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NASA NTRSDate not supplied

A Novel Framework for Multi-Path Data Fusion in Earth Observation and New Observing Strategies: Applications to Predicting Forest Canopy Height

Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.

Mark Moussa↗

NASA NTRSDate not supplied

Summary of Technical Interchange Meetings (TIMs) Designed to Enable Earth Independent Medical Operations (EIMO)

The Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program hosted a series of TIMs in 2023-2024 designed to stimulate discussion around specific topics with the goal of enabling EIMO. In context of the thematic constituent elements of EIMO, namely pre-mission planning, acute/emergent/prolonged medical decision making, supply/resource management and task load management, subject matter experts from industry, academia and government (NASA and other Agencies) provided valuable and actionable guidance and recommendations. Earth-based medical experts will remain indispensable for pre-mission planning, however, management of acute/emergent medical contingencies will require a gradual transition of medical care and decision making from terrestrial to space-based assets to enable support of astronaut health and performance and reduce overall mission risk. Key to achieving these enhancements is providing an integrated data system platform capable of utilizing multiple data streams in concert with a variety of on-board databases and passive monitoring of video and wearable sensors to enable a multi-modal, agentic AI-based clinical decision support system (CDSS) to support crew medical officer (CMO) medical decision-making. The EIMO series of TIMs (I-V) have proven to be instructive and portend a significant paradigm shift will be necessary to maintain crew health and performance on exploration class missions. Importantly, since the expected paradigm shift will be significantly different from the methods of operation that have been employed for the majority of missions from the inception of human spaceflight to date, any proposed methods must be deployed in the setting of ongoing operations early and be “tested, reviewed and practiced” while reliable back-up is available to facilitate an Enterprise-wide level of comfort and acceptance. Serious constraints on data transmission coupled with a large and expanding universe of on-board medical informatics data streams will necessitate implementation of a CDSS to supplant the current reliance on support provided by ground-based SMEs. Establishment of trust in the system by CMO/crew and the ground-based medical support team will be essential. Co-development of a CDSS with industry partners will assure that state of the art tools can be employed, and industry efficiencies can be leveraged. Training regimens, materials and tools must evolve to be responsive (just-in-time training) and facilitate autonomous execution of procedures. Proficiency metrics should be established and be based on validated competencies or milestones as opposed to a prescribed number of training hours. Training should be prioritized for broad, translatable skills that have universal application across a variety of medical conditions. Repetition was deemed to be the key to achieving proficiency and emphasis should lie in procedural training which is known to extinguish more rapidly than diagnostic skills. Advanced tools, e.g., extended reality, can provide more realistic and effective training. Use of advanced probabilistic risk assessment tools will be essential to optimize the medical system capability while carefully balancing risk relative to mass/power/volume limitations. Importance of factoring use-life of medical supplies and maintaining awareness of redundancy and opportunity to re-purpose under off nominal situations was emphasized. Consideration of adopting optimized performance standards vs. “good-enough” performance thresholds is warranted. The use of legacy systems as opposed to creating new systems may be preferable. Managing task load and associated cognitive load will be essential to maintain operational safety and behavioral health. ExMC aspires to create a shared EIMO paradigm and strategic vision for advancing medical system design through novel technologies, training, protocols, and support capabilities, built upon the spirit of successful strategies and innovations over the past six decades of space medicine operations.

