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All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗

NASA NTRS2003

Accelerating Large Data Analysis By Exploiting Regularities

We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical to Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid-body transformation of another. Our algorithms can also discover rigid-body motion of meshes in time-series data. Next, we describe a data model where we can utilize the results from the discovery process in order to accelerate large data visualizations. Where possible, we replace general curvilinear zones with rectilinear or cylindrical zones. In rigid-body motion cases we replace a time-series of meshes with a transformed mesh object where a reference mesh is dynamically transformed based on a given time value in order to satisfy geometry requests, on demand. The data model enables us to make these substitutions and dynamic transformations transparently with respect to the visualization algorithms. We present results with large data sets where we combine our mesh replacement and transformation techniques with out-of-core paging in order to achieve significant speed-ups in analysis.

Moran, Patrick J.↗

DOE OSTI2014

Automated management of radioactive sources in Saudi Arabia

For usage of radioactive substances, any facility has to register and take license from relevant authority of the country in which such facility is operating. In the Kingdom of Saudi Arabia (KSA), the authority for managing radioactive sources and providing licenses to organizations for its usage is the National Center of Radiation Protection (NCRP). This paper describes the system that automates registration and licensing process of the National Center of Radiation Protection. To provide 24×7 accesses to all the customers of NCRP, system is developed as web-based application that provide facility to online register, request license, renew license, check request status, view historical data and reports etc. and other features are provided as Electronic Services that would be accessible to users via internet. The system also was designed to streamline and optimize internal operations of NCRP besides providing ease of access to its customers by implementing a defined workflow through which every registration and license request will be routed. In addition to manual payment option, the system would also be integrated with SADAD (online payment system) that will avoid lengthy and cumbersome procedures associated with manual payment mechanism. Using SADAD payment option license fee could be paid through internet/ATM machine or branch of any designated bank, Payment will be instantly notified to NCRP hence delay in funds transfer and verification of invoice could be avoided, SADAD integration is discussed later in the document.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2022

Access of named data elements in coordination namespace

An approach is described that provides access to a named data element in a Coordination Namespace that is stored in a memory that is distributed amongst a set of nodes. A request of a name corresponding to the named data element is received from a requesting process and the approach responsively searches for the name in the Coordination Namespace. In response to determining an absence of data corresponding to the named data element, a pending state is indicated to the requesting process. In response to determining that the data corresponding to the named data element exists, a successful state is returned to the requesting process. In one embodiment, the successful state also includes providing the requesting process with access to the data corresponding to the named data element.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1972

The ATS F&G systems reliability program.

Assurance of reliability, quality, and proper testing requires a large coordinating effort and a means for connecting the various areas involved. All parts used on the spacecraft are required to meet strict specifications and consequently must be approved by the systems reliability manager. The parts program has access to a computer data bank into which all information concerning nonstandard parts approval requests has been stored. Through the data bank, the system collects and distributes timely information concerning quality and reliability to all departments that may be concerned.

Doyle, H.↗

DOE OSTI2018

Time-division multiplexing data bus

A two-to-one multiplexor comprises a first data input configured to hold data provided from a first preceding asynchronous pipeline stage and a second data input configured to hold data provided from a second preceding asynchronous pipeline stage, a 4-phase bundled data protocol facilitating communication between the first and the second data inputs and the first and second preceding asynchronous pipeline stage, an arbitration unit connected to the first data input and the second data input, and configured to select which of the data from the first and the second data inputs is released, a request release unit, and a reset unit wherein the arbitration unit, the request release unit, and the reset unit implement and complete a second 4-phase bundled data protocol facilitating communication with a succeeding asynchronous pipeline stage for transmission of the data chosen by the arbitration unit thereby providing asynchronous multiplexing of the data.

Hoff, James R.↗

DOE OSTI2021

Data shuffling with hierarchical tuple spaces

Methods and systems for shuffling data are described. A processor may generate pair data from source data. The processor may insert the pair data into local tuple spaces. In response to a request for a particular key, the processor may determine a presence of the requested key in a global tuple space. The processor may, in response to a presence of the requested key in the global tuple space, update the global tuple space. The update may be based on the pair data among the local tuple spaces including the existing key. The processor may, in response to an absence of the requested key in the global tuple space, insert pair data including the missing key from the local tuple spaces into the global tuple space. The processor may fetch the requested pair data, and may shuffle the fetched data to generate a dataset.

