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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

On the Development and Application of High Data Rate Architecture (HiDRA) in Future Space Networks

Historically, space missions have been severely constrained by their ability to downlink the data they have collected. These constraints are a result of relatively low link rates on the spacecraft as well as limitations on the time during which data can be sent. As part of a coherent strategy to address existing limitations and get more data to the ground more quickly, the Space Communications and Navigation (SCaN) program has been developing an architecture for a future solar system Internet. The High Data Rate Architecture (HiDRA) project is designed to fit into such a future SCaN network. HiDRA's goal is to describe a general packet-based networking capability which can be used to provide assets with efficient networking capabilities while simultaneously reducing the capital costs and operational costs of developing and flying future space systems.Along these lines, this paper begins by reviewing various characteristics of modern satellite design as well as relevant characteristics of emerging technologies (such as free-space optical links capable of working at 100+ Gbps). Next, the paper describes HiDRA's design, and how the system is able to both integrate and support the operation of not only today's high-rate systems, but also the high-rate systems likely to be found in the future. This section also explores both existing and future networking technologies, such as Delay Tolerant Networking (DTN) protocol (RFC4838 citeRFC:1, RFC5050citeRFC:2), and explains how HiDRA supports them. Additionally, this section explores how HiDRA is used for scheduling data movement through both proactive and reactive link management. After this, the paper moves on to explore a reference implementation of HiDRA. This implementation is currently being realized based on a Field Programmable Gate Array (FPGA) memory and interface controller that is itself controlled by a local computer running DTN software. Next, this paper explores HiDRA's natural evolution, which includes an integration path for software-defined networking (SDN) switches. This section also describes considerations for both near-Earth and deep-space instantiations of HiDRA, describing how differences in latencies between the environments will necessarily influence how the system is configured and the networks operate. Finally, this paper describes future work. This section includes a description of a potential ISS implementation which will allow rapid advancement through the technology readiness levels (TRL). This section also explores work being done to support HiDRA's successful implementation and operation in a heterogeneous network: such a network could include communications equipment spanning many vintages and capabilities, and one significant aspect of HiDRA's future development involves balancing compatibility with capability.

DTN↗
NASA NTRS2021

Extending the Licklider Transmission Protocol to Multi-Band Links

Most deep space missions return data to Earth using links operating at a single frequency band. Indeed, their data requirements are low enough that bandwidth regulations do not constrain the system. In contrast, spacecraft such as Kepler or Europa Clipper are transitioning to a new operational paradigm where engineering and science data are transmitted through simultaneous links operating at different frequency bands (henceforth termed multi-band links). This ensures, for instance, that critical data is correctly received using a well characterized X-band link, while science data at a much larger data rate can be returned efficiently (both in terms of bandwidth and energy) through a Ka-band link.Having a spacecraft establish two simultaneous links with a ground station opens a large span of potential improvements for space communications and mission operations. In this paper, we consider the problem of running a Licklider Transmission Protocol (LTP) session over a multi-band link. LTP is an implementation of a selective Automatic Repeat reQuest (ARQ) protocol, i.e. it ensures correct delivery of data over an error prone link with potentially long propagation delays. To maximize its efficiency in deep space environments, LTP operates in deferred-ACK mode and is typically included as one of the core protocols in the Delay Tolerant Networking (DTN) suite.The contributions of this paper are as follows: First, we propose an extension to LTP for multi-band links (denoted MBLTP) and sketch how it can be implemented without modifying the current definition of LTP data units. Next, we develop bounds on the performance of MBLTP when transmitting a single data file over a multi-band link. Three metrics are considered, file expected delivery time, total energy spent and bundle jitter. The results of the analytic model are first benchmarked against simulations to ensure validity, and then compared against the performance of both traditional LTP and Parallel LTP (PLTP).We demonstrate that MBLTP can significantly reduce the latency and jitter with which data products are delivered to destination over a deep space link compared to LTP at moderate energy cost. Similarly, we also demonstrate that MBLTP outperforms PLTP in all considered metrics.

