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5 results for “image board”
Cebulka (Polish dark web cryptomarket and image board) messages data
<h3><strong>General Information</strong></h3> <p>1. <strong>Title of Dataset</strong></p> <p>Cebulka (Polish dark web cryptomarket and image board) messages data.</p> <p>2. <strong>Data Collectors</strong></p> <p>Haitao Shi (The University of Edinburgh, UK); Patrycja Cheba (Jagiellonian University); Leszek Świeca (Kazimierz Wielki University in Bydgoszcz, Poland).</p> <p>3. <strong>Funding Information</strong></p> <p>The dataset is part of the research supported by the Polish National Science Centre (Narodowe Centrum Nauki) grant 2021/43/B/HS6/00710.</p> <p>Project title: “Rhizomatic networks, circulation of meanings and contents, and offline contexts of online drug trade” (2022-2025; PLN 956 620; funding institution: Polish National Science Centre [NCN], call: OPUS 22; Principal Investigator: Piotr Siuda [Kazimierz Wielki University in Bydgoszcz, Poland]).</p> <h3><strong>Data Collection Context</strong></h3> <p>4.<strong> Data Source</strong></p> <p>Polish dark web cryptomarket and image board called Cebulka (<a href="http://cebulka7uxchnbpvmqapg5pfos4ngaxglsktzvha7a5rigndghvadeyd.onion/index.php">http://cebulka7uxchnbpvmqapg5pfos4ngaxglsktzvha7a5rigndghvadeyd.onion/index.php</a>). </p> <p>5. <strong>Purpose</strong></p> <p>This dataset was developed within the abovementioned project. The project focuses on studying internet behavior concerning disruptive actions, particularly emphasizing the online narcotics market in Poland. The research seeks to (1) investigate how the open internet, including social media, is used in the drug trade; (2) outline the significance of darknet platforms in the distribution of drugs; and (3) explore the complex exchange of content related to the drug trade between the surface web and the darknet, along with understanding meanings constructed within the drug subculture.</p> <p>Within this context, Cebulka is identified as a critical digital venue in Poland’s dark web illicit substances scene. Besides serving as a marketplace, it plays a crucial role in shaping the narratives and discussions prevalent in the drug subculture. The dataset has proved to be a valuable tool for performing the analyses needed to achieve the project’s objectives.</p> <h3><strong>Data Content</strong></h3> <p>6. <strong>Data Description</strong></p> <p>The data was collected in three periods, i.e., in January 2023, June 2023, and January 2024.</p> <p>The dataset comprises a sample of messages posted on Cebulka from its inception until January 2024 (including all the messages with drug advertisements). These messages include the initial posts that start each thread and the subsequent posts (replies) within those threads. The dataset is organized into two directories. The “cebulka_adverts” directory contains posts related to drug advertisements (both advertisements and comments). In contrast, the “cebulka_community” directory holds a sample of posts from other parts of the cryptomarket, i.e., those not related directly to trading drugs but rather focusing on discussing illicit substances. The dataset consists of 16,842 posts.</p> <p>7. <strong>Data Cleaning, Processing, and Anonymization</strong></p> <p>The data has been cleaned and processed using regular expressions in Python. Additionally, all personal information was removed through regular expressions. The data has been hashed to exclude all identifiers related to instant messaging apps and email addresses. Furthermore, all usernames appearing in messages have been eliminated.</p> <p>8. <strong>File Formats and Variables/Fields</strong></p> <p>The dataset consists of the following files:</p> <ul> <li>Zipped .txt files (“cebulka_adverts.zip” and “cebulka_community.zip”) containing all messages. These files are organized into individual directories that mirror the folder structure found on Cebulka.</li> <li>Two .csv files that list all the messages, including file names and the content of each post. The first .csv lists messages from “cebulka_adverts.zip,” and the second .csv lists messages from “cebulka_community.zip.”