Andrade Costa, Carlos Henrique↗

DOE OSTI2025

University Data Management Pilot Utilizing the Nuclear Research Data System

Background In 2022, the Office of Science and Technology Policy (OSTP) issued a memo that significantly reshaped the landscape of access to federally funded research. The memo mandated that all taxpayer-funded research be made available to the public without delay upon publication, without an embargo period, superseding the 2013 OSTP public access policy. This public access policy promotes transparency and the democratization of knowledge, ensuring that the fruits of scientific endeavors funded by federal agencies could be immediately accessed and built upon by scientists, educators, students, and the public at large. To implement the requirements of the OSTP guidance and DOE Public Access Plan, the Office of Nuclear Energy (NE) has implemented public access plan guidance and has identified several areas where better data management practices would further expand public access to important nuclear energy related scientific data, reports, and other technical products. Significant NE supported efforts are already underway for data management and public access to important nuclear energy related data.1 2 To address gaps in data management practices, and improve retention and accessibility of data, NE is actively exploring enhanced data management options utilizing its high-performance computing resources administered by its Nuclear Scientific User Facility Program. A newly piloted system, the Nuclear Research Data System (NRDS) acts as a portal for data collection and dissemination. Nuclear Energy University Program Research and Development Portfolio According to Web of Science, NEUP has produced 2,345 journal publication that have been cited more than 61,000 times3 and countless conference proceedings. These publications are publicly available through OSTI.gov and in the open literature. Additional scientific and technical products including project milestones that are not publications and NEUP project final reports are vetted through OSTI.gov and released once reviewed and approved by DOE. Since 2009, NEUP has awarded close to 1,000 different R&D projects in technical areas across the NE research programs. As of June 2023, 512 NEUP reports are publicly available on OSTI. The underlying data for projects is still held at universities, and data transfer, co-location, and dissemination has not occurred in a systematic way. NEUP data is currently accessible through myriad university-based data repositories, or through direct requests to PIs. The program identified this patchwork of repositories, or often lack of publicly available data, as a significant barrier to an organized, accessible, and comprehensive solution to sharing data with the larger nuclear energy community. Approach The goal of this pilot project is to establish a pathway to a consolidated long-term repository for NEUP project data. To accomplish this goal, the pilot strives to accomplish the following objectives: Establish data collection standards, including a standard set of required supplementary information to contextualize and support raw data files. Work with the HPC group collect and upload information and to modify the NRDS system, as needed, to support a standardized approach. Resolve potential barriers to successful roll out of an expanded data collection strategy, including modifying data management plan guidelines and establishing a document and data release process that accounts for potential intellectual property and/or export control concerns. Results Overall, the pilot was successful in collecting 8,982 raw and processes data files, 220 reports, 56 calibration files, and 5,931 other supplementary documents. Supplementary documents included experimental plans, methods, journal publications and conference proceedings, milestone reports, and final reports. Figure 2 shows the number of data sets and supplementary project information provided by each project. Projects has significantly different input, depending on experimental data produced and completeness of the datasets provided.

Data collection↗

NASA NTRS2006

Patterns in Crew-Initiated Photography of Earth from ISS - Is Earth Observation a Salutogenic Experience?

