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NASA NTRSDate not supplied

Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗
NASA NTRS2024

Independent Scientific Review of Biological Assumptions and Planned Ultraviolet Treatment for Mars Sample Return Backward Planetary Protection

The Backward Planetary Protection Study Team (“the Study Team”), assembled by the Office of the Chief Scientist (OCS) at the request of the Office of Planetary Protection (OPP), has conducted numerous detailed discussions on the potential use of an active ultraviolet (UV) approach for minimizing or eliminating biological contamination of the exterior of the planned Orbiting Sample container (OS) of the Mars Sample Return (MSR) mission. This review of the backward planetary protection (BPP) considerations for the MSR mission focused on two key parameters that are opportunities for further process development to support implementation of planetary protection practices in meeting mission requirements. These parameters include: 1) Sub-cellular biological assumptions – should Mars missions consider self-replicating organisms only or do they need to consider non-self-replicating entities without a cellular organization such as viruses, prions, etc. and macromolecules capable of genetic information transfer, as well? 2) UV biocidal impact – what is the UV fluence in the Mars atmosphere and its associated potential biocidal credit? The assembled subject matter experts (SMEs) in the Study Team are listed in Appendix A, with a brief description of each member’s relevant expertise.

Mamta Patel Nagaraja↗
NASA NTRSDate not supplied

Exploration Exercise System (EES) Development

Exploration class missions will be required to have an exercise device that is lightweight, has a small footprint, and is capable of providing enough physical stimulus and exercise variability to be an effective countermeasure against muscle and bone loss that results from the microgravity environment. Exploration exercise device prototypes should be evaluated on the ground and in-orbit for feasibility of use in microgravity for long and short duration exploration missions and efficacy of the device to maintain multi-system health and performance. The European Enhanced Exploration Exercise Device (E4D) was selected as the exploration prototype device to be evaluated on ISS for efficacy and feasibility of use as a single multi-modality device for the exercise system for exploration missions. This effort supports the continued development, testing, and verification of E4D hardware and software, internal NASA integration (Human Health and Performance, ISS Vehicle Office, Engineering, and Flight Operations), and external integration across NASA, ESA, and the Danish Aerospace Company (DAC). Providing a feasibility and acceptability assessment from a physiological efficacy and hardware durability standpoint are critical for informing use and risk associated with use on exploration missions. Clearly defined objectives from end users, stakeholders, and Subject Matter Experts (SMEs) will be tested by crewmembers during acute use sessions and long duration use of the exercise device while on ISS. This effort will include a flight study where crewmembers will be asked to exercise using only the E4D during the duration of their mission and participate in a battery of physiological testing to evaluate the efficacy. Hardware is scheduled to launch in FY25 followed by 2 years of operational use after activation and checkout. A final recommendation will be provided to the Artemis program on acceptability of the device for exploration missions. The E4D needs a vibration isolation stabilization (VIS) system that serves as a platform for the exercise hardware to protect the vehicle from loads imparted during exercise. Exploration forward VIS systems will need to protect the vehicle and provide sufficient stabilization for the exerciser during performance of all critical exercises. These enabling capabilities need to be achieved within exploration vehicle power, thermal, mass, and volume limitations.

