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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗
DOE OSTI2025

Pooled Rideshare in the U.S.: An Exploratory Study of User Preferences

Pooled ridesharing offers on-demand, one-way, cost-effective transportation for passengers traveling in similar directions via a shared vehicle ride with others they do not know. Despite its potential benefits, the adoption of pooled rideshare remains low in the United States. This exploratory study aims to evaluate potential service improvements and features that may increase users’ willingness to adopt the service. The study analyzed transportation behaviors, rideshare preferences, and willingness to adopt pooled rideshare services among 8296 U.S. participants in 2025, building on findings from a 2021 nationwide survey of 5385 U.S. participants. The study incorporated 77 actionable items developed from the results of the 2021 survey to assess whether addressing specific user-generated topics such as safety, reliability, convenience, and privacy can improve pooled rideshare use. A side-by-side comparison of the 2021 and 2025 data revealed shifts in transportation behavior, with personal rideshare usage increasing from 22% to 28%, public transportation from 21% to 27%, and pooled rideshare from 6% to 8%, while personal vehicle (79%) use remained dominant. Participants rated features such as driver verification (94%), vehicle information (93%), peak time reliability (93%), and saving time and money (92–93%) as most important for improving rideshare services. A pre-to-post analysis of willingness to use pooled rideshare utilizing the actionable items as per respondents’ preferences showed improvement: “definitely will” increased from 15.9% to 20.1% and “probably will” rose from 35.6% to 47.7%. These results suggest that well-targeted service improvements may meaningfully enhance pooled rideshare acceptance. This study offers practical guidance for Transportation Network Companies (TNCs) and policymakers aiming to improve pooled rideshare as well as potential future research opportunities.

Transportation Network Companies (TNCs)↗
NASA NTRSDate not supplied

Zero-Trust Architecture for Autonomous Edge Computing

We are at the apex of an aviation revolution where autonomy will play a central role in enabling complex, multi-agent systems to communicate, interact, and collaborate on a myriad of applications spanning autonomous swarms to wild-fire management. Autonomy is not an absolute but rather a spectrum ranging from a system requiring significant human intervention to one requiring little to none [1]. For example, the extreme, in the case of an autonomous aircraft, is one that operates independently in the airspace interacting with all other elements (air traffic controllers, other pilots) as if it were a human pilot. Critical to this vision is an architecture that enables autonomous agents to interact with minimal latency. Edge computing is an emerging architecture where compute and storage is pushed to the ‘edge’ of the network in order to minimize the round-trip time from agent to resource thereby mitigating the latency associated with cloud-only based approaches. Additionally, services can generate massive amounts of data (e.g., video feeds), which may require analysis in near real-time. Moving this data to the cloud for further processing may not be feasible due to latency, bandwidth, and cost. Privacy, security, and reliability can also be improved by edge computing architectures. However, this geo-distributed and dynamic* architecture complicates the establishment of unambiguous network security boundaries and can lead to vulnerabilities including man in the middle attacks, replay attacks, physical security breaches of edge nodes, signal interception, etc. This motivates the need for zero-trust architectures [2–4] which de-emphasize the notion of static network perimeters and, as the name implies, do not instill any innate trust in any particular agent. It is required that all agents must be authorized and approved in every transaction. In this paper, we present a zero-trust architecture suitable for edge-computing applications that demand significant low-latency, security, privacy, and reliability.

zero trust↗
NASA NTRSDate not supplied

Results for the Subjective Habitability Acceptability Questionnaire From HERA C5 and C6

