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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Modeling and Analysis of Structural Dynamics for a One-Tenth Scale Model NGST Sunshield

New modeling and analysis techniques have been developed for predicting the dynamic behavior of the Next Generation Space Telescope (NGST) sunshield. The sunshield consists of multiple layers of pretensioned, thin-film membranes supported by deployable booms. Modeling the structural dynamic behavior of the sunshield is a challenging aspect of the problem due to the effects of membrane wrinkling. A finite element model of the sunshield was developed using an approximate engineering approach, the cable network method, to account for membrane wrinkling effects. Ground testing of a one-tenth scale model of the NGST sunshield were carried out to provide data for validating the analytical model. A series of analyses were performed to predict the behavior of the sunshield under the ground test conditions. Modal analyses were performed to predict the frequencies and mode shapes of the test article and transient response analyses were completed to simulate impulse excitation tests. Comparison was made between analytical predictions and test measurements for the dynamic behavior of the sunshield. In general, the results show good agreement with the analytical model correctly predicting the approximate frequency and mode shapes for the significant structural modes.

Johnston, John↗
NASA NTRSDate not supplied

Video That's Out of This World: A Snapshot of Video Distribution from Across the Solar System

While extraterrestrial scientific research advances to the Moon and Mars, so does the need for secure high-quality video links to capture data and share the experience. This paper describes the space to ground architecture needed to satisfy the cultural mantra challenging “Pictures or it didn’t happen!” as NASA travels beyond Low Earth Orbit. The Marshall Space Flight Center’s Delay Tolerant Network Marshall Enterprise (DTNME) protocol is the framework for video distribution beyond the terrestrial Internet, appropriate for communications across the solar system. Utilizing the International Space Station (ISS) as a test facility, NASA engineers and contractors will demonstrate new technologies and capabilities to transmit high-quality video imagery for NASA’s return to the Moon, Mars exploration, and next generation of explorers.

Jennifer L. Christopher↗
DOE OSTI2022

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A neural network for cloud masking in WorldView satellite imagery

This repository includes the cloud detection algorithm used in the manuscript 'Topography controls variability in circumpolar permafrost thaw pond expansion' by Abolt et al. It also includes a demonstration of the algorithm's use at a survey area in northern Alaska and a demonstration of training the algorithm. This dataset includes .pt place-text, .py python, .tif image, .ipynb Jupyter notebook, and .txt text files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

BBN-Based Portfolio Risk Assessment for NASA Technology R&D Outcome

The NASA Aeronautics Research Mission Directorate (ARMD) vision falls into six strategic thrusts that are aimed to support the challenges of the Next Generation Air Transportation System (NextGen). In order to achieve the goals of the ARMD vision, the Airspace Operations and Safety Program (AOSP) is committed to developing and delivering new technologies. To meet the dual challenges of constrained resources and timely technology delivery, program portfolio risk assessment is critical for communication and decision-making. This paper describes how Bayesian Belief Network (BBN) is applied to assess the probability of a technology meeting the expected outcome. The network takes into account the different risk factors of technology development and implementation phases. The use of BBNs allows for all technologies of projects in a program portfolio to be separately examined and compared. In addition, the technology interaction effects are modeled through the application of object-oriented BBNs. The paper discusses the development of simplified project risk BBNs and presents various risk results. The results presented include the probability of project risks not meeting success criteria, the risk drivers under uncertainty via sensitivity analysis, and what-if analysis. Finally, the paper shows how program portfolio risk can be assessed using risk results from BBNs of projects in the portfolio.

Geuther, Steven C.↗
NASA NTRS2009

Design Aspects of the VLBI2010 System - Progress Report of the IVS VLBI2010 Committee

This report summarizes the progress made in developing the next generation VLBI system, dubbed the VLBI2010 system. The VLBI2010 Committee of the International VLBI Service for Geodesy and Astrometry (IVS) worked on the design aspects of the new system. The report covers Monte Carlo simulations showing the impact of the new operating modes on the final products. A section on system considerations describes the implications for the VLBI2010 system parameters by considering the new modes and system-related issues such as sensitivity, antenna slew rate, delay measurement error. RF1, frequency requirements, antenna deformation, and source structure corrections_ This is followed by a description of all major subsystems and recommendations for the network, station. and antenna. Then aspects of the feed, polarization processing. calibration, digital back end, and correlator subsystems are covered. A section is dedicated to the NASA. proof-of-concept demonstration. Finally, sections tm operational considerations, on risks and fallback options, and on the next steps complete the report.

