Deep Space C3: high power uplinks
The uplink transmitters of the Deep Space Network (DSN) perform three support functions in support of space missions: navigation, command, uplink, and emergency recovery.
vacuum↗
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Results for “High Performance Networking”
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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All-solid-state interpenetrating network polymer electrolytes for long cycle life of lithium metal batteries
A star-shape polymer of 3-armed poly(ethylene glycol) methyl ether methacrylate-co-glycidyl methacrylate copolymer (3PPEGM-co-GMA) was synthesized using an atom transfer radical polymerization (ATRP) technique. All-solid-state interpenetrating network polymer electrolytes (INSPEs) were fabricated by simultaneous reaction of 3PPEGM-co-GMA and bisphenol A diglycidyl ether (BPDE) with polyetherdiamine (ED2003) in the presence of lithium bis(trifluoromethane) sulfonamide (LiTFSI). The INSPEs exhibited ionic conductivities higher than 10 -5 S cm -1 at room temperature, a high oxidation stability of 4.5 vs. Li/Li + and remarkable stability towards lithium metal. Li metal batteries with LiFePO 4 as the cathode and INSPEs as the electrolyte cycled at a current rate of 0.1C at 60 °C showed a high initial discharge capacity of 156.2 mA h g -1 and a stable cycling performance over 200 cycles with a high coulombic efficiency of 99%. In conclusion, the results demonstrate that the interpenetrating network polymer electrolytes are promising electrolytes for next generation lithium-based batteries with high ionic conductivity, improved safety, and stable electrochemical performance.
25 ENERGY STORAGE↗
Deep graph representations embed network information for robust disease marker identification
We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.
59 BASIC BIOLOGICAL SCIENCES↗
High-Performance Near-Infrared Quantum Emission from Color Centers in hBN
Color centers hosted in hexagonal boron nitride (hBN) have emerged as a highly promising platform for single-photon emission and spin-photon technologies relevant to quantum communication and quantum networking. As a wide bandgap van der Waals material, hBN can host optically active quantum defects across a broad spectral range. Here, we demonstrate a simple and scalable oxygen-plasma process that reproducibly creates single quantum emitters in hBN with blinking-free zero-phonon lines (ZPLs) spanning near-infrared (NIR) from 700 up to 971 nm. These emitters combine MHz-level brightness, single-photon purity up to 99.9%, and ultranarrow cryogenic line widths down to 2.7 GHz under quasi-resonant excitation, placing them in a particularly attractive regime for quantum photonics. Photostability measurements further reveal resistance to photobleaching, subnanometer spectral stability over long time scales, and near-shot-noise-limited intensity fluctuations. Analysis of the phonon sidebands shows weak vibronic coupling and ZPL-dominated emission, with Debye–Waller factors approaching 50%. Control experiments together with elemental mapping support oxygen incorporation as a necessary ingredient in activating the NIR emitter population, while first-principles calculations identify O N V N and O N V N H as the leading defect candidates. These results establish a high-performance NIR quantum-emitter platform in hBN for free-space quantum networking and future integrated quantum-photonic architectures.
2D materials↗
Normalizing flows for high-dimensional detector simulations
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimeter shower simulations with increasing phase space dimension. We use fast and expressive coupling spline transformations applied to the CaloChallenge datasets. In addition to the base flow architecture we also employ a VAE to compress the dimensionality and train a generative network in the latent space. We evaluate our networks on several metrics, including high-level features, classifiers, and generation timing. Our findings demonstrate that invertible neural networks have competitive performance when compared to autoregressive flows, while being substantially faster during generation.
