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At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗
DOE OSTI2021

Distributed, Intelligent Edge-Sensing for a Smarter Grid

The electric grid is undergoing major transformations and developments resulting in unprecedented levels of volatility, uncertainty, and stress on grid infrastructure. Smart sensors and methods aiding in advanced visibility and situational awareness are key for tackling these issues. In this work, a decentralized architecture is proposed, where sensing, local computation and control capability are embedded in the edge devices, communicating with a set of trusted 'data mules' in a 'delay-tolerant' manner, while functioning autonomously. This system has been designed and implemented as an overall platform – called Global Asset Monitoring, Management and Analytics (GAMMA) Platform intended to provide the backbone for a global array of sensors and actuators. Further, as a building block for advanced current sensing solutions, a smart, low-cost ‘clip-on’ current sensor based on PCB-embedded Rogowski coil has been developed. The sensor hosts a novel signal conditioning stage allowing an 'auto-tuning' feature, resulting in a universal current sensor design for measuring a wide range of currents, including faults for smart grid applications. Finally, the research proposes a method to instrument and monitor key parameters for the most common electric utility asset – the pole-top distribution transformer. The work done in this research enables scalable, edge-intelligent sensing solutions for monitoring grid infrastructure, allowing utility operators to gain advanced visibility in an economical way.

Kulkarni, Shreyas Bhalchandra↗
DOE OSTI2024

Edge majorana quasiparticles and qubits

Various embodiments described herein provide for a topological quantum computer that uses edge Majorana quasi-particles to form qubits. An inverted Indium Arsenide (InAs) and Gallium Antimonide (GaSb) heterostructure is disclosed that is a quantum spin Hall insulator. A layer of aluminum can be deposited over a nanotube that is placed across the layers of the heterostructure. Once the nanotube is removed, and a gate is formed on the heterostructure and the heterostructure is cooled so that the aluminum becomes superconducting, helical edge states are formed at the junction of the super conducting aluminum, the InAs, and the GaSb which creates a Majorana zero modes (MZMs) at zero magnetic field. The MZMs can be used to construct a topological qubit for fault-resistant topological quantum computation.

Pan, Wei↗
DOE OSTI2026

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.

Das, Arghya Ranjan [Purdue U.] (ORCID:000000018451↗
DOE OSTI2026

Towards Secure Autonomous Vehicles: An Integrated Edge and Multi-Modal Machine Learning Framework for Intrusion Detection

Autonomous vehicles (AVs) are vulnerable to cyberattacks targeting both internal communication networks and external perception sensors. While edge-based intrusion de- tection for Controller Area Network (CAN) buses offers real-time protection, it cannot detect cross-modal threats. Conversely, multi-modal fusion approaches improve coverage but often lack efficiency for in-vehicle deployment. This thesis integrates two complemen- tary solutions: (1) a lightweight, edge-deployable machine learning framework for CAN bus intrusion detection, and (2) a late-fusion system combining CAN FD and LiDAR data. Together, they form a hierarchical defense capable of handling single-modality and coordi- nated attacks. Simulations show that CAN-only models reach 93% accuracy on simulated DoS, spoofing, replay, and fuzzy attacks, while the fusion system achieves 0.87 AUC and 0.82 F1-score at 2 ms latency. This unified framework establishes a scalable, explainable, and field-ready strategy for AV cybersecurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Simulation of the Impact of Point Defects and Edge Dislocations on X-Ray Diffraction in Hexagonal (Ni,Co) 1+2 x Ti 1– x O 3 Thin Films

In this work, a computer code for simulating high-resolution X-ray diffraction (XRD) data from disordered crystals with arbitrary spatial composition and local lattice parameters is developed. Simulated patterns are compared with the experimental data collected on a single phase, highly crystalline (Ni 0.42 Co 0.58 ) 2.22 Ti 0.39 O 3 solid-solution thin film exhibiting a large number of subsidiary minima. As a case study, the edge dislocations in hexagonal (Ni,Co) 3 O 3 thin films with Burgers vector parallel and perpendicular to the a-axis and c-axis, respectively, are modeled. No peak profiles are assumed and thus issues related to profile fitting are avoided. Both macroscopic features, such as film thickness, and atomic-scale structure, such as dislocations, are simultaneously modeled. Simulations are run on a desktop machine. Commonly applied database-based phase identification routines can result in wrong phase identification, unless intensities are properly modeled. Modeling tools of diffractometer manufacturers are compared with the present approach. An example of how simulated reciprocal lattice patterns can be used to choose relevant measurement geometries in defected thin films is given.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Performance of Al-Mn Transition-Edge Sensor Bolometers in SPT-3G

SPT-3G is a polarization-sensitive receiver, installed on the South Pole Telescope, that measures the anisotropy of the cosmic microwave background (CMB) from degree to arcminute scales. The receiver consists of ten 150-mm-diameter detector wafers, containing a total of similar to 16,000transition-edge sensor (TES) bolometers observing at 95, 150, and 220 GHz. During the 2018-2019 austral summer, one of these detector wafers was replaced by a new wafer fabricated with Al-Mn TESs instead of the Ti/Au design originally deployed for SPT-3G. We present the results of in-laboratory characterization and on-sky performance of this Al-Mn wafer, including electrical and thermal properties, optical efficiency measurements, and noise-equivalent temperature. In addition, we discuss and account for several calibration-related systematic errors that affect measurements made using frequency-domain multiplexing readout electronics.

