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1,384 records · Page 77

SWIPE: Spectral Water Inversion Processor and Emulator

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.

SWIPE↗

Science Plan for the Deployment of the Third ARM Mobile Facility to the Southeastern United States at the Bankhead National Forest, Alabama (AMF3 BNF)

In 2018, the U.S. Department of Energy (DOE) held a workshop for the Atmospheric Radiation Measurement (ARM) (Mather and Voyles 2013) user facility to discuss critical climate challenges and locations where key ARM Mobile Facility (AMF) observational assets could impact Earth system modeling (ESM). As an outcome, the southeast United States (SE U.S.) was identified as a high-priority region to target climate-process studies that promote a deeper understanding of the climate system and bolster ARM interactions with the community to drive ESM advancement. The DOE ARM user facility is a globally recognized leader in deploying and operating strategically located observation sites around the world for studying the properties of aerosols and clouds and their interaction with radiation, precipitation, and the Earth’s surface. In partnering with the DOE Atmospheric System Research (ASR) program, ARM solicited a multi-agency Site Science Team approach to provide input and close interaction with ARM management towards a successful SE U.S. deployment of the ARM third Mobile Facility (AMF3) (Miller et al. 2016). These efforts included identifying key locations, science drivers and instruments, and measurement strategies to address the wider climate-process needs and ESM improvement. Community input served a vital role in establishing, refining, and informing the relevant drivers and decisions regarding this AMF3 deployment. The team has identified Northern Alabama (N. AL) as regionally representative to unlock the key opportunities that will improve our understanding and model representation of aerosol, cloud, and land surface processes and their couplings in the SE U.S. A defining aspect of the AMF3 deployment is its commitment to long-term (anticipated five-year) observations to mitigate potential seasonal-to-annual variability that often limits appropriate attribution of phenomena to local or larger-scale processes. The proposed location may leverage nearby surface networks and multi-agency and partner assets to enrich this multi-year deployment. One motivation is to understand the role of spatiotemporal variability (thermodynamic, land-surface) across aspects of the climate system, with our AMF3 team anticipating future demands on characterizing the relationships between local-to-regional cloud development and surface processes across a diverse patchwork of natural, managed, and urban landscapes as found throughout the N. AL regions. The main site targets an intact, representative, forested region – the Bankhead National Forest (BNF) – underscoring further team commitment to regionally important land atmosphere two-way interactive studies “from the canopy to the clouds”, with enhanced tower instrumentation augmenting traditional ARM capabilities adjacent to this site. Multiple supplemental sites will also be distributed across this region, prioritizing added needs for biodiversity. Anticipated high-priority cloud science themes will target N. AL as a regional SE U.S. hotbed for high-impact weather, convective cloud onset, and shallow to-deep cloud transitioning. Anticipated aerosol drivers will focus on chemical processes that control the evolution of organic aerosol, the seasonality and spatial distribution of water vapor and particle-phase water, and its role on aerosol optical properties. Anticipated land atmosphere drivers consider the two-way feedbacks between surface influence on aerosols, clouds, and precipitation properties and the associated radiative impacts on plant physiology and canopy-scale fluxes. Emphasis will include the study of the impact of surface processes on aerosols via precursor emission, and on clouds via moisture flux and thermal development.

Doppler lidar, aerosols, convection↗

Science Plan for the Deployment of the Third ARM Mobile Facility to the Southeastern United States at the Bankhead National Forest, Alabama (AMF3 BNF)