Ernst, Florian↗
Cooperative high-performance storage in the accelerated strategic computing initiative
The use and acceptance of new high-performance, parallel computing platforms will be impeded by the absence of an infrastructure capable of supporting orders-of-magnitude improvement in hierarchical storage and high-speed I/O (Input/Output). The distribution of these high-performance platforms and supporting infrastructures across a wide-area network further compounds this problem. We describe an architectural design and phased implementation plan for a distributed, Cooperative Storage Environment (CSE) to achieve the necessary performance, user transparency, site autonomy, communication, and security features needed to support the Accelerated Strategic Computing Initiative (ASCI). ASCI is a Department of Energy (DOE) program attempting to apply terascale platforms and Problem-Solving Environments (PSEs) toward real-world computational modeling and simulation problems. The ASCI mission must be carried out through a unified, multilaboratory effort, and will require highly secure, efficient access to vast amounts of data. The CSE provides a logically simple, geographically distributed, storage infrastructure of semi-autonomous cooperating sites to meet the strategic ASCI PSE goal of highperformance data storage and access at the user desktop.
Gary, Mark↗
Topology-Dependent Performance of Free-Space Photonic Quantum Networks Under Noise
Photonic quantum communication enables secure and high-fidelity information transfer beyond classical limits, with direct relevance to emerging quantum networks operating in free-space environments. While physical-layer models of depolarizing noise, Gamma–Gamma turbulence statistics, entanglement swapping, and decoy-state QKD security bounds are individually well established, prior work typically treats these components in isolation or under fixed network assumptions. In this work, we develop a unified topology-aware analytical framework that simultaneously integrates free-space optical link budgets, turbulence-induced visibility degradation, depolarizing qubit noise, multi-hop entanglement cascade dynamics, teleportation fidelity thresholds, CHSH nonlocality certification, and asymptotic decoy-state secret key rate bounds across star, mesh, and ring graph structures. Rather than introducing new physical channel models, we demonstrate that identical physical links exhibit fundamentally different end-to-end performance once embedded within different network topologies. Mesh architectures minimize visibility cascade through hop-count reduction but incur quadratic hardware scaling. Star topologies minimize link count but concentrate noise and synchronization overhead at the hub. Ring configurations offer linear hardware scaling with multiplicative fidelity degradation. The results establish topology as a first-order design parameter in near-term free-space quantum networks operating without full quantum repeater infrastructures. While motivated by distributed multi-agent architectures, the framework applies broadly to terrestrial, airborne, and satellite-assisted photonic quantum communication systems.
QKD↗
Optimizing deep learning hyper-parameters through an evolutionary algorithm. In: MLHPC '15 Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, Article No. 4
There has been a recent surge of success in utilizing Deep Learning (DL) in imaging and speech applications for its relatively automatic feature generation and, in particular for convolutional neural networks (CNNs), high accuracy classification abilities. While these models learn their parameters through data-driven methods, model selection (as architecture construction) through hyper-parameter choices remains a tedious and highly intuition driven task. To address this, Multi-node Evolutionary Neural Networks for Deep Learning (MENNDL) is proposed as a method for automating network selection on computational clusters through hyper-parameter optimization performed via genetic algorithms.
Young, Steven R.↗
A Tool for Verification and Validation of Neural Network Based Adaptive Controllers for High Assurance Systems
High reliability of mission- and safety-critical software systems has been identified by NASA as a high-priority technology challenge. We present an approach for the performance analysis of a neural network (NN) in an advanced adaptive control system. This problem is important in the context of safety-critical applications that require certification, such as flight software in aircraft. We have developed a tool to measure the performance of the NN during operation by calculating a confidence interval (error bar) around the NN's output. Our tool can be used during pre-deployment verification as well as monitoring the network performance during operation. The tool has been implemented in Simulink and simulation results on a F-15 aircraft are presented.
