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The potential of image data compression techniques to satisfy the anticipated requirements of imaging spectrometer missions is discussed. Noiseless coding, rate controlled compression, cluster compression, and error protection are addressed.
Optimal allocation and usage of resources is a key to effective management. Resources of concern to TRAC are: Manpower (PSY), Money (Travel, contracts), Computing, Data, Models, etc. Management activities of TRAC include: Planning, Programming, Tasking, Monitoring, Updating, and Coordinating. Existing systems are insufficient, not completely automated, manpower intensive, and has the potential for data inconsistency exists. A system is proposed which suggests a means to integrate all project management activities of TRAC through the development of a sophisticated software and by utilizing the existing computing systems and network resources. The systems integration proposal is examined in detail.
A search was conducted to locate periods of nearly simultaneous solar-wind and high latitude topside-ionospheric data during magnetic storms. The focus was on the 20-yr interval from 1965 to 1985 when both solar-wind and Alouette/ISIS topside-sounder data are potentially available. The search yielded 125 large magnetic storms (minimum Dst less than 100) and 280 moderate magnetic storms (minimum Dst between -60 and -100). Solar wind data were available for most, but not all, of these storms. A search of the available high-latitude topside electron-density Ne(h) profiles available from the National Space Science Data Center (NSSDC), both from manual inspection of 35-mm film ionograms in the 1960s and more recent auto-processing of ISIS-2 topside digital ionograms using the TOPIST software, during 9-day intervals associated with the 125 large magnetic storm minimum Dst times yielded the following results: 31 intervals had 10 or more manual-scaled profiles (21 intervals had more than 100 profiles and 5 of these had more than 1,000 profiles), and 34 intervals had 10 or more TOPIST profiles (2 intervals had more than 100 profiles). In addition, a search of the available Alouette-2, ISIS-1 and ISIS-2 digital ionograms during the above periods has yielded encouraging initial results in that many ISIS-1 ionograms were found for the early time intervals. Future work will include the search for 35-mm film ionograms during selected intervals. This presentation will illustrate the results of this investigation to date.
The Y-dwarf WISE 1828+2650 is one of the coldest known Brown Dwarfs with an effective temperature of ~300 K. Located at a distance of just 10 pc, previous model-based estimates suggest WISE 1828+2650 has a mass of ~5-10 M J , making it a valuable laboratory for understanding the formation, evolution and physical characteristics of gas giant planets. However, previous photometry and spectroscopy have presented a puzzle with the near-impossibility of simultaneously fitting both the short (0.9-2.0 μm) and long wavelength (3-5 μm) data. A potential solution to this problem has been the suggestion that WISE 1828+2650 is a binary system whose composite spectrum might provide a better match to the data. Alternatively, new models being developed to fit JWST/NIRSpec and MIRI spectroscopy might provide new insights. This article describes JWST/NIRCam observations of WISE 1828+2650 in 6 filters to address the binarity question and to provide new photometry to be used in model fitting. We also report Adaptive Optics imaging with the Keck 10 m telescope. We find no evidence for multiplicity for a companion beyond 0.5 AU with either JWST or Keck. Companion articles will present low and high resolution spectra of WISE 1828 obtained with both NIRSpec and MIRI.
The Y-dwarf WISE 1828+2650 is one of the coldest known brown dwarfs with an effective temperature of∼300K. Located at a distance of just 10 pc, previous model-based estimates suggest WISE1828+2650 has a mass of∼5–10MJ, making it a valuable laboratory for understanding the formation, evolution, and physical characteristics of gas giant planets. However, previous photometry and spectroscopy have presented a puzzle, with the near impossibility of simultaneously fitting both the short-(0.9–2.0μm)and long-wavelength(3–5μm)data. A potential solution to this problem has been the suggestion that WISE 1828+2650 is a binary system whose composite spectrum might provide a better match to the data. Alternatively, new models being developed to fit JWST/NIRSpec, and MIRI spectroscopy might provide new insights. This article describes JWST/NIRCam observations of WISE 1828+2650 in six filters to address the binarity question and to provide new photometry tobe used in model fitting. We also report adaptive optics imaging with the Keck I0 m telescope. We find no evidence for multiplicity for a companion beyond 0.5 au with either JWST or Keck. Companion articles will present low-and high-resolution spectra of WISE 1828 obtained with both NIRSpec and MIRI.
Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.
This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with spectra containing similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and qualitative distribution analysis. Additionally, we propose a probabilistic classification model that can utilize spectral data, temporal data, or both to determine if the incorporation of temporal information improves radioisotope identification. Our findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated to develop more robust and optimal methods for utilizing this temporal information in applications requiring radioisotope identification.
Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. In addition to providing curated reference site condition datasets for four U.S. areas, our assessment highlights the need for more publicly available metocean and soil condition data.
Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. Further information on the datasets and how they were created can be found in: Biglu, M., M. Hall, E. Lozon, S. Housner. 2024. Reference Site Conditions for Floating Wind Arrays in the United States. Golden, CO: National Renewable Energy Laboratory (NREL). NREL/TP-5000-89897. The data are also available at: https://github.com/FloatingArrayDesign/SiteConditions The content of each dataset is as follows: _NOW23_wind.txt: Hourly NOW-23 wind data up to a height of 400 meter. _metocean_1hr.txt: Hourly time series including wind, wave, surface current and temperature data. _Summary.xlsx: Metocean data, including extreme values, joint probability distributions and monthly statistics. _usSEABED_soil.csv: Extract of the usSEABED database for this specific site. _bathymetry_200m.txt (and 500m, 1000m): Gridded seabed depth data.
Since the Apollo era the electric potential of the Moon has been a subject of interest and debate. Deployed by three Apollo missions, Apollo 12, Apollo 14 and Apollo 15, the Suprathermal Ion Detector Experiment (SIDE) determined the sunlit lunar surface potential to be about +10 Volts using the energy spectra of lunar ionospheric thermal ions accelerated toward the Moon. More recently, the Lunar Prospector (LP) Electron Reflectometer used electron distributions to infer negative lunar surface potentials, primarily in shadow. We will present initial results from a study to combine lunar surface potential measurements from both SIDE and the LP/Electron Reflectometer to calibrate an advanced model of lunar surface charging which includes effects from the plasma environment, photoemission, secondaries ejected by ion impact onto the lunar surface, and the lunar wake created downstream by the solar wind-lunar interaction.
Data management program activities centered around the analyses of selected far-term Office of Applications (OA) objectives, with the intent of determining if significant data-related problems would be encountered and if so what alternative solutions would be possible. Three far-term (1985 and beyond) OA objectives selected for analyses as having potential significant data problems were large-scale weather forecasting, local weather and severe storms forecasting, and global marine weather forecasting. An overview of general weather forecasting activities and their implications upon the ground based data system is provided. Selected topics were specifically oriented to the use of satellites.
Geopotential heights of constant pressure surfaces are retrieved from global positioning system (GPS) radio occultation data. In order to assess accuracy a subset of data obtained by GPS/MET during spring 1995 and summer 1995 are compared to the output of the European Centre for Medium-Range Weather Forecasts (ECMWF) global model. The root-mean-square measurement error is 20 m throughout the upper troposphere and lower stratosphere. Furthermore, the ECMWF global model contains enhanced errors in the southeast Pacific. In probing the data for potential utility in climate studies, a Bayesian interpolation technique is used to map the geopotential height fields in the upper troposphere during the summer. Despite limitations of the GPS/MET data set the global average 300-mbar geopotential height over a 2-week period in summer 1995 is determined with an accuracy of 7 m. By obtaining greater coverage and partially resolving synoptic variability, a future constellation of 16 orbiting receivers could obtain global average geopotential height estimates in the upper troposphere with an accuracy of 1 m each day. Accuracy would be somewhat worse for regional studies, except in the tropics where synoptic variability is depressed.
