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NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Update and Future Direction

NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at: NASA SSRI Knowledge Base | Explore. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content; development of an Application Programming Interface (API); improved user interfaces; scalable and searchable Best Practices and Lessons Learned (BPLL) lists with ratings; and custom website analytics. This presentation and paper will discuss the motivation for and development of the SSRI Knowledge Base, demonstrate the existing tool, and outline plans for further development. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.

Small Spacecraft↗

NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Use Case Review and Future Functionality and Content Direction

NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at https://s3vi.ndc.nasa.gov/ssri-kb/. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months, significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content, an Application Programming Interface (API), improved user interfaces, new interfaces for crowdsourcing of content and user ratings, and custom website analytics to inform future development. This presentation and paper will discuss the motivation for the SSRI Knowledge Base, review educational use case(s), and outline plans for further development. The 2022 session topic that best fits the abstract (select only one): Coordinating Successful Educational Programs

Small Spacecraft↗

NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Update and Future Direction

The Small Satellite Reliability Initiative (SSRI)Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. This presentation and paper will discuss the motivation for and development of the SSRI Knowledge Base, demonstrate the existing tool, and outline plans for further development. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. NASA’s Small Spacecraft Systems Virtual Institute (S3VI) funds the SSRI Knowledge Base. The S3VIis jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.

Small Spacecraft↗

NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Update and Future Direction

Small Spacecraft↗

NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Use Case Review and Future Functionality and Content Direction

The Small Satellite Reliability Initiative (SSRI) Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat Community. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. This presentation and paper will discuss the motivation for the SSRI Knowledge Base, review educational use cases, and outline plans for further development. The SSRI is a collaborative activity with broad participation from civil, U.S. Department of Defense, and both national and international commercial space systems providers and stakeholders. NASA’s Small Spacecraft Systems Virtual Institute (S3VI) funds the SSRI Knowledge Base. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate. This presentation and paper will discuss the motivation for the SSRI Knowledge Base, review educational use case(s), and outline plans for further development.

Small Spacecraft↗

NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Use Case Review and Future Functionality and Content Direction

NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at: NASA SSRI Knowledge Base | Explore. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content; development of an Application Programming Interface (API); improved user interfaces; scalable and searchable Best Practices and Lessons Learned (BPLL) lists with ratings; and custom website analytics. This presentation and paper will discuss the motivation for and development of the SSRI Knowledge Base, review of potential use case(s), and outline plans for further development and content generation. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.

Small Spacecraft↗

NASA NTRS2012

Space Operations Learning Center Facebook Application

The proposed Space Operations Learning Center (SOLC) Facebook module, initially code-named Spaceville, is intended to be an educational online game utilizing the latest social networking technology to reach a broad audience base and inspire young audiences to be interested in math, science, and engineering. Spaceville will be a Facebook application/ game with the goal of combining learning with a fun game and social environment. The mission of the game is to build a scientific outpost on the Moon or Mars and expand the colony. Game activities include collecting resources, trading resources, completing simple science experiments, and building architectures such as laboratories, habitats, greenhouses, machine shops, etc. The player is awarded with points and achievement levels. The player s ability increases as his/her points and levels increase. A player can interact with other players using multiplayer Facebook functionality. As a result, a player can discover unexpected treasures through scientific missions, engineering, and working with others. The player creates his/her own avatar with his/her selection of its unique appearance, and names the character. The player controls the avatar to perform activities such as collecting oxygen molecules or building a habitat. From observations of other successful social online games such as Farmville and Restaurant City, a common element of these games is having eye-catching and cartoonish characters, and interesting animations for all activities. This will create a fun, educational, and rewarding environment. The player needs to accumulate points in order to be awarded special items needed for advancing to higher levels. Trophies will be awarded to the player when certain goals are reached or tasks are completed. In order to acquire some special items needed for advancement in the game, the player will need to visit his/her neighboring towns to discover the items. This is the social aspect of the game that requires the player to go out of his/her own establishment to explore what is in the neighborhood. Spaceville will take advantage of Facebook s successful architecture to inspire a new audience of scientists and engineers for the future.

