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NASA NTRS2006

Marshall Space Flight Center Propulsion Systems Department (PSD) Knowledge Management (KM) Initiative

NASA Marshall Space Flight Center's Propulsion Systems Department (PSD) is four months into a fifteen month Knowledge Management (KM) initiative to support enhanced engineering decision making and analyses, faster resolution of anomalies (near-term) and effective, efficient knowledge infused engineering processes, reduced knowledge attrition, and reduced anomaly occurrences (long-term). The near-term objective of this initiative is developing a KM Pilot project, within the context of a 3-5 year KM strategy, to introduce and evaluate the use of KM within PSD. An internal NASA/MSFC PSD KM team was established early in project formulation to maintain a practitioner, user-centric focus throughout the conceptual development, planning and deployment of KM technologies and capabilities within the PSD. The PSD internal team is supported by the University of Alabama's Aging Infrastructure Systems Center of Excellence (AISCE), lntergraph Corporation, and The Knowledge Institute. The principle product of the initial four month effort has been strategic planning of PSD KNI implementation by first determining the "as is" state of KM capabilities and developing, planning and documenting the roadmap to achieve the desired "to be" state. Activities undertaken to suppo~th e planning phase have included data gathering; cultural surveys, group work-sessions, interviews, documentation review, and independent research. Assessments and analyses have beon pedormed including industry benchmarking, related local and Agency initiatives, specific tools and techniques used and strategies for leveraging existing resources, people and technology to achieve common KM goals. Key findings captured in the PSD KM Strategic Plan include the system vision, purpose, stakeholders, prioritized strategic objectives mapped to the top ten practitioner needs and analysis of current resource usage. Opportunities identified from research, analyses, cultural1KM surveys and practitioner interviews include: executive and senior management sponsorship, KM awareness, promotion and training, cultural change management, process improvement, leveraging existing resources and new innovative technologies to align with other NASA KM initiatives (convergence: the big picture). To enable results based incremental implementation and future growth of the KM initiative, key performance measures have been identified including stakeholder value, system utility, learning and growth (knowledge capture, sharing, reduced anomaly recurrence), cultural change, process improvement and return-on-investment. The next steps for the initial implementation spiral (focused on SSME Turbomachinery) have been identified, largely based on the organization and compilation of summary level engineering process models, data capture matrices, functional models and conceptual-level svstems architecture. Key elements include detailed KM requirements definition, KM technology architecture assessment, - evaluation and selection, deployable KM Pilot design, development, implementation and evaluation, and justifying full implementation (estimated Return-on-Investment). Features identified for the notional system architecture include the knowledge presentation layer (and its components), knowledge network layer (and its components), knowledge storage layer (and its components), User Interface and capabilities. This paper provides a snapshot of the progress to date, the near term planning for deploying the KM pilot project and a forward look at results based growth of KM capabilities with-in the MSFC PSD.

Caraccioli, Paul↗

NASA NTRS2019

Enabling Real-time Multi-messenger Astrophysics Discoveries with Deep Learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

E A Huerta↗

NASA NTRS2015

Technical Challenges and Opportunities of Centralizing Space Science Mission Operations (SSMO) at NASA Goddard Space Flight Center

The NASA Goddard Space Science Mission Operations project (SSMO) is performing a technical cost-benefit analysis for centralizing and consolidating operations of a diverse set of missions into a unified and integrated technical infrastructure. The presentation will focus on the notion of normalizing spacecraft operations processes, workflows, and tools. It will also show the processes of creating a standardized open architecture, creating common security models and implementations, interfaces, services, automations, notifications, alerts, logging, publish, subscribe and middleware capabilities. The presentation will also discuss how to leverage traditional capabilities, along with virtualization, cloud computing services, control groups and containers, and possibly Big Data concepts.

