To aid in parameterization of mechanistic, statistical, and machine learning models of hydrologic systems in the contiguous United States (CONUS), flow-conditioned parameter grids (FCPGs) have been generated describing upstream basin mean elevation, slope, land cover class, latitude, and 30-year climatologies of mean total annual precipitation, minimum daily air temperature, and maximum daily air temperature. Additional datasets of upstream basin area and binary stream presence-absence are provided to help validate queries against the flow-conditioned data. These data are provided as virtual raster tile (vrt) mosaics of cloud optimized GeoTIFFs to allow point queries of the data (see Distribution Information) without requiring downloading the whole dataset.

Real-time neuron detection and neural activity extraction are critical components of real-time neural decoding. They are modeled effectively in dataflow graphs. However, these graphs and the components within them in general have many parameters, including hyper-parameters associated with machine learning sub-systems. The dataflow graph parameters induce a complex design space, where alternative configurations (design points) provide different trade-offs involving key operational metrics including accuracy and time-efficiency. In this paper, we propose a novel optimization framework that automatically configures the parameters in different neural decoders. The proposed optimization framework is evaluated in depth through two case studies. Significant performance improvement in terms of accuracy and efficiency is observed in both case studies compared to the manual parameter optimization that was associated with the published results of those case studies. Additionally, we investigate the application of efficient multi-threading strategies to speed-up the running time of our parameter optimization framework. Our proposed optimization framework enables efficient and effective estimation of parameters, which leads to more powerful neural decoding capabilities and allows researchers to experiment more easily with alternative decoding models.

This dataset is a continuous parameter grid (CPG) of upstream contributing area data (flow accumulation) in the Pacific Northwest. Source data come from the U.S. Geological Survey National Elevation Dataset and NHDPlus Version 2.

This dataset was produced by the U.S. Geological Survey (USGS) in cooperation with the Wyoming Water Development Office for the purpose of calculating basin characteristics in preparation for the Wyoming StreamStats application. These datasets are raster representations of various environmental, geological, and land use attributes with the Wyoming StreamStats study area and will be served in the Wyoming StreamStats application to describe delineated watersheds. The StreamStats application provides access to spatial analytical tools that are useful for water-resources planning and management, and for engineering and design purposes. The map-based user interface can be used to delineate drainage areas, get basin characteristics and estimates of flow statistics. To aid in parameterization of mechanistic, statistical, and machine learning models of hydrologic systems in the Wyoming StreamStats study area, flow-conditioned parameter grids (FCPGs) have been generated describing upstream ...

Hypoplastic left heart syndrome (HLHS) is a congenital heart disease responsible for 23% of infant cardiac deaths each year in the United States. HLHS patients are born with an underdeveloped left heart, requiring several surgeries to reconstruct the aorta and create a single ventricle circuit known as the Fontan circulation. While survival into early adulthood is becoming more common, Fontan patients suffer from reduced cardiac output, putting them at risk for a multitude of complications. These patients are monitored using chest and neck MRI imaging, but these scans do not capture energy loss, pressure, wave intensity, or hemodynamics beyond the imaged region. This study develops a framework for predicting these missing features by combining imaging data and computational fluid dynamics (CFD) models. Predicted features from models of HLHS patients are compared to those from control patients with a double outlet right ventricle (DORV). We infer patient-specific parameters through the proposed framework. In the calibrated model, we predict pressure, flow, wave intensity (WI), and wall shear stress (WSS). Results reveal that HLHS patients have lower compliance than DORV patients, resulting in lower WSS and higher WI in the ascending aorta and increased WSS and decreased WI in the descending aorta.

This parameter file is for VIC5 baseline simulation over the Colorado River basin.

The spatial resolution is 1/16 degree.

A few extra grid cells in the Mexico near the boarder is also unnecessarily included, which do not drainage to the CRB.

Users can get rid of those pixels with a more precise domain mask.

Also include VIC source code (see the readme.txt in the zipped file for details)

The updates in Sep, 2022 includes the natural flow dataset used in the study for VIC streamflow evaluation

The bold ones represent the selected parameter using grid search [25].

