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Research is reported dealing with problems of digital data transmission and computer communications networks. The results of four individual studies are presented which include: (1) signal processing with finite state machines, (2) signal parameter estimation from discrete-time observations, (3) digital filtering for radar signal processing applications, and (4) multiple server queues where all servers are not identical.
The feasibility of using data compression to improve link efficiency as an alternative to increased transmitter power, reducing receiver noise figures, increasing antenna gain through more stringent orbiter attitude constraints, etc. is studied. A method of encoding digital data is developed which permits low band-width encoding as well as a unique system of adaptive run length encoding. The effectiveness of these techniques for the air-to-ground link and for the bandwidth-limited ground-to-ground data link used for the orbiter downlink data is evaluated. Results are presented.
National Climatic Data Center (NCDC) data management issues are discussed, such as nature of holdings, history of the site, popularity of data sets, media/technology used for storage, volume distributed per month, mode of distribution, most frequently encountered problems, type of media requested/used, and evolution of media. Current holdings at NCDC are 107.8 terabytes of digital data and about 0.3 terabytes of manuscript data. The nexrad radar system is expected to generate approximately 88 terabytes per year by 1996.
GLAS-2, a modified version of the GLAS-1 technique developed by Negri et al. (1984) for estimating convective precipitation from GEO-satellite IR data, is introduced and demonstrated on the data set for the Florida Area Cumulus Experiment in 1980. In GLAS-2, rain cells are located by identification of all local blackbody-temperature minima below 253 K in an array of GOES digital data, and rain parameters are assigned on the basis of the one-dimensional cloud model of Adler and Mack (1984). The demonstration results are presented in graphs and tables and compared with those of GLAS-1 and a number of other methods as well as radar and rain-gage measurements. The false-alarm rate for GLAS-2 is found to be 0.54, somewhat better than that of other satellihte methods; further improvements are suggested.
The measured bit-error-rate (BER) performance of a fiber optic data link to be used in satellite communications systems is presented and discussed. In the testing, the link was measured for its ability to carry high burst rate, serial-minimum shift keyed (SMSK) digital data similar to those used in actual space communications systems. The fiber optic data link, as part of a dual-segment injection-locked RF fiber optic link system, offers a means to distribute these signals to the many radiating elements of a phased array antenna. Test procedures, experimental arrangements, and test results are presented.
The measured bit-error-rate (BER) performance of a fiber optic data link to be used in satellite communications systems is presented and discussed. In the testing, the link was measured for its ability to carry high burst rate, serial-minimum shift keyed (SMSK) digital data similar to those used in actual space communications systems. The fiber optic data link, as part of a dual-segment injection-locked RF fiber optic link system, offers a means to distribute these signals to the many radiating elements of a phased array antenna. Test procedures, experimental arrangements, and test results are presented.
NASA's Safe Autonomous Systems Operations (SASO) project goal is to define and safely enable all future airspace operations by justifiable and optimal autonomy for advanced air, ground, and connected capabilities. This work showcases how Increasingly Autonomous Systems (IAS) could create operational transformations beneficial to the enhancement of civil aviation safety and efficiency. One such IAS under development is the Traffic Data Manager (TDM). This concept is a prototype 'intelligent party-line' system that would declutter and parse out non-relevant air traffic, displaying only relevant air traffic to the aircrew in a digital data communications (Data Comm) environment. As an initial step, over 22,000 data points were gathered from 31 Airline Transport Pilots to train the machine learning algorithms designed to mimic human experts and expertise. The test collection used an analog of the Navigation Display. Pilots were asked to rate the relevancy of the displayed traffic using an interactive tablet application. Pilots were also asked to rank the order of importance of the information given, to better weight the variables within the algorithm. They were also asked if the information given was enough data, and more importantly the "right" data to best inform the algorithm. The paper will describe the findings and their impact to the further development of the algorithm for TDM and, in general, address the issue of how can we train supervised machine learning algorithms, critical to increasingly autonomous systems, with the knowledge and expertise of expert human pilots.
