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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Office of university affairs management information system: Users guide and documentation

Data on the NASA-University relationship are reported that encompass research in over 600 schools through several thousand grants and contracts. This user-driven system is capable of producing a variety of cyclical and query-type reports describing the total NASA-University profile. The capabilities, designed as part of the system, require a minimum of user maintenance in order to ensure system efficiency and data validity to meet the recurrent Statutory and Executive Branch information requirements as well as ad hoc inquiries from NASA general management, Congress, other Federal agencies, private sector organizations, universities and individuals. The data base contains information on each university, the individual projects and the financial details, current and historic, on all contracts and grants. Complete details are given on the system from its unique design features to the actual steps required for daily operation.

Distin, J.↗
DOE OSTI2022

Integrating terrestrial scavenging ecology into contemporary wildlife conservation and management

Scavenging plays a vital role in maintaining ecosystem health and contributing to ecological functions; however, research in this sub-discipline of ecology is underutilized in developing and implementing wildlife conservation and management strategies. We provide an examination of the literature and recommend priorities for research where improved understanding of scavenging dynamics can facilitate the development and refinement of applied wildlife conservation and management strategies. Due to the application of scavenging research broadly within ecology, scavenging studies should be implemented for informing management decisions. In particular, a more direct link should be established between scavenging dynamics and applied management programs related to informing pharmaceutical delivery and population control through bait uptake for scavenging species, prevention of unintentional poisoning of nontarget scavenging species, the epidemiological role that scavenging species play in disease dynamics, estimating wildlife mortalities, nutrient transfer facilitated by scavenging activity, and conservation of imperiled facultative scavenging species. This commentary is intended to provide information on the paucity of data in scavenging research and present recommendations for further studies that can inform decisions in wildlife conservation and management. Additionally, we provide a framework for decision making when determining how to apply scavenging ecology research for management practices and policies. Due to the implications that scavenging species have on ecosystem health, and their overall global decline as a result of anthropic activities, it is imperative to advance studies in the field of scavenging ecology that can inform applied conservation and management programs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Learning Lessons from the X-37 Project

The X-37 was planned as an automated vehicle capable of flight-testing new aerospace technologies in combined environments that are beyond the capability of existing ground or flight platforms. Flight demonstration with the X-37 architecture and configuration in relevant environments was planned to reduce the risk of developing launch vehicle technologies for sustainable, affordable exploration and other aerospace applications. Current plans are for the X-37 Approach and Landing Test Vehicle (ALTV) to be atmospheric tested in 2005 from Scaled Composite's White Knight carrier aircraft at up to 40,000 feet over California's Mojave Spaceport, with and turnaround maintenance performed. The fight Operations Control Center will conduct the mission, using a streamlined operations concept. Taxi-tow and captive-carry tests will be conducted prior to the atmospheric-test series. Sponsored by the Defense Advanced Research Projects Agency (DARPA) with NASA participation, technical objectives are to: (1) mature Computed Air Data System/Remote Pressure Sensor technology, (2) manage energy during Terminal Area Energy Management/Heading Alignment Cone maneuvers, and (3) validate the aerodatabase. The X-37 Project began in 1999 under a cooperative agreement as an element of NASA's Future X Program and transitioned to a NASA Research Announcement under the Space Launch Initiative. In mid-2004, NASA transferred ownership to DARPA, with its heritage of performing high-risk, high-payoff research and development. NASA contributes technical expertise, including risk analysis and system integration. The Boeing Company is the prime contractor, with nationwide suppliers. This recent partnership exemplifies the synergy attainable when NASA Centers, other Government agencies, and industry work together toward a common goal - contributing to the knowledge base for U.S. exploration and other aerospace endeavors. The X-37 team represents a range of space transportation disciplines - from engineering to management. Some members have been with the project since its inception. All have gained priceless experience during the design, manufacturing, and testing of the ALTV, as well as through developing advanced orbital flight technologies, such as state-of-the-art Thermal Protection Systems and hot structures. Throughout this process, the X-37 Project team captures lessons that are directly applicable to other such efforts. The upcoming ALTV flights offer another dimension of data and first-hand experience that will prove invaluable to those designing new generations of reusable spacecraft. And ongoing technology developments will expand the aerospace knowledge base. Delivering prototype hardware is always a risky proposition. During the course of the X-37 effort, the team has experienced many challenging opportunities, delivering significant accomplishments and learning numerous lessons in the process. The ability to manage the risk landscape is key to overcoming obstacles, especially technical hurdles that are encountered in progressing hardware from design to flight. The approach to managing risk under this partnership is evolving but, in general, the team allocates resources to reduce the likelihood of severe-consequence risks, thus maximizing mission success and ensuring that the X-37 Project delivers value to its stakeholders. As the team sharpens its focus on operations, it continues to contribute knowledge to those who would undertake high-risk, high-payoff research and development and provides valuable experience to implement the exploration vision.