The Geospatial Fabric for National Hydrologic Modeling (Viger and Bock, 2014; Bock and others, 2021) is a dataset of hydrographic features and spatial data for use within the National Hydrologic Model that covers the conterminous United States (CONUS), Hawaii, and most major river basins that flow in from Canada. This U.S. Geological Survey (USGS) data release consists of the geospatial fabric features and other related spatial datasets created to expand the National Hydrologic Model to Alaska. The National Hydrologic Model database contains parameters for hydrologic response units (HRUs) and stream segments needed to run the NHM. These parameters are generated using python scripts to process input datasets such as digital elevation models, soil maps, and land cover classifications. Many of the parameters were left at their default model value as they would need to be calibrated as part of the PRMS model development process. Please refer to the Supplemental Information and the Process Description elements of this metadata record for more details on the source datasets and scripts used to generate these parameters.

Overview of Data

The site includes data only for the two subjects: Ceu-pacific and JBilling. For both the subjects, the “.model” shows the model created from the business rules obtained from respective websites, and “_HighLevelTests.csv” shows the tests generated. Among csv files, we show tests generated by both BUSTER and Exhaust as well.

Paper Abstract

Test cases that drive an application under test via its graphical user interface (GUI) consist of sequences of steps that perform actions on, or verify the state of, the application user interface. Such tests can be hard to maintain, especially if they are not properly modularized—that is, common steps occur in many test cases, which can make test maintenance cumbersome and expensive. Performing modularization manually can take up considerable human effort. To address this, we present an automated approach for modularizing GUI test cases. Our approach consists of multiple phases. In the first phase, it analyzes individual test cases to partition test steps into candidate subroutines, based on how user-interface elements are accessed in the steps. This phase can analyze the test cases only or also leverage execution traces of the tests, which involves a cost-accuracy tradeoff. In the second phase, the technique compares candidate subroutines across test cases, and refines them to compute the final set of subroutines. In the last phase, it creates callable subroutines, with parameterized data and control flow, and refactors the original tests to call the subroutines with context-specific data and control parameters. Our empirical results, collected using open-source applications, illustrate the effectiveness of the approach.

This data set consists of physics-based Delft3D-FLOW and SWAN hydrodynamic models input files used to study the wave-induced 3D flow over spur-and-groove (SAG) formations. SAG are a common and impressive characteristic of coral reefs. They are composed of a series of submerged shore-normal coral ridges (spurs) separated by shore-normal patches of sediment (grooves) on the fore reef of coral reef environments. Although their existence and geometrical properties are well documented, the literature concerning the hydrodynamics around them is sparse. Here, the three-dimensional flow patterns over SAG formations, and a sensitivity of those patterns to waves, currents, and SAG geometry were examined. Shore-normal shoaling waves over SAG formations were shown to drive two circulation cells: 1) a cell on the lower fore reef with offshore flow over the spur and onshore flow over the groove, except near the seabed where velocities were always onshore; and 2) a cell on the upper fore reef with offshore surface velocities and onshore bottom currents, which result in depth-averaged onshore and offshore flow over the spurs and grooves, respectively. These input files accompany the modeling conducted for the following publication: da Silva, R.F., Storlazzi, C.D., Rogers, J.S., Reyns, J., and McCall, R., 2020, Modeling three-dimensional flow over spur-and-groove morphology: Coral Reefs, https://doi.org/10.1007/s00338-020-02011-8.