Petrachenko, Bill↗
DOE OSTI2025

Mitigating Battery Cell Failure: Role of Ag‐Nanoparticle Fillers in Solid Electrolyte Dendrite Suppression

Abstract The development of solid‐state batteries (SSBs) with lithium Li metal anodes holds significant promise for enhancing the energy density and safety of next‐generation energy storage systems. However, their commercialization is hindered by challenges related to Li dendrite formation, which can lead to short circuits and battery failure. In this study, the role of Ag nanoparticles embedded within solid electrolytes (SE) is investigated in suppressing dendrite propagation. The results demonstrate that Ag nanoparticles effectively mitigate two key failure mechanisms: (1) dendrite growth within porous networks at low current densities and (2) stress intensification‐induced SE fracture at higher current densities (12 mA cm −2 ). Ex situ characterization using focused‐ion beam – scanning electron microscopy (FIB–SEM) and energy dispersive X‐ray spectroscopy (EDS,) reveals that Ag nanoparticles migrate alongside advancing Li dendrites, promoting homogeneous dendrite growth and reducing the likelihood of localized stress concentrations. Additionally, the incorporation of Ag nanoparticles is shown to facilitate a more uniform Li distribution toward the anode side, which can potentially enable the use of higher charging rates in SSBs. This study provides a new perspective on Li dendrite suppression and presents new opportunities for enhancing the performance and safety of SSBs.

Diallo, Mouhamad Said↗
NASA NTRS1988

Patterns of the cosmic microwave background from evolving string networks

A network of cosmic strings generated in the early universe may still exist today. As the strings move across the sky, they produce, by gravitational lensing, a characteristic pattern of anisotropies in the temperature of the cosmic microwave background. The observed absence of such anisotropies places constraints on theories in which galaxy formation is seeded by strings, but it is anticipated that the next generation of experiments will detect them.

Bouchet, Francois R.↗
DOE OSTI2024

Bacterial microcompartments as a next-generation metabolic engineering tool: utilizing nature's solution for confining challenging catabolic pathways

Advancements in synthetic biology have facilitated the incorporation of heterologous metabolic pathways into various bacterial chassis, leading to the synthesis of targeted bioproducts. However, total output from heterologous production pathways can suffer from low flux, enzyme promiscuity, formation of toxic intermediates, or intermediate loss to competing reactions, which ultimately hinder their full potential. The self-assembling, easy-to-modify, protein-based bacterial microcompartments (BMCs) offer a sophisticated way to overcome these obstacles by acting as an autonomous catalytic module decoupled from the cell's regulatory and metabolic networks. More than a decade of fundamental research on various types of BMCs, particularly structural studies of shells and their self-assembly, the recruitment of enzymes to BMC shell scaffolds, and the involvement of ancillary proteins such as transporters, regulators, and activating enzymes in the integration of BMCs into the cell's metabolism, has significantly moved the field forward. These advances have enabled bioengineers to design synthetic multi-enzyme BMCs to promote ethanol or hydrogen production, increase cellular polyphosphate levels, and convert glycerol to propanediol or formate to pyruvate. These pioneering efforts demonstrate the enormous potential of synthetic BMCs to encapsulate non-native multi-enzyme biochemical pathways for the synthesis of high-value products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Next-Generation NASA Earth-Orbiting Relay Satellites: Fusing Microwave and Optical Communications