Kent Lawrence Kalogera↗
NASA NTRSDate not supplied

Exploration Exercise System (EES) Physiology

Exploration class missions will be required to have an exercise device that is lightweight, has a small footprint, and is capable of providing enough physical stimulus and exercise variability to be an effective countermeasure against muscle, bone, aerobic fitness, and sensorimotor loss that results from the microgravity environment. Ground studies, inflight studies, and results analyses are required to inform Moon2Mars Design Reference Missions (DRMs) and vehicle designs. This activity encompasses two related projects, an evaluation study called Zero T2 and a requirements development for an exploration treadmill (ET). Exploration class missions’ mass/power/volume restrictions have resulted in the development of exercise devices that are motorized and flywheel-based to provide both aerobic and resistive training on one platform. These devices provide a variety of full body resistance exercise options as well as rowing and cycling for aerobic exercise, but do not provide ambulatory exercise via a treadmill. Because a treadmill has been available for use on the ISS since 2001, we do not understand the efficacy of exploration exercise modalities on muscle performance, aerobic fitness, bone health, or sensorimotor performance. A retrospective study was performed to quantify the association between total exercise (including specific contributions of treadmill exercise) and functional performance upon landing. Due to the relatively small variation in treadmill usage inflight, there is a need to do a controlled study where inflight crewmembers will not use the ISS treadmill (called T2) for the duration of their flights. There will be 3 arms to the Zero T2 study: 1) participants use all currently available exercise platforms aboard ISS (control group), 2) participants use only ARED and CEVIS, and 3) participants use only European Enhanced Exploration Device (E4D). (Please see the TechPort Entry for Exploration Exercise System Development for further detail on the E4D.) This study started in Oct 2020 and will continue through Sep 2027. In a parallel effort, a team will be developing requirements for an exploration treadmill. This project will be broken into two phases. Phase 1 will develop and implement energy prediction models based on available in-flight exercise data to quantify energy expenditure during 0g treadmill running. Phase 2 will then use the available biomechanical and metabolic evidence from Phase 1 to determine the hardware specification requirements essential to providing the desired exercise stimulus. Subject Matter Experts (SMEs) will then leverage the Standardized Process for Evaluating Exercise Devices (SPEED) developed by the team to 1) assess available technologies for candidate treadmills which meet defined requirements, 2) perform hands-on evaluations of top candidates, 3) provide evaluation outcomes and recommendations to developers. This project will begin in 2025 and continue through Sep 2030.

Kent Lawrence Kalogera↗
NASA NTRS2024

Ensuring Safe Decision-Making on the Moon and Mars: Cognitive Performance Assessment for Exploration Class Mission EVA

Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future partial gravity EVA on the Moon and Mars are expected to be higher compared to microgravity EVA currently conducted from the International Space Station (ISS). Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient data to characterize cognitive performance prior to, during, and following EVA. Furthermore, it is still unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis (CTA) of EVA to characterize the procedures, the cognitive demands required, and the critical safety decisions associated with decrements in cognitive performance. We used the applied cognitive task analysis method as outlined by Militello and Hutton (1998) and conducted interviews with subject matter experts (SMEs) in EVA at NASA Johnson Space Center in summer 2024. Interviews focused on exploration EVA and elicited feedback from SMEs on the cognitive skills required for specific EVA tasks including cognitive strategies, critical cues, and decision-making strategies. A cognitive demands table was assembled to consolidate and synthesize the information from all interviews. The information from this cognitive task analysis will inform ongoing work to identify the key cognitive domains for safe decision-making during exploration mission class EVA on the Moon and Mars. This work will importantly provide a link between cognitive domains identified in the cognitive task analysis and operationally-relevant performance metrics collected during spaceflight and simulation EVA that can be tracked and assessed by NASA mission operations. Ultimately, this work will aid in the development and application of exploration class cognitive performance monitoring capabilities to improve safe decision-making and inform EVA planning on future spaceflight missions to the Moon and Mars.

Steven R Anderson↗
NASA NTRSDate not supplied

Insights and Observations from Operating a Geostationary Laser Communication Relay Mission: Operational Lessons from NASA’s Laser Communications Relay Demonstration (LCRD) and Associated Optical Ground Stations (OGSs)

The NASA Laser Communications Relay Demonstration (LCRD) has operated on orbit for the last two years. The LCRD Mission consists of a geostationary payload and two optical ground stations. This technology demonstration mission is NASA’s first two-way, end-to-end optical communications relay. LCRD has performed an extensive experiment campaign to analyze laser communication performance for extended operations. This paper discusses various lessons learned while operating the optical relay mission from early commissioning through two years of operation. Before launch, engineers and operators performed analyses to generate operational procedures based on expected LCRD performance. However, only operation on orbit can supply actual performance data. Additional topics covered in this paper include pre-launch and commissioning testing, ephemeris generation and cadence, environmental configurations, and accommodation concerns. The addition of Integrated Laser Communications Relay Demonstration Low Earth Orbit User Modem and Amplifier Terminal (ILLUMA-T) to the optical network supplemented the LCRD team’s operational experiences and enabled them to garner new lessons learned. The authors of this paper include day-to-day flight leads who consulted with on-console operators, engineers, and subject matter experts analyzing data and experiment results to gather the lessons learned described in this paper. LCRD is a joint project involving NASA Goddard Space Flight Center (GSFC), the California Institute of Technology Jet Propulsion Laboratory (JPL), and Massachusetts Institute of Technology Lincoln Laboratory (MIT LL).

