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562 results for “faults”
Supporting data for 'Fast universal quantum gate above the fault-tolerance threshold in silicon'
<p>Data supporting for paper 'Fast universal quantum gate above the fault-tolerance threshold in silicon'.</p> <p>All the data are stored in the HDF5 format that can be conveniently loaded by the xarray Python package.</p>
An Exploratory Study on Faults in Web API Integration in a Large-Scale Payment Company: Appendix
<p>Appendix of our "An Exploratory Study on Faults in Web API Integration in a Large-Scale Payment Company: Appendix" paper.</p>
Travel times of temporary seismic arrays and 3-D Vp and Vs models in the mid-to-south segment of the Red River fault, China
<p>The travel times of the temporary seismic arrays are manually picked. The 3-D Vp and Vs models beneath the mid-to-south segment of the Red River fault are obtained based on the improved double-difference tomography method and abundant data. </p>
Supplementary Dataset for "A new mechanical perspective on a shallow megathrust near-trench slip from the high- resolution fault model of the 2011 Tohoku-Oki earthquake"
<p>This dataset contains the results obtained by the analysis in this study, such as the digital data of the slip distribution and stress drop estimated by Kubota et al.(2022)</p>
UAV-FD: a dataset for actuator fault detection in multirotor drones
<p>This dataset collects real flight data from a hexarotor under the effects of a chipped blade. A conventional ArduPilot based controller is employed, where the ArduPilot firmware is customized to increase the signal logging rate of the IMU variables, thus capturing enough information at higher frequencies. Additional variables are available, including on-board measurements, commands, estimations, and parameters; in particular, the actual speed of each motor is measured as well.</p> <p>The purpose of the UAV-FD dataset is to accelerate the research on actuator fault diagnosis for multirotor vehicles.</p>
Geodetic datasets for analysis of southern San Andreas fault geometry from 2017-2021 shallow creep
<p>Line-of-sight (LOS) Sentinel-1 InSAR velocities, residual fault-parallel velocities, GNSS vectors, and fault nodes used a study of the shallow structure of the Southern San Andreas Fault in the Coachella Valley.</p>
Frictional Properties and Healing Behavior of Tectonic Mélanges: Implications for the Evolution of Subduction Fault Zones
<p>Here, we report data from velocity-step experiments using rocks collected from ancient subduction fault zones, the Lower Mugi and Makimine mélanges of the Cretaceous Shimanto belt. The two mélanges preserve paleotemperature records corresponding to the updip and downdip limits of the seismogenic zone and deformation recording a lower versus higher degree of pressure solution. The compostions of the mélanges analyzed with X-ray diffraction (XRD) are also included in the datasets. Our data show that the Lower Mugi mélange sample exhibits velocity-weakening to velocity-neutral behavior under low normal stress, and the Makimine mélange sample shows velocity-strengthening behavior under high normal stress. This is consistent with the slip behavior observed at the depths they have been subducted to along the plate interface. We also perform a series of slide-hold-slide experiments under different hydrothermal conditions using the Lower Mugi mélange sample to evaluate the role of pressure solution in fault healing and its dependency on temperature. The results show that healing rates increase in tests operated at higher temperatures, which suggests the importance of pressure solution healing along plate interfaces.</p>
Dating strike-slip ductile shear through combined zircon-, titanite- and apatite U–Pb geochronology along the southern Tan-Lu Fault zone, East China
<p>This is the dataset for <em>"Dating strike-slip ductile shear through combined zircon-, titanite- and apatite U–Pb geochronology along the southern Tan-Lu Fault zone, East China"</em>. Including the EMPA and geochronology data.</p>
Datasets for "Trapdoor fault activation: a step towards caldera collapse at Sierra Negra, Galápagos, Ecuador", Journal of Geophysical Research: Solid Earth