Science↗

NASA NTRS2024

Digital Lunar Exploration Sites (DLES) Terrain Crafting

Humans will soon be returning to the surface of the Moon with NASA’s Artemis program. The Artemis program is an international collaboration that will consist of a complex series of space systems and missions to explore the lunar surface and pave the way for the future exploration of Mars. NASA and its partners rely heavily on simulation for lighting and navigation studies as well as training astronauts, flight controllers, and mission support staff. The NASA Exploration Systems Simulations (NExSyS) team in the Simulation and Graphics Branch (ER7) in the Engineering Directorate at NASA’s Johnson Space Center has built up many simulation products to support this effort, one of which is the Digital Lunar Exploration Sites (DLES). DLES is a collection of products used to simulate and render the lunar surface in a digital environment. We discussed and presented an overview of the DLES products at the 2022 IEEE Aerospace Conference in Big Sky, MT with a paper titled "Digital Lunar Exploration Sites". This “DLES Terrain Crafting” paper will expand on the information previously provided in “DLES” paper and dive deeper into the details of the terrain crafting process and the toolsets used to support this task. The best digital data currently available of the lunar surface is provided by the Lunar Reconnaissance Orbiter (LRO). Its Lunar Orbiter Laser Altimeter (LOLA) achieves an impressive resolution of 5m per pixel at the Lunar South Pole (LSP) and can generate datasets covering a large continuous region near the LSP. There are a few additional methods, such as Shape from Shading which can infer higher resolution data (up to 1m per pixel) from the LRO Narrow Angle Camera (NAC) images. However, surface-based simulations require higher-resolution data, and this paper will discuss the process of enhancing the terrain to meet that need. The process begins with capturing statistical data of craters in the regions of interest using images provided by the LRO NAC. This data is then used to scatter artificial features which are not captured in the truth data, resulting in an enhanced DEM with a much higher resolution of 20cm per pixel. Many tools were built up to assist in the creation of these artificial Digital Elevation Models (DEM), which this paper will discuss in detail. DEMs themselves are a very powerful representation of a planetary surface, and many operations and tools can utilize the data they contain. This paper includes a description of the rendering of the lunar surface in a graphics engine, generation of contact patches to simulate tire to ground interaction, and ray tracing utilities to model Line of Sight (LOS) interactions with the terrain. This paper will also explore some new tool sets currently under development which aim to utilize Machine Learning (ML) to assist in the identification of craters from LRO NAC imagery. While this is not a novel idea, the NExSyS team is developing a unique approach which may result in more robust identification of crater characteristics.

Artemis↗

NASA NTRSDate not supplied

Modern Scientific Data Governance Framework

Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies and stewardship best practices tend to be disconnected from operational data management practices and enforcement and mainly exist in well-meaning documents or reports. These governance policies are, at best, partially implemented and rarely monitored or audited. In addition, existing governance policies keep adding additional data management steps that require a human, ‘a data steward’, in the loop, and the cost of data management can no longer scale proportionately with the current and future increased data volume and complexity. The goal for developing an updated data governance framework is to modernize scientific data governance to the reality of Big data and align it with the current technology trends such as cloud computing and AI. The goals of this framework are two folds. One is to ensure thoroughness that the governance adequately covers the entire data life cycle. Two, provide a practical approach that offers a consistent and repeatable process for different projects. Three core principles ground this framework. First, focus on just enough governance and prevent data governance from becoming a roadblock toward the scientific process. Remove any unnecessary processes and steps. Second, automate data management steps where possible. Actively remove steps that require ‘human in the loop’ within the management process to be efficient and scale with increasing data. Third, all the processes should continually be optimized using quantified metrics to streamline the monitoring and auditing workflows.

Rahul Ramachandran↗

NASA NTRS1974

The origin of the diffuse background gamma radiation

Recent observations provided evidence for diffuse background gamma radiation extending to energies beyond 100 MeV, and evidence of isotropy and implied cosmological origin. Significant features in the spectrum of this background radiation were observed which provide evidence for its origin in nuclear processes in the early stages of big-bang cosmology, and connect these processes with the galaxy formation theory. A test of the theory is in future observations of the background radiation in the 100 MeK to 100 GeV energy range which are made with large orbiting spark-chamber satellite detectors. The theoretical interpretations of present data, their connection with baryon-symmetric cosmology and galaxy formation theory, and the need for future observations are discussed.

