Results for “Digital Infrastructure” — Page 13

DOE OSTI2026

APOLLO: a facility-scale differentiable virtual accelerator for Fermilab

As the design complexity of modern accelerators grows, there is more interest in using advanced simulations that have fast execution time or yield additional insights like gradients. The FAST/IOTA facility has been working on implementing and experimentally validating an end-to-end digital twin that is both fast and gradient-aware, allowing for rapid prototyping of new software and experiments with minimal beam time costs. Our framework integrates physics and ML codes for linac and ring simulation through a set of generic interfaces between surrogate and physics-based sections. To reproduce device inputs and outputs, system state is exposed as a deterministic discrete event simulator. Because Fermilab is undergoing control system transition, both EPICS and ACNET frontends are supported. Recently, we have begun transitioning to a new community lattice standard, PALS, as well as developing standardized infrastructure for data ingest and normalization to prepare for model calibration during FAST proton injector commissioning. We discuss implementation details as well as challenges, and future plans to extend modelling to main complex proton accelerators like PIPII and Booster.

Kuklev, Nikita [Fermilab]↗

NASA NTRS2016

Human Exploration Spacecraft Testbed for Integration and Advancement (HESTIA)

The proposed paper will cover ongoing effort named HESTIA (Human Exploration Spacecraft Testbed for Integration and Advancement), led at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) to promote a cross-subsystem approach to developing Mars-enabling technologies with the ultimate goal of integrated system optimization. HESTIA also aims to develop the infrastructure required to rapidly test these highly integrated systems at a low cost. The initial focus is on the common fluids architecture required to enable human exploration of mars, specifically between life support and in-situ resource utilization (ISRU) subsystems. An overview of the advancements in both integrated technologies, in infrastructure, in simulation, and in modeling capabilities will be presented, as well as the results and findings of integrated testing,. Due to the enormous mass gear-ratio required for human exploration beyond low-earth orbit, (for every 1 kg of payload landed on Mars, 226 kg will be required on Earth), minimization of surface hardware and commodities is paramount. Hardware requirements can be minimized by reduction of equipment performing similar functions though for different subsystems. If hardware could be developed which meets the requirements of both life support and ISRU it could result in the reduction of primary hardware and/or reduction in spares. Minimization of commodities to the surface of mars can be achieved through the creation of higher efficiency systems producing little to no undesired waste, such as a closed-loop life support subsystem. Where complete efficiency is impossible or impractical, makeup commodities could be manufactured via ISRU. Although, utilization of ISRU products (oxygen and water) for crew consumption holds great promise of reducing demands on life support hardware, there exist concerns as to the purity and transportation of commodities. To date, ISRU has been focused on production rates and purities for propulsion needs. The meshing of requirements between all potential users, producers, and cleaners of oxygen and water is crucial to guiding the development of technologies which will be used to perform these functions. Various new capabilities are being developed as part of HESTIA, which will enable the integrated testing of these technologies. This includes the upgrading of a 20' diameter habitat chamber to eventually support long duration (90+ day) human-in-the-loop testing of advanced life support systems. Additionally, a 20' diameter vacuum chamber is being modified to create Mars atmospheric pressures and compositions. This chamber, designated the Mars Environment Chamber (MEC), will eventually be upgraded to include a dusty environment and thermal shroud to simulate conditions on the surface of Mars. In view that individual technologies will be in geographically diverse locations across NASA facilities and elsewhere in the world, schedule and funding constraints will likely limit the frequency of physical integration. When this is the case, absent subsystems can be either digitally or physically simulated. Using the Integrated Power Avionics and Software (iPAS) environment, HESTIA is able to bring together data from various subsystems in simulated surroundings, insert faults, errors, time delays, etc., and feed data into computer models or physical systems capable of reproducing the output of the absent subsystems for the consumption of a local subsystems. Although imperfect, this capability provides opportunities to test subsystem integration and interactions at a fraction of the cost. When a subsystem technology is too immature for integrated testing, models can be produced using the General-Use Nodal Network Solver (GUNNS) capability to simulate the overall system performance. In doing so, even technologies not yet on the drawing board can be integrated and overall system performance estimated. Through the integrated development of technologies, as well as of the infrastructure to rapidly and at a low cost, model, simulate, and test subsystem technologies early in their development, HESTIA is pioneering a new way of developing the future of human space exploration.

