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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Satcom systems and technologies into the 21st century

The role of communications satellites in the domestic U.S. telecommunications market for the next 25 years is examined, the focus being on the new satellite communications systems concepts and their enabling technologies. A simple procedure for the identification of critical satellite technologies in the years 2000-2015 is described. Study results indicate that by the year 2015, satellites will find commercial applications in the distribution of space-gathered data, supercomputer network, scientific visualization to remote areas, mobile services, VSAT, broadband ISDN, and data services.

Kwan, Robert K.↗
NASA NTRS1989

Method and apparatus for disabling an echo canceller on a digital telecommunications network

A method and apparatus for disabling an echo canceller in a framed digital network from a remote terminal that is copuled to the network through an unframed data link. Control is effected by sending N different bit sequences from the remote terminal, where N is the number of different framing alignments that may be imposed on the unframed data by the network. The different sequences are chosen so that one will be framed by the network as the requisite control tone regardless of which framing alignment is actually imposed.

Schroeder, George L.↗
NASA NTRS2018

MoonBEAM: A Beyond Earth-Orbit Gamma-Ray Burst Detector for Multi-Messenger Astronomy

Moon Burst Energetics All-sky Monitor (MoonBEAM) is a CubeSat concept of deploying gamma-ray detectors in cislunar space to increase gamma-ray burst detections and improve localization precision with the timing triangulation technique. A gamma-ray instrument in cislunar orbit will have greatly reduced sky blockage compared to instruments in low Earth orbit. Working in conjunction with another instrument in low Earth orbit, MoonBEAM can also help constrain the arrival direction of the wavefront to an annulus on the sky by utilizing the light arrival times between the different orbits. This method has been demonstrated by the Interplanetary Gamma- Ray Burst Timing Network. However, delays in data downlink for instruments outside the Tracking and Data Relay Satellite network prevent rapid follow-up observations. We present here a gamma-ray CubeSat concept in Earth-Moon L3 halo orbit that is capable of faster response and provide a timing baseline for localization improvement. Such an instrument would aid in the gravitational wave follow-up observations in other wavelengths to identify the gamma-ray burst afterglow and kilonova emission. Reducing the region of interest makes identifying afterglows much faster, allowing for rapid on-source observations and monitoring of the rise and decay times. It will also prevent source confusion between two transients and enable robust association. A gamma-ray detection could also increase the confidence of a simultaneous but marginal gravitational wave signal, extending the detection horizon. MoonBEAM is a 12U CubeSat concept of deploying gamma-ray detectors in cislunar space to increase gamma-ray burst detections and improve localization precision with the timing triangulation technique. Such an instrument would probe the extreme processes in cosmic collision of compact objects and facilitate multi-messenger time-domain astronomy to explore the end of stellar life cycles and black hole formations.

gamma-ray Cubesat↗
NASA NTRS2020

Impact of Jason-2/T2L2 Ultra-Stable-Oscillator Frequency Model on DORIS stations coordinates and Earth Orientation Parameters

Doppler Orbitography and Radiopositioning Integrated on Satellite (DORIS)is a tracking technique based on a one-way ground to space Doppler link.For Low Earth Orbit (LEO) satellites, DORIS shows a robust capability interms of data coverage and availability, due to a wide and well-distributedground network, where data are made available by the International DorisService (IDS). However, systematic errors remain in the DORIS data, suchas instabilities of the on-board clock due to radiation encountered in space,which limit the accurate determination of station positions.The DORIS on-board clock frequency stability is degraded by the increased radiation found in the region of the South Atlantic Anomaly (SAA)and has been shown to degrade station position estimation. This paper in-troduces a new model correction to the DORIS data for the frequency ofthe Jason-2 Ultra Stable Oscillator (USO), derived from the Time Transferby Laser Link (T2L2) experiment (Belli and Exertier, 2018). We show thata multi-satellite DORIS solution including this T2L2-corrected data appliedto the frequency modelling for The DORIS data, improves the estimationof station coordinates. We show the tie residuals with respect to collocatedGPS stations are improved by several millimeters. We also demonstratethat the 117-day (Jason-2) draconitic signal in the geophysical parametersis reduced, implying that the origin of this signal is not just solar radiationpressure mis-modeling, but also radiation-induced clock perturbations onthe Jason-2 DORIS Ultra-Stable-Oscillator (USO). Finally we demonstratethrough comparisons with the International Earth Rotations and ReferenceSystems Service (IERS) C04 series for Earth Orientation Parameters (EOP),that the estimation of EOP is improved in both a Jason-2 DORIS-only anda multi-satellite DORIS solution for EOP.

