Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “SCORE”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Natural Language Understanding Approach for Digitizing Aircraft Ground Taxi Instructions

Advancements in natural language processing (NLP) technologies offer a unique opportunity to furnish aircraft crews, primarily pilots, with digital instructions for taxiing operations. Digital taxi instructions, delivered either as text or graphics, can streamline taxiing procedures, thereby reducing radio congestion, minimizing communication errors, and enhancing aircraft monitoring. Techniques used for natural language understanding (NLU), a subset of NLP focused on machine comprehension of natural language, can extract taxi instructions directly from verbal radio communications. This capability paves the way for implementing a digital taxi communication framework with minimal adjustments to the existing air traffic controller operations. This paper delves into a novel application of NLU: the automated generation of digital taxi instructions from air traffic controller speech. We detail the development of an annotation scheme to represent aircraft ground traffic communications within the US National Airspace System (NAS), employing intent classification (IC) and slot filling (SF) to extract taxi instructions using NLU models. Several neural network models were trained on a dataset annotated with our scheme, achieving notable accuracy and 𝐹1 scores. Our research demonstrates the feasibility of using NLU to automatically generate digital taxi instructions, showcasing its potential to streamline the implementation of digital taxi communications.

ATC↗
NASA NTRSDate not supplied

Spacesuit Sizing by Multivariate Simulation: Case Study for Liquid Cooling and Ventilation Garment

A technique was developed to assess the fit of spacesuit garment liquid cooling and ventilation garment (LCVG). A fit scoring scheme was built from the spacesuit sizing requirements and guidelines, and the accommodated proportion of the wearer population was estimated from statistically synthesized pseudo population data. The method evaluated different scenarios of LCVG sizing systems including 1) baseline 7-size system, 2) 7-sizes with target body dimensions iteratively adjusted for maximum fit, and 3) 9-sizes with iterative adjustments. Overall, the baseline sizing system showed 81% accommodation rate, while the hypothetical new systems in-tegrating iterative adjustments for 7- and 9-sizes predictively indicated that accommodation could improve up to 92 and 97%. The proposed method provided a structured metric to quantify fit performance of a garment sizing system, which can be further applied to different spacesuit component, PPE and garment designs.

Han Kim↗
NASA NTRS2024

Summarizing Multiple Aspects of Triple Collocation Analysis in a Single Diagram

With the ongoing expansion of global observation networks, it is expected that we shall routinely analyze records of geophysical variables such as temperature from multiple collocated instruments. Validating datasets in this situation is not a trivial task because every observing system has its own bias and noise. Triple collocation is a general statistical framework to estimate the error characteristics in three or more observational-based datasets. In a triple colocation analysis, several metrics are routinely reported but traditional multiple-panel plots are not the most effective way to display information. A new formula of error variance is derived for connecting the key terms in the triple collocation theory. A diagram based on this formula is devised to facilitate triple collocation analysis of any data from observations, as illustrated using three aerosol optical depth datasets from the recent Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). An observational-based skill score is also derived to evaluate the quality of three datasets by taking into account both error variance and correlation coefficient. Several applications are discussed and sample plotting routines are provided.

triple collocation↗
NASA NTRS2025

Modeling and Simulation Credibility Assessments of Musculoskeletal Computational Models for Simulating Astronaut Injuries Due to a Poor Spacesuit Fit

The musculoskeletal (MS) system of astronauts is subject to physiological changes, potentially leading to injuries due to the exposure to different gravitational environments experienced during spaceflight. These injuries can occur while an astronaut is performing an Extravehicular Activity (EVA) in space, on lunar or planetary surfaces or while wearing a spacesuit during terrestrial training for an EVA. The OpenSim MS modeling software can assess EVA induced MS injury mechanisms such as muscle strains, ligament injuries and joint injuries. One area of concern, since there are only a few different spacesuit sizes with limited adjustability, is the possibility of a poorly fitting spacesuit. This can cause unnatural joint motions and torques resulting in various MS injuries. A credibility assessment of the OpenSim modeling and simulation procedures is performed per NASA-STD-7009A to provide information on the credibility of the model’s use in simulating EVA related injury mechanisms. The credibility assessment evaluated various OpenSim models against the following eight credibility factors: data pedigree, input pedigree, code verification, solution verification, conceptual validation, referent validation, results uncertainty and results robustness (sensitivity). The models evaluated for EVA injuries will require additional credibility factor analysis and upgrades to the model features, such as adding ligaments to a whole-body model, to reliably predict and analyze the EVA injuries expected to occur due to a poor spacesuit fit. The degree of elevation strategy required to increase the credibility assessment scores will depend on the model complexity and the injury mechanism.