<p>The following files were used in the analysis from "Trapdoor fault activation: a step towards caldera collapse at Sierra Negra, Galápagos, Ecuador", <em>Journal of Geophysical Research: Solid Earth</em>:</p> <p><strong>alos2_csk/alos2_sm1_dsc_20180504_20180713/:</strong> Includes DEM used in processing of the ALOS-2 SM1 descending interferogram spanning 4 May 2018–13 July 2018 (dem.2alks_2rlks.crop.*); geocoded SAR offsets in pixels (range resolution=1.43 m/pixel; azimuth resolution=2.01 m/pixel; denseOffsets.bil.2alks_2rlks.geo*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.2alks_2rlks.geo.*); geocoded, unwrapped interferometric phase (filt_topophase.unw.2alks_2rlks.geo.*); geocoded incidence and heading angle for the interferogram (los.rdr.2alks_2rlks.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/alos2_sm3_asc_20180114_20180701/:</strong> Includes DEM used in processing of the ALOS-2 SM3 ascending interferogram spanning 14 January 2018–1 July 2018 (dem.crop.*); geocoded, unwrapped interferometric phase (filt_topophase.unw.geo.*); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/alos2_wd1_dsc_147_180518_180629/</strong>: Includes DEM used in processing of the ALOS-2 WD1 descending interferogram spanning 18 May 2018–29 June 2018 (crop.dem.*); geocoded, unwrapped interferometric phase (filt_180629-180518_2rlks_14alks.unw.geo.*) ; geocoded incidence and heading angle for the interferogram (180629-180518_2rlks_14alks.los.geo.*); geocoded coherence for the interferogram (180629-180518_2rlks_14alks.cor.geo.*); geocoded mask for the interferogram (filt_topophase.unw.masked.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_asc_20180617_20180703/:</strong> Includes DEM used in processing of the COSMO-SkyMed ascending interferogram spanning 17 June 2018–3 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.54 m/pixel; azimuth resolution=2.48 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_asc_20180703_20180719/</strong>: Includes DEM used in processing of the COSMO-SkyMed ascending interferogram spanning 3 July 2018–19 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.54 m/pixel; azimuth resolution=2.48 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_dsc_20180618_20180704/:</strong> Includes DEM used in processing of the COSMO-SkyMed descending interferogram spanning 18 June 2018–4 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.70 m/pixel; azimuth resolution=2.45 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>alos2_csk/csk_dsc_20180704_20180720/: </strong>Includes DEM used in processing of the COSMO-SkyMed descending interferogram spanning 4 July 2018–20 July 2018 (dem.crop.*); geocoded SAR offsets in pixels (range resolution=1.70 m/pixel; azimuth resolution=2.45 m/pixel; denseOffsets.bil.geo.*); geocoded SNR of SAR offsets (denseOffsets_snr.bil.geo); geocoded incidence and heading angle for the interferogram (los.rdr.geo.*); all in ISCE format.</p> <p><strong>S1.zip</strong>: Unwrapped, geocoded interferometric phase in meters for Sentinel-1 ascending and descending interferograms, spanning time periods of interest. </p> <p><strong>S1_20180630_20180706_asc_mask_nan_ref.grd: </strong>Unwrapped, geocoded, and masked interferometric phase for Sentinel-1 ascending interferogram spanning 30 June 2018–6 July 2018.</p> <p><strong>S1_20180701_20180707_desc_mask_nan_ref.grd: </strong>Unwrapped, geocoded, and masked interferometric phase for Sentinel-1 descending interferogram spanning 1 July 2018–7 July 2018.</p> <p><strong>tandemx12m_crop.grd</strong>: TanDEM-X 12 meter DEM in meters.</p> <p><strong>pleaides_tandemx12m_diff.grd</strong>: Difference between the TanDEM-X 12 meter DEM and the Pléiades-derived DEM, computed from images on 29 October 2018 and 6 December 2019.</p> <p><strong>trapdoorFaultSlip.zip</strong>: Discretized trapdoor fault patch dip-slip modeled to fit deformation from Sentinel-1 ascending interferograms, estimated using the Classic Slip Inversion software.</p> <p><strong>trapdoorFaultTraces.zip</strong>: Caldera and trapdoor fault traces, derived from <em>Bell et al. 2021</em>.</p> <p><strong>SN14_tilt_10s_2018-19.txt</strong>: Text filt containing date-time (sampled at 10 s, in matplotlib date-time number format), N-S tilt and E-W tilt. Tilt values can be converted to microradians by multiplying by a factor of 0.00129. Tilt data obtained from authors of <em>Bell et al. 2021</em>. For use of this dataset, please cite <a href="https://doi.org/10.1038/s41467-021-21596-4">https://doi.org/10.1038/s41467-021-21596-4</a>.</p>
Supplementary material for 'The MAP metric in Information Retrieval Fault Localization'