NASA is currently considering architectures and concepts for the generation of relay satellites that will replace the Tracking and Data Relay Satellite (TDRS) constellation, which has been flying since 1983. TDRS-M, the last of the second TDRS generation, launched in August 2017, extending the life of the TDRS constellation beyond 2030. However, opportunities exist to re-engineer the concepts of geosynchronous Earth relay satellites. The needs of the relay satellite customers have changed dramatically over the last 34 years since the first TDRS launch. There is a demand for greater bandwidth as the availability of the traditional RF spectrum for space communications diminishes and the demand for ground station access grows. The next generation of NASA relay satellites will provide for operations that have factored in these new constraints. In this paper, we describe a heterogeneous constellation of geosynchronous relay satellites employing optical and RF communications. The new constellation will enable new optical communications services formed by user-to-space relay, space relay-to-space relay and space relay-to-ground links. It will build upon the experience from the Lunar Laser Communications Demonstration from 2013 and the Laser Communications Relay Demonstration to be launched in 2019.Simultaneous to establishment of the optical communications space segment, spacecraft in the TDRS constellation will be replaced with RF relay satellites with targeted subsets of the TDRS capabilities. This disaggregation of the TDRS service model will allow for flexibility in replenishing the needs of legacy users as well as addition of new capabilities for future users. It will also permit the U.S. government access to launch capabilities such as rideshare and to hosted payloads that were not previously available. In this paper, we also explore how the next generation of Earth relay satellites provides a significant boost in the opportunities for commercial providers to the communications space segment. For optical communications, the backbone of this effort is adoption of commercial technologies from the terrestrial high-bandwidth telecommunications industry into optical payloads. For RF communications, the explosion of software-defined radio, high-speed digital signal processing technologies and networking from areas such as 5G multicarrier will be important. Future commercial providers will not be limited to a small set of large aerospace companies. Ultimately, entirely government-owned and -operated satellite communications will phase out and make way for commercial business models that satisfy NASAs satellite communications requirements. The competition being provided by new entrants in the space communications business may result in a future in which all NASA communications needs can be satisfied commercially.

Space Communications↗
NASA NTRS2018

Next-Generation NASA Earth-Orbiting Relay Satellites: Fusing Optical and Microwave Communications

NASA is currently considering architectures and concepts for the generation of relay satellites that will replace the Tracking and Data Relay Satellite (TDRS) constellation, which has been flying since 1983. TDRS-M, the last of the second TDRS generation, launched in August 2017, extending the life of the TDRS constellation beyond 2030. However, opportunities exist to re-engineer the concepts of geosynchronous Earth relay satellites. The needs of the relay satellite customers have changed dramatically over the last 34 years since the first TDRS launch. There is a demand for greater bandwidth as the availability of the traditional RF spectrum for space communications diminishes and the demand for ground station access grows. The next generation of NASA relay satellites will provide for operations that have factored in these new constraints. In this paper, we describe a heterogeneous constellation of geosynchronous relay satellites employing optical and RF communications. The new constellation will enable new optical communications services formed by user-to-space relay, space relay-to-space relay and space relay-to-ground links. It will build upon the experience from the Lunar Laser Communications Demonstration from 2013 and the Laser Communications Relay Demonstration to be launched in 2019.Simultaneous to establishment of the optical communications space segment, spacecraft in the TDRS constellation will be replaced with RF relay satellites with targeted subsets of the TDRS capabilities. This disaggregation of the TDRS service model will allow for flexibility in replenishing the needs of legacy users as well as addition of new capabilities for future users. It will also permit the U.S. government access to launch capabilities such as rideshare and to hosted payloads that were not previously available.In this paper, we also explore how the next generation of Earth relay satellites provides a significant boost in the opportunities for commercial providers to the communications space segment. For optical communications, the backbone of this effort is adoption of commercial technologies from the terrestrial high-bandwidth telecommunications industry into optical payloads. For RF communications, the explosion of software-defined radio, high-speed digital signal processing technologies and networking from areas such as 5G multicarrier will be important. Future commercial providers will not be limited to a small set of large aerospace companies. Ultimately, entirely government-owned and -operated satellite communications will phase out and make way for commercial business models that satisfy NASA's satellite communications requirements. The competition being provided by new entrants in the space communications business may result in a future in which all NASA communications needs can be satisfied commercially.

