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A method for retrieving atmospheric temperatures in a severe storm situation was investigated. Retrieval was accomplished through the aid of satellite radiance measurements and nearby radiosondes. A set of coefficients was derived which when multiplied by the measured radiances, yielded smaller temperature retrieval errors than the minimum-information retrieval method.
A three-dimensional analysis of turbofan forced mixer nozzle aerodynamics demonstrates that the complex flow structure is dominated by geometrically induced secondary flow rather than by turbulence. The test apparatus consisted of a fixed upstream model section and a rotating shroud. The Mach number of the fan and core streams at the mixing plane (lobe exit) was 0.45, the bypass ratio was about 4, and the Reynolds number based on the shroud radius was 1,100,000. The three velocity components near the exit plane of the lobes were measured using flow angularity probes to provide information about the mixer inflow conditions for turbulent computations. The validity of a previous computer code was demonstrated in a comparison of the nozzle exit temperature data with the computed temperature distributions. The mechanism most responsible for the generation of secondary flow within the lobes is due to the turning of the fan and core streams in opposite radial directions.
The James Webb Space Telescope Instrument Support Integration Module (ISIM) is being designed and developed at the Goddard Space Flight Center. The ISM Thermal Distortion Testing (ITDT) program was started with the primary objective to validate the ISM mechanical design process. The ITDT effort seeks to establish confidence and demonstrate the ability to predict thermal distortion in composite structures at cryogenic temperatures using solid element models. This-program's goal is to better ensure that ISIM meets all the mechanical and structural requirements by using test results to verify or improve structural modeling techniques. The first step to accomplish the ITDT objectives was to design, and then construct solid element models of a series 2-D test assemblies that represent critical building blocks of the ISIM structure. Second, the actual test assemblies consisting of composite tubes and invar end fittings were fabricated and tested for thermal distortion. This paper presents the development of the GSFC Cryo Distortion Measurement Facility (CDMF) to meet the requirements of the ISIM 2-D test. assemblies, and other future ISIM testing needs. The CDMF provides efficient cooling with both a single, and two-stage cryo-cooler. Temperature uniformity of the test assemblies during thermal transients and at steady state is accomplished by using sapphire windows for all of the optical ports on the radiation shields and by using .thermal straps to cool the test assemblies. Numerical thermal models of the test assemblies were used to predict the temperature uniformity of the parts during cooldown and at steady state. Results of these models are compared to actual temperature data from the tests. Temperature sensors with a 0.25K precision were used to insure that test assembly gradients did not exceed 2K lateral, and 4K axially. The thermal distortions of two assemblies were measured during six thermal cycles from 320K to 35K using laser interferometers. The standard deviation for all of the distortion measurements is less than 0.5 microns, which falls within the ISIM requirement of 3 microns.
Deep space missions with large payloads require high specific impulse and relatively high thrust to achieve mission goals in reasonable time frames. Nuclear Thermal Rockets (NTR) are capable of producing a high specific impulse by employing heat produced by a fission reactor to heat and therefore accelerate hydrogen through a rocket nozzle providing thrust. Fuel element temperatures are very high (up to 3000 K) and hydrogen is highly reactive with most materials at high temperatures. Data covering the effects of high‐temperature hydrogen exposure on fuel elements are limited. The primary concern is the mechanical failure of fuel elements due to large thermal gradients; therefore, high‐melting‐point ceramics‐metallic matrix composites (cermets) are one of the fuels under consideration as part of the Nuclear Cryogenic Propulsion Stage (NCPS) Advance Exploration System (AES) technology project at the Marshall Space Flight Center. The purpose of testing and analytical modeling is to determine their ability to survive and maintain thermal performance in a prototypical NTR reactor environment of exposure to hydrogen at very high temperatures and obtain data to assess the properties of the non‐nuclear support materials. The fission process and the resulting heating performance are well known and do not require that active fissile material to be integrated in this testing. A small‐scale test bed; Compact Fuel Element Environmental Tester (CFEET), designed to heat fuel element samples via induction heating and expose samples to hydrogen is being developed at MSFC to assist in optimal material and manufacturing process selection without utilizing fissile material. This paper details the analytical approach to help design and optimize the test bed using COMSOL Multiphysics for predicting thermal gradients induced by electromagnetic heating (Induction heating) and Thermal Desktop for radiation calculations.
