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Data catalog series for space science and applications flight missions. Volume 3B: Descriptions of data sets from low- and medium-altitude scientific spacecraft and investigations

The main purpose of the data catalog series is to provide descriptive references to data generated by space science flight missions. The data sets described include all of the actual holdings of the Space Science Data Center (NSSDC), all data sets for which direct contact information is available, and some data collections held and serviced by foreign investigators, NASA and other U.S. government agencies. This volume contains narrative descriptions of data sets from low and medium altitude scientific spacecraft and investigations. The following spacecraft series are included: Mariner, Pioneer, Pioneer Venus, Venera, Viking, Voyager, and Helios. Separate indexes to the planetary and interplanetary missions are also provided.

Jackson, John E.↗

Cube Quest Challenge: A Government Prize for Advanced CubeSat Technologies for Affordable Deep Space Science and Exploration Missions

NASA STMD Centennial Challenges Program operates government prize programs for the public benefit. Cube Quest Challenge awards prizes to citizen inventors who advance CubeSat state of the art, enabling affordable NASA science and exploration missions. Cube Quest will take place in lunar orbit or at 4M km. CubeSat developers will make advancements in communications, propulsion and radiation tolerance suitable for future deep space missions. Cube Quest may inspire other ambitious government challenges.

spacecraft↗

Data catalog series for space science and applications flight missions. Volume 5A: Descriptions of astronomy, astrophysics, and solar physics spacecraft and investigations. Volume 5B: Descriptions of data sets from astronomy, astrophysics, and solar physics spacecraft and investigations

The main purpose of the data catalog series is to provide descriptive references to data generated by space science flight missions. The data sets described include all of the actual holdings of the Space Science Data Center (NSSDC), all data sets for which direct contact information is available, and some data collections held and serviced by foreign investigators, NASA and other U.S. government agencies. This volume contains narrative descriptions of data sets of astronomy, astrophysics, solar physics spacecraft and investigations. The following spacecraft series are included: Mariner, Pioneer, Pioneer Venus, Venera, Viking, Voyager, and Helios. Separate indexes to the planetary and interplanetary missions are also provided.

Kim, Sang J.↗

Guidelines for submitting data to the National Space Science Data Center

The mission of the National Space Science Data Center (NSSDC) is to disseminate space science data for further analysis beyond that provided by the principal investigators (PIs) or team leaders (TLs) and their coworkers. Consequently, the NSSDC is responsible for the acquisition, organization, storage, retrieval, announcement, and distribution of scientific data obtained mainly from satellites and spacecraft. Any scientist may acquired data from the NSSDC and use them in further studies, either alone or in conjunction with data from ground-based or spacecraft experiments. With the responsibility for archiving data is the concomitant responsibility for distributing the documentation necessary to make those data usable. Since the group most knowledgeable about a particular experiment and its data is the PI or TL and his coworkers, and since the NSSDC cannot possibly supply the qualified personnel needed to write this documentation comprehensively, it is the responsibility of the PI or TL to provide the essential documentation. The NSSDC will support this effort by defining what is needed, by reviewing what is provided, and by reproducing and distributing the resulting documentation with the data. For a high-use data set, the NSSDC may publish the documentation as a Data Users Note; for a low-use data set, the NSSDC may distribute a Xerox, microfilm, or microfiche copy of the documentation.

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Space Shuttle to deploy Magellan planetary science mission

The objectives of Space Shuttle Mission STS-30 are described along with major flight activities, prelaunch and launch operations, trajectory sequence of events, and landing and post-landing operations. The primary objective of STS-30 is to successfully deploy the Magellan spacecraft into low earth orbit. Following deployment, Magellan will be propelled to its Venus trajectory by an Inertial Upper Stage booster. The objectives of the Magellan mission are to obtain radar images of more than 70 percent of Venus' surface, a near-global topographic map, and near-global gravity field data. Secondary STS-30 payloads include the Fluids Experiment Apparatus (FEA) and the Mesoscale Lightning Experiment (MLE).

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Rodent growth, behavior, and physiology resulting from flight on the Space Life Sciences-1 mission

A rodent-based spaceflight study is conducted to investigate physiological changes in rats vs humans and the effects of changes in the design of the Research Animal Holding Facility (RAHF) and the Animal Enclosure Module (AEM). Rats were housed in the AEM and the RAHF, and controls were kept in identical flight hardware on earth subjected to the same flight-environmental profile. Biosamples and organ weights are taken to compare the rats before and after flight, and food/water intake are also compared. Weight gain, body weight, and food consumptions in the flight rats are significantly lower than corresponding values for the control subjects. Flight rats tend to have smaller postexperiment spleens and hearts, and flight rats consumed more water in the AEM than in the RAHF. The rodents' behavior is analogous to humans with respect to physiological and reconditioning effects, showing that the rat is a good model for basic research into the effects of spaceflight on humans.