In 2018, the U.S. Department of Energy (DOE) held a workshop for the Atmospheric Radiation Measurement (ARM) (Mather and Voyles 2013) user facility to discuss critical climate challenges and locations where key ARM Mobile Facility (AMF) observational assets could impact Earth system modeling (ESM). As an outcome, the southeast United States (SE U.S.) was identified as a high-priority region to target climate-process studies that promote a deeper understanding of the climate system and bolster ARM interactions with the community to drive ESM advancement. The DOE ARM user facility is a globally recognized leader in deploying and operating strategically located observation sites around the world for studying the properties of aerosols and clouds and their interaction with radiation, precipitation, and the Earth’s surface. In partnering with the DOE Atmospheric System Research (ASR) program, ARM solicited a multi-agency Site Science Team approach to provide input and close interaction with ARM management towards a successful SE U.S. deployment of the ARM third Mobile Facility (AMF3) (Miller et al. 2016). These efforts included identifying key locations, science drivers and instruments, and measurement strategies to address the wider climate-process needs and ESM improvement. Community input served a vital role in establishing, refining, and informing the relevant drivers and decisions regarding this AMF3 deployment. The team has identified Northern Alabama (N. AL) as regionally representative to unlock the key opportunities that will improve our understanding and model representation of aerosol, cloud, and land-surface processes and their couplings in the SE U.S. A defining aspect of the AMF3 deployment is its commitment to long-term (anticipated five-year) observations to mitigate potential seasonal-to-annual variability that often limits appropriate attribution of phenomena to local or larger-scale processes. The proposed location may leverage nearby surface networks and multi-agency and partner assets to enrich this multi-year deployment. One motivation is to understand the role of spatiotemporal variability (thermodynamic, land-surface) across aspects of the climate system, with our AMF3 team anticipating future demands on characterizing the relationships between local-to-regional cloud development and surface processes across a diverse patchwork of natural, managed, and urban landscapes as found throughout the N. AL regions. The main site targets an intact, representative, forested region – the Bankhead National Forest (BNF) – underscoring further team commitment to regionally important land-atmosphere two-way interactive studies “from the canopy to the clouds”, with enhanced tower instrumentation augmenting traditional ARM capabilities adjacent to this site. Multiple supplemental sites will also be distributed across this region, prioritizing added needs for biodiversity. Anticipated high-priority cloud science themes will target N. AL as a regional SE U.S. hotbed for high-impact weather, convective cloud onset, and shallow-to-deep cloud transitioning. Anticipated aerosol drivers will focus on chemical processes that control the evolution of organic aerosol, the seasonality and spatial distribution of water vapor and particle-phase water, and its role on aerosol optical properties. Anticipated land-atmosphere drivers consider the two-way feedbacks between surface influence on aerosols, clouds, and precipitation properties and the associated radiative impacts on plant physiology and canopy-scale fluxes. Emphasis will include the study of the impact of surface processes on aerosols via precursor emission, and on clouds via moisture flux and thermal development.

54 ENVIRONMENTAL SCIENCES↗

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗

Launch Complex 34, SWMU CCO542022 DNAPL Source Zone Operations, Maintenance, and Monitoring, Site-Wide Long-Term Monitoring, and Hot Spot 6 Air Sparge System Annual Performance Monitoring Report Cape Canaveral Space Force Station, Florida

