Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks
Overview: Deep learning and Convolutional Neural Network (CNN); CNN for Tropical Cyclone Intensity Estimation; Preliminary results; Work in progress.
Hurricane intensity↗
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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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The deep space network, volume iii. space programs summary, 1 may - 30 jun. 1965
Tracking stations engineering and operations, and communications engineering, research, and development for Deep Space Network
DEEP SPACE↗
Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks, Frontiers in Earth Science: Dataset
This data package contains watershed modeling inputs and outputs as well as deep neural networks training and testing results used in "Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks" (Cromwell et al., 2021). We train various deep neural network models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models. For detailed information regarding watershed model description and DNNs setup, please refer to Cromwell et al., 2021. The deep_learning.zip file contains the inputs and outputs for the different deep neural networks. The watershed_model.zip file contains the inputs and outputs for watershed simulation using ATS. The figures.zip file contains raw figures and their corresponding scripts. For a more detailed description, please see the README.md file within each compressed file.
54 ENVIRONMENTAL SCIENCES↗
Calibration and Performance Measurements for the NASA Deep Space Network Aperture Enhancement Project (DAEP)
The NASA Deep Space Network (DSN) has recently constructed two new 34-meter antennas at the Canberra Deep Space Communications Complex (CDSCC). These new antennas are part of the larger DAEP project to add six new 34-meter antennas to the DSN, including two in Madrid, three in Canberra and one in Goldstone (California). The DAEP project included development and implementation of several new technologies for the S, X, Ka (26 GHz) and Ka (32 GHz) -band uplink and downlink electronics, as previously reported. The electronics upgrades were driven by several different considerations, including parts obsolescence, cost reduction, improved reliability and maintainability, and capability to meet future performance requirements. The new antennas are required to support TT&C links for all of the NASA deep-space spacecraft, as well as for several international partners. Some of these missions, such as Voyager 1 and 2, have very limited link budgets, which results in demanding requirements for system G/T performance. These antennas are also required to support radio science missions with several spacecraft, which dictate some demanding requirements for spectral purity, amplitude stability and phase stability for both the uplink and downlink electronics. After completion of these upgrades, a comprehensive campaign of tests and measurements took place to characterize the electronics and calibrate the antennas. Radiometric measurement techniques were applied to characterize, calibrate, and optimize the performance of the antenna parameters. These included optical and RF high-resolution holographic and total power radiometry techniques. These techniques, which are described in the article, resulted in the highest antenna aperture efficiency in the DSN, of 66% achieved, at the highest operating frequency of the antenna, which is Ka-Band (32-GHz). The other measurements and results described include antenna noise temperature, photogrammetry and holography alignment of antenna panels, beam-waveguide mirrors, and subreflector, antenna aperture efficiencies and G/T versus frequency, and antenna pointing models. The first antenna (DSS-35) was entered into operations in October, 2014 and the 2nd antenna (DSS-36) in October, 2016. This paper describes the measurement techniques and results of the testing and calibration for both antennas, along with the driving requirements.
Rochblatt, David J.↗
The road to the Deep Space Network
The history and current status of the NASA Deep Space Network, a system of 13 antennas of various designs and sizes located around the world to stay in continuous contact with spacecraft out to the edge of the solar system, are reviewed. A major step in the evolution of the Deep Space Network is considered to be the installation of 210-ft-diameter antennas to support sophisticated spacecraft sending data at higher rate back to earth at Goldstone in 1966 and at Tidbinbilla and Robledo de Chevala in 1973. New 340-meter multifrequency beam-waveguide antenna, in which weather sensitive microwave components were to be put in an equipment room, was installed for research and development at Goldstone in 1990.
Waff, Craig B.↗
NASA Deep Space Network Operations Scheduling
The functioning of the Deep Space Network Operations Scheduling, Jet Propulsion Laboratory, CA is reviewed. The primary objectives of the Operations Scheduling are: to schedule the worldwide global allocation of ground communications, tracking facilities, and equipment; and to provide deep space telecommunications for command, tracking, telemetry, and control in support of flight mission operations and tests. Elements of the earth set are Deep Space Stations (DSS) which provide the telecommunications link between the earth and spacecraft; NASA Communications Network; Network Data Processing Area; Network Operations Control Area which provides operational direction to the DSS; Mission Control and Computing systems; and Mission Support areas which provide flight control of the spacecraft. Elements of the space set include mission priorities and requirements which determine the spacecraft queue for allocating network resources. Scheduling is discussed in terms of long-range (3 years), mid-range (8 weeks), and short-range (2 weeks).
Enari, D. M.↗
A New Deep Space Network for the Next Century
NASA's Deep Space Network (DSN) is responsible for providing communications, tracking, and
Deep↗
The Deep Space Network, volume 17
The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Network Control System are described.
Source record↗
The deep space network, volume 18
The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Network Control System are described.
Source record↗
Understanding and Verifying Deep Neural Networks
Explore the source record for details and available documents.
