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7,129 results for “injection”
Supporting data for “Climate Intervention through Stratospheric Aerosol Injection may partially mitigate marine heatwaves"
Although climate intervention aims to lower the global average temperature, the potential impact of Stratospheric Aerosol Injection on marine heatwaves (MHW) has not been thoroughly examined. This spatial dataset provides global and regional MHW metrics—such as frequency, maximum intensity, and duration—from the Community Earth System Model, version 2 (CESM2), using the baseline scenario SSP2-4.5, referred to as a no climate intervention scenario, and the ARISE-SAI ensemble. The ARISE-SAI model uses the SSP2-4.5 scenario, introducing stratospheric aerosol injection at approximately 21 km in 2035, aiming to keep global mean surface air temperature near 1.5°C for ARISE-SAI-1.5 and near 1.0°C for ARISE-SAI-1.0 above pre-industrial levels. The dataset includes global MHW properties for the historical period (1990-2009), the current period under SSP2-4.5 emission scenario (2015-2034), and future scenarios under SSP2-4.5, ARISE-SAI-1.5, and ARISE-SAI-1.5 for 2050-2059 and 2060-2069.
Design of an elastic porous injectable biomaterial for tissue regeneration and volume retention: raw dataset
<p>Raw dataset for the publication:</p> <p><strong>Design of an elastic porous injectable biomaterial for tissue regeneration and volume retention</strong></p>
The role of injection method on residual trapping at the pore-scale in continuum-scale samples: segmented data
<p>The experiments in this work explore the role of a variable injection rate on gas saturation and residual trapping. There are 2 experiments in this work H2L (high to low injection rate) and L2H (low to high injection rate). The workflow for processing the micro-CT images to get the segmented images is described in [1]. </p><p>The following scans are included in this repository NB. all data for this repository is segmented micro-CT data.: </p><ol><li>Dry scan prior to experiment = merged_binning_2_38_1927</li><li>H2L during high flow = merged_segmented_flow_09_h2lh_merged</li><li>H2L during low flow = merged_segmented_flow_11_h2ll_2_merged</li><li>H2L at the end of drainage (no flow) =merged_segmented_flow_16_dra1_pd5_merged</li><li>H2L at the end of imbibition (no flow) =merged_segmented_flow_21_imb1_pi1_merged</li><li>L2H during low flow = merged_segmented_flow_29_2_l2hl_merged</li><li>L2H during high flow = merged_segmented_flow_30_l2hh_merged</li><li>L2H at the end of drainage (no flow) =merged_segmented_flow_31_dra2_pd1_merged</li><li>L2H at the end of imbibition (no flow) =merged_segmented_flow_33_imb2_pi1_merged</li></ol>
Transcriptomic atlas reveals organ-specific disease tolerance in sickle cell mice: dataset bone marrow HbAA mice injected or not with heme
<p>The objective of this experiment was to explore the transcriptome of the HbSS Townes mouse model of sickle cell disease. Townes model mice carry several human hemoglobin knock-in genes replacing the endogenous mouse genes and may be useful in studying sickle cell disease. All mice were genotyped, age- and sex-matched littermates. All HbAA (control, normal human hemoglobin) vs HbSS (sickle cell disease, mutated human hemoglobin) mice were used for experimentations at 6-8 weeks of age, to limit intra-group heterogeneity. Hemin (Ferriprotoporphyrin IX) was purchased from Frontiers Scientific and injected intravenously (iv.) in a retroorbital sinus at a concentration of 24 µmol/kg. Control mice received PBS instead. Mice were anesthetized with isoflurane 2-3% for injections, blood collection and sacrifice. All mice were sacrificed by cervical dislocation, 4 hours after injection.</p> <p>This dataset contains the results of the HbAA mice with and without heme.</p> <p>The corresponding HbSS mice with and without heme are deposited under number 10.5281/zenodo.10962782</p> <p>Bone marrow RNA was extracted by Macherey Nagel kit, according to the manufacturer’s instructions. The quality and quantity of mRNA were evaluated using a 2100<br>bioanalyzer with TNA 6000 NanoKits (all Agilent Technologies, Palo Alto, CA, USA). RNA Integrity Numbers superior to 7 were eligible for subsequent reverse transcription into cDNA. RNAseq was performed at the GenomIC plateform Cochin Institute INSERM U1016. After RNA extraction, RNA quality (RNA integrity number) was estimated. 