Gupta, Pramod↗
Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes
Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Proposal for superconducting quantum networks using multioctave transduction to lower frequencies
We propose networking superconducting quantum circuits by transducing their excitations (typically 4–8 GHz) to 200–500 MHz photons for transmission via superconducting cryogenic coaxial cables. This frequency down-conversion reduces transmission losses and, counterintuitively, reduces noise. We introduce a multioctave asymmetrically threaded superconducting quantum interference device circuit capable of the required efficient, high-rate transduction. For a 100-meter cable with 𝑄 𝑖 = 10 5 at 10 mK, we theoretically show that our approach achieves single-photon fidelities of 0.962 at 200 MHz versus 0.772 at 8 GHz, and triples the lower bound on quantum channel capacity. In conclusion, this method enables highly efficient networking of superconducting qubits while maintaining high fidelities, combining improved performance with the practical advantages of flexible, compact coaxial cables.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Intelligent systems technology infrastructure for integrated systems
Significant advances have occurred during the last decade in intelligent systems technologies (a.k.a. knowledge-based systems, KBS) including research, feasibility demonstrations, and technology implementations in operational environments. Evaluation and simulation data obtained to date in real-time operational environments suggest that cost-effective utilization of intelligent systems technologies can be realized for Automated Rendezvous and Capture applications. The successful implementation of these technologies involve a complex system infrastructure integrating the requirements of transportation, vehicle checkout and health management, and communication systems without compromise to systems reliability and performance. The resources that must be invoked to accomplish these tasks include remote ground operations and control, built-in system fault management and control, and intelligent robotics. To ensure long-term evolution and integration of new validated technologies over the lifetime of the vehicle, system interfaces must also be addressed and integrated into the overall system interface requirements. An approach for defining and evaluating the system infrastructures including the testbed currently being used to support the on-going evaluations for the evolutionary Space Station Freedom Data Management System is presented and discussed. Intelligent system technologies discussed include artificial intelligence (real-time replanning and scheduling), high performance computational elements (parallel processors, photonic processors, and neural networks), real-time fault management and control, and system software development tools for rapid prototyping capabilities.
Lum, Henry, Jr.↗
Advanced data management system architectures testbed
The objective of the Architecture and Tools Testbed is to provide a working, experimental focus to the evolving automation applications for the Space Station Freedom data management system. Emphasis is on defining and refining real-world applications including the following: the validation of user needs; understanding system requirements and capabilities; and extending capabilities. The approach is to provide an open, distributed system of high performance workstations representing both the standard data processors and networks and advanced RISC-based processors and multiprocessor systems. The system provides a base from which to develop and evaluate new performance and risk management concepts and for sharing the results. Participants are given a common view of requirements and capability via: remote login to the testbed; standard, natural user interfaces to simulations and emulations; special attention to user manuals for all software tools; and E-mail communication. The testbed elements which instantiate the approach are briefly described including the workstations, the software simulation and monitoring tools, and performance and fault tolerance experiments.
Grant, Terry↗
StarNAV: An Architecture for Autonomous Spacecraft Navigation by the Relativistic Perturbation of Starlight
Exploration missions to the outer Solar System (e.g., Neptune, Kuiper belt objects) or to the interstellar medium present several challenges for conventional spacecraft designs. One of the greatest challenges is a means for navigation, as Earth-based tracking with the Deep Space Network (DSN) becomes less desirable due to high cost, decreased performance at long ranges, and long light-time delays. Indeed, light time delays at Neptune are over four hours (one way), making control of spacecraft during critical events nearly impossible—and this problem only becomes worse as we move to the Kuiper belt or to interstellar space. The need for autonomous spacecraft navigation is well established and is prominently featured in the 2015 NASA Technology Roadmap (e.g., TA 5.4.2.6 & 5.4.2.8). This has led to investment in various technologies to accomplish this task, with the majority of recent work focusing on optical navigation (OPNAV; TA 5.4.4.1) [1], X-ray pulsar navigation (XNAV; TA 5.6.1) [2], or DSN one-way ranging with the Deep Space Atomic Clock (DSAC; TA 5.4.1.1) [3]. This NIAC study suggests a new (and completely different) way of autonomously navigating a spacecraft anywhere in the Solar System or beyond. We call this new technique StarNAV.