Lessons Learned↗
NASA NTRSDate not supplied

Artificial Intelligence (AI) Methods for Augmenting the IMPACT Tool Evidence Library

Development of the Evidence Library for use with the IMPACT probability risk assessment tool took several years and involved a staggering amount of effort from a multi-disciplinary team. A very significant amount of the labor effort to collect, assess and finalize the Clinical Finding Form (CliFF) for each of the 119 medical conditions was provided by physician subject matter experts from the Exploration Medical Capability (ExMC) Element Clinical and Science Team. Many AI tools such as ChatGPT are excellent at summarizing large amounts of information and the current project was initiated to determine how such tools might streamline laborious processes, e.g., review and summarization of many scientific research publications, to execute key steps more efficiently in the process of developing CliFFs. The process for collecting the evidence which is found in the CliFFs is well documented in the Evidence Library Methods document (ELM; HRP-48036*). Using ELM and the CliFF development instructions as a guideline, a team of developers is leveraging Microsoft Azure AI tools and services along with open-source frameworks, to construct an AI-assisted automated pipeline. This pipeline is designed to search, retrieve, and process the necessary data sources, and ultimately help generate the final version of a CliFF. Currently, the large language model evaluates the relevance of each source material to spaceflights, either as direct evidence or as an analog. Additionally, the model assists in extracting keywords and generating brief summaries to enhance augmented retrieval and search processes in later stages of CliFF development. Once the data is ready, the model can perform semantic search and retrieval, generating and extracting valuable information for the CliFF. For instance, it can handle epidemiological statistical data, such as incidence rates and the likelihood of best or worst-case scenarios. The steps that required reading and summarizing articles were viewed as providing the greatest return on investment since large language models are very efficient and accurate in summarizing large amounts of text. Since labor effort to complete the original CliFF was not recorded with sufficient granularity, comparisons with an AI tool-generated CliFF will provide merely an approximation of time saved. Upon completion of the process, the CliFF for the medical condition “appendicitis” generated with the support of AI-based methods will serve as a proof-of-concept and will be compared to the original appendicitis CliFF to determine if use of the tools resulted in content and conclusory similarity. Based upon the results from face validation of the two CliFFs, modifications to the process will be made if necessary and additional condition CliFFs will be evaluated. Ultimately, CliFFs for the entire set of medical conditions will be created with the assistance of AI tools. Depending on the cost savings realized, CliFFs for additional medical conditions can be created to expand the Evidence Library. Future direction includes specifying the characteristics of the reviewer (prompting the AI tools to generate output assuming the reviewer is a sub-specialist physician, or nurse or EMT/medic) to determine if the effects on AI-generated output are different based on knowledge, skills and abilities. *Exploration Medical Capability Evidence Library Methods, HRP-48036 Rev A, July 2022.

Ali Al↗
NASA NTRSDate not supplied

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

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

Jay Lemery↗
NASA NTRS2025

Evaluating the Viability of Compact and Portable X-Ray Systems for an Exploration Medical System in a Ground Demonstration