Deep space missions with large payloads require high specific impulse and relatively high thrust to achieve mission goals in reasonable time frames. Nuclear Thermal Rockets (NTR) are capable of producing a high specific impulse by employing heat produced by a fission reactor to heat and therefore accelerate hydrogen through a rocket nozzle providing thrust. Fuel element temperatures are very high (up to 3000 K) and hydrogen is highly reactive with most materials at high temperatures. Data covering the effects of high‐temperature hydrogen exposure on fuel elements are limited. The primary concern is the mechanical failure of fuel elements due to large thermal gradients; therefore, high‐melting‐point ceramics‐metallic matrix composites (cermets) are one of the fuels under consideration as part of the Nuclear Cryogenic Propulsion Stage (NCPS) Advance Exploration System (AES) technology project at the Marshall Space Flight Center. The purpose of testing and analytical modeling is to determine their ability to survive and maintain thermal performance in a prototypical NTR reactor environment of exposure to hydrogen at very high temperatures and obtain data to assess the properties of the non‐nuclear support materials. The fission process and the resulting heating performance are well known and do not require that active fissile material to be integrated in this testing. A small‐scale test bed; Compact Fuel Element Environmental Tester (CFEET), designed to heat fuel element samples via induction heating and expose samples to hydrogen is being developed at MSFC to assist in optimal material and manufacturing process selection without utilizing fissile material. This paper details the analytical approach to help design and optimize the test bed using COMSOL Multiphysics for predicting thermal gradients induced by electromagnetic heating (Induction heating) and Thermal Desktop for radiation calculations.
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This report documents the quantification of uncertainty of the calculated temperature data for the Advanced Gas Reactor (AGR) 5/6/7 fuel irradiation experiment conducted in the Advanced Test Reactor at Idaho National Laboratory in support of the Advanced Reactor Technologies? research and development program. Recognizing uncertainties inherent in physics and thermal simulations of the AGR 5/6/7 capsules, the results of the numerical simulations are used in combination with statistical analysis methods to improve qualification of measured data. The calculated fuel temperatures for AGR tests are also used for validation of the fission product transport and fuel performance simulation models. These crucial roles of the calculated fuel temperatures in ensuring achievement of the AGR experimental program objectives require accurate determination of the model temperature uncertainties. This report covers temperature uncertainty results for each of the five AGR 5/6/7 capsules. To quantify the uncertainty of calculated temperatures determined using the ABAQUS finite element heat transfer code, this study identifies and analyzes model parameters of potential importance to the calculated temperatures of fuel compacts and thermocouples. The selection of input parameters for uncertainty quantification is based on the ranking of their influences upon temperature predictions. Thus, selected input parameters include those with high sensitivity and those with the largest uncertainty. Propagation of model parameter uncertainty and sensitivity is then used to quantify the overall uncertainty of calculated temperatures. Measurement uncertainty, analysis of modeling assumptions, and expert judgment are used as the basis to quantify the uncertainty range for selected input parameters. The input uncertainties are dynamic, accounting for the effect of unplanned events and changes in thermal properties of capsule components over extended exposure to high temperatures and fast neutron irradiation. The sensitivity analysis performed in this work went beyond the traditional local sensitivity. Using experimental design, analysis of pairwise interactions of model parameters was performed to establish sufficiency of the time dependent first order (linear) expansion terms in constructing the temperature response surface. To achieve completeness, uncertainty propagation made use of pairwise noise correlations of model parameters. Furthermore, using an interpolation scheme over the input parameter domain, the analysis obtains time dependent sensitivity over the test campaign duration. This allows computation of uncertainty for the calculated fuel temperatures and the calculated graphite temperatures at thermocouple locations during the entire irradiation period.
This dataset supports a broader study examining the effects of intermittency on sediment respiration. The dataset provides sediment and surface water geochemistry and in situ sensor data from 7 perennial and 7 intermittent streams in San Antonio, Texas. Each stream/site was visited both in summer during base flow (July-September 2023) and winter during peak flow (January-February 2024). Related data were collected and will be published separately in collaboration with A. Veach. The data package was originally published in April 2025. It was updated in June 2025 (v2; modified and new files) and September 2025 (v3; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocol; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) sediment grain size data; (4) sediment iron (II) data and averages; (5) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment percent carbon and nitrogen; (11) sediment X-ray diffraction (XRD) data; (12) gravimetric moisture and averages; (13) a subfolder with sediment incubation respiration data, scripts, and plots; (14) surface water and sediment FTICR methods; and (15) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: The data processing methods for FTICR described in “v3_WHONDRS_AV1_Methods_Codes.csv” mistakenly indicate that users should process the data in Formultitude. The corrected description should read: “Both unprocessed and processed data are provided to allow users flexibility in data processing. Instructions and scripts for processing the data using CoreMS are included.” CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package.
