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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems

Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. Here this paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated subregion. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.

42 ENGINEERING↗

NASA NTRS1980

LANDSAT technology transfer to the private and public sectors through community colleges and other locally available institutions

Major first year accomplishments are summarized and plans are provided for the next 12-month period for a program established by NASA with the Environmental Research Institute of Michigan to investigate methods of making LANDSAT technology readily available to a broader set of private sector firms through local community colleges. The program applies a network where the major participants are NASA, university or research institutes, community colleges, and obtain hands-on training in LANDSAT data analysis techniques, using a desk-top, interactive remote analysis station which communicates with a central computing facility via telephone line, and provides for generation of land cover maps and data products via remote command.

Rogers, R. H.↗

NASA NTRS1995

Performance Evaluation Tools for Next Generation Scalable Computing Platforms

The Federal High Performance and Communications (HPCC) Program continue to focus on R&D in a wide range of high performance computing and communications technologies. Using its accomplishments in the past four years as building blocks towards a Global Information Infrastructure (GII), an Implementation Plan that identifies six Strategic Focus Areas for R&D has been proposed. This white paper argues that a new generation of system software and programming tools must be developed to support these focus areas, so that the R&D we invest today can lead to technology pay-off a decade from now. The Global Computing Infrastructure (GCI) in the Year 2000 and Beyond would consists of thousands of powerful computing nodes connected via high-speed networks across the globe. Users will be able to obtain computing in formation services the GCI with the ease of using a plugging a toaster into the electrical outlet on the wall anywhere in the country. Developing and managing the GO requires performance prediction and monitoring capabilities that do not exist. Various accomplishments in this field today must be integrated and expanded to support this vision.

Yan, Jerry C.↗

NASA NTRSDate not supplied

An Overview of NASA’s Newest Engineering Model, ORDEM 4.0

Since the mid-1990s, one of the most important products produced by the NASA Orbital Debris Program Office (ODPO) has been the Orbital Debris Engineering Model (ORDEM). This series of models distills down our knowledge of the orbital debris environment to compute debris fluxes on satellites in a given orbit. This information can be used by spacecraft and upper stage designers and operators to design missions for better protection against the debris environment. The current version of the model is ORDEM 3.2, but the ODPO is working on the next generation of ORDEM, to be designated ORDEM 4.0. ORDEM 4.0 will include many known features from previous models, such as the ability to input a spacecraft orbit and time and to compute the flux as a function of debris size, impact speed, impact direction, and debris material densities, as well as uncertainty information on the flux. ORDEM 4.0 will update debris populations using the most recent measurements, including radar observations by the Haystack Ultrawideband Satellite Imaging Radar (HUSIR), NASA’s Goldstone radar, data from the new Space Surveillance Network Space Fence, and observations of Geosynchronous Earth Orbits (GEO) using the Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT). The latest in situ impact data from returned hardware surfaces will be used. In addition, ORDEM 4.0 will introduce a parameterized debris shape model based on laboratory hypervelocity impact tests, such as DebriSat. This will allow analysts to implement shape characteristics in their damage equations and more accurately predict impact damage risk by debris of different shapes and orientations. This paper provides an overview of some of the new features forthcoming in ORDEM 4.0 and a status report on its development.

Mark Matney↗

NASA NTRSDate not supplied

An Overview of NASA’s Newest Engineering Model, ORDEM 4.0

Mark Matney↗

DOE OSTI2025

Emerging applications: Neuromorphic computing and reservoir computing

The emergence of doped hafnium oxide (HfO 2 )-based ferroelectric films has enabled highly scalable and silicon-compatible ferroelectric devices, opening new frontiers in neuromorphic and reservoir computing. Among these, ferroelectric field-effect transistors (FeFETs) are particularly promising due to their analog memory characteristics and unique polarization dynamics. These properties make FeFETs ideal candidates for artificial synapses in neuromorphic architectures, supporting deep neural networks and spiking neural networks based on leaky-integrate-and-fire (LIF) mechanisms. Beyond neuromorphic computing, FeFETs also play a crucial role in physical reservoir computing, leveraging their intrinsic nonlinear and history-dependent behavior for efficient real-time learning. This approach offers significant advantages for time-series processing and edge artificial intelligence (AI) applications, addressing the growing need for energy-efficient computing. As a result, this article explores the principles, key demonstrations, and future potential of FeFET-based neuromorphic and reservoir computing, highlighting their impact on next-generation AI hardware.

