LIVE-Viasat Real-World Satellite QoE Database contains 179 videos from real-world streaming, encompassing a range of distortions. Enhanced by a study with 54 participants providing detailed QoE feedback, our work not only provides a rich analysis of the determinants of subjective QoE but also delves into how various streaming impairments influence user behavior, thereby offering a more holistic understanding of user satisfaction.
LIVE-Viasat Real-World Satellite QoE Database contains 179 videos from real-world streaming, encompassing a range of distortions. Enhanced by a study with 54 participants providing detailed QoE feedback, our work not only provides a rich analysis of the determinants of subjective QoE but also delves into how various streaming impairments influence user behavior, thereby offering a more holistic understanding of user satisfaction.
The Biomolecular Adsorption Database, BAD, is an archive of the data regarding protein adsorption on flat solid surfaces, as reported in peer-reviewed literature.
The following dataset includes two types of RF IQ samples: Synthetic and Indoor Over-The-Air datasets.
This dataset has been used in a conference paper published in 2025 DySPAN: I Can’t Believe It’s Not Real: CV-MuSeNet: Complex-Valued Multi-Signal Segmentation.
Paper abstract:
This study adopts a comprehensive approach that integrates finite element simulations with experimental validation to investigate the potential application of ultrasonic imaging technology for downhole fallen objects detection. The study first employed finite element simulations to model the impact of fallen objects on acoustic wave propagation, with a focus on examining the correlations between the reflected signal and the fallen objects' spatial position, size, and the probe's excitation frequency.
This dataset contains deep-sea measured sound velocity profile (SVP) data, which was used in a hybrid experiment that integrates both real-world measurements and simulations for ultra-short baseline (USBL) acoustic positioning. The dataset supports research on underwater acoustic propagation, sound ray tracing, and positioning accuracy improvements. By utilizing actual deep-sea SVP data, the hybrid experimental approach enhances the realism and reliability of USBL performance evaluation.
This dataset contains deep-sea measured sound velocity profile (SVP) data, which was used in a hybrid experiment that integrates both real-world measurements and simulations for ultra-short baseline (USBL) acoustic positioning. The dataset supports research on underwater acoustic propagation, sound ray tracing, and positioning accuracy improvements. By utilizing actual deep-sea SVP data, the hybrid experimental approach enhances the realism and reliability of USBL performance evaluation.
This dataset contains deep-sea measured sound velocity profile (SVP) data, which was used in a hybrid experiment that integrates both real-world measurements and simulations for ultra-short baseline (USBL) acoustic positioning. The dataset supports research on underwater acoustic propagation, sound ray tracing, and positioning accuracy improvements. By utilizing actual deep-sea SVP data, the hybrid experimental approach enhances the realism and reliability of USBL performance evaluation.
In recent years, the fusion of artificial intelligence and semantic web technologies has paved the way for innovative approaches to managing and utilizing information. With the growing demand for structured gastronomical data, there is a need for well-defined ontologies that facilitate recipe organization, ingredient classification, nutritional insights, and personalized diet recommendations. The dataset presents a multilingual recipe ontology and knowledge graph, capturing critical relationships between ingredients, nutrition, cooking actions, and recipe planning.
Modern power systems face growing risks from cyber-physical attacks, necessitating enhanced resilience due to their societal function as critical infrastructures. The challenge is that defense of large-scale systems-of-systems requires scalability in their threat and risk assessment environment for cyber-physical analysis including cyber-informed transmission planning, decision-making, and intrusion response. Hence, we present a scalable discrete event simulation tool for analysis of energy systems, called DESTinE.
MagNet is a large-scale dataset designed to enable researchers modeling magnetic core loss using machine learning to accelerate the design process of power electronics. The dataset contains a large amount of voltage and current data of different magnetic components with different shapes of waveforms and different properties measured in the real world. Researchers may use these data as pairs of excitations and responses to build up dynamic magnetic models or calculate the core loss to derive static models.
