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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Multivariate normality

Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

Crutcher, H. L.↗
NASA NTRS1983

Amplitude calibration of spaceborne synthetic aperture radars

Problems encountered during attempts to calibrate SAR imagery, recent successful experiments conducted with SEASAT SAR data, and a proposed program for the calibration and validation of the radar imagery from the forthcoming SIR-B SAR are discussed. The SEASAT SAR data for 10 passes over Death valley, California, were processed with a modified digital correlator. The procedure included a preliminary screening of the data to check for raw data saturation, compensation of waveforms and estimation of the amplitude of the pilot tone. All data was normalized to this pilot tone signal to reduce the effects of variable gains in the data links and ground receivers. The digital correlation algorithm generated image data. Evaluation of 6 passes results in a maximum pass to pass gain variation of only 1.1 dB and a standard deviation amongst the passes of 0.35 dB.

Held, D. N.↗
NASA NTRS1983

Amplitude calibration of spaceborne synthetic aperture radars

Problems encountered during attempts to calibrate SAR imagery, recent successful experiments conducted with SEASAT SAR data, and a proposed program for the calibration and validation of the radar imagery from the forthcoming SIR-B SAR are discussed. The SEASAT SAR data for 10 passes over Death Valley, California, were processed with a modified digital correlator. The procedure included a preliminary screening of the data to check for raw data saturation, compensation of waveforms and estimation of the amplitude of the pilot tone. All data was normalized to this pilot tone signal to reduce the effects of variable gains in the data links and ground receivers. The digital correlation algorithm generated image data. Evaluation of 6 passes results in a maximum pass to pass gain variation of only 1.1 dB and a standard deviation amongst the passes of 0.35 dB. previously announced in STAR as N83-26215

Held, D. N.↗
NASA NTRS1987

Two-phase reduced gravity experiments for a space reactor design

Future space missions researchers envision using large nuclear reactors with either a single or a two-phase alkali-metal working fluid. The design and analysis of such reactors require state-of-the-art computer codes that can properly treat alkali-metal flow and heat transfer in a reduced-gravity environment. New flow regime maps, models, and correlations are required if the codes are to be successfully applied to reduced-gravity flow and heat transfer. General plans are put forth for the reduced-gravity experiments which will have to be performed, at NASA facilities, with benign fluids. Data from the reduced-gravity experiments with innocuous fluids are to be combined with normal gravity data from two-phase alkali-metal experiments. Because these reduced-gravity experiments will be very basic, and will employ small test loops of simple geometry, a large measure of commonality exists between them and experiments planned by other organizations. It is recommended that a committee be formed to coordinate all ongoing and planned reduced gravity flow experiments.

Antoniak, Zenen I.↗
NASA NTRS1979

Extreme Mean and Its Applications

Extreme value statistics obtained from normally distributed data are considered. An extreme mean is defined as the mean of p-th probability truncated normal distribution. An unbiased estimate of this extreme mean and its large sample distribution are derived. The distribution of this estimate even for very large samples is found to be nonnormal. Further, as the sample size increases, the variance of the unbiased estimate converges to the Cramer-Rao lower bound. The computer program used to obtain the density and distribution functions of the standardized unbiased estimate, and the confidence intervals of the extreme mean for any data are included for ready application. An example is included to demonstrate the usefulness of extreme mean application.

Swaroop, R.↗
NASA NTRS1974

Reflection of a thick planar shock wave from a coplanar surface

The reflection of a thick shock wave from an adsorbing surface has been studied for argon and neon test gases using the direct simulation Monte Carlo method and laboratory shock-tube experiments. In the simulation, collisions between gas atoms were calculated with a point-center repulsive intermolecular potential, and collisions between gas atoms and the surface were described by a simple two-parameter model which includes the effects of adsorption and thermal accommodation. Experimental data included normal stress and temperature time histories on the surface of a shock-tube and wall. These data and published data of density variations near the end wall were compared with simulation results to assess the influence of adsorption and accommodation on the flowfield and the surface and also to infer optimum values for the surface-interaction model parameters. Good quantitative agreement between simulation and laboratory data was found with this simple surface-interaction model.

