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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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An abstracted bibliography of 59 articles addressing the analysis of X-ray observations from the UHURU X-Ray Observatory (SAS-1) is presented. Investigations of galactic and extragalactic X-ray sources, X-ray binaries, and transient and burst phenomena are highlighted.
The Midwestern astronomical data reduction and analysis facility (MADRAF) PDS microdensitometer which was the first astronomical PDS which was provided with a Motorola 6800 microprocessor control/interface and will be interfaced to a 32-bit instruction computer operating in a multiuser environment is outlined. Command and response handshaking are handled on an RS 232 serial line while data transfer occurs on a dedicated Direct Memory Access line. This set of conditions necessitated the creation of a specialized device for the system. The general outline of this software is discussed. The availability of an extraordinarily powerful computer opened the possibility of a user operating program of great versatility with diverse data destinations and formats, automatic documentation, and an extensive set of utilities which facilitate operations.
A major obstacle in evaluating the residual acceleration environment in an orbiting space laboratory is the amount of data collected during a given mission: gigabytes of data will be available as SAMS units begin to fly regularly. Investigators taking advantage of the reduced gravity conditions of space should not be overwhelmed by the accelerometer data which describe these conditions. We are therefore developing a data reduction and analysis plan that will allow principal investigators of low-g experiments to create experiment-specific residual acceleration data bases for post-flight analysis. The basic aspects of the plan can also be used to characterize the acceleration environment of earth orbiting laboratories. Our development of the reduction plan is based on the following program of research: the identification of experiment sensitivities by order of magnitude estimates and numerical modelling; evaluation of various signal processing techniques appropriate for the reduction, supplementation, and dissemination of residual acceleration data; and testing and implementation of the plan on existing acceleration data bases. The orientation of the residual acceleration vector with respect to some set of coordinate axes is important for experiments with known directional sensitivity. Orientation information can be obtained from the evaluation of direction cosines. Fourier analysis is commonly used to transform time history data into the frequency domain. Common spectral representations are the amplitude spectrum which gives the average of the components of the time series at each frequency and the power spectral density which indicates the power or energy present in the series per unit frequency interval. The data reduction and analysis scheme developed involves a two tiered structure to: (1) identify experiment characteristics and mission events that can be used to limit the amount of accelerator data an investigator should be interested in; and (2) process the data in a way that will be meaningful to the experiment objectives. A general outline of the plan is given.
Bistatic-radar data reduction and Explorer 35 reflectivity and bandwidth tables with polarization spectra
The PISCES (Prototype Imaging Spectrograph for Coronagraphic Exoplanet Studies) is a lenslet array based integral field spectrograph (IFS) designed to advance the technology readiness of the WFIRST (Wide Field Infrared Survey Telescope)-AFTA (Astrophysics Focused Telescope Assets) high contrast Coronagraph Instrument. We present the end to end optical simulator and plans for the data reduction pipeline (DRP). The optical simulator was created with a combination of the IDL (Interactive Data Language)-based PROPER (optical propagation) library and Zemax (a MatLab script), while the data reduction pipeline is a modified version of the Gemini Planet Imager's (GPI) IDL pipeline. The simulations of the propagation of light through the instrument are based on Fourier transform algorithms. The DRP enables transformation of the PISCES IFS data to calibrated spectral data cubes.
We propose the development of an expert system tool for the management and reduction of complex datasets. the proposed work is an extension of a successful prototype system for the calibration of CCD (charge coupled device) images developed by Dr. Johnston in 1987. (ref.: Proceedings of the Goddard Conference on Space Applications of Artificial Intelligence). The reduction of complex multi-parameter data sets presents severe challenges to a scientist. Not only must a particular data analysis system be mastered, (e.g. IRAF/SDAS/MIDAS), large amounts of data can require many days of tedious work and supervision by the scientist for even the most straightforward reductions. The proposed Expert Data Reduction Assistant will help the scientist overcome these obstacles by developing a reduction plan based on the data at hand and producing a script for the reduction of the data in a target common language.
A detailed discussion of the application of a previously method to determine vehicle flight attitude using a single camera onboard the vehicle is presented with emphasis on the digital computer program format and data reduction techniques. Application requirements include film and earth-related coordinates of at least two landmarks (or features), location of the flight vehicle with respect to the earth, and camera characteristics. Included in this report are a detailed discussion of the program input and output format, a computer program listing, a discussion of modifications made to the initial method, a step-by-step basic data reduction procedure, and several example applications. The computer program is written in FORTRAN 4 language for the Control Data 6000 series digital computer.
