An analysis of the ground temperature and reflectivity pattern about St. Louis, Missouri, using HCMM satellite data
Explore the source record for details and available documents.
SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
The Ultraviolet Imaging Telescope (UIT), part of the Astro-1 Spacelab Mission, was used to obtain high-resolution surface brightness distribution data in six ultraviolet wavelength bands for the bright reflection nebula NGC 7023. The corresponding data from the visible leads us to conclude that the scattering in the near and far ultraviolet in this nebula is more strongly forward directed than it is in the visible. The UIT also obtained near-UV and far-UV images of the giant OB association NGC 206 in M32. Photometry was obtained for thirty massive stars. We analyzed UV imagery of two Sb bulges and two E galaxies obtained with UIT. For NGC 1275, the measured UV colors indicate a mass function which extends to eight solar masses but no higher. Other aspects of this investigation are discussed.
This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Engineering Experiment Station, Temple University, and Quanta Technology, LLC. The in-kind support came from OSIsoft (acquired by AVEVA), which provided their PI Historian software to demonstrate the use case of streaming PMU data. The first section of the report describes the project goals and objectives related to the development of Machine Learning (ML) models capable of detecting and classifying events by processing phasor measurements captured in the field by Phasor Measurement Units (PMUs). The data for this study was contributed by the utilities/ISOs from the Western and Eastern interconnects and ERCOT, further referred to as Interconnect B (IC B), Interconnect A (IC A), and Interconnect C (IC C), respectively. The approach that the BDSMART Research Team proposed and the key research tasks defined by the team are outlined in this section. The next section describes the technical approach. We first discuss the data constraints related to the PMU measurements and data interpretation constraints imposed by the data contributors. They provided neither the topological information of the grid nor PMU placement locations and captured recorded data at very few locations in the system with the reporting rate of either 30 or 60 fps. The recordings are mostly positive sequence voltage, frequency, and ROCOF, and in some limited cases, three-phase voltages and currents. We then reflect on the bad data issues that stem from poor recording practices and vague definitions of the PMU status bits to supposedly be used for bad data identification. Finally, the data discovery points to imprecise time stamps with incomplete event start/end time, as well as inconsistent and incomplete event labeling, which combined make the implementation of the data models using supervising learning quite challenging. Following the data discovery study, we hypothesize that because the IC B data has the most complete labels, we should focus our model development on that data and then test it on data from other interconnects. We also define the common metrics used to evaluate the results from the ML algorithm tests. We concluded this section by summarizing the common ML models we used and explaining how we implemented and tested them. The issues from this section are expanded in the Training Dataset Report from this project. The final section of this report deals with the accomplishments and conclusions. As the accomplishments, we formulate the problem we are solving and what is achieved by solving the problem. We then reflect on each of the analytics tools we developed and point out the performance of each tool when applied to solving the mentioned problems. We reference this work for further details to the papers we published on each tool. In the conclusions, we give recommendations on how to improve future PMU recording practices to facilitate the ML algorithm implementation and guidance for the future standardization work aimed at clarifying the ambiguities associated with the PMU status bits. We finally list future tasks that can bring about further improvements in the proposed algorithms. The issues from this section are expanded in the Training, and Test Dataset Report filed at the project completion date.
Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges) in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.
Spacecraft and balloon radiometry data, theories, and mathematical model of reflectance and scattering of solar radiation by earth
We report the results of simultaneous Chandra and RXTE observations of the Seyfert 1 galaxy Mkn 509. We deconvolve the broad and narrow Fe-K emission-line components for which we measure rest-frame equivalent widths of 119+/-18 eV and 57+/-13 eV respectively. The broad line has a FWHM of 57,600((sup 14,400)(sub -21,000)) km/s and the narrow line is unresolved, with an upper limit on the FWHM of 4,940 km/s. Both components must originate in cool matter since we measure rest-frame center energies of 6.36((sup +0.13)(sub -0.12)) keV and 6.42+/-0.01 keV for the broad and narrow line respectively. This rules out He-like and H-like Fe for the origin of both the broad and narrow lines. If, as is widely accepted, the broad Fe-K line originates in Thomson-thick matter (such as an accretion disk), then one expects to observe spectral curvature above approximately 10 keV, (commensurate with the observed broad line), characteristic of the Compton-reflection continuum. However our data sets very stringent limits on deviations of the observed continuum from a power law. Light travel-time delays cannot be invoked to explain anomalies in the relative strengths of the broad Ferry line and Compton-reflection continuum since they are supposed to originate in the same physical location. We are forced to conclude that both the broad and narrow Fe-K lines had to originate in Thomson-thin matter during our observation. This result, for a single observation of just one source, means that our understanding of Fe K line emission and Compton reflection from accreting X-ray sources in general needs to be re-examined. For example, if an irradiated accretion disk existed in Mkn 509 at the time of the observations, the lack of spectral curvature above approximately 10 keV suggests two possibilities. Either the disk was Thomson-thick and highly ionized, having negligible Fe-K line emission and photoelectric absorption or the disk was Thomson-thin producing some or all of the broad Fe-K line emission. In the former case, the broad Fe-K line had to have produced in a Thomson-thin region elsewhere. In both cases the predicted spectral curvature above approximately 10 keV is negligible. An additional implication of our results is that any putative obscuring torus in the system, required by unification models of active galaxies, must also be Thomson-thin. The same applies to the optical broad line region (BLR) if it has a substantial covering factor.