This Annual Performance Monitoring Report (PMR) for the Dense Non-Aqueous Phase Liquid (DNAPL) Source Zone (DSZ), Site-Wide Long-Term Monitoring (LTM), and Hot Spot (HS) 6 Air Sparge (AS) System presents the results of Year 13 operation of the hydraulic containment (HC) Interim Measure (IM), the results of performance monitoring direct-push technology (DPT) sampling and monitoring well sampling conducted in the DSZ, results of the biennial site-wide LTM event, and the results of operations and performance sampling of the HS 6 AS IM at Launch Complex 34 (LC34), located at Cape Canaveral Space Force Station (CCSFS), Florida. This site has been designated Solid Waste Management Unit (SWMU) CC054 under the Kennedy Space Center (KSC) Resource Conservation and Recovery Act (RCRA) Corrective Action Program. For the site-wide biennial LTM event, a total of 55 monitoring wells were sampled for volatile organic compounds (VOCs) in February 2023 and one well was sampled for polychlorinated biphenyls (PCBs) in December 2022. One well planned for VOC sampling was found to be destroyed and could not be sampled (CW0002). The LTM wells are screened in two lithologic zones: Layer 1 (0 to 25 feet below land surface [bls]) and Layer 2 (25 to 30 ft bls), and are located in the outlying areas of LC34 to monitor groundwater conditions within the Low-Concentration Plume (LCP), defined as concentrations exceeding Groundwater Cleanup Target Levels (GCTLs), and the High-Concentration Plume (HCP), defined as concentrations exceeding Natural Attenuation Default Concentrations (NADCs). Results from the biennial sampling event indicated overall plume stability and delineation for the plume, which extends over 300 acres. The operational period for Year 13 of the HCS was from April 1, 2022 to March 31, 2023. Operational runtime for the system was 90 percent during Year 13, with downtime events attributed to planned maintenance, system repairs, and power outages. As of March 31, 2023, a total of 313,673,241 cumulative gallons of groundwater containing 88,337 pounds of VOCs have been removed by the HCS. Influent concentrations of trichloroethene (TCE) have decreased since startup from approximately 280,000 μg/L (January 2010) to 12,000 μg/L (March 2023). During the reporting period, all effluent concentrations from the HCS (aqueous and vapor) were below regulatory reporting limits, indicating the system continues to operate as intended. Performance monitoring was conducted in December 2022 within the DSZ to evaluate TCE contamination. Groundwater samples were collected via DPT at nine locations, consistent with previous events between 2017 and 2021. Full vertical profile sampling was completed at each DPT from 8 to 98 feet bls, at 5-foot intervals. The DPT performance monitoring results are summarized in this PMR. The results revealed TCE remains at concentrations greater than 11,000 μg/L in the DSZ (1-percent solubility, indicative of DNAPL) at eight of the nine DPT locations and at depths ranging from 8 to 98 feet bls. In addition to DPT sampling, groundwater samples were collected from deep monitoring wells in the DSZ area (Layers 7 and 8) in December 2022 to verify vertical delineation. Layer 7/8 monitoring well results were non-detect in December 2022, with exception of three wells (IW0162, IW043D2, and IW044D2), where TCE, cis-1,2-dichloroethene (cDCE), and/or vinyl chloride (VC) were detected above GCTLs. These wells are screened 105 to 115 feet bls, which is below the existing recovery well capture zone. TCE was first detected in IW0162 in December 2021 and has since been sampled at least monthly to monitor TCE concentrations. The maximum TCE concentration during this operational period was 15,000 μg/L at IW0162 in March 2023. Because of the increased TCE concentrations in this well, a new recovery well (RW21D), screened 86 to 106 feet bls, was installed in January 2023 and incorporated into existing HCS operations. The HS 6 AS IM was initiated in 2018 with 160 AS wells, and expanded in 2019 with an additional 140 AS wells. Quarterly performance monitoring was reduced to semi-annual in 2020. The HS 6 AS system remained operational during the reporting period covered under this report. The results of the HS 6 system operation and semi-annual performance monitoring are summarized in this report. Semi-annual monitoring results collected in April and October 2022 show concentrations of contaminants of concern (cDCE, trans-1,2-dichloroethene, and vinyl chloride) are generally decreasing and not impacting the surface water drainage canal, indicating the HS 6 IM is meeting objectives. A Phase Two Expansion of the HS 6 AS IM was recently completed. As of the date of this report, the expansion became operational in August 2023 and the first quarter of monitoring was conducted in November 2023. Details of the construction, start-up, and performance monitoring will be included in a future Annual PMR. Overall, the tasks associated with Year 13 operation of the HC IM, operation of the HS 6 AS IM, and biennial site-wide sampling were performed in accordance with recommendations included in the previous 2021 LC34 (Year 12) annual report. Evaluation of results from the HC IM and HS 6 IM show that these systems are operating as designed and meeting performance objectives. Results from the site-wide biennial LTM program also show that the overall network of monitoring wells is adequate to continue monitoring plume-wide conditions.