Stecker, F. W.↗

NASA NTRS1999

Ocean Wave Studies with Applications to Ocean Modeling and Improvement of Satellite Altimeter Measurements

Combining analysis of satellite data (altimeter, scatterometer, high-resolution visible and infrared images, etc.) with mathematical modeling of non-linear wave processes, we investigate various ocean wave fields (on scales from capillary to planetary), their role in ocean dynamics and turbulent transport (of heat and biogeochemical quantities), and their effects on satellite altimeter measuring accuracy. In 1998 my attention was focused on long internal gravity waves (10 to 1000 km), known also as baroclinic inertia-gravity (BIG) waves. We found these waves to be a major factor of altimeter measurements "noise," resulting in a greater uncertainty [up to 10 cm in terms of sea surface height (SSH) amplitude] in the measured SSH signal than that caused by the sea state bias variations (up to 5 cm or so). This effect still remains largely overlooked by the satellite altimeter community. Our studies of BIG waves address not only their influence on altimeter measurements but also their role in global ocean dynamics and in transport and turbulent diffusion of biogeochemical quantities. In particular, in collaboration with Prof Peter Weichman, Caltech, we developed a theory of turbulent diffusion caused by wave motions of most general nature. Applied to the problem of horizontal turbulent diffusion in the ocean, the theory yielded the effective diffusion coefficient as a function of BIG wave parameters obtainable from satellite altimeter data. This effort, begun in 1997, has been successfully completed in 1998. We also developed a theory that relates spatial fluctuations of scalar fields (such as sea surface temperature, chlorophyll concentration, drifting ice concentration, etc.) to statistical characteristics of BIG waves obtainable from altimeter measurements. A manuscript is in the final stages of preparation. In order to verify the theoretical predictions and apply them to observations, we are now analyzing Sea-viewing Wide Field of view Sensor (SeaWiFS) and Field of view Sensor (SeaWiFS) and Advanced Very High-Resolution Radiometer (AVHRR) data on sea surface temperature (SST) and chlorophyll concentration jointly with TOPEX/POSEIDON data on SSH variations.

Glazman, Roman E.↗

NASA NTRS1993

Use of negotiated rulemaking in developing technical rules for low-Earth orbit mobile satellite systems

Technical innovations have converged with the exploding market demand for mobile telecommunications to create the impetus for low-earth orbit (LEO) communications satellite systems. The so-called 'Little LEO's' propose use of VHF and UHF spectrum to provide position - location and data messaging services. The so-called 'Big LEO's' propose to utilize the RDSS bands to provide voice and data services. In the United States, several applications were filed with the U.S. Federal Communications Commission (FCC) to construct and operate these mobile satellite systems. To enable the prompt introduction of such new technology services, the FCC is using innovative approaches to process the applications. Traditionally, when the FCC is faced with 'mutually exclusive' applications, e.g. a grant of one would preclude a grant of the others, it uses selection mechanisms such as comparative hearings or lotteries. In the case of the LEO systems, the FCC has sought to avoid these time-consuming approaches by using negotiated rulemakings. The FCC's objective is to enable the multiple applicants and other interested parties to agree on technical and service rules which will enable the grant of all qualified applications. With regard to the VHF/UHF systems, the Advisory Committee submitted a consensus report to the FCC. The process for the systems operating in the bands above 1 GHz involved more parties and more issues but still provided the FCC useful technical information to guide the adoption of rules for the new mobile satellite service.

Taylor, Leslie A.↗

NASA NTRS2018

Introduction to NASA Goddard Workshop on Artificial Intelligence

Artificial Intelligence (AI) is a collection of advanced technologies that allows machines to think and act, both humanly and rationally, through sensing, comprehending, acting and learning. AI's foundations lie at the intersection of several traditional fields Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Although the inception of AI started in the 1950's, it has recently made a strong comeback in all aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few.This workshop will be investigating how AI technologies can be adapted or developed to address the following challenges: Discover events of interest and correlations in large amounts of science data; improve the outcomes of science modeling and data assimilation using improved data processing, integration, and analysis. Design advisors for mission planning and operations, including anomaly detection and spacecraft health monitoring. Develop tools for engineering support, including advanced manufacturing, orbit determination, new component design and system engineering. Customize intelligent user interfaces, including visual analytics and natural language processing.