Jay Lemery↗

NASA NTRSDate not supplied

Increasing Data Discovery and Re-Use: The Space Life Sciences Ontology

Two of the most important goals of the adoption of the FAIR principles are increasing the ability of agents to find and re-use research data. Achieving these goals for space life sciences research is even more pressing, given the relatively expensive and scarce nature of these data. We have reported in the past on the progress made by exemplar life sciences data systems towards implementing FAIR, showing gaps particularly in the “interoperability area” of the principles; the lack of common conceptual models for space life science research is one reason for this gap. There were few available resources that define, annotate, categorize or otherwise relate various kinds of metadata describing the acquisition, nature, and intent of investigational space life sciences data. To address this gap, NASA is working with the Open Biological and Biomedical Ontology Foundry (https://obofoundry.org/) to develop the Space Life Science Ontology (SLSO) that is intended to support archival and other kinds of systems that operate using these data. The scope of the ontology includes concepts regarding those aspects of investigation design and execution specific or unique to space environments, such as types of specialized equipment, operating organizations, and documentation. The ontology is continually being developed and published to the life science community (https://github.com/nasa/LSDAO/); at the time of this publication, the SLSO newly and uniquely defines 30 types (classes), 90 properties, and 14 relations specific to space life sciences metadata. In addition, the SLSO reuses (imports) some 2,360 types (classes), 49 properties, and 393 relations from other ontologies that are relevant to these kinds of metadata. In addition to its role as a common conceptualization for space biomedical research activities, the SLSO can also be used to provide automated support for traditionally difficult and expensive activities such as data curation and cross-system data integration and analysis.

fair↗

NASA NTRSDate not supplied

Improving Satellite-Based Hotspot Detection Through Deep Learning-Enabled Smoke Recognition

While geostationary satellites, such as the GOES-R series, provide wildland fire hotspot readings at a high temporal resolution, they are prone to false negative readings and decreased confidence. One cause of decreased hotspot confidence is cloud contamination. Smoke produced from wildfire is often misinterpreted as cloud contamination, resulting in inaccurate and unsure sensor readings. To this end, we built a deep learning image segmentation model to identify smoke and cloud in true color satellite images. The model is pre-trained using self-supervised learning on over 10,000 GOES-R images to learn the underlying structure of satellite imagery. Then, the model is fine-tuned on a set of 130 labeled documents using supervised learning. The resulting model performs multi-class image segmentation with 85% accuracy and runs in under a minute on a standard personal computer. When paired alongside hotspot data, the model’s outputs can help increase confidence in wildfire location by identifying cases of cloud contamination that are due to smoke. The resulting model can be deployed in a stand-alone application or bundled in an Open Data Integration for wildland fire management (ODIN) application.

Earth observation↗

DOE OSTI2011

CHALLENGES IN DATA INTENSIVE ANALYSIS AT SCIENTIFIC EXPERIMENTAL USER FACILITIES

This chapter will discuss the critical data intensive analysis and visualiza-tion challenges faced by the experimental science community at large scale and laboratory based facilities. The chapter will further highlight initial solutions under development through community efforts and lay out perspectives for the future, such as the potential of more closely linked experimental and computational science approaches, methods to achieve real time analysis capabilities and the challenges and opportunities of data integration across experimental scales, levels of theory and varying techniques.

Chemical Imaging↗

DOE OSTI2013

A Mathematical Framework for the Analysis of Cyber-Resilient Control Systems

The increasingly recognized vulnerability of industrial control systems to cyber-attacks has inspired a considerable amount of research into techniques for cyber-resilient control systems. The majority of this effort involves the application of well known information security (IT) techniques to control system networks. While these efforts are important to protect the control systems that operate critical infrastructure, they are never perfectly effective. Little research has focused on the design of closed-loop dynamics that are resilient to cyber-attack. The majority of control system protection measures are concerned with how to prevent unauthorized access and protect data integrity. We believe that the ability to analyze how an attacker can effect the closed loop dynamics of a control system configuration once they have access is just as important to the overall security of a control system. To begin to analyze this problem, consistent mathematical definitions of concepts within resilient control need to be established so that a mathematical analysis of the vulnerabilities and resiliencies of a particular control system design methodology and configuration can be made. In this paper, we propose rigorous definitions for state awareness, operational normalcy, and resiliency as they relate to control systems. We will also discuss some mathematical consequences that arise from the proposed definitions. The goal is to begin to develop a mathematical framework and testable conditions for resiliency that can be used to build a sound theoretical foundation for resilient control research.

