DSIF development
DSIF operations and development of wideband data systems for 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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Viking Mars launch set for August 11
The 1975-1976 Viking Mars Mission is described in detail, from launch phase through landing and communications relay phase. The mission's scientific goals are outlined and the various Martian investigations are discussed. These investigations include: geological photomapping and seismology; high-resolution, stereoscopic horizon scanning; water vapor and thermal mapping; entry science; meteorology; atmospheric composition and atmospheric density; and, search for biological products. The configurations of the Titan 3/Centaur combined launch vehicles, the Viking orbiters, and the Viking landers are described; their subsystems and performance characteristics are discussed. Preflight operations, launch window, mission control, and the deep space tracking network are also presented.
Panagakos, N.↗
Automation of the deep space network
Approaches to automating the radiofrequency system used in the deep space network (DSN) are discussed. The goal is to operate DSN, which communicates with U.S. space probes that travel beyond the moon, in an unattended mode in order to reduce operating costs. The philosophy of a hierachical automation plan, which emphasizes distributed control and the utilization of DSN hardware development engineers to develop automation software, is discussed, and software development as well as common software and common microcontroller hardware are considered. It is planned that a station controller will serve as the operator interface and control and coordinate the operation of three system controllers (RF system, digital system, and antenna system). These systems are described.
Crow, R. B.↗
Radio astronomy
The activities of the Deep Space Network in support of radio and radar astronomy operations during July and August 1980 are reported. A brief update on the OSS-sponsored planetary radio astronomy experiment is provided. Also included are two updates, one each from Spain and Australia on current host country activities.
Taylor, R. M.↗
A conceptual 34-meter antenna feed configuration for joint DSN/SETI use from 1 to 10 GHz
The very satisfactory performance of a conceptual 34-m DSS-12 type HA-Dec antenna feed sysem over the frequency range of 1 to 10 GHz is demonstrated. A seven-feedhorn baseline design is developed which will allow Search for Extra-Terrestrial Intelligence (SETI) investigations using each horn over a 1.4:1 frequency range. A gain/system noise temperature (G/T) figure of merit is calculated for the frequency range of each horn; it is found that system performance down to 20 deg elevation is possible with a G/T degradation of less than 3 dB at every frequency. The design presented here will allow shared but independent antenna use by the Deep Space Network (DSN) and SETI with a minimum of operational impacts to DSN functions and no intrusions into the DSN microwave equipment configuration.
Slobin, S. D.↗
A Ten-Meter Ground-Station Telescope for Deep-Space Optical Communications: A Preliminary Design
This article describes a telescope design for a 10-m optical ground station for deep-space communications. The design for a direct-detection optical communications telescope differs dramatically from a telescope for imaging applications. In general, the requirements for optical manufacturing and tracking performance are much less stringent for direct detection of optical signals. The technical challenge is providing a design that will operate in the daytime/nighttime conditions required for a Deep Space Network tracking application. The design presented addresses these requirements. The design will provide higher performance at lower cost than existing designs.
Britcliffe, M.↗
An Improved Ionospheric Correction Method for Wide-Area Augmentation Systems
A powerful approach for generating ionospheric corrections in wide area differential GPS applications has been developed, that can be applied to the Federal Aviation Administration's Wide Area Augmentation System (WAAS). This approach has been used to support ionospheric calibration for NASA's Deep Space Network and will be supporting real-time operations for global ionospheric specification and, possibly, single-frequency satellite altimeter calibration. It is a real-time, grid-based technique relying on a computationally efficient Kalman-type filter to produce accurate, smoothly varying ionospheric correction maps over the coverage area. Formal error maps are also computed, providing vertical delay errors over the WAAS grid, which are useful in integrity monitoring. This solution to the ionospheric correction problem is self-calibrating, since GPS transmitter inter-frequency biases are obtained as a by-product of the mapping procedure. This contrasts with other techniques in which bias values must be provided from some additional source.