MOTIVATION FOR INCLUDING X-RAY CAPABILITIES For upcoming exploration missions, the need for enhanced medical care becomes critical due to extended mission durations, significant communication delays, and minimal evacuation opportunities. Previous evidence by our team has revealed that among the 119 medical conditions targeted for management during spaceflight within NASA Exploration Medical Capability’s IMPACT Condition List, at least 36 could benefit from radiography (XR). Utilizing XR for diagnosis and management is hypothesized to significantly improve management of crew health by enabling the immediate evaluation and confirmation of potential injuries or illnesses. Beyond clinical applications, XR also holds potential for non-destructive testing (NDT). This includes applications such as assessing the structural integrity of the spacecraft, analyzing surface and meteorite samples, and inspecting onboard electronics. THREE CANDIDATE X-RAY SYSTEMS CHOSEN FOR GROUND DEMONSTRATION The Exploration Medical Capability Element (ExMC) and the Exploration Medical Integrated Product Team (XMIPT) of the Mars Campaign Office initiated early background work for ground demonstrations. In FY21, ExMC published a Concept of Operations to guide requirements development. By FY23, XMIPT and yet2, a technology scouting and open innovation consulting firm, had completed a market survey and trade study to identify potential miniature XR systems. Selection criteria included commercial-off-the-shelf availability, low mass and volume, and regulatory compliance. The top three candidate devices—Remedi REMEX-KA6, MinXray Impact, and FujiFilm Xair—were acquired to characterize the requirements and capabilities of each device. To facilitate testing, phantoms, and radiographic personal protective equipment (PPE) were purchased, and a dedicated space was designated for XRS usage at Glenn Research Center. During this presentation, the mass, volume, and power requirements for each of the three piloted devices are revealed, as well as information regarding the detector, mA, and kV of the devices. GOAL AND OBJECTIVES OF A MINI XRS GROUND DEMONSTRATION The primary goal of ExMC/XMIPT technology demonstrations is to bridge the gap in available, flight-ready medical device technology by flight-testing diagnostic and treatment technologies essential for managing medical conditions during exploration missions. These technologies must adhere to vehicle constraints such as mass, volume, power, and data requirements, integrate seamlessly with medical decision-support tools, and support increasingly Earth-independent operations. There are three main objectives for the future ground demonstration of these three devices. First, we aim to determine the full capabilities of these three miniature XR systems within the context of the spaceflight environment. While medical applications are the primary focus for the miniature XR, a comprehensive exploration of non-medical uses has been initiated by an XMIPT-sponsored NASA SPARK campaign to identify collaborators. Second, we plan to establish criteria and to use insights gained from evaluating each miniature XR against those criteria to select the most suitable system among the three candidates. Third, we intend to evaluate their suitability for flight certification, which includes assessing its durability for launch, reentry, and exposure to high background radiation, as well as its compatibility with existing data architecture systems. Numerous subject matter experts from NASA and partner institutions will support these objectives.

C A Haddix↗
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 NTRSDate not supplied

Artificial Intelligence (AI) Methods for Automating the Impact Tool Evidence Library

INTRODUCTION: The development of the Evidence Library for use with the IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) probability risk assessment tool involved a multilayered, time intensive process of data collection and analysis by subject matter experts from the Exploration Medical Capability (ExMC) Element Clinical and Science Team to produce clinical findings forms (CliFFs) for 120 medical conditions. Artificial Intelligence Large Language Models (LLMs) can be leveraged to facilitate this process, thus reducing labor and time. TOPIC: CliFFs contain information about medical conditions as they pertain to spaceflight. This includes condition definitions, incidence data, crew task impairment estimates caused by conditions, treatment protocols and references to literature used for gathering condition evidence. Guided by the Evidence Library Methods document and the CliFF development instructions, a team has leveraged Microsoft Azure AI services and open-source documentation to construct an AI-assisted automated pipeline for CliFF development. This process is designed to search, retrieve, and evaluate the applicable data, and ultimately generate a completed CliFF. The LLM evaluates the relevance of each of the source materials to spaceflight, either as direct evidence or as an analog. The model extracts keywords and generates brief summaries to enhance search and retrieval in later stages of CliFF development. For instance, it can calculate epidemiological statistical data, such as incidence rates and the likelihood of best or worst-case scenarios. APPLICATION: Large Language Models (LLMs) can efficiently summarize large amounts of text. Leveraging this technology will automate data retrieval and evidence gathering for medical databases, like the IMPACT tool, by aiding in the labor-intensive process of analyzing large bodies of literature and organizing it into a formatted document like a CliFF. This added efficiency will enable expeditious expansion of the Evidence Library with additional medical conditions and update previous CLiFFs as new technology becomes available.

Ali Al↗
NASA NTRSDate not supplied

Ensuring Safe Decision-Making on the Moon and Mars: Cognitive Performance Assessment for Exploration Class Mission EVA

Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future partial gravity EVAs on the Moon and Mars are expected to be higher compared to microgravity EVAs currently conducted from the International Space Station. Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient data to characterize cognitive performance prior to, during, and following EVA. Furthermore, it is still unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis (CTA) of EVA to characterize the procedures, the cognitive demands required, and the critical safety decisions associated with decrements in cognitive performance. We conducted a cognitive task analysis with 15 astronauts and subject matter experts in EVA operations and research. Interviews focused on surface exploration EVA and elicited feedback from experts on the cognitive skills required for specific EVA tasks, including cognitive strategies, critical cues, and decision-making strategies. A cognitive demands table was assembled to consolidate and synthesize the information from all interviews. The information from this cognitive task analysis informs ongoing exploration EVA for Moon to Mars. This work identifies the specific cognitive challenges that astronauts are likely to encounter during surface exploration EVA, and provides the foundation for: (1) prioritized and targeted cognitive performance measurement and functional performance tests, (2) EVA simulation design at varying levels of cognitive workload, and (3) the development of training and other technologies that can improve safe decision-making and inform EVA planning on future spaceflight missions to the Moon and Mars.

