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At least 307 records · Page 17

The Mars mapper science and mission planning tool

The Mars Mapper Program (MOm) is an interactive tool for science and mission design developed for the Mars Observer Mission (MO). MOm is a function of the Planning and Sequencing Element of the MO Ground Data System. The primary users of MOm are members of the science and mission planning teams. Using MOm, the user can display digital maps of Mars in various projections and resolutions ranging from 1 to 256 pixels per degree squared. The user can overlay the maps with ground tracks of the MO spacecraft (S/C) and footprints and swaths of the various instruments on-board the S/C. Orbital and instrument geometric parameters can be computed on demand and displayed on the digital map or plotted in XY-plots. The parameter data can also be saved into files for other uses. MOm is divided into 3 major processes: Generator, Mapper, Plotter. The Generator Process is the main control which spawns all other processes. The processes communicate via sockets. At any one time, only 1 copy of MOm may operate on the system. However, up to 5 copies of each of the major processes may be invoked from the Generator. MOm is developed on the Sun SPARCStation 2GX with menu driven graphical user interface (GUI). The map window and its overlays are mouse-sensitized to permit on-demand calculations of various parameters along an orbit. The program is currently under testing and will be delivered to the MO Mission System Configuration Management for distribution to the MO community in 3/93.

Lo, Martin W.↗

A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS)

Global anthropogenic emission inventories remain vital for understanding the sources of atmospheric pollution and the associated impacts on the environment, human health, and society. Rapid changes in today's society require that these inventories provide contemporary estimates of multiple atmospheric pollutants with both source sector and fuel type information to understand and effectively mitigate future impacts. To fill this need, we have updated the open-source Community Emissions Data System (CEDS) (Hoesly et al., 2019) to develop a new global emission inventory, CEDSGBD-MAPS. This inventory includes emissions of seven key atmospheric pollutants (NOx; CO; SO2; NH3; non-methane volatile organic compounds, NMVOCs; black carbon, BC; organic carbon, OC) over the time period from 1970–2017 and reports annual country-total emissions as a function of 11 anthropogenic sectors (agriculture; energy generation; industrial processes; on-road and non-road transportation; separate residential, commercial, and other sectors (RCO); waste; solvent use; and international shipping) and four fuel categories (total coal, solid biofuel, the sum of liquid-fuel and natural-gas combustion, and remaining process-level emissions). The CEDSGBD-MAPS inventory additionally includes monthly global gridded (0.5°?×?0.5°) emission fluxes for each compound, sector, and fuel type to facilitate their use in earth system models. CEDSGBD-MAPS utilizes updated activity data, updates to the core CEDS default scaling procedure, and modifications to the final procedures for emissions gridding and aggregation. Relative to the previous CEDS inventory (Hoesly et al., 2018), these updates extend the emission estimates from 2014 to 2017 and improve the overall agreement between CEDS and two widely used global bottom-up emission inventories. The CEDSGBD-MAPS inventory provides the most contemporary global emission estimates to date for these key atmospheric pollutants and is the first to provide global estimates for these species as a function of multiple fuel types and source sectors. Dominant sources of global NOx and SO2 emissions in 2017 include the combustion of oil, gas, and coal in the energy and industry sectors as well as on-road transportation and international shipping for NOx. Dominant sources of global CO emissions in 2017 include on-road transportation and residential biofuel combustion. Dominant global sources of carbonaceous aerosol in 2017 include residential biofuel combustion, on-road transportation (BC only), and emissions from the waste sector. Global emissions of NOx, SO2, CO, BC, and OC all peak in 2012 or earlier, with more recent emission reductions driven by large changes in emissions from China, North America, and Europe. In contrast, global emissions of NH3 and NMVOCs continuously increase between 1970 and 2017, with agriculture as a major source of global NH3 emissions and solvent use, energy, residential, and the on-road transport sectors as major sources of global NMVOCs. Due to similar development methods and underlying datasets, the CEDSGBD-MAPS emissions are expected to have consistent sources of uncertainty as other bottom-up inventories. The CEDSGBD-MAPS source code is publicly available online through GitHub: https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS (last access: 1 December 2020). The CEDSGBD-MAPS emission inventory dataset (both annual country-total and monthly global gridded files) is publicly available under https://doi.org/10.5281/zenodo.3754964 (McDuffie et al., 2020c).

