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NASA NTRS2016

GC31G-1182: Opennex, a Private-Public Partnership in Support of the National Climate Assessment

The NASA Earth Exchange (NEX) is a collaborative computing platform that has been developed with the objective of bringing scientists together with the software tools, massive global datasets, and supercomputing resources necessary to accelerate research in Earth systems science and global change. NEX is funded as an enabling tool for sustaining the national climate assessment. Over the past five years, researchers have used the NEX platform and produced a number of data sets highly relevant to the National Climate Assessment. These include high-resolution climate projections using different downscaling techniques and trends in historical climate from satellite data. To enable a broader community in exploiting the above datasets, the NEX team partnered with public cloud providers to create the OpenNEX platform. OpenNEX provides ready access to NEX data holdings on a number of public cloud platforms along with pertinent analysis tools and workflows in the form of Machine Images and Docker Containers, lectures and tutorials by experts. We will showcase some of the applications of OpenNEX data and tools by the community on Amazon Web Services, Google Cloud and the NEX Sandbox.

datasets↗

NASA NTRS2023

NASA GRC ICME Schema for Materials Data Management: An Executive Summary

Integrated Computational Materials Engineering (ICME) has received a growing emphasis in attention due its potential impact on rapid material design, reduction in cost and time to market for new applications, and the promise of ‘fit-for-purpose’ materials coupled with recent advances in high performance computing and material characterization tools. However, for an organization to implement ICME practices for material discovery and design, a series of both technical and cultural challenges must be overcome to foster an environment that enables efficient, traceable, and predictive multiscale simulations of material behavior to enable virtual design of materials. In 2016, NASA sponsored a 2040 Vision study to define the potential 25-year future state required for integrated multiscale modeling of materials and systems to improve both the associated time and cost for aerospace and aeronautical innovation. The study envisions a cyber-physical-social ecosystem of experimentally validated computational models, tools, and techniques, along with the associated digital tapestry, that can enable rapid, optimized, ‘fit-for-purpose’ design of materials, components, and systems. A key requirement for such an ecosystem is the development of a robust information management system for materials across their full lifecycle, including material pedigree, experimental (real) and virtual (simulation) data, developed material models, and the implementation of models in engineering applications, such that process-structure-property-performance relationships can be established, thereby enabling the virtual design and optimization of materials. Such an information management system must be able to effectively capture: i) material information at each length scale; ii) test data and analysis; iii) associated material models; and iv) material and model deployment in engineering applications. These systems must also provide traceability between experimental and virtual representations of the material to ensure, when appropriate, the material digital twin is maintained. Additionally, this robust material information management system must be able to seamlessly connect with both commercial and an organization’s in-house software tools, be they analysis tools, other material databases, product lifecycle management (PLM) or simulation data management (SDM) tools, etc., such that automation of the design and analysis of a material across multiple length scales is possible. In this paper, an executive summary of the NASA GRC ICME Schema for materials information management is presented. The database best practices and schema design philosophy specifically for ICME materials data management and an overview description of each element in the schema is given, along with its associated role in an ICME workflow. Additionally, auxiliary tools that interact with the database and provide judicious automation with regards to importing, exporting, and analyzing materials data are presented. Such tools are critical to an ICME ecosystem, not only for their role in enabling optimization, but also in relieving users of tedious manual tasks, thus helping to promote adoption and combat the cultural challenges organizations face in enabling ICME.