Steven R Anderson↗
NASA NTRSDate not supplied

Preliminary Development of Multi-Vehicle (m:N) Operations with NASA Langley’s Remote Vehicle Operations Center

To achieve the vision of Advanced Air Mobility (AAM), a transition from localized operations of aircraft to remote operations is being pursued across many use cases. This transition will allow fewer human operators to manage more increasingly autonomous aircraft (i.e., m operators managing N vehicles, or m:N). To study this operational concept, the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has developed a prototype remote vehicle operations center and ground control station (GCS) software to conduct research with simulated and real flight operations. To date, flight operations at LaRC have been limited to one vehicle per operator. However, the current paper describes initial development and considerations for enabling m:N flight operations at LaRC. Further, the research described in this paper provides the foundation for a concept of operations (ConOps) that will be developed to support remote operators managing multiple increasingly autonomous vehicles, with the goal of exploring human-autonomy teaming (HAT) concepts that enable more advanced m:N operations. Two key enablers have been identified to facilitate successful m:N operations: GCS software updates for multi-vehicle management and procedural updates for vehicle handoffs during off-nominal events. Additionally, specific modifications were identified across five key areas: technology and software, team structure, inter-team communication, contingency plans, and operator decision flows. Next steps in forming the LaRC m:N ConOps will include working with subject-matter experts to identify off-nominal scenarios, implementing the recommended GCS functionality for m:N operations, and performing integration testing of facility capabilities and new operational procedures. Although the future m:N ConOps will be tailored to the NASA LaRC remote operations facility and flight range, it is intended to be a transparent, accessible, and reality-based exemplar for external organizations seeking to create or evaluate their own m:N operational concepts.

m:N↗
NASA NTRS2024

Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications

This technical report presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2M global time series samples from NASA’s Harmonized Landsat and Sentinel-2 data archive at 30m resolution, the new 300M and 600M parameter models incorporate temporal and location embeddings for enhanced performance across various geospatial tasks. Through extensive benchmarking with GEOBench, the 600M version outperforms the previous Prithvi-EO model by 8% across a range of tasks. It also outperforms six other geospatial foundation models when benchmarked on remote sensing tasks from different domains and resolutions (i.e. from 0.1m to 15m). The results demonstrate the versatility of the model in both classical earth observation and high-resolution applications. Early involvement of end-users and subject matter experts (SMEs) are among the key factors that contributed to the project’s success. In particular, SME involvement allowed for constant feedback on model and dataset design, as well as successful customization for diverse SME-led applications in disaster response, land use and crop mapping, and ecosystem dynamics monitoring. Prithvi-EO-2.0 is available on Hugging Face and IBM terratorch, with additional resources on GitHub. The project exemplifies the Trusted Open Science approach embraced by all involved organizations.

Daniela Szwarcman↗
NASA NTRSDate not supplied

Increasing Discovery and Usability of Earth Science Satellite Data with My NASA Data

For 20 years, the My NASA Data project at NASA Langley Research Center has developed innovative approaches to increase the use of NASA’s satellite data by learners. My NASA Data offers a variety of authentic Earth Science datasets and a data visualization tool, eliminating the need for educators and/or learners to obtain specialized knowledge of GIS data formats and software to access and use authentic Earth Science data. While there is no shortage of available data, as federal government agencies such as NASA house petabytes of freely accessible Earth Science datasets, much of the data are only available for download and visualization in specialized formats and software, limiting their accessibility to educators and learners, especially those in primary and secondary school. Using the Google Earth Engine platform, the My NASA Data team has recently reinvented their data visualization tool, called the Earth System Data Explorer (ESDE). The ESDE gives users the capability to explore over 60 Earth Science satellite datasets in a multitude of formats such as maps, graphs, and data table Its new and improved user interface design was developed based on the preferences of educators, whom the My NASA Data project has over 20 years’ experience working with. Earth Science and GIS Subject Matter Experts (SMEs) structured the data in a professional and scientific manner. During Fiscal Year 2023, the My NASA Data website received over 1 million digital engagements, with over one-third being visitors to the data visualization tool. These metrics highlight the interest in a visualization tool that is simple and free to use with reliable and trusted datasets. The ESDE empowers users to readily relate and analyze NASA Earth Science data within their area of interest. The team used a user-centered design (UCD) framework to receive and incorporate feedback into the application’s design. Core requested features include the ability to create time series graphs, comparative analysis of maps, and download the data as CSV file. Responses indicate that advances in data visualization tools such as the ESDE make authentic Earth Science data more accessible. This presentation will cover how the My NASA Data project develops tools to enhance data discovery and accessibility, as well as how SME and user suggestions are incorporated.