36 MATERIALS SCIENCE↗

NASA NTRS1992

Evolutionary Telemetry and Command Processor (TCP) architecture

A low cost, modular, high performance, and compact Telemetry and Command Processor (TCP) is being built as the foundation of command and data handling subsystems for the next generation of satellites. The TCP product line will support command and telemetry requirements for small to large spacecraft and from low to high rate data transmission. It is compatible with the latest TDRSS, STDN and SGLS transponders and provides CCSDS protocol communications in addition to standard TDM formats. Its high performance computer provides computing resources for hosted flight software. Layered and modular software provides common services using standardized interfaces to applications thereby enhancing software re-use, transportability, and interoperability. The TCP architecture is based on existing standards, distributed networking, distributed and open system computing, and packet technology. The first TCP application is planned for the 94 SDIO SPAS 3 mission. The architecture enhances rapid tailoring of functions thereby reducing costs and schedules developed for individual spacecraft missions.

Schneider, John R.↗

NASA NTRS2002

Utilization of Internet Protocol-Based Voice Systems in Remote Payload Operations

Due to limited crew availability to support science and the large number of experiments to be operated simultaneously, telescience is key to a successful International Space Station (ISS) science program. Crew, operations personnel at NASA centers, and researchers at universities and companies around the world must work closely together to per orm scientific experiments on-board ISS. The deployment of reliable high-speed Internet Protocol (IP)-based networks promises to greatly enhance telescience capabilities. These networks are now being used to cost-effectively extend the reach of remote mission support systems. They reduce the need for dedicated leased lines and travel while improving distributed workgroup collaboration capabilities. NASA has initiated use of Voice over Internet Protocol (VoIP) to supplement the existing mission voice communications system used by researchers at their remote sites. The Internet Voice Distribution System (IVoDS) connects remote researchers to mission support "loopsll or conferences via NASA networks and Internet 2. Researchers use NODS software on personal computers to talk with operations personnel at NASA centers. IVoDS also has the ;capability, if authorized, to allow researchers to communicate with the ISS crew during experiment operations. NODS was developed by Marshall Space Flight Center with contractors & Technology, First Virtual Communications, Lockheed-Martin, and VoIP Group. NODS is currently undergoing field-testing with full deployment for up to 50 simultaneous users expected in 2002. Research is being performed in parallel with IVoDS deployment for a next-generation system to qualitatively enhance communications among ISS operations personnel. In addition to the current voice capability, video and data/application-sharing capabilities are being investigated. IVoDS technology is also being considered for mission support systems for programs such as Space Launch Initiative and Homeland Defense.

Chamberlain, jim↗

DOE OSTI2025

Unveiling X-ray absorption signatures of boron nitride via first-principles simulation and machine learning

Boron nitride (BN) allotropes hold great promise in many advanced applications ranging from optical and photonic devices to energy storage and battery systems to tribological components. The diverse functionalities of this material stem from BN’s highly tunable structural and electronic properties, which are governed by the versatile boron–nitrogen bonding configurations. Exploring the structural landscape of BN can unveil novel structures possessing unique properties suited for specific applications, therefore accelerating the design of next-generation advanced functional materials. In this work, we leverage boron K-edge X-ray absorption spectroscopy (XAS) as an effective probe for local structural features and chemical environments. A total of 210 BN crystal structures are generated via analogies to the extensive array of carbon allotropes, and XAS is simulated for each unique local motif within the resulting collection of structures. A mapping between structural features and spectral signatures was established by synergizing first-principle simulations with data-driven based post-analysis approaches. Specifically, we developed a neural network model that can satisfactorily predict spectra line shapes from local structural descriptors. Toward automatic spectroscopic interpretation of any new BN structures, supervised machine learning models, trained on this structure–spectrum dataset, can accurately infer local coordination environments from simulated XAS, highlighting the strength of this unique approach of combining high-fidelity first-principles simulation and machine-learning to accelerate target design of novel BN materials via rational understanding of local structure-spectrum correlations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