Christopher A. Gallo↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗
NASA NTRSDate not supplied

Behavioral Health Benefits of the ISS Pick-and-Eat Crop Growth System

Summary: We summarize findings related to assessing the pick-and-eat salad-crop productivity and acceptability of the ISS food system as a countermeasure to maintain behavioral health in long-duration spaceflight. BACKGROUND Plants are a potential countermeasure for the stresses of living in space. Caring for plants and eating fresh food have been shown to serve as a psychological benefit for previous astronauts and for others in analogous environments such as Antarctica [1,2]. Gardening can be therapeutic in terrestrial settings [3]. These effects may carry over or be more pronounced in austere environments such as spaceflight. It can reduce stress and increase sensory stimulation and enjoyment [2]. Growing crops may offer astronauts meaningful and engaging work as they care for living things in an austere environment away from Earth’s nature and as they provide sustenance for their crew. We examined the extent to which interacting with plants (e.g., tending to, consuming) was related to behavioral health outcomes in long-duration space missions. METHOD Participants were 27 astronauts that interacted with the plant system VEG-04, VEG-05, and HRF-VEG (VEG-03 I-L, PH-04). In total, there were 106 in-flight observations. Participants completed monthly measures of mood and well-being, enjoyment and time spent performing specific crop growth and consuming the plants, meaningfulness, performance, connections with others and the Earth, desire to work with and eat plants, and experiences with struggling or dying plants. Higher ends of the 7-point Likert scale indicated more positive outcomes, lower ends of the scale were anchored with more negative outcomes, and the scale midpoints indicated neutral positions. Participants selected a specific survey version based on whether they interacted with the plant system(s) or not, and we included only the scores from the crewmembers that participated in any given activity. RESULTS & DISCUSSION Results indicated that participants generally rated tasks related to the plants and crop growth as enjoyable. Consuming the harvested plants and voluntary viewing were the most enjoyable activities. Tending to the plants was reported as moderately enjoyable, indicating BHP benefits from this work task. The BHP impacts of whether the task was engaging, meaningful, supported well-being, or was demanding for a crewmember were generally positive and increased over time. As shown in the Figure, working with plants was reported as more engaging, meaningful, and beneficial to well-being over time, while the demand from the task was moderate to low and consistent over time. Engaging, meaningful, and supporting well-being were strongly, positively correlated with each other, which is consistent with research that suggests engaging and meaningful work can support well-being. Plant activities such as voluntary viewing, watering, harvesting, and consuming, were positively related to task engagement and meaning, and enjoyment and well-being. Findings suggest that working with plants and consuming them can be a behavioral health countermeasure for long-duration spaceflight. REFERENCES [1] Schlacht I, et al. (2020) Impact of plants in isolation: The EDEN-ISS…in Antarctica. In Advances in Human Factors of Transportation, Washington DC. [2] Vessel E, Russo S (2015) Effects of reduced sensory stimulation and…countermeasures…. NASA/TM-2015-218576. [3] Odeh R, Guy C (2017) Gardening for therapeutic people-plant interactions during LDSM. Open Agriculture, 2(1), 1-13. ACKNOWLEDGEMENTS This research was supported by the Human Health and Performance Contract NNJ15HK11B, and NASA grant MTL #1075 (PI G.D. Massa).