Deep Neural Networks↗
Application of deep neural networks to improve diagnostic accuracy of rheumatoid arthritis using diffuse optical tomography
A set of deep neural network models for rheumatoid arthritis (RA) classification using a highway network, a convolutional neural network and a residual network is proposed based on the data of diffuse optical tomography (DOT) utilising near-infrared light, which ensures early diagnosis of pathophysiological changes resulting from inflammation. A numerical model of the finger is used to generate images to overcome the inherent problem of insufficient clinical DOT images available. The proposed deep neural network models are applied to automatically classify simulated DOT images of inflamed and non-inflamed joints and transfer learning is also used to improve the performance of the classification. The results demonstrate that all three deep neural network methods improve the diagnostic accuracy as compared to the widely applied support vector machine (SVM), especially for high inter-subject variability databases. In cases of distinct modelled severity of disease, residual network achieved the highest accuracy (> 99 %), and both of highway and convolutional neural networks reached 99 %, respectively. However, as the severity of the modelled disease is reduced, this accuracy is reduced to 75.2 % for residual networks. The results indicate that transfer learning can improve the performance of deep neural network methods on RA classification from DOT data and highlight their potential as a computer aided tool in DOT diagnostic systems. (paper)
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Maintenance of time and frequency in the Jet Propulsion Laboratory's Deep Space Network using the Global Positioning System
The Deep Space Network (DSN), managed by the Jet Propulsion Laboratory for NASA, must maintain time and frequency within specified limits in order to accurately track the spacecraft engaged in deep space exploration. Various methods are used to coordinate the clocks among the three tracking complexes. These methods include Loran-C, TV Line 10, Very Long Baseline Interferometry (VLBI), and the Global Positioning System (GPS). Calculations are made to obtain frequency offsets and Allan variances. These data are analyzed and used to monitor the performance of the hydrogen masers that provide the reference frequencies for the DSN Frequency and Timing System (DFT). Areas of discussion are: (1) a brief history of the GPS timing receivers in the DSN, (2) a description of the data and information flow, (3) data on the performance of the DSN master clocks and GPS measurement system, and (4) a description of hydrogen maser frequency steering using these data.
Clements, P. A.↗
Space Programs Summary no. 37-43, volume III for the period November 1 to December 31, 1966. The deep space network
Advanced communications techniques and control capability studies for Deep Space Network
INTERPLANETARY FLIGHT↗
Complexity-Based Link Assignment for NASA’s Deep Space Network for Follow-the-Sun Operations
NASA’s Deep Space Network (DSN) recently underwent a paradigm shift in its operations approach called Follow the Sun Operations (FtSO) in an effort to increase efficiency for forthcoming expansion of the network. This change requires each Deep Space Communications Complex (DSCC) to remotely control the other two complexes’ antennas during their local day shift, in contrast to locally controlling only their own antennas 24x7. Remote operations increases the workload of each complex during their day shift, specifically that of the Link Control Operators (LCOs), and presents a new challenge for planning and managing the distribution of responsibility for each link. A new DSN software assembly, the Link Complexity and Maintenance (LCM) software, was developed to support workload management for LCOs, as well as for planning site-local maintenance activities. The LCM deployment was a vital part of the transition to FtSO in November 2017. This paper discusses the architecture of LCM, its feature set, and lessons learned during its development and rollout.
Lee, Carlyn↗
U-net architected deep material network training with microstructure local field information
The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.
36 MATERIALS SCIENCE↗
(abstract) Spacecraft Doppler Tracking with the Deep Space Network in the Search for Gravitational Waves
The Deep Space Network generates accurate radio science data observables for investigators who use radio links between spacecraft and the Earth to examine small changes in the phase and/or amplitude of the signal to study a wide variety of structures and phenomena in space. Several such studies are directed at aspects of the theory of general relativity such as gravitational redshift and gravitational waves. A gravitational wave is a propagating, polarized gravitational field, a ripple in the curvature of space-time. In Einstein's theory of general relativity, the waves are propagating solutions of the Einstein field equations. Their amplitudes are dimensionless strain amplitudes that change the fractional difference in distance between test masses and the rates at which separated clocks keep time. Predicted by all relativistic theories of gravity, they are extremely weak (the ratio of gravitational forces to electrical forces is about 10(sup -40)) and are generated at detectable levels only by astrophysical sources - very massive sources under violent dynamical conditions. The waves have never been detected but searches in the low-frequency band using Doppler tracking of many spacecraft have been conducted and others are being planned. Upper limits have been placed on the gravitational wave strength with the best sensitivities to date are for periodic waves being 7 x 10(sup -15).
gravitational waves theory of general relativity D↗
Proposed Array-based Deep Space Network for NASA
The current assets of the Deep Space Network (DSN) of the National Aeronautics and Space Administration (NASA), especially the 70-m antennas, are aging and becoming less reliable. Furthermore, they are expensive to operate and difficult to upgrade for operation at Ka-band (321 GHz). Replacing them with comparable monolithic large antennas would be expensive. On the other hand, implementation of similar high-sensitivity assets can be achieved economically using an array-based architecture, where sensitivity is measured by G/T, the ratio of antenna gain to system temperature. An array-based architecture would also provide flexibility in operations and allow for easy addition of more G/T whenever required. Therefore, an array-based plan of the next-generation DSN for NASA has been proposed. The DSN array would provide more flexible downlink capability compared to the current DSN for robust telemetry, tracking and command services to the space missions of NASA and its international partners in a cost effective way. Instead of using the array as an element of the DSN and relying on the existing concept of operation, we explore a broader departure in establishing a more modern concept of operations to reduce the operations costs. This paper presents the array-based architecture for the next generation DSN. It includes system block diagram, operations philosophy, user's view of operations, operations management, and logistics like maintenance philosophy and anomaly analysis and reporting. To develop the various required technologies and understand the logistics of building the array-based lowcost system, a breadboard array of three antennas has been built. This paper briefly describes the breadboard array system and its performance.
telemetry↗
The deep space network, volume 2 Progress report, Jan. - Feb. 1971
Deep Space Network, Ground Communications Facility, Space Flight Operations Facility, and Deep Space Instrumentation Facility research and development - Vol. 2
Source record↗