Control Theory↗

DOE OSTI2013

Beyond climate-smart agriculture: toward safe operating spaces for global food systems

Agriculture is considered to be climate-smart when it contributes to increasing food security, adaptation and mitigation in a sustainable way. This new concept now dominates current discussions in agricultural development because of its capacity to unite the agendas of the agriculture, development and climate change communities under one brand. In this opinion piece authored by scientists from a variety of international agricultural and climate research communities, we argue that the concept needs to be evaluated critically because the relationship between the three dimensions is poorly understood, such that practically any improved agricultural practice can be considered climate-smart. This lack of clarity may have contributed to the broad appeal of the concept. From the understanding that we must hold ourselves accountable to demonstrably better meet human needs in the short and long term within foreseeable local and planetary limits, we develop a conceptualization of climate-smart agriculture as agriculture that can be shown to bring us closer to safe operating spaces for agricultural and food systems across spatial and temporal scales. Improvements in the management of agricultural systems that bring us significantly closer to safe operating spaces will require transformations in governance and use of our natural resources, underpinned by enabling political, social and economic conditions beyond incremental changes. Establishing scientifically credible indicators and metrics of long-term safe operating spaces in the context of a changing climate and growing social-ecological challenges is critical to creating the societal demand and political will required to motivate deep transformations. Answering questions on how the needed transformational change can be achieved will require actively setting and testing hypotheses to refine and characterize our concepts of safer spaces for social-ecological systems across scales. This effort will demand prioritizing key areas of innovation, such as (1) improved adaptive management and governance of social-ecological systems; (2) development of meaningful and relevant integrated indicators of social-ecological systems; (3) gathering of quality integrated data, information, knowledge and analytical tools for improved models and scenarios in time frames and at scales relevant for decision-making; and (4) establishment of legitimate and empowered science policy dialogues on local to international scales to facilitate decision making informed by metrics and indicators of safe operating spaces.

Gulledge, Jay↗

DOE OSTI2011

Diagnostics and Prognostics Tools for Assessing Remaining Useful Life of Nuclear Power Plant Materials

In recent years, there has been renewed interest in expanding the use of nuclear power to provide sustainable, carbon-free energy. As part of these activities in the USA, there are major initiatives focused on "life extension" for existing light-water nuclear power reactors (LWR) from 60 to 80 (or 100) years. To enable longer term operation, a range of advanced diagnostics and prognostics methods that are suitable for on-line, continuous, in-plant monitoring over extended time periods (months to years) are necessary. A central issue in life extension for the current fleet of LWRs is the early detection and monitoring of materials degradation. Material aging and degradation due to stresses and irradiation is a critical element in assessing potential for the failure of components in legacy nuclear power plants. A related issue is the ability to estimate remaining useful life (RUL) of components and systems based on condition assessment or degradation information. Detection of early stage damage in materials and assessment of remaining life is important in proactive or prognostic-based life management of legacy nuclear power plants. These approaches go beyond what is currently included in "condition-based maintenance," this strategy can potentially improve safety and reduce costs by detecting damage and scheduling appropriate maintenance/mitigation strategies early in the component lifecycle. For early detection of degradation, novel nondestructive (i.e., without destroying the utility of the specimen) tests that are suitable for continuous monitoring over extended time periods are needed, as are new techniques for data integration. The challenge of predicting remaining life starting from earlier phases of degradation is also largely unsolved and will require new prognostics tools. This paper will discuss the development and application of advanced diagnostics and prognostics tools to the life extension problem. The focus of these activities will be on ferritic and stainless steels, and degradation mechanisms common to an LWR operating environment in these materials. The use of micromagnetic measurements to assess early stages of degradation will be discussed. The measurements will be used, along with models of degradation accumulation over time, to assess the remaining useful life using a Bayesian prognostic algorithm. The primary advantage of a Bayesian approach is the ability to incorporate a priori information to constrain the solution of the problem and mitigate uncertainties in the measurements. Further, a Bayesian approach enables the incorporation of additional information as it becomes available, which can be used to update, and refine, the RUL estimates. Results of the proposed techniques on experimentally acquired measurements will be presented in the full paper.