1μg of high-quality total RNA sample (RIN &gt;7) was processed to build up the libraries, using TruSeq Stranded mRNA kit (Illumina) according to manufacturer instructions. Briefly, purified poly-A containing mRNA molecules were fragmented and reverse-transcribed using random primers. Replacement of dTTP by dUTP during second strand synthesis allowed us to achieve strand specificity. Addition of a single A base to the cDNA was followed by ligation of Illumina adapters.<br>Libraries were quantified by qPCR using KAPA Library Quantification Kits for Illumina Libraries (KapaBiosystems, Wilmington, MA). Library profiles were assessed using DNA High Sensitivity LabChip kits on an Agilent Bioanalyzer. Libraries were sequenced on an Illumina Nextseq 500 instrument using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, primary analysis based on AOZAN software (ENS, Paris), was applied to demultiplex and control the quality of the raw data (based of FastQC modules / version 0.11.5).</p> <p>The dataset here represents 4 groups of mice, 4 mice per group as follows: HbAA PBS, HbAA heme, HbSS PBS, HbSS heme. </p> <p> </p>
Data for: Impact of SO2 injection profiles on simulated volcanic forcing for the Sarychev 2009 eruptions - investigating the importance of using high vertical resolution methods when compiling SO2 data
<p>The files are data assosicated with the study High-resolution stratospheric volcanic SO2 injections in WACCM. The files are associated with four differnt simulaions described in the paper: M16, S21-1D, S21-3D and No-Volc. The files with "input" in the name are the SO2 input files used in the WACCM (Whole Atmosphere Community Climate Model) simulations in the paper. The files with "monthly_averages" in the filenames are monthly averages of model output data the variables used in the paper. </p> <p>The CALIOP_monthly_averages.nc file is monthly average of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) satellite data used in the study to evaluate the WACCM simulations. </p> <p> </p>
Optimizing Production and Storage: Carlsberg's Injection-Molding Operations
<p>The research paper investigates the optimization of production and storage for a custom molder, using a dataset that includes production times, weekly production hours, stockroom capacity, storage space per case, contribution per case, and customer limits for different types of glass produced using specific dies. The paper aims to determine the optimal production quantities for each type of glass to maximize the total contribution, taking into account production constraints and customer demand.</p>
Dataset for "Reduced ice loss from Greenland under stratospheric aerosol injection"
<p>Dataset for the paper "Reduced ice loss from Greenland under stratospheric aerosol injection"<br>(<em>Journal of Geophysical Research: Earth Surface</em>, 128 (11), e2023JF007112, <a href="https://doi.org/10.1029/2023JF007112">doi: 10.1029/2023JF007112</a>).</p> <p>Please see the README for details.</p> <p>V1.1.1: README and metadata updated.<br>V1.1: Scripts related to the ISIMIP-method downscaling, SEMIC code, as well as configuration and input files for SICOPOLIS and Elmer/Ice added.<br>V1: Results of new simulations that include both atmospheric and oceanic forcing.<br>V0.9.1: Crucial bug fix in the files ElmerIce_MIROC-ESM-CHEM-{RCP85,RCP45,G4}_2D_final.nc (those in V0.9 were faulty).<br>V0.9: Scalar variables: now distinguished between state and flux variables. 2D variables added.<br>V0.5: Scalar variables as functions of time.</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.</p>
Supplementary Data for Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay
<p>This dataset contains the interpolation tables for use with the DM21cm code release as part of "Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay." For details on usage, see the public github repository at: https://github.com/yitiansun/DM21cm. </p>
Spatial transcriptome data from coronal mouse brain sections after striatal injection of heme and heme-hemopexin
<p>This dataset and the associated Python notebooks are related to the publication "Spatial transcriptome data from coronal mouse brain sections after striatal injection of heme and heme-hemopexin".</p>
Test and numerical data of a vapour-injection scroll compressor in a heat pump with R1234ze(E)
<p>The dataset contains the experimental results of a water-to-water heat pump tested at the lab for different water temperatures. The refrigerant used is the HFO R1234ze(E). The scroll compressor is equipped with an eco port. The numerical results of a validated semi-empirical model are also included for a standard suction pressure drop model and an improved one.</p>
Test dataset of VLT/SPHERE/IRDIS H2 observation Injected with simulated disks.