</li> </ul> <h3><strong>Ethical Considerations</strong></h3> <p>9. <strong>Ethics Statement</strong></p> <p>A set of data handling policies aimed at ensuring safety and ethics has been outlined in the following paper:</p> <p>Harviainen, J.T., Haasio, A., Ruokolainen, T., Hassan, L., Siuda, P., Hamari, J. (2021). Information Protection in Dark Web Drug Markets Research [in:] Proceedings of the 54th Hawaii International Conference on System Sciences, HICSS 2021, Grand Hyatt Kauai, Hawaii, USA, 4-8 January 2021, Maui, Hawaii, (ed.) Tung X. Bui, Honolulu, HI, pp. 4673-4680.</p> <p>The primary safeguard was the early-stage hashing of usernames and identifiers from the messages, utilizing automated systems for irreversible hashing. Recognizing that automatic name removal might not catch all identifiers, the data underwent manual review to ensure compliance with research ethics and thorough anonymization.</p>
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
<p>This dataset contains the video clips used to produce the results presented in:</p><p>Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.</p>
Chemical imaging data collected on wood board sections after impregnation-treatment with phenol formaldehyde resin
<p>This dataset contains UV microspectrophotometry (UMSP) and near infrared (NIR) imaging data from the following publication: Altgen M., Awais M. Altgen D., Klüppel A., Koch G., Mäkelä M., Olbrich A., Rautkari L. (2022) Chemical imaging to reveal the resin distribution in impregnation-treated wood at different spatial scales. Materials & Design, DOI: <a href="https://doi.org/10.1016/j.matdes.2022.111481">https://doi.org/10.1016/j.matdes.2022.111481</a>.</p> <p>The data was obtained from beech wood board sections (75x15x25 mm<sup>3</sup>) that were impregnation-treated with a low molecular weight phenol formaldehyde resin. Experimental details can be found in the publication.</p> <p>The file “NIR sample IDs with weight and dimensional changes.csv” contains the sample IDs and the weight percent gains caused by the resin treatment of each sample in the dataset. To generate the NIR image files, regions of interest of 881 x 384 pixels (subsets a,b,c,d) or 2401 x 375 pixels were selected from the raw image files to produce an image that contains the sample surrounded by background. The spectral data was corrected using the calibration reflectance target values and then converted to absorbance. Each NIR image is stored in a separate MATLAB file (.mat) with the sample ID as the file name.</p> <p>The file "UMSP sample IDs.csv" contains the sample IDs of the UMSP images. The folder "UMSP image profiles.zip" contains the corresponding UMSP image profiles, which are stored as excel files (.xlsx) with the sample IDs as file names. The files contain the absorbance at 278 nm per pixel with a pixel resolution of 0.25 x 0.25 µm<sup>2</sup>.</p>
Driving environmental images from on-board camera and RTK-based localization for autonomous vehicles
<p>This dataset contains images from an on-board frontal camera as well as information of the vehicle state, including: accurate global localization, heading, speed, acceleration and steering angle position. The data was captured from one of the vehicle instrumenhted vehicles of the <a href="https://autopia.car.upm-csic.es/">AUTOPIA group</a> at the surrounding of the <a href="https://www.car.upm-csic.es/">Centre for Automation and Robotics</a>, in Arganda del Rey (Madrid, Spain).</p> <p>The dataset is aimed to help users in developing and improving image-based road detection algorithms, as well as machine learning end-to-end approaches using driving information provided (steering angle, vehicle speed, etc.).</p> <p>The dataset has also been used to develop algorithms for online map adaptation based on computer vision. Please, refer to:</p> <p>"Artuñedo, A. (2020). Decision-making Strategies for Automated Driving in Urban Environments. In Springer Theses. Springer International Publishing. <a href="https://doi.org/10.1007/978-3-030-45905-5">https://doi.org/10.1007/978-3-030-45905-5</a>"</p> <p><strong>Image information</strong></p> <p>The sequence of images is provided in PNG format, and is named following the pattern: 'l_sA_nsB.png', where A and B are the second and nanosecond when the image was captured, so that A+B*1e-9 is the capture time. The images were captuted with the camera Bumblebee 2 0,8 MP Color FireWire 1394a 3,8mm (Sony ICX204) with the following technical features:</p> <ul> <li>Sensor type: CCD</li> <li>Sensor format: 1/3"</li> <li>Pixel size 4.65 µm</li> <li>Focal length: 3.8mm</li> <li>Fames per second: 20 fps</li> <li>Resolution: 1024 x 768</li> </ul> <p><strong>Camera position</strong></p> <p>The camera is placed at a height 1290 mm measured from the ground plane. With respect to the vehicle frame, it has a yaw of -2º and a pitch 5.5º, in order to focus the field of view in the road.