*.avi; *.csv; *.txt; *.zip

The dataset used in this study consists of Airborne LiDAR Bathymetry (ALB) waveform data collected by the Norwegian Mapping Authority via Field Geospatial AS. It covers the Fjøløy Island area in Stavanger, Norway, a region characterized by fjords and diverse submerged environments. The dataset is proprietary and was provided to the authors under a research collaboration agreement.

Two subsets were extracted from the full dataset:

• Dataset 1: 6,379 waveform files
• Dataset 2: 4,428 waveform files

Each waveform file contains:

The GestDoor dataset contains wearable sensor data collected to support research in biometric authentication through arm movements during door-opening interactions. Using two 6-degree-of-freedom (6-DOF) inertial measurement units (IMUs) worn on the wrist and upper arm, 11 participants performed four types of door-opening tasks—left-hand pull, left-hand push, right-hand pull, and right-hand push—across up to three sessions. The dataset includes 3,330 samples comprising accelerometer and gyroscope signals at 100 Hz, along with session metadata.

Brain-Computer Interface (BCI) technology makes possible a direct interface between the brain and external devices through the interpretation of neural signals. It is essential to have patient's native language-containing datasets when designing BCI-based solutions for neurological disorders. Current BCI research, though, lacks language-specific datasets, notably for languages like Telugu, which has over 90 million speakers in India. We developed an Electroencephalograph (EEG)-based Brain-Computer Interface (BCI) dataset consisting of EEG signal samples for Telugu Vowels and Consonants.

With the advent of 6G Open-RAN architecture, multiple operational services can be simultaneously executed in RAN, leveraging the near-Real-Time Radio Intelligent Controller (near-RT-RIC) and real-time (RT) nodes. The architecture provides an ideal platform for Federated Learning (FL): The xAPP is hosted in the near-RT-RIC to perform global aggregation, whereas the Open Radio Unit (ORU) allocates power to users to participate in FL in a RT manner. This paper identifies power and latency optimization as critical factors for enhancing FL in a stochastic environment.

This dataset comprises synchronized multi-modal physiological recordings—functional Near-Infrared Spectroscopy (fNIRS), Electroencephalography (EEG), Electrocardiography (ECG), and Electromyography (EMG)—collected from 16 participants exposed to emotion-eliciting video stimuli. It includes raw signals, event markers, and Python scripts for data import and preprocessing. Special emphasis is placed on fNIRS, which, though less common in affective computing, provides valuable hemodynamic insights that complement electrical signals from EEG, ECG, and EMG.

This repository contains resources for EEG data processing and cognitive load recognition using a Multi-Head Attention EEGNet model. It includes original EEG data, MATLAB code for preprocessing, and Python code for classification.

With the ethics approval obtained from our institution, this study acquired 30 subjects aged between 18 to 29 to conduct research. Informed written consents were attained from all participants. The selection of participants follows a standardized and rigorous protocol that they have to meet the following requirements:

Motion analysis provide important information in rehabilitation, performance evaluation, and movement symmetry assessment, with applications including neurology, biomedicine, surgery, and sports monitoring. The integration of wearable sensors and signal processing forms a robust interdisciplinary platform for such analysis. Specific methods are based on monitoring physiological and motion responses during controlled exercises that simulate real-world motion scenarios.

This is a data for cosmetics dataset. The International Patent Classification (IPC) is a standardized, hierarchical system used worldwide to categorize the technical content of patents. It is administered by the World Intellectual Property Organization (WIPO). The IPC system breaks down technology into sections, classes, subclasses, and groups, each representing specific technical domains. By assigning IPC codes to patent documents, patent offices and researchers can systematically organize, search, and analyze patent information across various industries and technological fields.

The growing adoption of declarative software specification languages, coupled with their inherent difficulty in debugging, has underscored the need for effective and automated repair techniques applicable to such languages. Researchers have recently explored various methods to automatically repair declarative software specifications, such as template-based repair, feedback-driven iterative repair, and bounded exhaustive approaches. The latest developments in Large Language Models (LLMs) provide new opportunities for the automatic repair of declarative specifications.