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NASA NTRSDate not supplied

Manual Crew Override of Vehicle Landings Following G-Transitions

BACKGROUND Manual control during exploration spaceflight consists of both planned automated supervisory control and unplanned crew override. This crew override capability is critical to enable overall mission success during landing contingencies. However, the introduction of manual override capabilities must be implemented to enable crews to mitigate risks introduced by human error. Adaptive changes in the sensorimotor system can manifest during g-transitions as spatial disorientation. While training and landing aids enable successful landing through disorientation, these adaptive changes may increase cognitive demand that needs to be accounted for in the manual control strategy. It is important to characterize these effects as soon as possible following the G-transition to develop appropriate countermeasures. METHODS In this study, we will examine operational piloting tasks following International Space Station (ISS) missions in a simulated lunar landing using a six-degree-of-freedom (6DOF) motion base. The motion-based simulation will be implemented in our laboratory at the Johnson Space Center (JSC) and will be available within hours following the return from commercial crew landings. The primary goals of this study are (1) to understand the impact of spaceflight on crew ability to perform manual crew override tasks, (2) to examine how adaptive changes in vestibular and cognitive function relate to changes in manual crew override proficiency, and (3) compare performance during late “just-in-time” on-board training with early post-flight crew performance. The impact of spaceflight on piloting capability will be assessed from pre- versus post-flight changes in crewmembers assigned to either short duration (< 30 day) or long duration (~6- month) missions to the International Space Station (ISS). Individual differences in post-flight vestibular and cognitive changes include motion sickness reports, measures of tilt motion perception accuracy and precision, and dual-task tracking. During the 6DOF lunar simulation, the crew will manually takeover attitude and rate-of-descent to the nominal or re-designated landing aim point during the approach phase. The outcome measures for the lunar crew override tasks will be the percent time maintaining actual vehicle states, e.g., attitude and rate-of-descent, within recommended guidance during the landing approach, number and maximum deviation outside limits, and root mean square error (RMSE). Given that “just-in-time” (JIT) training is an operational expectation for the Human Landing System (HLS) program, all participants will perform late inflight JIT training for each manual crew override task in which they will participate. Crew proficiency will be captured inflight during JIT training that will be implemented on a laptop with hand controllers to allow the crewmember to practice the landing task procedures like the approach implemented for JIT training with Shuttle landing and ISS telerobotic tasks. COUNTERMEASURE UPDATES The lunar lander simulation has been implemented onto a two-laptop display system where subjects select from alternative landing points, based upon avoiding hazards that the on-board system identifies during the landing. The subject makes inputs using a rotational hand controller (i.e., joystick) and a translational hand controller, which are processed by simulated vehicle dynamics to update the vehicle attitude and rate of descent. Subjects will utilize a combination of flight, situation, and status displays to monitor the state of the simulated vehicle. The simulation exports tilt and translation movements to a 6DOF motion base synced to visual movements and provides representative vestibular cues to the subject while performing the task inside an enclosed cabin featuring a simulated lunar out-the-window view and a heads-down lander cockpit view. RELEVANCE This project will deliver an operational demonstration of crew override capability following spaceflight and identify potential deficits that may require remediation. Comparison of individual vestibular and cognitive changes with crew override performance will help better characterize the manual control risks associated with sensorimotor alterations. The inclusion of “just-in-time” on-board training will ensure we are characterizing changes in override proficiency with this expected countermeasure in place. ACKNOWLEDGEMENTS: The authors acknowledge contributions from Draper, HLS Joint Test Panel, Crew HLS Interfaces for Piloting Working Group, and Dynamic Skills Trainer (DST) Lab toward the development of the lunar landing simulation. This project is funded by the Human Health Countermeasures Element.