DORIS↗
NASA NTRSDate not supplied

Simplifying Analysis of Hierarchical HDF5 and NetCDF4 Files with Xarray-Datatree

NASA’s Earth Observing System Data and Information System (EOSDIS) contains thousands of Earth science datasets from satellites, models, and field campaigns. EOSDIS data are stored in formats that are well supported by the Earth Science community. These formats include the Hierarchical Data Format (HDF), with derivative flavors such as HDF-5 and the Network Common Data Format (NetCDF-4). The HDF specification allows for a directory-like hierarchy within a single file, known as "groups". Observational data and associated metadata within a single file can be distributed amongst multiple internal groups, which can also be nested to multiple levels. Working with datasets that have a group hierarchical structure can be difficult because of the nested structure of groups. Widely used packages, such as xarray, have data models that do not accommodate the hierarchical structure within HDF files, requiring users to traverse the file and open different HDF groups as separate, unrelated objects. Xarray-datatree is a Python package developed to solve the difficulty of traversing HDFs with a hierarchical group structure by creating a tree-like hierarchical data structure in xarray. The tree-like structure allows each group to be accessed once a DataTree object is instantiated. The migration of xarray-datatree into the xarray core library will reduce barriers to accessing Earth science data by eliminating the need to understand and traverse the specific hierarchy of a grouped HDF file.

Eni Awowale↗
DOE OSTI2024

Learning Constitutive Relations From Soil Moisture Data via Physically Constrained Neural Networks

Abstract The constitutive relations of the Richardson‐Richards equation encode the macroscopic properties of soil water retention and conductivity. These soil hydraulic functions are commonly represented by models with a handful of parameters. The limited degrees of freedom of such soil hydraulic models constrain our ability to extract soil hydraulic properties from soil moisture data via inverse modeling. We present a new free‐form approach to learning the constitutive relations using physically constrained neural networks. We implemented the inverse modeling framework in a differentiable modeling framework, JAX, to ensure scalability and extensibility. For efficient gradient computations, we implemented implicit differentiation through a nonlinear solver for the Richardson‐Richards equation. We tested the framework against synthetic noisy data and demonstrated its robustness against varying magnitudes of noise and degrees of freedom of the neural networks. We applied the framework to soil moisture data from an upward infiltration experiment and demonstrated that the neural network‐based approach was better fitted to the experimental data than a parametric model and that the framework can learn the constitutive relations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Mission Operations and Data Systems Directorate's operational/development network (MODNET) at Goddard Space Flight Center

A brief, informal narrative is provided that summarizes the results of all work accomplished during the period of the contract; June 1, 1987 through September 30, 1988; in support of Mission Operations and Data Systems Directorate's Operational Development Network (MODNET). It includes descriptions of work performed in each functional area and recommendations and conclusions based on the experience and results obtained.

Source record↗
DOE OSTI2021

D2U: Data Driven User Emulation for the Enhancement of Cyber Testing, Training, and Data Set Generation

Whether testing intrusion detection systems, conducting training exercises, or creating data sets to be used by the broader cybersecurity community, realistic user behavior is a critical component of a cyber range. Existing methods either rely on network level data or replay recorded user actions to approximate real users in a network. Our work is the first to produce generative models trained on actual user data (sequences of application usage) collected on endpoints. Once trained to the user's behavioral data, these models can generate novel sequences of actions %that appear to come from the same distribution as the training data. These sequences of actions are then fed to our custom software via configuration files, which replicate those behaviors on end devices. Notably, our models are platform agnostic and could generate behavior data for any emulation software package. In this paper we present our model generation process, software architecture, and an initial evaluation of the fidelity of our models. Our software is currently deployed in a cyber range to help evaluate the efficacy of defensive cyber technologies. We suggest additional ways that the cyber community as a whole can benefit from more realistic user behavior emulation. The data used to train our model, as well as sample configuration files produced by the model, are available at [redacted].