Banker, Brian F.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRSDate not supplied

Towards Sustainable Aviation With Efficient Airspace Operations

In November 2021, the Federal Aviation Administration published the United States Aviation Climate Action Plan to accelerate innovation across the U.S. aviation ecosystem. In response, NASA has established the Sustainable Flight National Partnership to engage with industry, academia, and other agencies to accomplish net-zero carbon emissions by 2050. As part of the SFNP Mission, NASA is conducting a series of real world operational demonstrations in the current National Airspace System with a focus on delivering real world sustainability benefits. This paper presents the current work and future plan for the SFNP Ops Demo Series and describes the cloud based infrastructure used for virtual deployment of decision support tools to flight operators and Air Traffic Controllers to help improve operational efficiency of the National Airspace System. Validation results are shared along with the key sustainability benefits such as jet fuel savings and reduction in CO2 emissions. The operational efficiency metrics such as delay savings are also reported.

Sustainable Flight National Partnership↗

NASA NTRSDate not supplied

Exploring Digital Transformation for NASA Nuclear Flight Safety

The U.S. National Aeronautics and Space Administration’s (NASA)’s Nuclear Flight Safety discipline is exploring opportunities to combine incremental advancements in many contributing areas in a way that produces a transformative change for how work is performed. More specifically, after providing some general NASA and space nuclear policy background, the authors will describe concepts and efforts that enable: (i) the use of objectives-d riven approaches (in concert with internal and external constraints) to establish a mission risk posture; (ii) the use of that risk posture in the planning process to risk-inform the selection of Safety and Mission Success (S&MS) methods and models; (iii) use of model-based and machine-assisted techniques to manage the complex and ponderous amount of information and interfaces that typify spaceflight efforts; (iv ) the means by which that infrastructure can directly feed an assurance case (including use of systems modelling language, ontological formulation, and semantic web technology) so as to address known weaknesses in our ability to communicate and manage that complexity; and (v) use of that case-assured framework to demonstrate that one did the adequate and sufficient S&MS work and that the S&MS work was done competently.

Donald Helton↗

NASA NTRS1996

Support of Helicopter 'Free Flight' Operations in the 1996 Olympics

The microcosm of activity surrounding the 1996 Olympic Games provided researchers an opportunity for demonstrating state-of-the art technology in the first large-scale deployment of a prototype digital communication/navigation/surveillance system in a confined environment. At the same time it provided an ideal opportunity for transportation officials to showcase the merits of an integrated transportation system in meeting the operational needs to transport time sensitive goods and provide public safety services under real-world conditions. Five aeronautical CNS functions using a digital datalink system were chosen for operational flight testing onboard 91 aircraft, most of them helicopters, participating in the Atlanta Short-Haul Transportation System. These included: GPS-based Automatic Dependent Surveillance, Cockpit Display of Traffic Information, Controller-Pilot Communications, Graphical Weather Information (uplink), and Automated Electronic Pilot Reporting (downlink). Atlanta provided the first opportunity to demonstrate, in an actual operating environment, key datalink functions which would enhance flight safety and situational awareness for the pilot and supplement conventional air traffic control. The knowledge gained from such a large-scale deployment will help system designers in development of a national infrastructure where aircraft would have the ability to navigate autonomously.