This report documents the development and deployment of the Data Reduction Expert Assistant (DRACO). The system was successfully applied to two astronomical research projects. The first was the removal of cosmic ray artifacts from Hubble Space Telescope (HST) Wide Field Planetary Camera data. The second was the reduction and calibration of low-dispersion CCD spectra taken from a ground-based telescope. This has validated our basic approach and demonstrated the applicability of this technology. This work has been made available to the scientific community in two ways. First, we have published the work in the scientific literature and presented papers at relevant conferences. Secondly, we have made the entire system (including documentation and source code) available to the community via the World Wide Web.
Neurophysiological and cardiovascular data collection during flights, instant analysis and reduction of data, and pattern and correlation analysis
Boundary layer and wake survey data reduction techniques in compressible flows
Computerized data reduction techniques for obtaining local electron temperature variations in plasma beams produced by magnetoplasmadynamic accelerators
The Large Binocular Telescope Interferometer (LBTI) is a versatile instrument designed for high angular resolution and high-contrast infrared imaging (1.5-13 micrometers). In this paper, we focus on the mid-infrared (8-13 micrometers) nulling mode and present its theory of operation, data reduction, and on-sky performance as of the end of the commissioning phase in 2015 March. With an interferometric baseline of 14.4 m, the LBTI nuller is specifically tuned to resolve the habitable zone of nearby main-sequence stars, where warm exozodiacal dust emission peaks. Measuring the exozodi luminosity function of nearby main-sequence stars is a key milestone to prepare for future exo-Earth direct imaging instruments. Thanks to recent progress in wavefront control and phase stabilization, as well as in data reduction techniques, the LBTI demonstrated in 2015 February a calibrated null accuracy of 0.05% over a 3 hr long observing sequence on the bright nearby A3V star Beta Leo. This is equivalent to an exozodiacal disk density of 15-30 zodi for a Sun-like star located at 10 pc, depending on the adopted disk model. This result sets a new record for high-contrast mid-infrared interferometric imaging and opens a new window on the study of planetary systems.
This paper presents a digital processing system that produces the Seasat synthetic-aperture radar (SAR) imagery. The system consists of a SEL 32/77 host minicomputer and three AP-120B array processors. The partitioning of the SAR processing functions and the design of software modules is described. The rationale for selecting the parallel array processor architecture and the methodology for developing the parallel processing scheme on this system is described. This system attains a Seasat SAR data reduction speed of 2.5 h per 25-m resolution 4-look and 100 km x 100 km image frame. A preliminary performance evaluation of this parallel processing system and potential future applications for remote sensing data reduction are described.
Since 1984 Marshall Space Flight Center was actively engaged in research and development concerning autonomous power systems. Much of the work in this domain has dealt with the development and application of knowledge-based or expert systems to perform tasks previously accomplished only through intensive human involvement. One such task is the health status monitoring of electrical power systems. Such monitoring is a manpower intensive task which is vital to mission success. The Hubble Space Telescope testbed and its associated Nickel Cadmium Battery Expert System (NICBES) were designated as the system on which the initial proof of concept for intelligent power system monitoing will be established. The key function performed by an engineer engaged in system monitoring is to analyze the raw telemetry data and identify from the whole only those elements which can be considered significant. This function requires engineering expertise on the functionality of the system, the mode of operation and the efficient and effective reading of the telemetry data. Application of this expertise to extract the significant components of the data is referred to as data reduction. Such a function possesses characteristics which make it a prime candidate for the application of knowledge-based systems' technologies. Such applications are investigated and recommendations are offered for the development of intelligent data reduction systems.
Experience with fiber-optic spectrometers has demonstrated improvements in observing efficiency for clusters of 30 or more objects that must in turn be matched by data reduction capability increases. The Medusa Automatic Reduction System reduces data generated by multiobject spectrometers in the form of two-dimensional images containing 44 to 66 individual spectra, using both software and hardware improvements to efficiently extract the one-dimensional spectra. Attention is given to the ridge-finding algorithm for automatic location of the spectra in the CCD frame. A simultaneous extraction of calibration frames allows an automatic wavelength calibration routine to determine dispersion curves, and both line measurements and cross-correlation techniques are used to determine galaxy redshifts.
The computational procedures that are involved in exhaust emissions data reduction and the use of these computational procedures for determining the quality of the data that is obtained from exhaust measurements were considered. Four problem areas were calculated: (1) the various methods for performing the carbon balance, (2) the method for calculating water correction factors, (3) the method for calculating the exhaust molecular weight, and (4) assessing the quality of the data.
Automatic carpet plotting applicability to reduction of wind tunnel data
The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.