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190 results for “intrusions”

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zenodo36/100

Experimental data generated on the study of partial intrusion and extrusion of C8-CH3 silica

Open access to experimental data generated by the project <a href="https://www.electro-intrusion.eu/en">Electro-Intrusion</a> (101017858, Horizon 2020, European Union) along with the research on the stability of hydrophobic porous materials to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2). Underlying data for the publication Paulo, G. et al. Partial Water Intrusion and Extrusion in Hydrophobic Nanopores for Thermomechanical Energy Dissipation. The Journal of Physical Chemistry C 2024, 128(29), 12036-12045. <a title="DOI URL" href="https://doi.org/10.1021/acs.jpcc.4c02900">https://doi.org/10.1021/acs.jpcc.4c02900</a>. Data related to Figures 2, 4 and 5 in the article.

opencc-by-4.0Oct 2024View details →

zenodo36/100

Experimental and theoretical data to study intrusion pressure and contact angle for a lyophobic material, Cu2L

/* ********** /* This work is licensed under a Creative Commons Attribution 4.0 International License. /* **********   Open access to theoretical and experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union). Research pertaining to Task 3.1 (WP3). Underlying data for the publication Merchiori, S. et al. Counterintuitive Trend of Intrusion Pressure with Temperature in the Hydrophobic Cu2(tebpz) MOF. Small 2024, 20,2402173. https://doi.org/10.1002/smll.202402173.  Data related to Figures 1, 2.

