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NASA NTRS2006

Conflict Detection Using Variable Four-Dimensional Uncertainty Bounds to Control Missed Alerts

Decision-support tools for maintaining pairwise aircraft separation rely on conflict detection to alert the operator when the predicted trajectories of aircraft will result in a loss of separation. But aircraft frequently do not follow their predicted trajectories exactly. This can cause missed alerts and the failure of strategic separation procedures. We present a technique for modeling a bounded region of uncertainty around a four-dimensional predicted trajectory and an algorithm for detecting conflicts between trajectories modeled in this way that avoids missed alerts as long as the aircraft remain within the specified regions of uncertainty. In addition, we present an algorithm for detecting the intrusion of a trajectory modeled in this way into an area hazard modeled as a polygonal region. The size of the region of uncertainty can vary along the trajectory continually and independently in the along-path, cross-track, and vertical dimensions, providing an opportunity to reduce the likelihood of false alerts while protecting against typical prediction errors. The algorithm has been implemented in the Autonomous Operations Planner, a NASA Langley prototype decision support tool for airborne self-separation.

Karr, David A.↗

NASA NTRSDate not supplied

Designing for Advanced Aerial Mobility: Human-Autonomy Teaming and In-Time System-Wide Safety Assurance

The continued growth of aviation shall require new innovative technologies and operational concepts to meet the ever-increasing demands on air transportation. The NASA Advanced Air Mobility (AAM) project focuses on emerging aviation markets, such as Urban Air Mobility (UAM). UAM is defined as “...a safe and efficient system for air passenger and cargo transportation within an urban area. It is inclusive of small package delivery and other urban unmanned aerial system services and supports a mix of onboard/ground-piloted and increasingly autonomous operations” ([1]). The AAM project emphasizes technology development and validating system-level concepts and solutions in coordination with other NASA Aeronautics Research Mission Directorate (ARMD) projects to enable UAM metro- and micro-plex vertiport and airspace concepts of operations. The NASA AAM research portfolio includes the concepts of Remote Supervisor-in-Command (RSC) and Fleet and Airspace Manager (FAM) as possible human roles for consumer fleet providers. NASA research in RSC is focused on development of guidelines and standards for remote pilots/operators passively and actively controlling a large fleet of autonomous aircraft. For FAM, flight and ground system concepts and technologies to enable high density homogeneous operations at increased scale from vertiport(s), and coordination with other humans in the systems (e.g., UAM urban airspace manager, Air Traffic Control) are key research areas. The envisioned UAM operations are posited to require autonomous systems to enable functions ranging from fleet and resource management to vehicle control. Although automation has become increasingly sophisticated and ubiquitous in civil aviation, autonomy represents a significant evolution in automation, which has generally been limited in functional scope and capability. As autonomy takes on increasing responsibilities, humans and machines will be required to work together in new and different ways [2], rather than traditional design approaches focused on how machines (i.e., autonomy) can do the work of people. The emerging field of human-autonomy teaming (HAT) represents a comprehensive and prioritized research-driven approach to enable the success of future emerging aviation market applications through capabilities and principles that facilitate humans and machine working and thinking better together. The NASA Transformational Tools and Technologies (TTT) Autonomous System (AS) Sub-project was created to assist with the transition into higher levels of autonomy to enable new modes of air transportation, such as UAM. TTT-AS has identified HAT as a key research need to enable UAM while maintaining today’s ultra-safe aviation system safety levels. The latter challenge has been taken up by the NASA System-Wide Safety (SWS) Project, which recognizes that aviation safety, as it evolves, shall require new ways of thinking about safety to include integration of a wide-range of existing and new safety systems and practices, enhanced tools and technologies, increased access to data and data fusion, improved data analysis capabilities, enhanced in-time risk monitoring and detection, hazard prioritization and mitigation, safety assurance decision-support, and in-time integrated system analytics [3].The operational concept of UAM represents a variety of work that has been termed, “work-as-imagined” to characterize the idea that how people think that work is done and how work is actually done are often not the same [4]. To ensure design success and system safety, looking at “work-as-done” provides a comparative approach toward UAM concept and technology design through examination of corresponding analogs found today in aviation (e.g., on-demand operations) and other transportation domains (e.g., port operations). The paper shall discuss various alternative applications with specific focus on airline operation center (AOC) operations, and unmanned aerial system (UAS) command-and-control to inform scaled-versions of FAM and RSC, respectively, and with consideration of the national airspace system contextual environment. The tenets and principles of the HAT field and current NASA research efforts under the TTT-AS sub-project shall also be described. Finally, the SWS sub-project efforts to develop In-Time System-Wide Safety Assurance (ISSA) and In-Time Safety Management Systems (IASMS) are discussed in terms of how “in-time” safety assurance may be conceptualized for the on-demand mobility air taxi “work-as-imagined” operational concept [5]. As part of this effort, concepts from the emerging field of resilience engineering, are being studied. Traditional approaches to aviation safety have focused on what can go wrong and how to prevent it. Another approach to thinking about system safety should reflect not only “avoiding things that go wrong” (protective safety) but also “ensuring that things go right” (productive safety), that enables a system to exhibit the resilient performance [6] necessary for the success of the future aviation system emerging concepts of operations. The paper shall describe efforts focused on how productive safety and resilience may enable a more complete approach to system safety thinking and design of ISSA and IASMS for UAM. Future directions and research needs shall also be discussed.