behavioral health↗
NASA NTRS2025

Human Spaceflight Applications of Novel Miniature X-Ray Technologies

INTRODUCTION: Radiography (XR) has long been a cornerstone of terrestrial medical imaging, though it has not yet been used in the spaceflight environment. Medical systems for human spaceflight missions are constrained by mass, volume, and power, and until recently, XR systems have been considered too large and power-consuming for spaceflight diagnostic and therapeutic applications. However, the rise of commercial spaceflight and NASA’s refocused efforts on returning crews to the Moon for long-duration missions have introduced a higher degree of medical risk to human spaceflight and require a re-evaluation when optimizing medical system design. Over the last decade, XR devices have miniaturized while maintaining good diagnostic and therapeutic sensitivity and specificity, making new in-flight medical and non-medical XR applications a possibility. Initial research identified several medical conditions where miniature XR would be beneficial for the diagnosis and/or management of medical conditions arising in space, though a more in-depth analysis is required to identify whether XR may add value to the management of such conditions. With this presentation, we aim to introduce the potential utility of miniature XR, review prior work highlighting where XR may be beneficial, and evaluate how miniature XR may reduce medical risk in human spaceflight missions. METHODS: IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a risk assessment tool developed by NASA to advance exploration mission medical system design by quantitatively estimating mission medical risk. IMPACT v1.0 includes a novel evidence library baselined to exploration environments, an expanded list of 119 medical conditions, medical capabilities and resources critical for management of these medical conditions, and the ability for rapid and iterative analysis in the setting of modifiable design reference missions (DRMs). Our first analysis identified which of the 119 medical conditions XR had diagnostic or therapeutic utility for. Subject matter experts (SMEs) recorded which XR views would be performed under ideal terrestrial circumstances for diagnosis/management of each condition, as well as which views are pragmatic for spaceflight limitations. A second analysis utilized IMPACT to identify significant conditions that contribute greatest to medical risk during a notional long-duration Lunar orbit and Lunar surface DRM. Medical system risk estimates include loss of crew life (LOCL), need for return to definitive care (RTDC; medical evacuation), and an estimate of crew task time affected (TTA). Using a standardized semi-quantitative scoring methodology, a deeper evaluation of each of the most significant medical conditions was performed. Data from both of these separate analyses were used to hypothesize what ideal and pragmatic XR studies may impact clinical management of the most significant conditions predicted to lead to medical risk. RESULTS: Approximately 1/3 of the IMPACT conditions were identified as being more effectively or comprehensively assessed or treated with the addition of miniature XR technology. The resulting conditions benefitting diagnostically and therapeutically from XR are revealed, as well as the ideal and pragmatic XR views and medical procedures benefitting from XR. The conditions of clinical significance and those most contributing to risk are also displayed. Among the conditions that contribute greatest to LOCL, four conditions for which XR may improve the diagnosis and management of include: decompression sickness, traumatic shock, dental abscess, and respiratory failure. Among conditions that contributed to RTDC, the evaluation and management of wrist fracture is likely improved by XR. For conditions leading to crew TTA, evaluation and management of EVA shoulder injuries, upper and lower extremity strains, back strains, and EVA hand injuries are likely improved by XR. DISCUSSION: Miniature XR in spaceflight has the potential to improve the evaluation and management of a substantial portion of conditions that most contribute to medical risk. This presentation is an introduction to the possibilities miniature XR provides for future human spaceflight missions and subsequent presenters will expand on potential applications in more detail. LEARNING OBJECTIVES: 1) Understand the previous limitations of using radiography in the management of spaceflight medical conditions; 2) Evaluate the findings from the IMPACT tool analysis, which allows quantification of the benefit miniature XR could provide for managing high-risk medical conditions in long-duration lunar orbit and surface missions, focusing on improvements in crew health outcomes; 3) Analyze case studies where miniature XR technology could reduce the medical risks associated with spaceflight missions, specifically in diagnosing and managing conditions such as decompression sickness, traumatic shock, and EVA-related injuries.

