POWER DATA ANOMALY DETECTION PACKAGE
SF-25-150 Software package for developing anomaly detection models for electric power operational data
PLATHOTTAM, SIBY JOSE [Argonne National Laboratory↗
SEARCH · Engineering Papers
Results for “Data Packages”
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.
ftmsRanalysis: An R package for exploratory data analysis and interactive visualization of FT-MS data
The high-resolution and mass accuracy of Fourier transform mass spectrometry (FT-MS) has made it an increasingly popular technique for discerning the composition of soil, plant and aquatic samples containing complex mixtures of proteins, carbohydrates, lipids, lignins, hydrocarbons, phytochemicals and other compounds. Thus, there is a growing demand for informatics tools to analyze FT-MS data that will aid investigators seeking to understand the availability of carbon compounds to biotic and abiotic oxidation and to compare fundamental chemical properties of complex samples across groups. We present ftmsRanalysis, an R package which provides an extensive collection of data formatting and processing, filtering, visualization, and sample and group comparison functionalities. The package provides a suite of plotting methods and enables expedient, flexible and interactive visualization of complex datasets through functions which link to a powerful and interactive visualization user interface, Trelliscope. Example analysis using FT-MS data from a soil microbiology study demonstrates the core functionality of the package and highlights the capabilities for producing interactive visualizations.
97 MATHEMATICS AND COMPUTING↗
The Application of Modern Optimization Packages in Multisensor Data Assimilation
PyDDA is an expandable framework that integrates data from weather radars and forecasting models using SciPy’s optimization package to create meteorological fields.
Radar↗
A three-dimensional potential-flow program with a geometry package for input data generation
Information needed to run a computer program for the calculation of the potential flow about arbitrary three dimensional lifting configurations is presented. The program contains a geometry package which greatly reduces the task of preparing the input data. Starting from a very sparse set of coordinate data, the program automatically augments and redistributes the coordinates, calculates curves of intersection between components, and redistributes coordinates in the regions adjacent to the intersection curves in a suitable manner for use in the potential flow calculations. A brief summary of the program capabilities and options is given, as well as detailed instructions for the data input, a suggested structure for the program overlay, and the output for two test cases.
Halsey, N. D.↗
Rarefied aerodynamics and upper atmospheric flight results from the Orbiter High Resolution Accelerometer Package experiment
Data from the High Resolution Accelerometer Package (HiRAP) experiment on the Space Shuttle Orbiter have been analyzed for 10 flights to produce lift-drag (L/D) ratios in the free molecule, transition, and continuum flow flight regimes. Freestream density is also obtained in the altitude range from 60 km to 160 km using an aerodynamic model based upon earlier flight analysis of accelerometer data. The results of the L/D analysis of the latest four flights have been compared with the aerodynamic model developed from the first six HiRAP missions. The new data agree with the established flight-derived model, thereby validating earlier analysis. However, the arithmetic mean of the 10 flight-derived L/D values in the free molecular flow regime is 0.053 (+ or - 0.1) as compared to the initial value of 0.10 (+ or - 0.3), determined from STS-6. Updates to the Orbiter aerodynamic model values and calibration factors have been incorporated into the density calculations for all 10 flights to provide realistic absolute density. The derived density altitude profiles, which have been normalized to the U.S. Standard Atmosphere 1976, are characterized by a large amplitude wave pattern relative to the standard as seen in the initial flight analysis.
Blanchard, R. C.↗
Skylab program earth resources experiment package: Ground truth data for test sites (SL-2)
Field measurements were performed at selected ground sites in order to provide comparative calibration measurements of sensors for the Earth Resources Experiment Package. Specifically, the solar radiation (400 to 1300 namometers) and thermal radiation (8-14 micrometers) were measured. Sites employed for the thermal measurements consisted of warm and cold water lakes. The thermal brightness temperature of the lake water, the temperature and humidity profile above the lake, and near surface meteorology (wind speed, pressure, etc.) were measured near the time of overpass. Sites employed for the solar radiation measurements were two desert type sites. Ground measurements consisted of: (1) direct solar radiation - optical depth; (2) diffuse solar radiation; (3) total solar radiation, (4) target directional (normal) reflectance; (5) target hemispherical reflectance; and (6) near surface meteorology.
