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Results of research on the use of convolutional codes in data communications are presented. Convolutional coding fundamentals are discussed along with modulation and coding interaction. Concatenated coding systems and data compression with convolutional codes are described.
Reproducibility of scientific research relies on accurate and precise citation of data and the provenance of that data. Earth science data are often the result of applying complex data transformation and analysis workflows to vast quantities of data. Provenance information of data processing is used for a variety of purposes, including understanding the process and auditing as well as reproducibility. Certain provenance information is essential for producing scientifically equivalent data. Capturing and representing that provenance information and assigning identifiers suitable for precisely distinguishing data granules and datasets is needed for accurate comparisons. This paper discusses scientific equivalence and essential provenance for scientific reproducibility. We use the example of an operational earth science data processing system to illustrate the application of the technique of cascading digital signatures or hash chains to precisely identify sets of granules and as provenance equivalence identifiers to distinguish data made in an an equivalent manner.
A sophisticated, multi-channel computerized data acquisition and processing system was developed at the NASA LeRC for use in noise experiments. This technology, which is available for transfer to industry, provides a convenient, cost-effective alternative to analog tape recording for high frequency acoustic measurements. This system provides 32-channel acquisition of microphone signals with an analysis bandwidth up to 100 kHz per channel. Cost was minimized through the use of off-the-shelf components. Requirements to allow for future expansion were met by choosing equipment which adheres to established industry standards for hardware and software. Data processing capabilities include narrow band and 1/3 octave spectral analysis, compensation for microphone frequency response/directivity, and correction of acoustic data to standard day conditions. The system was used successfully in a major wind tunnel test program at NASA LeRC to acquire and analyze jet noise data in support of the High Speed Civil Transport (HSCT) program.
Providing web-based data of complex and sensitive astromaterials (including meteorites and lunar samples) in novel formats enhances existing preliminary examination data on these samples and supports targeted sample requests and analyses. We have developed and tested a rigorous protocol for collecting highly detailed imagery of meteorites and complex lunar samples in non-contaminating environments. These data are reduced to create interactive 3D models of the samples. We intend to provide these data as they are acquired on NASA's Astromaterials Acquisition and Curation website at http://curator.jsc.nasa.gov/.
The Second Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on May 6, 7, and 8, 1986. It was attended by 100 people from seven countries. Papers were presented by 24 attendees; summaries of 19 of the papers are published in these Workshop Proceedings. The Second Workshop was divided into three sessions: Calibration, the Atmosphere, and Data Techniques; Geological Research; and Botanical and Geobotanical Research. Because many of the AIS researchers have had an additional year to work with their data, a general advance was seen in the utilization of the data for solving earth science problems. The major conclusions that can be drawn from the Workshop are: (1) Almost everyone now working with AIS data is attempting to take into account in data analysis the effects of the atmosphere and sensor calibration and performance. As a result, information extraction techniques are becoming more sophisticated and effective. (2) The emphasis on work in the geological disciplines is beginning to shift to the application of AIS data for solving geological problems now that the efficacy of the data for mineral identification has been demonstrated. (3) In the botanical sciences, interpretation of AIS data is becoming more quantitative. Several statistical techniques are under development for extracting the subtle differences in plant spectra in the region of the near infrared and short wavelength infrared. In summary, an overall advance was seen in the ability to effectively utilize the fundamentally new class of data acquired with imaging spectrometers. There was also a general feeling at the Workshop however, that much work remains to be done in remote sensing research before the maximum potential of these data for solving earth science problems is realized.
Tracking and data acquisition networks and communications systems for support of space missions, studying data processing, unified S Band System and Data Relay Satellite System
Noise measurement data on various types of blown flap configurations for STOL aircraft, discussing integration effects between jet and flap assembly
Recent research on filter synthesis is summarized, and the implications in optical computation, image processing, image restoration, and image detection are examined.
