ethics) and their applicability to AI systems during all lifecycle phases. The first two phases were summarized as a form of ethical genealogy for artificial intelligence, specific to NASA’s ethical approaches. In the third and last phase of the workshop the participants evaluated notional examples of artificial intelligence to qualify and quantify its ability to adhere to the organizational ethics approaches, using the Ethical AI Certification and Maturity Model™. The workshop uses the concept of genealogy, in the traditional sense: the study and traceability of lines of ancestors in the process of evolutionary development from earlier forms. However, as it is applied to an Ethical AI definition, it is providing the insights to the necessary and mandatory traceability of content, data, metrics, telemetry, elements, and structures which are used in the AI’s lifecycle to foster and measure AI ethics in all steps of its lifecycle. The Ethical Artificial Intelligence Quantification Workshop provided NASA with the opportunity to apply the Ethical AI Certification and Maturity Model™, in combination with existing and well-known decision-making and quality control methods to identify the metrics and measurements for an Ethical AI and assess its ethical condition and quality aligned with NASA ethics approaches. The result of the workshop is the capacity for NASA to apply the maturity model assessment to its AI Systems as desired and if necessary, publish the ability of these AI Systems to adhere to the organizational ethical goals. AI ethics frameworks need to be customized for each application domain, for example, individual NASA Mission Directorates. General principles that work in one area such as AI/Machine Learning-based text analysis (the ethics of information-extraction) may need to be adapted for another such as sense-and-avoid decision-making in a flight environment. The workshop was conducted among approximately twenty NASA subject matter experts, so the elements noted above should be considered examples, not definitive NASA ethical AI principles, genealogy, etc. Generating a definitive AI ethics framework for an organization as diverse as NASA would require far more discussion, debate, review, etc. However, the workshop provided valuable insight into mechanisms and processes for quantifying AI ethical qualities.

Artificial Intelligence↗
NASA NTRSDate not supplied

HelioSwarm: The Swarm is the Observatory

HelioSwarm will transform our understanding of space plasma turbulence by being the first-of-its-kind simultaneous, multiscale observatory comprising multiple spacecraft. HelioSwarm was competitively selected under the Heliophysics Explorers Program 2019 Medium-Class Explorer (MIDEX) Announcement of Opportunity. The central powered-ESPA hub spacecraft is co-orbited by eight SmallSat Node spacecraft, together moving through a High Earth Orbit to obtain data in various solar wind regimes. The mission architecture is that of hub-and-spoke, with the larger hub serving as a communications relay between the nodes and DSN. Mission operations, management, and technical oversight are provided by NASA Ames Research Center; the spacecraft are provided by Northrop Grumman and BCT. The instrument suite includes foreign-contributed instruments and U.S. ones, all under the oversight of University of New Hampshire (which is also the Principal Investigator’s home institution and Science Operations Center). The mission timeline from launch through conclusion of the one-year science mission is provided along with a summarized concept of operations, with particular emphasis on placing the nodes in their proper relative orbit loops to form the geometry needed for science collection at apogee. A brief discussion of how a combination of legacy tools and custom-created swarm analysis tools are used to design the swarm and sort and visualize the collected science data and telemetry in context is provided. Finally, an exploration of the pathfinding nature of HelioSwarm and some implications for future large scientific swarms is offered.

Heliophysics↗
NASA NTRSDate not supplied

HelioSwarm: The Swarm is the Observatory

The HelioSwarm Mission will transform our understanding of space plasma turbulence by being the first-of-its-kind simultaneous, multiscale observatory comprising multiple spacecraft. HelioSwarm was competitively selected under the Heliophysics Explorers Program 2019 Medium-Class Explorer (MIDEX) Announcement of Opportunity. The central powered-ESPA Hub spacecraft is co-orbited by eight SmallSat Node spacecraft, together moving through a High Earth Orbit to obtain data in various solar wind regimes. The mission architecture is that of hub-and-spoke, with the larger hub serving as a communications relay between the Nodes and DSN. Mission operations, management, and technical oversight are provided by NASA Ames Research Center; the spacecraft are provided by Northrop Grumman and BCT. The instrument suite includes foreign-contributed instruments and U.S. ones, all under the oversight of University of New Hampshire (which is also the Principal Investigator’s home institution and Science Operations Center). The mission timeline from launch through conclusion of the one-year science mission is provided along with a summarized concept of operations, with particular emphasis on placing the Nodes in their proper relative orbit loops to form the geometry needed for science collection at apogee. A brief discussion of how a combination of legacy tools and custom-created swarm analysis tools are used to design the swarm and sort and visualize the collected science data and telemetry in context is provided. Finally, an exploration of the pathfinding nature of HelioSwarm and some implications for future large scientific swarms is offered.

