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NASA NTRSDate not supplied

NASA Technology Maturation Plan for In-space Manufacturing of Metals

As the International Space Station’s (ISS) life approaches its end, NASA intends to travel back to the Moon and establish a sustainable presence, paving a pathway towards Mars. A fundamental shift in the current logistics strategy is required to support extended missions. On-demand manufacturing enables reduced operational cost and increased long term sustainability providing a pathway towards reducing NASA’s logistics burden. The In-Space Manufacturing (ISM) portfolio at Marshall Space Flight Center is developing additive polymers, metals, and electronics manufacturing technologies to enable a sustainable presence on the Moon and enable long-duration transit missions. Manufacturing systems for in-space applications must meet a unique set of constraints requiring a maturation path independent from processes targeted for terrestrial use. In May 2023, the On Demand Manufacturing of Metals (ODMM) project, part of the ISM portfolio funded through the Game Changing Development (GCD) program office, was canceled; however, prior to cancelation, the engineering team developed a technology maturation plan for in-space manufacturing of metallic components. The status of ODMM at closeout and an overview of the technology maturation plan for ODMM are discussed.

In Space Manufacturing↗
NASA NTRSDate not supplied

Gateway at the Crossroads of Sustainable Lunar Exploration

The Gateway Program has made substantial design and development progress toward delivering a small, human-tended lunar space station purposefully designed to enable sustainable human exploration. The Program integrates partners and providers organizationally and physically as part of the spacecraft. The Power and Propulsion Element (PPE) and the Habitation and Logistics Outpost (HALO) with the European System Providing Refueling, Infrastructure and Telecommunications (ESPRIT) HALO Lunar Communications System (HLCS) have begun manufacturing the long lead components and will be launched first as a Co-Manifested Vehicle (CMV). The International Habitat (I-Hab) and ESPRIT Refueling Module (ERM) are passing life cycle milestones and include capabilities key for human crewmembers, such as windows, private sleeping quarters, and galley functions. The Logistics Module (LM) may provide a variety of services to Gateway depending on each mission. Requirements for the airlock have been developed, including requests that it support the integrated spacecraft with functions like augmenting heat rejection capabilities, and interfaces with new spacesuits will soon be developed in more detail. As a critical element of the architecture for solar system exploration, Gateway implements key tenets and features of international interoperability standards necessary to operate with multiple visiting vehicles and lunar assets, especially avionics, communications, and docking. Specific choices such as software architecture and standards, power standards, and robotics standards make it possible to utilize heritage or proprietary technology, yet still operate as one spacecraft. Engineering teams are evaluating many possible future missions to be executed at or utilizing the Gateway. The system architecture protects for an evolvable, extensible, and flexible capability. Designing systems robust enough to serve as a cornerstone of exploration activities for decades while remaining adaptable is not without its challenges. The detailed integration activities have revealed challenges and the need to mature key technologies. Refueling is a key component of achieving long life for Gateway, with unique operations to plan, safety concerns to mitigate, and risk reduction activities to conduct to better understand the system. The constraints and impacts of the design of visiting vehicles is also an important concern, with orientation constraints, control of attitude and orbit of the Gateway with docked visiting vehicles. Tradeoffs between robust maintainable systems and lightweight, compact systems must be balanced. Opportunities still exist for adding additional advanced capabilities to increase and extend Gateway’s benefits, such as intravehicular robotics, autonomous Guidance Navigation and Control (GN&C), and augmented control propulsion, heat rejection, or other services.