Lui, Ben↗

NASA NTRS2004

Constructing an Educational Mars Simulation

Working in the Educational Programs Office, my task this summer is to model a 3D habitat that will be part of a future Mars base. With the President's charge to further explore mars by way of robotic-led and human-led missions, there has been a surge in the activity regarding the "red planet". Since all present designs are merely conjecture, I have some creative freedom in deciding what the habitat will look like. To get ideas for what a Mars habitat might be like, I looked at several references including websites and NASA documents. One of these was a NASA Technical Memorandum about Space Transportation Systems that I looked at to get insight on spaceship design. Information about the planet's environment, such as the gravity and the weather, is useful as well when designing the structure. The main software that I am using is Lightwave 3D and Modeler 7.5 that comes along with it. Lightwave is very complex in that it lets you model, surface, and animate so there was a lot to learn. To learn the software I watched a series of instructional videos, looked at online tutorials, and referenced several books. Modeling is like shaping clay with a computer. Every item modeled is made of smaller shapes called polygons. For example, each side of a box would be a different polygon. Modelers must be careful to design with users' systems in mind. Having a model made with too many polygons can slow down a walk-through, but it usually improves the small details on a model. Getting speed and quality proved tricky. An important thing for me to remember when modeling the habitat was to save space. Also, I must consider that technology in the future will be much different than now, so I must be especially creative. My project will be used in an educational walkthough simulation in which users can interact with the environment. I worked closely with intern Stephen Henke who built a Mars Rover, terrain and programmed code for the simulation. This summer's project will help me with future aspirations in computer graphics. Modeling is a valuable skill that I appreciate having the chance to learn and practice.

Henke, Stephen A.↗

DOE OSTI2025

Unsupervised Process Anomaly Detection and Identification Using the Leave-One-Variable-Out Approach

Automated anomaly detection and identification can signal equipment issues and pinpoint causes in large-scale industrial systems. For systems with limited failure history, unsupervised machine learning methods can be utilized as they do not require past failures. This study introduces the leave-one-variable-out (LOVO) model, which masks one variable at a time to predict the others, learning underlying process correlations. Detection performance was assessed with synthetic and experimental data, while identification performance used only synthetic data due to its ability to generate labeled anomaly types. For detection using synthetic data, the LOVO model generally outperformed comparative models; while using experimental data, the comparative methods outperformed the LOVO model. However, the comparative methods required selecting a latent size, and these conclusions pertain to using the optimal size. In practice, it would not be feasible to always select the optimal value, and incorrect selections impacted performance. In contrast, the LOVO model does not require a latent space. For identification using synthetic data, the LOVO model was slightly outperformed in interpretability and repeatability but still demonstrated impressive results. These outcomes suggest that the LOVO model is an effective model and may be more easily implemented without the challenging tuning process of selecting a latent size.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Reduced-order modeling for efficient cross section library development in high-temperature gas reactor pebble-bed depletion analysis

Accurate modeling of running-in and equilibrium conditions in pebble-bed reactors (PBRs) requires precise microscopic multigroup neutron cross sections. In Griffin, deterministic neutronics calculations rely on multivariate interpolation over large cross section libraries, resulting in significant memory usage and performance bottlenecks. This work, together with a companion paper on Griffin integration, explores reduced-order models (ROMs) to replace interpolation with lightweight surrogates. Several ROM techniques are benchmarked, with deep neural networks (DNNs) demonstrating superior memory efficiency, scalability, and predictive accuracy. A total of 295 DNNs were trained to build a comprehensive isotope library, integrated into Griffin through a custom LibTorch interface for depletion analysis. Initial results demonstrate that DNN-based ROMs drastically reduce memory demands while preserving accuracy, enabling finer tabulations and additional state variables without overhead. In conclusion, the framework also supports online cross section generation and real-time DNN updates through transfer learning, improving fidelity by capturing self-shielding and evolving nuclide compositions during burnup.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2004