A Anderson↗
NASA NTRS2024

Orbital Debris Quarterly News, October 2024

- Three New On-orbit Fragmentations - NASA’s ODPO Wins Software of the Year Award - Aerodynamic Demise Model for Carbon Fiber Polymer Fiber - DebriSat: 10 years and Growing - Meeting Reports - Upcoming Meetings - NASA ODPO Abstracts - Space Missions, and Satellite Box Score

Heather Cowardin↗
NASA NTRSDate not supplied

Dust-Proofing the Future: Materials Challenges for Lunar Exploration

As humanity expands its ventures into lunar exploration, the demand intensifies for materials capable of enduring extreme environmental and service conditions. The Moon’s surface presents numerous challenges, including ultra-high vacuum exposure, temperature extremes and intense radiation. Additionally, lunar regolith threatens component durability and reusability due to its abrasive nature. Lunar dust particles scoring, adhering or embedding into surfaces and within device-confined geometries, such as bearings, drive mechanisms and connectors, can cause premature component wear or failure. Furthermore, the interaction between the rocket plume and surface during vehicle landings and ascents creates severe erosive conditions near critical vehicle components and adjacent infrastructure. A key barrier hindering materials discovery for lunar dust tolerant applications is a lack of standard laboratory methods to evaluate material properties and performance under representative operating conditions. A case will be presented for research activities supporting identification, processing and characterization of candidate materials in conjunction with innovations in standardized testing and qualification. Results will highlight current and emerging test methods for simulating lunar dust exposure and component degradation, paving the way for a new generation of robust materials and coatings for space exploration.

lunar dust↗
NASA NTRSDate not supplied

Latest Development in Radiative Transfer Models and Retrieval Algorithms Using Principal Components

The radiative transfer model (RTMs) has a wide range of applications in satellite remote sensing and atmospheric radiation applications. However, millions of line-by-line (LBL) radiative transfer calculations at fine monochromatic frequencies are needed in order to properly calculate spectral contributions of water vapor and trace gases in the atmosphere in infrared and solar spectral regions. Therefore, fast and accurate RTMs are needed to efficiently process large amount of satellite data. A Principal Component-based Radiative Transfer Model (PCRTM) was first developed in 2004 at NASA Langley Research Centre to fulfil this need. By using PC-compression, one can reduce the data dimension significantly while maintaining original information content. The PCRTM can directly compute PC-scores and their derivatives with respect to retrieved parameters. The PCRTM can simulate the top-of-atmosphere (TOA) radiance or reflectance spectra from 0.250 µm (400000 cm-1) to 2000 µm (50 cm-1) with several orders of magnitude faster speed as compared to a LBL RTM. It is also extremely accurate compared to LBL RTM benchmarks (0.03 K RMS error in IR and 0.05% in solar). The PCRTM model has been developed for hyperspectral sensors such as AIRS, CrIS, IASI, NAST-I, SHIS, FIRST, and CLARREO-IR in thermal IR spectral region and CLARREO-Solar, CPF, TEMPO, EMIT, OMI, and SCIAMACHY in solar spectral region. The PCRTM accuracy has been demonstrated via RTM intercomparisons and with real satellite observations from AIRS, CrIS, IASI, SCHIAMACHY, and EMIT etc. In this presentation, we will describe two PCRTM-based inversion algorithms to retrieve atmospheric temperature, water vapor, and trace gas profiles, as well as cloud and surface properties from hyperspectral sounders such as AIRS, CrIS, IASI, and NAST-I. The first one is called Single Fieldof-view Sounder Atmospheric Product (SiFSAP) algorithm. It provides L2 products with 9-times higher area spatial resolution as compared to current cloud-clearing sounder algorithms. The SiFSAP L2 and L3 products are available at NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The second inversion algorithm is called Climate Fingerprinting Atmospheric Product (ClimFiSP) algorithm. It is designed to produce high quality climate products (trends, anomalies, daily, and monthly profiles of temperature, water vapor, traces, clouds, and surface properties) from multiple satellite sensors such AIRS on Aqua and CrIS on multiple satellites. This product will be available at NASA GES DISC later this year. The PCRTMbased high fidelity simulators for CLARREO and CPF have been used for sensor performance trade studies, algorithm development, and inter-satellite calibrations. We have also used PCRTM generated TOA radiance spectra to train an AI-based algorithm and successfully retrieved cloud properties from EMIT solar hyperspectral imagers.