Austin M Bollinger↗

NASA NTRS2014

MARGInS: Model-Based Analysis of Realizable Goals in Systems

Under NASAs Constellation effort, the Exploration Technology Development Program funded research toward a system validation capability that applied machine learning and test-case generation techniques to the analysis of black-box system behavior. The behavior analysis capability scaled to spaces of hundreds of input parameters and tens of thousands of test cases. Aerospace systems at the vehicle level, especially those systems which contain some level of autonomy, are best described by hybrid and non-linear mathematics. Even simplified models of such systems need parameter dimensionalities in the hundreds or thousands of parameters in order to capture sufficient fidelity. The System Safety Assessments (such as those described in the SAE ARP 4761A Safety Assessment Process guidelines) for these systems are prone to errorinteractions between the vehicles subsystems are complex, and can display emergent behaviors. NASA captured this new analysis in the Model-based Analysis of Realizable Goals in Systems (MARGInS) tool and applied it to the Pad Abort 1 (PA-1) simulation as part of the independent validation and verification cycle before the PA-1 flight test in May of 2010. MARGInS evaluated the adherence of the high-fidelity simulation to its requirements, and deter- mined the margins to failure from the expected nominal input conditions. Following the PA-1 test, the capabilities within the MARGInS framework have been extended with sophisticated statistical and white-box test case generation techniques and applied to other NASA missions. The frame- work now includes a critical factors analysis that was applied to NASAs Orion simulation and design. NASAs Aeronautics Research Mission Directorate (ARMD) leveraged the existing MARGInS framework for work on aviation safety for civil transport vehicles and for research on autonomy issues. The NASA ARMD effort created a time series output prediction capability that has been used to characterize trajectories for a plane with an adaptive control system, and a safety boundary detection capability that has been applied to an air traffic control concept of operation for the Federal Aviation Administration. The statistical and machine- learning based techniques within MARGInS have been successfully combined with concolic execution to improve the coverage of a critical unit by driving system-level inputs. The use case driving the concolic execution and MARGInS integration was inspired by the Air France 447 disaster in which the loss of a critical functionality (the airspeed calculation from the pitot tubes) led to loss of the entire plane with the people aboard. To illustrate capabilities and limitations, we will highlight the analyses for the applications listed above. We will then discuss the future plans for MARGInS and its interfaces with other tools.