Materials↗

NASA NTRSDate not supplied

Modeling the Worst-Case-Scenario of Soil Drydown for Agricultural Drought Monitoring in Kansas Using NASA Earth Observations and In-situ Parameters

Climatologists in Kansas have observed rapid shifts from wet to dry conditions and rapid intensification of drought conditions for the state in recent years. As a leading state in agricultural production, drought events can lead to decreased crop yields and impact the state’s economy. The exponential decay of soil moisture content is a major consequence of drought and can exacerbate these impacts on agricultural production. This study examined the rate of soil moisture drydown in Kansas to understand and monitor the worst-case-scenario of short-term soil water shortage. Soil Moisture Active Passive (SMAP) L-band Radiometer rootzone soil moisture imagery from March 2015 to June 2019 was used to estimate the minimum and maximum soil moisture capacity throughout the state. In situ soil moisture parameters, such as the exponential decay constant, were approximated from the Kansas Mesonet system. These variables were inputs into an exponential decay model that forecasted gridded rootzone soil moisture in a scenario that assumed no precipitation for a user-identified period of time. The forecasts were output on the order of weeks to capture the rapid shifts in soil moisture content. Another model was used to identify areas in Kansas currently below a given percentage of the relative soil moisture saturation, measured as a function of minimum, maximum, and current soil moisture. This model forecasted the number of days until each cell in the soil moisture grid reached the input percentage of relative soil moisture saturation. The gridded model outputs and workflow were developed in partnership with the Kansas Water Office and Kansas Office of the State Climatologist at Kansas State University, as a part of their drought monitoring efforts.

NASA DEVELOP↗

NASA NTRS2010

Polyphony: A Workflow Orchestration Framework for Cloud Computing

Cloud Computing has delivered unprecedented compute capacity to NASA missions at affordable rates. Missions like the Mars Exploration Rovers (MER) and Mars Science Lab (MSL) are enjoying the elasticity that enables them to leverage hundreds, if not thousands, or machines for short durations without making any hardware procurements. In this paper, we describe Polyphony, a resilient, scalable, and modular framework that efficiently leverages a large set of computing resources to perform parallel computations. Polyphony can employ resources on the cloud, excess capacity on local machines, as well as spare resources on the supercomputing center, and it enables these resources to work in concert to accomplish a common goal. Polyphony is resilient to node failures, even if they occur in the middle of a transaction. We will conclude with an evaluation of a production-ready application built on top of Polyphony to perform image-processing operations of images from around the solar system, including Mars, Saturn, and Titan.

Space Exploration,↗

NASA NTRS2019

2018 NISAR Applications Workshop: Agriculture and Soil Moisture

Agricultural lands cover the globe and play an essential role in not only sustaining a growing global population, but can have significant implications on the Earth system through land use change (e.g., deforestation, grazing, etc.). As such, countries around the world have dedicated programs for managing these lands. Accurate and timely information concerning the status of agricultural crops (soil moisture, crop health, crop type, etc.) is essential to those nations’ anthropogenic and ecological health as well as economy. The joint NASA/US Department of Agriculture Agricultural Research Service (USDA-ARS) workshop focused on advancing agriculture and soil moisture applications by using remote sensing data from the NASA-ISRO Synthetic Aperture Radar (NISAR) mission (expected launch 2022). Participants included representatives from the international agriculture community that are key players in facilitating integration of Earth Observations into decision support workflows including US Federal Agencies, nonprofits, and private sector. They included scientists, technicians, and program managers with a responsibility for data acquisition and exploitation such as product development, delivery, and use, as well as capacity building. Discussions were held over two and a half days to convey the broader agriculture and soil moisture community information needs, the mission and procedures for various representative participants and programs involved in the delivery of geospatial products, and the capabilities and status of the NISAR mission. Case studies were presented to demonstrate the current state of practice in the use of SAR remote sensing for applications of direct importance for the agriculture and soil moisture communities. Eleven organizations presented their information requirements in response to a set of questions provided by the NASA team, then the NASA team responded by describing the degree to which NISAR could meet these requirements. Discussion ensued about needed data product specifications to increase utility (e.g., projection, latency, etc.), tools and capacity building.