<pre># map_bench4bl This is the supplementary material, data, and evaluation source code for the paper "The MAP metric in Information Retrieval Fault Localization" by Thomas Hirsch and Birgit Hofer. ## Preliminaries ### Python environment - Python 3.8 - pandas - numpy - matplotlib ## Datasets The [Bench4BL](<em>https://github.com/exatoa/Bench4BL</em>) dataset has been used in this evaluation, with the addition of intermediate files taken from the [SABL](<em>http://dx.doi.org/10.5281/zenodo.4681242</em>) experiment performed on this Bench4BL dataset. All data used in our evaluation is included in this repository. However, if the data is to be re-imported directly from these benchmark and datasets they have to be downloaded first and their local paths have to be set in [paths.py](<em>paths.py</em>). ### Bench4BL The Bench4BL dataset was published with the paper "Bench4BL: Reproducibility study on the performance of IR-based bug localization" by Lee, J., Kim, D., Bissyandé, T.F., Jung, W. and Le Traon, Y.. The dataset can be obtained [here](<em>https://github.com/exatoa/Bench4BL</em>). Follow the steps described in the corresponding [README](<em>https://github.com/exatoa/Bench4BL/blob/master/README.md</em>) to set up the dataset. The Bench4BL dataset contains the _old subjects_ subdataset, containing 558 bugs from AspectJ, JDT, PDE, SWT, and ZXing that have been widely used in older IRFL studies. This _old subjects_ subdataset was used in answering our RQ1, as discussed below, the corresponding scripts use _old subjects_ in their name to highlight this. #### SABL The SABL dataset is the online appendix of the paper "An Extensive Study of Smell-Aware Bug Localization" by TTakahashi, A., Sae-Lim, N., Hayashi, S. and Saeki, M.. The dataset can be downloaded [here](<em>http://dx.doi.org/10.5281/zenodo.4681242</em>). The experiments in this dataset build on top of Bench4BL and intermediate files are provided in the datapackage. #### Rankings Rankings for BLIA, BRTracer, and BugLocator were produced by running these tools on Bench4BL locally. Rankings for AmaLgam and BLUiR were taken from the SABL experiment dataset. ## Structure ### Folders Bench4BL ground truths: - bench4bl_old_subjects_summary - bench4bl_summary Localization results of the included tools in Bench4BL: - bench4bl_localization_results - bench4bl_localization_results_sabl Target projects size metrics: - cloc_results - cloc_results_old_subjects Utility functions: - utils Output folders containing results, generated figures and tables: - results - results_old_subjects ### Scripts Scripts for re-importing data from Bench4BL and SABL datasets: - data_preparation_step_1_cloc_bench4bl.py - data_preparation_step_1_cloc_old_subjects_bench4bl.py - data_preparation_step_2_import_ground_truth_from_bench4bl.py - data_preparation_step_2_import_ground_truth_from_old_subjects_bench4bl.py - data_preparation_step_3_import_bench4bl_ranking_results.py - data_preparation_step_3_import_sabl_ranking_results.py Utilities: - paths.py - utils/bench4bl_utils.py - utils/Logger.py ### Evaluation scripts for the corresponding research questions: **Dataset analysis:** - rq_0_dataset_analysis_bench4bl_issues.py **RQ1: How big is the average ground truth in Bench4BL datasets, and what proportion of bugs have a ground truth containing multiple files?** - rq_1_bench4bl_ground_truth_size.py - rq_1_old_subjects_bench4bl_ground_truth_size.py RQ2: Do the IRFL tools included in Bench4BL truncate their results? - rq_2_ranking_lengths.py **RQ3: How strong is $AP_{asrd}$ overestimating $AP_{mb}$ for truncated BugLocator retrieval results on the Bench4BL dataset? RQ3a: How strong is $AP_{asrd}$ overestimating $AP_{mb}$ for truncated BugLocator retrieval results when considering the bloated ground truth issue found in Bench4BL?** - rq_3_truncating_BugLocator_rankings_bench4bl.py **RQ3b: How strong is $AP_{asrd}$ overestimating $AP_{mb}$ for truncated BugLocator retrieval results when undefined $AP$ values are simply ignored?** - rq_3b_undefined_ap_BugLocator_rankings_bench4bl.py ## Licence All code and results are licensed under [CCA v4](<em>https://creativecommons.org/licenses/by/4.0/</em>), according to LICENSE file. Other licences may apply for some tools and datasets contained in this repo: [cloc-1.92.pl](<em>https://github.com/AlDanial/cloc</em>) under GPL v2, [Bench4BL](<em>https://github.com/exatoa/Bench4BL</em>) and [SABL](<em>http://dx.doi.org/10.5281/zenodo.4681242</em>) under CCA 4.0.</pre>
Supplementary data for "Fault-tolerant quantum algorithm for symmetry-adapted perturbation theory"