Heliophysics↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism, behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

On The Processing of Log Files for Monitoring Antenna Health

In order to improve the quality of geodetic results, we have developed an infrastructure for timely processing of telemetry from IVS observing stations. We check every hour for new log files with telemetry from both VLBI observing sessions, single dish experiments, and stow-in data collection and automatically process them. The telemetry data we use is the system temperature, phase calibration phases and amplitudes, system equivalent flux density, and the differences between formatter clock and GPS clock. For the system temperature and phase calibration, processing includes filtering out outliers and computing averages and rms of the scatter in each scan. Furthermore, for the phase calibration we also compute the group delay and detect spurious signals. Cleaned and post-processed telemetry is archived. Our process detects abnormalities, such as, anomalously high system temperature, unstable phase calibration phases, jumps in the GPS and formatter clock differences, and others. With our procedure, the latency of detection of station abnormalities is reduced to less than two hours. Early detection of abnormalities reduces the amount of affected data since station personnel get early alerts. We discuss our experience of running this system since 2022.

VLBI↗
NASA NTRSDate not supplied

On The Processing of Log Files for Monitoring Antenna Health

In order to improve the quality of geodetic results, we have developed an infrastructure for timely processing of telemetry from IVS observing stations. We check every hour for new log files with telemetry from both VLBI observing sessions, single dish experiments, and stow-in data collection and automatically process them. The telemetry data we use is the system temperature, phase calibration phases and amplitudes, system equivalent flux density, and the differences between formatter clock and GPS clock. For the system temperature and phase calibration, processing includes filtering out outliers and computing averages and rms of the scatter in each scan. Furthermore, for the phase calibration we also compute the group delay and detect spurious signals. Cleaned and post-processed telemetry is archived. Our process detects abnormalities, such as, anomalously high system temperature, unstable phase calibration phases, jumps in the GPS and formatter clock differences, and others. With our procedure, the latency of detection of station abnormalities is reduced to less than two hours. Early detection of abnormalities reduces the amount of affected data since station personnel get early alerts. We discuss our experience of running this system since 2022.

Phase Calibration↗
NASA NTRS1986

Relative performance of 8.5-GHz and 32-GHz telemetry links on the basis of total data return per pass

The performance of X-band (8.5-GHz) and 32-GHz telemetry links is compared on the basis of the total data return per DSN station pass. Differences in spacecraft transmitter efficiency, transmit circuit loss, and transmitting antenna area efficiency and pointing loss are not considered in these calculations. Thus, the performance differentials calculated in this memo are those produced by a DSN 70-m station antenna gain and clear weather receiving system noise temperature and by weather. These calculations show that, assuming mechanical compensation of the DSN 70-m antenna for 32-GHz operation, a performance advantage for 32 GHz over X-band of 8.2 dB can be achieved for at least one DSN station location. Even if only Canberra and Madrid are used, a performance advantage of 7.7 dB can be obtained for at least one DSN station location. A system using a multiple beam feed (electronic compensation) should achieve similar results.

Koerner, M. A.↗
NASA NTRS1991

Ulysses spacecraft control and monitoring system

The baseline Ulysses spacecraft control and monitoring system (SCMS) concepts and the converted SCMS, residing on a DEC/VAX 8350 hardware, are considered. The main functions of the system include monitoring and displaying spacecraft telemetry, preparing spacecraft commands, producing hard copies of experimental data, and archiving spacecraft telemetry. The SCMS system comprises over 20 subsystems ranging from low-level utility routines to the major monitoring and control software. These in total consist of approximately 55,000 lines of FORTRAN source code and 100 VMS command files. The SCMS major software facilities are described, including database files, telemetry processing, telecommanding, archiving of data, and display of telemetry.