Validation↗

NASA NTRS2023

State-of-the-Art: Small Spacecraft Technology

When the first edition of NASA’s Small Spacecraft Technology State-of-the-art report was published in 2013, 247 CubeSats and 105 other non-CubeSat small spacecraft under 50 kilograms (kg) had been launched worldwide, representing less than 2% of launched mass into orbit over multiple years. In 2013 alone, around 60% of the total spacecraft launched had a mass under 600 kg, and of those under 600 kg, 83% were under 200 kg and 37% were nanosatellites (1). Of the total 1,849 spacecraft launched in 2021, 94% were small spacecraft with an overall mass under 600 kg, and of those under 600 kg, 40% were under 200 kg, and 11% were nanosatellites (1). Since 2013, the fight heritage for small spacecraft has increased by over 30% and has become the primary source to space access for commercial, government, private, and academic institutions. The total number of spacecraft launched in the past 10 years is 5,681 and 45% of those had a mass. As with all previous editions of this report, the 2022 edition captures and distills a wealth of new information available on small spacecraft systems from NASA and other publicly available sources. This report is limited to publicly available information and cannot reflect major advances in development that are not publicly disclosed. We encourage any opportunity to publish mission outcomes and technology development milestones (e.g., via conference papers, press releases, company website) so they can be reflected in this report. Overall, this report is a survey of small spacecraft technologies sourced from open literature; it does not endeavor to be an original source, and only considers literature in the public domain to identify and classify devices. Commonly used sources for data include manufacturer datasheets, press releases, conference papers, journal papers, public filings with government agencies, news articles, presentations, the compendium of databases accessed via NASA’s Small Spacecraft Systems Virtual Institute (S3VI) Information Search, and engagement with companies. Data not appropriate for public dissemination, such as proprietary, export controlled, or otherwise restricted data, are not considered. As a result, this report includes many dedicated hours of desk research performed by subject matter experts reviewing resources noted above. Content in this 2022 edition is based on data available by October 2022. This report should not be considered as a comprehensive overview of all the technologies but a great reference for the current state-of-the-art SmallSat technologies. The organizational approach for each chapter is relatively consistent with previous editions and includes an introduction of the technology, current development status of the technology’s procurable systems, and summary tables of technologies surveyed. The content in each chapter is uniquely organized to present a mini-stand-alone report on spacecraft subsystems. As in previous years, chapters include information from previous editions but are updated with new and maturating technologies and reference missions. Tables in each section provide a convenient summary of the technologies discussed, with explanations and references in the body text. The authors have attempted to isolate trends in the small spacecraft industry to point out which technologies have been adopted after successful demonstration missions. Lastly, the authors tried to use the terms “SmallSat,” “microsatellite,” “nanosatellite,” and “CubeSat” in a consistent manner, even as these terms are often used interchangeably in the space industry. Every subsystem chapter contains updated information to reflect the growth in the small spacecraft market. Significant changes are included in several chapters. The “Complete Spacecraft Platforms” chapter now includes information on the two main market options, hosted payload services and dedicated buses. The “Power” chapter provides information on the development of solid-state batteries with significantly higher energy than the current state-of-theart lithium-ion batteries. A large effort was made to update the “Communications” chapter to appropriately capture the recent technology maturation of optical communications for SmallSats. The “Ground Data Systems and Mission Operations” chapter was updated to reflect the recent establishment of the Near Space Network and influx of SmallSat Optical Ground Stations. The “Guidance, Navigation and Control” chapter was updated to include Lidar sensor technology. The “Deorbit Systems” chapter includes a discussion of recently proposed changes by the Federal Communications Commission (FCC) to limit a spacecraft’s lifetime to no longer than 5 years after end-of-mission. The “Identification and Tracking” Chapter includes updated information on the progress of SmallSat tracking. Finally, this report now encompasses technology funded by NASA’s Small Spacecraft Technology (SST) program’s SmallSat Technology Partnerships (STP) initiative which is described further in this Introduction. The reader can find the included SST technology in the “On the Horizon” section of the “Thermal Systems”, “Communications”, and “Guidance, Navigation, and Control” chapters. A central element of this report is to list state-of-the-art technologies by NASA standard Technology Readiness Level (TRL) as defined by the 2020 NASA Engineering Handbook, found in NASA NPR 7123.1C NASA Systems Engineering Processes and Requirements. The authors have endeavored to independently verify the TRL value of each technology by reviewing and citing published test results or publicly available data to the best of their ability. Where test results and data disagree with vendors’ own advertised TRL, the authors have attempted to engage the vendors to discuss the discrepancy. Readers are strongly encouraged to follow the references cited in the literature describing the full performance range and capabilities of each technology. Readers of this report should reach out to individual companies to further clarify information. It is important to note that this report takes a broad system-level view. To attain a high TRL, the subsystem must be in a flight-ready configuration with all supporting infrastructure—such as mounting points, power conversion, and control algorithms—in an integrated unit. An accurate TRL assessment requires a high degree of technical knowledge on a subject device, and an in-depth understanding of the mission (including interfaces and environment) on which the device was flown. There is variability in TRL values depending on design factors for a specific technology. For example, differences in TRL assessment based on the operating environment may result from the thermal environment, mechanical loads, mission duration, or radiation exposure. If a technology has flown on a mission without success, or without providing valid confirmation to the operator, such claimed “flight heritage” was discounted. The authors believe TRLs are most accurately determined when assessed within the context of a program’s unique requirements. While the overall capability of small spacecraft has matured since the 2021 edition of this report, technologies are still being developed to make deep space SmallSat missions more routine and more cost effective. Future editions of this report may include content dedicated to the rapidly growing fields of assembly, integration, and testing services, and mission modeling and simulation–all of which are now extensively represented at small spacecraft conferences. Many of these subsystems and services are still in their infancy, but as they evolve and reliable conventions and standards emerge, the next iteration of this report may also evolve to include additional chapters.