<p>Supplementary data belonging to "Fault-tolerant quantum algorithm for symmetry-adapted perturbation theory".</p> <p>The data includes geometries for the molecules in the paper as well as the Hamiltonian matrix elements, orbital coefficients and overlap matrices to reproduce the data in the paper.</p>
Supplementary material: Accuracy of finite fault slip estimates in subduction zone regions with topographic Green's functions and seafloor geodesy
<p># Code attached to the manuscript entitled "Accuracy of finite fault slip estimates in subduction zone regions with topographic Green's functions and seafloor geodesy"</p> <p>These python scripts are for producing Figure 1 (trench-perpendicular topographic profile for regions where some megathrust earthquakes occurred) and the average topographic profile that is used in the paper.</p> <p> </p>
Limited Northward Expansion of the Tibetan Plateau in the Late Cenozoic: Insights from the Cherchen Fault in the Southeastern Tarim Basin
<p>Supporting information submitted: Figure S1 to Figure S3</p> <p>Figures, captions, and introduction text are included in the uploaded file. </p>
Replication Package for the paper "AI-based Fault-proneness Metrics for Source Code Changes"
<p>This is the replication package for the paper "<em>AI-based Fault-proneness Metrics for Source Code Changes</em>", submitted at the <em>IWSM-Mensura '23 </em>conference.</p> <p>The archive is a <em>Docker </em>image file with a fully setup and working environment to re-execute the experiments involved in the manuscript. We pre-loaded all libraries and codeBERT models to ease the replication process and avoid compatibility issues, as the environment cannot be easily managed using <em>Dockerfile</em>s.</p> <p>To run the image, a <em>Docker</em> installation is needed. Once downloaded, from the command line type:</p> <pre><code>docker load -i </path/to/downloaded/ai-proneness-replication.tar></code></pre> <p>After the loading process, you can run the container by typing:</p> <pre><code>docker run -it mensura/ai-proneness-replication:1.0</code></pre> <p>All the source code and the dataset to re-execute the experiment is located into the <em>/Replication</em> folder. The folder contains the results of our experimentation in CSV and MS Excel format, along with the following subdirectories:</p> <ul> <li><em>dataset</em>: a replication of the used dataset. The file <em>dataset.csv</em> gives information on all the entries, while the <em>code </em>folder contains a subdirectory for each sample, named by its id. In the folder, the file <em>old.txt </em>and<em> </em><em>new.txt </em>refers to the older and newer version of the method, respectively; <em>gitdiff.txt </em>stores the raw <em>git-diff</em> command output, while <em>diff.html</em> stores a more human-readable version of the differences.</li> <li><em>ai-fault-proneness-tk-replication</em>: the Java code used to apply Tree Kernel techniques on the dataset (we used JDK-11, embedded within the container). To build and execute the package, refer to the file <em>README.md</em> in the folder. For convenience, we also provided an executable JAR file <em>ai-fault-proneness-tk-replication-1.0-jar-with-dependencies.jar </em>that can be run directly and saves the output in a CSV file in the <em>results</em> folder of the replication package.</li> <li><em>code-embeddings-and-analysis</em>: python scripts to execute the <em>codeBERT</em>-based approaches and to extract the <em>diff</em> statistics. To execute all the steps, a convenience shell script <em>execute.sh</em> has been pre-loaded and can be executed to automatize all the process.</li> </ul>
Viscoelasticity modeling of clay minerals by dynamic viscoelasticity measurement and its implications for earthquake faulting
<p>We conducted dynamic viscoelastic measurements on three clay minerals, kaolinite, illite and smectite with water. These concentrated (dense) suspension systems of clay minerals were investigated using a high-temperature and high-fluid-pressure rheometer to determine their viscoelastic properties, which help further the understanding of tectonic and non-tectonic phenomena in the shallow unconsolidated portion of the lithosphere. Our results suggested that the rheological properties resulting from the network structure of the clay mineral were temperature, pressure and peak shear strain rate dependent. In addition, it was observed during this study that the amount of change in the phase angle varied systematically with the type of clay mineral. This suggests that the viscoelastic behaviour of unconsolidated systems saturated with fluid varies with the type of clay minerals that compose it. (Abstract)</p>