This paper presents the results of the HELIOS (Hardened Extremely Long Life In-formation Optical Storage) mission on the International Space Station (ISS) which tested a unique solution for the long-term storage and retrieval of data in space. For this mission Creative Technology (CTech) developed test media—termed WORF (Write Once, Read Forever)—to validate whether this patented technology will survive all critical parameters for harsh space-based environments including microgravity and ionizing radiation. The HELIOS experiment confirmed that the WORF media is impervious to ionizing radiation, microgravity, solar (plasma) eruptions, and the stress from 8 Gs of the launch including extreme temperature expo-sure. The principal results indicate that there has been no discernible degradation of the media after 8 months on the ISS as compared to a control set of media stored on the ground. This data validated the media’s survivability for harsh space environments for long-term and deep space missions. In addition to the space environment, we are confident that WORF technology can be used for data storage where space-related and other long-term or archival integrity is critical such as: geospatial collections from satellites; space weather archives; past, ongoing, and future space mission media and documentation files; the deep space Gate-way program; as well as Big Data applications such as the Vera C. Rubin astronomical observatory (formerly the LSST). WORF technology for the HELIOS experiment uses a proven archival media, redesigned, re-purposed and patented by CTech to store digital data for long periods, measured in decades and possibly centuries. The media stores standing waves embedded in a substrate that capture the precise col-ors or wavelengths projected onto the media. The colors represent numerical data, with each data location storing multiple superimposed wavelengths, which facilitate the storage of multiple data bytes (rather than just zeros and ones); advanced mathematical permutations allow for extremely large data density equal to or greater than contemporary data storage de-vices. These colors cannot fade or degrade over time since the standing waves are physically stabilized (fully oxidized) metallic silver; no dyes are embedded for this storage system, and silver ions resist micro-bacterial and fungal contamination
The Planetary Data System (PDS) is an active science data archive managed by scientists for NASA's planetary science community. With the advent of the World Wide Web the majority of the archive has been placed on-line as a science digital libraty for access by scientists, the educational community, and the general public.
The geometric flexibility of additively manufactured metals and ceramics generates a very large and open design space that requires advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to understand the design space, evaluate potential designs, and more efficiently generate optimized results. The Transformational Challenge Reactor (TCR) program is leveraging advances in several scientific areas—including materials, manufacturing, sensors and control systems, data analytics, and high-fidelity modeling and simulation—to accelerate the design, manufacturing, qualification, and deployment of advanced nuclear energy systems. Through a manufacturing-informed design approach, the TCR program seeks to integrate digital data for rapid nuclear innovation; accelerate the adoption of advances in manufacturing, materials, and computational sciences for nuclear applications; and dramatically reduce deployment costs and timelines for new nuclear reactor technologies. This report documents efforts under the TCR program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, which drives the evolution of the cooling channel shapes to minimize temperature peaking and material stress. Outcomes from these activities provide design information and feedback into the core design efforts.
The structure of the turbulence in the mixing region for the first few diameters downstream from the outlet of a circular subsonic jet is characterized at three Mach numbers, 0.3, 0.5, and 0.7, with most of the measurements taken at M = 0.3. Profiles of turbulence intensity showed that downstream of the lip intensity is independent of axial distance, while in the core intensity varies by a factor of eight between the jet outlet and the end of the core. A digital data reduction program was used to calculate the auto- and cross-correlations of axial velocity fluctuations and the power spectral densities. Convection velocities were measured using broadband, hot wire signals and signals that were digitally filtered for band-passes about center frequencies of 0.8, 1.3, 1.6, and 3.2 kHz. The center frequency of 1.3 kHz corresponded to the peak energy in the core spectrum. The results support the hypothesis that the coherent pressure field is driven by the intermittent fluctuations at the core boundary, which in turn are related to the large (low frequency) eddies.
A program is described for obtaining a reliable, parametric set of measurements in the exhaust plumes of solid propellant rocket motors. Plume measurements included pressures, temperatures, forces, heat transfer rates, particle sampling, and high-speed movies. Approximately 210,000 digital data points and 15,000 movie frames were acquired. Measurements were made at points in the plumes via rake-mounted probes, and on the surface of a large plate impinged by the exhaust plume. Parametric variations were made in pressure altitude, propellant aluminum loading, impinged plate incidence angle and distance from nozzle exit to plate or rake. Reliability was incorporated by continual use of repeat runs. The test setup of the various hardware items is described along with an account of test procedures. Test results and data accuracy are discussed. Format of the data presentation is detailed. Complete data are included in the appendix.