Desiray Wilson↗
NASA NTRS2024

JPSS-4 VIIRS Version 2 at-Launch Relative Spectral Response Characterization

The JPSS-4 VIIRS sensor, the last in a JPSS program that will eventually span some 25+ years of on-orbit data collection, has completed its pre-launch test program. The test program included measurements for characterizing the VIIRS relative spectral response (RSR) in support of the Sensor and Environmental Data Records that will be generated from JPSS-4 VIIRS on-orbit observations after launch. Subject matter experts of the Government Team’s VIIRS DAWG have analyzed the VIIRS spectral measurements and produced the VIIRS spectral characterization, in the form of band averaged and supporting detector level RSR for each VIIRS band. The characterization is based upon the analysis of independent SpMA dual monochromator (all bands) and GSFC GLAMR laser system (reflectance bands only) spectral measurements. The SpMA and GLAMR measurements for reflectance bands (DNBLGS and DNBMGS, I1-I3, M1-M11) were combined to produce a “fused” RSR. For emissive bands (I4, I5, M12-M16), the SpMA measurements provide the entire characterization. The effort has led to the VIIRS Version 2 RSR product, the official at-launch RSR characterization for the JPSS-4 VIIRS mission, which is currently slated to be the next launch (expected Fall 2027) of the JPSS program. As expected, the JPSS-4 RSR are a close match to those of JPSS-3 and JPSS-2 (NOAA-21) VIIRS, while showing some spectral position and shape differences with SNPP and JPSS-1 (NOAA-20) VIIRS. An assessment on compliance with spectral performance metrics finds that VIIRS bandaverage RSR are compliant on nearly all metrics, with only a single minor exception. The Version 2 RSR will be available under EAR99 restrictions to the science community on the restricted access NASA Sharepoint.

SDR↗
NASA NTRS2024

Updates to NASA’S Break-in-Prebreathe Flight Rules Due to Type II Decompression Sickness Risk Considerations

Investigation of a central neurological decompression sickness (DCS) case during hypobaric ground testing at Johnson Space Center (JSC) identified a ~3 min break-in-prebreathe (BiP) late in the prebreathe (13 min prior to depressurization) as the leading credible cause for the DCS case. This occurred despite applicable prebreathe payback rules being followed. Applicable NASA rules, for ground and flight, directed 2:1 payback of breaks up to 10 min in duration, regardless of when a break occurs relative to depress. Breaks lasting longer than 10 min require a complete restart of the prebreathe protocol. The adequacy of NASA’s BiP rules was evaluated prior to resuming any hypobaric ground testing or International Space Station (ISS) extravehicular activity (EVA). Of particular interest to the authors was the timing of the BiP in relation to the overall prebreathe timeline. Although there exists no agreed-upon definition, a ‘late’ BiP means towards the end of the prebreathe period, and in particular, the last hour prior to depressurization. The following information sources were reviewed prior to formulating recommendations: i) the type II DCS case report and findings from the investigation; ii) documented rationale for existing flight rules, iii) consultations with subject matter experts involved in definition of existing flight rules (several of whom had since left NASA); iv) relevant published literature; v) tissue gas loading model estimates; and vi) NASA’s operational experience with late breaks in prebreathe.

Andrew F J Abercromby↗
NASA NTRSDate not supplied

HWO Technology Roadmapping - High Sensitivity UV and Visible Band Technologies for Instrumentation

We report on ongoing efforts by the HWO Project Office at NASA Goddard to define technology development roadmaps for a variety of technologies relevant to the astrophysics instruments on the mission. We have been soliciting input from a variety of subject matter experts (SMEs) to define the necessary development paths to achieve a TRL of 5 by 2029, the necessary cost and investment, and the schedule over which the work can be achieved. This planning is directed by the performance requirements coming out of the START science working group studies and will enable the Project Office to define the Observatory Technology Maturation Plan by Summer 2025.

Paul Scowen↗