The deep space network Technical report, 1 Sep. - 31 Oct. 1968
Description, mission support, engineering projects, network operations, facilities, structures, and utilities of deep space network
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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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Earth-Mars Telecommunications and Information Management System (TIMS): Antenna Visibility Determination, Network Simulation, and Management Models
This report presents the results of our research on Earth-Mars Telecommunications and Information Management System (TIMS) network modeling and unattended network operations. The primary focus of our research is to investigate the feasibility of the TIMS architecture, which links the Earth-based Mars Operations Control Center, Science Data Processing Facility, Mars Network Management Center, and the Deep Space Network of antennae to the relay satellites and other communication network elements based in the Mars region. The investigation was enhanced by developing Build 3 of the TIMS network modeling and simulation model. The results of several 'what-if' scenarios are reported along with reports on upgraded antenna visibility determination software and unattended network management prototype.
Odubiyi, Jide↗
ATS-6 - Technical aspects of the Health/Education Telecommunications Experiment
An overview is given of the HET experiment on ATS-6. The paper is divided into nine parts, including a technical overview, a preliminary evaluation of the HET demonstration, a review of operations at the Denver uplink terminal, a discussion of remote ground terminals, a review of C-band comprehensive terminals and of S-band comprehensive terminals, and parts devoted to general network operations, technical management and effectiveness of the network, and the site equipment operator.
Boor, J. L.↗
The deep space network
The objectives, functions, and organization of the Deep Space Network are summarized along with deep space station, ground communication, and network operations control capabilities. Mission support of ongoing planetary/interplanetary flight projects is discussed with emphasis on Viking orbiter radio frequency compatibility tests, the Pioneer Venus orbiter mission, and Helios-1 mission status and operations. Progress is also reported in tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations.
Source record↗
DSN test and training system
Key characteristics of the Deep Space Network Test and Training System were presented. Completion of the Mark III-75 system implementation is reported. Plans are summarized for upgrading the system to a Mark III-77 configuration to support Deep Space Network preparations for the Mariner Jupiter/Saturn 1977 and Pioneer Venus 1978 missions. A general description of the Deep Space Station, Ground Communications Facility, and Network Operations Control Center functions that comprise the Deep Space Network Test and Training System is also presented.
H. C. Thorman↗
More About Architecture For Intelligent Robotic Control
Boolean neural networks proposed to implement part of intermediate level of hierarchical architecture of control system for artificially intelligent control of robot hand. Concept described in "Architecture for Intelligent Control of Robotic Tasks" (NPO-17871). Rule level of architecture implemented in two Boolean neural networks operated and updated in alternation. No explicit programming of network. Internal configuration not unique but, depends on initial state and history of previous adaptations. Accepts new rules sequentially presented by external controller.
Fiorini, Paolo↗
Airport-Noise Levels and Annoyance Model (ALAMO) user's guide
A guide for the use of the Airport-Noise Level and Annoyance MOdel (ALAMO) at the Langley Research Center computer complex is provided. This document is divided into 5 primary sections, the introduction, the purpose of the model, and an in-depth description of the following subsystems: baseline, noise reduction simulation and track analysis. For each subsystem, the user is provided with a description of architecture, an explanation of subsystem use, sample results, and a case runner's check list. It is assumed that the user is familiar with the operations at the Langley Research Center (LaRC) computer complex, the Network Operating System (NOS 1.4) and CYBER Control Language. Incorporated within the ALAMO model is a census database system called SITE II.