Complex 34↗

High-Reliability Waveguide Vacuum/Pressure Window

The NASA Deep Space Network (DSN) uses commercial waveguide windows on the output waveguide of Ka-band (32 GHz) low-noise amplifiers. Mechanical failure of these windows resulted in an unacceptable loss in tracking time. To address this issue, a new Ka-band WR-28 waveguide window has been designed, fabricated, and tested. The window uses a slab of low-loss, low-dielectric constant foam that is bonded into a 1/2-wave-thick waveguide/flange. The foam is a commercially available, rigid, closed-cell polymethacrylimide. It has excellent electrical properties with a dielectric constant of 1.04, and a loss tangent of 0.01. It is relatively strong with a tensile strength of 1 MPa. The material is virtually impermeable to helium. The finished window exhibits a leak rate of less than 3x10(exp -3)cu cm/s with helium. The material is also chemically resistant and can be cleaned with acetone. The window is constructed by fabricating a window body by brazing a short length of WR-28 copper waveguide into a standard rectangular flange, and machining the resulting part to a thickness of 4.6 mm. The foam is machined to a rectangular shape with a dimension of 7.06x3.53 mm. The foam is bonded into the body with a two-part epoxy. After curing, the excess glue and foam are knife-trimmed by hand. The finished window has a loss of less than 0.08 dB (2%) and a return loss of greater than 25 dB at 32 GHz. This meets the requirements for the DSN application. The window is usable for most applications over the entire 26-to-40-GHz waveguide band. The window return loss can be tuned to a required frequency by var y in g the thickness of the window slightly. Most standard waveguide windows use a thin membrane of material bonded into a recess in a waveguide flange, or sandwiched between two flanges with a polymer seal. Designs using the recessed window are prone to mechanical failure over time due to constraints on the dimensions of the recess that allow the bond to fail. Designs using the sandwich method are often permeable to helium, which prohibits the use of helium leak detection. At the time of this reporting, 40 windows have been produced. Twelve are in operation with a combined operating time of over 30,000 hours without a failure.

Britcliffe, Michael J.↗

Cooperative Research and Development Agreement With Georgetown University Report: National Institutes of Health, National Center for Advancing Translational Sciences Clinical and Translational Science Award

Oak Ridge National Laboratory (ORNL) is participating with Georgetown University (GU) as a subrecipient in response to the National Institutes of Health (NIH), National Center for Advancing Translational Sciences (NCATS) Clinical and Translational Science Award (CTSA) (U54 Clinical Trial Optional) funding opportunity announcement. This Cooperative Research and Development Agreement is put in to place to facilitate the development and implementation of clinical interventions that demonstrably improve human health is currently a complex, recursive, and inefficient process that leads to delays of years or decades before discoveries in biomedical research result in health benefits for patients and communities. NCATS conducts and supports research in the science of translation, to discover the mechanistic and operational principles of the intervention development and dissemination process, thereby providing the scientific foundation for improvements in translational efficiency that will accelerate the realization of interventions that improve human health. Under NCATS’ leadership, the CTSA Program supports a national network of medical research institutions called hubs. GU is the lead institution in one of the NIH hubs that was created as a result of a previous NIH CTSA. The missions of the GU have historically included the advancement of health through research in the clinical and biomedical sciences, the education of future leaders in medical and nursing practice and academia, and the provision of compassionate and scientifically competent patient care and service to the Washington, DC community and the nation. GU is the lead institution for the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS), a multi-institutional partnership of medical research institutions forged from a desire to promote clinical research and translational science. Through multiple collaborations among these institutions, GHUCCTS is transforming clinical research and translational science in order to bring new scientific advances to health care. Oak Ridge National Laboratory is the Department of Energy's (DOE) largest science and energy laboratory. Managed since April 2000 by a partnership of the University of Tennessee and Battelle, ORNL was established in 1943 as a part of the secret Manhattan Project to pioneer a method for producing and separating plutonium. During the 1950s and 1960s, ORNL became an international center for the study of nuclear energy and related research in the physical and life sciences. With the creation of DOE in the 1970s, ORNL's mission broadened to include a variety of energy technologies and strategies. Today the laboratory supports the nation with a peacetime science and technology mission that is just as important as, but very different from, its role during the Manhattan Project. ORNL is home to the world's premier center for high performance supercomputing to enable scientific discovery. ORNL has extensive expertise in various areas of computer science that are uniquely situated to support GU. Additionally, ORNL’s leading computational user facilities present a unique opportunity to leverage the largest scale machines for open science in support of the stated mission of the NCATS CTSA. ORNL's partnership with GU will offer unparalleled opportunity in data analytics, deep-learning, artificial intelligence, and urban dynamics.