This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.
Compressibility measurements and thermodynamic properties data for parahydrogen were extended to higher temperatures and pressures. Results of an experimental program are presented in the form of new pressure, volume and temperature data in the temperature range 23 to 300 K at pressures up to 800 bar. Also given are tables of thermodynamic properties on isobars to 1000 bar including density, internal energy, enthalpy, entropy, specific heats at constant volume and constant pressure, velocity of sound, and surface derivatives. The accuracy of the data is discussed and comparisons are made with previous data.
This dataset includes geoelectrical monitoring data acquired between October 2021 and November 2022, soil moisture and temperature data, groundwater data obtained from borehole SNIB covering the period from June 2021 to September 2022, and hydrological modelling results. The data were acquired to investigate how variations in bedrock type and topography, and vegetation cover control subsurface flow dynamics. To provide insights into the subsurface flow dynamics and their controls, a monitoring transect was installed at the Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO, measuring the spatio-temporal variations of soil moisture, soil and snow temperature, subsurface electrical resistivity variations, and groundwater dynamics. Field data are organized in a folder structure, with Electrical Resistivity Tomography (ERT) data being provided as one file per measurement, and data of the soil moisture and temperature sensors being provided as text files covering the entire monitoring period. The ‘Locations.csv’ file contains the location of all sensors, given in NAD83 – UTM Zone 13N. ERT monitoring data has been processed to filter data based on reciprocal errors (data with errors > 30% were removed), a linear error model was fitted to each survey, and to ensure a constant set of measurements for time-lapse inversion, filtered data were interpolated and assigned a 100% measurement error. Soil moisture and temperature data were acquired at 15 min intervals, and averaged to provide 1h data. Weather data and borehole data (groundwater depth, conductivity and temperature) were acquired at 30 min intervals, and are provided as daily measurements; all measurements are averaged, except of precipitation values, which are given as daily accumulation. The hydrological model was set up along the ERT monitoring transect, and net infiltration was used as surface boundary condition and derived from the weather data. Four different results are provided, (1) results for a parameterization using hydraulic permeability and porosity as derived from the ERT data through petrophysical relationships, and (2) three simplified model results, using 1 to 3 geological layers above the bedrock. Modelling was performed using PFLOTRAN, and for each model the PFLOTRAN input files are provided. The result files include weekly hydrological modelling results (e.g., saturation, velocities, pressures), as well as the model parameterization. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.
Wind tunnel tests were performed to determine pressures, heat transfer rates, and gas recovery temperatures in the base region of a rocket firing model of the space shuttle integrated vehicle during simulated yawed flight conditions. First and second stage flight of the space shuttle were simulated by firing the main engines in conjunction with the SRB rocket motors or only the SSME's into the continuous tunnel airstream. For the correct rocket plume environment, the simulated altitude pressures were halved to maintain the rocket chamber/altitude pressure ratio. Tunnel freestream Mach numbers from 2.2 to 3.5 were simulated over an altitude range of 60 to 130 thousand feet with varying angle of attack, yaw angle, nozzle gimbal angle and SRB chamber pressure. Gas recovery temperature data derived from nine gas temperature probe runs are presented. The model configuration, instrumentation, test procedures, and data reduction are described.
Surface and atmospheric radiation are being measured by space-borne conical scanning microwave radiometers with high accuracy and spatial resolution since 1980s'. These radiometers include Special Sensor Microwave Imager (SSM/I) onboard various NOAA's polar-orbiting satellites, Special Sensor Microwave Imager Sounder (SSMIS) onboard Defense Meteorological Satellite Program (DMSP) satellites, and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI), and Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard NASA and JAXA's satellites. Atmospheric temperature, humidity, clouds, and precipitation are retrieved using brightness temperature data at frequencies between 22 GHz – 183 GHz. Some of the brightness temperature and retrieved rain rate data made by these imagers are assimilated in NASA Global Modelling and Assimilation Office (GMAO)'s weather and climate data sets including the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its version 2 (MERRA-2) data sets. However, those radiance data are assimilated with the Goddard Earth Observing System model, version 5 (GEOS-5) only in clear-sky conditions. Recently, we started assimilate GPM/GMI radiance data with GEOS-5's atmospheric data assimilation system GSI under all-sky conditions in order to have better constraints in analyzed hydrological properties. GMAO plans to assimilate more microwave radiometers' brightness temperature data in its analyses. In this talk, we will discuss TMI and AMSR2 data's impact in temperature, humidity, clouds and precipitation under all-sky conditions in future GEOS reanalysis.