<p>Test data sets used in Juillard et. al. (2023) and , Juillard et. al. (2024), used to compare three different algorithms for processing data sets using ADI alone and to compare the three different strategies: RDI, ADI, and ARDI.</p> <p>This test pipeline consists of a total of 60 test data sets composed of five different disk morphologies, injected at three different contrast levels ($10^{-3}$, $10^{-4}$, and $10^{-5}$), into four different observing ADI sequences of stars without any known circumstellar signal, reflecting different observing conditions. </p> <div><strong><u>CONTENT </u></strong></div> <div> </div> <div>You will find in this folder the battery of test dataset in the compressed folder "test_cubes_sphere.zip". It contains 60 folders, one for each test dataset, with two files in each: "angle.fits" and "cube.fits"</div> <div> </div> <div>Additionaly there folders containg the empty cubes, injected disks, star flux, mask that reprensent the location of the apperture where the flux of the disk was integrated to compute contrast. The Jupyter notebook "asses_quality_of_disk_estimate.ipynb" shows how to compare a disk estimate. We also provided an example disk estimate (file 'X_3_2_0.fits') to try out the notebook.</div> <div> </div> <div>Finally, a folder named "Ref_lib_sphere" containes the different set of references frames library used in the publication Juillard et. al. (2024). In this folder, "ref_lib_X" corresponds to the optimal references for testing using empty cube X, "randref" corresponds to "shuffled" (same for every cube), and "randref_nooverlap_X" corresponds to "excluded." </div> <div>In the “Optimal” case, we used the most correlated frames, which is the same selection used in the first series, where we compare ADI, RDI, and ARDI. In the “Shuffled” case, we randomly selected a sample from the reference libraries dedicated to each of our four ADI test cubessequences into one common reference library. In this test, the random selection contains 25% of frames from the “Optimal” reference library. In the “Excluded” case, we selected for each ADI cube a sample only from references dedicated to the three other ADI cubes, creating a selection that excludes optimal references. The PCC computed for each of the three selections of references is presented in Fig. 5 of Juillard et. al. (2024).</div> <div> </div> <div><span><strong>ABOUT THE DATA</strong></span></div> <p>The data sets, obtained through the High-Contrast Data Center (HCDC), were acquired using the Infrared Dual-Band Imager and Spectrograph (IRDIS, Dohlen et al. 2008; Vigan et al. 2010) camera of the Spectro-Polarimetric High-contrast Exoplanet Research coronagraphic system on the Very Large Telescope (VLT/SPHERE, Beuzit et al. 2019). The test data sets all consist of the $H$2 channel from the dual-band $H$23 set. They were chosen to exhibit a diverse range of characteristics, including low Strehl ratio with a 26\degr\ rotation (ID #1), wind-driven halo (ID #2), an unstable speckle field (ID #3), and good Strehl ratio with an 80\degr\ field rotation (ID #4). The raw data processed with the data handling software (Pavlov et al. 2008) of the HCDC (Delorme et al. 2017), which performs dark, flat, and bad pixel correction on a coronagraphic sequence. <br>For future reference, we computed the mean and standard deviation of the Pearson correlation coefficients (PCC) between each unique pair of frames in the ADI cube. The mean PCC are as follows: Cube #1: $\mu = 0.99$; Cube #2: $\mu = 0.97$; Cube #3: $\mu = 0.93$; Cube #4: $\mu = 0.96$, with standard deviations below $0.001$ for all the cubes.</p> <p><br>The injected disks represent a range of scenarios for both debris and protoplanetary disks. As detailed in Juillard et.al 2023, this selection consists of two 75\degr\ inclined disks with varying sharpness levels (A and B), a 45\degr\ inclined disk with two concentric rings (C), a nearly face-on disk with azimuthal flux variation (D), and a hydrodynamical simulation of a disk with embedded spiral structures and a companion (E). <br>The contrast of the injected disks is determined by measuring the integrated flux within a full width at half-maximum (FWHM)-sized aperture, centered at the peak intensity of the disk, and then dividing this value by the integrated flux within an FWHM-sized aperture of the stellar point spread function. However, we made an exception for the synthetic disk E, where we measured the flux at the companion location.