Mcduffie, Erin↗

Development of a prototype spatial information processing system for hydrologic research

Significant advances have been made in the last decade in the areas of Geographic Information Systems (GIS) and spatial analysis technology, both in hardware and software. Science user requirements are so problem specific that currently no single system can satisfy all of the needs. The work presented here forms part of a conceptual framework for an all-encompassing science-user workstation system. While definition and development of the system as a whole will take several years, it is intended that small scale projects such as the current work will address some of the more short term needs. Such projects can provide a quick mechanism to integrate tools into the workstation environment forming a larger, more complete hydrologic analysis platform. Described here are two components that are very important to the practical use of remote sensing and digital map data in hydrology. Described here is a graph-theoretic technique to rasterize elevation contour maps. Also described is a system to manipulate synthetic aperture radar (SAR) data files and extract soil moisture data.

Sircar, Jayanta K.↗

BOREAS TE-1 Soils Data Over The SSA Tower Sites in Raster Format

The BOREAS TE-1 team collected various data to characterize the soil-plant systems in the BOREAS SSA. This data set was gridded from vector layers of soil maps that were received from Dr. Darwin Anderson (TE-1), who did the original soil mapping in the field during 1994. The vector layers were gridded into raster files that cover approximately 1 square kilometer over each of the tower sites in the SSA. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Hall, Forrest G.↗

BOREAS TE-20 Soils Data Over the NSA-MSA and Tower Sites in Raster Format

The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set was gridded from vector layers of soil maps that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994. The vector layers were gridded into raster files that cover the NSA-MSA and tower sites. The data are stored in binary, image format files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Center (DAAC).

Hall, Forrest G.↗

Datasets and U-Net Model for "A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma"

This dataset has results and the model associated with the publication Ciulla et al., (2024). It contains a U-Net semantic segmentation model (unet_model.h5) and associated code implemented in tensorflow 2.0 for the model training and identification of oil and gas well symbols in USGS historical topographic maps (HTMC). Given a quadrangle map (7.5 minutes), downloadable at this url: https://ngmdb.usgs.gov/topoview/, and a list of coordinates of the documented wells present in the area, the model returns the coordinates of oil and gas symbols in the HTMC maps. For reproducibility of our workflow, we provide a sample map in California and the documented well locations for the entire State of California (CalGEM_AllWells_20231128.csv) downloaded from https://www.conservation.ca.gov/calgem/maps/Pages/GISMapping2.aspx. Additionally, the locations of 1,301 potential undocumented orphaned wells identified using our deep learning framework or the counties of Los Angeles and Kern in California, and Osage and Oklahoma in Oklahoma are provided in the file found_potential_UOWs.zip. The results of the visual inspection of satellite imagery in Osage County is in the file visible_potential_UOWs.zip. The dataset also includes a custom tool to validate the detected symbols in the HTMC maps (vetting_tool.py). More details about the methodology can be found in the associated paper: Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S.C., and Varadharajan, C. (2024) A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma. Accepted for publication in Environmental Science and Technology. The geographical coordinates provided correspond to the locations of potential undocumented orphaned oil and gas wells (UOWs) extracted from historical maps. The actual presence of wells need to be confirmed with on-the-ground investigations. For your safety, do not attempt to visit or investigate these sites without appropriate safety training, proper equipment, and authorization from local authorities. Approaching these well sites without proper personal protective equipment (PPE) may pose significant health and safety risks. Oil and gas wells can emit hazardous gasses including methane, which is flammable, odorless and colorless, as well as hydrogen sulfide, which can be fatal even at low concentrations. Additionally, there may be unstable ground near the wellhead that may collapse around the wellbore. This dataset was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

Artificial Intelligence↗

ESS-DIVE Reporting Format for Amplicon Abundance Table

While standardized sequencing data is available in public repositories and efforts such as MIxS for common sample collection and processing metadata are well established, the lack of common bioinformatic processing metadata has hindered the ability to do large-scale metaanalyses and the potential for data re-use by non-experts such as ecosystem, watershed, or earth system modelers. To address this need for Department of Energy researchers, we have developed an amplicon reporting format which captures both sample preparation and bioinformatic processing metadata and stores processed amplicon data as a paired abundance table and sequencing file to maximize the potential for re-use of these data. To aid in the adoption of accessible and reproducible analysis workflows, this reporting format was developed in concert with amplicon functionality within the Department of Energy’s Systems Biology Knowledgebase (KBase) to ensure common data and metadata requirements and facilitate seamless transfer between these platforms.This dataset contains support documentation for the amplicon reporting format (README.md and instructions.md), templates for both bioinformatic and sequencing metadata (amplicon_bioinformatic_metadata_template_2021_10_03.csv and amplicon_sequencing_metadata_template_2021_10_03.csv), a crosswalk indicating how this reporting format relates to the current MIxS format (ESSDIVE-MIxS_crosswalk.csv), a list of available instrument terms (amplicon_seq_instrument_terms_2021_10_03.csv), a map between QIIME2 parameter settings and metadata fields (amplicon_qiime2_plugin_metadata_map.csv), a data dictionary (amplicon_CSV_dd.csv), and file-level metadata (amplicon_FLMD.csv).