This paper explores realistic nonstationary atmospheric boundary layer (ABL) turbulence arising from nonstationarity at the mesoscale, particularly within offshore low-level jets with implications to offshore wind farms, using high-fidelity multiscale large-eddy simulations (LES). To this end, we analyzed the single-point turbulence statistical structure of a North-Atlantic offshore LLJ event simulated using high-resolution LES (AMR-Wind). The nonstationary LLJ is simulated using a mesoscale-to-microscale coupled (MMC) simulation procedure involving data assimilation of mesoscale velocity and temperature data from the Weather Research and Forecasting (WRF) model. Unlike the assimilation of mesoscale velocity data into the LES, the direct assimilation of temperature profiles had a strong impact on turbulence stratification, thereby causing erroneous predictions of turbulence both above and within the jet layer. Various approaches to mitigate this effect have resulted in multiple (four) variants of this MMC strategy. Outcomes from this work clearly show that the turbulence within the low-level jet is a strong function of the MMC approach as the turbulence structure within the low-level jet is dependent on the flux of residual turbulence from outside the jet, which in turn depends on the temperature forcing history. Additionally, the turbulence predicted by all these different methods (as well as the observation data) show similar deviations from equilibrium as evidenced by comparisons with idealized atmospheric turbulence structure obtained using the same numerical method. In general, we observe that the predicted LLJ turbulence tends to differ from canonical ABL turbulence with comparable shear. Particularly, the combination of shear and turbulence observed in such nonstationary low-level turbulence cannot be matched using equilibrium settings and therefore, represents a critical use-case for both testing and leveraging meso–micro coupling strategies.
The oscillating quartz crystal viscometer has been used to investigate possible viscoelastic behavior in synthetic lubricating fluids and to obtain viscosity-pressure-temperature data for these fluids at temperatures to 300 F and pressures to 40,000 psig. The effect of pressure and temperature on the density of the test fluids was measured concurrently with the viscosity measurements. Viscoelastic behavior of one fluid, di-(2-ethylhexyl) sebacate, was observed over a range of pressures. These data were used to compute the reduced shear elastic (storage) modulus and reduced loss modulus for this fluid at atmospheric pressure and 100 F as functions of reduced frequency.
Continued analysis of the pressure and temperature data returned by the two Vega mission balloons has revealed an apparently significant difference in mean atmospheric static stability between the two data sets. Furthermore, the stability is time dependent within each data set. The 6.5 K temperature contrast between the two balloons remains, and appears to have a counterpart in the contrast between two of the Pioneer Venus probes at these levels, which has been attributed to planetary scale waves. Comparisons of the Vega 2 lander data with those of the Pioneer Venus Large Probe shows relatively close agreement in the state properties and in the atmospheric static stability profiles as well.
This paper presents a scalable, autonomous framework for diagnostics of wind turbine gearbox failures using a multi-feature supervisory control and data acquisition data set spanning multiple years. Because of the size of the data set studied, all algorithms were constructed to be scalable using a Spark-Hadoop data framework. An unsupervised approach was used to detect significant failures based on predetermined criteria. The initial criteria selected were an abnormal spike in turbine component temperature followed by a turbine power off, which helps reduce the number of potential false alarms. To detect abnormal spikes in component temperature, a model was introduced to adjust the temperature data for effects caused by ambient temperature and normal temperature increases when loaded. This study evaluates methods for normalizing temperature sensor data to identify anomalies. The models that performed the best were a linear regression and a multivariate polynomial regression. The proposed process for finding failures has tunable parameters that can be adjusted to be more or less sensitive. The combination of sensor data normalization and application of these criteria is successful in finding turbine failures resulting in downtime. The proposed methods can operate without failure or maintenance logs and can be utilized for offline analysis of large high-resolution data sets.