*.csv
This dataset accompanies the study “Universal Metrics to Characterize the Performance of Imaging 3D Measurement Systems with a Focus on Static Indoor Scenes” and provides all measurement data, processing scripts, and evaluation code necessary to reproduce the results. It includes raw and processed point cloud data from six state-of-the-art 3D measurement systems, captured under standardized conditions. Additionally, the dataset contains high-speed sensor measurements of the cameras’ active illumination, offering insights into their optical emission characteristics.
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A significant portion of the end users of electricity consists of residential consumers, often exceeding that of other consumer categories, particularly in developing countries. Effective demand side management strategies increasingly rely on Artificial Intelligence (AI) and Machine Learning (ML), yet their success depends on access to high-quality, comprehensive datasets.
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The COVID-19 Vaccine Misinformation Aspects Dataset contains 3,822 English tweets discussing COVID-19 vaccine misinformation, collected from Twitter/X between December 31, 2020, and July 8, 2021.
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The medical biometric dataset comprises 10,000 records collected across 23 patients spanning different demographics, biometric profiles, and temporal variations between 2022 and 2023. It is accumulated from various hospitals, digital health records, and biometric-enabled healthcare security systems.
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This dataset contains synthetic smart meter data with simulated cyber attacks, designed to support research in anomaly detection, cybersecurity, and energy consumption analysis. The dataset is based on 159 users from the Smart Meters in London dataset, selected for their regular consumption patterns. This larger dataset can be found in
https://www.kaggle.com/datasets/jeanmidev/smart-meters-in-london,
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CO2 Emissions Data Visualization Project – I Hug Trees
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This dataset provides a comprehensive record of wind power generation and its relationship with oceanic-atmospheric indices, facilitating advanced forecasting and analytical research in renewable energy. The dataset comprises 12 input parameters, including average wind speed, which serves as a crucial predictor, while wind power generation acts as the output variable.
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The manual generation of access control policies from an organization’s high-level requirement specifications is a laborious and error-prone process. Mistakes in this manual policy generation process cause access control failures that may lead to data breaches. As a solution, previous research pro- posed automated access control policy generation frameworks. However, existing approaches suffer from several limitations, such as the inability to handle complex access requirements due to the lack of domain adaptation, making them highly unreliable.
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The attached zip contains both data and analysis scripts for the first two particle-in-cell examples in the associated paper. In particular,
(1) The BeamExpansion folder contains output z vs r phase space data from the quasi-Helmholtz FEMPIC code used in the paper at 3 ns (fempic.csv), as well as the equivalent data from an XOOPIC simulation (xoopic.txt). The analysis is done in the ipython notebook file in the folder. All of the plots in the paper was generated using this same script.
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The recent developments in the field of the Internet of Things (IoT) bring alongside them quite a few advantages. Examples include real-time condition monitoring, remote control and operation and sometimes even remote fault remediation. Still, despite bringing invaluable benefits, IoT-enriched entities inherently suffer from security and privacy issues. This is partially due to the utilization of insecure communication protocols such as the Open Charge Point Protocol (OCPP) 1.6. OCPP 1.6 is an application-layer communication protocol used for managing electric vehicle chargers.
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