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Northern Africa and the Eastern Mediterranean area has faced lots of natural catastrophes to earthquakes last decade. The primary active tectonic structure concentrated in Sub-Saharan Africa. A recent assessment of earthquake seismicity characteristics has been conducted in North Africa.The database of historical and instrumental earthquakes is one of the most crucial tools for evaluating the risk of earthquakes.
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PPE Usage Dataset
This repository provides the Personal Protective Equipment (PPE) Usage Dataset, designed for training deep neural networks (DNNs). The dataset was collected using the EFR32MG24 microcontroller and the ICM-20689 inertial measurement unit, which features a 3-axis gyroscope and a 3-axis accelerometer.
The dataset includes data for four types of PPE: helmet, shirt, pants, and boots, categorized into three activity classes: carrying, still, and wearing.
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The dataset were compiled and obtained using the HarmonyERP (https://www.harmonyerp.cloud/en/) software used by KNS Otomotiv (www.knsotomotiv.com/en/) which operates with the ATO model. The KNS company produces parts in various categories such as air duct systems, service sets, continuous LED lighting systems, grab bars, and baskets for commercial vehicles (buses).
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The dataset is generated using VISSIM, a microscopic traffic simulation software. The simulated environment accurately reproduces mixed traffic conditions involving both autonomous and conventional vehicles in the autonomous driving demonstration zone of Pangyo, South Korea. In setting up the simulation, various real-world factors are carefully incorporated, including the number and width of lanes, roadway gradients, and the configuration of traffic signal phase systems.
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This dataset is collected at KAIST, Daejeon, and KAIST by ISILAB to research seamless indoor-outdoor detection. The collecting device is a Raspberry Pi 4B+ with touchscreen UI connected with a Pmod Nav module and a PmodGPS. This collection has a rough three-month time span, which mitigates the specific time-specific bias. Further, in the collection, we also swap the wiring to simulate the device bias. The dynamic calibration is not applied to the dataset; searchers may choose to apply the dataset or not.
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Training and testing the accuracy of machine learning or deep learning based on cybersecurity applications requires gathering and analyzing various sources of data including the Internet of Things (IoT), especially Industrial IoT (IIoT). Minimizing high-dimensional spaces and choosing significant features and assessments from various data sources remain significant challenges in the investigation of those data sources. The research study introduces an innovative IIoT system dataset called UKMNCT_IIoT_FDIA, that gathered network, operating system, and telemetry data.
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The dataset are served for community-imbalanced graph sampling algorithm performance experiments. In the algorithm performance experiment, we selected 30 graph datasets, 15 of which were derived from real-world graph datasets (https://snap.stanford.edu/data/), and 15 were adapted from real-world datasets or simulated datasets.
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This is a dataset that contains the testing results presented in the manuscript "Exploring the Potential of Offline LLMs in Data Science: A Study on Code Generation for Data Analysis", and it aims to assess offline LLMs' capabilities in code generation for data analytics tasks. Best utilization of the dataset would occur after thorough understanding of the manuscript. A total of 250 testing results were generated. They were merged, leading to the creation of this current dataset.
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The PermGuard dataset is a carefully crafted Android Malware dataset that maps Android permissions to exploitation techniques, providing valuable insights into how malware can exploit these permissions. It consists of 55,911 benign and 55,911 malware apps, creating a balanced dataset for analysis. APK files were sourced from AndroZoo, including applications scanned between January 1, 2019, and July 1, 2024. A novel construction method extracts Android permissions and links them to exploitation techniques, enabling a deeper understanding of permission misuse.
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The SINEW 15 2023 Biomarker dataset was extracted from the sensor data collected by a longitudinal study called Sensors IN-home for Elder Wellbeing (SINEW).
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42 Views