resilience↗

NASA NTRS2020

Gila Water Resources III - Modeling the Impacts of Post-fire Restoration Methods on Vegetation Recovery in the Gila National Forest

In recent years, wildfires in New Mexico’s Gila National Forest have become increasingly common and more severe. Wildfires can have powerful impacts on hydrology and soil stability, including erosion, flooding, and debris-flows that threaten lives and infrastructure downstream. Vegetation restoration treatments like seeding and mulching can mitigate these effects and facilitate ecosystem recovery. Understanding the effectiveness of various restoration methods is vital to planning a cost-effective and successful post-fire recovery strategy. The immediate response to a fire on US Forest Service land is coordinated by a Burned Area Emergency Response (BAER) team, a group responsible for mitigating immediate post-fire risks to human life, property, and critical natural and cultural resources. This study created a proof-of-concept methodology for a decision-support tool designed to help BAER teams identify the restoration treatments most likely to succeed in a given burned area. Leveraging random forest regression, Google Earth Engine, and Landsat 7 and 8 Earth observations, this study modeled vegetation recovery after the 2013 Silver Fire for seeded areas, seeded/mulched areas, and untreated areas. Treatment type and initial burn severity were the largest drivers of vegetation recovery across the landscape. Seeded/mulched areas showed higher recovery levels than untreated areas three months post-fire, but by four years post-fire, treated and untreated areas displayed similar recovery levels. To produce a robust predictive tool for the Gila National Forest, the model should be trained on many more fires and incorporate post-fire weather conditions into the process. Such a model will help partners ensure efficient resource use and plan effective post-fire restoration strategies.

DEVELOP Project Summary↗

NASA NTRSDate not supplied

Human Capabilities Assessments for Autonomous Missions: A Multi-Team Research Effort to Reduce Risk in the Human-System Integration Architecture for Future Deep-Space Missions