Wind SWE Key Parameter data: proton density, thermal speed, flow velocity vectors, and spacecraft position vectors. Various versions differ slightly from each other. The version at MIT has flow velocity vectors experessed bby using Geocentric Solar Ecliptic, GSE, Cartesian and spherical representations and GSE Cartesian position vectors. The version available via nssdcftp and FTPBrowser has temperature instead of thermal speed and has no flow direction angles. The CDAWeb version has flow velocity and spacecraft position vectors in both GSE and Geocentric Solar Magnetospheric, GSM, coordinates, flow dynamic pressure, NmV^2, and velocity and density quality flags. The data were progressively despiked in passing from CDAWeb to MIT to nssdcftp/FTPBrowser.Use of the Quality Variables:Quality flags are set in the analysis program that generates the KP data. Previous descriptions of their meaning were out of date.Good data is indicated by a quality flag equal to 0.The quality flags for each parameter are given as integers 4 bytes long, integer4.The individual bits for each quality value are set or cleared in the analysis code by adding or subtracting a power of 2 as follows. To set the first bit, add 1, the second bit, add 2, the third bit, add 4, the fourth bit, add 8, and so on. See the table below.+------------------------------------------------------------------------------------------------------------------------------+| Bit | Set Value | MEANING ||------------------------------------------------------------------------------------------------------------------------------|| 1 | 1 | Three point parabolic fits to proton peaks were not attempted. || 2 | 2 | Non-linear least squares fit was not attempted. || 3 | 4 | Three point parabolic fits to proton peaks failed. || 4 | 8 | Non-linear least squares fit failed. || 5 | 16 | Alpha parameters not valid since the non-linear least squares fit was done for protons only. || | | Not enough good energy channels to do simultaneous alpha fit. This value applies to iqual_core(5) only. || 6 | 32 | Analysis code unable to get good value for spin period. || 7 | 64 | SWE instrument in mode 1, calibration state mode. Key parameters are produced in mode 1, science mode. || 8 | 128 | Three point fits done for cup 1 only. Split collector ratio of currents used to get the north/south angle. || | | Either cup 2 turned off, or cup 2 densities were low indicating noise associated with vibration. || 9 | 256 | Fewer than ten fc_blocks in spectrum. Analysis skipped. || 10 | 512 | Alpha particle non-linear fit produced values of density and thermal speed that do not seem reasonable. || 11 | 1024 | Three point parabolic fits to proton peaks done for cup 2 only. Probably Cup 1 is turned off. || | | The ratio of currents on split collectors used to get north/south angle. || 12 | 2048 | Single width windows. Delta E over E 6.5% instead of the default 13%. || 13 | 4096 | Tracking mode operation. || 14 | 8192 | Limited tracking mode scan, not a full scan. |+------------------------------------------------------------------------------------------------------------------------------+Particular flag settings:+-------------------------------------------------------------------------------------------+| Flag Value | Meaning ||-------------------------------------------------------------------------------------------|| 4098 | Tracking mode operation is full scan (4096) and No non-linear fits (2) || 14338 | Tracking mode operation is full scan (4096) and Limited tracking mode (8192) |+-------------------------------------------------------------------------------------------+Comments: Note that in bit 4 of the quality flag the non-linear fit may be reported as good for protons and, at the same time, not good for alphas. Non-linear fits are not done for Key Parameters, KPs, but those parameter values are excellent and should be used to do science. Non-linear fits are available are available in this data product, but they have problems which suggests strongly that the KP parameters should be used, see the paper by Kasper et al., Physics-based tests to identify the accuracy of solar wind ion measurements: A case study with the Wind Faraday Cups, J. Geophys. Res., 111, A03105, DOI: 101029/2005JA011442.* Note that all quality flag values that are even numbers are for data values when non-linear fits were not attempted.For the complete guide to the quality flag values see https://cdaweb.gsfc.nasa.gov/wind_swe_quality.html.Note that this SPASE Numerical Description only describes the MESSENGER Magnetometer data stored in Common Data Files. Other links to Wind SWE data in non CDF format are listed in the following table:+--------------------------------------------------------------------------------------------------------------------------------------------+| SPASE Repository Resource ID | Data Source | Data Access URL ||--------------------------------------------------------------------------------------------------------------------------------------------|| SMWG/Repository/MIT_CSR | ASCII via ftp from MIT | ftp://space.mit.edu/pub/plasma/wind/kp_files/ || SMWG/Repository/NASA/GSFC/SPDF | ASCII via ftps from SPDF | ftps://spdf.gsfc.nasa.gov/pub/data/wind/swe/ascii/swe_kp_unspike/ || SMWG/Repository/NASA/GSFC/SPDF | ASCII via https from SPDF | https://spdf.gsfc.nasa.gov/pub/data/wind/swe/ascii/swe_kp_unspike/ || SMWG/Repository/NASA/GSFC/SPDF | Subset, plot and list via FTPBrowser | https://omniweb.gsfc.nasa.gov/ftpbrowser/wind_swe_kp.html |+--------------------------------------------------------------------------------------------------------------------------------------------+