opencc-by-4.0Oct 2024View details →

zenodo36/100

High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula

Abstract: Saltwater intrusion (SWI) on coastal farmlands can change the soil properties (physical and chemical), rendering it unusable for agricultural purposes. Globally, over a quarter of arable land is negatively impacted by soil salinization, including more than 50% of irrigated land. These salt-impacted lands account for more than 30% of food production worldwide. However, the visible impacts of SWI on coastal ecosystems are challenging to map due to the fine spatial resolution of the salt patches. Here we provide the first mapping of the early visual evidences of SWI impacts on the Delmarva (Delaware, Maryland, Virginia) Peninsula region's farmlands by quantifying and mapping the proportions of the farmlands where the spectral signature of a white salt patch was detected. We focus our effort on fourteen counties on the Delmarva Peninsula. We utilized very high-resolution (1-m) aerial imagery from the National Agriculture Imagery Program (NAIP) and seasonal information derived from the moderate resolution (30-m) Landsat satellite imagery collection. Using a Random Forest algorithm with 100 trees and over 94,240 reference points for training and testing, we developed high-resolution geospatial datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland counties witnessed an average of 79% increase in the salt patches on farmlands. The average increase across the state of Delaware is 81%. Virginia experienced an average of 243% increase in these salt patches. While the expansion rate is alarming, the absolute area with these salt deposits remained rather small even in 2017: about 122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland. Visible white salt patches remained a small fraction of total farmlands in each of these counties, ranging between 0.01% and 0.18% in 2011-2013, and between 0.01% and 0.39% in 2016-2017. -  - - - - - - - - - - - - - - -  - - - - - - - - - - - - - - This collection of gridded data layers provides the spatial distribution of salt patches along with seven other land cover classes for 14 counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula in the United States of America (USA). We developed high-resolution datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The geospatial datasets are classified images for each time-step and have eight land cover categories as shown below: Raster value             Land cover/use category 1                                  Forest 2                                  Marsh 3                                  Salt patch 4                                  Built 5                                  Open water 6                                  Farmland 7                                  Bare soil 8                                  Other vegetation   Input Data: These geospatial data layers are derived using aerial data from the National Agriculture Imagery Program (NAIP) and satellite data from Landsat 5, 7, and 8. We accessed ortho-rectified NAIP images from June-July 2011 (Maryland), May 2012 (Virginia), September 2013 (Delaware), June 2016 (Virginia), June 2017 (Maryland), and July-August 2017 (Delaware) on the Google Earth Engine (GEE) platform. Cloud-masked top-of-atmosphere (TOA) reflectance images from Landsat 5 (2011, 2012), Landsat 7 (2013), and Landsat 8 (2016, 2017) were obtained using GEE. We derived several spectral indices from the original NAIP and Landsat bands and then used those as inputs into a Random Forest (RF) classifier on GEE. Methods: NAIP data contains 4 spectral bands (red, blue, green, and near-infrared) and have a 1 m spatial resolution. Several spectral indices were calculated from the NAIP imagery and used as input into the RF classifier, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and a Shadow Index (SI). A Principal Component Analysis (PCA) was used to generate four additional bands. In addition, four smoothed NAIP bands were generated using a 3x3 boxcar kernel. NDVI = (Near-infrared – Red) / (Near-infrared + Red) NDWI = (Green – Near-infrared) / (Green + Near-infrared) SI = (256 – Blue) * (256 + Blue) In order to address limited spectral resolution of the NAIP data and lack of year-long coverage, we incorporated seasonal information from Landsat data. Landsat is a series of satellites launched by the National Aeronautics and Space Administration (NASA) with satellite images distributed through the United States Geological Survey (USGS). Landsat data includes red, green, blue, near-infrared, shortwave infrared, aerosol, cirrus, panchromatic, and