In future exploration missions beyond low earth-orbit, crew will have to execute complex operations and respond to off-nominal events, without real-time support from Mission Control. It is anticipated that increased reliance on automated systems, including human-centric vehicle and information architecture, will need to be designed to support the crew; increased risk to performance, health, and safety may occur if these are not implemented appropriately. The Human Factors and Behavioral Performance Element (HFBP) in the NASA Human Research Program supports research to characterize and mitigate such human health and performance risks, including the Risk of Adverse Outcome Due to Inadequate Human Systems Integration Architecture (HSIA). The HSIA risk addresses the integration of onboard capability and the crew roles and responsibilities necessary to enable the crew to respond effectively and efficiently in the increasingly autonomous mission operations environment. In 2017, HFBP released the “Human Capabilities Assessments for Autonomous Missions” (HCAAM) research topic to address HSIA related questions. HCAAM is a major NASA research effort that has assembled a multidisciplinary team from seven institutions to work closely with design and engineering efforts on research towards developing and refining human performance standards, guidelines and automation tools. The scientific focus is on quantitative assessment of human capabilities relevant to future deep-space missions during which earth/spacecraft communication is so delayed and intermittent that the crew must be able to function autonomously. The integrated strategy of the HCAAM project characterizes human capabilities and limitations related to potential performance decrements during long duration exploration mission spaceflight as relevant to both routine and complex task performance; defines system characteristics that reduce the likelihood or impact of potential decrements in human performance capabilities; performs integrated assessment of intelligent system responses within the context of an operational environment with relevant NASA tools, systems, and data structures in order to determine positive or negative interactions and validate recommended approaches; and proposes specific updates to existing standards and guidelines for inclusion in NASA handbooks for the design of future spacecraft intelligent systems that provide crew performance assessment/feedback, and to also serve as decision-support aids for the onboard crew (i.e., NASA-STD-3001, and NASA/SP-Human Integration Design Handbook (HIDH)). The scientific research vectors being addressed by the seven HCAAM teams include: - crew task performance (accuracy, efficiency) (crew + automation) - crew performance (accuracy, efficiency) - crew Situation Awareness - procedure design and multi-modal enhancement - concurrent tasking (mixed manual + some level of autonomy) - task handover - crew self-planning and time-lining - task design - trust in automation, real-time calibration - human multi-sensory feedback and guidance - human trust in on-board software-based intelligent assistants - virtual assistants The presentation will highlight plans and progress made in each of these research areas as well as the methods by which surrogate astronaut crews in the NASA JSC HERA spaceflight analog facility will function as human test subjects for all of the HCAAM research projects.

HCAAM VNSCOR↗

NASA NTRSDate not supplied

Supporting Crew Medical Decisions on Deep Space Missions: A Real-Time Performance Monitoring

Crewed missions into deep space will require astronauts to respond autonomously to safety- and time-critical anomalies. These and other potential issues will require the continuous monitoring and recognition of potentially subtle, but complex, anomalous data patterns. NASA continues to investigate real-time metrics for Extravehicular Operations1 and for providing feedback to improve performance2. Since it is unknown how long-term exposure to deep space will affect crew health, continuous monitoring of clinical and subclinical status is warranted. NASA is characterizing crew medical decision-support needs and identifying the recommended requirements of a Clinical Decision Support System (CDSS) within the Exploration Medical Capability Element (ExMC) of the Human Research Program3. Early detection, and continuous monitoring, of performance may provide an effective prognostic capability for assessing crew health state, and the identified requirements could be provided for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. There is evidence to suggest that automatic, unobtrusive collection of keystroke features, such as alphanumeric key latencies, backspaces or typing rhythm, could be a valuable performance screening tool. Keystroke features show significant differences between control subjects and patients with overt clinical illness, such as multiple sclerosis4 and Parkinson’s disease5, as well as behavioral health and performance issues, such as mild cognitive impairment6 and depression7. This work is exploring a capability for the assessment of early task performance decline in crew as they perform daily activities. Timestamped keyboard entries are continuously collected and stored. Nominal keystroke interactions are compared to those obtained after exposure to spaceflight stressors, e.g., fatigue, to determine the methods sensitivity. This work will pave the way toward an objective detection tool that may be deployed in a spaceflight setting.