Deloach, R.↗
Optimizing the design and operation of water networks: Two decomposition approaches
We consider the design and operation of water networks simultaneously. Water network problems can be divided into two categories: the design problem and the operation problem. The design problem involves determining the appropriate pipe sizing and placements of pump stations, while the operation problem involves scheduling pump stations over multiple time periods to account for changes in supply and demand. Our focus is on networks that involve water co-produced with oil and gas. While solving the optimization formulation for such networks, we found that obtaining a primal (feasible) solution is more challenging than obtaining dual bounds using off-the-shelf mixed-integer nonlinear programming solvers. Therefore, we propose two methods to obtain good primal solutions. One method involves a decomposition framework that utilizes a convex reformulation, while the other is based on time decomposition. To test our proposed methods, we conduct computational experiments on a network derived from the PARETO case study.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Evaluating Space Network (SN) scheduling operations concepts through statistical analysis
The Network Control Center (NCC) currently uses the NCC Data System (NCCDS) to schedule customer spacecraft communication requests for the Space Network (SN). The NCC/Request Oriented Scheduling Engine (NCC/ROSE), which implements an operational concept called flexible scheduling, is being tested as a potential replacement for the NCCDS scheduler in an effort to increase the efficiency of the NCC scheduling operations. This paper describes the high fidelity benchmark tests being conducted on NCC/ROSE, the evaluation techniques used to compare schedules, and the results of the tests. This testing will verify the increases in efficiency and productivity that can help the NCC meet the anticipated scheduling loads well into the next century.
Kwadrat, Carl↗
Concorde with the airlines
The only supersonic aircraft in airline service, Concorde, offers the first actual test of supersonic cruise feasibility and the only real experience relative to passenger, airline, and community acceptance. The dominant characteristic of Concorde operations is low aircraft utilization, due partly to the restricted route network. Operating costs, the maintenance/reliability record and associated dispatch delays are discussed. Problems with overwater operations, and the secondary boom phenomena are examined. Monthly average load factors for various routes, major causes of technical delays, aircraft technical performance, and aircraft tracks are graphically depicted.
Leyman, C. S.↗
DeepONet-Assisted Optimization of Surface Topography for Transition Delay in a Mach 4.5 Boundary Layer
We use deep learning, an ensemble variational technique (EnVar), and direct numerical simulations(DNS) to design an optimal topography for a two-dimensional roughness element that delays the on-set of laminar-turbulent transition in a Mach 4.5 flat-plate boundary layer. Deep operator networks (DeepONets), which have the known ability to learn complex nonlinear operators within dynamical systems, are used for machine learning. For the baseline configuration of a smooth flat plate, the second-mode waves at the DNS inflow cause a quick nonlinear breakdown of the high-speed boundary layer within the computational domain. Results reported in the present study validate the ability of DeepONets to model the transition delay via a given topography of the roughness element. The computing cost to optimize the rough-ness element for minimal skin-friction drag is substantially lowered by the DeepONets-based reduced-order model. In comparison to the baseline method of EnVar optimization based on DNS alone, the DeepONets-based EnVar optimizer is able to delay transition past the outflow boundary of the computational domain while utilizing almost 5–6 times fewer DNS.
Machine Learning↗
NOCA upgrade
The network operations control area (NOCA) of the space flight operations facility (SFOF) is being upgraded in both hardware and software capabilities to provide the visibility and operability necessary for the operations control function to be performed within the present technological environment of the tracking network. The hardware implementation currently in progress is addressed. It provides a standard operating console with increased display capacity and improved ergonomics as a replacement for the existing, outmoded, circular consoles original to the SFOF.
Gould, G. D.↗
Throughput and packet delay analysis for the H-network - CSMA/CD with adaptive and nonadaptive backoff protocols
The structure of a high-data-rate (about 7 Mbytes/sec) local area network named the H-Network is described which is used to provide point to point analysis and broadcast communication in a multiprocessor environment, the Homogeneous Multiprocessor (Dimopoulos, 1983). The throughput and packet delay characteristics for the H-Network operating under CSMA/CD are presented, using adaptive and nonadaptive backoff protocols. The network is found to perform quite well, with an average utilization factor of over 60 percent and a maximum packet delay of less than 500 microsec for the case of 10 competing stations.