BACKGROUND The impact of habitability on psychological and performance outcomes is recognized as an important factor in habitat and vehicle design—especially as missions increase in length. Formal evaluation of habitability’s impact is possible using the Spaceflight Habitability Acceptability Questionnaire (SHAQ), which was psychometrically validated by Roma et al. in 2022 [1]. The SHAQ survey systematically quantifies the relationships between habitat areas (e.g., bedroom, kitchen, hygiene area, office, etc.) and behavioral health and performance (BHP) outcomes (i.e., individual and team performance, mood, stress, sleep, social interaction), and considers the moderating effects of key habitability aspects (i.e., privacy, control, convenience, efficiency, comfort, social density) on that relationship. METHOD Respondents were HERA participants from Campaign 5 and 6 (n = 32). Data from three environments were used in our SHAQ analyses: SHAQ in HERA (in-mission), SHAQ at Home (pre-mission), and SHAQ in Hotel (pre- and post-mission for C6, just pre-mission for C5). For each habitat area, respondents are asked how that area layout impacts six BHP outcomes on a 201-point visual analog scale with the anchors: impairs, has no effect, and facilitates. They also had the option to respond “Not Applicable”. Follow up questions asked why six key habitability aspects (e.g., privacy) influenced the way the habitat area affected a given outcome. Participants indicated whether a habitat area’s habitability aspects were inadequate (low score) to adequate (midpoint) to ideal (high score), using a 201-point visual analog scale as well but were binned to a 7-point scale for analyses. RESULTS AND CONCLUSION When comparing across environments, ratings of BHP outcomes were worse in HERA compared to home and pre-mission hotel, but rebounded for the post-mission hotel such that post-mission hotel rating were often higher than all previous ratings. Within HERA, ratings of how a habitat area impacted an outcome skewed towards facilitation with communal areas (i.e., galley and office) rated most facilitating. The follow up questions about why six key aspects related to habitability moderated outcomes indicated that most habitat areas were rated as having “adequate” habitability characteristics. Privacy was the only moderator with average scores below adequate for all habitat areas. The office had the most moderators scoring above adequate for all habitat areas. Ratings indicated small to moderate effects of layouts on BHP outcomes: with facilitating layouts averaging between 4 and 5, and impairing layouts averaging between 2 and 4. The sleeping module layout facilitated sleep, with ratings indicating adequate comfort but inadequate privacy. The galley layout facilitated social interaction and mood, with ratings indicating adequate efficiency and convenience. The office layout facilitated social interaction, team and individual performance, with ratings similarly indicating adequate efficiency and convenience. The hygiene module worsened individual performance, mood and stress, with ratings indicating less than adequate privacy and social density. Our findings suggest there is value in having dedicated team and individual spaces. REFERENCES [1] Roma, P. G., Landon, L.B., Spencer, C. A., Whitmire, A. M., & Williams, T. J. (2022). SHAQ: Development and Validation. Human Factors: Special Issue on Spaceflight, 65(6), 1074-1104. ACKNOWLEDGEMENTS This project was funded by the NASA Human Research Program directed task Human Factors and Behavioral Performance in HERA (PI = S. Bell) to the BHP Lab. KBR Inc. and JES Tech authors were supported by KBR’s Human Health and Performance Contract NNJ15HK11B with NASA.

behavioral health↗
DOE OSTI2025

Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML

Internet of Things (IoT) and Edge Computing (EC) are rapidly becoming an integral part of the modern society. By 2030, there is estimated to be over 40 billion active and connected IoT devices [1]. This rapid progress also comes with a significant implication on cybersecurity. Back-end infrastructure and systems have a much broader attack than they did previously due to vulnerable IoT/EC devices being connected to wireless networks. This expanding attack surface is a growing concern because IoT/EC are increasingly being used in critical systems such as power grids, health care, and smart homes. To effectively address a problem of this scale, cognitive cyber methods—which can autonomously detect and react to cyber attacks as they develop—are needed. To address this, we bring Artificial Intelligence (AI) and Machine Learning (ML) to IoT/EC devices, using tinyML to monitor voluminous IoT data against cyber threats, and using Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. We propose a novel three-layer architecture: (1) an IoT layer for tinyML-based inference, (2) an edge layer for ML model training, and (3) a cloud layer for FL operations. Using the publicly available 11-class N-BaIoT dataset [2], we demonstrate that this architecture mitigates resource constraints at the IoT layer while improving detection accuracy over standard two-layer designs. An outlier-resistant scaler, feature reduction, and quantization enable the tinyML model to maintain detection accuracy with a reduced model size. Additionally, federated learning that only utilizes the intersection (across heterogenous devices) of the reduced feature set achieves superior detection accuracy compared to locally trained models.