Space Communications↗
DOE OSTI2022

Designing Porous Ion Emitters for Thermal Ionization Mass Spectrometry: Evaluating Metal-Organic Frameworks

In this work we describe the first exploration of metal-organic frameworks (MOFs) as ‘next generation’ ion emitters for thermal ionization mass spectrometry (TIMS). MOFs were identified as promising candidates for this application given the synthetic control over their desired structural properties. This tunability results in well-ordered, high surface area, high porosity frameworks with targeted sorption affinities. Here we explored an aluminum-based, bipyridine-containing MOF (MOF-253) with and without incorporating the high work function metal, rhenium (Re). After analysis of a Nd-bearing MOF, we hypothesized that the well-dispersed, sponge-like interconnected network of the degraded structure would enhance Nd ionization more than traditional TIMS loading activators (i.e., phosphoric acid). Compared to filaments loaded with phosphoric acid that require an additional benzene carburization step, the Nd ionization efficiencies (atoms detected relative to atoms loaded) for heated filaments loaded with MOF-253 were similar (~1%). Electron microscopy after TIMS analysis demonstrated that the MOF was retained on the filament and, as hypothesized, preserved sub-micron porosity post analysis. While these results are preliminary, they demonstrate that MOFs have potential to enhance ionization and exceed the performance of traditional loading techniques by forming nano-porous ion emitters (nano-PIEs). Thus, further experimentation is likely to exceed this performance through more specific selection of the base MOF structure and modifications to porosity and composition. This work represents a novel application of MOFs and a next step in the pursuit of advanced thermal ionization with potential to expand across the periodic table.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2026

NGEE Arctic Integrated Modeling (IM2): Improved subgrid hillslope hydrologic connectivity

This data product represents the integration of new code capability for arctic tundra hillslope hydrologic processes into the Energy Exascale Earth System Model (E3SM), through the E3SM Land Model (ELM) component. This code integration is the result of collaborative effort between the NGEE Arctic project and the E3SM project. The current ELM represents water movement primarily through vertical processes, such as precipitation, canopy interception, evaporation, infiltration, and soil water movement. Lateral water movement—such as surface runoff, subsurface flow, and river transport—plays a significant role in the hydrological cycle, especially in regions with varied topography. While E3SM includes a runoff routing component representing water transport in the river network, the lateral transport of water at the subgrid scale within the land model has previously not been taken into account. With the recent development of topographic units within the ELM subgrid data structure, there is an opportunity to simulate hillslope hydrologic connectivity by introducing water transport along topographic gradients. We expect that more realistic representation of hillslope hydrologic processes will lead to improved predictions of both soil water content and river network flows. Lateral transport of water at and near the surface is represented as a sub-grid process in this new code development. Water is tracked as it moves from higher to lower elevations within a gridcell. This capability uses the nested hierarchical sub-grid scheme within ELM to connect water fluxes from sub-grid elements with higher elevation to those with lower elevation. This data record consists of a single document (pdf format) that describes the theoretical basis for the hillslope hydrology processes added to ELM, and describes the modifications made to the ELM code. The Methods section of this metadata record includes a link to the public E3SM code repository where the exact code modifications as integrated in E3SM can be accessed. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Thornton, Peter E [ORNL] (ORCID:0000000247595158)↗
DOE OSTI2022

The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era

Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.

47 OTHER INSTRUMENTATION↗
NASA NTRS2019

NASA Near Earth Network (NEN) DVB-S2 Demonstration Testing for Enhancing Data Rates for CubeSat/SmallSat Missions

National Aeronautics and Space Administration (NASA) CubeSat/SmallSat missions are expected to grow rapidly in the next decade. As the number of spacecraft on a ground network grows, employing higher data rates could reduce loading by reducing the contact time per day required. CubeSats also need to communicate directly to earth from space from longer distances than low earth orbit (LEO). These challenges motivate the need for bandwidth and power efficient modulation and coding techniques. Today, Digital Video Broadcast, Satellite Second Generation (DVB-S2) is a communications standard for larger satellites. DVB-S2 uses power and bandwidth efficient modulation and coding techniques to deliver performance approaching Radio Frequency (RF) channel theoretical limits. NASA’s Near Earth Network (NEN) conducted a demonstration test at the Wallops Flight Facility in spring of 2019 for CubeSat/SmallSat missions for enhancing data rate performance in NASA’s S-band 5 MHz channel. The goal is to upgrade NEN with DVB-S2 to increase science data return and enable greater numbers of CubeSats. This paper presents the NEN DVB-S2 demonstration testing objectives and performance measurement results. Results of the demonstration testing are compared with evolving SmallSat/CubeSat radios. DVB-S2 S-band transmitter development concepts for SmallSats/CubeSats and use of DVB-S2 by future missions are discussed.

Wong, Yen↗