54 ENVIRONMENTAL SCIENCES↗

On Data Transfers Over Wide-Area Dedicated Connections

Dedicated wide-area network connections are employed in big data and high-performance computing scenarios, since the absence of cross-traffic promises to make it easier to analyze and optimize data transfers over them. However, nonlinear transport dynamics and end-system complexity due to multi-core hosts and distributed file systems make these tasks surprisingly challenging. We present an overview of methods to analyze memory and disk file transfers using extensive measurements over 10 Gbps physical and emulated connections with 0–366 ms round trip times (RTTs). For memory transfers, we derive performance profiles of TCP and UDT throughput as a function of RTT, which show concave regions in contrast to entirely convex regions predicted by previous models. These highly desirable concave regions can be expanded by utilizing large buffers and more parallel flows. We also present Poincar´e maps and Lyapunov exponents of TCP and UDT throughputtraces that indicate complex throughput dynamics. For disk file transfers, we show that throughput can be optimized using a combination of parallel I/O and network threads under direct I/O mode. Our initial throughput measurements of Lustre filesystems mounted over long-haul connections using LNet routers show convex profiles indicative of I/O limits.

Rao, Nageswara S.↗

Model Data Archive for Manuscript Titled "Evaluation of a Coupled Surface–Subsurface Hydrologic Model Using Dense Water‑Level Sensors in a Mixed Urban–Rural Watershed"

This archive provides scripts, input files, and datasets used for the implementation and evaluation of a fully coupled surface–subsurface hydrologic model in the Neches River Basin, southeast Texas. The study uses the Advanced Terrestrial Simulator (ATS) to simulate coupled surface–subsurface hydrologic processes over a mixed urban–rural watershed and evaluates model performance using a dense network of 136 in situ water-level sensors, nine U.S. Geological Survey (USGS) stream gauges, and SSEBop-derived evapotranspiration estimates during the period October 2014–June 2024. The workflow is implemented primarily in Python 3 using the Watershed Workflow package. The Jupyter notebooks can be executed using open-source software such as Anaconda JupyterLab or Visual Studio Code. Other data files include TXT, CSV, XML, SHP, TIF, NetCDF, HDF5, and ExodusII files, which can be processed using the provided Python scripts. ATS input files are provided in XML format and can be edited using any commonly used text editor. This archive contains: *Scripts and input files used to generate the ATS model setup, including watershed discretization, mesh generation, parameter mapping, and model configuration. *Jupyter notebooks used for preprocessing observational data, evaluating streamflow, water levels, and evapotranspiration, computing performance metrics, and generating the figures presented in the manuscript. *ATS simulation outputs and processed observational datasets, including OneRain and DD6 water-level sensors, USGS streamflow observations, GIS data, and supporting spatial datasets used throughout the study.

Dense water-level sensor network↗

Vertical deformation at western part of Sumatra

This research tries to make advancement in GPS signal processing to estimate the interseismic vertical deformation field at western part of Sumatra Island. The data derived by Continuous Global Positioning System (CGPS) from Badan Informasi Geospasial (BIG) between 2010 and 2012. GPS Analyze at Massachusetts Institute of Technology (GAMIT) software and Global Kalman Filter (GLOBK) software are used to process the GPS signal to estimate the vertical velocities of the CGPS station. In order to minimize noise due to atmospheric delay, Vienna Mapping Function 1 (VMF1) is used as atmospheric parameter model and include daily IONEX file provided by the Center for Orbit Determination in Europe (CODE) as well. It improves GAMIT daily position accuracy up to 0.8 mm. In a second step of processing, the GLOBK is used in order to estimate site positions and velocities in the ITRF08 reference frame. The result shows that the uncertainties of estimated displacement velocity at all CGPS stations are smaller than 1.5 mm/yr. The subsided deformation patterns are seen at the northern and southern part of west Sumatra. The vertical deformation at northern part of west Sumatra indicates postseismic phase associated with the 2010 and 2012 Northern Sumatra earthquakes and also the long-term postseismic associated with the 2004 and 2005 Northern Sumatra earthquakes. The uplifted deformation patterns are seen from Bukit Tinggi to Seblat which indicate a long-term interseismic phase after the 2007 Bengkulu earthquake and 2010 Mentawai earthquake. GANO station shows a subsidence at rate 12.25 mm/yr, indicating the overriding Indo-Australia Plate which is dragged down by the subducting Southeast Asian Plate.

