Real-time tracking of electricians in distribution rooms is essential for ensuring operational safety. Traditional GPS-based methods, however, are ineffective in such environments due to complex non-line-of-sight (NLOS) conditions caused by dense cabinets and thick walls that obstruct satellite signals. Existing solutions, such as video-based systems, are prone to inaccuracies due to NLOS effects, while wearable devices often prove inconvenient for workers.
Human Body is an extremely intricate and modern structure and involves a huge number of capacities. All these muddled capacities have been comprehended by man him, part-by-part their exploration and tests. As science and innovation advanced, pharmaceutical turned into a necessary part of the exploration. Continuously, restorative science turned into an altogether new branch of science. Starting today, the Health Sector involves Medical establishments i.e. Healing facilities, HOSPITALs and so forth innovative work foundations and medicinal universities.
Human Body is an extremely intricate and modern structure and involves a huge number of capacities. All these muddled capacities have been comprehended by man him, part-by-part their exploration and tests. As science and innovation advanced, pharmaceutical turned into a necessary part of the exploration. Continuously, restorative science turned into an altogether new branch of science. Starting today, the Health Sector involves Medical establishments i.e. Healing facilities, HOSPITALs and so forth innovative work foundations and medicinal universities.
Now a day’s education plays a great role in development of any country. Many of education organizations try to increase education quality. One of the aspects of this improvement is managing of school resources.
This dataset selects the CIFAR-10 small-scale general object recognition dataset, which contains a total of 60,000 RGB color images with a size of 32×32, belonging to 10 categories. The dataset is divided into a training set of 40,000 images, a testing set of 10,000 images, and a validation set of 10,000 images. The images in the dataset are processed with bit masking, and the image classification results are compared with those of the original dataset, providing an experimental basis for the study of how bit masking improves the accuracy and speed of the algorithm.
This dataset contains anonymized responses from 600 Egyptian citizens collected in March 2025 to assess public perceptions of artificial intelligence (AI) and deepfake technologies used in the animation of ancient pharaonic statues and symbols. The survey was conducted as part of a broader research study titled "Animating the Sacred: The Ethical and Cultural Implications of AI-Powered Awakening of Pharaonic Symbols Using Deepfake Techniques."
The diameter of the rivet hole is 5mm. In the experiments at the Cooperative Institute, the AE sensor spacing was set to 130mm, where the centers of sensor 1 and sensor 2 were 90mm from each end of the test piece. The waveform flow data obtained in the experiment only retained the information from 30 minutes before the crack initiation to the fracture of the test piece, and the image data of the test piece during this period were recorded.
High-precision, high-resolution ultra-deep field astronomical observations are essential for detecting special celestial bodies and extremely rare astronomical events. Space astronomical telescopes can achieve this by employing the fine image stabilization system (FISS) to generate line-of-sight (LOS) dithering, enabling scientific instruments to obtain higher-resolution astronomical images through resampling and fusion algorithms. To meet the requirement for sub-pixel dithering control in the FISS of space telescopes, an adaptive control algorithm based on a single neuron is proposed.
Plant diseases remain a significant threat to global agriculture, necessitating rapid and
accurate detection to minimize crop loss. This paper presents a lightweight, end-to-end system for plant
leaf disease detection and severity estimation, optimized for real-time field deployment. We propose a
custom Convolutional Neural Network (CNN), built using PyTorch, trained on the PlantVillage dataset
to classify leaves as healthy or diseased with a test accuracy of 92.06%. To enhance its practical relevance,
This dataset contains hourly electricity demand data and corresponding weather indicators collected from 2021 to 2023. The electricity data was sourced from the U.S. Energy Information Administration (EIA), covering both winter and summer periods across three years. Weather features—including temperature, wind speed, and humidity—were collected to capture the external conditions affecting demand. All files are stored in CSV format and aligned by timestamp. This dataset supports research in time series forecasting, demand prediction, and energy systems modeling.
DRIVE Dataset: The DRIVE dataset consists of 40 fundus images collected from 7 patients with retinal disease and 33 healthy individuals. Each image has a resolution of 584 × 565 pixels. A total of 20 images are used for training, while the remaining 20 images are designated for testing. Each image has two annotations provided by two different experts, and we use the first annotation as the ground truth.
DRIVE Dataset: The DRIVE dataset consists of 40 fundus images collected from 7 patients with retinal disease and 33 healthy individuals. Each image has a resolution of 584 × 565 pixels. A total of 20 images are used for training, while the remaining 20 images are designated for testing. Each image has two annotations provided by two different experts, and we use the first annotation as the ground truth.