Stavros, Natasha↗

NASA NTRSDate not supplied

Crew Health and Performance Integrated Data System Platform Project Updates

Future human exploration missions introduce a new paradigm as crews move further from the resupply and near real-time ground support typical of Low Earth Orbit missions today. Without immediate support from ground-based personnel, exploration crews will be more reliant on inflight data and technology to respond to emergencies and anomalies. Today, in-flight data is often siloed, unsynchronized, and largely inaccessible in real time. Many data sets require manual entry and/or data transfer between vehicles and the ground. These issues contribute to risks in supporting crew autonomy for future exploration missions. An integrated data system platform is needed to mitigate these risks by supporting a new generation of technologies and employing advanced analytical and predictive modeling techniques to enable crew autonomy for future exploration missions. The Crew Health and Performance Integrated Data System Platform (CHP-IDSP) project is laying a foundation for future in-flight informatics by providing a back-end architecture for collecting, storing, and integrating multiple sources of data generated by and around the crew. This cohesive integration point will streamline the management of CHP data (e.g., environmental, exercise, medical, sleep, performance, etc.) and facilitate situation awareness and decision support required by the crew and remote support of exploration missions. This presentation will describe the ongoing development effort of the path-to-flight CHP-IDSP software and the demonstration of its core capabilities. This includes a brief history of the project, the human-centered process used to identify data needs and workflows feeding the development of scenarios and requirements, and current subsystem development status. Current integrations, including the Chiron exploration electronic health record application, will be discussed. Future work includes collaboration with additional CHP domains and a flight technology demonstration.

Data integration↗

NASA NTRSDate not supplied

Crew Health and Performance Integrated Data Service Platform (CHP-IDSP): Project Updates

Future human exploration missions introduce a new paradigm as crews move further from the resupply and near real-time ground support typical of Low Earth Orbit missions today. Without immediate support from ground-based personnel, exploration crews will be more reliant on inflight data and technology to respond to emergencies and anomalies. Today, in-flight data is often siloed, unsynchronized, and largely inaccessible in real time. Many data sets require manual entry and/or data transfer between vehicles and the ground. These issues contribute to risks in supporting crew autonomy for future exploration missions. An integrated data services platform is needed to mitigate these risks by supporting a new generation of technologies and employing advanced analytical and predictive modeling techniques to enable crew autonomy for future exploration missions. The Crew Health and Performance Integrated Data System Platform (CHP-IDSP) project is laying a foundation for future in-flight informatics by providing a back-end architecture for collecting, storing, and integrating multiple sources of data generated by and around the crew. This cohesive integration point will streamline the management of CHP data (e.g., environmental, exercise, medical, sleep, performance, etc.) and facilitate situation awareness and decision support required by the crew and remote support of exploration missions. This presentation will describe the ongoing development effort of the path-to-flight CHP-IDSP software and the demonstration of its core capabilities. This includes a brief history of the project, the human-centered process used to identify data needs and workflows feeding the development of scenarios and requirements, and current subsystem development status. Current integrations, including the Chiron exploration electronic health record application, will be discussed. Future work includes collaboration with additional CHP domains and a flight technology demonstration.

Software↗

NASA NTRS2004

Grid Enabled Geospatial Catalogue Web Service

Geospatial Catalogue Web Service is a vital service for sharing and interoperating volumes of distributed heterogeneous geospatial resources, such as data, services, applications, and their replicas over the web. Based on the Grid technology and the Open Geospatial Consortium (0GC) s Catalogue Service - Web Information Model, this paper proposes a new information model for Geospatial Catalogue Web Service, named as GCWS which can securely provides Grid-based publishing, managing and querying geospatial data and services, and the transparent access to the replica data and related services under the Grid environment. This information model integrates the information model of the Grid Replica Location Service (RLS)/Monitoring & Discovery Service (MDS) with the information model of OGC Catalogue Service (CSW), and refers to the geospatial data metadata standards from IS0 19115, FGDC and NASA EOS Core System and service metadata standards from IS0 191 19 to extend itself for expressing geospatial resources. Using GCWS, any valid geospatial user, who belongs to an authorized Virtual Organization (VO), can securely publish and manage geospatial resources, especially query on-demand data in the virtual community and get back it through the data-related services which provide functions such as subsetting, reformatting, reprojection etc. This work facilitates the geospatial resources sharing and interoperating under the Grid environment, and implements geospatial resources Grid enabled and Grid technologies geospatial enabled. It 2!so makes researcher to focus on science, 2nd not cn issues with computing ability, data locztic~, processir,g and management. GCWS also is a key component for workflow-based virtual geospatial data producing.