58 GEOSCIENCES↗

GPFA-AB_Phase1ReservoirTask2DataUpload

This submission to the Geothermal Data Repository (GDR) node of the National Geothermal Data System (NGDS) in support of Phase 1 Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. The files included in this zip file contain all data pertinent to the methods and results of this task’s output, which is a cohesive multi-state map of all known potential geothermal reservoirs in our region, ranked by their potential favorability. Favorability is quantified using a new metric, Reservoir Productivity Index, as explained in the Reservoirs Methodology Memo (included in zip file). Shapefile and images of the Reservoir Productivity and Reservoir Uncertainty are included as well.

15 GEOTHERMAL ENERGY↗

Appalachian Basin Play Fairway Analysis: Revised 2016 Combined Risk Factor Analysis

This submission contains information used to compute the combined risk factors for deep geothermal energy opportunities in the Appalachian Basin, in the context of a the Play Fairway Analysis project. The risk factors are sedimentary rock reservoir quality, thermal resource quality, potential for induced seismicity, and utilization for direct-use heating of neighborhoods. The methods used to combine the risk factors included taking the average, the geometric mean, and the minimum of the four risk factors. Combined risk maps are provided for three different sedimentary rock reservoir metrics. Combined risk maps are also provided for the three geologic risk factors alone (thermal, reservoir, and seismic), and for the three risk factors that exclude reservoir quality (utilization, seismicity, and thermal qualities). The 2015 data submission should be visited to obtain associated shapefiles, which include: 1) definition of the High and Medium priority play fairways (Inner_Fairway, and Outer_Fairway), 2) definition of the US Census Places (usCensusPlaces), 3) places (cities) of interest in the region (Places_of_Interest) identified as geothermal play fairways, 4) the point centers of the raster cells (Raster_Center_Locations), and 5) locations of industries and special-use communities (e.g., colleges and military bases) identified as low temperature heat users (Industries). The 2015 submission also includes: 1) a methodology memo that explains how the risk factors were combined (GPFA-AB_combining_risk_factors.pdf), 2) the earthquake-based seismic risk map, and 3) supporting information with details of the calculations or processing used in generating these data files. More details on each file are given in the spreadsheet "list_of_contents.xlsx" in the folder "Supporting_Information". Code used to calculate values is available at https://github.com/calvinwhealton/geothermal_pfa under the folder "combining_metrics". Note that the 2016 code is currently under the branch named "combining_metrics_2016" in the folder called "combining_metrics". This branch may be merged with the master branch in the future. Many files contained within this submission update and replace the indicated files contained in: Cornell University. (2015). Risk Factor Analysis in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB) [data set]. Retrieved from https://gdr.openei.org/submissions/622. doi:10.15121/1261942

15 GEOTHERMAL ENERGY↗

Utah FORGE: GPS Data - Deformation Monitoring

This data bundle contains raw GPS data files and resulting data using Trimble Business Center and NOAA OPUS to process the GPS data in spreadsheets with station coordinates and elevations. A map of station locations is included along with the final processing report which includes pre-processing, pre-stack time migration, and pre-stack depth migration. It also contains a readme.text file goes into further explanation.

15 GEOTHERMAL ENERGY↗

Design and implementation of wireless dose logger network for radiological emergency decision support system

A decision support system (DSS) is implemented in Radiological Safety Division, Indira Gandhi Centre for Atomic Research for providing guidance for emergency decision making in case of an inadvertent nuclear accident. Real time gamma dose rate measurement around the stack is used for estimating the radioactive release rate (source term) by using inverse calculation. Wireless gamma dose logging network is designed, implemented, and installed around the Madras Atomic Power Station reactor stack to continuously acquire the environmental gamma dose rate and the details are presented in the paper. The network uses XBee–Pro wireless modules and PSoC controller for wireless interfacing, and the data are logged at the base station. A LabView based program is developed to receive the data, display it on the Google Map, plot the data over the time scale, and register the data in a file to share with DSS software. The DSS at the base station evaluates the real time source term to assess radiation impact.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

SU-F-BRD-02: Application of ARCHERRT-- A GPU-Based Monte Carlo Dose Engine for Radiation Therapy -- to Tomotherapy and Patient-Independent IMRT