Data of 15 years of space flight and laboratory tests of plant seeds of 20 species, mainly on the combined and separate effects of launch vibration, ionizing radiation and weightlessness, are surveyed. It is concluded that plants do not show a pronounced response to space flight factors. Conditions of return to Earth, the number of heavy cosmic ray particles striking biological targets and effects of change in magnetic an electromagnetic fields have been little studied, and that more study of growing plants in space is needed.
The design of the facility is described in terms of its use as an investigation tool for evaluating crop growth in space with reference to required emerging technologies. NASA's CELSS Test Facility (CTF) is designed to permit the measurement of crop-plant productivity under microgravity conditions including biomass production, food production, water transpiration, and O2/CO2 exchanges. Crucial hardware tests and qualifications are identified to assure the operation of CTF technologies in space including the nutrient-delivery, water-condensation, and gas-liquid-mixing subsystems. The design concept and related scientific requirements are described and shown to provide microgravity crop research. The CTF is expected to provide data for plant research and for concepts for bioregenerative life-support systems for applications to Martian, lunar, and space-station missions.
The proposed research is to use the DOE ARM ground-based measurements and retrievals as the ground-truth references for validating satellite cloud results and retrieving algorithms. This validation effort includes four different ways: (1) cloud properties on different satellites, therefore different sensors, TRMM VIRS and TERRA MODIS; (2) cloud properties at different climatic regions, such as DOE ARM SGP, NSA, and TWP sites; (3) different cloud types, low and high level cloud properties; and (4) day and night retrieving algorithms. Validation of satellite-retrieved cloud properties is very difficult and a long-term effort because of significant spatial and temporal differences between the surface and satellite observing platforms. The ground-based measurements and retrievals, only carefully analyzed and validated, can provide a baseline for estimating errors in the satellite products. Even though the validation effort is so difficult, a significant progress has been made during the proposed study period, and the major accomplishments are summarized in the follow.
For nearly a century Ludwig Prandtl's lifting-line theory remains a standard tool for understanding and analyzing aircraft wings. The tool, said Prandtl, initially points to the elliptical spanload as the most efficient wing choice, and it, too, has become the standard in aviation. Having no other model, avian researchers have used the elliptical spanload virtually since its introduction. Yet over the last half-century, research in bird flight has generated increasing data incongruous with the elliptical spanload. In 1933 Prandtl published a little-known paper presenting a superior spanload: any other solution produces greater drag. We argue that this second spanload is the correct model for bird flight data. Based on research we present a unifying theory for superior efficiency and coordinated control in a single solution. Specifically, Prandtl's second spanload offers the only solution to three aspects of bird flight: how birds are able to turn and maneuver without a vertical tail; why birds fly in formation with their wingtips overlapped; and why narrow wingtips do not result in wingtip stall. We performed research using two experimental aircraft designed in accordance with the fundamentals of Prandtl's second paper, but applying recent developments, to validate the various potentials of the new spanload, to wit: as an alternative for avian researchers, to demonstrate the concept of proverse yaw, and to offer a new method of aircraft control and efficiency.
Acoustic data obtained using a small turbofan engine are further analyzed with the aim to ultimately enable improved prediction methods and mitigation techniques for turbofan core/combustor noise. The relative impact of this propulsion-noise source for civilian-transport aircraft on airport community noise is expected to significantly increase in the future. Simultaneous high-data-rate acoustic measurements acquired using a circumferential infinite-tube-probe array at the core-nozzle exit in conjunction with a far-field microphone array are processed. The test matrix contains engine operational points from idle to engine-maximum setting, with measurements repeated for different circumferential array configurations, as well as for redundancy. The combustor-noise contribution to the far-field noise signature is obtained using an advanced source-separation method that correlates far-field microphone measurements with a modal decomposition of the unsteady pressure field at the core-nozzle exit. The advantages of the present approach compared to the classical two-signal source-separation method are discussed.
