*.avi; *.csv; *.txt; *.zip
The dataset used in the study consists of different IoT network traffic data files each IoT traffic data has files containing benign, i.e. normal network traffic data, and malicious traffic data related to the most common IoT botnet attacks which are known as the Mirai botnet.
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Open set face recognition on a small dataset, in terms of the amount of image samples per individual, is a hard and active area of study. This study investigates the open set face verification and face identification problems on the IFPLD dataset, which consists of only one frontal and one profile image per individual.
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To gather the dataset, we asked two participants to perform six basic knife activities. The layout of the system experiment is provided in Fig. 4. As it illustrates, we put the receiver on the right side and the ESP32 transceiver on the left side of the performing area. The performing area is a cutting board (30 x 46 cm) in this experiment. Each participant performs each activity five times in the performing area.
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We build a large-scale dataset for term name generation, which contains the GO terms about Homo sapiens (humankind and yeast). We collect the term ID, term name and the corresponding genes’ ID from \href{http://geneontology.org/}{Gene Ontology Consortium}.
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Motion analysis forms a very important research topic with a general mathematical background and applications in different areas including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detection and recording of the moving body position and the simultaneous acquisition of signals from further sensors. The application is related to monitoring of physical activity and the use of wearable sensors of the heart rate and acceleration during different motion patterns.
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This dataset consists of two parts:
1)original VSP data
---- syn.txt -> synthetic VSP record
---- SEAM.xlsx -> the 66-th shot of SEAM Phase I RPSEA Elastic Simulations
---- aN.txt -> real VSP record in the Dong area
2)coressponding up- and downgoing separation results
---- results_Syn -> abalation experimental results for self validation
---- results_SEAM -> comparison experimental results on SEAM open data
---- results_Dong -> comparison experimental results on real VSP data
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The project provides trained models of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for outdoor insulator detection and classification of the surface contamination, such as salt, snow, cement, soil and wet soil. The project is based on YOLOv3 implementation developed by Ultralytics/YOLOv3. The models were trained on custom insulator dataset consisting of 11816 images of different type insulators under various conditions.
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Mission-integrated ⪅8nT magnetometer (MAG) data at Jupiter and Saturn, 100-minute averages. Data consist of the only available MAG measurements that spanned at least 6 months (180 Earth days), and include the Cassini–Huygens, Galileo, and Juno missions. Steven P. Joy and Joe Mafi (UCLA & NASA Planetary Data System/Planetary Plasma Interactions Node) provided concatenated and RTN-rotated Jupiter MAG 1-minute averages from Galileo and Juno, including random samples of field swelling.
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Many algorithms for compressed sensing are studied. The common guarantee for the reconstruction algorithm is restricted isometry property (RIP), which is shown to only hold under ideal assumptions. However, in practice, more than one ideal condition is often violated and there is no RIP-based guarantee application. Based on this discrepancy, we propose a new oblique subspace thresholding pursuit (ObSTP) algorithm. It is guaranteed by the restricted biorthogonality property (RBOP) which requires no ideal assumptions.
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The greenhouse remote sensing image dataset we produced contains 2101 tiles and 23914 greenhouses. And in the data set, 37.9% of dense scenes were added, so that the model trained through this data set could better adapt to the dense scene detection task.
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