Al-Mn↗
DOE OSTI2020

Commercially Fabricated Antenna-Coupled Transition Edge Sensor Bolometer Detectors for Next-Generation Cosmic Microwave Background Polarimetry Experiment

We report on the development of commercially fabricated multi-chronic antenna-coupled transition edge sensor (TES) bolometer arrays for cosmic microwave background (CMB) polarimetry experiments. The orders of magnitude increase in detector count for next-generation CMB experiments require a new approach in detector wafer production to increase fabrication throughput. Here, we describe collaborative efforts with a commercial superconductor electronics fabrication facility (SeeQC, Inc.) to fabricate antenna-coupled TES bolometer detectors. We have successfully fabricated an operational dual-polarization, dichroic sinuous antenna-coupled TES detector array on a 150-mm-diameter wafer. The fabricated detector arrays have average yield of 95% and excellent uniformity across the wafer. Both RF characteristics and TES bolometer properties are suitable for CMB observations. We successfully fabricated different types of TES bolometers optimized for frequency multiplexing readout, time-domain multiplexing readout, and microwave SQUID multiplexing readout. We also demonstrated high production throughput. We discuss the motivation, design considerations, fabrication processes, test results, and how industrial detector fabrication could be a path to fabricate hundreds of detector wafers for future CMB polarimetry experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Characterization of Transition Edge Sensors for the Simons Observatory

The Simons Observatory is building both large (6 m) and small (0.5 m) aperture telescopes in the Atacama Desert in Chile to observe the cosmic microwave background CMB radiation with unprecedented sensitivity. Simons Observatory telescopes in total will use over 60,000 transition edge sensor (TES) detectors spanning center frequencies between 27 and 285 GHz and operating near 100 mK. TES devices have been fabricated for the Simons Observatory by NIST, Berkeley, and HYPRES/SeeQC corporation. Iterations of these devices have been tested cryogenically in order to inform the fabrication of further devices, which will culminate in the final TES designs to be deployed in the field. The detailed design specifications have been independently iterated at each fabrication facility for particular detector frequencies. We present test results for prototype devices, with emphasis on NIST high frequency detectors. A dilution refrigerator was used to achieve the required temperatures. Measurements were taken both with 4-lead resistance measurements and with a time-domain Superconducting Quantum Interference Device (SQUID) multiplexer system. The SQUID readout measurements include analysis of current versus voltage (IV) curves at various temperatures, square wave bias step measurements, and detector noise measurements. Lastly, normal resistance, superconducting critical temperature, saturation power, thermal and natural time constants, and thermal properties of the devices are extracted from these measurements.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

In-Flight Gain Monitoring of SPIDER’s Transition-Edge Sensor Arrays

We report experiments deploying large arrays of transition-edge sensors (TESs) often require a robust method to monitor gain variations with minimal loss of observing time. We propose a sensitive and non-intrusive method for monitoring variations in TES responsivity using small square waves applied to the TES bias. We construct an estimator for a TES’s small-signal power response from its electrical response that is exact in the limit of strong electrothermal feedback. We discuss the application and validation of this method using flight data from SPIDER, a balloon-borne telescope that observes the polarization of the cosmic microwave background with more than 2000 TESs. This method may prove useful for future balloon- and space-based instruments, where observing time and ground control bandwidth are limited.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Absolute Energy Measurements with Superconducting Transition-Edge Sensors for Muonic X-ray Spectroscopy at 44 keV

Superconducting transition-edge sensor (TES) microcalorimeters have great utility in X-ray applications owing to their high energy resolution, good collecting efficiency and the feasibility of being multiplexed into large arrays. In this work, we develop hard X-ray TESs to measure the absolute energies of muonic-argon (R-Ar) transition lines around 44 keV and 20 keV. TESs with sidecar absorbers of different heat capacities were fabricated and characterized for their energy resolution and calibration uncertainty. Here, we achieved similar to 1 eV absolute energy measurement accuracy at 44 keV, and < 12 eV energy resolution at 17.5 keV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Quantifying uncertainty in uranium concentration measurements via K-edge densitometry