Jahns, G.↗

Orbit determination covariance analysis for the Deep Space Program Science Experiment mission

To define an appropriate orbit support procedure for the DSPSE mission, detailed permission orbit determination covariance analyses have been performed for the translunar and trans-Geographos mission phases. Preliminary analyses were also performed for the lunar mapping mission phase. These analyses are designed to assess the tracking patterns and the amount of tracking data needed to obtain orbit solutions of required accuracy for each mission phase and before and after each major orbit perturbation, such as orbit maneuvers and flybys of the Earth and Moon. In addition to operational orbit determination procedures, these analyses identify major error sources, estimate their contribution to orbital errors, and address possible strategies to reduce orbit determination error. For the lunar orbit phase, several lunar gravity error modeling approaches have been investigated. The covariance analysis results presented in this paper will serve as a guide for providing orbit determination support for the DSPSE mission.

Beckman, M.↗

In-Situ Resource Utilization: Laying the Foundation for "Living off the Land"

The technology to manufacture rocket propellants, breathing and life-support gases, fuel cell reagents, and other consumables on Mars using indigenous Martian resources as feedstock in the production process is known as In-Situ Resource Utilization (ISRU). Several studies of the long-term, committed exploration of Mars by humans show that ISRU is essential ... an enabling technology. The recognized value of ISRU to human exploration is reflected in the NASA Strategic Plan. In the description of the "Strategies and Outcomes" of the Human Exploration and Development of Space (HEDS) Enterprise, the NASA Strategic Plan states: The [HEDS] Enterprise relies on the robotic missions of the Space Science Enterprise to provide extensive knowledge of the geology, environment, and resources of planetary bodies. The Space Science Enterprise missions will also demonstrate the feasibility of utilizing local resources to "live off the land."

Kaplan, D. I.↗

NASA's Advanced Solar Sail Propulsion System for Low-Cost Deep Space Exploration and Science Missions that Use High Performance Rollable Composite Booms

Several low-cost solar sail technology demonstrator missions are under development in the United States. However, the mass saving derived benefits that composites can offer to such a mass critical spacecraft architecture have not been realized yet. This is due to the lack of suitable composite booms that can fit inside CubeSat platforms and ultimately be readily scalable to much larger sizes, where they can fully optimize their use. With this aim, a new effort focused at developing scalable rollable composite booms for solar sails and other deployable structures has begun. Seven meter booms used to deploy a 90 m2 class solar sail that can fit inside a 6U CubeSat have already been developed. The NASA road map to low-cost solar sail capability demonstration envisioned, consists of increasing the size of these composite booms to enable sailcrafts with a reflective area of up to 2000 m2 housed aboard small satellite platforms. This paper presents a solar sail system initially conceived to serve as a risk reduction alternative to Near Earth Asteroid (NEA) Scout's baseline design but that has recently been slightly redesigned and proposed for follow-on missions. The features of the booms and various deployment mechanisms for the booms and sail, as well as ground support equipment used during testing, are introduced. The results of structural analyses predict the performance of the system under microgravity conditions. Finally, the results of the functional and environmental testing campaign carried out are shown.

Fernandez, Juan M.↗

Data catalog series for space science and applications flight missions. Volume 4A: Descriptions of meteorological and terrestrial applications spacecraft and investigations

The National Space Science Data Center (NSSDC) provides data from and information about space science and applications flight investigations in support of additional studies beyond those performed as the principal part of any flight mission. The Earth-orbiting spacecraft for investigations of the earth and its atmosphere is discussed. Geodetic tracking data are included in this category. The principal subject areas presented are meteorology and earth resources survey, and the spacecraft selection is made according to those subjects. All experiments on board the spacecraft are described. No attempt is made to reference investigations that are related to the above disciplines, but that are described in other volumes of this series.

Ng, C. Y.↗

Mission Overview STS-107: Providing 24/7 Space Science Research

Columbia's 16-day mission is dedicated to a mixed complement of competitively selected and commercially sponsored research in the space, life and physical sciences. An international crew of seven, including the first Israeli astronaut, will work 24 hours a day in two alternating shifts to carry out experiments in the areas of astronaut health and safety; advanced technology development; and Earth and space sciences.

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Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