</p> <p>The reference frames were selected from a set of archival IRDIS observations taken with the same filter, coronagraph, and exposure time as the test data sets. These reference targets were observed between 2014 December 11 and 2021 June 1, and the raw data were calibrated through the same process as the test data sets. For each test data set, the PCC was calculated between the frames of the data set and the reference targets, excluding any observations of the data set star taken at different epochs. The PCC was calculated within a circular annulus between 0\farcs31 and 0\farcs67, which captures both the dominant speckle region and position of the waffle pattern, used for precise star centering of a coronagraphic sequence (Zurlo et al. 2014), if it was included in the observation. For each frame in the data set, the 300 best correlated reference frames were identified, and those that appeared in this selection for more than 30\% of the data set frames were selected for the final reference library. </p>
Behavioral economics approach to reduce injectable discontinuation rate in rural Ethiopia
<p>Data used for the study titled "Application of behavioral economics principles to reduce injectable contraceptive discontinuation rate in rural Ethiopia: A stratified-pair, cluster-randomized field study" is reposited here. Data was analyzed using Stata 15.1. The repository includes the data and the Stata do-files that replicates the study results. The study manuscript has been submitted to Gates Open Research. </p>
Behavioral economics approach to reduce injectable discontinuation rate in rural Ethiopia
<p>Data used for the study titled "Application of behavioral economics principles to reduce injectable contraceptive discontinuation rate in rural Ethiopia: A stratified-pair, cluster-randomized field study" is reposited here. Data was analyzed using Stata 15.1. The repository includes the data and the Stata do-files that replicates the study results. The study manuscript has been submitted to Gates Open Research. </p>
Data: An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction
<p><strong>Dataset supporting the manuscript "</strong>An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction<strong>" by the authors of this dataset.</strong></p> <p><strong>Where to start</strong></p> <p>This Zenodo repository contains both raw data and runnable code for the manuscript "An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction". The runnable code is best executed directly at CodeOcean (https://doi.org/10.24433/CO.6934377.v1). Alternatively, CodeOcean capsules are Docker images and can be run locally after download and unzipping. The full CodeOcean capsule is stored here as "CodeOceanCapsule_Injectable_meta_biomaterial.zip", it contains all the information and data to full reproduce the evaluation underpinning the manuscript " An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction".</p> <p>Quantitative raw data, in the form of text files, Excel files and R-data files useful for the data evaluation are included in "CodeOceanCapsule_Injectable_meta_biomaterial.zip". As especially the numerical simulation files are rather voluminous (100GB), we also provide a copy of the capsule without this large part, which however otherwise remains runnable for most evaluations ("CodeOceanCapsule_Injectable_meta_biomaterial_no_raw_simulation.zip"), and, for lightweight documentation of the code section only "CodeOceanCapsule_Injectable_meta_biomaterial_code_only.zip". The results of a capsule run are also provided, as "CodeOceanCapsule_Injectable_meta_biomaterial_results_run_4899036.zip".</p> <p>Besides archival of the CodeOcean evaluation capsule, this repository contains additional imaging data from which some of the quantitative data treated in the CodeOcean capsule was extracted, and additionally raw files for the illustrative figures in the manuscript. This data is contained in the files "Raw_images_For_Figure_1.zip", "Raw_images_For_Figure_3.zip", "Raw_images_For_Figure_4.zip"; "Raw_images_For_Figure_5.zip", "Raw_images_For_SFigure_S6.zip", "Raw_images_For_SFigure_S8.zip", "Raw_images_For_SFigure_S9.zip", "Raw_images_For_SFigure_S19.zip".</p> <p><strong>External dependencies</strong></p> <p>To facilitate centralized software development and installation, custom R and Python libraries used by the CodeOcean capsule "CodeOceanCapsule_Injectable_meta_biomaterial.zip" are hosted on Github, with releases archived in separate Zenodo repositories. These libraries are included automatically during the build phase of the CodeOcean capsule.