Chen, Ai-Jun↗

NASA NTRS2012

The Invasive Species Forecasting System (ISFS): An iRODS-Based, Cloud-Enabled Decision Support System for Invasive Species Habitat Suitability Modeling

The Invasive Species Forecasting System (ISFS) is an online decision support system that allows users to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of interest, such as a national park, monument, forest, or refuge. Target customers for ISFS are natural resource managers and decision makers who have a need for scientifically valid, model- based predictions of the habitat suitability of plant species of management concern. In a joint project involving NASA and the Maryland Department of Natural Resources, ISFS has been used to model the potential distribution of Wavyleaf Basketgrass in Maryland's Chesapeake Bay Watershed. Maximum entropy techniques are used to generate predictive maps using predictor datasets derived from remotely sensed data and climate simulation outputs. The workflow to run a model is implemented in an iRODS microservice using a custom ISFS file driver that clips and re-projects data to geographic regions of interest, then shells out to perform MaxEnt processing on the input data. When the model completes, all output files and maps from the model run are registered in iRODS and made accessible to the user. The ISFS user interface is a web browser that uses the iRODS PHP client to interact with the ISFS/iRODS- server. ISFS is designed to reside in a VMware virtual machine running SLES 11 and iRODS 3.0. The ISFS virtual machine is hosted in a VMware vSphere private cloud infrastructure to deliver the online service.

Gill, Roger↗

NASA NTRS2022

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.

Natural Language Processing↗

NASA NTRSDate not supplied

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

Natural Language Processing↗

NASA NTRSDate not supplied

Boundary Representation Tolerance Impacts on Mesh Generation and Adaptation

The control of discretization error is critical to obtaining reliable simulation results. Re-cent progress has matured anisotropic mesh adaptation, which automates discretization error control for complex geometries. However, the meshing process can fail when geometric model Boundary REPresentation (BREP) tolerances are larger than boundary layer surface nor-mal and tangential spacing requirements. Geometry sources are often created in Mechanical Computer-Aided Design (MCAD) systems, which are designed to produce models for manufacturing and not the stricter requirements of viscous flow analysis. BREP tolerances are strained by three factors: the inherent complexity of the model (e.g., large range of scales and complex topology), the numerical difficulties arising from surface/surface intersections, and the omission of crucial data during export and translation. Manual preparation of geometry is commonly employed to enable expert-guided mesh generation, which severely inhibits work-flow automation. Accommodation of loose BREP tolerances is required for automated mesh processes, because barrier issues may not be detected until well into the solution process. To raise awareness of this class of geometry issues and their impact on simulation, a survey of these barrier issues is presented with example mitigation techniques. This awareness may also impact geometry creation workflows to prevent the introduction of these artifacts.