Sanchez Net, Marc↗
DOE OSTI2019

Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints

Subsurface nuclear magnetic resonance (NMR) logs acquired in the wellbore environment are sensitive to fluid-filled pore size distribution, fluid mobility, permeability, and porosity in the near-wellbore reservoir volume. NMR response of a formation layer is processed to extract the T2 distribution, which approximates the fluid-filled pore size distribution. NMR logs are acquired in limited number of wells due to financial and operational challenges, which adversely affects reservoir characterization. We developed here two neural-network-based machine learning techniques, long short-term memory (LSTM) network and variational autoencoder with a convolutional layer (VAEc) network, to process the ‘easy-to-acquire’ formation mineral and fluid saturation logs to generate synthetic NMR T2 distributions in the absence of ‘hard-to-acquire’ NMR T2 distribution log. Both the predictive models are trained and tested on limited wireline log measurements randomly selected from a 300-ft depth interval of the Bakken shale formation. Synthesis performances of LSTM and VAEc models in terms of R 2 are 0.78 and 0.75, respectively. Noise is inevitable in logging data due to the complex wellbore and formation conditions. Notably, both the predictive models robustly synthesize the fluid-filled pore size distributions in the presence of 50% noise in input logs and 30% noise in training T2 data. The performance of the proposed methodology improves with access to larger volume of training data from other formation types. The proposed method is critical to the synthesis of in situ fluid-filled pore size distributions in shale formations under data constraints due to financial and operational challenges.

58 GEOSCIENCES↗
DOE OSTI2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

The deep space network, Volume 11

Deep Space Network progress in flight project support, Tracking and Data Acquisition research and technology, network engineering, hardware and software implementation, and operations are presented. Material is presented in each of the following categories: description of DSN; mission support; radio science; support research and technology; network engineering and implementation; and operations and facilities.

Source record↗
NASA NTRS1975

The deep space network

Summaries are given of Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations.

Source record↗
NASA NTRS1977

The Deep Space Network

The various systems and subsystems are discussed for the Deep Space Network (DSN). A description of the DSN is presented along with mission support, program planning, facility engineering, implementation and operations.

Source record↗
NASA NTRS1978

The Deep Space Network

The Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operation is reported.

Source record↗
NASA NTRS1979

The Deep Space Network

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is presented.

Source record↗
NASA NTRS1980

The deep space network

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is presented.

Source record↗
NASA NTRS1980

The telecommunications and data acquisition report

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implemention, and operations is documented. In addition, developments in Earth based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

Renzetti, N. A.↗
NASA NTRS1981

Telecommunications and data acquisition

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported. In addition, developments in Earth based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

Renzetti, N. A.↗
NASA NTRS1982

RFI measurement survey at Goldstone in November and December 1981

A measurement survey was conducted to measure the emissions from the transmitters at Deep Space Station 14 and the Spaceflight Tracking and Data Network station. Both intended and spurious emissions were measured from DSN operational transmitters. The quiescent electromagnetic environment was also measured.

Taylor, R. M.↗
NASA NTRS1986

The Telecommunications and Data Acquisition Report

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is documented. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics and the radio search for extraterrestrial intelligence are reported.

Posner, E. C.↗
NASA NTRS1988

DSN 64-meter antenna L-band (1668-MHz) microwave system performance overview

In 1985, L-band (1668 MHz) receive-only feed systems were installed on the three Deep Space Network 64 meter antennas to provide tracking support for two non-NASA spacecraft. The specifications, design approach, and operational test results are presented. The L-band microwave system met all of its tracking goals and is currently being upgraded to include a C-band (5000 MHz) uplink.

Withington, J.↗
NASA NTRS1993

Mars Observer mission status and orbit insertion phase planning

The Mars Observer spacecraft will reach Mars on August 24, 1993. Launch and cruise phase operations have gone smoothly, with few spacecraft anomalies. The orbit insertion phase, which spans a three month period and involves a series of seven maneuvers, has been redesigned postlaunch making use of excess spacecraft velocity change (Delta V) capability to advance the mapping phase, now planned to begin November 24, 1993. This is highly desirable since it moves the start of the mapping phase away from solar conjunction and the expected dust storm disturbances. The redesign process involved making tradeoffs between science, operations, and maneuver requirements. At Mars, the spacecraft will continuously record data with a single daily playback through the Deep Space Network's 34-meter high-efficiency antennas for one Martian year.