This paper covers a wide variety of issues associated with the implementation and use of these frequency bands for deep space communications. Performance issues, such as ground station pointing stability, ground antenna gain, antenna pattern, and propagation effects such as due to atmospheric, charged-particle and space loss at 37 GHz, will be addressed in comparison to the 32 GHz Ka-band deep space allocation. Issues with the use of and competition for this spectrum also will be covered. The state of the hardware developed (or proposed) for operating in this frequency band will be covered from the standpoint of the prospects for achieving higher data rates that could be accommodated in the available bandwidth. Hardware areas to be explored include modulators, digital-to-analog converters, filters, power amplifiers, receivers, and antennas. The potential users of the frequency band will be explored as well as their anticipated methods to achieve the potential high data rates and the implications of the competition for bandwidth.
Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. Here, we propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. We hope this publication and the proposed Recommended Metadata for Biological Images (REMBI) will stimulate discussions about their implementation and future extension.
We describe and report the status of a neutrino-triggered program in IceCube that generates real-time alerts for gamma-ray follow-up observations by atmospheric-Cherenkov telescopes (MAGIC and VERITAS). While IceCube is capable of monitoring the whole sky continuously, high-energy gamma-ray telescopes have restricted fields of view and in general are unlikely to be observing a potential neutrino-flaring source at the time such neutrinos are recorded. The use of neutrino-triggered alerts thus aims at increasing the availability of simultaneous multi-messenger data during potential neutrino flaring activity, which can increase the discovery potential and constrain the phenomenological interpretation of the high-energy emission of selected source classes (e.g. blazars). Here, the requirements of a fast and stable online analysis of potential neutrino signals and its operation are presented, along with first results of the program operating between 14 March 2012 and 31 December 2015.
The paper considers problems of optimum resolution, periodicity, and wavelength bands used for snow mapping. Analog and digital methods were used for application of satellite data; techniques were developed for producing steamflow forecasts, hydroelectric power generation regulation data, irrigation potentials, and information on the availability of drinking water supplies. Future systems will utilize improved spectral band selection, new spectral regions, higher repetition rates, and more rapid access to satellite data.
Award DE-FE-0000880 from the Department of Energy to the Ground Water Protection Council (GWPC) focuses on state and federal priorities in the areas of state RBDMS development, connectivity between state systems and FracFocus.org, and data sharing initiatives across agencies. The overarching goals of these projects are to: upgrade the Risk Based Data Management System (RBDMS) to a web based application; upgrade the RBDMS user interface to make it more intuitive and easier to use increase connectivity between state RBDMS systems and FracFocus; provide outreach to states and agencies through training programs, special meetings, and state by state outreach on all deliverables; provide greater functionality for non-IT users; update and install RBDMS modules in additional state programs (IL, CA, WY, WV); and; help make data access more transparent. The primary objective is to enhance the Risk Based Data Management System (RBDMS) by adding new components relevant to current environmental topics such as hydraulic fracturing, increasing field inspection capabilities, creating linkages between FracFocus and state programs, upgrading eForm capabilities, and analyzing potential for data sharing. The GWPC will work with state agencies to develop an RBDMS module(s) that meets these needs.
When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. Here, we benchmark two other modalities of evolving structures, contour exploration (CE) and dimer-method (DM) searches against MD for their ability to produce diverse and robust density functional theory training data sets for MLIPs. We also discuss the generation of initial structures which are either from known structures or from random structures in detail to further formalize the structure-sourcing processes in the future. The polymorph-rich zirconium-oxygen composition space is used as a rigorous benchmark system for comparing the performance of MLIPs trained on structures generated from these structural evolution methods. Using Behler–Parrinello neural networks as our MLIP models, we find that CE and the DM searches are generally superior to MD in terms of spatial descriptor diversity and statistical accuracy.