Turner, Susan↗
DOE OSTI2023

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins

Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.

3D visualization↗
NASA NTRSDate not supplied

Developing a Cybersecurity Architecture for Extensible Traffic Management (xTM)

This paper explores the development of a cybersecurity architecture tailored for Extensible Traffic Management (xTM) to address emerging challenges in managing diverse aerial vehicles within the National Airspace System (NAS). Driven by technological advances and the rise of uncrewed aerial systems (UAS), urban air mobility (UAM), and high-altitude traffic (ETM), the NAS is undergoing a paradigm shift. Traditional air traffic management, reliant on traditional Federal Aviation Administration (FAA) control, will give way to decentralized coordination among autonomous and semi-autonomous systems. The proposed xTM Security Architecture, designed as a high-level framework, focuses on ensuring the confidentiality, integrity, and availability of data and operations in this evolving ecosystem. Utilizing threat modeling, the research identifies potential risks across key flight phases, operations and use cases to offer security control recommendations. Key objectives include analyzing interactions between novel airspace entrants and existing NAS traffic, cataloging vulnerabilities, and developing mitigative strategies to ensure safety, operational stability, and secure data exchanges. This research lays the groundwork for regulatory and industry adaptation, providing critical insights into managing cybersecurity risks in this complex, multi-domain environment.

UAM↗
NASA NTRS1976

Telemetry packetization for improved mission operations

The requirements for mission-operations data management will accelerate sharply when the Space Transportation System (i.e., Space Shuttle) becomes the primary vehicle for research from space. These demands can be satisfied most effectively by providing a higher-level source encoding function within the spaceborne vehicle. An Instrument Telemetry Packet (ITP) concept is described which represents an alternative to the conventional multiplexed telemetry frame approach for acquiring spaceborne instrument data. By providing excellent data-integrity protection at the source and a variable instrument bandwidth capability, this ITP concept represents a significant improvement over present data acquisition procedures. Realignments in the ground telemetry processing functions are described which are intended to take advantage of the ITP concept and to make the data management system more responsive to the scientific investigators.

Greene, E. P.↗
NASA NTRS2024

Assessment of Some IASMS-relevant Data Sources for Aviation Safety

An In-time Aviation Safety Management System (IASMS) [1,2] is a set of services, functions, and capabilities (SFCs) necessary for monitoring known hazards and emergent risks, assessing safety data for anomalies, precursors, and trends, mitigating hazards that reach safety thresholds, and assuring efficacy of controls in mitigating hazards. An IASMS will continually monitor the NAS to collect data on the status of aircraft, air traffic management systems, weather, and airports. Within the NASA Aeronautics Research Mission Directorate (ARMD) System-Wide Safety (SWS) project’s technical challenge called In-time Aviation Safety Management Systems (IASMS) for Commercial Aviation Operations, which we often refer to as Technical Challenge 6 (TC-6), we have performed an assessment of several aviation data sources we have found that are relevant to assessing the safety of the National Airspace System (NAS) in the context of an IASMS. This assessment includes understanding the nature of the data themselves and using some data analytics tools on these data to show how they can be used to identify potential safety issues. We also describe how the data and analytics are part of a system that can allow for other data and analytics to be performed and for the results to be visualized for use by appropriate operators to identify potential safety issues and develop mitigations. This report is a step toward the ultimate goal of TC-6, which is to develop a prototype IASMS system that demonstrates the potential of an IASMS and inspire operators to build analogous systems to make the best possible use of the significant investments that they make in collecting, storing, and managingdata related to their operations.