Dodd, Suzanne R.↗
NASA NTRS2013

The NASA Meter Class Autonomous Telescope: Ascension Island

The Meter Class Autonomous Telescope (MCAT) is the newest optical sensor dedicated to NASA s mission to characterize the space debris environment. It is the successor to a series of optical telescopes developed and operated by the JSC Orbital Debris Program Office (ODPO) to monitor and assess the debris environment in (1) Low Earth Orbit (LEO), (2) Medium Earth Orbit (MEO), and (3) Geosynchronous Orbit (GEO), with emphasis on LEO and GEO altitudes. A joint NASA-Air Force Research Labs project, MCAT is a 1.3m optical telescope dedicated to debris research. Its optical path and sensor yield a large survey fence at the cutting edge of current detector performance. It has four primary operational observing modes, two of which were not computationally feasible a decade ago. Operations are supported by a sophisticated software suite that monitors clouds and weather conditions, and controls everything from data collection to dome rotation to processing tens of GB of imagery data nightly. With fainter detection limits, precision detection, acquisition and tracking of targets, multi-color photometry, precision astrometry, automated reacquisition capability, and the ability to process all data at the acquisition rate, MCAT is capable of producing and processing a volume and quality of data far in excess of any current (or prior) ODPO operations. This means higher fidelity population inputs and eliminating the multi-year backlog from acquisition-to-product typical of optical campaigns. All of this is possible given a suitable observing location. Originally planned for the island of Legan, part of the Kwajalein Atoll Islands, recent developments have led to a change in venue. Specifically, the Ground-based Electro-Optical Deep Space Surveillance, or GEODSS, System of telescopes is the United States major tracking system for deep space. This network consists of telescopes in Maui, Hawaii; Diego Garcia (Indian Ocean), and Socorro, New Mexico. A fourth optical telescope, though smaller in size, has been operating in conjunction with this effort until recently in M.ron, Spain. With the M.ron site closing, a significant gap in longitude exists between the New Mexico and Diego Garcia sites. This longitudinal gap is well covered by placing a telescope on Ascension Island (7degrees 58'20" S, 14degrees 24'4"W), in the Atlantic Ocean. Ascension Island offers the benefits of both location and weather. The near equatorial location affords the opportunity to access under-sampled low-inclination orbits and new GEO longitudes, while simultaneously filling in the GEODSS longitudinal gap. Ascension Island is a volcanic, desert island, receiving only 7" of rain per year on average. With consistent trade winds blowing from the SSE direction off Africa, the combination of an island location with consistent winds will create the smooth laminar flow sought after by all astronomical sites, which creates stable atmospheric ('seeing') conditions. Finally, this low population island has minimal lighting, resulting in very dark skies, ideal for an observatory.

Lederer, S. M.↗
NASA NTRS2015

An Updated Process for Automated Deepspace Conjunction Assessment

There is currently a high level of interest in the areas of conjunction assessment and collision avoidance from organizations conducting space operations. Current conjunction assessment activity is mainly focused on spacecraft and debris in the Earth orbital environment [1]. However, collisions are possible in other orbital environments as well [2]. This paper will focus on the current operations of and recent updates to the Multimission Automated Deep Space Conjunction Assessment Process (MADCAP) used at the Jet Propulsion Laboratory for NASA to perform conjunction assessment at Mars and the Moon. Various space agencies have satellites in orbit at Mars and the Moon with additional future missions planned. The consequences of collisions are catastrophically high. Intuitive notions predict low probability of collisions in these sparsely populated environments, but may be inaccurate due to several factors. Orbits of scientific interest often tend to have similar characteristics as do the orbits of spacecraft that provide a communications relay for surface missions. The MADCAP process is controlled by an automated scheduler which initializes analysis based on a set timetable or the appearance of new ephemeris files either locally or on the Deep Space Network (DSN) Portal. The process then generates and communicates reports which are used to facilitate collision avoidance decisions. The paper also describes the operational experience and utilization of the automated tool during periods of high activity and interest such as: the close approaches of NASA's Lunar Atmosphere & Dust Environment Explorer (LADEE) and Lunar Reconnaissance Orbiter (LRO) during the LADEE mission. In addition, special consideration was required for the treatment of missions with rapidly varying orbits and less reliable long term downtrack estimates; in particular this was necessitated by perturbations to MAVEN's orbit induced by the Martian atmosphere. The application of special techniques to non-operational spacecraft with large uncertainties is also studied. Areas for future work are also described. Although the applications discussed in this paper are in the Martian and Lunar environments, the techniques are not unique to these bodies and could be applied to other orbital environments.

collision↗