PCRTM↗
NASA NTRSDate not supplied

Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network

Transverse cirrus bands (TCBs) are bands of upper-level clouds regularly seen in mesoscale and synoptic-scale weather systems. In tropical cyclones, their appearance has been subjectively linked to intensification and the diurnal cycle. However, these hypothesized relationships have not been rigorously tested due to the subjective nature of TCBs in satellite images. A machine learning technique that successfully identifies TCBs objectively in imagery from the GOES-16 Advanced Baseline Imager (ABI) has been developed to solve this problem. The technique uses a U-Net convolutional neural network (CNN) that assigns a probability to each pixel in an image based on the likelihood of the pixel being associated with a TCB. Using the U-Net CNN, a database of TCBs from 2019 to 2022 was developed for the Atlantic tropical cyclone basin by defining an appropriate probability threshold that defines the difference between TCB and non-TCB pixels. This threshold is where the Jaccard score, calculated using manually identified TCBs and model identified TCBs, is maximized. Statistics for TCB occurrence will also be presented, including the relationships between TCBs and storm relative motion, shear relative direction, cardinal direction, tropical cyclone intensity, tropical cyclone intensification rates, and time of day.

John Mark Mayhall↗
NASA NTRS2024

Modeling and Simulation Credibility Assessments of Musculoskeletal Computational Models for Simulating Astronaut Injuries Due to a Poor Spacesuit Fit

The musculoskeletal (MS) system of astronauts is subject to physiological changes, potentially leading to injuries due to the exposure to different gravitational environments experienced during spaceflight. These injuries can occur while an astronaut is performing an Extravehicular Activity (EVA) in space, on lunar or planetary surfaces or while wearing a spacesuit during terrestrial training for an EVA. The OpenSim MS modeling software can assess EVA induced MS injury mechanisms such as muscle strains, ligament injuries and joint injuries. One area of concern, since there are only a few different spacesuit sizes with limited adjustability, is the possibility of a poorly fitting spacesuit. This can cause unnatural joint motions and torques resulting in various MS injuries. A credibility assessment of the OpenSim modeling and simulation procedures is performed per NASA-STD-7009A to provide information on the credibility of the model’s use in simulating EVA related injury mechanisms. The credibility assessment evaluated various OpenSim models against the following eight credibility factors: data pedigree, input pedigree, code verification, solution verification, conceptual validation, referent validation, results uncertainty and results robustness (sensitivity). The models evaluated for EVA injuries will require additional credibility factor analysis and upgrades to the model features, such as adding ligaments to a whole-body model, to reliably predict and analyze the EVA injuries expected to occur due to a poor spacesuit fit. The degree of elevation strategy required to increase the credibility assessment scores will depend on the model complexity and the injury mechanism.

Christopher A Gallo↗
NASA NTRSDate not supplied

Developing Natural Language Processing and Supervised Learning Techniques to Classify Mars Tasks

As NASA's Human Research Program (HRP) prepares for long-duration Mars missions, understanding astronaut tasks is crucial. This study, conducted at NASA Glenn Research Center (GRC), employed Natural Language Processing (NLP) and machine learning techniques to analyze and classify Mars tasks. A list of 1,058 Mars tasks was provided by HRP experts including binary labeling of 18 Human System Task Categories (HSTCs). We developed an NLP model using Google's BERT language model to capture the semantic and syntactic nuances of these tasks. Supervised training was initially applied to a subset of the NLP-analyzed tasks to assess the model's effectiveness in classifying the remaining tasks. Incorporating HSTC descriptions significantly enhanced the classification accuracy for 9 out of the 18 HSTCs and reduced training time. To address the issue of severe class imbalance in the HSTC data, we introduced innovative weighting and sampling techniques for data augmentation. We then fine-tune BERT to implement a pairwise relatedness scoring method, allowing us to cluster tasks based on their relatedness and similarity, getting a step closer to labeling the tasks without supervision. In this presentation we guide you through data preprocessing, deciphering key syntax components using BERT, and performing supervised classification of the Mars tasks. This work showcases the potential use of advanced NLP techniques to analyze Mars missions to be incorporated into various crew health and performance analyses.