thermal bands. All bands are collected at a 30 m resolution except the panchromatic band, which is collected at a 15 m resolution and the thermal bands which are collected at a 100 m resolution. In this work, Landsat 5 was used for 2011 and 2012, Landsat 7 was used for 2013, and Landsat 8 was used for 2016 and 2017. Landsat data (spatial resolution: 30 m) were fused with NAIP data to a resolution of 1 m. An Enhanced Vegetation Index (EVI) was calculated from Landsat bands for each of the four seasons (June-August, September-November, December-February, and March-May) and was then used as input into the RF classifier. Seasonal data was reduced using a median reducer. Landsat thermal bands for each season were also used in the classification. Again, bands were smoothed using a 3x3 boxcar kernel. EVI = (NIR-Red) / (NIR+6*Red-7.5*Blue+1) A Random Forest (RF) classifier was used with input data comprised of the four NAIP bands, four PCA bands from NAIP, three indices from NAIP, four smoothed NAIP bands, four smoothed seasonal EVI bands from Landsat, and four smoothed seasonal thermal bands (from Landsat 5) or eight when (for Landsat 7 or 8) – all sampled to a 1 m resolution to match the NAIP input bands. Due to the high resolution of the input data, there is a considerable 'salt-and-pepper' effects or speckle effects on the classified image, especially for the salt deposit class and its surroundings. As a post-processing step to reduce such speckle effects, we applied a majority filter to the classified image using eight pixel neighbors. For example, any solitary salt patch pixel was reclassified as the majority land cover within the immediate neighborhood. Furthermore, we considered only patches of 10 or more connected 'salt patch' pixels as a valid salt signature. We also used a road mask to minimize the confusion between impervious streets and salt deposits. Accuracy assessment: A total of 94,240 reference points were collected from ground surveys and visual interpretation of NAIP imagery from both time periods. 70% of these points were used to train the RF classifier and 30% were used to test accuracy. We calculated user’s accuracy, producer’s accuracy, overall accuracy, kappa statistic, and the F-Score as shown below. <table> <tbody> <tr> <td> Delaware 2013 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 88.46% </td> <td> 90.89% </td> <td> 0.90 </td> <td> 86.37% </td> <td> 0.83 </td> </tr> <tr> <td> Marsh </td> <td> 84.03% </td> <td> 80.13% </td> <td> 0.82 </td> </tr> <tr> <td> Salt patch </td> <td> 97.02% </td> <td> 71.18% </td> <td> 0.82 </td> </tr> <tr> <td> Built </td> <td> 94.56% </td> <td> 95.06% </td> <td> 0.95 </td> </tr> <tr> <td> Water </td> <td> 91.01% </td> <td> 96.63% </td> <td> 0.94 </td> </tr> <tr> <td> Farmland </td> <td> 83.16% </td> <td> 86.19% </td> <td> 0.85 </td> </tr> <tr> <td> Bare Soil </td> <td> 87.70% </td> <td> 87.30% </td> <td> 0.88 </td> </tr> <tr> <td> Other Vegetation </td> <td> 82.46% </td> <td> 84.20% </td> <td> 0.83 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Delaware 2017 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 95.07% </td> <td> 90.30% </td> <td> 0.93 </td> <td> 91.37% </td> <td> 0.90 </td> </tr> <tr> <td> Marsh </td> <td> 88.86% </td> <td> 92.44% </td> <td> 0.91 </td> </tr> <tr> <td> Salt patch </td> <td> 91.82% </td> <td> 85.59% </td> <td> 0.89 </td> </tr> <tr> <td> Built </td> <td> 87.58% </td> <td> 93.54% </td> <td> 0.90 </td> </tr> <tr> <td> Water </td> <td> 92.68% </td> <td> 90.48% </td> <td> 0.92 </td> </tr> <tr> <td> Farmland </td> <td> 91.61% </td> <td> 93.61% </td> <td> 0.93 </td> </tr> <tr> <td> Bare Soil </td> <td> 95.67% </td> <td> 87.67% </td> <td> 0.91 </td> </tr> <tr> <td> Other Vegetation </td> <td> 91.16% </td> <td> 90.24% </td> <td> 0.91 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Maryland 2011 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 88.32% </td> <td> 90.97% </td> <td> 0.90 </td> <td> 87.20% </td> <td> 0.85 </td> </tr> <tr> <td> Marsh </td> <td> 87.14% </td> <td> 82.08% </td> <td> 0.85 </td> </tr> <tr> <td> Salt patch </td> <td> 96.74% </td> <td> 78.76% </td> <td> 0.87 </td> </tr> <tr> <td> Built </td> <td> 89.02% </td> <td> 88.50% </td> <td> 0.89 </td> </tr> <tr> <td> Water </td> <td> 92.74% </td> <td> 96.10% </td> <td> 0.94 </td> </tr> <tr> <td> Farmland </td> <td> 84.31% </td> <td> 89.83% </td> <td> 0.87 </td> </tr> <tr> <td> Bare Soil </td> <td> 87.53% </td> <td> 90.05% </td> <td> 0.89 </td> </tr> <tr> <td> Other Vegetation </td> <td> 86.11% </td> <td> 80.57% </td> <td> 0.83 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Maryland 2017 