To provide for the well-being of crewmembers on future exploration missions, understanding how space station crewmembers handle the inherently stressful isolation and confinement during long-duration missions is important. A recent retrospective survey of previously flown astronauts found that the most commonly reported psychologically enriching aspects of spaceflight had to do with their Perceptions of Earth. Crewmembers onboard the International Space Station (ISS) photograph Earth through the station windows. Some of these photographs are in response to requests from scientists on the ground through the Crew Earth Observations (CEO) payload. Other photographs taken by crewmembers have not been in response to these formal requests. The automatically recorded data from the camera provides a dataset that can be used to test hypotheses about factors correlated with self-initiated crewmember photography. The present study used objective in-flight data to corroborate the previous questionnaire finding and to further investigate the nature of voluntary Earth-Observation activity. We examined the distribution of photographs with respect to time, crew, and subject matter. We also determined whether the frequency fluctuated in conjunction with major mission events such as vehicle dockings, and extra-vehicular activities (EVAs, or spacewalks), relative to the norm for the relevant crew. We also examined the influence of geographic and temporal patterns on frequency of Earth photography activities. We tested the hypotheses that there would be peak photography intensity over locations of personal interest, and on weekends. From December 2001 through October 2005 (Expeditions 4-11) crewmembers took 144,180 photographs of Earth with time and date automatically recorded by the camera. Of the time-stamped photographs, 84.5% were crew-initiated, and not in response to CEO requests. Preliminary analysis indicated some phasing in patterns of photography during the course of a mission (significant quadratic and trimodal models). There was also a small but significant increase in photo activity on the weekends. In contrast, fewer photos were taken during major station events and for a period of time immediately preceding those events. Data on photography patterns presented here represent a relatively objective group-level measure of Earth observing activities on ISS. Crew Earth Observations offers a self-initiated positive activity that may be important in salutogenesis (maintenance of well-being) of astronauts on long-duration missions. Consideration should be given to developing substitute activities for crewmembers in future exploration missions where there will not be the opportunity to look at Earth, such as on long-duration transits to Mars.

Robinson, Julie A.↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Biologically Relevant Space Radiation Data

RadLab, a new component of the NASA Open Science Data Repository (OSDR), is a platform built upon a database of radiation data relevant to space biology. RadLab provides visual and programmatic interfaces for interrogation of its database, as well as a submission process for inclusion of data from investigators. The RadLab application programming interface (API) implements a request syntax enabling users to retrieve data filtered by various combinations of parameters (detector type, location, direction, timespan, etc), which are delivered in machine-readable text formats, ready to be ingested by downstream analysis pipelines; while the graphical user interface (GUI) provides easy means to iteratively modify query parameters and incorporates a number of standard analyses and visualizations (time series plots, geospatial visualizations, detector comparison). Investigators from many countries, including US, Russia, Japan, Canada, the Czech Republic, Germany, Hungary, and Italy, have committed to provide data from their instruments located on the ISS; RadLab will also include data from other spacecraft in LEO (e.g., the Space Shuttle, the Mir space station), BLEO (e. g. BioSentinel, Mars Orbiter, among others), and on other celestial bodies (e. g. Chang’e 4, Curiosity). The first release of RadLab has been made available to the public. Once fully operational, RadLab will provide a comprehensive and ever-growing compendium of space radiation data, facilitating straightforward access to multiple types of readings and enabling space biology researchers to perform intercomparisons of detectors and to determine the radiation environment of research missions, both via programmatic retrieval of these data and via the graphical analysis toolkit; as well as a user-friendly submission portal for ingesting data from space agencies and research institutions. Radiation scientists will be able to use RadLab to gain a deeper understanding of the space radiation environment for future human space exploration. The RadLab Working Group has been formed to foster close collaborations among data contributors and users, to identify data sources, to put in place standards for data normalization, to guide the development of features of the analysis toolkit, to establish the use of RadLab in space radiation biology research, and eventually to provide a forum for discussing relevant research issues that can take advantage of RadLab's capabilities.

radiation↗

NASA NTRS1967

A user-oriented time-shared online system.

Computerized method for on-line data analysis, elucidating SLIP language, request, processing, interpretation, time sharing, background activity, etc

LANGUAGE PROGRAMMING↗

NASA NTRS2024

Appendix N. Pc Calculation Approaches – UPDATE 3/20/24

This document represents a technical content update to Appendix N of the NASA Spacecraft Conjunction Assessment and Collision Avoidance Best Practices Handbook (OCE-51, available on NODIS) requested by the Consultative Committee for Space Data Standards (CCSDS) to document the background on why NASA populates certain fields of the Conjunction Data Message (CDM) in certain ways. This update was requested to be used as reference material for the CCSDS CDM documentation update and so is being published as a white paper out of cycle from Handbook updates. When the full Handbook is next updated, these Appendix redlines may be incorporated into the official document. Two versions are attached - the redlines to the published Handbook for use in understanding what has changed and a clean copy.

conjunction assessment↗

DOE OSTI2021

Error-controlled, progressive, and adaptable retrieval of scientific data with multilevel decomposition