Visible/short-wave infrared spectral data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) show absorptions attributed to hematite at Vera Rubin ridge (VRR), a topographic feature on northwest Mt. Sharp. The goals of this study are to determine why absorptions caused by ferric iron are strongly visible from orbit at VRR, and to improve interpretation of CRISM data throughout lower Mt. Sharp. These goals are achieved by analyzing coordinated CRISM and in situ spectral data along the Curiosity Mars rover’s traverse. VRR bedrock within areas that have the deepest ferric absorptions in CRISM data also have the deepest ferric absorptions measured in situ. This suggests strong ferric absorptions are visible from orbit at VRR because of the unique spectral properties of VRR bedrock. Dust and mixing with basaltic sand additionally inhibit the ability to measure ferric absorptions in bedrock stratigraphically below VRR from orbit. There are two implications of these findings: (1) Ferric absorptions in CRISM data initially dismissed as noise could be real, and ferric phases are more widespread in lower Mt. Sharp than previously reported, (2) Patches with the deepest ferric absorptions in CRISM data are, like VRR, reflective of deeper absorptions in the bedrock. One model to explain this spectral variability is late-stage diagenetic fluids that changed the grain size of ferric phases, deepening absorptions. Curiosity’s experience highlights the strengths of using CRISM data for spectral absorptions and associated mineral detections, and the caveats in using these data for geologic interpretations and strategic path planning tools.
The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) measures reflected light in 224 contiguous spectra bands in the 0.4 to 2.45 micron region of the electromagnetic spectrum. Numerous studies have used these data for mineralogic identification and mapping based on the presence of diagnostic spectral features. Quantitative mapping requires conversion of the AVIRIS data to physical units (usually reflectance) so that analysis results can be compared and validated with field and laboratory measurements. This study evaluated two different AVIRIS calibration techniques to ground reflectance: an empirically-based method and an atmospheric model based method to determine their effects on quantitative scientific analyses. Expert system analysis and linear spectral unmixing were applied to both calibrated data sets to determine the effect of the calibration on the mineral identification and quantitative mapping results. Comparison of the image-map results and image reflectance spectra indicate that the model-based calibrated data can be used with automated mapping techniques to produce accurate maps showing the spatial distribution and abundance of surface mineralogy. This has positive implications for future operational mapping using AVIRIS or similar imaging spectrometer data sets without requiring a priori knowledge.
A LIDQA evaluation for geologic applications of a LANDSAT TM scene covering the Wind River/Bighorn Basin area, Wyoming, is examined. This involves a quantitative assessment of data quality including spatial and spectral characteristics. Analysis is concentrated on the 6 visible, near infrared, and short wavelength infrared bands. Preliminary analysis demonstrates that: (1) principal component images derived from the correlation matrix provide the most useful geologic information. To extract surface spectral reflectance, the TM radiance data must be calibrated. Scatterplots demonstrate that TM data can be calibrated and sensor response is essentially linear. Low instrumental offset and gain settings result in spectral data that do not utilize the full dynamic range of the TM system.
Although many radar profiles and images of the areas within 20 deg of Mercury's equator had been obtained from 1971, at both Goldstone and Arecibo radar facilities, surprisingly little geological analysis had been done with these data until recently. Topographic profiles and radar roughness reflectivity images which can be derived from these data will be crucial in completing the geological mapping of Mercury now underway at the U.S. Geological Survey. Earth based radar observations of Mercury and the other terrestrial planets as well are a potentially very valuable tool in the determination of the physical nature of their surfaces. Processing of available radar data must be completed to establish any systematic relationship between raar reflectivities and roughness, density, dielectric constant, and other related geological parameters.