Li, Mingyan [ORNL] (ORCID:0009000569532640)↗
NASA NTRSDate not supplied

Artificial Intelligence Enhancements to Imagery for Space Operations

Philosophy classes still ponder the question asked by Dr. George Berkely, an Anglican Bishop and philosopher in the 1600’s-- “If a tree falls in a forest and no one is around to hear it, does it make a sound?” With that in mind, I ask the following—If a still image or motion imagery from a space mission cannot be found during a search, does it exist? Since the beginning of spaceflight, imagery has been a key form of data collected. Whether for mere curiosity (what does Earth look like from Space?), or for operational reasons (did the solar panel deploy?), or for engineering purposes (what was that object that floated away from the spacecraft?), imagery has been included in space missions. To be useful, though, the image or motion imagery must be accessible and accessed when needed. During the analog era, that typically meant captions and numbers associated with the physical media. With “born digital” imagery, it is possible to add metadata to the image data file. This metadata might include the date and time of capture, mission, camera, exposure data, and similar data fields. Many modern cameras embed some basic metadata into the image file at the moment of capture. The reality, though, is even with today’s born-digital enhancements with embedded metadata at the time of capture, reviewing and cataloging still and motion imagery is very labor intensive. Humans review the imagery for sensitive content (privacy concerns, imagery containing proprietary data/subject matter), and to identify imagery containing crew members or imagery that should be reviewed for engineering or scientific reasons. All this review and manual data entry is very time-consuming. Many improvements in Artificial Intelligence (AI), Machine Learning, and processing power now make it possible to identify persons, objects, motion, color, audio with sensitive content, and other details after or while the imagery is captured.

Rodney Grubbs↗
DOE OSTI2024

Ultrasound Interim cyber-physical research evaluation

Recent events have presented Medical Practitioners with concerns about safety and privacy implications associated with GE’s VScan MIot Ultrasound Device. Concerns relate to the devices potential risk to broadcast location data that can pose serious risks to device users and patients. This report was commission in collaboration with Augusta University to determine if device concerns pose real threat to operators and patients. In effort to ensure all potential threat vectors are targeted, the initial step was to analyze the supply chain. Here the devices are CT scanned to look for any hardware anomalies that could present threat vectors to users of these devices. (See SRNL-STI-2024-00225 for detailed analysis). No anomalies were found associated with the hardware utilized within the GE VScan Air.

42 ENGINEERING↗
NASA NTRS2025

Habitability and Human Factors Assessment (iSHORT, SHAQ, and SHU)

BACKGROUND As long-duration off-planet habitats become a reality, a consideration of habitability and human factors (HF) is crucial. The habitat is more than just a place to live and work. It is also the crew’s perception of the space, and the psychological impacts of size, layout, and usage over time; all of which can support or strain behavioral health and performance (BHP). A previous International Space Station (ISS) habitability study used the iSHORT (Space Habitability Observation Reporting Tool) to collect detailed data about habitability and human factors and inform NASA Standards. Of the previous iSHORT study, only one of the six ISS subjects had a duration of one year; all other ISS and ground analog subjects had shorter mission durations from one week to six months. It is necessary to collect new data with a focus on long-duration exploration missions of > 6 months and on planetary surface habitat design. New data is also needed to compare the iSHORT to other habitability measures. One measure, the SHAQ (Subjective Habitability and Acceptability Questionnaire), assesses the intersection of psychology and habitability. Another complementary measure, the Scale for Habitat Usability (SHU), is a brief subjective scale that captures how habitat design impacts perceived usability of the built environment in relation to task performance. OBJECTIVE Our study aims to (1) understand how individual well-being and team dynamics may relate to HF concerns over time, (2) capture how habitability and HF change over time, (3) compare the three habitability measures (iSHORT, SHAQ, SHU), (4) assess habitats to capture HF design concerns and related BHP impacts of a planetary habitat, and (5) inform future standards for HF design. METHOD Data are being collected on crews living and working in long-duration spaceflight analogs. Individual-level data collections are repeated at regular intervals throughout the missions on several habitat areas, activities, and key equipment (i.e., points of interest). These points of interest (POIs) include the kitchen/galley, crew quarters, and other work and living areas. Assessments include evaluations of privacy, comfort, convenience, control, efficiency, and social density through the lens of subsequent outcomes like sleep, individual performance, group activities performance, stress, mood, and social interactions. Pre- and post-mission evaluations will also allow comparison with homes, pre- and post-mission hotels, and a retrospective reflection of living and working in a long-duration analog. INITIAL DATA COLLECTIONS In this poster, we will describe the measures and data yield. Since the research protocol was designed, the study team has collected iSHORT Standalone four times, nine collections of SHAQ, and three collections of iSHORT with SHAQ. Data collection is ongoing. SUMMARY A novel assessment suite has been developed to further aid the comparison and complementary understanding of the habitability and human factors measures, which will allow for efficient deployment of these measures in analogs and/or spaceflight in near-term research as well as support well-being and performance through design.