The Forest Pest Management Division (FPMD) of the Pennsylvania Bureau of Forestry has the responsibility for conducting annual surveys of the State's forest lands to accurately detect, map, and appraise forest insect infestations. A standardized, timely, and cost-effective method of accurately surveying forests and their condition should enhance the probability of suppressing infestations. The repetitive and synoptic coverage provided by LANDSAT (formerly ERTS) makes such satellite-derived data potentially attractive as a survey medium for monitoring forest insect damage over large areas. Forest Pest Management Division personnel have expressed keen interest in LANDSAT data and have informally cooperated with NASA/Goddard Space Flight Center (GSFC) since 1976 in the development of techniques to facilitate their use. The results of this work indicate that it may be feasible to use LANDSAT digital data to conduct annual surveys of insect defoliation of hardwood forests.
The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its rst two years of operation (July 2014{2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the rst phase began operations in 2000. New in DR14 is the rst public release of data from the extended Baryon Oscillation Sky Survey (eBOSS); the rst data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as The Cannon; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scienti c use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020.
The objective of the program is to develop and engine test hardware and software technologies that will enable active condition monitoring to be implemented on hot gas path turbine blades in large industrial gas turbines. The specific objectives are (1) to fabricate and install Smart Turbine Blades with thermally sprayed sensors and high temperature wireless telemetry systems in a gas turbine engine, (2) to integrate the component engine test data with remaining useful life (RUL) models and develop an approach for networking the component RUL data with Siemens' Power Diagnostics® engine monitoring system. These significant advances carried out in Phase 1 in temperature wide bandgap telemetry, along with new induced power driver and receiver geometry combined with an innovative approach to transmit digital data wirelessly will enable the opportunity to proceed with more technical innovation. The Phase 2 program focused on validation testing of sensor-wireless telemetry package in spin rig and advanced operation-based assessment (OBA) model utilizing artificial intelligence. Significant efforts were dedicated on the download of the technology onto components to be tested an actual gas turbine engine for full realization of active condition monitoring for Smart Turbine Blades.
To provide data with long-term stability and accuracy, the pulse amplitude modulation (PAM) decommutator was synchronized to the PAM-return to zero wavetrain, and each channel was sampled with a common sample and hold circuit and digitized sequentially. The digital value of each channel was then scaled by the digital value of the calibration channels. The corrected digital value of each channel was stored for one complete frame and then transferred to the multiplexer-demultiplexer at a high rate in one block of serial digital data. A test model was built to demonstrate this design approach taken for the PAM decom and performance data was provided. The accuracies obtained with various signal to noise ratios are shown.
A residual gas analyzer (RGA), a device for measuring the amounts and species of various gases present in a vacuum system is discussed. In a recent update of the RGA, it was shown that the use of microprocessors could revolutionize data acquisition and data reduction. This revolution is exemplified by the Inficon 1Q200 RGA which was selected to meet the needs of this update. The Inficon RGA and the Zilog microcomputer were interfaced in order the receive and format the digital data from the RGA. This automated approach is discussed in detail.
The Airborne Infrared Disaster Assessment System (AIRDAS) is a four-channel scanner designed and built at NASA-Ames for the specific task of supporting research and applications on fire impacts on terrestrial and atmospheric processes and also of serving as a vital instrument in the assessment of natural and man-induced disasters. The system has been flown on numerous airframes including the Navajo, King-Air, C0130, and Lear Jet 310 and a 206. The system includes a configuration composed of a 386 PC computer workstation, a non-linear detector amplifier, a sixteen-bit digitizer, dichroic filters, and Exabyte 8500 5Gb Tape output, VHS tape output, a Rockwell GPS and a 2-axis gyro. The AIRDAS system collects digital data in four wavelength regions, which can be filtered: band 1 (0.61-0.68 microns), band 2 (1.57-1.7 microns), band 3 (3.6-5.5 microns), and band 4 (5.5-13.0 microns), an FOV of 108 degrees, an IFOV of 2.62 mrads, and a digitized swath width of 720 pixels. The inclusion of the non-linear detector amplifier allows for the accurate measurement of emitted temperature from fires and hot spots. Lab testing of the scanner has indicated temperature assessments of 800 C without detector saturation. This has advantages over previous systems which were designed for thermal measurement of earth background temperatures, and were ill-equipped for accurate determination of high intensity conditions. The scanner has been flown successfully on data collection missions since 1992 in the western US as well as Brazil. These and other research and applications responses will be presented along with an assessment of future directions with the system.a