Le Moigne, Jacqueline↗

NASA NTRS1974

The origin of the diffuse background gamma-radiation

Recent observations have now provided evidence for diffuse background gamma radiation extending to energies beyond 100 MeV. There is some evidence of isotropy and implied cosmological origin. Significant features in the spectrum of this background radiation have been observed which provide evidence for its origin in nuclear processes in the early stages of the big-band cosmology and tie in these processes with galaxy fromation theory. A crucial test of the theory may lie in future observations of the background radiation in the 100 MeV to 100 GeV energy range which may be made with large orbiting spark-chamber satellite detectors. A discussion of the theoretical interpretations of present data, their connection with baryon symmetric cosmology and galaxy formation theory, and the need for future observations are given.

Stecker, F. W.↗

NASA NTRSDate not supplied

Pyroscopegridding: Geo-Leo Aerosol Data Fusion (an Open-Source Package)

The retrieval of aerosol optical depths (AODs) from sun-synchronous polar orbiting satellites, such as MODISs, VIIRSs, OMI, TROPOMI, etc., has been widely adopted as a method for obtaining information regarding particulate matter (PM) and related atmospheric processes. However, the advent of recently launched geostationary satellites, such as GOES-16/17/18, Himawari-8/9, and Meteosat Third Generation (MTG), has led to an increased temporal resolution of AOD observations (order of 10 minutes), resulting in typically one or more images per hour during daylight hours, compared to the once-per-day observations obtained from LEO satellites. By integrating these observations, the diurnal cycle of global AOD can be characterized at local, regional, and global scales. The scientific community is still examining the novel data from geostationary satellite observations and evaluating methods for effectively merging these observations with differing spatial and temporal resolutions. This presents a significant ""Big Data"" challenge, encompassing not only data storage, but also data discoverability, accessibility, and migration within cloud computing environments. This study presents our attempts at fusing Level 2 aerosol data from six satellites, three of which are geostationary (GOES-16/17 and Himawari-8) and three of which are polar orbiting (TERRA/MODIS, AQUA/MODIS, and SNPP-VIIRS), using the Dark Target aerosol retrieval algorithm. The ability to fuse remote sensing products on demand into desired temporal and spatial domains empowers researchers and practitioners to more efficiently work with satellite and sensor data. It is our hope that through making our open-source package and accompanying functionality available, the scientific community will have improved access to aerosol data processing resources.

Jennifer Wei↗

NASA NTRS1988

Stellar nucleosynthesis and chemical evolution of the solar neighborhood

Current theoretical models of nucleosynthesis (N) in stars are reviewed, with an emphasis on their implications for Galactic chemical evolution. Topics addressed include the Galactic population II red giants and early N; N in the big bang; star formation, stellar evolution, and the ejection of thermonuclearly evolved debris; the chemical evolution of an idealized disk galaxy; analytical solutions for a closed-box model with continuous infall; and nuclear burning processes and yields. Consideration is given to shell N in massive stars, N related to degenerate cores, and the types of observational data used to constrain N models. Extensive diagrams, graphs, and tables of numerical data are provided.

Clayton, Donald D.↗

NASA NTRS2018

NASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval System

NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.