Data from: Thermal springs and active fault network of the central Colca River basin, Western Cordillera, Peru, published in Journal of Volcanology and Geothermal Research
<p>We used hydrogeochemical analysis of 35 water samples from springs and geysers, together with isotopic (δ<sup>18</sup>O and δD) analysis, chemical and mineral studies of precipitates collected in the field around these outflows, and field observations to study the thermal system of the Colca River basin in S Peru. We aimed to determine the geochemistry of thermal waters, identify fluid sources and their origin, estimate reservoir temperature, and discuss the regional tectonic and volcanic framework. Our findings presented in Tyc et al. (2022; https://doi.org/10.1016/j.jvolgeores.2022.107513) corroborate a heterogeneous and complex geothermal system in the central region of the Colca River basin. This system exhibits contrasting hydrogeochemical and physical characteristics, variable isotope compositions, distinct reservoir temperatures, and associated precipitates near thermal springs. The control of water chemistry in this area is closely linked to the activity of the Ampato-Sabancaya magmatic chamber and the presence of tectonic structures, which enable intricate interactions between meteoric waters, magmatic fluids, and gases.</p> <p>Here, we present datasets used in the article (Tyc et al., 2022; https://doi.org/10.1016/j.jvolgeores.2022.107513), including:</p> <p>- Physicochemical characteristics of water samples collected by authors in the field in September 2012 and August–September 2017 (Table 1)</p> <p>- Chemical and isotopic composition of water samples collected by authors in the field in September 2012 and August–September 2017 (Table 2) and those monitored by INGEMMET in years 2013-2018 (Table 3)</p> <p>- Chosen molecular ratios discussed in Tyc et al., 2022 (Table 4)</p> <p>- Calculated reservoir temperature with the use of different Na/K geothermometers (Table 5)</p> <p>- Mineral phases in efflorescences precipitating at the water sampling sites (Table 6).</p> <p>Thirty-five sets of water samples were collected in the field in September 2012 and August–September 2017 using polyethylene bottles of high density (Table 1). Consequently, these were analyzed in the Water Analysis Laboratory at the University of Silesia in Katowice (Poland; Table 2). Water temperatures, pH, and electrical conductivity were measured in the field using portable pH meter CP-315 and conductivity meter CC-315, both with temperature sensors, with an accuracy of ±0.1 °C, ±0.01 pH, and ± 0.1% (up to 19.999 mS/cm) or ± 0.25% (above 20.00 mS/cm), respectively. Discharge of springs was estimated if possible (Table 1). Both cations and anions were analyzed by ion chromatography using Methron 850 Professional Ion Chromatograph with separate Metrosept C4–150 and A-supp 7–250 columns for cations and anions, respectively (Tables 2 and 4). Analysis of water analyses collected by INGEMMET in years 2013-2018 was performed at the INGEMMET Chemical Laboratory in Lima with the use of ion chromatography (Dionex ICS 5000) for the determination of anions and inductively coupled plasma optical emission spectrometry (ICP-OES) – VARIAN for cations (Table 3). Isotopic analyses (δ<sup>2</sup>H, δ<sup>18</sup>O) of 17 water samples collected in 2017 were performed at the Stable Isotope Laboratory Institute of Geological Sciences Polish Academy of Sciences (Table 2). The δ<sup>2</sup>H values of studied H<sub>2</sub>O were measured using the H-Device peripheral coupled to MAT 253 IRMS (Thermo Scientific) in a dual inlet system. For the determination of δ<sup>18</sup>O in H<sub>2</sub>O, an equilibration technique was used. The analysis used the GasBench II peripheral device (Thermo Scientific) coupled to MAT 253 IRMS with a continuous He flow. The AquaChem 4.0.284 software was used to evaluate the water samples' geochemical properties and calculate reservoir temperature for thermal waters (Table 5). Precipitates found at the water sampling sites were collected separately into plastic bags with strings and sealed boxes. These samples were subsequently analyzed at the Institute of Earth Sciences, University of Silesia in Katowice. The qualitative chemical composition and mineral characteristics were examined using a Philips XL 30 ESEM/TMP scanning electron microscope coupled with an energy-dispersive spectrometer (EDS; EDAX type Sapphire). The phase composition of the precipitates was determined through X-ray diffraction (XRD) using a Philips PW 3710 diffractometer. The XRD data were analyzed and interpreted using the X'Pert HIGHScore Plus software (Table 6).</p>