clinical↗

NASA NTRSDate not supplied

An Analysis of Depolarization Streaks for Anticipating Lightning in Thundersnow

Infrequent lightning events, particularly in stratiform precipitation, present a unique decision-support challenge to National Weather Service (NWS) forecasters and core partners. Anticipating thundersnow events, which are rare compared to warm season lightning, are especially difficult to anticipate due to slanted updrafts within the comma-head region of a mid-latitude cyclone. Researchers and operational forecasters have observed depolarization streaks in differential reflectivity (ZDR) as a result of ice crystal layers prior to thundersnow initiation. These depolarization streaks form as a result of ice crystals aligning themselves with enhancements in the electric field in stratiform precipitation and are associated with ZDR values near zero. During the 7 March 2018 winter storm, a teacher in New Jersey was struck by lightning while dismissing students and depolarization streaks were observed in weather radar observations. Therefore, it is advantageous to examine depolarization streaks in ZDR to determine whether it can be used to anticipate lightning potential in winter-time events. Multiple events will be analyzed using the National Lightning Detection Network (NLDN) and the Geostationary Lightning Mapper (GLM) to determine if thundersnow flashes coincide with ZDR depolarization streaks in weather radars. This study also expands on collaboration between the NWS Huntsville Forecast Office and NASA Short-term Prediction Research and Transition (SPoRT) program to determine how depolarization streaks can be used operationally to anticipate lightning within stratiform regions. Preliminary work has indicated noticeable potential in correlating depolarization streak as a precursor to thundersnow flashes in a variety of geographical regions.

Autumn Millard↗

NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗

NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Milad Memarzadeh↗

NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗

NASA NTRSDate not supplied

Measured and Modeled Vineyard Canopy Development and Water Use

Two publicly available applications relevant to vineyard irrigation management are described. OpenET is a satellite-based system that applies an ensemble of remote sensing methods to enable wide-area monitoring of evapotranspiration (ET) and related measures such as vegetation canopy development (via the NDVI spectral index). Data are freely available at one-quarter acre spatial resolution, and may be automatically aggregated to the individual block level. The satellite-based daily ET data were compared with in-situ eddy covariance measurements collected by micro-meteorological instrumentation in a Central Coast vineyard over a three-year period (2020-2022). Estimation uncertainties were reasonably consistent with prior reports for GRAPEX sites in the Central Valley and North Coast. The CropManage (CM) web application is a free software tool developed and operated U.C. Cooperative Extension for ET-based irrigation scheduling of major specialty crops. CM provides specific guidance for irrigation events in terms of irrigation system runtime. Applied water recommendations are based largely on estimated ET, derived from assumed canopy cover and associated crop coefficients, since the last irrigation or rainfall event. The application was recently adapted to vineyards by accounting for presence of winter/spring cover crop, and vine water stress imposed by regulated deficit irrigation. A 2022 field campaign involved 12 Central Coast and San Joaquin Valley commercial vineyards, and OpenET data were used to help evaluate CM output. Maximum percent vine cover was compared to estimates derived from average July satellite NDVI. The difference for 10 sites lacking midseason groundcover ranged from 0-7% between datasets with average agreement near 4%. Cumulative ET estimates agreed with OpenET to within 12% at the majority of sites, while larger discrepancies at the remaining sites may require additional data collection and analysis during the 2023 season. Satellite based systems such as OpenET have the potential to help parameterize CropManage and similar agricultural decision-support systems.

Measured↗

NASA NTRSDate not supplied

Satellite-assisted Evaluation of Irrigation Application Efficiency for Lettuce in the Salinas Valley

The Salinas Valley region of California’s Central Coast is a majorU.S. vegetable producer. Lettuce is a leading commodity, grown on about 100,000 acres with annual value near $1.4B (2022 Monterey County Ag Report). Vegetables crops in the area aretypically well watered and fertilized in pursuit of commercial yield and quality standards. Information on irrigation application efficiency, meanwhile, can enhance waterresource sustainability and mitigate impacts of nitrate infiltrationon groundwater quality. The Crop Consumptive Use Fraction (CCUF), previously recommended by the California Department of Water Resources, is a metric that expresses evapotranspiration (ET) of applied water relative to total applied water. More recently under Assembly Bill 1668 addressing water conservation and drought planning, the metric can be used to help quantify application efficiency to support preparation of agricultural water management plans in the state. This study will report CCUF evaluations from several commercial lettuce plantings in the Salinas Valley during the 2022 dry season (late spring-early fall). The evaluations were derived from satellite-based observations of cropET from the OpenETcloud application, and applied water monitored by in-line flowmetersand supplemented by grower reports of irrigation system runtime. The study will also compare observed (satellite-based) ET with modeled ET from the CropManage irrigationand nutrient management decision-support system operated by Cooperative Extension. Additional study in cool-season vegetables is ongoing in 2023.