diagnostics↗

DOE OSTI2014

Overview of the 2014 Edition of the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhEP Handbook)

The International Reactor Physics Experiment Evaluation Project (IRPhEP) is a widely recognized world class program. The work of the IRPhEP is documented in the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhEP Handbook). Integral data from the IRPhEP Handbook is used by reactor safety and design, nuclear data, criticality safety, and analytical methods development specialists, worldwide, to perform necessary validations of their calculational techniques. The IRPhEP Handbook is among the most frequently quoted reference in the nuclear industry and is expected to be a valuable resource for future decades.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Sirocco Storage Server v. pre-alpha 0.1

Sirocco is a parallel storage system under development, designed for write-intensive workloads on large-scale HPC platforms. It implements a keyvalue object store on top of a set of loosely federated storage servers that cooperate to ensure data integrity and performance. It includes support for a range of different types of storage transactions. This software release constitutes a conformant storage server, along with the client-side libraries to access the storage over a network.

Curry, Matthew L.↗

DOE OSTI2018

Deployable sensor system using mesh networking and satellite communication

A sensor system may be configured for continuous operation in a low resource environment and/or in extreme environmental conditions. The sensor system may have sufficient processing capabilities to provide scientific computing for pre-processing, quality control, statistical analysis, event classification, data compression and corrections (e.g., spikes in the data), autonomous decisions and actions, triggering other nodes, and information assurance functions that provide data confidentiality, data integrity, authentication, and non-repudiation. The hardware may have both mesh networking and satellite and cellular communication capability, and may be available for relatively low cost. Such a network provides the flexibility to have potentially any number of nodes be completely independent from one another. Thus, the network may scale across a diverse terrain.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Knowledge catalysts

A computer implemented method integrates data from remote disparate data sources by processing a non-transitory media. The non-transitory media stores instructions for detecting data sets in different formats hosted in a plurality of heterogeneous databases that are accessible through a distributed network. The method extracts schema data from the plurality of heterogeneous databases and identifies related fields in two or more of the heterogeneous databases. The method links the related fields in the two or more of the plurality of heterogeneous databases and makes the data accessible through a virtual warehouse. As schemas change, as new data sources and analysis artifacts are created, the computer implemented method and system can act as a meta-data store, a provenance tracking device, and/or a knowledge management service.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Conceptual Design Report for the I2 Instrumentation Experiment in ATRC

The US Department of Energy In-Pile Instrumentation (I2) program is tasked with developing advanced sensors for in-pile service as well as supporting the engineering required to implement existing nuclear sensor technologies in US high-power test reactors. The ATRC instrumentation test has been conceived to address both of these missions, specifically with respect to neutron flux measurements. Space has been allocated in the test for Self-Powered Neutron Detectors (SPNDs), fission chambers, and Micro-Pocket Fission Detectors (MPFDs). The real-time flux readings obtained from these instruments will be compared with time integrated data obtained from passive neutron dosimetry (i.e., “flux” wires). This work will be complementary to data obtained from the TREAT MIMIC-N experiment. This experiment is expected to support instrumentation efforts related to the proposed Medium-I loop and to instrumenting loop experiments conducted in the ATR center flux trap. While the MIMIC-N experiment was conducted in a dry environment, the ATRC I-19 experiment will be conducted in an aqueous environment, similar to the center flux trap loop experiments but will take place at ambient temperature and pressure, and at a much lower neutron flux. One of the challenges of the ATRC I-19 experiment will be low amplitude signals produced by the sensors in the low neutron fluxes of ATRC. However, related work conducted several years ago in ATRC was able to reliably detect signals using a similar instrument set.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Systematic Analyzer Of Numeric Data

This system is meant to take large amounts of digital, automatically gathered data, and then organize it into a form that shows data integrity and plot, graph.