These datasets are used for training and testing MLP-Net.

Wastewater treatment facilities must manage water quality during both average flow and extreme events, including wet and dry weather periods. Constituent concentrations can increase during dry weather flow events, since facilities experience reduced incidental infiltration and influent flows may be lower as a result. A script was developed using self-reported data taken from the California Integrated Water Quality System Project (CIWQS) to clean, filter, and organize a batch dataset of influent flows, constituent concentrations, and constituent mass loadings from California wastewater treatment facilities. The script develops two summary data sheets for each facility: one that contains total suspended solids (TSS) data and one that contains biochemical oxygen demand (BOD) data. Data from 426 wastewater treatment facilities was accessed through CIWQS. 104 facilities had adequate BOD data and 105 facilities had adequate TSS data. These summary sheets can be used to quickly assess an individual facility’s dry weather or baseline influent flows. A separate script uses these sheets to prepare a composite summary of BOD, TSS, and influent flow during recent drought-related water use restrictions and the following period of eased restrictions.

This repository contains hydrological parameter sets derived using the hybrid regionalization method for three distinct streamflow signatures: Streamflow Signatures: Q10: Represents low flow, indicating the nonexceedance probability of 0.1 for daily streamflow. Q90: Represents high flow, with a nonexceedance probability of 0.9 for daily streamflow. Qmean: Indicates the mean annual flow. Parameters for 464 CAMELS Basins: CAMELS_1000_parameters.csv: Contains 1,000 ensemble parameter sets generated using the Latin hypercube sampling method for CLM5, encompassing 15 hydrological parameters. CAMELS_q10_behavioral_parameter_num.csv: Provides the behavioral ensemble parameter sets for the Q10 streamflow signature for each basin. The associated ID number refers to entries in the CAMELS_1000_parameters.csv file. A minimum of 10 ensemble parameter sets are available for each basin. CAMELS_q90_behavioral_parameter_num.csv: Similar to the above file but for the Q90 streamflow signature. CAMELS_qmean_behavioral_parameter_num.csv: Corresponds to the Qmean streamflow signature, similar to the previous files. Parameters for 50,629 1/8° CONUS Land Grid Cells: CONUS_350_parameters.csv: Contains 350 ensemble parameter sets derived using the Latin hypercube sampling method for CLM5's 15 hydrological parameters within 1/8° CONUS land grid cells. CONUS_q10_behavioral_parameter_num.csv: Holds the behavioral ensemble parameter sets for the Q10 streamflow signature, organized for each grid cell. The ID number relates to entries in CONUS_350_parameters.csv. A minimum ofmore » 10 ensemble parameter sets are provided for each grid cell. CONUS_q90_behavioral_parameter_num.csv: Similar to the above file but focusing on the Q90 streamflow signature. CONUS_qmean_behavioral_parameter_num.csv: Corresponds to the Qmean streamflow signature, following a similar structure to the previous files. « less