Dimopoulos, Nikitas J.↗
Earth-Space Link Attenuation Estimation via Ground Radar Kdp
A method of predicting attenuation on microwave Earth/spacecraft communication links, over wide areas and under various atmospheric conditions, has been developed. In the area around the ground station locations, a nearly horizontally aimed polarimetric S-band ground radar measures the specific differential phase (Kdp) along the Earth-space path. The specific attenuation along a path of interest is then computed by use of a theoretical model of the relationship between the measured S-band specific differential phase and the specific attenuation at the frequency to be used on the communication link. The model includes effects of rain, wet ice, and other forms of precipitation. The attenuation on the path of interest is then computed by integrating the specific attenuation over the length of the path. This method can be used to determine statistics of signal degradation on Earth/spacecraft communication links. It can also be used to obtain real-time estimates of attenuation along multiple Earth/spacecraft links that are parts of a communication network operating within the radar coverage area, thereby enabling better management of the network through appropriate dynamic routing along the best combination of links.
Bolen, Steven M.↗
Link monitor and control operator assistant: A prototype demonstrating semiautomated monitor and control
This article describes the approach, results, and lessons learned from an applied research project demonstrating how artificial intelligence (AI) technology can be used to improve Deep Space Network operations. Configuring antenna and associated equipment necessary to support a communications link is a time-consuming process. The time spent configuring the equipment is essentially overhead and results in reduced time for actual mission support operations. The NASA Office of Space Communications (Code O) and the NASA Office of Advanced Concepts and Technology (Code C) jointly funded an applied research project to investigate technologies which can be used to reduce configuration time. This resulted in the development and application of AI-based automated operations technology in a prototype system, the Link Monitor and Control Operator Assistant (LMC OA). The LMC OA was tested over the course of three months in a parallel experimental mode on very long baseline interferometry (VLBI) operations at the Goldstone Deep Space Communications Center. The tests demonstrated a 44 percent reduction in pre-calibration time for a VLBI pass on the 70-m antenna. Currently, this technology is being developed further under Research and Technology Operating Plan (RTOP)-72 to demonstrate the applicability of the technology to operations in the entire Deep Space Network.
Lee, L. F.↗
High-speed, cross-continental networks - A necessity
The networking requirements that the Earth Observing System (EOS) program has levied on NASA and the national and international operational networking communities is addressed. The objectives of the EOS program are listed and include the development and operation of an observing system to acquire essential, global earth science data on a long-term, sustained basis and in a manner which maximizes the scientific utility of the data and simplifies data analysis. Network design issues are also discussed, including protocol selection, the Transport Control Protocol/Internet Protocol suite, the Open Systems Interconnect network suite, and the internet evolution. Open and closed network issues are also discussed. A number of networks now in place are assessed including NASNet, and ESNet. These offer possible options and/or solutions to be considered by the NASA Headquarters EOS Data and Information Systems Program.
Wong, C. M.↗
Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems
Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.
97 MATHEMATICS AND COMPUTING↗
ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers
Deep neural networks (DNNs) have heavily relied on traditional computational units, such as CPUs and GPUs. However, this conventional approach brings significant computational burden, latency issues, and high power consumption, limiting their effectiveness. This has sparked the need for lightweight networks such as ExtremeC3Net. Meanwhile, there have been notable advancements in optical computational units, particularly with metamaterials, offering the exciting prospect of energy-efficient neural networks operating at the speed of light. Yet, the digital design of metamaterial neural networks (MNNs) faces precision, noise, and bandwidth challenges, limiting their application to intuitive tasks and low-resolution images. In this study, we proposed a large kernel lightweight segmentation model, ExtremeMETA. Based on ExtremeC3Net, our proposed model, ExtremeMETA maximized the ability of the first convolution layer by exploring a larger convolution kernel and multiple processing paths. With the large kernel convolution model, we extended the optic neural network application boundary to the segmentation task. To further lighten the computation burden of the digital processing part, a set of model compression methods was applied to improve model efficiency in the inference stage. The experimental results on three publicly available datasets demonstrated that the optimized efficient design improved segmentation performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from 461.07 MMacs to 166.03 MMacs. The large kernel lightweight model ExtremeMETA showcased the hybrid design’s ability on complex tasks.
large convolution kernel↗