Deiwert, G. S.↗
NASA NTRS1976

Flight-test measurement of the noise reduction of a jet transport delayed flap approach procedure

A delayed flap approach procedure was flight tested using the NASA CV-990 airplane to measure and analyze the noise produced beneath the flight path. Three other types of landing approaches were also flight tested to provide a comparison of the noise reduction benefits to the delayed flap approach. The conventional type of approach was used as a baseline to compare the effectiveness of the other approaches. The decelerating approach is a variation of the delayed flap approach. A detailed comparison of the ground perceived noise generated during the approaches is presented. For this comparison, the measured noise data were normalized to compensate for variations in aircraft weight and winds that occurred during the flight tests. The data show that the reduced flap approach offers some noise reduction, while the delayed flap and decelerating approaches offer significant noise reductions over the conventional approach.

Foster, J. D.↗
NASA NTRS1991

Maintaining consistency in distributed systems

In systems designed as assemblies of independently developed components, concurrent access to data or data structures normally arises within individual programs, and is controlled using mutual exclusion constructs, such as semaphores and monitors. Where data is persistent and/or sets of operation are related to one another, transactions or linearizability may be more appropriate. Systems that incorporate cooperative styles of distributed execution often replicate or distribute data within groups of components. In these cases, group oriented consistency properties must be maintained, and tools based on the virtual synchrony execution model greatly simplify the task confronting an application developer. All three styles of distributed computing are likely to be seen in future systems - often, within the same application. This leads us to propose an integrated approach that permits applications that use virtual synchrony with concurrent objects that respect a linearizability constraint, and vice versa. Transactional subsystems are treated as a special case of linearizability.

Birman, Kenneth P.↗
DOE OSTI2025

COMPASS-FME Synoptic Sites Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-0 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-1 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024 This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Identification of wood energy resources in central Michigan

Existing biomass studies were compiled for determining their applicability in measuring forest biomass in an entirely new way. Over sixty tree-weight tables were prepared from existing tables or formulas. An estimate of forest biomass was made on a defined area by using Landsat Satellite data analysis, existing forest cover type maps and actual weighting of the entire biomass. Control plots were cruised for normal volume data and weight data, harvested and weighed to determine actual tonnage yields.

Hudson, W. D.↗
NASA NTRS2013

Evaluation of Delamination Onset and Growth Characterization Methods under Mode I Fatigue Loading

Double-cantilevered beam specimens of IM7/8552 graphite/epoxy from two different manufacturers were tested in static and fatigue to compare the material characterization data and to evaluate a proposed ASTM standard for generating Paris Law equations for delamination growth. Static results were used to generate compliance calibration constants for reducing the fatigue data, and a delamination resistance curve, GIR, for each material. Specimens were tested in fatigue at different initial cyclic GImax levels to determine a delamination onset curve and the delamination growth rate. The delamination onset curve equations were similar for the two sources. Delamination growth rate was calculated by plotting da/dN versus GImax on a log-log scale and fitting a Paris Law. Two different data reduction methods were used to calculate da/dN. To determine the effects of fiber-bridging, growth results were normalized by the delamination resistance curves. Paris Law exponents decreased by 31% to 37% after normalizing the data. Visual data records from the fatigue tests were used to calculate individual compliance constants from the fatigue data. The resulting da/dN versus GImax plots showed improved repeatability for each source, compared to using averaged static data. The Paris Law expressions for the two sources showed the closest agreement using the individually fit compliance data.