Dissemination of information about the technologies of the Vision Research Lab through the World Wide Web

The Vision Research Lab at NASA John Glenn Research Center is headed by Dr. Rafat Ansari. Dr. Ansari and other researchers have developed technologies that primarily use laser and fiber optics to non-invasively detect different ailments and diseases of the eye. One of my goals as a LERCIP intern and ACCESS scholar for the 2004 summer is to inform other NASA employees, researchers and the general public about these technologies through the development of a website. The website incorporates the theme that the eye is a window to the body. Thus by investigating the processes of the eye, we can better understand and diagnosis different ailments and diseases. These ailments occur in not only earth bound humans, but astronauts as well as a result of exposure to elevated levels of radiation and microgravity conditions. Thus the technologies being developed at the Vision Research Lab are invaluable to humans on Earth in addition to those astronauts in space. One of my first goals was to research the technologies being developed at the lab. The first several days were spent immersing myself in the various articles, journals and reports about the theories behind Dynamic Light Scattering, Laser Doppler Flowmetry, Autofluoresence, Raman Spectroscopy, Polarimetry and Oximetry. Interviews with the other researchers proved invaluable to help understand these theories as well gain hands on experience with the devices being developed using these technologies. The rest of the Vision Research Team and I sat down and discussed how the overall website should be presented. Combining this information with the knowledge of the theories and applications of the hardware being developed, I worked out different ideas to present this information. I quickly learned Paint Shop Pro 8 and FrontPage 2002, as well as using online tutorials and other resources to help design an effective website. The Vision Research Lab website incorporates the anatomy and physiology of the eye, different diseases that affect the eye and the technologies being develop at the lab to help diagnosis these diseases. It also includes background information on Dr. Ansari as well as other researchers involved in the lab and it includes segments on patents, awards and achievements. There are links to help viewers navigate to internal and external websites to further investigate different ideas and hrther understand the implications of these technologies at being developed.

Dorais, Christopher M.↗

NASA NTRS1991

A strongly goal-directed close-range vision system for spacecraft docking

In this presentation, we will propose a strongly goal-oriented stereo vision system to establish proper docking approach motions for automated rendezvous and capture (AR&C). From an input sequence of stereo video image pairs, the system produces a current best estimate of: contact position; contact vector; contact velocity; and contact orientation. The processing demands imposed by this particular problem and its environment dictate a special case solution; such a system should necessarily be, in some sense, minimalist. By this we mean the system should construct a scene description just sufficiently rich to solve the problem at hand and should do no more processing than is absolutely necessary. In addition, the imaging resolution should be just sufficient. Extracting additional information and constructing higher level scene representations wastes energy and computational resources and injects an unnecessary degree of complexity, increasing the likelihood of malfunction. We therefore take a departure from most prior stereopsis work, including our own, and propose a system based on associative memory. The purpose of the memory is to immediately associate a set of motor commands with a set of input visual patterns in the two cameras. That is, rather than explicitly computing point correspondences and object positions in world coordinates and trying to reason forward from this information to a plan of action, we are trying to capture the essence of reflex behavior through the action of associative memory. The explicit construction of point correspondences and 3D scene descriptions, followed by online velocity and point of impact calculations, is prohibitively expensive from a computational point of view for the problem at hand. Learned patterns on the four image planes, left and right at two discrete but closely spaced instants in time, will be bused directly to infer the spacecraft reaction. This will be a continuing online process as the docking collar approaches.

Boyer, Kim L.↗

NASA NTRS2020

A Reinforcement Learning Framework for Space Missions in Unknown Environments

A land-and-traverse mission to icy worlds such as Europa and Enceladus is challenging due to lack of prior knowledge regarding the terrain conditions. Previous work [1] showed that rovers with high degrees of freedom (DoF) can achieve robust traversal by leveraging redundant modes for mobility to counter terrain uncertainty (e.g. walking, driving, or inch-worming). This paper presents a generic and scalable reinforcement learning scheme for enabling on-board decision making on rovers to automatically switch between modes of traversal based on online performance feedback. The objective is to maximize energy efficiency, minimize operator input and successfully negotiate unstructured terrain conditions without relying on exhaustive prior knowledge. The proposed methodology is well grounded in the literature on reinforcement learning and has been adapted to address conformance to validation and verification requirements and JPL flight operations history of using per-sol prescribed sequences for a space mission.