</p> <p>This concerns the Python discrete particle simulation particleShear (DOI: <a href="https://doi.org/10.5281/zenodo.4589212">10.5281/zenodo.4589212</a>), and the R packages textureAnalyzerGels (for analysis of mechanical compression curves, DOI: <a href="https://doi.org/10.5281/zenodo.4589276">10.5281/zenodo.4589276</a>), rheologyEvaluation (for analysis of oscillatory sweep rheology, DOI: <a href="https://doi.org/10.5281/zenodo.4594353">10.5281/zenodo.4594353</a>), particleShearEvaluation (evaluation of the output of the Python simulations, DOI: <a href="https://doi.org/10.5281/zenodo.4594649">10.5281/zenodo.4594649</a>), plot.counts (convenience functions for scientific plotting, DOI: <a href="https://doi.org/10.5281/zenodo.4589498">10.5281/zenodo.4589498</a>) and reproducibleCalculationTools (numerical comparision of subsequent evaluations to validate reproducibility, DOI: <a href="https://doi.org/10.5281/zenodo.4594515">10.5281/zenodo.4594515</a>).</p> <p>For automated evaluation of ImageJ macros from Excel files, we also developed an Excel macro runner plugin in ImageJ, termed PoreSizeExcel (DOI: <a href="https://doi.org/10.5281/zenodo.4589546">10.5281/zenodo.4589546</a>). While the R and Python libraries listed above are actively loaded and used by the CodeOcean capsule, we used the PoreSizeExcel ImageJ plugin manually to streamline our quantitative image treatment, but not in a fully automated fashin.</p> <p>The Zenodo archives cited above reproducibly provide the state of the libraries as used for evaluation of this dataset, we continue to develop the libraries and continuously make them available at Github ( at <a href="https://github.com/tbgitoo">https://github.com/tbgitoo</a> ).</p> <p><strong>Version history</strong></p> <p>This is the third version of this Zenodo repository.</p> <p>We undertook major efforts from version v1.0 to the present version v2.0 to increase reprodubility of evaluation (via the use of the CodeOcean platform) and via separation of generic libraries (listed above, and installable on their own independently of this particular project) from specific project-associated data and evaluation (here). For this reason, while the data is maintained and in part completed due to new experiments having been carried out in the mean time, the structure of the repository has undergone major changes from v1.0 to the present version v2.0.</p> <p>With this version v3.0 we added raw data on cell transplantation, and completed the CodeOcean capsule, including adaptation to peer review changes to the manuscript.</p>
Cataloging Dependency Injection Anti-Patterns in Software Systems
<p><strong>Background</strong> Dependency Injection (DI) is a commonly applied mechanism to decouple classes from their dependencies in order to provide better modularization of software. In the context of Java, the availability of a DI specification and popular frameworks, such as Spring, facilitate DI usage in software projects. However, bad DI implementation practices can have negative consequences, such as increasing coupling, hindering the achievement of DI's main goal. Even though the literature suggests the existence of DI anti-patterns, there is no detailed documentation of such bad practices. Moreover, there is no evidence on their occurrence and perceived usefulness from the developer's point of view. </p> <p><strong>Aims</strong> Our goal is to review the reported DI anti-patterns in order to analyze their completeness and to propose and evaluate a novel catalog of Java DI anti-patterns. </p> <p><strong>Method</strong> We propose a catalog containing twelve Java DI anti-patterns. We selected four open-source and two closed-source software projects that adopt a DI framework and developed a tool to statically analyze the occurrence of the candidate DI anti-patterns within their source code. Also, we conducted a survey through face to face interviews with three experienced developers that regularly apply DI. We extended the survey in order to gather the perception of a set of fifteen expert and novice developers through an online questionnaire. </p> <p><strong>Results</strong> At least nine different DI anti-patterns appeared frequently in the analyzed projects. In addition, the feedback received from the developers confirmed the relevance of the catalog. Besides, the respondents expressed their willingness to refactor instances of anti-patterns from source code.</p> <p><strong>Conclusions</strong> The catalog contains Java DI anti-patterns that occur in practice and are useful. Sharing it with practitioners may help them to avoid such anti-patterns.</p>