J C W Miller↗
DOE OSTI2026

Dataset 3: A National Dataset on Actionable Items in Improving Pooled Rideshare, 2025.

Dataset 3: A National Dataset on Actionable Items in Improving Pooled Rideshare.” 2025. Dataset Description: Pooled Rideshare Acceptance Survey - Phase 3 (2025, N = 8,296). This dataset represents the third and final phase of a national survey aimed at understanding user acceptance and preferences related to pooled rideshare (PR) services in the United States. Building on insights from earlier phases, this phase expands both the sample size and the depth of analysis to support policymaking, transportation planning, and service design for sustainable mobility systems. The Phase 3 survey was administered online to a nationally representative sample of 8,296 U.S. adults. The sample includes a wide range of demographics. The survey retained core questions from previous phases while introducing 77 detailed service features (actionable items) to evaluate potential improvements to PR offerings. Each feature was designed to assess whether a specific improvement, such as enhanced safety measures, real-time ride tracking, or user training would increase participants’ willingness to adopt PR services. In addition, behavioral predictors, current rideshare habits, environmental attitudes, and perceived barriers (e.g., safety, privacy, and comfort) were captured. - Phase_3_Final - The dataset contains rows corresponding to individual respondents and columns representing survey items, demographic characteristics, and response values. The data is available in both .CSV and .SAV formats. - Phase_3_Final_MapFile - Accompanying this dataset is a data dictionary explaining each variable, value range, and coding schema. An .XLSX format of the full survey instrument is included to support interpretation and reuse of the dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

FiberFlex: Real-time FPGA-based Intelligent and Distributed Fiber Sensor System for Pedestrian Recognition

In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing Field Programmable Gate Arrays (FPGA) high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 W of power, 33 W in the optical DAS system and 22 W in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let’s use fiber optic touch instead.