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 88.66% </td> <td> 88.66% </td> <td> 0.89 </td> <td> 87.34% </td> <td> 0.84 </td> </tr> <tr> <td> Marsh </td> <td> 87.65% </td> <td> 88.35% </td> <td> 0.88 </td> </tr> <tr> <td> Salt patch </td> <td> 93.29% </td> <td> 68.30% </td> <td> 0.79 </td> </tr> <tr> <td> Built </td> <td> 93.44% </td> <td> 86.92% </td> <td> 0.90 </td> </tr> <tr> <td> Water </td> <td> 92.36% </td> <td> 92.36% </td> <td> 0.92 </td> </tr> <tr> <td> Farmland </td> <td> 83.84% </td> <td> 94.55% </td> <td> 0.89 </td> </tr> <tr> <td> Bare Soil </td> <td> 92.86% </td> <td> 82.61% </td> <td> 0.87 </td> </tr> <tr> <td> Other Vegetation </td> <td> 86.56% </td> <td> 76.00% </td> <td> 0.81 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Virginia 2012 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 84.65% </td> <td> 91.18% </td> <td> 0.88 </td> <td> 86.88% </td> <td> 0.84 </td> </tr> <tr> <td> Marsh </td> <td> 87.03% </td> <td> 84.39% </td> <td> 0.86 </td> </tr> <tr> <td> Salt patch </td> <td> 97.67% </td> <td> 72.41% </td> <td> 0.83 </td> </tr> <tr> <td> Built </td> <td> 90.97% </td> <td> 86.24% </td> <td> 0.89 </td> </tr> <tr> <td> Water </td> <td> 94.17% </td> <td> 87.39% </td> <td> 0.91 </td> </tr> <tr> <td> Farmland </td> <td> 86.87% </td> <td> 91.81% </td> <td> 0.89 </td> </tr> <tr> <td> Bare Soil </td> <td> 85.82% </td> <td> 83.04% </td> <td> 0.84 </td> </tr> <tr> <td> Other Vegetation </td> <td> 83.60% </td> <td> 80.59% </td> <td> 0.82 </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> Virginia 2016 </td> </tr> <tr> <td> Categories </td> <td> User’s accuracy </td> <td> Producer’s accuracy </td> <td> F-score </td> <td> Overall </td> <td> Kappa </td> </tr> <tr> <td> Forest </td> <td> 84.65% </td> <td> 86.00% </td> <td> 0.85 </td> <td> 85.83% </td> <td> 0.83 </td> </tr> <tr> <td> Marsh </td> <td> 86.25% </td> <td> 90.72% </td> <td> 0.88 </td> </tr> <tr> <td> Salt patch </td> <td> 90.61% </td> <td> 62.12% </td> <td> 0.74 </td> </tr> <tr> <td> Built </td> <td> 88.70% </td> <td> 77.72% </td> <td> 0.83 </td> </tr> <tr> <td> Water </td> <td> 92.12% </td> <td> 84.41% </td> <td> 0.88 </td> </tr> <tr> <td> Farmland </td> <td> 85.17% </td> <td> 90.36% </td> <td> 0.88 </td> </tr> <tr> <td> Bare Soil </td> <td> 83.30% </td> <td> 88.54% </td> <td> 0.86 </td> </tr> <tr> <td> Other Vegetation </td> <td> 84.49% </td> <td> 84.17% </td> <td> 0.84 </td> </tr> </tbody> </table> While our datasets have an overall high accuracy, a few caveats should be considered when utilizing the data for other applications. Misclassifications of salt patches might arise from a flooding event immediately prior to the image acquisition or spectral similarity with marsh. Misclassifications might also arise from spectral similarities between crop fields and other vegetation, which typically encompasses open fields and lawns. Shadows are sometimes misclassified as water, or built. The algorithm used in this work often under-predicted salt patches, because the typical bright white signature of these patches can be altered when those areas become wet, leading these areas to be classified as crop fields. Some of the areas classified as salt patches might be bleached siliceous minerals visible on the soil surface.   Data format: The spatial resolution of all the derived datasets is 1 m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis. Code: Sample code is available at <a href="https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806">https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806</a>. The user would need to upload study area boundaries and reference points in order to successfully run these codes. Datasets for download: <ul> <li>Two zipped data layers for Delaware:</li> </ul> <ol> <li>DE_3counties_2013</li> <li>DE_3counties_2017</li> </ol> These data layers cover 3 counties: Kent, New Castle, Sussex. <ul> <li>Two zipped data layers for Maryland:</li> </ul> <ol> <li>MD_9counties_2011</li> <li>MD_9counties_2017</li> </ol> These data layers cover 9 counties: Caroline, Cecil, Dorchester, Kent, Queen Anne's, Somerset, Talbot, Wicomico, Worcester. <ul> <li>Two zipped data layers for Virginia:</li> </ul> <ol> <li>VA_2counties_2012</li> <li>VA_2counties_2016</li> </ol> These data layers cover 2 counties: Accomack, Northampton. We also provided a color map (DELMARVA_ColorMap.clr) that can be used with these data files.  Data citation: Mondal, P., Walter, M., Miller, J., Epanchin-Niell, R., Yawatkar, V., Nguyen, E., Gedan, K. and Tully, K. 2022. High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula. Available at: 10.5281/zenodo.6685695. Accessed DAY MONTH YEAR.