AI-Enhanced Co-Design for Next-Generation Microelectronics: Innovating Innovation (Workshop Report)

On April 6-8, 2021, Sandia National Laboratories hosted a virtual workshop to explore the potential for developing AI-Enhanced Co-Design for Next-Generation Microelectronics (AICoM). The workshop brought together two themes. The first theme was articulated in the 2018 Department of Energy Office of Science (DOE SC) “Basic Research Needs for Microelectronics” (BRN) report, which called for a “fundamental rethinking” of the traditional design approach to microelectronics, in which subject matter experts (SMEs) in each microelectronics discipline (materials, devices, circuits, algorithms, etc.) work near-independently. Instead, the BRN called for a non-hierarchical, egalitarian vision of co-design, wherein “each scientific discipline informs and engages the others” in “parallel but intimately networked efforts to create radically new capabilities.” The second theme was the recognition of the continuing breakthroughs in artificial intelligence (AI) that are currently enhancing and accelerating the solution of traditional design problems in materials science, circuit design, and electronic design automation (EDA).

36 MATERIALS SCIENCE↗

DOE OSTI2021

Machine Learning Tools Set for Natural Gas Fuel Cell System Design

This study is focusing on leveraging the system design tools set for the next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design, and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides a user-friendly graphic user interface built within Jupyter Notebooks, and the complete tools set is open-source public available.

Wang, Dewei↗

NASA NTRS2010

Implementation of a Relay Coordination System for the Mars Network

Mars network relay operations involve the coordination of lander and orbiter teams through long-term and short-term planning, tactical changes and post-pass analysis. Much of this coordination is managed through email traffic and point-to-point file data exchanges. It is often difficult to construct a complete and accurate picture of the relay situation at any given moment, as there is no centralized store of correlated relay data. The Mars Relay Operations Service (MaROS) is being implemented to address the problem of relay coordination for current and next-generation relay missions. The service is provided for the purpose of coordinating communications sessions between landed spacecraft assets and orbiting spacecraft assets at Mars. The service centralizes a set of functions previously distributed across multiple spacecraft operations teams, and as such greatly improves visibility into the end-to-end strategic coordination process. Most of the process revolves around the scheduling of communications sessions between the spacecraft during periods of time when a landed asset on Mars is geometrically visible by an orbiting spacecraft. These "relay" sessions are used to transfer data both to and from the landed asset via the orbiting asset on behalf of Earth-based spacecraft operators. This paper will discuss the relay coordination problem space, overview the architecture and design selected to meet system requirements, and describe the first phase of system implementation

Allard, Daniel A.↗

DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1994

Digital Linear Tape (DLT) technology and product family overview

The demand that began a couple of years ago for increased data storage capacity continues. Peripheral Strategies (a Santa Barbara, California, Storage Market Research Firm) projects the amount of data stored on the average enterprise network will grow by 50 percent to 100 percent per year. Furthermore, Peripheral Strategies says that a typical mid-range workstation system containing 30GB to 50GB of storage today will grow at the rate of 50 percent per year. Dan Friedlander, a Boulder, Colorado-based consultant specializing in PC-LAN backup, says, 'The average NetWare LAN is about 8GB, but there are many that have 30GB to 300GB.....' The substantial growth of storage requirements has created various tape technologies that seek to satisfy the needs of today's and, especially, the next generations's systems and applications. There are five leading tape technologies in the market today: QIC (Quarter Inch Cartridge), IBM 3480/90, 8mm, DAT (Digital Audio Tape) and DLT (Digital Linear Tape). Product performance specifications and user needs have combined to classify these technologies into low-end, mid-range, and high-end systems applications. Although the manufacturers may try to position their products differently, product specifications and market requirements have determined that QIC and DAT are primarily low-end systems products while 8mm and DLT are competing for mid-range systems applications and the high-end systems space, where IBM compatibility is not required. The 3480/90 products seem to be used primarily in the IBM market, for interchangeability purposes. There are advantages and disadvantages for each of the tape technologies in the market today. We believe that DLT technology offers a significant number of very important features and specifications that make it extremely attractive for most current as well as emerging new applications, such as Hierarchical Storage Management (HSM). This paper will demonstrate why we think that the DLT technology and family of DLT products will become the technology of choice for most new applications in the mid-range and high-end (non-IBM) markets.