59 BASIC BIOLOGICAL SCIENCES↗

Geothermal Direct-Use Applications for the District Energy System in Bucharest, Romania

The city of Bucharest, Romania, hosts the second-largest district energy system (DES) in the world. Geothermal resources can be considered as a supplementary heat source to support the demand for domestic hot water and space heating in the winter and shoulder seasons. The National Laboratory of the Rockies (NLR) has conducted a study that considers geothermal energy to serve a fraction of the existing district heating network operated by Electrocentrale Bucure?ti (ELCEN), the utility operating the DES. Lower Cretaceous and Jurassic limestones make up the main geothermal aquifer underlying Bucharest, which hosts temperatures suitable for district heating (up to 90 degrees C to the north of the city). Anomalous geothermal gradients have been observed to the north of the city, where a pumped well has produced 82 degrees C brine at the wellhead to feed the Therme Bucharest Spa. An anomalous gradient has also been reported to the southeast of the city (35 degrees C/km). NLR modeled the building heating loads of a small portion of the DES (a block of nine prototypical buildings) in its Urban Renewable Building and Neighborhood Optimization (URBANopt ) platform. To simulate meeting a baseload benchmark of 20 MWth deliverable to a small portion of the DES, the NLR team used GEOPHIRES to model production scenarios for (1) hydrothermal systems coupled with heat pumps targeting the main geothermal aquifer in the north, (2) enhanced geothermal systems targeting hot dry rock in the southeast, and (3) huff-and-puff systems targeting a gradient of 25 degrees C/km. Finally, NLR conducted a high-level sensitivity study around the techno-economics of these systems. The outcomes of this work indicate that hot dry rock geothermal resources that can deliver at least 90 degrees C hot water to the Geothermal District Energy System (GeoDES) offer a possible solution for supplemental geothermal heat delivered to the existing DES.

15 GEOTHERMAL ENERGY↗

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↗

The circum-Chryse region as a possible example of a hydrologic cycle on Mars: Geologic observations and theoretical evaluation

The transection and superposition relationships among channels, chaos, surface materials units, and other features in the circum-Chryse region of Mars were used to evaluate relative age relationships and evolution of flood events. Channels and chaos in contact (with one another) were treated as single discrete flood-carved systems. Some outflow channel systems form networks and are inferred to have been created by multiple flood events. Within some outflow channel networks, several separate individual channel systems can be traced to a specific chaos which acted as flood-source area to that specific flood channel. Individual flood-carved systems were related to widespread materials units or other surface features that served as stratigraphic horizons. Chryse outflow channels are inferred to have formed over most of the perceivable history of Mars. Outflow channels are inferred to become younger with increasing proximity to the Chryse basin. In addition, outflow channels closer to the basin show a greater diversity in age. The relationship of subsequent outflow channel sources to the sources of earlier floods is inferred to disfavor episodic flooding due to the progressive tapping of a juvenile near-surface water supply. Instead, we propose the circum-Chryse region as a candidate site of past hydrological recycling. The discharge rates necessary to carve the circum-Chryse outflow channels would have inevitably formed temporary standing bodies of H2O on the Martian surface where the flood-waters stagnated and pooled (the Chryse basin is topographically enclosed). These observations and inferences have led us to formulate and evaluate two hypotheses: (1) large amounts of the sublimated H2O off the Chryse basin flood lakes precipitated (snowed) onto the flood-source highlands and this H2O was incorporated into the near surface, recharging the H2O sources, making possible subsequent deluges; and (2) ponded flood-water in Chryse basin drained back down an anti basinward dipping subsurface layer accessed along the southern edge of the lake, recharging the flood-source aquifers. H2O not redeposited in the flood-source region was largely lost to the hydrologic cycle. This loss progressively lowered the vitality of the cycle, probably by now killing it. Our numerical evaluations indicate that of the two hypotheses we formulated, the groundwater seep cycle seems by far the more viable. Optimally, approx. 3/4 of the original mass of an ice-covered cylindrical lake (albedo 0.5, 1 km deep, 100-km radius, draining along its rim for one quarter of its circumference into substrata with a permeability of 3000 darcies) can be modeled to have moved underground (on timescales of the order of 10(exp 3) years) before the competing mechanisms of sublimation and freeze down choked off further water removal. Once underground, this water can travel distances equal to the separation between Chryse basin and flood-source sites in geologically short (approx. 10(exp 6) year-scale) times. Conversely, we calculate that optimally only approx. 40% of the H2O carried from Chryse can condense at the highlands, and most of the precipitate would either collect at the base of the highlands/lowlands scarp or sublimate at rates greater than it would accumulate over the flood-source sites. Further observations from forthcoming missions may permit the determination of which mechanisms may have operated to recycle the Chryse flood-waters.