Wide-Area↗
Chapter 7: Predictive Analytics in Future Power Systems: A Panorama and State-of-the-Art of Deep Learning Applications
The challenges surrounding the optimal operation of power systems are growing in various dimensions, due in part to increasingly distributed energy resources and a progression towards large-scale transportation electrification. Currently, the increasing uncertainties associated with both renewable energy generation and demand are largely being managed by increasing operational reserves - potentially at the cost of suboptimal economic conditions - in order to maintain the reliability of the system. This chapter looks at the big picture role of forecasting in power systems from generation to consumption and provides a comprehensive review of traditional approaches for forecasting generation and load in various contexts. This chapter then takes a deep dive into the state-of-the-art machine learning and deep learning approaches for power systems forecasting. Furthermore, a case study of multi-time-horizon solar irradiance forecasting using deep learning is discussed in detail. Smart grids form the backbone of the future interdependent networks. For addressing the challenges associated with the operations of smart grid, development and wide adoption of machine learning and deep learning algorithms capable of producing better forecasting accuracies is urgently needed. Along with exploring the implementation and benefits of these approaches, this chapter also considers the strengths and limitations of deep learning algorithms for power systems forecasting applications. This chapter, thus, provides a panoramic view of state-of-the-art of predictive analytics in power systems in the context of future smart grid operations.
24 POWER TRANSMISSION AND DISTRIBUTION↗
The deep space network, volume 15
The DSN progress is reported in flight project support, TDA research and technology, network engineering, hardware and software implementation, and operations. Topics discussed include: DSN functions and facilities, planetary flight projects, tracking and ground-based navigation, communications, data processing, network control system, and deep space stations.
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On the influence of over-parameterization in manifold based surrogates and deep neural operators
Constructing accurate and generalizable approximators (surrogate models) for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. The main question is what type of surrogate models we should construct and should these models be under-parameterized or over-parameterized. In this work, we propose new developments and perform comparisons for two promising approaches: manifold-based polynomial chaos expansion (m-PCE) and the deep neural operator (DeepONet), and we examine the effect of over-parameterization on generalization. While m-PCE enables the construction of a mapping by first identifying low-dimensional embeddings of the input functions, parameters, and quantities of interest (QoIs), a neural operator learns the nonlinear mapping via the use of deep neural networks. Here, we demonstrate the performance of these methods in terms of generalization accuracy by solving the 2D time-dependent Brusselator reaction-diffusion system with uncertainty sources, modeling an autocatalytic chemical reaction between two species. We first propose an extension of the m-PCE by constructing a mapping between latent spaces formed by two separate embeddings of the input functions and the output QoIs. To further enhance the accuracy of the DeepONet, we introduce weight self-adaptivity in the loss function. We demonstrate that the performance of m-PCE and DeepONet is comparable for cases of relatively smooth input-output mappings. However, when highly non-smooth dynamics is considered, DeepONet shows higher approximation accuracy. We also find that for m-PCE, modest over-parameterization leads to better generalization, both within and outside of distribution, whereas aggressive over-parameterization leads to over-fitting. In contrast, an even highly over-parameterized DeepONet leads to better generalization for both smooth and non-smooth dynamics. Furthermore, we compare the performance of the above models with another recently proposed operator learning model, the Fourier Neural Operator, and show that its over-parameterization also leads to better generalization. Taken together, our studies show that m-PCE can provide very good accuracy at very low training cost, whereas a highly over-parameterized DeepONet can provide better accuracy and robustness to noise but at higher training cost. In both methods, the inference cost is negligible.
97 MATHEMATICS AND COMPUTING↗
Distrubtion Tolerant Network Technology Flight Validation Report: DINET
In October and November of 2008, the Jet Propulsion Laboratory installed and tested essential elements of Delay/Disruption Tolerant Networking (DTN) technology on the Deep Impact spacecraft. This experiment, called Deep Impact Network Experiment (DINET), was performed in close cooperation with the EPOXI project which has responsibility for the spacecraft. During DINET some 300 images were transmitted from the JPL nodes to the spacecraft. Then, they were automatically forwarded from the spacecraft back to the JPL nodes, exercising DTN's bundle origination, transmission, acquisition, dynamic route computation, congestion control, prioritization, custody transfer, and automatic retransmission procedures, both on the spacecraft and on the ground, over a period of 27 days. All transmitted bundles were successfully received, without corruption. The DINET experiment demonstrated DTN readiness for operational use in space missions.
networking↗
Trinity University Campus-Wide Deep Dive
In May 2019, staff members from the Engagement and Performance Operations Center (EPOC) and the Lonestar Education And Research Network (LEARN) met with researchers and staff at Trinity University for the purpose of a Campus-Wide Deep Dive into research drivers. The goal of this meeting was to help characterize the requirements for five campus research use cases and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support. Profiled use cases included: Physics and Neuroscience, Computer Science and Engineering, Classical Studies and Archeology, Biological Sciences, and Geosciences. Material for this event included the written documentation from each of the research areas at Trinity University, documentation about the current state of technology support, and a write-up of the discussion that took place in person.