GenAI↗
NASA NTRSDate not supplied

Behavioral Health Benefits of the ISS Pick-and-Eat Crop Growth System

Summary: We summarize findings related to assessing the pick-and-eat salad-crop productivity and acceptability of the ISS food system as a countermeasure to maintain behavioral health in long-duration spaceflight. BACKGROUND Plants are a potential countermeasure for the stresses of living in space. Caring for plants and eating fresh food have been shown to serve as a psychological benefit for previous astronauts and for others in analogous environments such as Antarctica [1,2]. Gardening can be therapeutic in terrestrial settings [3]. These effects may carry over or be more pronounced in austere environments such as spaceflight. It can reduce stress and increase sensory stimulation and enjoyment [2]. Growing crops may offer astronauts meaningful and engaging work as they care for living things in an austere environment away from Earth’s nature and as they provide sustenance for their crew. We examined the extent to which interacting with plants (e.g., tending to, consuming) was related to behavioral health outcomes in long-duration space missions. METHOD Participants were 27 astronauts that interacted with the plant system VEG-04, VEG-05, and HRF-VEG (VEG-03 I-L, PH-04). In total, there were 106 in-flight observations. Participants completed monthly measures of mood and well-being, enjoyment and time spent performing specific crop growth and consuming the plants, meaningfulness, performance, connections with others and the Earth, desire to work with and eat plants, and experiences with struggling or dying plants. Higher ends of the 7-point Likert scale indicated more positive outcomes, lower ends of the scale were anchored with more negative outcomes, and the scale midpoints indicated neutral positions. Participants selected a specific survey version based on whether they interacted with the plant system(s) or not, and we included only the scores from the crewmembers that participated in any given activity. RESULTS & DISCUSSION Results indicated that participants generally rated tasks related to the plants and crop growth as enjoyable. Consuming the harvested plants and voluntary viewing were the most enjoyable activities. Tending to the plants was reported as moderately enjoyable, indicating BHP benefits from this work task. The BHP impacts of whether the task was engaging, meaningful, supported well-being, or was demanding for a crewmember were generally positive and increased over time. As shown in the Figure, working with plants was reported as more engaging, meaningful, and beneficial to well-being over time, while the demand from the task was moderate to low and consistent over time. Engaging, meaningful, and supporting well-being were strongly, positively correlated with each other, which is consistent with research that suggests engaging and meaningful work can support well-being. Plant activities such as voluntary viewing, watering, harvesting, and consuming, were positively related to task engagement and meaning, and enjoyment and well-being. Findings suggest that working with plants and consuming them can be a behavioral health countermeasure for long-duration spaceflight. REFERENCES [1] Schlacht I, et al. (2020) Impact of plants in isolation: The EDEN-ISS…in Antarctica. In Advances in Human Factors of Transportation, Washington DC. [2] Vessel E, Russo S (2015) Effects of reduced sensory stimulation and…countermeasures…. NASA/TM-2015-218576. [3] Odeh R, Guy C (2017) Gardening for therapeutic people-plant interactions during LDSM. Open Agriculture, 2(1), 1-13. ACKNOWLEDGEMENTS This research was supported by the Human Health and Performance Contract NNJ15HK11B, and NASA grant MTL #1075 (PI G.D. Massa).

countermeasure↗
NASA NTRSDate not supplied

LLMs and GenAI Tools to Depict Contributions of Human Systems to Spaceflight Tasks Execution

Recent advancements in Artificial Intelligence and Machine Learning (AI/ML) technologies, particularly Large Language Models (LLMs) capable of sophisticated syntax analysis, offer substantial potential in automating complex processes, thereby saving time and human resources. This study explores the development of an LLM-driven model designed to analyze and categorize a diverse set of Mars mission tasks into 18 predefined Human System Task Categories (HSTCs) based on their textual descriptions. As part of developing the Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA projects Performance Risk Model (PRisM) proof-of-concept, we established a framework to project performance scores from small-scale tests onto a preliminary list of Mars tasks. The foundation of our model was a comprehensive spreadsheet populated by NASA experts and clinicians, which detailed each Mars task alongside binary indicators of HSTC involvement. This dataset enabled the initial application of supervised ML, training and testing on existing HSTC labels. The HSTCs were originally defined from a medical system perspective, focusing on task impairments due to deteriorated human health. To expand our model's scope to include categories impacting performance, we face the challenge of generating binary labels (0 or 1) for new categories without pre-existing data. We address this by employing Generative AI (GenAI) software to determine whether a given task involved a new category by asking, "Does task A involve using category B?" We validate our approach by comparing the GenAI's binary classifications with the expert-provided labels for existing HSTCs. Notably, we utilize Ollama [4], a locally hosted GenAI tool that does not require cloud access, thus safeguarding NASA's proprietary data from unauthorized exposure. This study demonstrates the feasibility of leveraging cutting-edge AI tools to advance research, paving the way for automation and rapid decision-making in space exploration.