This study quantifies the uncertainty in uranium concentration predictions of fluoride and chloride-based salts within a steel pipe using K-edge densitometry. Modeling and simulation was conducted with the Monte Carlo N-Particle Transport (MCNP) code. The quality of of this technique’s prediction in a pipe requires proper characterization of the pipe’s thickness, which is dependent on the source size and axial offset from the pipe centerline. The thickness was determined as either the center-line thickness seen by the X-ray source or an average value determined through random sampling. Generally, the predicted concentrations were slightly better at lower offset with the random sampling thickness and using the center-line thickness for the highest offsets. For a line-beam source and varying axial offsets, the relative error of concentration was within 1% of the true value but uncertainty increased by 2 orders of magnitude. Similarly, for no axial offset, the relative error was significantly less than 1% while no trend for uncertainty was found. However, at the largest possible offset for a given source size, the concentrations become erroneous and greater than the allowable 1% relative error. Furthermore, high offsets tended to increase the variance of the transmission spectra by 3 orders of magnitude.

Characterization and Analytical Technique↗
DOE OSTI2022

Universal location of Yang–Lee edge singularity for a one-component field theory in 1 ≤ d ≤ 4

Herein we determine the universal location of the Yang–Lee edge singularity in the entire relevant domain of spatial dimensions 1 ≤ d ≤ 4 for the Ising universality class. To that end, we present analytical results for d = 1,2,4 and near four dimensions. For d = 3 and a set of fractional dimensions, we perform numerical calculations using a systematic Functional Renormalization Group approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

S K-edge XAS of Cu Ⅱ , Cu Ⅰ , and Zn Ⅱ oxidized Dithiolene complexes: Covalent contributions to structure and the Jahn-Teller effect

We report reduced dithiolene ligands are bound to high valent Mo centers in the active site of the oxotransferase family of enzymes. Related model complexes have been studied with great insight by Prof. Holm and his colleagues. This study focuses on the other limit of dithiolene chemistry: an investigation of the 2-electron oxidized dithiolene bound to low-valent late transition metal (TM) ions (Zn II , Cu I , and Cu II ). The bonding descriptions of the oxidized dithiolene [N,N-dimethyl piperazine 2,3-dithione (Me 2 Dt 0 )] complexes are probed using S K-edge X-ray absorption spectroscopy (XAS) and the results are correlated to density functional theory (DFT) calculations. These experimentally supported calculations are then extended to explain the different geometric structures of the three complexes. The Zn II (Me 2 Dt 0 ) 2 complex has only ligand-ligand repulsion so it is stabilized at the D 2d symmetry limit. The Cu I (Me 2 Dt 0 ) 2 complex has additional weak backbonding thus distorts somewhat from D 2d toward D 2h symmetry. The Cu II (Me 2 Dt 0 ) 2 complex has a strong σ donor bond that leads to both a large Jahn-Teller stabilization to D 2h and an additional covalent contribution to the geometry. The combined strong stabilization results in the square planar, D 2h structure. This study quantifies the competition between the ligand-ligand repulsion and the change in electronic structures in determining the final geometric structures of the oxidized dithiolene complexes, and provides quantitative insights into the Jahn-Teller stabilization energy and its origin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

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

Cognitive cyber↗
DOE OSTI2020

Insights into Copper Sulfide Formation from Cu and S K edge XAS and DFT studies

An understanding of the fundamentals of the reaction between CuO with trace amounts of H 2 S to form CuS products is critical for the optimal utilization of this process in sulfur removal applications. Unfortunately, CuS is a complex material, featuring various Cu 2-x S compounds (with 0 ≤ x ≤ 1), distorted crystal phases, and varying electronic structures and coordination environments of Cu and S ions. In this work, we combine ex situ and in situ X-ray absorption spectroscopy (XAS) at S and Cu K edges, fixed bed sorption experiments, DFT simulations, and other characterization techniques to speciate the CuS products formed at different temperatures (298–383 K) and from CuO sorbents with different crystallite sizes (2.8–40 nm). The results of our analysis identify the formation of a distorted CuS layer at the surface of CuO crystals with disulfide groups with shorter Cu–S bonds and higher delocalization of the positive charge of the Cu center into (S 1– )2. This distorted CuS layer dominates the XAS signal at lower temperatures (298–323 K) and at the initial stages of sulfidation at higher temperatures (353 and 383 K) where conversion is low (<40%). First-principles atomistic simulations confirm the thermodynamic favorability of the formation of surface (S 1– )2 on both CuO (111) and ($\bar{1}11$) surfaces, providing further support for our experimental observations. Furthermore, these simulations reveal that the presence of disulfide bonds stabilized surface hydroxyl groups, leading to lower Gibbs Free Energies of their surface migration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