Purpose: As a module of ARCHER -- Accelerated Radiation-transport Computations in Heterogeneous EnviRonments, ARCHER{sub RT} is designed for RadioTherapy (RT) dose calculation. This paper describes the application of ARCHERRT on patient-dependent TomoTherapy and patient-independent IMRT. It also conducts a 'fair' comparison of different GPUs and multicore CPU. Methods: The source input used for patient-dependent TomoTherapy is phase space file (PSF) generated from optimized plan. For patient-independent IMRT, the open filed PSF is used for different cases. The intensity modulation is simulated by fluence map. The GEANT4 code is used as benchmark. DVH and gamma index test are employed to evaluate the accuracy of ARCHER{sub RT} code. Some previous studies reported misleading speedups by comparing GPU code with serial CPU code. To perform a fairer comparison, we write multi-thread code with OpenMP to fully exploit computing potential of CPU. The hardware involved in this study are a 6-core Intel E5-2620 CPU and 6 NVIDIA M2090 GPUs, a K20 GPU and a K40 GPU. Results: Dosimetric results from ARCHER{sub RT} and GEANT4 show good agreement. The 2%/2mm gamma test pass rates for different clinical cases are 97.2% to 99.7%. A single M2090 GPU needs 50~79 seconds for the simulation to achieve a statistical error of 1% in the PTV. The K40 card is about 1.7∼1.8 times faster than M2090 card. Using 6 M2090 card, the simulation can be finished in about 10 seconds. For comparison, Intel E5-2620 needs 507∼879 seconds for the same simulation. Conclusion: We successfully applied ARCHER{sub RT} to Tomotherapy and patient-independent IMRT, and conducted a fair comparison between GPU and CPU performance. The ARCHER{sub RT} code is both accurate and efficient and may be used towards clinical applications.

60 APPLIED LIFE SCIENCES↗

Pxl

The code consists of several scripts. The image is fetched from a predefined URL, downloaded, and is displayed in the virtual environment at a predefined location. The image is also dynamically scaled to fit in the virtual environment depending on its size. The image is sent to have each pixel's value written to a CSV file. While the CSV is being written, a dictionary is populated with values that map each pixel to a temperature reading. The image's height, width, position and references to its corners are sent to make a button that is the same size as the image and placed right behind the image in the virtual scene. The button defines behavior that should be triggered when the user pushes the image. Unity provides visual cues that alert the user when they are about to touch something in the virtual environment. The code holds references to the cues for the left and right hands. The button queries the cue tracker and receives the position of the active cue in the form of a 3D vector. The 3D vector is compared to the bottom left corner of the image to transform the world space coordinates of the cue to the pixel space of the image. The specific pixel is approximated relative to how far left and up the user touched from the bottom left corner of the image. The approximated pixel value, which is represented as a coordinate in 2D space, is used to retrieve the temperature value associated with the approximated pixel. A window of text is then updated to display the temperature reading.

. Otis, KrystianeS [Idaho National Laboratory (INL↗

PALS Python v0.1.0

This is a Python implementation for the Particle Accelerator Lattice Standard (PALS). To define the PALS schema, Pydantic is used to map to Python objects, perform automatic validation, and serialize/deserialize data classes to/from many modern file formats. Various modern file formats (e.g., YAML, JSON, TOML, XML, etc.) are supported, which makes the implementation of the schema-following files in any modern programming language easy (e.g., Python, Julia, C++, LUA, Javascript, etc.). Here, we do Python.

Huebl, Axel [Lawrence Berkeley National Laboratory↗

Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product Report

Objective analysis (OA) is a method of mapping unstructured data to a structured grid. In the context of scanning radar data, OA is used to interpolate data in antenna coordinates (range, azimuth, and elevation) onto a regularly spaced Cartesian grid (Trapp and Doswell 2000). The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product (VAP) uses the Python ARM Radar Toolkit (Py-ART), a data model-driven interactive architecture for working with weather radar data, to map the data to a Cartesian grid (Helmus and Collis 2016). MMCG, with Py-ART built in, has the ability to take radar data in antenna coordinates and map the gates to a Cartesian grid using inverse distance weight functions such as Cressman (square) and Barnes (exponential), but also can filter the data during the interpolation. MMCG also allows arbitrary formulations for the radius of influence, which are matched to particular radar scanning strategies. This creates a complex parameter space for optimizing the retention of storm structure detail while minimizing artifacts. MMCG takes data processed with ARM’s Corrected Precipitation Radar Moments in Antenna Coordinates (CMAC) VAP and maps it to a Cartesian grid as the output product. A variety of fields that have been mapped to the Cartesian grid are then saved to plots to complement each grid file.

54 ENVIRONMENTAL SCIENCES↗