CAD↗

NASA NTRSDate not supplied

Assessing the Needs of NASA's Near Real-Time Earth Observation Products

"The 2017-2027 Decadal Survey for Earth Science and Applications from Space stated that NASA's Earth Science with planned implementation of applications provides sustained earth observations for societal benefits [1]. The Decadal Survey indicated that data latency is invaluable for time-sensitive applications including disaster risk reduction, wildland fire carbon emissions quantification, real-time measurements of the state of the hydrologic systems and many more. Data latency refers to the time between earth observation and data products available to users. During the past 13 years, NASA's Land, Atmosphere Near Real-Time Capability for Earth Observing Systems (LANCE) continues to provide free access to earth observation products that are made available much quicker than routine processing allows. The latency of most LANCE data products is Near Real-time (NRT) which is defined as less than three hours from satellite observations [2]. LANCE is managed by the Earth Science Data and Information System (ESDIS) Project at NASA Goddard Space Flight Center [3], and a User Working Group (UWG) is responsible for providing guidance to LANCE. LANCE data are used by direct users and brokers who add value to the data [4]. NASA Earth Applied Sciences Program (ASP) is one of the primary users of LANCE, which collaborates with partner organizations and provides support to scientists to solve problems in applications of earth observations. ASP promotes the use of LANCE NRT data products to demonstrate applications in decision making, facilitates end-user feedback to the science team to improve data products, and provides information on future demands for research. LANCE supports applications that need a rapid response including detecting wildland fires and volcanic eruptions, tracking smoke, ash and dust plumes, monitoring air quality and tracking extreme weather events such as hurricanes, landslides, and floods. To gather feedback regarding the availability, accessibility and actionability of NASA's NRT data products for societal benefit, three surveys and a few discussions with experts involved in the topic within ASP were conducted from the perspective of users. Feedback has been collected from users who are interested in using low latency NASA data within application communities of agriculture, disasters, water resources, health and air quality, ecological conservation, wildland fires and capacity building. Analysis-ready NRT data products in a variety of formats have been mentioned many times in the collected feedback, especially for applied users with little to no experience using research-grade earth observation products. Users prefer to have products that can be easily integrated into their existing workflows and take their analysis to the data. HDF5 is a commonly used data format for research, but typically requires some conversion to a more friendly format for applications and regular use in decision-making. Users prefer the GeoTIFF data format that can be directly ingested into a GIS mapping software and platform for data analysis and visualization. For example, LANCE’s fire, flood, SO2 and Black Marble Nighttime Blue/Yellow Composite data products have been integrated into NASA Disasters Mapping Portal, which is an GIS-based open data portal, for users in the disaster management community. There are 291 LANCE NRT layers available through GIBS and Worldview, where users can download a snapshot in GeoTIFF format. Operational users expect data to be processed as close to the user as possible. The collected feedback indicates that LANCE fire products within 3 hours latency would meet the needs of the wildland fire community. The ideal latency for volcanic application is 10-15 minutes. Users in Volcanic Ash Advisory Centers (VAAC) reported that the first forecast volcanic product should be issued within 75 minutes from the volcano eruption [5]. Overall, for disaster applications, data latency within 3 hours is useful while latency greater than 12 hours is not timely enough for operational use. Capacity building and training are critical for users to be able to access, interpret and use data products and tools for their decision making, especially for applied users with limited experience using earth observation products. LANCE data products have been used in a number of capacity building projects domestically and internationally [6]. As LANCE continues to bring new products into the system, users request training to utilize LANCE new and upcoming data products and capabilities in their applications. Due to the limitation of bandwidth and downstream flow paths, users in some developing countries need tools to select and download data for a specific area of interest instead of bulk downloads. The collected feedback also shows the lack of available SAR satellite low latency data products. The advantages of SAR to monitor conditions and changes on the ground through darkness, clouds, volcanic ash, and other atmospheric conditions, are appealing to low latency users. For example, terabytes of low latency but cloudy optical images are not helpful in rapidly identifying the extent of flood or fire impacts. LANCE could be complemented with low latency measurements via the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission [7]. Requests for higher spatial resolution products are expressed. A user from the wildland fire management community reported that products with 30-m spatial resolution could be used to detect small fires. The 30-m Landsat OLI fire data is now part of NASA’s Fire Information for Resource Management System (FIRMS) US/Canada [8]. Within the open and free NASA resources, LANCE disseminates NRT data products in a manner that allows them to be accessible and understandable to both scientific and applied users. In many application areas, latency plays an important or even decisive role where low latency earth observations help people to observe areas of interest, detect and track changes in the environment and make timely decisions. NASA’s Earth Applied Sciences Program promotes the use of LANCE NRT products and builds a bridge between application users and research teams. The collected feedback indicates data latency within 3 hours is useful for most of the applications, and shows the needs of user-friendly, analysis-ready products, and requests training on LANCE’s new and upcoming data products. User feedback has been provided to LANCE UWG for guidance and recommendations, and for translating findings into something actionable.

