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562 results for “Faults”
SubsurfaceBreaks v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces: Input and Processed Data
<p>This companion dataset relates to the manuscript "<strong>SubsurfaceBreaks</strong> <strong>v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces"</strong>, by Michał Michalak, Christian Gerhards and Peter Menzel.</p> <p>There are several groups of files:</p> <ul> <li>a file with parameters (params.txt) of the generated homoclinal interfaces (slopes) such as dip angle, dip direction, level of noise).</li> <li>files 0-999 are generated using the code from GitHub. (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_synthetic_subsurface_slopes) for generating synthetic slopes. Every slope is in a separate file (.txt files) and it is possible to upload the slope to ParaView for further inspection: Delaunay triangulation, normal vectors and dip vectors have their own .vtu files. The .txt files (0-999) can be uploaded for training using the Python script (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_subsurface_slopes_training_testing_evaluating_revision.ipynb).</li> <li>KSH_input.txt corresponds to real data from Kraków-Silesian Homocline. Every row corresponds to a point representing a geological horizon separating Middle Jurassic geological units: Kościeliska sandstones from ore-bearing clays. This data set can be used to calculate geometric attributes using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes).</li> <li>KSH_input_output_0 corresponds to an output file from processing the KSH_input.txt file using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes). This file should be uploaded to the Python script to identify fault-related features on a real subsurface slope.</li> </ul>
Switched capacitor filter transient responses for fault detection
<h2>Dataset Description</h2> <p>This dataset comprises both measured and simulated responses of second-order switched capacitor filters designed for failure detection. The study encompasses two types of filters:</p> <ul> <li> Overdamped</li> <li> Underdamped</li> </ul> <p>The measured data is segregated into two subsets:</p> <h3>Training Data</h3> <p>The training data exclusively contains non-failure responses. It serves as the foundation for model training and validation.</p> <h3>Test Data</h3> <p>The test data includes both failure and non-failure responses. Each response is labeled to indicate whether it is a non-failure or failure response, specifying which capacitor has been modified and its new value.</p> <p>For the simulated data, it is exclusively utilized for testing purposes. For each simulated case (% deviation of the nominal value), a metadata file named "*_metadata.csv" is provided. Each row in this file meticulously outlines the characteristics of the response corresponding to the elements in the accompanying .npy file.</p>
Differential crustal modification mechanism across the central-southern segment of Tanlu fault zone, eastern China
<div> <p>The folder contains: RFs, Discontinuities.txt and Velocity_model.</p> <p>RFs:<br>Receiver function waveforms.</p> <p>Discontinuities.txt:<br>This text file contains data related to discontinuities, with the meaning of each column listed in the first row of the file.</p> <p>Velocity_model:<br>This folder contains text files named station names, each representing the inverted velocity model for the corresponding station. The first line of each text file indicates the latitude and longitude of the station. From the second line to the last line, the velocity model is represented as follows: the first column indicates depth, the second column represents Vs (shear wave velocity), the third column indicates the error of Vs, the fourth column represents Vp, and the fifth column indicates the ratio Vp/Vs.</p> <p> </p> </div>
Error Governor for Active Fault Tolerance in PID Control of MIMO Systems
<p>The dataset complements the article under review, "Error Governor for Active Fault Tolerance in PID Control of MIMO Systems," by reporting the simulation results obtained from testing the proposed Error Governor approach on the Zagi flying-wing UAV model.</p> <p>Run the main file "plot_images.m" to generate plots from the simulation data.</p>
Data Sets of Timing and Kinematics of Late Cenozoic Strike-slip Faults in Western Tibetan Plateau: New Constraints from Provenance Analysis and Detrital Thermochronology
<p>Data Sets of Wei et al. (2023). The manuscript is titled Timing and Kinematics of Late Cenozoic Strike-slip Faults in Western Tibetan Plateau: New Constraints from Provenance Analysis and Detrital Thermochronology</p>
Estimated seismicity of Venusian wrinkle ridges based on fault scaling relationships
<p>These files contain all lengths of wrinkle ridges from Bilotti & Suppe (1999), and the code I used to calculate scalar moment release from these faults. We apply segmentation and apply a maximum vertical slip extent. We calculate a moment release rate, or annual moment release, by summing the scalar moments and dividing by a deformation time. The fault lengths are in the last (3rd) column of the Excel file (wrinkleridges_midXY_frank.xlsx) in meters.</p> <p>Please see this reference for more information:<br> Sabbeth, L., Smrekar, S.E., and Stock, J.M. "Estimated seismicity of Venusian wrinkle ridges based on fault scaling<br> relationships." <em>Earth and Planetary Science Letters</em>, 619 (2023): 118308. https://doi.org/10.1016/j.epsl.2023.118308.</p> <p>We develop the method in this paper: https://doi.org/10.1029/2022JE007686</p>
Parallel window decoding enables scalable fault tolerant quantum computation
<p>Dataset containing raw data presented in the publication <em>"Parallel window decoding enables scalable fault tolerant quantum computation"</em> as well as the stim circuits used to sample circuit-level noise.</p> <p> </p>
Data of resulting velocity and anisotropic models, and travel-times of the Tanlu fault zone
Open the record for dataset details and reuse information.