99 GENERAL AND MISCELLANEOUS↗
University of Cincinnati Campus-Wide Deep Dive
In April 2019, staff members from the Engagement and Performance Operations Center (EPOC) and the The Ohio Academic Resources Network (OARnet) met with faculty and staff from the University of Cincinnati to perform a Campus-Wide Deep Dive. The goal of this interaction was to help characterize a number of scientific and educational drivers for the entire campus, and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support. Material for this event includes both the written documentation from the University of Cincinnati, but also a writeup of the discussion that took place in person on April 26, 2019. Each of the highlighted use case highlights specific challenges, but a number of themes emerged that the campus is encouraged to address. In particular, the lack of available local compute and storage resources is impacting several groups and others can greatly benefit from a fully featured Science DMZ infrastructure to facilitate remote data access and transfer.
99 GENERAL AND MISCELLANEOUS↗
Baylor University Campus-Wide Deep Dive
In January 2020, staff members from the Engagement and Performance Operations Center (EPOC) and the Lonestar Education And Research Network (LEARN) met with researchers and staff at Baylor University for the purpose of a Campus-Wide Deep Dive into research drivers. The goal of this meeting was to help characterize the requirements for five campus research use cases and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support. Profiled scientific use cases included: - Experimental High Energy Physics (HEP) - Proton Computed Tomography (pCT) - Nutrition and Relation to Digestive Microbiome - Baylor University Core Research Facilities - Molecular Quantum-dot Cellular Automata (QCA), and Material Science of Quantum Computing - Modeling and Simulation of Low-Dimensional and Nano-Structured Materials - Computational Fluid Dynamics Material for this event included the written documentation from each of the research areas at Baylor University, documentation about the current state of technology support, and a write-up of the discussion that took place in person. The Case Studies highlighted the ongoing challenges that Baylor University has in supporting a cross-section of established and emerging research use cases. Each Case Study mentioned unique challenges which were summarized into common needs. These included: - Tradeoffs for network/software security, and usability of the resulting infrastructure. Better communication to set expectations and understand realities is required. - Computation use on campus is widespread and healthy. While no major problems were uncovered, upgrades to maintain current usage patterns and encourage growth will be required. - Storage is a critical need for enterprise use cases and research. In particular, a campus wide ‘storage architecture’ to support research use cases (e.g. instruments, data sharing) is required in the 2-5 year time window. - Instrumentation on campus is healthy and expanding. Technology must scale with this in the form of computation and storage. - Working with LEARN to upgrade network capacity (in multiples of 10G, or upgrades to 100G) will be required in the 1-3 year time frame. - Network monitoring and visibility will help to establish external science use cases. - Data sharing via portal systems is not currently a critical need, but growing in scope. EPOC can assist Baylor with options.
99 GENERAL AND MISCELLANEOUS↗
Space Programs Summary No. 37-51, Volume 2 for the Period March 1 to April 30, 1968. the Deep Space Network
Deep Space Network - mission support, advanced engineering, development and implementation, and operations and facilities
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A method for precision measurement of synchronization errors in tracking-station clocks
Tracking station clock synchronization error measurement using Deep Space Network Mark I ranging system, discussing system theory, design, operation, economy and versatility
Borncamp, F.↗
Viking Mars encounter
Various phases of planetary operations related to the Viking mission to Mars are described. Topics discussed include: approach phase, Mars orbit insertion, prelanding orbital activities, separation, descent and landing, surface operations, surface sampling and operations starting, orbiter science and radio science, Viking 2, Deep Space Network and data handling.
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Fuel cells feasibility
The technical and economic status of fuel cells is assessed with emphasis on their potential benefits to the Deep Space Network. The fuel cell, what it is, how it operates, and what its outputs are, is reviewed. Major technical problems of the fuel cell and its components are highlighted. Due to these problems and economic considerations it is concluded that fuel cells will not become commercially viable until the early 1990s.
Schonfeld, D.↗