Distributed↗
NASA NTRS2016

Sleep Environment Recommendations for Future Spaceflight Vehicles

Current evidence demonstrates that astronauts experience sleep loss and circadian desynchronization during spaceflight. Ground-based evidence demonstrates that these conditions lead to reduced performance, increased risk of injuries and accidents, and short and long-term health consequences. Many of the factors contributing to these conditions relate to the habitability of the sleep environment. Noise, inadequate temperature and airflow, and inappropriate lighting and light pollution have each been associated with sleep loss and circadian misalignment during spaceflight operations and on Earth. As NASA prepares to send astronauts on long-duration, deep space missions, it is critical that the habitability of the sleep environment provide adequate mitigations for potential sleep disruptors. We conducted a comprehensive literature review summarizing optimal sleep hygiene parameters for lighting, temperature, airflow, humidity, comfort, intermittent and erratic sounds, and privacy and security in the sleep environment. We reviewed the design and use of sleep environments in a wide range of cohorts including among aquanauts, expeditioners, pilots, military personnel and ship operators. We also reviewed the specifications and sleep quality data arising from every NASA spaceflight mission, beginning with Gemini. Finally, we conducted structured interviews with individuals experienced sleeping in non-traditional spaces including oil rig workers, Navy personnel, astronauts, and expeditioners. We also interviewed the engineers responsible for the design of the sleeping quarters presently deployed on the International Space Station. We found that the optimal sleep environment is cool, dark, quiet, and is perceived as safe and private. There are wide individual differences in the preferred sleep environment; therefore modifiable sleeping compartments are necessary to ensure all crewmembers are able to select personalized configurations for optimal sleep. A sub-optimal sleep environment is tolerable for only a limited time, therefore individual sleeping quarters should be designed for long-duration missions. In a confined space, the sleep environment serves a dual purpose as a place to sleep, but also as a place for storing personal items and as a place for privacy during non-sleep times. This need for privacy during sleep and wake appears to be critically important to the psychological well-being of crewmembers on long-duration missions.

Flynn-Evans, Erin E.↗
NASA NTRSDate not supplied

Watching Without Seeing a Tool to Surveil Astronaut Health Outcomes While Maintaining Astronaut Medical Privacy

BACKGROUND The Privacy Act of 1974 regulates the use a nd disclosure of personally identifiable information by US Federal agencies. The Act applies to biographical, financial, a nd other identity-linked information, a s well a s personal health information (PHI). As such, the use of astronaut PHI is limited to authorized personnel for preapproved uses, with data reporting often limited to aggregated information about groups. These limitations on the use a nd reporting of astronaut PHI complicates surveillance efforts, wherein epidemiologists a t the National Aeronautics and Space Administration (NASA)monitor the incidence of targeted health conditions in the astronaut population, or to discover emerging trends of aging and disease. Stratification on one or more covariates –particularly time-period, sex, a nd mission participation –can lead to extremely small datasets such that the reporting of results is potentially attributable to individuals. An additional challenge is the small size of the astronaut population, both in terms of numbers of individuals a s well a s in terms of density of exposure time. Such small datasets yield volatile rate estimates that are difficult to interpret. To a id the epidemiological surveillance efforts, a surveillance tool is required that can (a) satisfy the need for rapid computation of condition-specific incidence and mortality rates; (b) improve the statistical estimates of these estimated rates; and (c) maintain astronaut privacy. Here we describe a nd demonstrate such a tool. METHODS We devised a system that models incidence a nd mortality rates rather than calculating them directly. This ha s the advantage of using all the available data to derive the estimates, lea ding to rates that a re not attributable to any one individual, a nd a re a s numerically stable a s they can be given the extremely limited data. The system models disease endpoints using a Poisson regression model with exposure density (measured in person-years) a s a n offset term. By doing so the model is estimating event counts per person-year, equivalent to modeling the rates directly. It uses a standard (pre-specified)set of covariates; the system does not engage in “model-building” as model parsimony is not the goa l. Instead, it is explicitly recognized that if a covariate is not statistically significant a nd not a confounder then it will likely have very little effect on the estimate of the incidence a nd mortality rates. Users are able to specify the disease endpoint of interest and the covariates over which they would like to stratify. The system then uses the resulting model to compute the estimated rates for the user-chosen configuration of variables as visualizes those either over an age range within a specified time-period, or over time for astronauts with a specified age range. RESULTS The first iteration of the tool computes incidence a nd mortality rates for cardiovascular conditions and cancers. Code ha s been developed to retrieve the appropriate data from the IMPALA analysis platform, compute the models for incidence a nd mortality, a nd then use those models to generate the corresponding rate curves. A companion graphical user interface allows the user to specify the curves and visualize the results. CONCLUSIONS It is important to note that the rapid surveillance tool described here is neither meant to be a definitive assessment of the incidence or mortality of any particular disease or condition in the astronaut population, nor is it meant to be used for research purposes. Rather, it is meant as an early indicator that in-depth investigation may be warranted. By automating a repetitive process and leveraging carefully curated astronaut health outcomes, the tool makes possible a rapid “first look” into known areas of concern, and, if used judiciously, may surface new areas of concern for long-term astronaut health. This work is supported in part by the Translational Research Institute for Space Health (TRISH) through NASA Cooperative Agreement NNX16AO69A.