Molly S Anderson↗
NASA NTRSDate not supplied

iMETRO (Integrated Mobile Evaluation Testbed for Robotics Operations) Facility

Exploration crew time in space is precious – every hour could yield immense scientific discovery. However, overhead tasks such as logistics, maintenance, and assembly greatly limit crew time available for science and exploration. Robotic remote operations capabilities offer a solution, but operating mobile dexterous robots in human centered environments presents many unknowns and challenges, therefore testing is needed. iMETRO is a NASA JSC robotics test facility for terrestrial robotic technology adaptation for space exploration use cases, including logistics, maintenance, and science utilization. iMETRO focuses on Intra-Vehicular task environments, such as surface habitats, pressurized rover cabins, and space station modules (both Gateway & LEO). Its goal is to advance the Technology Readiness Levels (TRL) of remote space robot operations systems with Earth supervision.

Robotics↗
NASA NTRS2024

Short Window Intra-Spacecraft RFID Localization

Logistics management has emerged as a key component to activities conducted in space. The RFID Enabled Autonomous Logistics Management (REALM) system has played a key role in providing cargo tracking capabilities in the noisy environment of the ISS. Currently, the inferencing engines used by REALM to predict the location of RFID tagged items operate on an hour of data. Movements aboard space stations occur on the scales of seconds. In this work we propose a new inferencing engine, that produces an embedding space that represents the location of RFID marked cargo on the scale of 30 seconds to 2 minutes of data, allowing for the categorization of movement of cargo, and predictions of a coarse location in less time than existing engines.

RFID↗
NASA NTRSDate not supplied

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

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.

ATM↗
NASA NTRSDate not supplied

Autonomous RPOD for Arbitrarily Configured Spacecraft with Anomaly Detection

Autonomous GN&C is a necessary component for a sustainable deep-space logistics architecture. The challenges for establishing robust autonomy are numerous, from state uncertainty, to anomaly detection and recovery. In this work, previous work investigating autonomous GN&C for arbitrary thruster configurations and mass properties is expanded to include state uncertainty and anomaly detection. Logistics vehicles with off-center-of-mass thruster configurations and in the presence of large but realistic state uncertainties are simulated in a Rendezvous, Proximity Operations and Docking scenario. Furthermore, stuck and non-functional thrusters are simulated, demonstrating the vehicle's ability to identify and overcome thruster anomalies. The simulations demonstrate that even with these realistic ambiguities, the vehicle is able to converge to the desired pose.

GN&C↗
NASA NTRSDate not supplied

Supportability Concepts for Crewed Deep Space Exploration

Supportability—defined as the set of system characteristics that influence the logistics and support required to enable safe and effective operations—will be a much larger driver of mass, risk, and crew time for future human space exploration due to the more challenging mission context. For Mars, systems must operate in a logistically isolated environment for much longer durations than previous missions, which results in a higher probability of system failure and therefore an increased need for maintenance or contingency options. Mars missions also lack access to quick aborts, which increases the consequences of an unrecoverable system failure. Together, this higher likelihood and consequence of failure results in an increase in supportability-related risk. Supportability analysis is an important part of systems development that helps designers better understand the impacts of system and mission decisions on risk, mass, and crew time. The real-world processes that drive maintenance requirements and other supportability-related characteristics are probabilistic, and therefore they require different conceptual approaches and models than are used for more deterministic aspects of space systems. This paper provides an overview of supportability analysis, addresses key concepts, and provides examples of how supportability analysis can be incorporated into system development. Specifically, system supportability involves stochastic processes, and therefore must be evaluated using probabilistic models. These models can be used to perform sensitivity analysis even if system characteristics are not yet fully defined. Failure rates cannot be measured directly, but tests provide valuable data that can help refine those estimates. Human spaceflight architectures are complex, and exhibit coupled behavior that should be examined with integrated systems analysis that includes an assessment of supportability.