Large Vision-Language Models (LVLMs) struggle with distractions, particularly in the presence of irrelevant visual or textual inputs. This paper introduces the Irrelevance Robust Visual Question Answering (IR-VQA) benchmark to systematically evaluate and mitigate this ``multimodal distractibility". IR-VQA targets three key paradigms: irrelevant visual contexts in image-independent questions, irrelevant textual contexts in image-dependent questions, and text-only distractions.
Agriculture is the backbone of Mizoram’s state economy as the majority of the people use agriculture and its allied sector as their livelihood. According to the 2011 census, more than 50% of the people are still engaged in agriculture and its related activities. Jhum cultivation or shifting cultivation is the primary farming pattern in the state. However, this traditional farming method is no longer effective and productive, due to various reasons such as resource limitations due to population pressure, and a shortened jhum cycle period of 3-4 years (i.e., the ideal cycle is 14-18).
dataset on Indian banknotes that was collected to aid research in fields like financial technology, security, and machine learning. The collection contains notes for both older and more recent generations of Indian currency, including ₹1, ₹2, ₹5, ₹10, ₹20, ₹50, ₹100, ₹200, and ₹500. Each note has been carefully scanned and sorted. Important details have been noted, including the note's design, serial number, and security features including small printed text, security threads, and watermarks. We systematically gathered, checked, and labeled every note in order to create this dataset.
The incorporation of Internet of Things (IoT) technology with agriculture has transformed several farming practices, bringing unparalleled simplicity and efficiency. This article explores the robust integration of IoT and blockchain technology(BIoT) in agricultural operations, offering insight into the resulting BIoT system’s design. This study investigates the potential benefits of merging the IoT and blockchain technologies in agriculture. A system for tracking plant growth using sensors and blockchain-integrated IoT has been developed and analyzed.
A collection of Python pickles objects containing a Pandas DataFrame. Each Dataframe corresponds to the postprocessed firing rate (fr) in Hz and mean amplitude of the spikes (AMP) in microV/s of the vagus nerve recordings obtained from 12 adult female Sprague-Dawley rats. Additionally, the blood-glucose level in mg/dL is included. The fr and AMP signals have 0.1 miliseconds of resolution, whereas the glucose level was measured approximately every 5 minutes. Temporal variations are due to experimental factors. The number of available glucose samples changes across recordings.
The dataset consists of two primary files: dataset.json and analysis_script.ipynb. The dataset.json file contains structured records of AI-assisted psychological therapy sessions, including emotion recognition, NLP techniques, cognitive behavioral therapy (CBT) patterns, hypnotherapy data, user feedback, and therapy outcomes. The analysis_script.ipynb Jupyter Notebook provides data preprocessing, visualization, and statistical analysis of therapy session outcomes.
This paper presents a Tube-based Robust Model Predictive Control (Tube-RMPC) strategy for autonomous vehicle control, designed to address model parameter uncertainties and variations in road-tire adhesion coefficients under complex driving conditions. The proposed approach enhances the representation of vehicle dynamics by introducing a unified vehicle-tire modeling framework, capturing nonlinear characteristics more effectively. To facilitate real-time implementation, the model is systematically linearized and discretized, ensuring computational efficiency.
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.
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.
Monitoring bird populations is crucial to conserve biodiversity and protect wetland ecosystems. Unmanned aerial vehicle (UAV) remote sensing is characterized by high image resolution, strong maneuverability, and convenient data acquisition, and is thus well suited for monitoring bird species in wetland environments. However, strong light reflection from the water surface interferes with the accurate identification of birds on UAV remote sensing images.
Monitoring bird populations is crucial to conserve biodiversity and protect wetland ecosystems. Unmanned aerial vehicle (UAV) remote sensing is characterized by high image resolution, strong maneuverability, and convenient data acquisition, and is thus well suited for monitoring bird species in wetland environments. However, strong light reflection from the water surface interferes with the accurate identification of birds on UAV remote sensing images.
The datasets include six publicly datasets for dynamic graph analysis: UCI captures social interactions among UC Irvine students; Digg records user interactions on the news-sharing website; Email-Eu-core details email communications in a European research institution; ia-contacts-dublin tracks human contacts in Dublin; sx-mathoverflow and sx-askubuntu are two temporal networks datasets formed from user activities on StackOverflow.