Machine learning↗

NASA NTRSDate not supplied

Open Source Application of Fusing Aerosol Products from GEO and LEO Satellites

Retrieving aerosol optical depths (AODs) from sun-synchronous polar orbiting (aka low earth orbit, LEO) satellites, such as MODISs, and VIIRSs, OMI, TROPOMI, etc, has become well-established as a tool for extracting information on particulate matter (PM) and related processes in the atmosphere. However, with recently launched geostationary satellites (GEO), such as GOES-16/17/18, and Himawari-8/9, and Meteosat Third Generation (MTG) they provide a much higher temporal resolution (order of 10 minutes), typically an image once or more per hour during daylight compared to LEO once per day. By combining these observations, we may be able to characterize the diurnal cycle of global AOD at the local, regional and global scale. While the science community is still exploring the new data from GEO observations, we have been thinking about how to properly combine/merge/fuse those data considering differences in their spatial and temporal resolutions. However, this poses a “Big Data” challenge. The big data challenge is not just about data storage, but also about data discoverability, and accessibility, and even more, about data migration/mirroring in the cloud-computing environment. This paper is merely showing some of the efforts and approaches we have attempted in fusing six satellites’ Level 2 aerosol data (three are from GEO (GOES-16/17 and Himawari-8), and the other three are from LEO (TERRA/MODIS, AQUA/MODIS, SNPP-VIIRS) from Dark Target (DT) aerosol retrieval algorithm. Having the on-demand capability of fusing remote sensing products onto the desired temporal and spatial domain enables researchers and application practitioners to better manipulate and work with satellite and sensor data. It is our hopeWe hope that by making such an open-source package, and the accompanying functionality, the scientific community will be granted easier access to aerosol data processing resources. The MEaSUREs Program (Making Earth System Data Records for Use in Research Environments) expands our understanding of the Earth's current system through atmospheric and surface measurements. In an effort to aid the scientific research component and improve open source methods, this project developed Python code for fusing six satellite Level 2 aerosol data (three are from geostationary satellites (GEO), and the other three are from low earth orbital satellites (LEO)) from Dark Target Aerosol Retrieval Algorithm.

Jennifer Wei↗

NASA NTRS2017

GeoDash: Assisting Visual Image Interpretation in Collect Earth Online by Leveraging Big Data on Google Earth Engine

Collect Earth Online (CEO) is a free and open online implementation of the FAO Collect Earth system for collaboratively collecting environmental data through the visual interpretation of Earth observation imagery. The primary collection mechanism in CEO is human interpretation of land surface characteristics in imagery served via Web Map Services (WMS). However, interpreters may not have enough contextual information to classify samples by only viewing the imagery served via WMS, be they high resolution or otherwise. To assist in the interpretation and collection processes in CEO, SERVIR, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries, developed the GeoDash system, an embedded and critical component of CEO. GeoDash leverages Google Earth Engine (GEE) by allowing users to set up custom browser-based widgets that pull from GEE's massive public data catalog. These widgets can be quick looks of other satellite imagery, time series graphs of environmental variables, and statistics panels of the same. Users can customize widgets with any of GEE's image collections, such as the historical Landsat collection with data available since the 1970s, select date ranges, image stretch parameters, graph characteristics, and create custom layouts, all on-the-fly to support plot interpretation in CEO. This presentation focuses on the implementation and potential applications, including the back-end links to GEE and the user interface with custom widget building. GeoDash takes large data volumes and condenses them into meaningful, relevant information for interpreters. While designed initially with national and global forest resource assessments in mind, the system will complement disaster assessments, agriculture management, project monitoring and evaluation, and more.