Oesch, T↗
NASA NTRS2015

Applying a Space-Based Security Recovery Scheme for Critical Homeland Security Cyberinfrastructure Utilizing the NASA Tracking and Data Relay (TDRS) Based Space Network

Protection of the national infrastructure is a high priority for cybersecurity of the homeland. Critical infrastructure such as the national power grid, commercial financial networks, and communications networks have been successfully invaded and re-invaded from foreign and domestic attackers. The ability to re-establish authentication and confidentiality of the network participants via secure channels that have not been compromised would be an important countermeasure to compromise of our critical network infrastructure. This paper describes a concept of operations by which the NASA Tracking and Data Relay (TDRS) constellation of spacecraft in conjunction with the White Sands Complex (WSC) Ground Station host a security recovery system for re-establishing secure network communications in the event of a national or regional cyberattack. Users would perform security and network restoral functions via a Broadcast Satellite Service (BSS) from the TDRS constellation. The BSS enrollment only requires that each network location have a receive antenna and satellite receiver. This would be no more complex than setting up a DIRECTTV-like receiver at each network location with separate network connectivity. A GEO BSS would allow a mass re-enrollment of network nodes (up to nationwide) simultaneously depending upon downlink characteristics. This paper details the spectrum requirements, link budget, notional assets and communications requirements for the scheme. It describes the architecture of such a system and the manner in which it leverages off of the existing secure infrastructure which is already in place and managed by the NASAGSFC Space Network Project.

Cybersecurity↗
DOE OSTI2024

Learning of networked spreading models from noisy and incomplete data

Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, in this work, we introduce a universal learning method based on a scalable dynamic message-passing technique that addresses these challenges often encountered in real data. The algorithm leverages available prior knowledge on the model and on the data, and reconstructs both network structure and parameters of a spreading model. We show that a linear computational complexity of the method with the key model parameters makes the algorithm scalable to large network instances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery. This framework is constructed by assuming that the target variables are sampled from a Gaussian distribution conditioned on the inputs. Consequently, the overall formulation sets up a procedure to predict the hyperparameters of this distribution which are then used to compute an objective function given training data. We demonstrate that this framework has the ability to provide for prediction confidence intervals based on the assumption of a probabilistic posterior, given an appropriate model architecture and adequate training data. The applicability of the present framework to cases with noisy measurements and limited observations is also assessed. To demonstrate the capabilities of this framework, we consider canonical regression problems of fluid dynamics from the viewpoint of reduced-order modeling and spatial data recovery for four canonical data sets. The examples considered in this study arise from (i) the shallow-water equations, (ii) a two-dimensional cylinder flow, (iii) the wake of a NACA0012 airfoil with a Gurney flap, and (iv) the NOAA sea surface temperature data set. Furthermore, the present results indicate that the probabilistic neural network not only produces a machine-learning-based fluid flow surrogate model but also systematically quantifies the uncertainty therein to assist with model interpretability.

42 ENGINEERING↗
DOE OSTI2025

Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies

This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies. First, a network-aware Gaussian process (GP) termed Vertex-Degree Kernel (VDK-GP), developed in prior work, is used to estimate voltage–power functions for a few network configurations. The paper introduces a novel multi-task vertex degree kernel (MT-VDK) that amalgamates the learned VDK-GPs to determine power flows for unseen networks, with a significant reduction in the computational complexity and hyperparameter requirements compared to alternate approaches. Simulations on the IEEE 30-Bus network demonstrate the retention and transfer of power flow knowledge in both N-1 and N-2 contingency scenarios. The MT-VDK-GP approach achieves over 50 % reduction in mean prediction error for novel N-1 contingency network configurations in low training data regimes (50–250 samples) over VDK-GP. Additionally, MT-VDK-GP outperforms a hyper-parameter based transfer learning approach in over 75 % of N-2 contingency network structures, even without historical N-2 outage data. Furthermore, the proposed method demonstrates the ability to achieve PVEs using sixteen times fewer power flow solutions compared to Monte-Carlo sampling-based methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1992

Neural network architectures to analyze OPAD data

A prototype Optical Plume Anomaly Detection (OPAD) system is now installed on the space shuttle main engine (SSME) Technology Test Bed (TTB) at MSFC. The OPAD system requirements dictate the need for fast, efficient data processing techniques. To address this need of the OPAD system, a study was conducted into how artificial neural networks could be used to assist in the analysis of plume spectral data.