UAV surveying data and surface rupture for the 2022 Ms 6.9 Menyuan earthquake, along Haiyuan fault system, NE Tibet
<p>The unmanned aerial vehicle (UAV) data was acquired by a DJI (Dajiang Innovations Science and Technology Co., Ltd.) Phantom 4 RTK. High resolution digital elevation and orthophoto models (DEM/DOM) was produced by Agisoft Metashape Professional software.</p> <p>The LLL1-12 and TLS1-5 images are the DOMs covering the surface ruptures along the Leng Long Ling fault and Tuolai Shan fault from west to east, respectively.</p> <p>The 2022 Menyuan earthquake surface rupture (.kmz file) was obtained based on the interpretation of UAV DOM data. </p> <p>The DEM files are used to calculate the offsets in the Menyuan earthquake paper (<em>The 2022, Ms 6.9 Menyuan earthquake: surface rupture, Paleozoic suture re-activation, slip-rate and seismic gap along the Haiyuan fault system, NE Tibet</em>).</p> <p> </p> <p> </p>
Online appendix for "Leveraging Execution Trace with ChatGPT: A Case Study on Automated Fault Diagnosis" (New Ideas and Emerging Results Track in ICSME 2023)
<p>All the prompts we prepared for ChatGPT and the fault diagnosis results</p> <ul> <li>prompt_*: Prompt for ChatGPT <ul> <li>prompt_ChatGPT_setup_*.txt: Prompts to setup ChatGPT before starting the question to ChatGPT</li> <li>The other prompts: Prompts input to ChatGPT for fault diagnosis</li> </ul> </li> <li>result_*: Response from ChatGPT</li> <li>without_trace: Prompt or result when execution trace is not entered in ChatGPT</li> <li>with_trace: Prompt or result when execution trace is entered in ChatGPT</li> </ul>
Replication package of "How Do Deep Learning Faults Affect AI-Enabled Cyber-Physical Systems in Operation? A Preliminary Study Based on DeepCrime Mutation Operators"
<p>Cyber-Physical Systems (CPSs) combine digital cyber technologies with physical processes. As in any other software system, in the case of CPSs, the use of Artificial Intelligence (AI) techniques in general, and Deep Neural Networks (DNNs) in particular, is contantly increasing. While recent studies have considerably advanced the field of testing AI-enabled systems, it has not yet been investigated how different Deep Learning (DL) bugs affect AI-enabled CPSs in operation. This work-in-progress paper presents a preliminary evaluation on how such bugs can affect CPSs in operation by using a mobile robot as a case study system. For that, we generated DL mutants by using operators proposed by Humbatova et al., which are operators based on real-world DL faults. Our preliminary investigation suggests that such bugs are more difficult to detect when they are deployed in operation rather than when testing their DNN in an off-line setup, which contrast with related studies.</p> <p> </p> <p>This repository provides the replication data employed in our study.</p>
Squirrel-Cage Induction Motor Fault Diagnosis Dataset
<p>The <strong>Squirrel Cage Induction Motor Fault Diagnosis Dataset</strong> is a multi-sensor data collection gathered to expand research on anomaly detection, fault diagnosis, and predictive maintenance, mainly using non-invasive methods such as thermal observation or vibration measurement. The measurements were gathered using an advanced <em>Wrocław University of Science and Technology</em> laboratory designed to simulate and study motor defects. The collected dataset is licensed under a <em>Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International</em> License.</p> <p>Available data:</p> <ul> <li>thermal images</li> </ul> <p>The example dataset utilization is presented in the GitHub repository: <a href="https://github.com/MatPiech/motor-fault-diagnosis/tree/main">motor-fault-diagnosis</a></p> <p>Related publications:</p> <ul> <li><a href="https://ein.org.pl/Unraveling-Induction-Motor-State-through-Thermal-Imaging-and-Edge-Processing-A-Step,170114,0,2.html">Unraveling Induction Motor State through Thermal Imaging and Edge Processing: A Step towards Explainable Fault Diagnosis</a></li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.