R J Reynolds↗
DOE OSTI2025

Fully Homomorphic Encryption

This code implements a Fully Homomorphic Encryption (FHE) system, enabling secure computation on encrypted data without requiring decryption. It supports encryption, decryption, and homomorphic operations like matrix multiplication and addition. This code is adaptable for integrating FHE into linear-time invariant (LTI) systems, including digital control and filtering. With proper configuration from subject matter expertise, encrypted system parameters and signals can be manipulated to perform tasks like state updates, output calculations, and convolution in the encrypted domain. By preserving the structure of LTI systems while ensuring privacy, the framework facilitates secure applications in areas such as autonomous systems, signal processing, and industrial automation. The code initializes the encryption system using parameters provided in the env dictionary. These parameters include the ciphertext modulus, key dimension, plaintext fixed-point scaling factor, and noise bound. During initialization, a secret key is generated, which is essential for encrypting and decrypting data securely. The modular design allows users to tailor these parameters to specific use cases or security requirements. The code implements multiple cryptographic schemes. The learning with errors (LWE) encryption method encodes cleartext message to their plaintext fixed-point representation then encrypted into ciphertext space with additive noise. This noise ensures the security of the scheme, relying on the computational hardness of the LWE problem. The code also includes the Gentry-Sahai-Waters (GSW) scheme based off the LWE problem. Homomorphic matrix multiplication is performed between the LWE and GSW to encrypted data. This is achieved using a decomposition function on the LWE ciphertext during the multiplication operation. For higher-dimensional data, the code includes a method to encrypt entire matrices (GSWMat) using GSW encryption. These encrypted matrices can then be used for homomorphic matrix multiplications (MatMult). The decryption function uses the secret key to recover the original plaintext, removing the added noise and scaling that was originally applied during encryption.

Lois, Roberts [Idaho National Laboratory (INL), Id↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
NASA NTRS1981

Tri-state delta modulation system for Space Shuttle digital TV downlink

Future requirements for Shuttle Orbiter downlink communication may include transmission of digital video which, in addition to black and white, may also be either field-sequential or NTSC color format. The use of digitized video could provide for picture privacy at the expense of additional onboard hardware, together with an increased bandwidth due to the digitization process. A general objective for the Space Shuttle application is to develop a digitization technique that is compatible with data rates in the 20-30 Mbps range but still provides good quality pictures. This paper describes a tri-state delta modulation/demodulation (TSDM) technique which is a good compromise between implementation complexity and performance. The unique feature of TSDM is that it provides for efficient run-length encoding of constant-intensity segments of a TV picture. Axiomatix has developed a hardware implementation of a high-speed TSDM transmitter and receiver for black-and-white TV and field-sequential color. The hardware complexity of this TSDM implementation is summarized in the paper.