Moore, Jeffrey M.↗

The Circum-Chryse Region as a Possible Example of a Hydrologic Cycle on Mars: Geologic Observations and Theoretical Evaluation

The transection and superposition relationships among channels, chaos, surface materials units, and other features in the circum-Chryse region of Mars were used to evaluate relative age relationships and evolution of flood events. Channels and chaos in contact (with one another) were treated as single discrete flood-carved systems. Some outflow channel systems form networks and are inferred to have been created by multiple flood events. Within some outflow channel networks, several separate individual channel systems can be traced to a specific chaos which acted as flood-source area to that specific flood channel. Individual flood-carved systems were related to widespread materials units or other surface features that served as stratigraphic horizons. Chryse outflow channels are inferred to have formed over most of the perceivable history of Mars. Outflow channels are inferred to become younger with increasing proximity to the Chryse basin. In addition, outflow channels closer to the basin show a greater diversity in age. The relationship of subsequent outflow channel sources to the sources of earlier floods is inferred to disfavor episodic flooding due to the progressive tapping of a juvenile near-surface water supply. Instead, we propose the circum-Chryse region as a candidate site of past hydrological recycling. The discharge rates necessary to carve the circum-Chryse outflow channels would have inevitably formed temporary standing bodies of H2O on the Martian surface where the flood-waters stagnated and pooled (the Chryse basin is topographically enclosed). These observations and inferences have led us to formulate and evaluate two hypotheses: (1) large amounts of the sublimated H2O off the Chryse basin flood lakes precipitated (snowed) onto the flood-source highlands and this H2O was incorporated into the near surface, recharging the H2O sources, making possible subsequent deluges; and (2) ponded flood-water in Chryse basin drained back down an anti basinward dipping subsurface layer accessed along the southern edge of the lake, recharging the flood-source aquifers. H2O not redeposited in the flood-source region was largely lost to the hydrologic cycle. This loss progressively lowered the vitality of the cycle, probably by now killing it. Our numerical evaluations indicate that of the two hypotheses we formulated, the groundwater seep cycle seems by far the more viable. Optimally, approximately 3/4 of the original mass of an ice-covered cylindrical lake (albedo 0.5, 1 km deep, 100-km radius, draining along its rim for one quarter of its circumference into substrata with a permeability of 3000 darcies) can be modeled to have moved underground (on timescales of the order of 10(exp 3) years) before the competing mechanisms of sublimation and freeze down choked off further water removal. Once underground, this water can travel distances equal to the separation between Chryse basin and flood-source sites in geologically short (approximately 10(exp 6) year-scale) times. Conversely, we calculate that optimally only approximately 40% of the H2O carried from Chryse can condense at the highlands, and most of the precipitate would either collect at the base of the highlands/lowlands scrap or sublimate at rates greater than it would accumulate over the flood-source sites. Further observations from forthcoming missions may permit the determination of which mechanisms may have operated to recycle the Chryse flood-waters.

Moore, Jeffrey M.↗

A data analytical approach for assessing the efficacy of Operational Technology active defenses against insider threats

In recent years, the need for Operational Technology (OT) defenses has been recognized, serving as an additional line of defense when Information Technology (IT) defenses are bypassed. This is no longer considered an uncommon possibility when dealing with advanced persistent threat (APT) actors expected to be state-sponsored and receiving insider assistance. In these extreme adversarial situations, OT defenses aim to provide another layer of defense for the system, introduced directly at the physical process level, as described by the sensors data, the system model, and control actions. Just like IT defenses, two schools of thought, i.e., passive and active defenses, have emerged to address this challenge. In active defenses, representing the focus of this paper, known signatures, synthesized based on the system's unique characteristics, are inserted into the system. In contradistinction, passive methods rely solely on observing system behavior in search of patterns of normal behavior with deviations thereof representing abnormal behavior. In their most sophisticated implementations, both passive and active defenses rely on the use of data analytics to identify the patterns and synthesize the observed and/or inserted signatures. Past research has shown that passive defenses may be bypassed by APT actors relying on data analytics and their intimate knowledge of the system to evade detection by respecting the patterns identified by the defenders. Here this manuscript explores the use of active defenses under the assumption that the attacker has privileged access to the system, including access to the system's model and sensors data. Specifically, this manuscript assesses the ability of active defenses to remain invisible to the attackers, and discusses the associated challenges that must be addressed to ensure their resiliency against APT actors.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Thomas Leps Internship Abstract