Locally Injective Mappings Benchmark
<p>We are glad to release the benchmark dataset of 2D/3D meshes in our Siggraph 2020 paper <a href="https://duxingyi-charles.github.io/publication/lifting-simplices-to-find-injectivity/">Lifting Simplices to Find Injectivity</a> The dataset collects challenging examples from recent papers on fixed boundary injective mappings. It also includes hundreds of newly created examples. The dataset includes <em>10743</em> triangular mesh examples and <em>904</em> tetrahedral mesh examples. The examples are divided into 3 categories, 2D parameterization, 3D parameterization and 3D deformation.</p> <p>We hope that our dataset offers a benchmark for future research in this area.</p> <p>A more detailed introduction to the dataset can be find <a href="https://github.com/duxingyi-charles/Locally-Injective-Mappings-Benchmark">here</a>.</p>
Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus
<p>Description for Zenodo Data Set DOI:10.5281/zenodo.45507</p> <p>This dataset accompanies the article, submitted to Physics of Plasmas, titled "Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus," by Garnier, Mauel, Roberts, Kesner, and Woskov. </p> <p>---------------------------------------------</p> <p>Data is presented as HDF5 datafiles <br /> (see https://www.hdfgroup.org/HDF5/) <br /> as HDF4 datafiles<br /> (see https://www.hdfgroup.org/release4/doc/index.html)<br /> and as CSV datafiles <br /> (see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).</p> <p>Data files are associated with FIGURES 2, 3, 4, 5, 6</p> <p>---------------------------------------------<br /> start of figure list<br /> ---------------------------------------------<br /> FIGURE 1: No data set</p> <p>---------------------------------------------<br /> FIGURE 2: (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br /> S140529016_DensityData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 8 micro-sec<br /> samples: 250,000 x 4 channels + 250,000 (total)<br /> Unit: radian "Interferometer"<br /> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br /> S140529016_PDAData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000 x 16 channels<br /> Unit: A.U. "PDA-1"</p> <p>TOTAL ECRH Injected Heating Power<br /> S140529016_ECRHData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000<br /> Unit: kW "Microwave-Power"</p> <p>S140529016_LoopVoltage.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 80 micro-sec<br /> samples: 25,000 <br /> Unit: milli-Volt x sec "Loop-Voltage"</p> <p>---------------------------------------------<br /> FIGURE 3(a): (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br /> S140529016_DensityData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 8 micro-sec<br /> samples: 250,000 x 4 channels + 250,000 (total)<br /> Unit: radian "Interferometer"<br /> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br /> S140529016_PDAData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000 x 16 channels<br /> Unit: A.U. "PDA-1"</p> <p>---------------------------------------------<br /> FIGURE 4 (a), (b), (c): (CSV Files)</p> <p>time period: 5.0 - 6.0 sec<br /> Ensemble Window: 8 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Line-Density-Coherence.csv<br /> Frequency (Hz) Hz<br /> d(nl-1)^2 dimensionless<br /> d(nl-2)^2 dimensionless <br /> d(nl-3)^2 dimensionless<br /> d(nl-4)^2 dimensionless<br /> Kappa 1-2 dimensionless<br /> Kappa 1-3 dimensionless<br /> Kappa 1-4 dimensionless</p> <p>Isat-Coherence.csv <br /> Frequency (Hz) Hz<br /> d(I)^2 dimensionless<br /> Kappa 8deg dimensionless<br /> Kappa 16deg dimensionless<br /> Kappa 24deg dimensionless</p> <p>Float-Potential-Coherence.csv<br /> Frequency (Hz) Hz<br /> d(Pot)^2/Te^ dimensionless<br /> Kappa 8deg dimensionless<br /> Kappa 16deg dimensionless<br /> Kappa 24deg dimensionless</p> <p>---------------------------------------------<br /> FIGURE 5 (a), (b): (CSV Files)</p> <p>Figure 5(a)<br /> time period: 5.0 - 6.0 sec<br /> Ensemble Window: 8 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Float-Isat-CrossPhase.csv <br /> Frequency (Hz) Hz<br /> alpha-Float degree<br /> alpha-Isat degree</p> <p>Figure 5(b)<br /> time period: 6.02 - 6.05 sec<br /> Ensemble Window: 1.6 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Float-Isat-DuringCrossPhase.csv <br /> Frequency (Hz) Hz<br /> alpha-Float degree<br /> alpha-Isat degree<br /> kappa-Float dimensionless<br /> kappa-Isat dimensionless<br /> ---------------------------------------------<br /> FIGURE 6: No data set</p> <p>---------------------------------------------<br /> FIGURE 7: No data set</p> <p>---------------------------------------------<br /> FIGURE 8: No data set</p> <p>---------------------------------------------<br /> FIGURE 9: No data set</p> <p>---------------------------------------------<br /> FIGURE 10: No data set</p> <p>---------------------------------------------<br /> end of figure list<br /> ---------------------------------------------<br /> ---------------------------------------------<br /> start of file list<br /> ---------------------------------------------<br /> Filename Size <br /> ----------------------------------------------<br /> Potential-Time-Angle-Data.h5 254.68 KB <br /> All-Probe-Data.h5 9.81 MB <br /> Average-Probe-Data.h5 1.06 MB <br /> Average-Isat-Data.h5 422.22 KB <br /> S140529016_PDAData.hdf 6.80 MB <br /> S140529016_DensityData.hdf 6.00 MB <br /> S140529016_ECRHData.hdf 803.39 KB <br /> S140529016_LoopVoltage.hdf 203.39 KB <br /> Line-Density-Coherence.csv 420.59 KB <br /> Isat-Coherence.csv 258.51 KB <br /> Float-Potential-Coherence.csv 257.90 KB<br /> Float-Isat-DuringCrossPhase.csv 48.87 KB <br /> Float-Isat-CrossPhase.csv 138.47 KB <br /> LDX-Pellet-Supplementary.pdf 1.84 MB <br /> S140529016_frPlots.mp4 3.24 MB <br /> ---------------------------------------------<br /> end of file list<br /> ---------------------------------------------</p>
A join of the Kepler DR24 injections table with the robovetter table
<p>A join of the injection and robovetter results for Kepler DR24. The references for these data are:</p> <ul> <li>Christiansen et al. (2016): http://adsabs.harvard.edu/abs/2016ApJ...828...99C</li> <li>Coughlin et al. (2016): http://adsabs.harvard.edu/abs/2016ApJS..224...12C</li> <li>Mullally et al. (2016): http://adsabs.harvard.edu/abs/2016PASP..128g4502M</li> </ul>
Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus
<p><strong>Description for Zenodo Data Set DOI:10.5281/zenodo.220992</strong></p> <p>This dataset accompanies the article, to appear in Physics of Plasmas, titled "Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus," by Garnier, Mauel, Roberts, Kesner, and Woskov. </p> <p>---------------------------------------------</p> <p>Data is presented as HDF5 datafiles <br> (see https://www.hdfgroup.org/HDF5/) <br> as HDF4 datafiles<br> (see https://www.hdfgroup.org/release4/doc/index.html)<br> and as CSV datafiles <br> (see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).</p> <p>Data files are associated with FIGURES 2, 3, 4, 5</p> <p>Data plotted in other figures are derived from the dataset as described<br> in the paper.</p> <p>---------------------------------------------<br> start of figure list<br> ---------------------------------------------<br> <strong>FIGURE 1: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 2:</strong> (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br> S140529016_DensityData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 8 micro-sec<br> samples: 250,000 x 4 channels + 250,000 (total)<br> Unit: radian "Interferometer"<br> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br> S140529016_PDAData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000 x 16 channels<br> Unit: A.U. "PDA-1"</p> <p>TOTAL ECRH Injected Heating Power<br> S140529016_ECRHData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000<br> Unit: kW "Microwave-Power"</p> <p>S140529016_LoopVoltage.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 80 micro-sec<br> samples: 25,000 <br> Unit: milli-Volt x sec "Loop-Voltage"</p> <p>---------------------------------------------<br> <strong>FIGURE 3(a)</strong>: (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br> S140529016_DensityData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 8 micro-sec<br> samples: 250,000 x 4 channels + 250,000 (total)<br> Unit: radian "Interferometer"<br> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br> S140529016_PDAData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000 x 16 channels<br> Unit: A.U. "PDA-1"</p> <p>---------------------------------------------<br> <strong>FIGURE 4 (a), (b), (c), (d), (e), (f): </strong> (CSV Files)</p> <p>time period: 5.0 - 6.0 sec<br> Ensemble Window: 8 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(a) Fig4a-Line-Density-Coherence.csv<br> Frequency (Hz) Hz<br> d(nl-1)^2 dimensionless<br> d(nl-2)^2 dimensionless <br> d(nl-3)^2 dimensionless<br> d(nl-4)^2 dimensionless<br> Lambda 1-2 dimensionless<br> Lambda 1-3 dimensionless<br> Lambda 1-4 