Lignos, Demetrios↗

NASA NTRS2014

Intercomparison and Evaluation of Global Aerosol Microphysical Properties Among Aerocom Models of a Range of Complexity

Many of the next generation of global climate models will include aerosol schemes which explicitly simulate the microphysical processes that determine the particle size distribution. These models enable aerosol optical properties and cloud condensation nuclei (CCN) concentrations to be determined by fundamental aerosol processes, which should lead to a more physically based simulation of aerosol direct and indirect radiative forcings. This study examines the global variation in particle size distribution simulated by 12 global aerosol microphysics models to quantify model diversity and to identify any common biases against observations. Evaluation against size distribution measurements from a new European network of aerosol supersites shows that the mean model agrees quite well with the observations at many sites on the annual mean, but there are some seasonal biases common to many sites. In particular, at many of these European sites, the accumulation mode number concentration is biased low during winter and Aitken mode concentrations tend to be overestimated in winter and underestimated in summer. At high northern latitudes, the models strongly underpredict Aitken and accumulation particle concentrations compared to the measurements, consistent with previous studies that have highlighted the poor performance of global aerosol models in the Arctic. In the marine boundary layer, the models capture the observed meridional variation in the size distribution, which is dominated by the Aitken mode at high latitudes, with an increasing concentration of accumulation particles with decreasing latitude. Considering vertical profiles, the models reproduce the observed peak in total particle concentrations in the upper troposphere due to new particle formation, although modelled peak concentrations tend to be biased high over Europe. Overall, the multimodel- mean data set simulates the global variation of the particle size distribution with a good degree of skill, suggesting that most of the individual global aerosol microphysics models are performing well, although the large model diversity indicates that some models are in poor agreement with the observations. Further work is required to better constrain size-resolved primary and secondary particle number sources, and an improved understanding of nucleation an growth (e.g. the role of nitrate and secondary organics) will improve the fidelity of simulated particle size distributions.

Cloud physics↗

NASA NTRSDate not supplied

Target Tracking with Distributed Sensing and Optimal Data Migration

The paper presents an Extended Kalman Filter based framework for airborne target tracking using adaptive information fusion from multi-modal multi-rate distributed sensors network. First, the tracking algorithm execution location is determined using an optimal data migration strategy, which also computes the associated delays for each sensor data to arrive at the computing location. Next, the fast (zero-delay) sensors information is dynamically fused in the filter correction procedure at the arrival instance of each valid sensor reading. Finally, the target estimation is updated based on the valid slow (delayed) data, which are grouped according to the delay-time steps before application of the Larsen's method. This approach is applied to the synthetic sensor data generated by means of the ground based radar and camera models for the simulated target flight in Reflection simulation environment.

Distributed sensing↗

NASA NTRSDate not supplied

Target Tracking with Distributed Sensing and Optimal Data Migration

Distributed sensing↗

NASA NTRS2024

Future Lunar Surface Network Study: Final Project Report-Unlimited Data Rights

Nokia of America Corporation (Nokia) powered by the research and innovation of Nokia Bell Labs, is honored to have been asked by NASA to conduct a Systems Engineering and Integration (SE&I) study to evaluate an Initial Operating Capability (IOC) for a 3rd Generation Partnership Project (3GPP)-based future lunar surface communication network for the Artemis Program. In particular, the SE&I study has focused on developing an architecture and a 3GPP-based network solution that meets the requirements of the Artemis V mission and at the same time can be evolved and expanded to meet the needs and requirements of future missions in the Moon to Mars program. Nokia strongly believes that 3GPP-based communications and networking solutions present the most effective and future-proof technological path for advanced lunar surface communications (and beyond) for the next decades. 3GPP technologies (whether 4G, 5G or 6G in the future) have revolutionized voice, video, and data transmissions on Earth in consumer, enterprise, and industrial applications, and continue to enable significant gains in productivity, efficiency, and safety. The same benefits can be harnessed for space missions and the future lunar economy including, but not limited to the Artemis program. 3GPP technologies will revolutionize lunar surface communications by increasing data-rates, reducing latency, and providing critical voice, video and data communication capabilities across large surface areas while meeting the stringent reliability requirements of human-rated space flight missions.

Nokia Bell Labs↗