An optical navigation system is being flown as the backup system to the primary Deep Space Network telemetry for navigation and guidance purposes on Orion. This is required to ensure Orion can recover from a loss of communication, which would simultaneously cause a loss of DSN telemetry. Images taken of the Moon and Earth are used to give range and position information to the navigation computer for trajectory calculations and maneuver execution. To get telemetry data from these images, the size and location of the moon need to be calculated with high accuracy and precision. The reentry envelope for the Orion EM-1 mission requires the centroid and radius of the moon images to be determined within 1/3 of a pixel 3 sigma. In order to ensure this accuracy and precision can be attained, I was tasked with building precise dot grid images for camera calibration as well as building a hardware in the loop test stand for flight software and hardware proofing. To calibrate the Op-Nav camera a dot grid is imaged with the camera, the error between the image dot location and the actual dot location can be used to build a distortion map of the camera and lens system so that images can be fixed to display truth locations. To build the dot grid images I used the Electro Optics Lab optical bench Bright Object Simulator System, and gimbal. The gimbal was slewed to a series of elevations and azimuths. An image of the collimated single point light source was then taken at each position. After a series of 99 images were taken at different locations the single light spots were extracted from each image and added to a composite image containing all 99 points. During the development of these grids it was noticed that an intermittent error in the artificial "star" locations occurred. Prior to the summer this error was attributed to the gimbal having glitches in it's pointing direction and was going to be replaced, however after further examining the issue I determined it to be a software issue. I have since narrowed the likely source of the error down to a Software Development Kit released by the camera supplier PixeLink. I have since developed a workaround in order to build star grids for calibration until the software bug can be isolated and fixed. I was also tasked with building a Hardware in the Loop test stand in order to test the full Op-Nav system. A 4k screen displays simulated Lunar and Terrestrial images from a possible Orion trajectory. These images are then projected through a collimator and then captured with an Op-Nav camera controlled by an Intel NUC computer running flight software. The flight software then analyzes the images to determine attitude and position, this data is then reconstructed into a trajectory and matched to the simulated trajectory in order to determine the accuracy of the attitude and position estimates. In order for the system to work it needs to be precisely and accurately aligned. I developed an alignment procedure that allows the screen, collimator and camera to be squared, centered and collinear with each other within a micron spatially and 5 arcseconds in rotation. I also designed a rigid mount for the screen that was machined on site in Building 10 by another intern. While I was working in the EOL we received a $500k Orion startracker for alignment procedure testing. Due to my prior experience in electronics development, as an ancillary duty, I was tasked with building the cables required to operate and power the startracker. If any errors are made building these cables the startracker would be destroyed, I was honored that the director of the lab entrusted such a critical component with me. This internship has cemented my view on public space exploration. I always preferred public sector to privatization because, as a scientist, the most interesting aspects of space for me are not necessarily the most profitable. I was concerned that the public sector was faltering however, and that in order to improve human space exploration I would be forced into private sector. I now know that, at least at JSC, human spaceflight is still progressing, and exciting work is still being done. I am now actively seeking employment at JSC after I complete my Ph.D and have met with my branch chiefs and mentor to discuss transitioning to a grad Co-op position.