Supportability↗
NASA NTRS2024

A Distributed Simulation Framework Applied to Artemis Analysis, Studies, Integration, and Test

The National Aeronautics and Space Administration (NASA) established the Artemis Program, a series of missions to return humans to the Moon and explore further than before. To execute the Artemis missions, NASA is collaborating with commercial and international partners to create the necessary infrastructure and logistics plan that will establish a long term presence on the Moon ahead of exploring Mars. NASA and its partners are developing a collection of space and surface systems to support crewed missions to the lunar surface that will provide the mobility, habitation, logistics, and exploration support necessary for Artemis mission successes which includes robust scientific investigations. This paper details the design, capabilities, and uses of the Artemis Distributed Simulation (ADS) being developed by the NASA Exploration Systems Simulations (NExSyS) Team to support Artemis architecture studies. ADS utilizes international interoperability standards to connect a collection of independent vehicle and service simulations; these include but are not limited to elements such as rovers, landers, and habitation elements along with services like communications, environment, visualization, and data logging. ADS’s distributed nature allows for the complex aggregation of constituent Artemis elements; this includes efficient scenario modification with the addition or removal of individual simulations representing Artemis elements or services. This capability provides support for the rapid performance of various Artemis mission trade studies exploring alternate configurations. Currently, ADS uses NASA developed simulations for development and testing; however, through the use of international simulation interoperability standards, ADS provides an integration framework to incorporate dissimilar authoritative vendor simulations as Artemis systems mature and vendor simulations become available. Vendor simulations will be able to join ADS and interact with other Artemis elements and vehicles while limiting the exposure of proprietary data. This paper describes the expansion of an existing distributed simulation infrastructure to accommodate a collaborative and dynamic framework for the Artemis Program. This work includes updated federation designs, integration into existing NASA facilities, advancements in visualizations, and advancements in human driven inputs. This paper will also outline recently completed and ongoing support and collaboration with NASA studies and testing, namely results from energetics and Human-In-The-Loop (HITL) studies. The paper concludes with a plan for future developments and facility integration to enable enhanced studies in preparation for a return of humans to the lunar surface.

Artemis↗
NASA NTRSDate not supplied

Supportability Concepts for Crewed Deep Space Exploration

Supportability—defined as the set of system characteristics that influence the logistics and support required to enable safe and effective operations—will be a much larger driver of mass, risk, and crew time for future human space exploration due to the more challenging mission context. For Mars, systems must operate in a logistically isolated environment for much longer durations than previous missions, which results in a higher probability of system failure and therefore an increased need for maintenance or contingency options. Mars missions also lack access to quick aborts, which increases the consequences of an unrecoverable system failure. Together, this higher likelihood and consequence of failure results in an increase in supportability-related risk. Supportability analysis is an important part of systems development that helps designers better understand the impacts of system and mission decisions on risk, mass, and crew time. The real-world processes that drive maintenance requirements and other supportability-related characteristics are probabilistic, and therefore they require different conceptual approaches and models than are used for more deterministic aspects of space systems. This paper provides an overview of supportability analysis, addresses key concepts, and provides examples of how supportability analysis can be incorporated into system development. Specifically, system supportability involves stochastic processes, and therefore must be evaluated using probabilistic models. These models can be used to perform sensitivity analysis even if system characteristics are not yet fully defined. Failure rates cannot be measured directly, but tests provide valuable data that can help refine those estimates. Human spaceflight architectures are complex, and exhibit coupled behavior that should be examined with integrated systems analysis that includes an assessment of supportability.