Branstetter, James R.↗

DOE OSTI2025

Mechanical Solutions Scan Report

Power lines, poles, and towers are the backbone of the United States (U.S.) electric-power grid. These transmission and distribution networks route electricity from generator to loads. The characteristics of these routes are rapidly changing -- trending towards decentralized renewable generation, electric heating, vehicle charging, and large data-center loads. Coupled with aging infrastructure and the increased frequency of extreme weather events, there is concern about the future reliability and transmission capacity of conductors and adjacent components. This scan report seeks to provide an overview of mechanical solutions to challenges caused by extreme weather events associated with components of transmission and distribution infrastructure, including conductor heat sag, ice accumulation, wind, and wildfire. Many options could increase transmission capacity or reliability, and these are at various stages of technological readiness. Some have only been lab tested, while some have been widely deployed in the U.S. or overseas for decades. The solution categories and providers featured in this report are intended to be comprehensive at the time of publication and to serve as a reference for decision-makers concerned about transmission and distribution reliability. There are two other categories of large, complex solutions, which are not covered in this report: replacing existing conductors with advanced conductors and implementing digital grid enhancing technologies. A separate scan report titled “Advanced Conductor Scan Report,” which discusses advanced carbon-core conductors, was published by the Idaho National Laboratory (INL) in 2023. Information on digital technologies, such as dynamic line ratings, power-flow controllers, and other power electronics and communications-based devices, can be found on the Grid- Enhancing Technologies landing page. Mechanical grid-enhancing technologies, or solutions covered in this report, often do not require full equipment replacement and do not rely on digital components. Mechanical technologies are overlooked because they may be older, simpler, or seemingly “more obvious” than digital or carbon-core technologies. However, it is wise to consider mechanical solutions in a thorough evaluation of grid enhancing technology solutions.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Connected and Learning Based Optimal Freight Management for Efficiency

The management of the future heterogenous fleet is a complex decision-making problem. The heterogenous fleet is emerging as decarbonization technologies are deployed by fleets toward lowering the freight operation emissions in Medium and Heavy-duty vehicles. Traditionally, in fleets characterized by a homogeneous Diesel Internal Combustion Engine (ICE) powertrain, the process of fleet planning and operational optimization unfolds sequentially without the necessity to account for powertrain and vehicle-specific characteristics during dispatch decisions. Fleets with trucks less than 5 years old tend to maintain stable vehicle efficiency with minimal operational reliability risks for fleet managers. However, the landscape changes with the incorporation of emerging powertrain technologies, which lack extensive operational data and service experiences. This includes technologies like hybrid, Electric, Fuel Cell, or alternative fuel ICE. Operational decisions for fleets featuring heterogeneous powertrain technologies and facing limited access to alternative fueling and charging stations become intricate, requiring careful consideration and optimization at each dispatch. The difference in efficiency characteristics of emerging technologies, their range limitations, and the restricted availability of charging/alternative fueling infrastructure, coupled with sensitivity to driving conditions (e.g., EV range reduction in low temperatures) and their impact on component aging (such as batteries), become pivotal factors influencing the reliable and efficient freight transportation. To make the path toward low emission freight transportation efficient and reliable, an AI-assisted fleet management software is developed in this project to help fleet managers in optimizing both adoption of emerging powertrain decarbonization, connected and automated technologies and also operating the fleet after such technologies are deployed as schematically. Freight transportation requirements are different depending on the cargos to be shipped, customer requirements and regions of operations. This further highlights the need for software and digital solutions to tailor deployment and operation of emerging powertrain, connectivity, and automation technologies toward the specific fleet operation requirements. The fleet management optimizer was also integrated with a model of the fleet to simulate the operation of the fleet over 1 year of the baseline fleet operation (250,000+ shipments) indicating the significance of day-to-day variations on emissions and energy consumption of a freight transportation fleet. The results demonstrate ≥20% improvement in freight efficiency in terms of WTW CO2 per ton-mile of cargo shipments while all fleet operation constraints are enforced, and the cost (CapEx and OpEx) is minimized.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

An Integrated ML/AI Framework for Digitizing, Structuring and Searching DOE U-TRU-Fuels Data with Gap Analysis of Non-DOE Records

The U.S. Department of Energy (DOE) Advanced Fuels Campaign (AFC) is advancing transmutation fuel technologies to reduce long-lived radioactive waste by converting minor actinides into shorter-lived or stable elements through irradiation in sodium-cooled fast reactors. Key experiments such as AFC-1, AFC-2, FUels for the transmutation of Trans-URanium elements In phéniX (FUTURIX)-Fortes Teneurs en Actinides (FTA), and Experimental Breeder Reactor-II (EBR-II) X501 have provided fuel fabrication, irradiation, and performance data on various transuranic-bearing fuel forms. This report documents the creation of an artificial-intelligence assisted database, which has consolidated all DOE-owned data related to Transuranic (TRU)-bearing fuel experiments and stored across it across both the Idaho National Laboratory (INL) Nuclear Data Management and Analysis System and the INL high performance computing (HPC) infrastructure. A dedicated webpage, hosted on the INL HPC system, has been developed to support role-based access and data interaction. The database architecture allows researchers to navigate large, heterogeneous archives with far greater speed and accuracy than manual search and lays the foundation for future expansion into multimodal nuclear materials analysis environments. The database represents a major step towards a nationally integrated fuels database utilizing artificial intelligence tools.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Obstacles to Practical Digital Supply Chain Risk Management in the Energy Sector