Udalov, S.↗
NASA NTRSDate not supplied

Viability of Small Dimension Crew Quarters for Surface Habitation

During early planning for the Artemis program’s sustained phase of lunar activity, NASA planners have been held to work towards a NASA reference lunar lander concept. With this activity taking place prior to the awarding of a lander contract, NASA planners cannot assume which of several potential landers will be available. This has limited habitation team engineers to a 12-metric ton mass limit for the reference concept of the lunar Surface Habitat. Consequently, minimal approaches have been applied to many habitat systems and it is important to determine acceptable volume for crew quarters. A number of both NASA and non-NASA surface habitat concepts have proposed relatively small crew quarters due to this constraint. Consequently, there is a need to collect objective test data to confirm or refute the validity of small crew quarters. NASA-STD-3001 is looked to for guidance in its many standards but offers little to no help. While prior versions called for private habitation, the current version – Revision B – calls for “individual privacy” to “accommodate social retreat.” Proposed Revision C modifications change the language slightly to “accommodate sleep and social retreat.” This is not enough guidance to determine the size of a crew quarters or even its capabilities. Unfortunately, only a small number of US spacecraft have included crew quarters, primarily the International Space Station and the Skylab Space Station. The space shuttle orbiter sometimes flew a set of private bunks that some might consider a crew quarters. All of these are dramatically smaller than US standards for minimum jail cells. The first opportunity for NASA to test a small crew quarters in a surface habitat application has been created through the Exploration Atmospheres test series, which is evaluating human performance under reduced cabin pressures. The test is converting the 20-Foot Vacuum Chamber at Johnson Space Center into a habitat, with the lower level outfitted as an EVA test area and the upper two levels for human habitation. The test will place eight people (six test subjects and two technicians) inside the chamber for eleven days. All eight will sleep in private quarters during the test. Volume limitations in the chamber forced extremely small crew quarters, measuring approximately 2 meters in length, 0.85 meters in height, and 0.9 meters in width. The test cabin pressure of 8.2 psi and elevated oxygen also introduces significant material limitations, limiting outfitting options. Nonetheless, the crew quarters design requirements were to accommodate visual separation, auditory separation, olfactory separation, tactile separation, air flow control, lighting control, single person personal computing, physical work surface access, sleep accommodations, non-sleep rest/relaxation, meditation, stretching, two-person meetings, snacking, changing clothes, viewing appearance, video communication, and audio communication. This paper will detail the acceptability of the crew quarters as measured in the October 2021 Exploration Atmosphere test. Based on this data, the viability of the type of crew quarters used in the 20 Foot Chamber will be assessed. Design recommendations for a 30-60-day Surface Habitat crew quarters will be provided, along with recommendations for future testing.

Crew Quarters↗
NASA NTRS2016

Sleep Environment Recommendations for Future Spaceflight Vehicles

We conducted a comprehensive literature review summarizing optimal sleep hygiene parameters for lighting, temperature, airflow, humidity, comfort, intermittent and erratic sounds, and privacy and security in the sleep environment. We reviewed the design and use of sleep environments in a wide range of cohorts including among aquanauts, expeditioners, pilots, military personnel and ship operators. We also reviewed the specifications and sleep quality data arising from every NASA spaceflight mission, beginning with Gemini. Finally, we conducted structured interviews with individuals experienced in sleeping in non-traditional spaces including oilrig workers, Navy personnel, astronauts, and expeditioners. We also interviewed the engineers responsible for the design of the sleeping quarters presently deployed on the International Space Station. We found that the optimal sleep environment is cool, dark, quiet, and is perceived as safe and private. There are wide individual differences in the preferred sleep environment; therefore modifiable sleeping compartments are necessary to ensure all crewmembers are able to select personalized configurations for optimal sleep. It is possible to utilize lessons learned from prior spaceflight missions and from other industries in order to guide the design of an optimal sleep space suitable for long-duration spaceflight.

spaceflight↗
NASA NTRS2016

Sleep Environment Recommendations for Future Spaceflight Vehicles

Evidence from spaceflight and ground-based missions demonstrate that sleep loss and circadian desynchronization occur among astronauts, leading to reduced performance and, increased risk of injuries and accidents. We conducted a comprehensive literature review to determine the optimal sleep environment for lighting, temperature, airflow, humidity, comfort, intermittent and erratic sounds, privacy and security in the sleep environment. We reviewed the design and use of sleep environments in a wide range of cohorts including among aquanauts, expeditioners, pilots, military personnel, and ship operators. We also reviewed the specifications and sleep quality data arising from every NASA spaceflight mission, beginning with Gemini. We found that the optimal sleep environment is cool, dark, quiet, and is perceived as safe and private. There are wide individual differences in the preferred sleep environment; therefore modifiable sleeping compartments are necessary to ensure all crewmembers are able to select personalized configurations for optimal sleep.

Caddick, Zachary A.↗