Droidware is an Android malware dataset developed at the Cybersecurity Lab, GLA University, India. It comprises 253,527 applications, including 129,950 benign and 123,577 malicious samples. The dataset captures 68 features extracted from function call graphs, permissions, and Java source code, providing a comprehensive view of Android malware behavior. This latest and up-to-date dataset supports the training of AI-based malware detection models, aiding in the development of robust malware classification and threat mitigation strategies for cybersecurity research.
This dataset comprises volatile organic compound (VOC) profiles collected from blood culture broth samples using an electronic nose (E-nose) system. The samples include cultures positive for Candida spp., including C. albicans, C. glabrata, C. tropicalis, among others, as well as negative control samples. Each sample was exposed to the E-nose sensor array, which consists of multiple gas sensors sensitive to different VOC families.
The rapid diffusion and steep concentration gradients of ship exhaust plumes create substantial challenges in velocity field estimation and quantification. To address this limitation, this study develops FluentFluid, the first large-scale optical flow dataset specifically designed for plume motion analysis, as a standardized training benchmark. A novel deep optical flow network architecture guided by grayscale attention mechanisms is proposed. The architecture adopts a dual enhancement strategy through Effective Grayscale Pixels (EGPs) and Grayscale Attention Weights.
To establish comprehensive training data for fluid optical flow estimation, we developed a simulated ship emission dataset called FluentFluid. This dataset was generated through computational fluid dynamics simulations using ANSYS Fluent software, with focus on ship exhaust plume modeling. The synthesis pipeline began with the construction of a physical model of ship stack exhaust systems, followed by fluid motion simulation under controlled parametric variations, including exit velocity, stack position, turbulence intensity, and ambient wind conditions.
This dataset collects production data of G12 and M10 monocrystalline silicon photovoltaic cells at various stages including metal silicon smelting, polycrystalline silicon purification, monocrystalline silicon growth, silicon wafer cutting, and monocrystalline silicon solar cell manufacturing, covering different implementation processes. It aims to conduct in-depth analysis of the production process and provide a basis for optimizing the process and improving the performance of the cells.
<p>Electrocardiogram (ECG) interpretation is critical for diagnosing a wide range of cardiovascular conditions. To streamline and accelerate the development of deep learning models in this domain, we present a novel, image-based version of the PTB Diagnostic ECG Database tailored for use with convolutional neural networks (CNNs), vision transformers (ViTs), and other image classification architectures. This enhanced dataset consists of 516 grayscale .png images, each representing a 12-lead ECG signal arranged as a 2D matrix (12 × T, where T is the number of time steps).
The Travel Recommendation Dataset is a comprehensive dataset designed for building and evaluating conversational recommendation systems in the travel domain. It includes detailed information about users, destinations, and ratings, enabling researchers and developers to create personalized travel recommendation models. The dataset supports use cases such as personalizing travel recommendations, analyzing user behavior, and training machine learning models for recommendation tasks.
This dataset compilation brings together four significant traffic network datasets from California's transportation monitoring systems: METR-LA, PEMS-BAY, PEMS04, and PEMS08. The METR-LA dataset is collected from the traffic monitoring system in the Los Angeles area and records detailed traffic speed data. The PEMS-BAY, PEMS04, and PEMS08 datasets originate from the California Department of Transportation (Caltrans) Performance Measurement System, with PEMS-BAY recording traffic speed data, while PEMS04 and PEMS08 record traffic flow data.
The source data files and code files of the paper: optical chaos shift keying communication system via neural network-based signal reconstruction. The following data is included:
1. Source figure file in the paper;
2. Source code of the proposed scheme, include the simulation code for communication, secure analysis and parameter mismatch range.
3. The source Simulink module is included for time-delayed chaotic signal generation.
1.Dataset overview
This dataset is designed to support the HAR task of this study. Covered by (a) walking, (b) running, (c) going upstairs, (d) going downstairs, (e) high leg lifting, (f) skipping rope, and (g) rhombic extension Seven types of human movement data.
The files D.xlsx, E.xlsx, H.xlsx, R.xlsx, S.xlsx, U.xlsx, and W.xlsx are one-dimensional time series data of seven sports, with a data length of 400, and the number of data categories of 7.
Dataset for multiple signal DOA estmation. This the original dataset used in our research. we consider a ULA with M = 16 antenna elements spaced at half-wavelength distance (l = λ/2). We consider narrowband, non-coherent signals with different intermediate frequencies (IF) transmitted from different sources. The signals are sampled at the intermediate frequency with a sampling frequency of 2.5MHz and 300 sample points.
We evaluate the proposed FSDUF model on three publicly available social media benchmark datasets: Weibo {jin2017multimodal}, Twitter {boididou2015verifying}, and Pheme {zubiaga2017exploiting}.