The One Year Mission (1YM) by two astronauts on the International Space Station (ISS), starting in March 2015, offers a unique opportunity to expand multilateral collaboration by sharing data and resources among the partner agencies in preparation for planned space exploration missions beyond low Earth orbit. Agreements and protocols will be established for the collection, distribution, analysis and reporting of both research and clinical data. Data will be shared between the agencies sponsoring the investigators, and between the research and clinical medicine communities where common interests are identified. The assignment of only two astronauts, one Russian and the other American, to the 1YM necessitated creativity in bilateral efforts to maximize the biomedical return from the opportunity. Addition of Canadian, European and Japanese investigations make the effort even more integrative. There will be three types of investigations: joint, cross‐participation and data‐exchange. The joint investigations have US and Russian coprincipal investigators, and the data acquired will be their common responsibility. The other two types must develop data sharing agreements and processes specific to their needs. A multilateral panel of ISS partner space agencies will develop policies for international exchange of scientific information to meet their science objectives and priorities. They will promote archiving of space flight data and will inform each other and the scientific community at large about the results obtained from space life sciences studies. Integration tasks for the 1YM are based on current experience from the ISS and previous efforts on the Russian space station Mir. Closer coordination between international partners requires more common approaches to remove barriers to multilateral resource utilization on the ISS. Greater integration in implementation should increase utilization efficiency to benefit all participants in spaceflight human research. This presentation will describe the overarching principles for multilateral data collection, analysis and sharing and for data security for medical and research data shared between ISS partners prior to release in public forums.
A difficulty for many NASA researchers is that often the data to analyze is located remotely from the scientist and the data is too large to transfer for local analysis. Researchers have developed the Data Access Protocol (DAP) for accessing remote data. Presently one can use DAP from within IDL, but the IDL-DAP interface is both limited and cumbersome. A more powerful and user-friendly interface to DAP for IDL has been developed. Users are able to browse remote data sets graphically, select partial data to retrieve, import that data and make customized plots, and have an interactive IDL command line session simultaneous with the remote visualization. All of these IDL-DAP tools are usable easily and seamlessly for any IDL user. IDL and DAP are both widely used in science, but were not easily used together. The IDL DAP bindings were incomplete and had numerous bugs that prevented their serious use. For example, the existing bindings did not read DAP Grid data, which is the organization of nearly all NASA datasets currently served via DAP. This project uniquely provides a fully featured, user-friendly interface to DAP from IDL, both from the command line and a GUI application. The DAP Explorer GUI application makes browsing a dataset more user-friendly, while also providing the capability to run user-defined functions on specified data. Methods for running remote functions on the DAP server were investigated, and a technique for accomplishing this task was decided upon.
In 2014, a team of researchers, engineers and information technology specialists at NASA Langley Research Center developed a Big Data Analytics and Machine Intelligence Strategy and Roadmap as part of Langley's Comprehensive Digital Transformation Initiative, with the goal of identifying the goals, objectives, initiatives, and recommendations need to develop near-, mid- and long-term capabilities for data analytics and machine intelligence in aerospace domains. Since that time, significant progress has been made in developing pilots and projects in several research, engineering, and scientific domains by following the original strategy of collaboration between mission support organizations, mission organizations, and external partners from universities and industry. This report summarizes the work to date in Data Intensive Scientific Discovery, Deep Content Analytics, and Deep Q&A projects, as well as the progress made in collaboration, outreach, and education. Recommendations for continuing this success into future phases of the initiative are also made.
High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.
An amateur packet radio system operating in the very high frequency (VHF) range has been implemented in the Transport Systems Research Vehicle at the NASA Langley Research Center to provide an economical, bidirectional, real-time, ground-to-air data link. The packet system has been used to support flight research involving air traffic control (ATC), differential global positioning systems (DGPS), and windshear terminal doppler weather radar (TDWR). A data maximum rate of 2400 baud was used. Operational reliability of the packet system has been very good. Also, its versatility permits numerous specific configurations. These features, plus its low cost, have rendered it very satisfactory for support of data link flight experiments that do not require high data transfer rates.