Supportability↗
NASA NTRS2024

Gateway Program Development Progress

This paper provides an overview and status of Gateway, humanity’s first space station to orbit the Moon providing vital support for a sustained, long-term human return to the lunar surface and a steppingstone to Mars as part of the Artemis missions. As a lunar outpost, Gateway is a destination for deep space crew expeditions and science investigations, a port for deep space transportation, including landers transiting to the lunar surface or spacecraft embarking to deep space destinations beyond the Earth-Moon system. The National Aeronautics and Space Administration (NASA) leads the Program and is the integrator of the spaceflight capabilities and contributions of U.S. commercial partners and international partners to develop Gateway. This paper will provide an overview of Gateway’s major components in various stages of development. The entire Gateway spacecraft is at preliminary design level of maturity, with some components at or near critical design review. Gateway’s major components are the Power and Propulsion Element; the Habitation and Logistics Outpost; Deep Space Logistics; the International Habitation module; Gateway External Robotics System; European System Providing Refueling, Infrastructure and Telecommunications; and an Airlock. This paper will also provide an update on the status of the integration activities necessary to fly and operate this complex, next-generation integrated spacecraft for a minimum 15-year design life, including systems engineering integrated analysis cycles, the autonomous Vehicle System Manager software, verification and validation labs, and common vehicle equipment. Expanding on the successful partnership that has provided over 20 years of continuous crew operations in low-Earth orbit on the International Space Station, Gateway is an evolution of this extraordinary partnership leveraging the capabilities of each contributor to expand humankind’s sustained exploration deeper into the cosmos. Highlighting the international program with participation from multiple space agencies, this paper will also provide a status of Gateway multilateral governance structure and international agreements.

Gateway↗
NASA NTRSDate not supplied

Evaluating Lunar Water Processing System Model Configurations for Small Scale Oxygen and Hydrogen Production Within JAXA'S ISRU Technology

Introduction: In-Situ Resource Utilization (ISRU) refers to novel methods of extracting and processing local resources for use in life support and propulsion systems, reducing or eliminating the required consumables to be transferred from Earth. Current estimates of water-ice availability embedded in regolith within the Moon’s permanently shadowed regions (PSR’s) range between 1-5% by weight. However, the composition and characteristics of the “wet” regolith is unknown. Alternate ISRU excavation techniques and Concept of Operations (ConOps) must be explored to optimize surface system operations based on these factors. To assess the feasibility of different ISRU subsystem technologies and compare system architecture configurations, an interchangeable system model was generated to incorporate technologies spanning excavation of raw materials to storage of products and determine optimal arrangement of total system processing needs. Total Mass, Volume, and Power (M/V/P) requirements were computed for 168 design iterations of this water processing plant. System Model: In FY24, the System Engineering and Integration (SE&I) ISRU Modeling and Analysis (SIMA) team developed a lunar water processing system model using the Mission Analysis and Integration Tool (MAIT) to estimate the M/V/P for ISRU subsystems operating under a wide range of Hydrogen (H2) and Oxygen (O2) production targets for the Space Technology Mission Directorate (STMD) [1]. Based on Japan Aerospace Exploration Agency’s (JAXA) surface operational requirements, this system architecture was modified to include the ability to excavate consolidated icy regolith (versus granular ice excavation using Kennedy Space Center’s (KSC) ISRU Pilot Excavator, IPEx) and explore the feasibility of processing the lunar water both inside and outside of the PSR. For the consolidated icy regolith case study, excavation was performed via a mobility transport chassis outfitted with The Regolith Ice Drill for Exploring New Terrain (TRIDENT) for drilling [2] and the Cold Operable Lunar Deployable Arm (COLDArm) [3] for regolith transfer. The system model determines the required rover and payload. M/V/P to handle the required regolith processing rates. The regolith is then sorted and heated to sublimate the ice (via an auger dryer). The exiting high temperature, low pressure vapor is cleaned of volatiles (via cold trap) and electrolyzed to produce H2 and O2. These products are then dried, liquified with 20 K and 90 K cryocoolers (for H2 and O2, respectively), and stored in cylindrical tanks. Study Goals: Due to the different ConOps options of regolith transport to the ridge for processing versus processing it directly inside the PSR, as well as the unknown regolith/water-ice composition, new excavation techniques and their power configurations are being evaluated within a ISRU system architecture for production targets less than NASA’s pilot plant (1 mT). This analysis investigates the feasibility of numerous excavation techniques, power architectures, and logistical operations and determines an optimal system configuration with regards to M/V/P. It aims to investigate which parameters, both locally and globally, have the greatest effect on each subsystem within the plant. This can be used to identify the most critical components of the plant, and guide future decisions on allocating funding for research and development. The results from this study may provide subsystem developers with appropriate interfaces with excavation subsystems and downstream processes, and assessing the overall feasibility of each excavation technique, power architecture, and logistical timeframe. References: [1] Carlson, A. et. al. (2024) ICES. [2] Zacny, K., et. al. (2024) “ASCE Earth and Space”. [3] McCormick, R., et. Al. (2024) IEEE Xplore.