Extreme-scale simulations and high-resolution instruments have been generating an increasing amount of data, which poses significant challenges to not only data storage during the run, but also post-processing where data will be repeatedly retrieved and analyzed for a long period of time. The challenges in satisfying a wide range of post-hoc analysis needs while minimizing the I/O overhead caused by inappropriate and/or excessive data retrieval should never be left unmanaged. In this paper, we propose a data refactoring, compressing, and retrieval framework capable of 1) fine-grained data refactoring with regard to precision; 2) incrementally retrieving and recomposing the data in terms of various error bounds; and 3) adaptively retrieving data in multi-precision and multi-resolution with respect to different analysis. With the progressive data re-composition and the adaptable retrieval algorithms, our framework significantly reduces the amount of data retrieved when multiple incremental precision are requested and/or the downstream analysis time when coarse resolution is used. Experiments show that the amount of data retrieved under the same progressively requested error bound using our framework is 64% less than that using state-of-the-art single-error-bounded approaches. Parallel experiments with up to 1, 024 cores and ~ 600 GB data in total show that our approach yields 1.36× and 2.52× performance over existing approaches in writing to and reading from persistent storage systems, respectively.

Liang, Xin↗

NASA NTRS2015

Decision Making and Communications Process Assessment of NASA Using Three Change Requests from the Space Launch System Program

This thesis investigated the communication and decision making process as part of the Systems Engineering practices at the NASA/Marshall Center to determine its level of effectiveness. Data was collected across three change requests to assess how decisions were made, how the decisions were communicated, and whether a process mattered in the formulation and dissemination of those decisions. Data results revealed the comprehensive decision making process for the technical change requests to be effective. Evidence revealed that the process was sufficiently tailored to accommodate the need of each individual technical change which promoted effective communication amongst the stakeholders in the formulation of the strategic decision recommendations elevated to upper management. However, data results also revealed the dissemination of the final decision and approval of the change requests from the higher organizational level down to all stakeholders was less effective. An establishment of a culmination meeting at the end of the change request decision process in which to close the communication loop with all entities would be beneficial.

Hicks, Karen Campbell↗

DOE OSTI2015

Social Sensor Analytics: Making Sense of Network Models in Social Media

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013 and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.

event detection↗

DOE OSTI2019

Stitch v. 1.0 IO Library

Stitch is a new IO library focused around supporting simulations where only a small portion of the simulation space changes at each timestep. It improves storage efficiency by only storing the changed data and constructs on request a view of an arbitrary region at a given time by stitching together parts of various outputs. This enables scaling from a single process to 100s of nodes.

Lofstead, Gerald↗

DOE OSTI2019

Age-related changes in Miscanthus × giganteus phenology and the effect of nitrogen fertilization

Perennial plants go through noticeable morphological, developmental, and physiological changes as they age. Whether these age-related changes are driven by ontogenetic differences, are the result of different environmental conditions, or an artifact of plant size remains unknown. In this research we used the warm-season grass Miscanthus × giganteus as model herbaceous perennial species to study age-related changes on phenology. Age-related experiments are usually based on field experimental design that confound growing season and age effects and ignore variability generated during the establishment of the stand. Here, we used a staggered-start experimental design where stands are repeatedly planted over subsequent years providing the ability to separate plant age effects from environmental effects. We hypothesized (i) M. × giganteus would produce more plant structures and reach more advanced developmental stages in a given growing season as it aged. (ii) Juvenile stands would have faster developmental rates and start development sooner and (iii) Mature stands would start senescence sooner in the growing season. Finally, we studied nitrogen (N) fertilization effects on M. × giganteus phenology as to assess whether age-related changes could be an artifact of size and N dilution in mature bigger individuals. We hypothesized that (iv) fertilized juvenile stands would have similar dynamics as mature unfertilized stands. Data were collected bi-weekly on all established stands during 2016 and 2017. Ten stems were harvested from each plot and staged at the lab according to M. × giganteus morphological scale developed by Tejera & Heaton (2017). Time series progression were then analyzed using non-linear models and parameters were compared across stand ages, N treatments and growing seasons. R scripts used to analyze the data are available upon request.

Aging↗

DOE OSTI2019

GCD: Current Performance / Issues [Slides]

LANL purchased a man instrument (pulse dilation PMT) from the Sydor Instruments, Inc. The company requests to get experimental data obtained using the instrument, for their record.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