</p> <p> </p> <p><strong>Vehicle localization</strong></p> <p>The test vehicle is a prototype of Autonomous Vehicle developed by the <a href="https://autopia.car.upm-csic.es/">AUTOPIA reseach group</a> at the Centre for Automation and Robotics in Spain. Vehicle location mainly depends on a Trimble BX982 GNSS receiver using RTK. The GNSS antenna is installed near the rear axle of the vehicle, in the middle part of the vehicle. However, the location algorithm applies an EKF for combining GNSS measurements with different onboard sensors providing yaw rate, longitudinal acceleration and speed, steering wheel position and speed, etc.</p> <p>The vehicle localization file ('vehicle_data.csv') includes the following data at a sample rate of 20 Hz:</p> <ul> <li>Time (s)</li> <li>UTM East (m)</li> <li>UTM North (m)</li> <li>Orientation (º)</li> <li>Speed (m/s)</li> <li>Acceleration(m/s^2)</li> <li>Steering wheel angle (º)</li> </ul> <p>Disclaimer That the dataset comes "AS IS", without express or implied warranty and/or any liability exceeding mandatory statutory obligations. This especially applies to any obligations of care or indemnification in connection with the dataset. The dataset was created for our research purposes only and no quality assessment was done for the usage in products of any kind. We can therefore not guarantee for the correctness, completeness or reliability of the provided data set.</p>
Magnetic resonance imaging of water vapor absorption in simulated corrugated board
<p>This dataset contains all the raw source data that comprise the study:</p> <p><strong>Magnetic resonance imaging of water vapor absorption in simulated corrugated board </strong></p> <p>Tuomainen TV (1), Silvennoinen J (2), Mattila T (2), Kettunen MI (3), Nissi MJ (1)*</p> <p>Cellulose | DOI: 10.1007/s10570-023-05483-3</p> <p> </p> <p>1: University of Eastern Finland, Department of Technical Physics, Yliopistonranta 8, FI-70211, Kuopio, Finland</p> <p>2: Mondi Powerflute, Selluntie 142, FI-70420, Kuopio Finland</p> <p>3: University of Eastern Finland, A.I. Virtanen Institute for Molecular Sciences, Kuopio Biomedical Imaging Unit, Neulaniementie 2, FI-70150, Kuopio, Finland</p> <p> </p> <p>*Corresponding author: </p> <p>Mikko J. Nissi </p> <p>Department of Technical Physics, UEF </p> <p>POB 1627 </p> <p>FI-70211, Kuopio, Finland </p> <p>mikko.nissi [at] uef.fi </p> <p>+358-50-5955517</p> <p> </p> <p><strong>Study and data description</strong></p> <p>The purpose of this work was to investigate water vapor permeation through a layered structure composing of liner and fluting paperboard materials. The layered structure with paperboards separated with ring spacers was used to simulate the structure of a double-wall cardboard.</p> <p>This dataset contains MRI measurements (RARE pulse sequence) of simulated cardboard assemblies with different fluting materials (Fluting A,B,C) under high humidity. Furthermore, temperature and relative humidity data for possible pre-treatment (in .MAT files) are included.</p> <p>Included folders and files in the <em>paperboard_zenodo_repository.zip</em> are:</p> <ul> <li><strong>prep_paperboard</strong>: Temperature and relative humidity of the pre-treatment for simulated cardboard as .MAT files (can be opened with MATLAB). Two ,MAT-files are included: '14102021_paperboard_flutingCa_nacl_mgcl_RH_t.mat' and '09122021_paperboard_nacl_flutingAB_RH_t.mat'. In the .MAT file regarding Fluting A and Fluting B pre-treated with NaCl (09122021_paperboard_nacl_flutingAB_RH_t.mat) Fluting A is designated as x1NaCl and Fluting B as x2NaCl.</li> <li><strong>MRI_paperboard</strong>: Contains the MRI data as 'fid' and '2dseq' as well as metadata for each experiment obtained at 11.7 T (Bruker)</li> <li><strong>README.txt: </strong>More information on the file and folder structure and datatypes.</li> </ul> <p> </p> <p>Further information is provided in the included <strong>README.txt</strong>.</p>
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