Satellite-assisted↗

NASA NTRSDate not supplied

Optimization of Airport Runway Configuration with Forecast-Augmented Offline Reinforcement Learning

Runway configuration Management (RCM) governs the optimal utilization of runways based on variables such as traffic and meteorological conditions, making it a daunting task in air traffic management due to its dependency on volatile operational and environmental factors. This paper improves upon our previous work [1] on using offline model-free reinforcement learning for creating a Runway Configuration Assistance (RCA) decision-support tool. A novel integration of forecast data from LAMP (Localized Aviation Model Output Statistics Program) and TAF (Terminal Area Forecast) is introduced, enhancing the tool’s accuracy and also its adaptability to quick wind changes. The performance is evaluated using two major US airports, Charlotte Douglas International Airport (CLT) and Denver International Airport (DEN). To counter scalability issues presented by the addition of discrete forecast variables, we transitioned to a continuous state space model, ensuring scalability and inclusion of longer forecast data. The results of our experiments reflect significant improvements in the RCA tool’s prediction accuracy.

Sumanth Nethi↗

NASA NTRS2024

ISS Technology Demonstrations for Future Spaceflight Medical Systems

Throughout the history of human spaceflight, crewmembers have experienced various in-flight medical conditions including illness and injury. Planned missions to the Moon and Mars will require capabilities to maintain the health of future space travelers. Mass, power, and volume available in the vehicles and habitats for these missions will be severely constrained; resupply of resources will be limited or non-existent, as will opportunities for evacuation to Earth. Furthermore, ground-based support will be hampered by communication latencies and blackouts. These vehicle and mission constraints will necessitate a medical system that has been efficiently planned, providing on-board procedural guidance in addition to a variety of medical devices and consumable resources. Medical capabilities required for the diagnosis and treatment of potential medical conditions during future spaceflight missions may include real-time health monitoring, medical imaging, and biomarker analyses ( e.g., blood or urine). Terrestrial medicine shares these needs, thus many of these medical capabilities could likely be satisfied by Commercial-Off-The-Shelf (COTS) devices and methodologies; however, in some cases the unique space environment and increased mission duration will drive the need to modify technologies and the way care is provided. NASA’s Human Research Program (HRP) Exploration Medical Capability (ExMC) Element and Mars Campaign Office’s Exploration Medical Integrated Product Team (XMIPT) are working together to decrease medical risk during exploration missions. Flight-tested medical diagnostic and treatment technologies are necessary to effectively manage medical conditions relevant to exploration missions while meeting vehicle constraints, integrating with medical decision-support tools, and enabling increasingly Earth-independent operations. Several projects have leveraged the ISS as a testbed for exploration, including 1) i n- situ blood analysis, 2) medical inventory, 3) intravenous fluid generation, and 4) autonomous medical procedure guidance. Management of several in-flight medical conditions, such