Source record↗
Parallel Climate Data Assimilation PSAS Package Achieves 18 GFLOPs on 512-Node Intel Paragon
Several algorithms were added to the Physical-space Statistical Analysis System (PSAS) from Goddard, which assimilates observational weather data by correcting for different levels of uncertainty about the data and different locations for mobile observation platforms. The new algorithms and use of the 512-node Intel Paragon allowed a hundred-fold decrease in processing time.
weather prediction climate modeling data assimilat↗
Demonstration of object location, classification, and characterization by developed deep learning dust ablation trail analysis code package using plasma jets
Based on deep learning, a Dust Ablation Trail Analysis (DATA) code package is developed to detect dust ablation trails in tokamaks, which is intended to analyze a large amount data of tokamak dusts. To validate and benchmark the DATA code package, 2440 plasma jet images are exploited for the training and test of the deep learning DATA code package, since plasma jets resemble the shape and size of dust ablation clouds in tokamaks. After being trained by 1920 plasma jet images, the DATA code package is able to locate 100% plasma jets, classify plasma jets with the accuracy of >99.9%, and output image skeleton information for classified plasma jets. The DATA code package trained by the plasma jet images is also used to analyze the dust ablation trails captured in the Experimental Advanced Superconducting (EAST) tokamak with the satisfactory performance, further verifying its applicability in the fusion dust ablation investigation. Based on its excellent performance presented here, it is demonstrated that our DATA code package is able to automatically identify and analyze dust ablation trails in tokamaks, which can be used for further detailed investigations, such as the three-dimensional reconstruction of dusts and their ablation trails.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Hot Droughts and Forest Tree Dynamics in the Amazon - Statistical Models, Scripts, Data, and Outputs
This package contains data, outputs, equations, and R scripts for analyses for manuscript entitled "Hot droughts in the Amazon: A window to a future hypertropical climate" by J. Chambers et al., in particular it contains statistical models and analyses for the INPA BIONTE tree mortality study. The Models folder contains details for all statistical models in PDF files. The Scripts folder contains the R scripts for Bayesian Hierarchical Models (two text files) and SEMs (one text file) are separate and reasonably annotated. All data associated with these scripts are in the data folder. The Data folder contains two of the three CSV files used for the analyses and are called by the R scripts. Two of them are part of published datasets (`BIONTE_mortality-rates.csv` from Lima et al. 2024, DOI:10.15486/ngt/1898910 and `SPEI.csv` from Pastorello et al. 2023 DOI:10.15486/ngt/1958257) and also provided in this package for convenience (please see the corresponding datasets for usage and citation terms). The third dataset (`BIONTE_gapfilled_wd.csv`) contains sensitive information and can be obtained by contacting the manuscript lead author. The Outputs folder contains the two output files that provide extra information about the analyses. The file `figuresFeb2025d.pdf` contains all the figures from the manuscript - captions are in the manuscript. The file `ChambersMS.pdf` contains primary results from Bayesian statistical models, regression analyses, and validation steps applied to the tree mortality data from the INPA experiments. The document includes visual summaries, model diagnostics, and leave-one-out (LOO) validation results. A breakdown of file contents can be found in the README file that is part of this package.
54 ENVIRONMENTAL SCIENCES↗
The graphics and data acquisition software package
A software package was developed for use with micro and minicomputers, particularly the LSI-11/DPD-11 series. The package has a number of Fortran-callable subroutines which perform a variety of frequently needed tasks for biomedical applications. All routines are well documented, flexible, easy to use and modify, and require minimal programmer knowledge of peripheral hardware. The package is also economical of memory and CPU time. A single subroutine call can perform any one of the following functions: (1) plot an array of integer values from sampled A/D data, (2) plot an array of Y values versus an array of X values; (3) draw horizontal and/or vertical grid lines of selectable type; (4) annotate grid lines with user units; (5) get coordinates of user controlled crosshairs from the terminal for interactive graphics; (6) sample any analog channel with program selectable gain; (7) wait a specified time interval, and (8) perform random access I/O of one or more blocks of a sequential disk file. Several miscellaneous functions are also provided.