Lehmer, Jacob P.↗

DOE OSTI2020

NDMAS System and Process Description

The U. S. Department of Energy (DOE) has made a significant investment in research to develop the next generation of reactor technologies as well as to improve the performance and lengthen the life cycle of existing nuclear reactors. Data collected to demonstrate new concepts may also be used in the future to support licensing of these technologies. Provenance of these data must be preserved. The Nuclear Data Management and Analysis System (NDMAS) was established to manage and preserve data collected by fuels and materials research conducted by the high-temperature, gas-cooled reactor program. The scope of NDMAS is expanding to include other nuclear research programs that have the shared need to preserve the provenance of research data. Nuclear research funded by DOE is conducted by Idaho National Laboratory (INL), universities, other national laboratories, foreign research partners, and private companies. This research will generate a large amount of data from a variety of sources over a period of many years. Managing the data generated by the research and development projects presents a significant challenge for retaining data integrity and availability.

+↗

DOE OSTI2020

Land Model Testbed: Accelerating Development, Benchmarking and Analysis of Land Surface Models

A Land Model Testbed (LMT), designed to provide a computational framework for systematically assessing model fidelity and supporting rapid development of complex multiscale models, offers a general-purpose workflow for conducting large ensemble simulations of multiple land surface models, post-processing large volumes of model output, and evaluating model results. It leverages existing tools for launching model simulations and the International Land Model Benchmarking (ILAMB) package for assessing model fidelity through comparison with best-available observational datasets. Increased complexity and proliferation of uncertain parameters in process representations in land surface models has driven the need for frequent and intensive testing and evaluating of models to quantify uncertainties and optimize parameters such that results are consistent with observations. The LMT described here meets these needs by providing tools to run thousands of ensemble simulations simultaneously and post-process their output files, by automating execution of an enhanced version of ILAMB with site-specific benchmarks and multivariate functional relationships, and by offering ensemble diagnostics and a customizable dashboard for displaying model performance metrics and associated graphics. We envision the LMT capabilities will serve as a foundational computational resource for a proposed user facility focused on terrestrial multiscale model--data integration.

Sreepathi, Sarat↗

DOE OSTI2020

Deployable sensor system using mesh networking and satellite communication

Frigo, Janette↗

DOE OSTI2018

Experiment Design with Confidence: CoMET

The Nuclear Science User Facilities (NSUF) has been facilitating nuclear materials research since 2007 by providing access to national laboratory and other partner capabilities at no cost to the researchers. With funding from the Department of Energy – Office of Nuclear Energy, approximately 400 projects have been awarded to support research into radiation effects on fuels and materials. During these ten years, the NSUF has created tools to help researchers write better proposals and perform better research projects. In 2018, several of those tools have been combined to create a single point of access to NSUF information management resources. The Combined Materials Experiment Toolkit or CoMET, combines the Nuclear Energy Infrastructure Database (NEID), the Nuclear Fuels and Materials Library (NFML), and the NSUF proposal system, with a new subject matter expert database and an experiment design tool. The NEID contains information on 500 nuclear R&D facilities around the world, including 85 NSUF partner facilities. The NFML has information on nearly 10,000 material specimens, mostly irradiated materials, which can be used for research in NSUF projects. The new SME database includes contact information for NSUF researchers and scientists. The experiment design tool is an expert system that can guide a researcher through the process of designing an NSUF project by interacting with the other resources. Once a researcher has worked through this process, they will be able to save their experiment plan and input it into the NSUF proposal system. These tools are intended to aid both new and veteran researchers build solid research projects and take full advantage of NSUF resources. They also help the NSUF administer the hundreds of proposals that it receives each year. In the future, NSUF plans to add integrated data storage and retrieval to CoMET.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