opencc-by-4.0Dec 2022View details →

zenodo36/100

Simulation of Moisture intrusions into the Arctic: New insights from a mid-April 2020 case during MOSAiC

This dataset contains the model configuration files and selected output data of the large-eddy simulation experiments as described in the publication "Moisture intrusions into the Arctic: New insights from a mid-April 2020 case during MOSAiC" being submitted to Frontiers in Earth Science (Section Atmospheric Science)

opencc-by-4.0Jan 2023View details →

zenodo36/100

Dataset for "The Tectonics and Volcanism of Venus: New Modes Facilitated by Realistic Crustal Rheology and Intrusive Magmatism"

This repository contains data files to produce all the figures presented in the manuscript titled 'The Tectonics and Volcanism of Venus: New Modes Facilitated by Realistic Crustal Rheology and Intrusive Magmatism'. Models were run using the code StagYY (Tackley, 2008), and visualization and post-processing was done using the MATLAB files within this repository.

opencc-by-4.0Feb 2023View details →

zenodo36/100

An atomistically informed multiscale approach to the intrusion and extrusion in hydrophobic nanopores

Data from Molecular Dynamics simulations. Files ending in .dat are the "measurement" files and are the files required to compute the free energy and diffusivity from the simulation data. The normal sintax is eqX.dat where X is the imposed filling of the pore. The names of the folders represent the pressure at which the filling was taken (0 MPa, -20 MPa, 60 MPa) We also attach one trajectory file, 0.xyz and one LAMMPS output file, 0.log, which should be enough to reproduce the simulation script, in conjuction with 0.data, the initial condition.

opencc-by-4.0May 2023View details →

zenodo36/100

REVIEWING THE MORPHOEMETRIC PARAMETERS OF LUNAR INTRUSIVE DOMES USING LROC AND KAGUYA DATA

Auxiliary data for the poster presentation at the ELS 2023.

opencc-by-4.0Jun 2023View details →

dryad36/100

Data For: Seawater intrusion at the grounding line of Jakobshavn Isbrae, Greenland, from Terrestrial Radar Interferometry (TRI)

Jakobshavn Isbrae is a major outlet glacier in West Greenland that lost its protective ice shelf in 2002 and has been speeding up and retreating since. We image its grounding line for the first time with a ground portable radar interferometer deployed in 2016 and detect its migration at tidal frequencies. The southern half of the glacier develops a floating section (3 km x 3 km) that migrates in phase with the tide up to a distance of 1.5 km, which is far more than expected from flotation. We attribute the migration to kilometer-scale seawater intrusions, 10-20 cm in height, occurring at high tide. The intrusions reveal that the glacier bed must be 100-600 m deeper than expected on the south side, which illustrates that our knowledge of bed topography remains limited in this sector. We expect seawater intrusions to cause rapid melt of basal ice and play a major role in the glacier evolution.

opencc-zeroSep 2023View details →

zenodo36/100

Scaled laboratory experiments of analogue magma intrusion in granular material: X-ray Computed Tomography imagery and displacement data

This data set contains X-ray Computed Tomography (CT) images and surface displacement data of 15 scaled laboratory experiments of analogue magma intrusion in granular material. The experimental methodology and the experimental results were described in detail by Poppe et al. (2019). Displacement data of experiment SPCTIN14 was used by Poppe et al. (2023). When using the experimental imagery or their derivatives please reference at a minimum Poppe et al. (2019) and this data set (Poppe et al., 2023, Zenodo data set). The included explanatory notice reproduces the experimental method and presents the structure and file types contained in this data set.

opencc-by-4.0Oct 2023View details →

zenodo36/100

Artifact: SoK: Evaluations in Industrial Intrusion Detection Research

This collection resembles the artifact of our publication "SoK: Evaluations in Industrial Intrusion Detection Research," published in the Journal of Systems Research 2023, in which we performed a systematic mapping study on the literature of Industrial Intrusion Detection Systems (IIDSs). Within this paper, we systematically analyzed the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. This artifact contains our data extracted from the 609 publications under consideration in the survey. For further information, please refer to the respective publication.

opencc-by-4.0Oct 2023View details →

ClinicalTrials.gov36/100

Dealing With Intrusive Thoughts in OCD - a Comparison of Detached Mindfulness and Cognitive Restructuring

ClinicalTrials.gov study NCT03002753. IPD Sharing: Not stated. Countries: 1. Publications: 10.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad36/100