as bacterial and viral infections and acute radiation syndrome, is dramatically improved with ability to assess blood cell populations, electrolytes, and metabolites. I n December 2020 and January 2021 ExMC performed an ISS technology demonstration (Tech Demo) of the HemoCue® WBC DIFF analyzer (HemoCue, Brea, CA), a COTS device that was modified to enable functionality in a spaceflight environment. This Tech Demo marked the first time that hematology measurements were successfully performed real-time in microgravity. Also modified and demonstrated was the reusable Handheld Electrolytes and Lab Technology for Humans (rHEALTH) ONE analyzer (rHEALTH, Bedford, MA), which uses flow cytometry and sheath-based hydrodynamic focusing methodologies. The rHEALTH ONE ISS Tech Demo in May 2022 demonstrated test results obtained in-flight matched those on the ground. NASA currently relies on crew self-reporting to manage and maintain medical inventory on ISS.The ability to maintain an accurate inventory becomes more critical during long duration missions since the crew will need to be increasingly autonomous in finding and utilizing medical items, including those scenarios when alternative treatments need to be considered due to limited or no resupply. HRP’s Medical Consumables Tracking (MCT) project was developed by ZIN Technologies, Inc. (Cleveland, OH), and demonstrated real-time tracking of medical supplies aboard the ISS between December 2016 and July 2018. The MCT system design utilized Radio Frequency Identification Device (RFID) technology to perform automated inventory and was installed in the Crew Health Care System (CHeCS) Resupply Stowage Rack (RSR). The challenge of limited shelf life, exacerbated by the lack of resupply opportunities, affects a plethora of medical system components including consumables, pharmaceuticals, and intravenous (IV) fluid. In 2010, ExMC funded ZIN Technologies, Inc. (Cleveland, OH), to develop the Intravenous Fluid Generation (IVGEN) system. IV fluids were successfully generated with IVGEN using the potable water supply on ISS during ISS Expedition 23. The XMIPT is in the process of developing a miniaturized version of the original IVGEN hardware for a future Tech Demo aboard the ISS. Current ISS medical operations rely heavily on preflight training and real-time remote guidance, both of which become impractical or impossible for exploration missions. The primary goal of the Autonomous Medical Officer Support (AMOS) Software Tech Demos on ISS was to confirm telemedical proof-of-concept for autonomous medical imaging in an operational setting. This novel software tool shifts emphasis from preflight training and real-time remote guidance to in-flight just-in-time instruction, a new and necessary paradigm for crew medical autonomy. AMOS introduces a novel, streamlined skill management concept for exploration missions featuring comprehensive training and guidance modules for ultrasound examinations using the ISS Ultrasound 2 (a modified GE Vivid-q™; General Electric HealthCare, Chicago, IL). With no prior crew training or remote guidance, two Tech Demos on the ISS (April 2020 and June 2022) resulted in high quality, clinically useful image sets. We will provide a review of historical, current, and planned medical devices and technologies considered for inclusion within future spaceflight medical systems and summarize hardware development activities and medical device tech demos conducted on the ISS.