aviation safety↗
NASA NTRS1991

Environmental analysis using integrated GIS and remotely sensed data - Some research needs and priorities

This paper discusses some basic scientific issues and research needs in the joint processing of remotely sensed and GIS data for environmental analysis. Two general topics are treated in detail: (1) scale dependence of geographic data and the analysis of multiscale remotely sensed and GIS data, and (2) data transformations and information flow during data processing. The discussion of scale dependence focuses on the theory and applications of spatial autocorrelation, geostatistics, and fractals for characterizing and modeling spatial variation. Data transformations during processing are described within the larger framework of geographical analysis, encompassing sampling, cartography, remote sensing, and GIS. Development of better user interfaces between image processing, GIS, database management, and statistical software is needed to expedite research on these and other impediments to integrated analysis of remotely sensed and GIS data.

Davis, Frank W.↗
DOE OSTI2023

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a collection of objects stored within an RDate file, stateWaterRightsHarmonized.RData. Each object either describes how one should interact with the other objects, or contains the harmonized water rights data. Here is each object described in detail: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. The column headers for each data frame are: waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. basinNum - The alpha-numeric identifier of the WMA the record belongs to. priorityDate - The priority date associated with the right. allocatedFlow - The maximum flow of the allocation in cubic feet per second (ft3s-1). source - Whether the right is for surface water or groundwater. origWaterUse - The original stated water use from the state. waterUse - The water use category under the unified use categories established here. rightsByState_surface: A list of the cleaned surface water rights collected from each state. See rightsByState_ground for more details on how the data is formatted. fullRightsRecs: A list of the combined groundwater and surface water records for each state. Essentially, these lists are the merging of rightsByState_ground and rightsByState_surface by state. See rightsByState_ground for more details on how the data is formatted. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page.

Economics↗
DOE OSTI2024

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a series of various files organized by state sub-directories. The first two characters of each file name is the abbreviation for the state the in which the file contains data for. After the abbreviation is the text which describes the contents of the file. Here is each file type described in detail: XXFullHarmonizedRights.csv: A file of the combined groundwater and surface water records for each state. Essentially, this file is the merging of XXGroundwaterHarmonizedRights.csv and XXSurfaceWaterHarmonizedRights.csv by state. The column headers for each of this type of file are: state - The name of the state the data comes from. FIPS - The two digit numeric state ID code. waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. priorityDate - The priority date associated with the right. origWaterUse - The original stated water use from the state. waterUse - The water use category under the unified use categories established here. source - Whether the right is for surface water or groundwater. basinNum - The alpha-numeric identifier of the WMA the record belongs to. CFS - The maximum flow of the allocation in cubic feet per second (ft3s-1). Arizona is unique among the states, as its surface and groundwater resources are managed with two different sets of boundaries. So, for Arizona, the basinNum column is missing and instead there are two columns: surBasinNum - The alpha-numeric identifier of the surface water WMA the record belongs to. grdBasinNum - The alpha-numeric identifier of the groundwater WMA the record belongs to. XXStatePOD.shp: A shapefile which identifies the location of the Points of Diversion for the state's water rights. It should be noted that not all water right records in XXFullHarmonizedRights.csv have coordinates, and therefore may be missing from this file. XXStatePOU.shp: A shapefile which contains the area(s) in which each water right is claimed to be used. Currently, only Idaho and Washington provided valid data to included within this file. XXGroundwaterHarmonizedRights.csv: A file which contains only harmonized groundwater rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. XXSurfaceWaterHarmonizedRights.csv: A file which contains only harmonized surface water rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. Additionally, one file, stateWMALabels.csv, is not stored within a sub-directory. While we have referred to the spatial boundaries that each state uses to manage its water resources as WMAs, this term is not shared across all states. This file lists the proper name for each boundary set, by state.