The aerosol optical depth over the Konza Prairie, near Manhattan, Kansas, was recorded at various locations by five separate teams. These measurements were made in support of the First ISLSCP Field Experiment (FIFE) and used to correct imagery from a variety of satellite and aircraft sensors for the effects of atmospheric scattering and absorption. The results from one instrument are reported here for 26 days in 1987 and for 7 in 1989. Daily averages span a range of 0.05 to 0.28 in the midvisible wavelengths. In addition, diurnal variations are noted in which the afternoon optical depths are greater than those of the morning by as much as 0.07. A comparison between instruments and processing techniques used to determine these aerosol optical depths is provided. The first comparisons are made using summer 1987 data. Differences of as much as 0.05 (midvisible) are observed. Although these data allow reasonable surface reflectance retrievals, they do not agree to within the performance limits typically associated with these types of instruments. With an accuracy goal of 0.02 a preseason calibration/comparison experiment was conducted at a mountain site prior to the final field campaign in 1989. Good calibration data were obtained, and good agreement (0.01, midvisible) was observed in the retrieved optical depth acquired over the Konza. By comparing data from the surface instruments at different locations, spatial inhomogeneities are determined. Then, data from the airborne tracking sunphotometer allow one to determine variations as a function of altitude. Finally, a technique is proposed for using the in situ data to establish an instrument calibration.
Cloud cover is one of the most important variables affecting the earth radiation budget (ERB) and, ultimately, the global climate. The present investigation is concerned with several aspects of the effects of extended cloudiness, taking into account hourly visible and infrared data from the Geostationary Operational Environmental Satelite (GOES). A methodology called the hybrid bispectral threshold method is developed to extract regional cloud amounts at three levels in the atmosphere, effective cloud-top temperatures, clear-sky temperature and cloud and clear-sky visible reflectance characteristics from GOES data. The diurnal variations are examined in low, middle, high, and total cloudiness determined with this methodology for November 1978. The bulk, broadband radiative properties of the resultant cloud and clear-sky data are estimated to determine the possible effect of the diurnal variability of regional cloudiness on the interpretation of ERB measurements.
Over the next three years, the Public Service Company of New Mexico (PNM) plans to increase utility-scale solar photovoltaic (PV) capacity from today’s roughly 330MW to about 1600MW. This massive increase in variable generation—from about 15% to 75% of peak load—will require changes in how PNM operates their system. We characterize the 5 and 30-minute solar and wind forecast errors that the system is likely to experience in order to determine the level of reserves needed to counteract such events. Our focus in this study is on negative forecast error (in other words, shortfalls relative to forecast) – whereas excess variable generation can be curtailed if needed, a shortfall must be compensated for to avoid loss of load. Calculating forecast error requires the use of the same forecasting methods that PNM uses or a reasonable approximation thereof. For wind, we use a persistence forecast on actual 5-minute 2019 wind output data (scaled up to reflect the amount of wind capacity planned for 2025). For solar, we use a formula incorporating the clear sky index (CSI) for the forecast. As the solar on the grid now is a small fraction of what is planned for 2025, we generated 5-minute solar data using 2019 weather inputs. We find that to handle 99.9% of the 5-minute negative forecast errors, a maximum of 275MW of variable generation reserve during daylight hours, and a maximum of 75MW during non-daylight hours, should be sufficient. Note that this variable generation reserve is an additional reserve category that specifies reserves over and above what are currently carried for contingency reserve. This would require a significant increase in reserve relative to what PNM currently carries or can call upon from other utilities per reserve sharing agreements. This variable generation reserve specification may overestimate the actual level needed to deal with PNM’s planned variable generation in 2025. The forecasting methodologies used in this study likely underperform PNM’s forecasting – and better forecasting allows for less reserve. To obtain more precise estimates, it is necessary to consider load and use the same forecasting inputs and methods used by PNM.
This invention provides a method for automatically inspecting the surface of an object, such as an integrated circuit chip, whereby the data obtained by the light reflected from the surface, caused by a scanning light beam, is automatically compared with data representing acceptable values for each unique surface. A signal output provided indicated of acceptance or rejection of the chip. Acceptance is based on predetermined statistical confidence intervals calculated from known good regions of the object being tested, or their representative values. The method can utilize a known good chip, a photographic mask from which the I.C. was fabricated, or a computer stored replica of each pattern being tested.