Tavallali, Peyman↗

NASA NTRS2004

Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station

We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.

Lee, Charles↗

NASA NTRS2015

Online Dectection and Modeling of Safety Boundaries for Aerospace Application Using Bayesian Statistics

The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace.

Statistics↗

NASA NTRSDate not supplied

Plant Habitat 04 Citizen Outreach: The Space Chile Grow a Pepper Plant Challenge (2020-2021)

The Space Chile Grow a Pepper Plant Challenge (SCGAPPC) is a citizen outreach activity performed in conjunction with NASA technical demonstration, Plant Habitat (PH)-04 which grew a New Mexico chile pepper as the first fruit in the Advanced Plant Habitat (APH). Growing a related pepper variety from Chimayo, New Mexico, the activity tasked citizen “Space Farmers” to grow a pepper plant indoors, at home, or in controlled environments to document the details of growth, and search for solutions to growing consistently productive and flavorful peppers under these conditions. Launched in February of 2020, a scientist on the PH04 team shared an introduction on social media to provide information about the activity and instructions on how to participate. Each space farmer received an envelopecontaining seeds and a mission sticker. They conducted their own pepper growth trials to record environment and horticultural data and submit one pepper to the University of New Mexico for capsaicin analysis. This activity was available online at the onset of the COVID-19 shutdown. During 2020-2021, over 1,800 envelopes were mailed across the globe, tens of thousands of seed pouches were shared, the SCGAPPC was featured during a virtual Growing Beyond Earth Educator Workshop with the challenge of becoming a critical part of that program when students were working remotely. Citizen space farmers around the world contributed to space biology and crop production research while learning about CE agriculture. Members of the space biology community volunteered participation, skills, and interaction. An online community page provided a format for scientists, and the general publicto interact. A portion of data was documented in an online database. The primary outcome served to inform the publicabout NASA space crop production, while creating a channel to share experiences and compare results from a singlecrop growing under varying controlled environments.

Jacob Torres↗

DOE OSTI2025

LLM Generation of Online Courses from a Curated Set of Documents in the Nuclear Safeguards Domain

A multidisciplinary team at Argonne National Laboratory explores the application of advanced technologies to enhance knowledge transfer and retention within the nuclear safeguards domain. Specifically, it examines the feasibility of leveraging secure large language models (LLMs) to streamline the creation of e-learning modules for the U.S. National Nuclear Security Administration (NNSA) Office of International Nuclear Safeguards (NA-241). The initiative addresses the critical need for preserving institutional memory and accelerating skill development amidst the imminent retirement of senior professionals in the field in addition to supporting good knowledge management practices. The project integrates instructional design theory with cutting-edge AI technologies to transform curated document sets from the Safeguards Knowledge Repository (SKR) into modular online courses. By automating the generation of learning objectives and instructional content, the effort aims to reduce manual effort while maintaining high-quality educational outcomes. A limited measure of human supervision, however, ensures accuracy, relevance, and alignment with NNSA’s strategic priorities. Key findings highlight the potential of AI-assisted course generation to support safeguards professionals by creating structured, interactive learning experiences. The report underscores the importance of SME validation to address limitations in AI-generated content, such as terminology errors and gaps in coverage. Recommendations include adopting a structured workflow combining LLM acceleration with expert oversight to ensure accuracy, usability, and alignment with learner needs. This work demonstrates Argonne’s commitment to advancing national security and scientific excellence through innovative knowledge management solutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS2011

Feature Acquisition with Imbalanced Training Data

This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.

Thompson, David R.↗