Economics↗
DOE OSTI2024

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a series of various files organized by state sub-directories. The first two characters of each file name is the abbreviation for the state the in which the file contains data for. After the abbreviation is the text which describes the contents of the file. Here is each file type described in detail: XXFullHarmonizedRights.csv: A file of the combined groundwater and surface water records for each state. Essentially, this file is the merging of XXGroundwaterHarmonizedRights.csv and XXSurfaceWaterHarmonizedRights.csv by state. The column headers for each of this type of file are: state - The name of the state the data comes from. FIPS - The two-digit numeric state ID code. waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. priorityDate - The priority date associated with the right. origWaterUse - The original stated water use(s) from the state. waterUse - The water use category under the unified use categories established here. source - Whether the right is for surface water or groundwater. basinNum - The alpha-numeric identifier of the WMA the record belongs to. CFS - The maximum flow of the allocation in cubic feet per second (ft3s-1). Arizona is unique among the states, as its surface and groundwater resources are managed with two different sets of boundaries. So, for Arizona, the basinNum column is missing and instead there are two columns: surBasinNum - The alpha-numeric identifier of the surface water WMA the record belongs to. grdBasinNum - The alpha-numeric identifier of the groundwater WMA the record belongs to. XXStatePOD.shp: A shapefile which identifies the location of the Points of Diversion for the state's water rights. It should be noted that not all water right records in XXFullHarmonizedRights.csv have coordinates, and therefore may be missing from this file. XXStatePOU.shp: A shapefile which contains the area(s) in which each water right is claimed to be used. Currently, only Idaho and Washington provided valid data to include within this file. XXGroundwaterHarmonizedRights.csv: A file which contains only harmonized groundwater rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. XXSurfaceWaterHarmonizedRights.csv: A file which contains only harmonized surface water rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. Additionally, one file, stateWMALabels.csv, is not stored within a sub-directory. While we have referred to the spatial boundaries that each state uses to manage its water resources as WMAs, this term is not shared across all states. This file lists the proper name for each boundary set, by state. For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. This object holds the the data that is exported to created the xxGroundwaterHarmonizedRights.csv files. rightsByState_surface: A list of data frames with the cleaned surface water rights collected from each state. This object holds the the data that is exported to created the xxSurfaceWaterHarmonizedRights.csv files. fullRightsRecs: A list of the combined groundwater and surface water records for each state. This object holds the the data that is exported to created the xxFullHarmonizedRights.csv files. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page. The code related to the creation of this dataset can be viewed within HarDWR GitHub Repository/dataHarmonization.

Economics↗
NASA NTRS2022

San Diego Water Resources: Monitoring Pollution Plumes due to Storm and Wastewater Runoff in the San Diego Bay and Tijuana River Estuary to Inform Water Quality Management

Stormwater and wastewater runoff are a large source of pollutant discharge along the southern California coast and are a major concern to the health of local communities and ecosystems. In partnership with the Tijuana River National Estuarine Research Reserve and the California Department of Environmental Quality, NASA DEVELOP utilized satellite imagery to visualize and analyze the water quality of the Tijuana Estuary and southern California coast after major storm and wastewater events. Using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI), we estimated the extent and severity of plumes released from the Tijuana River Estuary. We used remotely sensed turbidity to map the extent of plumes, and used remotely sensed turbidity, Chlorophyll-a (chl-a), and colored dissolved organic matter (CDOM) to quantify and visualize stormwater, wastewater, and mixed plumes from 2013 to 2022. Furthermore, remotely sensed CDOM, turbidity, and chl-a were validated with in-situ data from NOAA and the San Diego Public Utilities in the San Diego coastal area to evaluate the accuracy of water quality data derived from satellite imagery. End products of this project include maps of stormwater, wastewater, and mixed plumes, tables illustrating the average area, CDOM, turbidity, and chl-a of each plume type, and validation graphs between satellite and in-situ data sources. These end products informed the environmental management of the Tijuana River National Estuarine Research Reserve and the public beaches in San Diego.