Astronaut health and performance↗

NASA NTRSDate not supplied

Objective Structured Clinical Evaluation (OSCE) of an Artificial Intelligence (AI) Clinical Decision Support System (CDSS) Tool

BACKGROUND Objective Structured Clinical Evaluations (OSCEs) have long been established as a robust methodology for summative assessment of clinical skills and decision-making during medical education. The recent integration of Artificial Intelligence (AI) into clinical decision-making processes has prompted the need for novel evaluation frameworks to assess the efficacy and reliability of AI clinical decision support system (CDSS) tools. This abstract outlines the process of quantitatively evaluating a novel CDSS (“Doc in a Box” Google 2024) trained on curated medical spaceflight data in the psychomotor domain as it interfaces with a human volunteer acting as the crew medical officer (CMO). PURPOSE The AI CDSS under review was developed as part of the Lunar Command and Control Interoperability (LuCCI) project, which is intended to address a gap in how Lunar Surface Systems (LSS) would interoperate across multiple programs, commercial partners, and international partners. The project objective is to define, prototype, integrate, and evaluate an interoperable lunar command, control, data, and software reference architecture to enable autonomy and informatics capability through common standards across LSS. A multi-modal AI-based CDSS compatible with Federated LSS will assist clinicians in diagnosing and managing complex medical conditions by providing evidence-based recommendations through predictive analytics. Given the critical role of decision-support as NASA continues to evolve its Earth-independent medical operations (EIMO), it is imperative to ensure that such AI tools perform reliably and align with clinical standards during progressive lunar and Martian exploration class missions. METHODS The OSCE framework, traditionally used for evaluating human clinicians, was adapted to assess the AI tool's decision-making capabilities in simulated clinical scenarios. In this adapted OSCE, the AI CDSS was tested across a series of structured clinical scenarios designed to mimic real-life spaceflight patient cases. These scenarios included a range of conditions and complexities, allowing for comprehensive assessment of the tool's performance. Key evaluation metrics included accuracy of diagnosis, timeliness of decision-making, and appropriate recommendations for therapies. The OSCE was scored by human physician evaluators who assessed the AI's recommendations in comparison with expert clinicians' medical decision making to ensure alignment with best practices and the standard of care. RESULTS Preliminary results indicate that the AI CDSS demonstrated high accuracy in diagnostic recommendations and decision support across various scenarios. However, certain limitations were noted, such as occasional discrepancies in handling complex or nuanced cases that required a more contextual understanding. Additionally, the tool scored higher on the diagnostic portion of the rubric, with lower scores in the therapeutic recommendations. These findings highlight the importance of continuous refinement and validation of AI tools through rigorous evaluation frameworks like the OSCE. The adaptation of OSCEs for AI tools presents several advantages, including a structured and reproducible approach to evaluation, the ability to test AI systems in diverse clinical scenarios, and the opportunity to benchmark AI performance against established clinical standards to permit charting of future progress as aerospace medicine evolves as a discipline. Remaining challenges include ensuring that these evaluations capture the full spectrum of clinical decision-making scenarios that will be confronted by CMOs during missions and adequately reflecting real-world variability of the austere spaceflight environment. CONCLUSION Employing OSCEs to evaluate AI clinical decision support tools offers a promising approach to validating their clinical utility and efficacy. This methodology not only provides insights into the tool's performance but also fosters ongoing improvement and alignment with standard of care practices. Future research should focus on refining these evaluation processes and addressing limitations to enhance the integration of AI tools in clinical spaceflight settings. REFERENCES Scott S, Hearns V, Barker MA. Testing Clinical Skills: A Look at the OSCE and USMLE Clinical Skills Exams. S D Med. 2019 Oct;72(10):451-453. Majumder MAA, Kumar A, Krishnamurthy K, Ojeh N, Adams OP, Sa B. An evaluative study of objective structured clinical examination (OSCE): students and examiners perspectives. Adv Med Educ Pract. 2019 Jun 5;10:387-397. Karam VY, Park YS, Tekian A, Youssef N. Evaluating the validity evidence of an OSCE: results from a new medical school. BMC Med Educ. 2018 Dec 20;18(1):313.

Ariana M Nelson↗

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↗

DOE OSTI2022

Times (and the Grid) are A-Changin'

<span style="background-color: rgb(255, 255, 255); font-family: "Neue Plak", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", Helvetica, Tahoma, Arial, sans-serif; font-size: 16px; color: rgb(111, 114, 135);">This webinar highlighted two datasets that provide forward-looking projections of electric grid GHG emissions for the United States. These data sets were created to help present-day analyses and decision-support tools incorporate what we know about the ongoing evolution of the U.S. electric grid. <span style="background-color: rgb(255, 255, 255); font-family: "Neue Plak", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", Helvetica, Tahoma, Arial, sans-serif; font-size: 16px; color: rgb(111, 114, 135);">Additionally, the webinar compared these two databases to other data sources to summarize differences (i.e., attributional versus consequential emissions rates, age and source of underlying data, regionalization approach, and boundary conditions) and highlighted the value and need for expert guidance when an analyst or developer is choosing how to represent electric-sector GHG emissions. <span style="background-color: rgb(255, 255, 255); font-family: "Neue Plak", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", Helvetica, Tahoma, Arial, sans-serif; font-size: 16px; color: rgb(111, 114, 135);">This webinar was intended to set the stage for ACLCA roundtable events to further discuss these issues.