dimensionless</p> <p>(b) Fig4b-Isat-Coherence.csv <br> Frequency (Hz) Hz<br> d(I)^2 dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(c) Fig4c-Float-Potential-Coherence.csv<br> Frequency (Hz) Hz<br> d(Pot)^2/Te^ dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(d) Fig4d-Line-Density-Coherence.csv<br> Frequency (Hz) Hz<br> d(nl-1)^2 dimensionless<br> d(nl-2)^2 dimensionless <br> d(nl-3)^2 dimensionless<br> d(nl-4)^2 dimensionless<br> Lambda 1-2 dimensionless<br> Lambda 1-3 dimensionless<br> Lambda 1-4 dimensionless</p> <p>(e) Fig4e-Isat-Coherence.csv <br> Frequency (Hz) Hz<br> d(I)^2 dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(f) Fig4f-Float-Potential-Coherence.csv<br> Frequency (Hz) Hz<br> d(Pot)^2/Te^ dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>---------------------------------------------<br> <strong>FIGURE 5 (a), (b):</strong> (CSV Files)</p> <p>Figure 5(a)<br> time period: 5.0 - 6.0 sec<br> Ensemble Window: 8 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(a) Fig5a-Float-Isat-CrossPhase.csv <br> Frequency (Hz) Hz<br> alpha-Float degree<br> alpha-Isat degree</p> <p>Figure 5(b)<br> time period: 6.02 - 6.05 sec<br> Ensemble Window: 1.6 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(b) Fig5b-Float-Isat-DuringCrossPhase.csv <br> Frequency (Hz) Hz<br> alpha-Float degree<br> alpha-Isat degree<br> kappa-Float dimensionless<br> kappa-Isat dimensionless<br> ---------------------------------------------<br> <strong>FIGURE 6: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 7: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 8:</strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 9: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 10:</strong> No data set</p> <p>---------------------------------------------<br> end of figure list<br> ---------------------------------------------<br> ---------------------------------------------<br> <strong>start of file list</strong><br> ---------------------------------------------<br> Filename Size <br> ----------------------------------------------<br> All-Probe-Data.h5 9.8 MB<br> Average-Isat-Data.h5 422 KB<br> Average-Probe-Data.h5 1.1 MB<br> Fig4a-Line-Density-Coherence.csv 413 KB<br> Fig4b-Isat-Coherence.csv 251 KB<br> Fig4c-Float-Potential-Coherence.csv 250 KB<br> Fig4d-Line-Density-Coherence.csv 103 KB<br> Fig4e-Isat-Coherence.csv 62 KB<br> Fig4f-Float-Potential-Coherence.csv 62 KB<br> Fig5a-Float-Isat-CrossPhase.csv 138 KB<br> Fig5b-Float-Isat-DuringCrossPhase.csv 36 KB<br> Potential-Time-Angle-Data.h5 255 KB<br> S140529016_DensityData.hdf 6 MB<br> S140529016_ECRHData.hdf 803 KB<br> S140529016_LoopVoltage.hdf 203 KB<br> S140529016_PDAData.hdf 6.8 MB</p> <p><br> LDX-Pellet-Supplementary.pdf 1.8 MB<br> S140529016_frPlots.mp4 3.2 MB<br> ---------------------------------------------<br> <strong>end of file list</strong><br> ---------------------------------------------</p> <p> </p> <p> </p>
Dataset of Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: A Systematic Literature Review
<p>Data set for the paper entitled “<strong>Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: a Systematic Literature Review</strong>”</p> <p>In this repo, we have some pictures and Excel files.</p> <ul> <li>Pictures are screenshots from the Parsifal tool (https://parsif.al/) which we use for performing the SLR.</li> <li>Excel files are as follows:</li> </ul> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 21.8789%;"><col style="width: 78.1211%;"></colgroup> <tbody> <tr> <td><strong>Excel’s file name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Keyword_analysis </td> <td>In this file, you can see the evolution of our keyword selection.</td> </tr> <tr> <td>Articles_InclusionExclusion_QA </td> <td>In this file, you can find all found papers until Feb. 27, 2025. In the last column of this excel file, we can see the status of each paper, if it has been included, or excluded by authors. For the included paper (their status is “Accepted”) you can see their quality score in the last column.</td> </tr> <tr> <td>Extracted_data </td> <td>In this file, we logged the result of data extraction from qualified paper. In the first sheet “Articles”, you can see a list of the read papers with corresponding data. Other sheets in this Excel file are driven from the “Article” sheet for data visualization. So, they are not important.</td> </tr> </tbody> </table> <p> <br>If you have any questions, you can read the corresponding paper and contact the authors.</p>
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