Los datos empleados en el análisis del estudio fueron obtenidos del sistema SAP del Departamento Comercial de la Compañía Nacional de Electricidad (CNEL EP) Unidad de Negocio Esmeraldas. Estos datos consisten en registros originales de consumo mensual de energía eléctrica facturada (expresada en kilovatios-hora, kWh) durante un periodo de 25 meses (enero de 2021 a enero 2023). Estos registros pertenecen a 136218 clientes aproximadamente de del sector residencial de la provincia de Esmeraldas.

Dataset description:

This contains ten categories of gas data, each category contains 5 concentrations, 10, 20, 30, 40, 50ppm.

There are 160 groups of 10, 20, 30, 40, each group contains 6000 sampled voltage signals, and the sampling frequency is 10HZ.

There are only 80 groups for 50ppm concentration, and each group also contains 6000 sampled voltage signals.

The label corresponding to each gas includes category and concentration, which can be split by gas category and concentration.

The Narrative question answering (QA) problem involves generating accurate, relevant, and human-like answers to questions based on the comprehension of a story consisting of logically connected paragraphs. However, this problem remains unexplored for the Arabic language because of the lack of Arabic narrative datasets. To address this gap, we present the Arabic-NarrativeQA dataset, which is the first dataset specifically designed for machine-reading comprehension of Arabic stories.

The "Queue Waiting Time Dataset" is a detailed collection of information that records the movement of waiting times in queues. This dataset contains important details such as the time of arrival, the start and finish times, the waiting time, and the length of the queue. The arrival time denotes the moment when customers enter the queue, while the start and finish times track the duration of the service process. The waiting time measures the time spent waiting in the queue, and the queue length shows the number of customers in the queue when a new customer arrives.

This dataset contains the Supplementary Information of the article "Discovering Mathematical Patterns Behind HIV-1 Genetic Recombination: a new methodology to identify viral features" (Manuscript DOI: 10.1109/ACCESS.2023.3311752).

This paper presents a cost-effective approach for building energy usage management through energy usage optimization of available building energy sources. An energy cost reduction model is developed considering grid energy usage and cost, generator energy usage and cost as well as carbon emissions tax penalties associated with scope 1 emissions. The model selects optimal times to use either Grid energy, generator energy, or a combination of the two to minimize the overall building energy usage cost.

ARImulti-mic: real-world speech recordings on a humanoid robot (ARI)

This dataset includes “real-world” experiments. A recording campaign was held in the acoustic laboratory at Bar-Ilan University. This lab is a [6×6×2.4]m room with a reverberation time controlled by 60 interchangeable panels covering the room facets.

Collective intelligence in biological groups can be employed to inspire the control of artificial complex systems, such as swarm robotics. However, modeling for the social interactions between individuals is still a challenging task.

Collective intelligence in biological groups can be employed to inspire the control of artificial complex systems, such as swarm robotics. However, modeling for the social interactions between individuals is still a challenging task.

This dataset contains video-clips of five volunteers developing daily life activities. Each video-clip is recorded with a Far InfraRed (FIR) camera and includes an associated file which contains the three-dimensional and two-dimensional coordinates of the main body joints in each frame of the clip. This way, it is possible to train human pose estimation networks using FIR imagery.

Recognizing and categorizing banknotes is a crucial task, especially for individuals with visual impairments. It plays a vital role in assisting them with everyday financial transactions, such as making purchases or accessing their workplaces or educational institutions. The primary objectives for creating this dataset were as follows:

We introduce an English Twitter dataset designed for the detection of online drug use, comprising 112,057 tweets accompanied by metadata. This dataset underwent manual annotation by a team of expert annotators consisting of around 30 members, these annotators, possessing diverse multidisciplinary backgrounds and expertise, committed over six months to meticulously label each tweet.

Different faults are experienced by a power system, particulary in transmission lines. In this dataset, the IEEE 5-Bus Model was used to different types of transmission line faults.

Indication of the label of the faults come from the time that the fault has been induced in the simulation.

This dataset aims to be utilized for machine learning algorithms, particularly in multi-class classification of the transmission line fault. In this simulation, each fault was induced at each transmission line one instance at a time during a certain period.

The Shining3D dataset consists of 1866, 272, and 272 videos for the training, validation, and testing sets, respectively. The uniform size of the images in the dataset is 640 × 480 pixels.  LabelMe software is employed to accurately mark the boundary and classify the region of each tooth in the datasets.  This dataset is usef for orthodontic treatment. Orthodontic treatment monitoring, one of the research direction of artificial intelligence, involves using current images and previous 3D models to estimate the relative position of individual teeth before and after orthodontic treatment.

The Aoralscan3 dataset includes 1573, 244, and 244 videos for the corresponding sets. The uniform size of the images in the dataset is 640 × 480 pixels.  LabelMe software is employed to accurately mark the boundary and classify the region of each tooth in the datasets.  This dataset is usef for orthodontic treatment. which is one of the research direction of artificial intelligence using current images and previous 3D models to estimate the relative position of individual teeth before and after orthodontic treatment.

The constructed Aoralscan3 tooth registration dataset includes 1667 samples for training, 156 samples for validation, and 176 samples for testing. Jaw models are generated from hospital patients by oral scanning. The ground truth of the relative pose of each tooth is generated by adding random jittering to the tooth models. For each tooth, ground truth relative pose information was generated by introducing random jittering to the tooth models. This dataset can be used for point cloud registration.

This dataset was used to support our work and provided to the review for reference.

Gamification is a rapidly evolving field that seeks to integrate game mechanics and elements into non-game contexts to enhance the learning experience of computer science students.

This dataset is from our study that challenges the conventional interpretation of electrocardiogram (ECG) measurements, suggesting a paradigm shift in our understanding. Traditionally, ECGs are seen as reflections of the electric potential on the body's surface, but we propose an alternative hypothesis: ECGs may represent the gradient of the electric potential rather than the potential itself. To investigate this, we use computational methods based on the boundary element method (BEM) within the SCIRun numerical package.

Cardiovascular diseases (CVD) are a leading global health concern. Comprehensive data on CVD and key biomarkers play a pivotal role in deciphering individual symptomatology. These biomarkers encompass a range of physiological indicators, such as cholesterol levels, blood pressure, and inflammatory markers like C-reactive protein.

 Enterprise green innovation has emerged as an urgent concern for manufacturing companies in the current context of global green and sustainable development. China's "peak carbon emissions" and "carbon neutrality" goals require companies to take proactive measures to reduce carbon emissions and improve energy efficiency, with the application of digital technology playing a crucial role in this process.

Abstract— Spacesuits are essential for ensuring the safety and functionality of astronauts during space missions. However, they can introduce challenges such as sensory deprivation, reduced dexterity, and lower cognitive performance, which can increase the risk of error. In our previous research, we have shown that adding sound transparency to a suit improves cognitive scores with the block design task. Building upon these findings, here we evaluate use of electro haptics feedback in place of traditional haptics.

Technology`s rapid growth has revolutionized education, inspiring teachers to integrate it into both pedagogical and andragogical approaches.

In order to maintain educational continuity, the pandemic pushed educational institutions and schools all over the world to immediately adopt remote learning and apply numerous digital tools and technology.

Technology has made communication and cooperation between students, instructors, and parents straightforward.

The Numerical Latin Letters (DNLL) dataset consists of Latin numeric letters organized into 26 distinct letter classes, corresponding to the Latin alphabet. Each class within this dataset encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in color, size, writing style, thickness, background, orientation, luminosity, and other attributes, making the dataset highly comprehensive and rich.