Tian Yao↗

NASA NTRSDate not supplied

Informing NLP Learning Tasks by Tracking User Features: An ASRS Use Case using Kaona

There has been growing interest in utilizing natural language processing (NLP) algorithms in Aviation Safety. This interest has extended to leveraging the decades of records publicly available on the Aviation Safety Reporting System (ASRS). While related literature has given more emphasis in lessons learned from the narratives, our prior work has focused on using NLP to support narrative search in the ASRS. Specifically, we evaluated if the use of alternative search mechanisms to keyword search, such as the retrieval of related narratives even without matching keywords could improve narrative discovery. A difficulty in experimenting alternative search mechanisms in any information retrieval task is the lack of ground truth. To address this limitation, we propose Kaona, a lightweight interface which enables the prototyping of alternative search retrieval tasks, by tracking user experience both explicitly (user-specified feedback), or implicitly (user navigation through interface affordances). Differently from distracting requests for feedback during user navigation, Kaona collects explicit feedback from users by mapping them to affordances which support the user workflow, while obtaining ground truth information for learning tasks.

human-computer-interaction↗

NASA NTRS2013

Advanced Multimission Operations System (ATMO)

The HiiHat toolbox developed for CAT/ENVI provides principal investigators direct, immediate, flexible, and seamless interaction with their instruments and data from any location. Offering segmentation and neutral region division, it facilitates the discovery of key endmembers and regions of interest larger than a single pixel. Crucial to the analysis of hyperspectral data from Mars or Earth is the removal of unwanted atmospheric signatures. For Mars and the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM), residual atmospheric CO2 absorption is both directly problematic and indicative of processing errors with implications to the scientific utility of any particular image region. Estimating this residual error becomes key both in selecting regions of low distortion, and also to select mitigating methods, such as neutral region division. This innovation, the ATMO estimator, provides a simple, 0-1 normalized scalar that estimates this distortion (see figure). The metric is defined as the coefficient of determination of a quadratic fit in the region of distorting atmospheric absorption (approx 2 micron). This mimics the behavior of existing CRISM team mineralogical indices to estimate the presence of known, interesting mineral signatures. This facilitates the ATMO metric's assimilation into existing planetary geology workflows.

Mandrake, Lucas↗

NASA NTRSDate not supplied

Automated Collection of Scientific Publications Linked to NASA Earth Science Datasets

NASA's Earth Observing System Data and Information System (EOSDIS) began dataset Digital Object Identifier (DOI) registration in 2012. The number of dataset DOIs registered as of January of 2023 exceeds 11,000. As the research community becomes aware of the importance of sharing data through Open Science and optimizing data reuse through Findability, Accessibility, Interoperability, and Reuse (FAIR) data management principles, datasets are increasingly being cited in scientific publications. When datasets are cited explicitly by DOI within published works, automated methods can be developed for collecting these published works from a variety of bibliometric sources. The coverage of the sources varies, so each source can collect citations that are only available within it. Using major citation databases such as Scopus and Web of Science, the Google Scholar search engine, the CrossRef Open Citation Index, and the dataset DOI registry DataCite, we present an automated workflow for dataset citation collection. By harvesting citations automatically, a citation library is created explicitly linking EOSDIS datasets to publications that cite them. Using Zotero, a free and open-source citation manager, we demonstrate how to access and browse this library by the tags indicating bibliometric sources, dataset DOI, and the dataset archive center. We also demonstrate temporary trends in the number of publications harvested from bibliometric sources.