ISRU↗
NASA NTRS2024

iMETRO (Integrated Mobile Evaluation Testbed for Robotics Operations) Facility

Crew time in space is precious – every hour could yield immense scientific discoveries and exploration milestones. However, overhead tasks such as logistics, maintenance, and assembly take a large portion of crew time. Remotely operated robotics capabilities offer a way to free the crew from many overhead tasks, but operating mobile dexterous robots in human-centered environments presents many unknowns and challenges for potential technology providers, limiting adoption for flight missions. iMETRO is a NASA JSC robotics test facility for terrestrial robotic technology adaptation to space exploration use cases, including logistics, maintenance, and science utilization. iMETRO focuses on Intra-Vehicular (IVA) environments, such as surface habitats, pressurized rover cabins, and space station modules (both Gateway & LEO). Its goal is to advance the Technology Readiness Levels (TRL) of integrated technologies for missions requiring remote space robotics operations.

iMETRO↗
NASA NTRS2026

On-Demand Production of Hypergols: Steps Towards Scalability

During FY 2025, the team at KSC focused on taking the 2024 proof-of-concept demonstration of hypergol production using novel methods and began scaling up production and increasing purity. The team was able to more than triple the production of hydrazine using a novel method with a drastic increase in the purity. The current method, albeit still limited to production rates on the order of mg/hr, provides a cost savings of approximately 14x when compared to market value pricing through the Defense Logistics Agency (DLA) website. The benefit of this technology is the ability to produce some hypergolic fuels using constituents in Earth’s atmosphere, eliminating most chemical commodity logistics. This technology also allows for the return of domestic production of hypergolic propellant.

Kenneth Engeling↗
DOE OSTI2025

Idaho National Laboratory’s Mobile Hot Cell Transportation: Engineering Solutions for Global Disused Sealed Radioactive Sources.

Title: Idaho National Laboratory’s Mobile Hot Cell Transportation: Engineering Solutions for Global Disused Sealed Radioactive Sources. Abstract: The Mobile Hot Cell (MHC), currently under development by Idaho National Laboratory (INL) for the Off-Site Source Recovery Project (OSRP), is designed to help international partners meet the unique challenges of end-of-life radioactive material management. The MHC will provide a critical resource for countries that require assistance securing and disposing of Disused Sealed Radioactive Sources (DSRS) and orphaned sources in challenging environments, allowing these sources to be secured against misuse and nefarious activities. The MHC is a rapidly deployable system for conditioning and preparing end-of-life radioactive sources for transportation or storage. It is designed to handle sources of up to 1,000 Ci Co-60 equivalent while maintaining full radiological and biological containment. It will be deployable within 48 hours of an alert, making it ideal for emergency situations. The MHC features an operational suite for control, support racks for electronics, pneumatics, and welding systems, and a modular robust steel structure providing radiological shielding and internal robotic support. This design allows configurations for multiple device types to be conditioned and the ability to safely manage routine issues such as leaking or damaged sources. The MHC has been designed with the transportation challenges of rapid deployment to difficult environments in mind. The system weighs approximately 150,000 pounds, with individual systems breaking down into pieces not exceeding 20,000 pounds. Components are to be transportable on standard 20ft ISO containers, with shielding shells on 20ft flat racks. It is estimated that a total of eight containers and flat racks will be required. The use of 20ft containers, as opposed to 40ft containers, minimizes the impact on less developed road infrastructures, enabling the MHC to be positioned in constrained environments such as hospital parking lots. The system’s modularity also allows for deployment using smaller equipment, such as a 10-ton boom truck or forklift, which is crucial given the potential logistical challenges in different countries. This transportation strategy, evaluated in collaboration with Utah State University, ensures the MHC can be deployed via ground, rail, sea, or air, addressing the primary concern of international transport logistics.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Community-Controlled Transportation: The Western New York E-Bike Library Network: Preprint