Bruce Yost↗

NASA NTRSDate not supplied

INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY

Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods. REFERENCES [1] Open science in space. Nature Medicine, 2021. 27(9): p. 1485-1485. [2] Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. [3] Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5. [4] Whetzel, P.L., et al., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res, 2011. 39(Web Server issue): p. W541-5.

informatics↗

NASA NTRSDate not supplied

INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY

knowledge↗

NASA NTRS2000

NPSS on NASA's IPG: Using CORBA and Globus to Coordinate Multidisciplinary Aeroscience Applications

Within NASA's High Performance Computing and Communication (HPCC) program, the NASA Glenn Research Center is developing an environment for the analysis/design of aircraft engines called the Numerical Propulsion System Simulation (NPSS). The vision for NPSS is to create a "numerical test cell" enabling full engine simulations overnight on cost-effective computing platforms. To this end, NPSS integrates multiple disciplines such as aerodynamics, structures, and heat transfer and supports "numerical zooming" between O-dimensional to 1-, 2-, and 3-dimensional component engine codes. In order to facilitate the timely and cost-effective capture of complex physical processes, NPSS uses object-oriented technologies such as C++ objects to encapsulate individual engine components and CORBA ORBs for object communication and deployment across heterogeneous computing platforms. Recently, the HPCC program has initiated a concept called the Information Power Grid (IPG), a virtual computing environment that integrates computers and other resources at different sites. IPG implements a range of Grid services such as resource discovery, scheduling, security, instrumentation, and data access, many of which are provided by the Globus toolkit. IPG facilities have the potential to benefit NPSS considerably. For example, NPSS should in principle be able to use Grid services to discover dynamically and then co-schedule the resources required for a particular engine simulation, rather than relying on manual placement of ORBs as at present. Grid services can also be used to initiate simulation components on parallel computers (MPPs) and to address inter-site security issues that currently hinder the coupling of components across multiple sites. These considerations led NASA Glenn and Globus project personnel to formulate a collaborative project designed to evaluate whether and how benefits such as those just listed can be achieved in practice. This project involves firstly development of the basic techniques required to achieve co-existence of commodity object technologies and Grid technologies; and secondly the evaluation of these techniques in the context of NPSS-oriented challenge problems. The work on basic techniques seeks to understand how "commodity" technologies (CORBA, DCOM, Excel, etc.) can be used in concert with specialized "Grid" technologies (for security, MPP scheduling, etc.). In principle, this coordinated use should be straightforward because of the Globus and IPG philosophy of providing low-level Grid mechanisms that can be used to implement a wide variety of application-level programming models. (Globus technologies have previously been used to implement Grid-enabled message-passing libraries, collaborative environments, and parameter study tools, among others.) Results obtained to date are encouraging: we have successfully demonstrated a CORBA to Globus resource manager gateway that allows the use of CORBA RPCs to control submission and execution of programs on workstations and MPPs; a gateway from the CORBA Trader service to the Grid information service; and a preliminary integration of CORBA and Grid security mechanisms. The two challenge problems that we consider are the following: 1) Desktop-controlled parameter study. Here, an Excel spreadsheet is used to define and control a CFD parameter study, via a CORBA interface to a high throughput broker that runs individual cases on different IPG resources. 2) Aviation safety. Here, about 100 near real time jobs running NPSS need to be submitted, run and data returned in near real time. Evaluation will address such issues as time to port, execution time, potential scalability of simulation, and reliability of resources. The full paper will present the following information: 1. A detailed analysis of the requirements that NPSS applications place on IPG. 2. A description of the techniques used to meet these requirements via the coordinated use of CORBA and Globus. 3. A description of results obtained to date in the first two challenge problems.

Lopez, Isaac↗

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↗