Hamer, P. A.↗
NASA NTRS1991

The impact of NRZ data asymmetry on the performance of a space telemetry system

The author derives an expression for the power spectral density of an asymmetric nonreturn-to-zero (NRZ) data stream. This formula is used to investigate how the data bandwidth varies with data asymmetry. The threshold levels of undesired spectral components that fall into the carrier tracking loop bandwidth are determined by examining the derived spectral density. Based on this formula, a simple technique is developed for the computation of bit signal-to-noise ratio (SNR) degradation due to data asymmetry. The telemetry bit SNR degradations derived using this technique are compared with the results obtained previously by Simon et al. (1978) and with measurements by the electronic systems test laboratory (ESTL) at the Johnson Space Center.

Nguyen, Tien M.↗
NASA NTRS1993

SpaceOps 1992: Proceedings of the Second International Symposium on Ground Data Systems for Space Mission Operations

The Second International Symposium featured 135 oral presentations in these 12 categories: Future Missions and Operations; System-Level Architectures; Mission-Specific Systems; Mission and Science Planning and Sequencing; Mission Control; Operations Automation and Emerging Technologies; Data Acquisition; Navigation; Operations Support Services; Engineering Data Analysis of Space Vehicle and Ground Systems; Telemetry Processing, Mission Data Management, and Data Archiving; and Operations Management. Topics focused on improvements in the productivity, effectiveness, efficiency, and quality of mission operations, ground systems, and data acquisition. Also emphasized were accomplishments in management of human factors; use of information systems to improve data retrieval, reporting, and archiving; design and implementation of logistics support for mission operations; and the use of telescience and teleoperations.

Source record↗
NASA NTRS1972

A Preliminary Study of a Buoy System for Acquisition, Transmission, and Management of Hydrological Data Obtained from In-situ Measurements

The requirements for a system of remotely located, data collection buoys are considered first for a prototype system to be used in conjunction with the Earth Resources Technology Satellite (ERTS-A), and then for a more advanced system. The necessary sensor characteristics for compatibility with the ERTS-A data collection platforms are considered, as well as possible sites for location of the prototype buoys. The advanced system is considered from the standpoint of continuous data collection both through satellite data relay and ground telemetry systems. Management of the data from a buoy system is analyzed, especially with regard to the advanced system.

Elliott, J. M.↗
NASA NTRS1994

SeaWiFS technical report series. Volume 15: The simulated SesWiFS data set, version 2

This document describes the second version of the simulated SeaWiFS data set. A realistic simulated data set is essential for mission readiness preparations and can potentially assist in all phases of ground support for a future mission. The second version improves on the first version primarily through additional realism and complexity. This version incorporates a representation of virtually every aspect of the flight mission. Thus, it provides a high-fidelity data set for testing several aspects of the ground system, including data acquisition, data processing, data transfers, calibration and validation, quality control, and mission operations. The data set is constructed for a seven-day period, 25-31 March 1994. Specific features of the data set include Global Area coverage (GAC), recorded Local Area Coverage (LAC), and realtime High Resolution Picture Transmission (HRPT) data for the seven-day period. A realistic orbit, which is propagated using a Brouwer-Lyddane model with drag, is used to simulate orbit positions. The simulated data corresponds to the command schedule based on the orbit for this seven-day period. It includes total (at-satellite) radiances not only for ocean, but for land, clouds, and ice. The simulation also utilizes a high-resolution land-sea mask. It includes the April 1993 SeaWiFS spectral responses and sensor saturation responses. The simulation is formatted according to July 1993 onboard data structures, which include corresponding telemetry (instrument and spacecraft) data. The methods are described and some examples of the output are given. The instrument response functions made available in April 1993 have been used to produce the Version 2 simulated data. These response functions will change as part of the sensor improvements initiated in July-August 1993.

Hooker, Stanford B.↗
NASA NTRS1972

Skylab 2 space vehicle data evaluation guide

Vehicle telemetry measurements for all stages of Skylab 2 are presented. The data sources for the launch vehicle and command service module are identified. The data evaluation guide format is described. The system for designating the components of the spacecraft is defined. A list of abbreviations for technical terms used in the reporting documents is included.

Source record↗