Shared Mobility Inc. (SMI) has used their mission of community-controlled transportation to partner with community based organizations on the launch and operations of E-Bike Libraries (EBLs) in Western New York (WNY) and beyond since 2021. For almost four years, the WNY EBLs have provided e-bikes at no cost to underserved communities in Buffalo and Niagara Falls. Participants used the e-bikes for commuting, recreational rides, community bike rides, running errands, and accessing essential services. This program significantly increased e-bike accessibility, with 71% of participants being first-time riders and 78% identifying as Black/African American. Challenges such as bike maintenance, battery charging logistics, and the need for suitable storage were addressed through partnerships with community-based organizations and adjustments to program logistics. Those lessons have been applied to additional EBLs in Pacoima, California and Carlisle, Pennsylvania, which have also been launched by SMI with a continued emphasis on the importance of community based approaches to sustainable transportation. All of these programs demonstrate the potential of e-bikes to provide an affordable, efficient, and fun transportation option, particularly for underserved communities. By leveraging community partnerships and focusing on accessibility and inclusivity, E-Bike Libraries can significantly contribute to sustainable transportation solutions and promote broader participation in the transportation electrification revolution.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3x) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. In conclusion, new methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.

13 HYDRO ENERGY↗
DOE OSTI2025

Exploring the environmental drivers of human blastomycosis cases in the Midwestern United States

Blastomycosis is a fungal infection endemic to the eastern United States (US) and Canada caused by the inhalation of the fungi Blastomyces spp. Currently, the environmental drivers of disease dynamics are poorly understood. The goal of our work was to explore what environmental conditions are associated with the annual presence of blastomycosis cases, and therefore are potentially explanatory of the ecological niche of Blastomyces. We examined the relationships between reported cases of blastomycosis in three Midwestern US states (Michigan, Minnesota, and Wisconsin) from 2007–2017 in relation to eleven hypothesized environmental conditions, including climate, stream and soil mineral content, and land cover variables. Then, we fit logistic regression models to explore the relationships between the environmental variables and yearly blastomycosis case occurrence. Mean soil moisture, stream sediment mercury content, percent of water within the county, and woody wetlands land cover were all positively associated with the presence of annual cases, with woody wetlands having the most consistent signal across the three states. We also found significant differences in the likelihood of case presence between US states that were not explained by the variables in our model, suggesting state-level differences in case reporting and disease awareness. Our results provide a perspective on potential biological hypotheses to further test regarding environmental controls on the life cycle and ecological niche of Blastomyces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Hybrid Anomaly Detection Approach for Obfuscated Malware

With the rapid evolution of malicious software, cyber threats have become increasingly sophisticated, employing advanced obfuscation techniques to evade traditional detection methods. This study presents a hybrid anomaly detection approach applied to obfuscated malware. Even though there is a large body of research in this field, existing malware detection techniques have some drawbacks, such as requiring large amounts of data, trustworthiness (imprecise results) of algorithms, and advanced obfuscation. To overcome these challenges, there is a need to employ solid and efficient techniques for malware detection. This paper proposes a hybrid approach, combining an autoencoder with traditional machine-learning methods to create an efficient malware detection framework. We used the malware memory dataset (MalMemAnalysis-2022) to evaluate this framework. The results indicate that our proposed approach can detect obfuscated malware when a deep autoencoder used for feature learning is combined with logistic regression, and it is extremely fast with an Accuracy, Detection Rate (DR), Matthew Correlation Coefficient(MCC), and Statistical Parity Difference

malware detection, Hybrid Anomly Detection, Obfusc↗