Infometrics↗

NASA NTRS2006

XBoard: A Framework for Integrating and Enhancing Collaborative Work Practices

Teams typically collaborate in different modes including face-to-face meetings, meetings that are synchronous (i. e. require parties to participate at the same time) but distributed geographically, and meetings involving asynchronously working on common tasks at different times. The XBoard platform was designed to create an integrated environment for creating applications that enhance collaborative work practices. Specifically, it takes large, touch-screen enabled displays as the starting point for enhancing face-to-face meetings by providing common facilities such as whiteboarding/electronic flipcharts, laptop projection, web access, screen capture and content distribution. These capabilities are built upon by making these functions inherently distributed by allowing these sessions to be easily connected between two or more systems at different locations. Finally, an information repository is integrated into the functionality to provide facilities for work practices that involve work being done at different times, such as reports that span different shifts. The Board is designed to be extendible allowing customization of both the general functionality and by adding new functionality to the core facilities by means of a plugin architecture. This, in essence, makes it a collaborative framework for extending or integrating work practices for different mission scenarios. XBoard relies heavily on standards such as Web Services and SVG, and is built using predominately Java and well-known open-source products such as Apache and Postgres. Increasingly, organizations are geographically dispersed, and rely on "virtual teams" that are assembled from a pool of various partner organizations. These organizations often have different infrastructures of applications and workflows. The XBoard has been designed to be a good partner in these situations, providing the flexibility to integrate with typical legacy applications while providing a standards-based infrastructure that is readily accepted by most organizations. The XBoard has been used on the Mars Exploration Rovers mission at JPL, and is currently being used or considered for use in pilot projects at Johnson Space Center (JSC) Mission Control, the University of Arizona Lunar and Planetav Laboratory (Phoenix Mars Lander), and MBART (Monterey Bay Aquarium Research Institute).

Shab, Ted↗

NASA NTRS2024

EdgeCortix SAKURA-I Machine-Learning, PCIe Accelerator SEE Heavy Ion Test Report

To enable autonomy in space, machine-learning and computer vision applications become invaluable for sensor processing. However, these algorithms are computationally complex and unfeasible for many embedded central processing units (CPUs) and usually require external coprocessors, such as graphics processing units (GPUs) or accelerators specific to the application, including application specific integrated circuits (ASICs). In power-constrained systems, GPUs tend to consume more power than is acceptable (>40W), so lower-power accelerators have shown promise to provide the performance needed under spacecraft constraints. For radiation engineers, developing methodologies that can properly test CPUs, GPUs, and accelerators, and enable comparisons between them remains a necessary complication to solve as the devices become more complex. The methodology in this test aims to be a start in developing a baseline single-event effect (SEE) test for client-device machine learning accelerators. This category of devices do not host their own operating system. This testing campaign is a continuation of a previous 200 MeV proton test performed in January 2024. This report covers two heavy ion tests of the SAKURA-I card: one in April 2024, and one in June 2024. Additional data was needed after the April test due to ion-range issues experienced at higher linear-energy transfers (LETs). These range issues are described in more detail in Section 8. This experiment characterizes SEEs and data error susceptibility of the EdgeCortix SAKURA-I machine-learning accelerator under heavy ions. The device was monitored for single event upsets (SEUs) and single event functional interrupts (SEFIs) at the Lawrence Berkeley National Laboratory’s 88-inch cyclotron. The SAKURA-I board accelerates machine-learning inference applications on a host computer through a PCIex16 connection. For the purposes of devising an end to end automated analysis workflow for this experiment, the YOLO-V5 and SSD300 objection-detection models, and the ResNet-50, EfficientNet, and MobileNetV2 image classification models were used as a representative suite of analytical machine-learning models.

Seth S Roffe↗