Ethan Gates↗
NASA NTRS2009

A Corrosion Risk Assessment Model for Underground Piping

The Pressure Systems Manager at NASA Ames Research Center (ARC) has embarked on a project to collect data and develop risk assessment models to support risk-informed decision making regarding future inspections of underground pipes at ARC. This paper shows progress in one area of this project - a corrosion risk assessment model for the underground high-pressure air distribution piping system at ARC. It consists of a Corrosion Model of pipe-segments, a Pipe Wrap Protection Model; and a Pipe Stress Model for a pipe segment. A Monte Carlo simulation of the combined models provides a distribution of the failure probabilities. Sensitivity study results show that the model uncertainty, or lack of knowledge, is the dominant contributor to the calculated unreliability of the underground piping system. As a result, the Pressure Systems Manager may consider investing resources specifically focused on reducing these uncertainties. Future work includes completing the data collection effort for the existing ground based pressure systems and applying the risk models to risk-based inspection strategies of the underground pipes at ARC.

Datta, Koushik↗
NASA NTRS2011

ISS Operations Cost Reductions Through Automation of Real-Time Planning Tasks

In 2008 the Johnson Space Center s Mission Operations Directorate (MOD) management team challenged their organization to find ways to reduce the costs of International Space station (ISS) console operations in the Mission Control Center (MCC). Each MOD organization was asked to identify projects that would help them attain a goal of a 30% reduction in operating costs by 2012. The MOD Operations and Planning organization responded to this challenge by launching several software automation projects that would allow them to greatly improve ISS console operations and reduce staffing and operating costs. These projects to date have allowed the MOD Operations organization to remove one full time (7 x 24 x 365) ISS console position in 2010; with the plan of eliminating two full time ISS console support positions by 2012. This will account for an overall 10 EP reduction in staffing for the Operations and Planning organization. These automation projects focused on utilizing software to automate many administrative and often repetitive tasks involved with processing ISS planning and daily operations information. This information was exchanged between the ground flight control teams in Houston and around the globe, as well as with the ISS astronaut crew. These tasks ranged from managing mission plan changes from around the globe, to uploading and downloading information to and from the ISS crew, to even more complex tasks that required multiple decision points to process the data, track approvals and deliver it to the correct recipient across network and security boundaries. The software solutions leveraged several different technologies including customized web applications and implementation of industry standard web services architecture between several planning tools; as well as a engaging a previously research level technology (TRL 2-3) developed by Ames Research Center (ARC) that utilized an intelligent agent based system to manage and automate file traffic flow, archiving f data, and generating console logs. This technology called OCAMS (OCA (Orbital Communication System) Management System), is now considered TRL level 9 and is in daily use in the Mission Control Center in support of ISS operations. These solutions have not only allowed for improved efficiency on console; but since many of the previously manual data transfers are now automated, many of the human error prone steps have been removed, and the quality of the planning products has improved tremendously. This has also allowed our Planning Flight Controllers more time to focus on the abstract areas of the job, (like the complexities of planning a mission for 6 international crew members with a global planning team), instead of being burdened with the administrative tasks that took significant time each console shift to process. The resulting automation solutions have allowed the Operations and Planning organization to realize significant cost savings for the ISS program through 2020 and many of these solutions could be a viable

Hall, Timothy A.↗
NASA NTRS2018

A Conceptual Enterprise Framework for Managing Scientific Data Stewardship

Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement. They often do not explicitly address the federal stewardship requirements and/or the uniqueness of geospatial data. This work proposes a data-centric conceptual enterprise framework for managing stewardship activities, based on the philosophy behind the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept. This framework, which includes the application of maturity assessment models, allows for quantitative evaluation of how organizations manage their stewardship activities and supports informed decision-making for continual improvement towards full compliance with federal, agency, and user requirements.