Teleseismic P-wave data in the Dunhua-Mishan fault (Tanlu northern segment)
<p>Teleseismic P-wave data in the Dunhua-Mishan fault (Tanlu northern segment) </p>
Data of receiver functions in the central and southern segments of the Tanlu fault zone
<p>Data of receiver functions in the central and southern segments of the Tanlu fault zone</p>
Receiver functions of the central and southern segments of the Tanlu fault zone
<p>Data of receiver functions in the central and southern segments of the Tanlu fault zone</p>
Receiver functions of the central and southern segments of the Tanlu fault zone
<p>Data of receiver functions in the central and southern segments of the Tanlu fault zone</p>
Basic data for manuscript "Identification Markers, Structural Characteristics and Evolution Mechanism of Strike-Slip Faults in the Southeastern Sichuan Basin, Southwestern China"
<p>The basic data used for the research involved in the manuscript "Identification Markers, Structural Characteristics and Evolution Mechanism of Strike-Slip Faults in the Southeastern Sichuan Basin, Southwestern China" includes the following figures: the position of the key research area in map view (numbered 00). Coherence attribute slices (numbered 01 to 19) of important seismic reflection interfaces for research obtained from 3D seismic data volume of each key research area. Horizontal time slices (numbered 20 to 24) obtained at equal intervals (20ms or 50ms) from 3D seismic data volume in each key research area. Seismic sections obtained from 3D seismic data volume of each key research area and seismic sections perpendicular to strike-slip fault for detailed analysis are also obtained at equal intervals (numbered 25 to 35). Photographs obtained from field observations were also provided (numbered 36 to 39). See the document "Details of basic data" for details of the above pictures.</p>
Joint Inversion (Receiver function and apparent S-wave velocity) Results of the central-southern segment of Tanlu fault zone
Open the record for dataset details and reuse information.
Conveyor Belt Idlers Acoustic Data set for Fault detection
<p>This dataset collecting the acoustic signal from 135 idlers of conveyor in real mine by using inspection robot. More information in details about this data is provide in this paper " <strong>Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler</strong>"</p> <p> </p>
vibro-acoustic data from test rig for fault detection
<p>This data set is include acoustic and vibration signal collected from test rig for fault detection purpose. More information in details about this data set is provided in following paper :</p> <p>"</p> <p><strong>Analysis of the vibro-acoustic data from test rig -comparison of acoustic and vibrational method</strong></p> <p>"</p>
Fault Tolerance, Diagnostics, and Prognostics in Aircraft Flight
**Abstract** In modern fighter aircraft with statically unstable airframe designs, the flight control system is considered flight critical, i.e. the aircraft will encounter a catastrophic event if the system fails. Consequently, the system design has to meet high levels of reliability and failure immunity. In this presentation, an overview will be provided on the basic concepts employed in flight critical system design for fault tolerance. Different methods of redundancy implementation, failure management, and health management will be discussed. Basic concepts in fault diagnostics will also be discussed, along with some new concepts for implementation of prognostics in reducing levels of physical system redundancy. **Bio** David S. Bodden is Lockheed Martin Senior Fellow. He recieved BS in Aerospace Engineering from Texas A&M University in 1976 and MS in Engineering Science and Mechanics from Virginia Tech in 1984. Mr. Bodden’s technical career has encompassed a broad scope of technology areas. He has worked in structural dynamics, advanced design, conceptual design, flight control systems, and prognostics. His management experience includes six years as Chief of the Control Law design and Analysis Group followed by seven years as the Senior Manager of Flight Control Systems. Mr. Bodden was selected as a Technical Fellow in Flight Controls at LM Aeronautics in 2002 and as a Senior Fellow in 2007. Mr. Bodden has authored numerous papers and technical proposals, and managed numerous technology development programs. He served on the AIAA Guidance, Navigation, and Control Technical Committee, he has served as the Chairman of the Lockheed Martin Corporate Task Force on Guidance and Control, he initiated and served as Chairman of the Lockheed Martin GNC Technology Focus Group, is former Chairman of the SAE Aerospace Control and Guidance Systems Committee, and he currently serves as Chairman of the Texas A&M Aerospace Advisory Board.
Regional seismicity (ML≥1.0) from 2008 to 2022 for the Haiyuan fault system
<p>This data is the regional seismicity (ML≥1.0) from 2008 to 2022 from the article "Strain Accumulation and Release on the Haiyuan Fault System from Joint Analysis of InSAR, GPS and Seismological Observations"</p>
Influence of the Canossa-San Romano Fault on the Quaternary deposits of the Roteglia Terrace (Northern Apennines, Italy)
<p>Data of publication in Atti della Soc. dei Nat. e Mat. di Modena, 153 (2022), pp. 49-66.</p>
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
<p>Trained agents to be used in the replication package of the paper "<em>Mutation Testing of Deep Reinforcement Learning Based on Real Faults</em>" submitted to the International Conference on Software Engineering (ICST) 2023. Authors are set to anonymous for the peer-review process.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.