Whitaker, Kevin W.↗
NASA NTRS2019

Advances in Neural Network Detection and Retrieval of Multilayer Clouds for CERES Using Multispectral Satellite Data

An artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels, the retrieved total cloud visible optical depth, and vertical humidity profiles is trained to detect multilayer (ML) ice-over-water cloud systems as identified by matched CloudSat and CALIPSO (CC) data. The multilayer ANN, or MLANN, algorithm is also trained to retrieve the optical depth and the top and base heights of the upper-layer ice clouds in ML systems. The trained MLANN was applied to independent MODIS data resulting in a combined ML and single layer hit rate of 80% (77%) for nonpolar regions during the day (night). The results are more accurate than currently available methods and the previous version of the MLANN. Upper-layer cloud top and base heights are accurate to ±1.2 km and ±1.6 km, respectively, while the uncertainty in optical depth is ±0.457 and ±0.556 during day and night, respectively. Areas of further improvement and development are identified and will be addressed in future versions of the MLANN.

CERES↗
NASA NTRS2005

High-Performance Satellite/Terrestrial-Network Gateway

A gateway has been developed to enable digital communication between (1) the high-rate receiving equipment at NASA's White Sands complex and (2) a standard terrestrial digital communication network at data rates up to 622 Mb/s. The design of this gateway can also be adapted for use in commercial Earth/satellite and digital communication networks, and in terrestrial digital communication networks that include wireless subnetworks. Gateway as used here signifies an electronic circuit that serves as an interface between two electronic communication networks so that a computer (or other terminal) on one network can communicate with a terminal on the other network. The connection between this gateway and the high-rate receiving equipment is made via a synchronous serial data interface at the emitter-coupled-logic (ECL) level. The connection between this gateway and a standard asynchronous transfer mode (ATM) terrestrial communication network is made via a standard user network interface with a synchronous optical network (SONET) connector. The gateway contains circuitry that performs the conversion between the ECL and SONET interfaces. The data rate of the SONET interface can be either 155.52 or 622.08 Mb/s. The gateway derives its clock signal from a satellite modem in the high-rate receiving equipment and, hence, is agile in the sense that it adapts to the data rate of the serial interface.

Beering, David R.↗
DOE OSTI2024

Fracture Characterization Via AI‐Assisted Analysis of Temperature Logs

Abstract Fractures control fluid flow, mass transport, and heat transfer in a geothermal reservoir. This makes accurate characterization of fracture networks a prerequisite for optimal design and control of a reservoir's exploitation. We develop a deep‐learning procedure to identify fracture locations via interpretation of temporally and spatially continuous downhole temperature measurements. A long short‐term memory fully convolutional network (LSTM‐FCN) is used both to capture long‐term dependencies in sequential temperature data and to distill local features around fractures. A wellbore and fractured‐reservoir thermal model is established to generate temperature data for network training. The trained LSTM‐FCN exhibits a unique ability to detect multiple fractures intersecting a borehole. We use the LSTM‐FCN algorithm to evaluate the effectiveness of different‐stage wellbore temperature measurements on fracture detection in a complex fractured system. Our experiments reveal that the use of various‐stage temperature information as an input feature set improves the robustness of fracture detection to noise interference. This study indicates the practical feasibility of obtaining accurate fracture‐network reconstructions from temperature signals, at reasonable computational cost.

Yang, Xiaoyu↗
NASA NTRS1974

Apollo experience report: A use of network simulation techniques in the design of the Apollo lunar surface experiments package support system

A case study of data-communications network modeling and simulation is presented. The applicability of simulation techniques in early system design phases is demonstrated, and the ease with which model parameters can be changed and comprehensive statistics gathered is shown. The discussion of the model design and application also yields an insight into the design and implementation of the Apollo lunar surface experiments package ground-support system.

Gustafson, R. A.↗
NASA NTRS1994

Global and regional kinematics with VLBI

Since a VLBI station cannot operate in isolation and since simultaneous operation of the entire VLBI network is impractical, it is necessary to design observing programs with periodic observing sessions using networks of 3-7 stations that, when treated together, will have the necessary interstation data and network overlaps to determine the desired rates of change. Thus, there has been a mix of global, intercontinental, transcontinental, and regional networks to make measurements ranging from plate motions to deformation over a few hundred km. Over time, even networks focusing on regional deformation using mobile VLBI included large stations removed by several thousand km to increase sensitivity, determine EOP more accurately, and provide better ties to the terrestrial reference frame (TRF). Analysis products have also evolved, beginning with baseline components, and then to full three-dimensional site velocities in a global TRF.

Ma, Chopo↗