SERVI↗

NASA NTRS2008

Proceedings of the 39th Lunar and Planetary Science Conference

Sessions with oral presentations include: A SPECIAL SESSION: MESSENGER at Mercury, Mars: Pingos, Polygons, and Other Puzzles, Solar Wind and Genesis: Measurements and Interpretation, Asteroids, Comets, and Small Bodies, Mars: Ice On the Ground and In the Ground, SPECIAL SESSION: Results from Kaguya (SELENE) Mission to the Moon, Outer Planet Satellites: Not Titan, Not Enceladus, SPECIAL SESSION: Lunar Science: Past, Present, and Future, Mars: North Pole, South Pole - Structure and Evolution, Refractory Inclusions, Impact Events: Modeling, Experiments, and Observations, Mars Sedimentary Processes from Victoria Crater to the Columbia Hills, Formation and Alteration of Carbonaceous Chondrites, New Achondrite GRA 06128/GRA 06129 - Origins Unknown, The Science Behind Lunar Missions, Mars Volcanics and Tectonics, From Dust to Planets (Planetary Formation and Planetesimals):When, Where, and Kaboom! Astrobiology: Biosignatures, Impacts, Habitability, Excavating a Comet, Mars Interior Dynamics to Exterior Impacts, Achondrites, Lunar Remote Sensing, Mars Aeolian Processes and Gully Formation Mechanisms, Solar Nebula Shake and Bake: Mixing and Isotopes, Lunar Geophysics, Meteorites from Mars: Shergottite and Nakhlite Invasion, Mars Fluvial Geomorphology, Chondrules and Chondrule Formation, Lunar Samples: Chronology, Geochemistry, and Petrology, Enceladus, Venus: Resurfacing and Topography (with Pancakes!), Overview of the Lunar Reconnaissance Orbiter Mission, Mars Sulfates, Phyllosilicates, and Their Aqueous Sources, Ordinary and Enstatite Chondrites, Impact Calibration and Effects, Comparative Planetology, Analogs: Environments and Materials, Mars: The Orbital View of Sediments and Aqueous Mineralogy, Planetary Differentiation, Titan, Presolar Grains: Still More Isotopes Out of This World, Poster sessions include: Education and Public Outreach Programs, Early Solar System and Planet Formation, Solar Wind and Genesis, Asteroids, Comets, and Small Bodies, Carbonaceous Chondrites, Chondrules and Chondrule Formation, Chondrites, Refractory Inclusions, Organics in Chondrites, Meteorites: Techniques, Experiments, and Physical Properties, MESSENGER and Mercury, Lunar Science Present: Kaguya (SELENE) Results, Lunar Remote Sensing: Basins and Mapping of Geology and Geochemistry, Lunar Science: Dust and Ice, Lunar Science: Missions and Planning, Mars: Layered, Icy, and Polygonal, Mars Stratigraphy and Sedimentology, Mars (Peri)Glacial, Mars Polar (and Vast), Mars, You are Here: Landing Sites and Imagery, Mars Volcanics and Magmas, Mars Atmosphere, Impact Events: Modeling, Experiments, and Observation, Ice is Nice: Mostly Outer Planet Satellites, Galilean Satellites, The Big Giant Planets, Astrobiology, In Situ Instrumentation, Rocket Scientist's Toolbox: Mission Science and Operations, Spacecraft Missions, Presolar Grains, Micrometeorites, Condensation-Evaporation: Stardust Ties, Comet Dust, Comparative Planetology, Planetary Differentiation, Lunar Meteorites, Nonchondritic Meteorites, Martian Meteorites, Apollo Samples and Lunar Interior, Lunar Geophysics, Lunar Science: Geophysics, Surface Science, and Extralunar Components, Mars, Remotely, Mars Orbital Data - Methods and Interpretation, Mars Tectonics and Dynamics, Mars Craters: Tiny to Humongous, Mars Sedimentary Mineralogy, Martian Gullies and Slope Streaks, Mars Fluvial Geomorphology, Mars Aeolian Processes, Mars Data and Mission,s Venus Mapping, Modeling, and Data Analysis, Titan, Icy Dwarf Satellites, Rocket Scientist's Toolbox: In Situ Analysis, Remote Sensing Approaches, Advances, and Applications, Analogs: Sulfates - Earth and Lab to Mars, Analogs: Remote Sensing and Spectroscopy, Analogs: Methods and Instruments, Analogs: Weird Places!. Print Only Early Solar System, Solar Wind, IDPs, Presolar/Solar Grains, Stardust, Comets, Asteroids, and Phobos, Venus, Mercury, Moon, Meteorites, Mars, Astrobiology, Impacts, Outer Planets, Satellites, and Rings, Support for Mission Operations, Analog Education and Public Outreach.

Source record↗

NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗

NASA NTRSDate not supplied

Big-data Efficient Automated Science Transfer (BEAST): an open-source software architecture for arc jet data management, modeling, and automation

Data management↗