Cyber supply chain risk management (C-SCRM) programs must consider operations that depend on the lifecycles of digital components such as hardware, firmware, software, and services. We integrate academic literature, historical incidents, and existing standards to identify obstacles faced by C-SCRM programs.

Business Process Management & Integration↗

DOE OSTI2026

SDN-Based Dynamic Cybersecurity Framework of IEC-61850 Communications in Smart Grid

In recent years, critical infrastructure and power grids have experienced a series of cyber-attacks, leading to temporary, widespread blackouts of considerable magnitude. Since most substations are unmanned and have limited physical security protection, cyber breaches into power grid substations present a risk. Nowadays, the susceptibility of SDN architecture to cyber-attacks has exhibited a notable increase in recent years, as indicated by research findings. This suggests a growing concern regarding the potential for cybersecurity breaches within the SDN framework. In this paper, we propose a hybrid intrusion detection system (IDS)-integrated SDN architecture for detecting and preventing the injection of malicious IEC 61850-based generic object-oriented system event (GOOSE) messages in a digital substation. Additionally, this program locates the fault’s location and, as a form of mitigation, disables a certain port. Furthermore, implementation examples are demonstrated and verified using a hardware-in-the-loop (HIL) testbed that mimics the functioning of a digital substation.

Liu, Chen-Ching [Virginia Tech] (ORCID:00000002894↗

DOE OSTI2026

Aligning NASA Earth Science Data Stewardship with FAIR Principles: Outcomes, Recommendations, and Future Directions

The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—offer a widely accepted framework for improving the sharing and reuse of digital scientific data by both human and machine users. Following these principles is critical for effective scientific data stewardship, broader scientific collaboration, and compliance with federal and agency data policies. This paper, based on the work of NASA’s Open, Free, and FAIR Working Group (O’FAIR WG) under the Earth Science Data Systems Program, presents an overview of how FAIR is being applied within NASA’s Earth science data landscape. It highlights ongoing progress and challenges, identifies FAIR-enabling resources, and offers recommendations and strategic actions to enhance the FAIRness of NASA-funded open and free Earth science data products. The FAIR-enabling resources identified underscore the vital role of NASA's existing enterprise processes, standards, tools, and infrastructures in supporting FAIR implementation. Our findings show strong performance in making NASA Earth science data more findable and accessible. However, further work is needed—especially in enhancing interoperability, so that different systems and tools can better understand and exchange data. This is especially important for enabling machine-driven discovery and analysis. We emphasize the importance of a balanced strategy that combines a centralized, top-down approach—focused on building enterprise-level capabilities and processes—with a decentralized, bottom-up approach driven by discipline-specific needs and community practices. We advocate for coordinated efforts to enhance (meta)data interoperability to facilitate seamless data and information sharing and exchange of Earth science data both within NASA and across other agencies managing Earth science data.

Data Product↗

DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems: Evaluating Radar, Lidar and Camera: Preprint

Intelligent transportation systems (ITS) are at the forefront in advancing the way we interact with and perceive the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as radar, lidar, and video imaging, which are the most popular modalities for ITS. Real-time perception data from these sensors allow intelligent infrastructure-side decision-making to improve the energy, efficiency, and safety at traffic intersections. As traffic departments across the United States transition from traditional loop detectors and emulators and embrace newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception that is reliable, inexpensive, and easy to set up and that has robust performance in varying weather conditions. However, choosing a sensor that checks all these boxes is not straightforward, as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long-range vehicles and weather resistance but lacks high resolution. Lidar is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines radar, lidar, and camera sensor capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like radar, lidar, and cameras offers the most robust solution for enhancing the safety and efficiency of ITS. Through this evaluation, we hope to draw attention to the necessity of the National Renewable Energy Laboratory's infrastructure perception and control framework, which presents a multisensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Engineering Against Digital Risk in CIP Applications: Cyber-Informed Engineering Use Cases

Cyber-Informed Engineering (CIE) addresses the reality that cyber attacks on engineered systems can have consequences far beyond data loss or disruption of digital networks. When control systems are compromised, safety, reliability, and performance of the physical process itself may be threatened. This presentation discusses engineered controls of 7 categories and the CIE database of controls that provides clear examples and guidance for defining and applying engineered controls in CIE. It explains what engineered controls are, how they differ from information security measures, and how they are integrated into system design.