Scientific data stewardship↗
DOE OSTI2025

Adaptive anomaly detection for identifying attacks in cyber-physical systems: A systematic literature review

Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods, which focus on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising techniques to detect evolving cyberattacks, with an emphasis on fast data processing and model adaptation. AAD has been researched extensively; however, to the best of our knowledge, our work is the first systematic literature review (SLR) on current research in this field. We present a comprehensive SLR, gathering 397 relevant papers and systematically analyzing 65 of them (47 research and 18 survey papers) on AAD in CPS from 2013 to November 2023. We introduce a novel taxonomy considering attack types, CPS application, learning paradigm, data management, and algorithms. Our findings show that most studies addressed either model adaptation or data processing, but rarely both simultaneously. This indicates a research gap in fully adaptive solutions. We also categorize algorithms, datasets, and attack characteristics, and summarize strengths and weaknesses across the literature. Our review provides a structured and accessible reference for researchers and practitioners, offering insights into key trends and highlighting limitations in current approaches. Finally, we outline several future research directions, including the need for integrated real-time processing and adaptive learning, explainability, and uncertainty quantification in AAD for CPS.

Adaptation↗
NASA NTRS2019

Cognitive Communications for NASA Space Systems

The growing complexity of spacecraft constellations, communication relay offerings, and mission architectures drives the need for the development of autonomous communication systems. NASA has traditionally launched single spacecraft missions that are served by the Space Communication and Navigation (SCaN) program. Operations on SCaN networks are typically scheduled weeks in advance, and often each asset serves a single user spacecraft at a time. Recent movement towards swarm missions could make the current approach unsustainable. Additionally, the integration of commercial communication service providers will substantially increase the data transfer options available to new missions. NASA science missions have found benefit in launching swarms of spacecraft, allowing coordinated simultaneous observations from different perspectives. Inter-spacecraft communication (mesh networking) is an enabler for this architecture, as are CubeSats that allow cost-effective provisioning of distributed mission assets. As more complex swarm missions launch, one challenge is coordinating communication within the swarm and choosing the appropriate mechanism for telemetry, tracking, control, and data services to and from Earth. Cognitive communications research conducted by SCaN aims to mitigate the increasing communication complexity for mission users by increasing the autonomy of links, networks, and service scheduling. By considering automation techniques including recent advances in artificial intelligence and machine learning, cognitive algorithms and related approaches enable increased mission science return, improved resource utilization for service provider networks, and resiliency in unpredictable or unplanned environments. The Cognitive Communications Project at the NASA Glenn Research Center develops applications of data-driven, non-deterministic methods to improve the autonomy of space communication. The project emphasizes development of decentralized space networks with artificial intelligence agents optimizing communication link throughput, data routing, and system-wide asset management. This paper discusses the objectives, approaches, and opportunities of the research to address growing needs of the space communications community.

Chelmins, David↗
NASA NTRS1994

Budget estimates: Fiscal year 1994. Volume 3: Research and program management

The research and program management (R&PM) appropriation provides the salaries, other personnel and related costs, and travel support for NASA's civil service workforce. This FY 1994 budget funds costs associated with 23,623 full-time equivalent (FTE) work years. Budget estimates are provided for all NASA centers by categories such as space station and new technology investments, space flight programs, space science, life and microgravity sciences, advanced concepts and technology, center management and operations support, launch services, mission to planet earth, tracking and data programs, aeronautical research and technology, and safety, reliability, and quality assurance.

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