Jamieson, Matthew↗

DOE OSTI2022

Verification and Validation of START: A Spent Nuclear Fuel Routing and Decision Support Tool

The Stakeholder Tool for Assessing Radioactive Transportation (START) is a web-based geospatial decision-support tool being developed by the US Department of Energy’s Office of Integrated Waste Management (IWM) to support federal interim storage for spent nuclear fuel (SNF) and associated transportation. START provides many functions for the IWM program including: serving as a communications tool for conveying geospatial data and information, an options analysis tool for exploring potential transport modes and routes for transporting SNF from nuclear power plants to future federal interim storage facilities, an emergency response planning tool for Tribes and States to identify training needs along potential SNF transport corridors, an environmental analysis tool for estimating potential radiation dose exposure from incident-free and incident-case SNF transport conditions, and a systems analysis support tool providing route-related inputs for system throughput analysis. As part of the START development process, a verification and validation (V&V) effort is being undertaken. In the initial V&V phase, several outputs of the START tool were checked such as the total distance, population and population densities within the buffer zone, and incident free dose. The V&V process is fluid as it will be utilized after each version change to ensure that the core functionalities of the tool are maintained and the results are consistent with the previous versions. Efforts have also been put into developing scripts to aid in the process of automating certain sections of the V&V work. As some of the V&V efforts use Environmental Systems Research Institute’s (ESRI) tools upon which the START framework is built, the START data were compared against outputs from tools like Quantum Geographic Information System (QGIS) for buffer zone populations and route lengths to ensure independence of the V&V process. Good agreement was observed between the START results and the independent V&V studies with the majority of the differences falling between 1% and 5% for populations within the buffer zone and route distance. This presentation describes the development and design of the START tool, V&V methods employed for various metrics of interest and their respective results, and future plans.

START, transportation, GIS, V&V↗

DOE OSTI2022

Verification and Validation of the Stakeholder Tool for Assessing Radioactive Transportation (START) – 22323

The U.S. Department of Energy (DOE) Office of Integrated Waste Management is planning for the eventual transportation, storage, and disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant and DOE sites. The Stakeholder Tool for Assessing Radioactive Transportation (START) is a web-based, geospatial decision-support tool developed for evaluating routing options and other aspects of transporting SNF and HLW, covering rail, truck, barge, and intermodal infrastructure and operations in the continental United States. The verification and validation (V&V) process is intended to independently assess START to provide confidence in the ability of START to accurately provide intended results. The V&V process checks the START tool using a variety of methods, ranging from independent hand calculations to comparison of START performance and results to those of other codes. The V&V activity was conducted independently from the START development team with opportunities to provide feedback and collaborate throughout the process. The V&V analyzed attributes of transportation routes produced by START, including route distance and both population and population density captured within buffer zones around routes. Population in the buffer zone, population density in the buffer zone, and route distance were all identified as crucial outputs of the START code and were subject to V&V tasks. Some of the improvements identified through the V&V process were standardizing the underlying population data in START, changing the projection of the population raster data, and changes to the methodology used for population density to improve its applicability for expected users. This collaboration also led to suggested improvements to some of the underlying shape file segments within START. A formal series of test routes went through the V&V process from START version 3.2.1 following the implementation of the recommended improvements to START. The V&V of the buffer zone population and distance reported in START were achieved by utilizing a custom workflow developed in QGIS. This platform was chosen because it is fully independent of START. The buffer zone population and route distances in START showed excellent agreement to independent V&V test results. Over 200 route test cases were run; in all cases, the percent difference in the population within the buffer zone was less than +/- 5%, and the majority of cases were below +/- 1%. These cases were also duplicated in ArcMap, which is Environmental Systems Research Institute’s (ESRI) GIS desktop application. The ArcMap results also show good agreement; however, they cannot be considered independent of START because START also uses ESRI software. The V&V tasks are currently addressing population density and estimated radiation dose in START as well as implementing further automation of the V&V process to allow larger suites of test routes to be run more easily and quickly by less experienced GIS users. These improvements to the V&V process will make it a valuable tool for the development team and the stakeholders so that each new iteration of START can easily be checked for quality and consistency. This paper and presentation will describe the V&V process, findings and implications, and ongoing V&V activities.

START, DOE, Transporation, dose, GIS↗