Crosier, W. G.↗
Machine Learning Based Software Package for Reproducible Data Analysis for Electron Microscopy
Advances in hardware and computation technologies are enabling large hyperspectral imaging during Electron Microscopy (EM), resulting in datasets being produced at rapidly increasing spatial and temporal resolutions. This presents exciting opportunities for microstructure analysis, but also poses great challenges in data analysis. Under this STTR program, QuesTek Innovations LLC, a leader in the field of integrated computational materials engineering (ICME), collaborated with Lawrence Livermore National Laboratory and Argonne National Laboratory. The team developed an open-source machine learning (ML) powered tool for EM data analysis of multiple systems and data types, and collected EM data for ML model development using the cutting-edge PicoProbe equipment at Argonne National Laboratory.
Lin, Yu↗
Selecting a general-purpose data compression algorithm
The National Space Science Data Center's Common Data Formate (CDF) is capable of storing many types of data such as scalar data items, vectors, and multidimensional arrays of bytes, integers, or floating point values. However, regardless of the dimensionality and data type, the data break down into a sequence of bytes that can be fed into a data compression function to reduce the amount of data without losing data integrity and thus remaining fully reconstructible. Because of the diversity of data types and high performance speed requirements, a general-purpose, fast, simple data compression algorithm is required to incorporate data compression into CDF. The questions to ask are how to evaluate and compare compression algorithms, and what compression algorithm meets all requirements. The object of this paper is to address these questions and determine the most appropriate compression algorithm to use within the CDF data management package that would be applicable to other software packages with similar data compression needs.
Mathews, Gary Jason↗
Remote Sensing and Information Technology for Large Farms
A method of applying of remote sensing (RS) and information management technology to help large farms produce at maximum efficiency is undergoing development. The novelty of the method does not lie in the concept of "precision agriculture," which involves variation of seeding, of application of chemicals, and of irrigation according to the spatially and temporally local variations in the growth stages and health of crops and in the chemical and physical conditions of soils. The novelty also does not lie in the use of RS data registered with other data in a geographic information system (GIS) to guide the use of precise agricultural techniques. Instead, the novelty lies in a systematic approach to overcoming obstacles that, heretofore, have impeded the timely distribution of reliable, relevant, and sufficient GIS data to support day-to-day, acre-to-acre decisions concerning the application of precise agricultural techniques to increase production and decrease cost. The development and promotion of the method are inspired in part by a vision of equipping farm machinery to accept GIS (including RS) data and using the data for automated or semiautomated implementation of precise agricultural techniques. Primary examples of relevant GIS data include information on plant stress, soil moisture, and effects of applied chemicals, all derived by automated computational analysis of measurements taken by one or more airborne spectroradiometers. Proper management and timeliness of the large amount of GIS information are of paramount concern in agriculture. Information on stresses and changes in crops is especially perishable and important to farmers. The need for timeliness and management of information is satisfied by use of computing hardware and software capable of (1) rapid georectification and other processing of RS data, (2) packaging the output data in the form of GIS plots, and (3) making the data available to farmers and other subscribers by Internet password access. It is a goal of this development program to make RS data available no later than the data after an aerial survey. In addition, data from prior surveys are kept in the data base. Farmers can, for example, use current and prior data to analyze changes.
Williams, John E.↗
JPL Earth Occultation Analysis Package: Status and Data Product
Explore the source record for details and available documents.
JPL Earth Occultation↗
Remote Sensing and Information Technology for Large Farms
A method of applying remote sensing (RS) and information-management technology to help large farms produce at maximum efficiency is undergoing development. The novelty of the method does not lie in the concept of precision agriculture, which involves variation of seeding, of application of chemicals, and of irrigation according to the spatially and temporally local variations in the growth stages and health of crops and in the chemical and physical conditions of soils. The novelty also does not lie in the use of RS data registered with other data in a geographic information system (GIS) to guide the use of precise agricultural techniques. Instead, the novelty lies in a systematic approach to overcoming obstacles that, heretofore, have impeded the timely distribution of reliable, relevant, and sufficient GIS data to support day-to-day, acre-to-acre decisions concerning the application of precise agricultural techniques to increase production and decrease cost. The development and promotion of the method are inspired in part by a vision of equipping farm machinery to accept GIS (including RS) data and using the data for automated or semi-automated implementation of precise agricultural techniques. Primary examples of relevant GIS data include information on plant stress, soil moisture, and effects of applied chemicals, all derived by automated computational analysis of measurements taken by one or more airborne spectroradiometers. Proper management and timeliness of the large amount of GIS information are of paramount concern in agriculture. Information on stresses and changes in crops is especially perishable and important to farmers. The need for timeliness and management of information is satisfied by use of computing hardware and software capable of (1) rapid geo-rectification and other processing of RS data, (2) packaging the output data in the form of GIS plots, and (3) making the data available to farmers and other subscribers by Internet password access. It is a goal of this development program to make RS data available no later than the data after an aerial survey. In addition, data from prior surveys are kept in the data base. Farmers can, for example, use current and prior data to analyze changes.