Murri, Gretchen B.↗
NASA NTRS1979

Covariance analyses of satellite-derived mesoscale wind fields

Statistical structure functions have been computed independently for nine satellite-derived mesoscale wind fields that were obtained on two different days. Small cumulus clouds were tracked at 5 min intervals, but since these clouds occurred primarily in the warm sectors of midlatitude cyclones the results cannot be considered representative of the circulations within cyclones in general. The field structure varied considerably with time and was especially affected if mesoscale features were observed. The wind fields on the 2 days studied were highly anisotropic with large gradients in structure occurring approximately normal to the mean flow. Structure function calculations for the combined set of satellite winds were used to estimate random error present in the fields. It is concluded for these data that the random error in vector winds derived from cumulus cloud tracking using high-frequency satellite data is less than 1.75 m/s. Spatial correlation functions were also computed for the nine data sets. Normalized correlation functions were considerably different for u and v components and decreased rapidly as data point separation increased for both components. The correlation functions for transverse and longitudinal components decreased less rapidly as data point separation increased.

Maddox, R. A.↗
NASA NTRS2001

The AutoBayes Program Synthesis System: System Description

AUTOBAYES is a fully automatic program synthesis system for the statistical data analysis domain. Its input is a concise description of a data analysis problem in the form of a statistical model; its output is optimized and fully documented C/C++ code which can be linked dynamically into the Matlab and Octave environments. AUTOBAYES synthesizes code by a schema-guided deductive process. Schemas (i.e., code templates with associated semantic constraints) are applied to the original problem and recursively to emerging subproblems. AUTOBAYES complements this approach by symbolic computation to derive closed-form solutions whenever possible. In this paper, we concentrate on the interaction between the symbolic computations and the deductive synthesis process. A statistical model specifies for each problem variable (i.e., data or parameter) its properties and dependencies in the form of a probability distribution, A typical data analysis task is to estimate the best possible parameter values from the given observations or measurements. The following example models normal-distributed data but takes prior information (e.g., from previous experiments) on the data's mean value and variance into account.

Fischer, Bernd↗
NASA NTRS1978

Nitrogen tetroxide scrubber data analysis

A major difficulty in the analysis of scrubber data is that of separating the physical effects, such as mass transfer, from the physico-chemical effects, such as reaction rates. This is especially true for the absorbtion of nitrogen tetroxide in the various liquids that were tested in the NASA-Kennedy Space Center Hypergolic Toxic Scrubber Program. A fruitful approach to correlating the data for outlet concentrations was to treat the overall absorbtion as a pseudo first-order absorbtion equation. This approach provided a method for normalizing the data to constant inlet concentration, constant sump liquor condition, and constant scrubbing time, and permitted evaluation of the test and fluid parameters that affected both absorbtion rate and scrubbing time. The analysis indicated that scrubber performance may be improved by optimizing liquor concentrations and liquor flowrate distributions.

Simon, E. D.↗
NASA NTRS1991

Chemical vapor deposition coatings for oxidation protection of titanium alloys

Results of an experimental investigation of the oxidation protection afforded to Ti-14Al-21Nb and Ti-14Al-23Nb-2V titanium aluminides and Ti-17Mo-3Al-3Nb titanium alloy by aluminum-boron-silicon and boron-silicon coatings are presented. These coatings are applied by a combination of physical vapor deposition (PVD) and chemical vapor deposition (CVD) processes. The former is for the application of aluminum, and the latter is for codeposition of boron and silicon. Coating thickness is in the range of 2 to 7 microns, and coating weights are 0.6 to 2.0 mg/sq cm. Oxidation testing was performed in air at temperatures to 1255 K in both static and hypersonic flow environments. The degree of oxidation protection provided by the coatings is determined from weight change measurements made during the testing and post test compositional analyses. Temperature-dependent total normal emittance data are also presented for four coating/substrate combinations. Both types of coatings provided excellent oxidation protection for the exposure conditions of this investigation. Total normal emittances were greater than 0.80 in all cases.

Cunnington, G. R.↗
DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