Bursty bulk flows (BBFs) play a crucial role in transporting energy, mass, and magnetic flux from the Earth’s magnetotail to the near-Earth region. However, their impulsive nature and small spatial scale pose significant difficulties for in-situ observations, given that only a handful number of spacecraft operate within the vast expanse of the magnetotail. Consequently, accurately predicting their behavior remains a challenging goal. In this study, we employ the XGBoost machine learning algotithm to predict the variation range of several essential BBF properties, including duration, magnetic field, plasma moments, and specific entropy parameters. The observed characteristics of a BBF are shaped by its formation in the downstream tail and its journey until it reaches the spacecraft. Therefore, we use both the background properties of the plasma sheet prior to the arrival of the BBF and the attributes of indirectly related variables during the BBF interval as inputs. Trained on 17 years of THEMIS data, we explore different input configurations. One approach involves incorporating optimal parameter combinations, utilizing as many input parameters as possible to predict upper and lower bounds of a target variable. Within this framework, we further apply the leave-one-feature-out method to quantitatively assess the contribution of each input, identifying the most dominant factor influencing BBFs in a statistical sense. Another approach involves cross-instrument prediction, leveraging measurements from a different payload. Our findings reveal that including observed background values enhances prediction accuracy by 10–20 percentage points. This study offers data-driven insights to improve BBF predictability, providing valuable guidance for future space weather monitoring and theoretical research.

The input parameters used by the parameter estimation framework performing the personalization and the corresponding output parameter values for all 15 patient data sets

Data for publication "Development of a dual-domain karst flow model under consideration of preferential film-flow dynamics and analysis of compartment-specific parameter sensitivities

A 3-dimensional (3D) synthetic model, using MODFLOW-NWT and MT3D-USGS, explores the new unsaturated zone heat transport capabilities in MT3D-USGS. Model simulations were used to explore various parameter sensitivities and unsaturated zone thicknesses and their impact on heat transport as water enters the subsurface as infiltration, flows through the unsaturated zone to the water table, and eventually exits the groundwater system as discharge to surface-water bodies. The 3D-model is patterned after a real-world watershed located in a region with a temperate, humid climate. Twenty-seven model simulations are included in this data release with the primary distinction between them the thickness of the unsaturated zone. Most model simulations either have a moderately think unsaturated zone or thick unsaturated zone. A small subset of model simulations do not simulate the downward movement of infiltration through the unsaturated zone but apply infiltration directly to the water table using the recharge (RCH) package. Twelve sensitivity run are included in the data release to help illuminate which parameters are important when engaged in heat transport simulation. This investigation begins to explore the impacts of a warming climate on groundwater temperature and ultimately on the temperature of groundwater discharge back to surface-water bodies. The synthetic model evaluated is a necessary first step to real-world applications that are more complicated by nature's heterogeneity. Temperatures simulated by MT3D-USGS were previously verified by comparing output from detailed 1-dimensional models to VS2DH output (https://doi.org/10.1111/gwat.13256). This USGS data release contains the input and output data files for the model simulations described in the associated journal article (https://doi.org/10.3390/w14233883). Model input files were developed from published information; no new datasets were collected as part of the modeling study associated with this data release. Details on data input sources and on processing of input and output files are documented in the associated journal article.

We will take advantage of recent advancements in laser technology to explore new measurement techniques for NASA’s wind tunnel facilities. Femtosecond (fs) lasers are 100,000x shorter pulse than typical 10 ns pulse lasers resulting in much higher power than existing previously laser technology. Can generates nonlinear effects: FLEET and FS CARS (FLEET = Femtosecond laser electronic excitation and tagging; CARS = Coherent anti-Stokes Raman Spectroscopy). Can use these techniques to measure velocity, temperature, pressure or density in wind tunnel flows with unprecedented precision accuracy. FLEET works very well in high pressure, low temperature N2 flows, such as our NTF. Such measurements will provide knowledge about flow fields as well as quantitative data for comparison with computations of the flow.

This is an exercise in optimizing the flow through a shrouded axial turbine to have the least resistance and to have optimal output and torque and energy. In this study, different variations of the original geometry of the current turbine designed by Hydrokinetic Energy Corp. (HEC) were evaluated for energy efficiency using Computational Fluid Dynamics (CFD). The objective was accomplished by a parametric study of the key geometric parameters for the shroud, the diffuser, and the hub. Project is part of the TEAMER RFTS 3 (request for technical support) program.