Long duration space travel will expose astronauts to extended periods of reduced gravity. Since gravity is not present to aid loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize the loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft is limited and therefore compact resistance exercise device prototypes are being developed. The Advanced Resistive Exercise Device (ARED) currently on the ISS is being used as a benchmark for the functional performance of these new devices. Biomechanical data collection and computational modeling aid the device design process by quantifying the joint torques and musculoskeletal forces that occur during exercises performed on the prototype devices. Computational models currently use OpenSim software, an open source code for musculoskeletal modeling, with biomechanical input data from subjects for estimation of muscle and joint loads. Subjects are instrumented with reflective markers for motion capture data collection while exercising on the Hybrid Ultimate Lifting Kit (HULK) prototype device. Ground reaction force data is collected with force plates under the feet and device loading is recorded through load cells internal to the HULK. This data is input into the OpenSim biomechanical model, which has been scaled to match the anthropometrics of the test subject, to calculate the loads on the body. Multiple exercises are performed and evaluated during a test session such as a full squat, single leg squat, heel raise and dead lift. Variables for these exercises include applied device load, narrow or wide foot stance, slow or fast cadence and the harness or long bar interface between the test subject and the device. Data from free weights are compared to the resistively loaded exercise device. The focus of this presentation is to summarize the results from the single-leg squat and heel raise exercises performed during three sessions occurring in 2015. Differences in loading configuration, cadence and stance produce differences in kinematics, joint toques and force and muscle forces.
Seven flightlines of Airborne Imaging Spectrometer (AIS) data were analyzed for an area of hydrothermally altered rocks. The data were reduced to reflectance relative to an average spectrum, and an automated procedure was used to produce a color coded image displaying absorption band information. Individual spectra were extracted from the AIS images to determine the detailed mineralogy. Two alteration types were mapped based upon mineralogy identified using the AIS data. The primary alteration type is quartz sericite pyrite alteration which occurs in northwest-trending zones in quartz monzonite porphyry. The AIS data allow identification of sericite (muscovite) based upon a strong absorption feature near 2.21 micron and weaker absorption features near 2.35 and 2.45 micron. The second alteration type occurs as a zone of argillic alteration associated with a granitic intrusion. Montmorillonite was identified based on a weak to moderate absorption feature near 2.2 micron and the absence of the two absorption features at longer wavelengths characteristic of sericite. Montmorillonite could be identified only where concentrations of sericite did not mask the montmorillonite spectrum.
The increasing number of space missions have resulted in an augmented density of artificial objects positioned in orbital domains near Earth. Knowing the material composition of the resident space object can be of value in object identification and risk assessment using remote sensing techniques. To perform this task, it is increasingly imperative to optically characterize spacecraft materials to identify unique material-specific spectroscopic markers. In this work, a variety of materials frequently utilized by the aerospace industry in spacecraft design and construction were analyzed using reflectance spectroscopy. The collected data provide a spectral characterization baseline for modern-day and historical spacecraft materials. The color index was computed for standard astronomical and suggested theoretical filter passbands and compared to each other in their capability to discriminate materials belonging to different classes. The color index was calculated from reflectance spectra of common spacecraft materials in their pristine, as-received conditions that fell under different family groupings. Different color-index combinations were studied using color-color diagrams. Visual and quantitative analysis of the color-color diagrams were used to evaluate the possibility of discriminating materials from one another by means of optical measurements. Results of the analysis show that polyimide and photovoltaic materials are most easy to discriminate from all other materials via color indices using different filter passbands evaluated in this study.
The paper presents further development in normalized contrast processing used in flash infrared thermography method. Method of computing normalized image or pixel intensity contrast, and normalized temperature contrast are provided. Methods of converting image contrast to temperature contrast and vice versa are provided. Normalized contrast processing in flash thermography is useful in quantitative analysis of flash thermography data including flaw characterization and comparison of experimental results with simulation. Computation of normalized temperature contrast involves use of flash thermography data acquisition set-up with high reflectivity foil and high emissivity tape such that the foil, tape and test object are imaged simultaneously. Methods of assessing other quantitative parameters such as emissivity of object, afterglow heat flux, reflection temperature change and surface temperature during flash thermography are also provided. Temperature imaging and normalized temperature contrast processing provide certain advantages over normalized image contrast processing by reducing effect of reflected energy in images and measurements, therefore providing better quantitative data. Examples of incorporating afterglow heat-flux and reflection temperature evolution in flash thermography simulation are also discussed.