Mona Matar↗
NASA NTRSDate not supplied

Performance Risk Model (PRisM) Proof-of-Concept: An Operational Decision Support Tool to Predict Crew Performance in Space from Available Performance Tests

The Crew Health and Performance-Probabilistic Risk Assessment (CHP-PRA) team at NASA Glenn Research Center has developed a range of tools to evaluate astronaut health during spaceflight and to optimize the medical set required for missions. Among these, the Performance Risk Model (PRisM) represents a novel advancement, extending CHP-PRA’s focus beyond medical systems into the domain of human performance. Such tool could be pivotal in optimizing astronaut capabilities during space travel, thereby enhancing overall mission success. PRisM leverages data from well-established performance assessments conducted during previous crewed space missions and Earth analogs to strategically predict outcomes for planned tasks, even when direct performance testing has not been conducted for those specific tasks. To evaluate performance, PRisM references the various metrics outlined in NASA-STD-3001 and employs a methodology to integrate different performance scales. This framework analyzes the contribution of various human system task categories (HSTCs) to task execution and compares these contributions to the HSTCs’ involvement in other known tests. The model further employs a Monte Carlo simulation to sample performance scores from their distribution in operationally relevant tests such as those in Mulavara et al. (2018) and, by leveraging similarities in HSTC involvement, transfers this knowledge to actual mission tasks, such as those outlined in the "Human Exploration of Mars: Preliminary List of Crew Tasks”. The current PRisM proof-of-concept includes analyses of the impact of exercise and specific medications on astronauts’ performance, with provisions to incorporate additional countermeasures as data becomes available. Furthermore, the tool is customizable to include any system necessary to fully encompass the domain of human systems and can be adapted to evaluate performance for any spaceflight activities as requested by operational stakeholders. PRisM has the potential to assist the Human Research Program in exploring the capabilities trade space for optimized crew performance.

performance modeling↗
NASA NTRS2025

Orbital Debris Quarterly News, Volume 29 Issue 1

In This Issue: - Two New On-orbit Fragmentations - Machine Learning Applications Supporting the ODPO - Radar Measurements of the Orbital Debris Environment from HUSIR and Goldstone: 2023 - Meeting Reports - Monthly Number of Objects in Earth Orbit by Object Type - Upcoming Meetings - Space Missions and Satellite Box Score

Heather Cowardin↗
NASA NTRS2024

Sensor Fault Detection in Smart Extraterrestrial Habitats Using Unsupervised Learning

Various types of sensors are needed to monitor the health state of smart deep-space habitats. However, measured data can be affected by sensor faults, which influence the health management system and consequently the decision-making. In this paper, an unsupervised learning approach based on convolutional autoencoders (CAEs) is developed to detect anomalies in temperature and pressure sensors. The proposed method is systematically investigated using a habitat simulator (HabSim). Several illustrative examples are demonstrated in the nominal and hazardous states of the habitat, including micrometeorite impact and fire scenarios. The performance of the proposed method using CAEs is compared with that of existing methods using auto-associative neural networks (AANNs) and variational autoencoders. This comparison is based on typical evaluation metrics, including precision, recall, F1 score, training time, and testing time. The effect of temperature–pressure coupling on the detection performance of CAEs and AANNs is explored by training different data-driven models, including one with temperature sensors, one with pressure sensors, and one with both temperature and pressure sensors. The effect of the number of faulty sensors on the performance of CAEs is studied, as with an increase in the number of faulty sensors, redundant information among the sensors is reduced. The capability of CAEs to change the number of sensors without redesigning the network architecture and retraining the neural network is investigated and demonstrated. The capabilities and limitations of the proposed solution are discussed.

Zixin Wang↗