Williams, John E.↗
Pypromice: A Python Package for Processing Automated Weather Station Data
The pypromice Python package is for processing and handling observation datasets from automated weather stations (AWS). It is primarily aimed at users of AWS data from the Geological Survey of Denmark and Greenland (GEUS), which collects and distributes in situ weather station observations to the cryospheric science research community. Functionality in pypromice is primarily handled using two key open-source Python packages, xarray (Hoyer & Hamman, 2017) and pandas (The pandas development team, 2020). A defined processing workflow is included in pypromice for transforming original AWS observations (Level 0, L0) to a usable, CF-convention-compliant dataset (Level 3, L3) (Figure 1). Intermediary processing levels (L1,L2) refer to key stages in the workflow, namely the conversion of variables to physical measurements and variable filtering (L1), cross-variable corrections and user-defined data flagging and fixing (L2), and derived variables (L3). Information regarding the station configuration is needed to perform the processing, such as instrument calibration coefficients and station type (one-boom tripod or two-boom mast station design, for example), which are held in a toml configuration file. Two example configuration files are provided with pypromice , which are also used in the package’s unit tests. More detailed documentation of the AWS design, instrumentation, and processing steps are described in Fausto et al. (2021).
pypromice↗
Tests of a Semi-Analytical Case 1 and Gelbstoff Case 2 SeaWiFS Algorithm with a Global Data Set
A semi-analytical algorithm was tested with a total of 733 points of either unpackaged or packaged-pigment data, with corresponding algorithm parameters for each data type. The 'unpackaged' type consisted of data sets that were generally consistent with the Case 1 CZCS algorithm and other well calibrated data sets. The 'packaged' type consisted of data sets apparently containing somewhat more packaged pigments, requiring modification of the absorption parameters of the model consistent with the CalCOFI study area. This resulted in two equally divided data sets. A more thorough scrutiny of these and other data sets using a semianalytical model requires improved knowledge of the phytoplankton and gelbstoff of the specific environment studied. Since the semi-analytical algorithm is dependent upon 4 spectral channels including the 412 nm channel, while most other algorithms are not, a means of testing data sets for consistency was sought. A numerical filter was developed to classify data sets into the above classes. The filter uses reflectance ratios, which can be determined from space. The sensitivity of such numerical filters to measurement resulting from atmospheric correction and sensor noise errors requires further study. The semi-analytical algorithm performed superbly on each of the data sets after classification, resulting in RMS1 errors of 0.107 and 0.121, respectively, for the unpackaged and packaged data-set classes, with little bias and slopes near 1.0. In combination, the RMS1 performance was 0.114. While these numbers appear rather sterling, one must bear in mind what mis-classification does to the results. Using an average or compromise parameterization on the modified global data set yielded an RMS1 error of 0.171, while using the unpackaged parameterization on the global evaluation data set yielded an RMS1 error of 0.284. So, without classification, the algorithm performs better globally using the average parameters than it does using the unpackaged parameters. Finally, the effects of even more extreme pigment packaging must be examined in order to improve algorithm performance at high latitudes. Note, however, that the North Sea and Mississippi River plume studies contributed data to the packaged and unpackaged classess, respectively, with little effect on algorithm performance. This suggests that gelbstoff-rich Case 2 waters do not seriously degrade performance of the semi-analytical algorithm.
Carder, Kendall L.↗
Thermal control coating degradation data from the Pegasus experiment packages.
Thermal control coating degradation data from aluminum case containing four sensors aboard Pegasus spacecraft
TEMPERATURE CONTROL↗