Leps, Thomas↗

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗

Mars Global Surveyor Ka-Band Frequency Data Analysis

The Mars Global Surveyor (MGS) spacecraft, launched on November 7, 1996, carries an experimental space-to-ground telecommunications link at Ka-band (32 GHz) along with the primary X-band (8.4 GHz) downlink. The signals are simultaneously transmitted from a 1.5-in diameter parabolic high gain antenna (HGA) on MGS and received by a beam-waveguide (BWG) R&D 34-meter antenna located in NASA's Goldstone Deep Space Network (DSN) complex near Barstow, California. The projected 5-dB link advantage of Ka-band relative to X-band was confirmed in previous reports using measurements of MGS signal strength data acquired during the first two years of the link experiment from December 1996 to December 1998. Analysis of X-band and Ka-band frequency data and difference frequency (f(sub x)-f(sub ka)/3.8) data will be presented here. On board the spacecraft, a low-power sample of the X-band downlink from the transponder is upconverted to 32 GHz, the Ka-band frequency, amplified to I-W using a Solid State Power Amplifier, and radiated from the dual X/Ka HGA. The X-band signal is amplified by one of two 25 W TWTAs. An upconverter first downconverts the 8.42 GHz X-band signal to 8 GHz and then multiplies using a X4 multiplier producing the 32 GHz Ka-band frequency. The frequency source selection is performed by an RF switch which can be commanded to select a VCO (Voltage Controlled Oscillator) or USO (Ultra-Stable Oscillator) reference. The Ka-band frequency can be either coherent with the X-band downlink reference or a hybrid combination of the USO and VCO derived frequencies. The data in this study were chosen such that the Ka-band signal is purely coherent with the X-band signal, that is the downconverter is driven by the same frequency source as the X-band downlink). The ground station used to acquire the data is DSS-13, a 34-meter BWG antenna which incorporates a series of mirrors inside beam waveguide tubes which guide the energy to a subterranean pedestal room, providing a stable environment for the feed and electronics equipment. A dichroic plate is used to reflect the X-band energy and pass the Ka-band energy to another mirror. The RF energy for each band is then focused onto a feed horn and low-noise amplifier package. After amplification and RF/IF downconversion, the IF signals are sent to the Experimental Tone Tracker (ETT), a digital phase-lock-loop receiver, which simultaneously tracks both X-band and Ka-band carrier signals. Once a signal is detected, the ETT outputs estimates of the SNR in a I -Hz bandwidth (Pc/No), baseband phase and frequency of the signals every I -sec. Between December 1996 and December 1998, the Ka-band and X-band signals from MGS were tracked on a regular basis using the ETT. The Ka-band downlink frequencies described here were referenced to the spacecraft's on-board USO which was also the X-band frequency reference (f(sub ka)= 3.8 f(sub x)). The ETT estimates of baseband phase at I -second sampled time tags were converted to sky frequency estimates. Frequency residuals were then generated for each band by removing a model frequency from each observable frequency at each time tag. The model included Doppler and other effects derived from spacecraft trajectory files obtained from the MGS Navigation Team. A simple troposphere correction was applied to the data. In addition to residuals, the USO frequencies emitted by the spacecraft were estimated. For several passes, the USO frequencies were determined from X-band data and from Ka-band data (referred to X-band by dividing by 3.8) and were found to be in good agreement. In addition, X-band USO frequency estimates from MGS Radio Science data acquired from operational DSN stations were available for comparison and were found to agree within the I Hz level. The remaining sub-Hertz differences were attributed to the different models and software algorithms used by MGS Radio Science and KaBLE-11. A summary of the results of a linear fit of the USO frequency versus time (day of year) is presented in Table I for an initial segment of passes.

Morabito, D.↗

The Vera C. Rubin Observatory Data Preview 1

We present Rubin Data Preview 1 (DP1), the first data from the National Science Foundation–Department of Energy Vera C. Rubin Observatory, comprising raw and calibrated single-epoch images, coadds, difference images, detection catalogs, and ancillary data products. DP1 is based on 1792 optical–near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera (LSSTComCam) on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile in late 2024. DP1 covers ∼15 deg 2 distributed across seven roughly equal-sized noncontiguous fields, each independently observed in six broad photometric bands, ugrizy. The median FWHM of the point-spread function across all bands is approximately 1"14, with the sharpest images reaching about 0." 58. The 5σ point-source depths for coadded images in the deepest field, the Extended Chandra Deep Field South, are u = 24.55, g = 26.18, r = 25.96, i = 25.71, z = 25.07, and y = 23.1. Other fields are no more than 2.2 mag shallower in any band, where they have nonzero coverage. DP1 contains approximately 2.3 million distinct astrophysical objects, of which 1.6 million are extended in at least one band in coadds, and 431 solar system objects, of which 93 are new discoveries. DP1 is approximately 3.5 TB in size and is available to Vera C. Rubin Observatory data rights holders via the Rubin Science Platform, a cloud-based environment for the analysis of petascale astronomical data. While small compared to future LSST releases, its high quality and diversity of data support a broad range of early science investigations ahead of full operations in 2026.

Ground-based astronomy↗