99 - GENERAL AND MISCELLANEOUS↗

NASA NTRS2012

Low-Cost Telemetry System for Small/Micro Satellites

A Software Defined Radio (SDR) concept uses a minimum amount of analog/radio frequency components to up/downconvert the RF signal to/from a digital format. Once in the digital domain, all other processing (filtering, modulation, demodulation, etc.) is done in software. The project will leverage existing designs and enhance capabilities in the commercial sector to provide a path to a radiation-hardened SDR transponder. The SDR transponder would incorporate baseline technologies dealing with improved Forward Error Correcting (FEC) codes to be deployed to all Near Earth Network (NEN) ground stations. By incorporating this FEC, at least a tenfold increase in data throughput can be achieved. A family of transponder products can be implemented using common platform architecture, allowing new products to be more quickly introduced into the market. Software can be reused across products, reducing software/hardware costs dramatically. New features and capabilities, such as encoding and decoding algorithms, filters, and bit synchronizers, can be added to the existing infrastructure without requiring major new capital expenditures, allowing implementation of advanced features in the communication systems. As new telecommunication technologies emerge, incorporating them into the SDR fabric will be easily accomplished with little or no requirements for new hardware. There are no preferred flight platforms for the SDR technology, so it can be used on any type of orbital or sub-orbital platform, all within a fully radiation hardened design.

Sims, William↗

NASA NTRS2008

Enhanced Flight Termination System (EFTS): Flight Demonstration and Results

The Enhanced Flight Termination System (EFTS) program was initiated and propelled due to the inadvertent terminations of Global Hawk and the Strategic Target System and the NASA Inspector General's assessment letter and recommendations regarding the exploration of low-cost, lightweight space COMSEC for FTS. Additionally, the standard analog and high alphabet systems most commonly used in FTS are secure, but not encrypted. A study group was initiated to select and document a robust, affordable, reliable technology that provides encrypted FTS capability. A flight demonstration was conducted to gain experience using EFTS in an operational environment, provide confidence in the use of the EFTS components, integrate EFTS into an existing range infrastructure to demonstrate the scalability of system components, to provide a command controller that generated the EFTS waveform using an existing range infrastructure, and to provide a report documenting the results of the demonstration. The primary goal of the demonstration was to obtain operational experience with EFTS. Areas of operational experience include: mission planning, pre-flight configuration and testing, mission monitoring and recording, vehicle termination, developing mission procedures. and post mission data reduction and other post mission activities. An Advanced Medium-Range Air-to-Air Missile (AMRAAM) was selected to support the EFTS demonstration due to interest in future use of EFTS by the AMRAAM program, familiarity of EFTS by range personnel, and the availability of existing operational environment to support EFTS testing with available program funding. For demonstration purposes, the AMRAAM was successfully terminated using an EFTS receiver and successfully demonstrating EFTS. The EFTS monitoring software with spectrum analyzer and digital graphical display of aircraft, missile, and target were also demonstrated.

Tow, David↗

NASA NTRS2023

Factories-in-Space for Servicing, Assembly, & Manufacturing

Space 2.0 is a promising frontier for scientific exploration and the advancement of commerce, security, and technology. To effectively harness this potential, it is imperative to establish a multifunctional, resilient, and sustainable infrastructure that enables the maintenance and production of space-based systems. This capability is a driver for mission success on-orbit and for interplanetary travel to other celestial bodies. Central to this infrastructure is the establishment of orbital manufacturing facilities, referred to as 'factories-in-space' (FiS), which serve as critical nodes in the supply chain for the servicing, assembly, and production of systems essential for space-based operations. This paper presents a framework for understanding the key principles and design considerations underpinning FiS.

Technology↗