NIAC Phase I↗
Evaluating a Cognitive Extension for the Licklider Transmission Protocol in a Spacecraft Emulation Testbed
In space communications, particularly when involving regions beyond cislunar space, the development of advanced networking solutions is essential to address the challenges posed by limited connectivity, substantial propagation delays, and radio signal variations. This study explores a data-driven approach to the Licklider Transmission Protocol (LTP), specifically focusing on dynamically adjusting the maximum payload size of segments. Prior research has emphasized the potential benefits of dynamically adjusting this parameter, introducing the concept of Cognitive LTP. This paper presents a software implementation of Cognitive LTP (CLTP) within an open-source Delay Tolerant Networking (DTN) framework, specifically the High-rate Delay Tolerant Networking (HDTN), and experimentally evaluates its performance under realistic space conditions. Leveraging the Cognitive Ground Testbed (CGT), developed by NASA GRC for spacecraft communication emulation, this study effectively bridges the gap between theoretical advancements and practical applications. By thoroughly analyzing CLTP’s functionality within the CGT, this research offers insights into the practical implications of adaptive networking strategies, emphasizing the importance of conducting tests in relevant environments for the maturation of space communication technologies.
Delay Tolerant Networking↗
Key Accomplishments & Results: Outdoor Lighting Accelerator
This document provides a high-level summary of the results of the Better Buildings Outdoor Lighting Accelerator, which worked with states, cities, regional energy networks, and utilities to accelerate the deployment of high-performance street and area lighting in the public sector.
Lighting, Exterior Lighting, Outdoor Lighting, LED↗
A Novel Sliding Mode Network Perimeter Controller
Congestion in urban areas is a chronic problem that faces many US and worldwide cities. It results in inefficient use of the infrastructure as well as increased fuel consumption and vehicle emissions. Congestion is intertwined with delay. Road users waste precious hours on the road, which reduces productivity. Throughout this decade researchers were and are developing tools and systems that alleviate this problem. Network perimeter control is one of these tools that has been studied extensively. It attempts to control the flow of vehicles entering a protected area so as not to reach the congested regime that is pre-determined using the Network Fundamental Diagram (NFD). In this paper, we present a novel and simple approach derived from sliding mode control theory that achieves the desired objective. Its main advantages over other control strategies include but are not limited to (1) ease of implementation, (2) minimal and straightforward model tuning, and (3) high performance. This controller was implemented and tested on a congested grid network. The results show that, the new logic performs as expected, producing network-wide delay savings that are consistent with other control approaches described in the literature. Furthermore, unlike other controllers, we demonstrate that the controller is robust to its hyper-parameter values, making the algorithm easy to implement and calibrate to local conditions. http://pubsindex.trb.org/
Sliding mode control, network perimeter control, n↗
Evaluation of a data fusion approach to estimate daily PM{sub 2.5} levels in North China
PM{sub 2.5} air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM{sub 2.5} exposure spatiotemporally in China, but all these have limitations. This study was to develop a data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM{sub 2.5} in grid cells with a resolution of 10 km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF-Chem). A data fusion technique was developed by fusing PM{sub 2.5} concentration predicted by KED and WRF-Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF-Chem and observations. Model performances were evaluated by comparing them with ground observations and the spatial prediction errors. KED and data fusion performed better at monitoring sites with a daily model R{sup 2} of 0.95 and 0.94, respectively and PM{sub 2.5} was overestimated by WRF-Chem (R{sup 2}=0.51). KED and data fusion performed better around the ground monitors, WRF-Chem performed relative worse with high prediction errors in the central of study domain. In our study, both KED and data fusion technique provided highly accurate PM{sub 2.5}. Current monitoring network in North China was dense enough to provide a reliable PM{sub 2.5} prediction by interpolation technique. - Highlights: • KED and data fusion model predicted daily PM{sub 2.5} with high accuracy. • WRF-Chem performed worse in PM{sub 2.5} prediction compared with KED and data fusion. • The PM{sub 2.5} monitoring network in North China was able to support reliable PM{sub 2.5} interpolation.
54 ENVIRONMENTAL SCIENCES↗