Short-term electricity demand forecasting is a fundamental part of the decision-making process of entities involved in electricity consumption management, since it allows the development of strategies to deal with variations in electricity demand in short periods of time. Developing a highly-accurate predictive model is necessary to understand and reflect the consumption behavior, as well as to adjust the generation program essentially to meet the demand at each moment. Therefore, a performance comparison has been made between statistical, machine learning and deep learning models for short-term forecasting. The deep learning models are based on recurrent neural networks, incorporating attention mechanisms in some of them. Hyperparameter tuning was also applied using a Bayesian optimization algorithm. A dataset was also developed including historical electricity demand and external factors such as weather and calendar variables recorded in Paraguay from 2009 to 2022. Models were evaluated from a set of numerical experiments using classical error metrics such as: mean squared error, root mean square error, mean absolute error and mean percentage absolute error. In addition, new special measures were introduced to analyze the error in this type of applications: the percentage error at peak hour and the maximum percentage error of the day to analyze the error in certain events.

CONNIE experiment installed 12 charge- coupled devices (CCDs) sensors near the Angra II nuclear reactor in Angra dos Reis (Brazil) aiming to detect low energy antineutrinos produced in the core of the reactor. For two years, these sensors recorded particle images catching mainly muons and other particles such as electrons and alphas that will be considered as external radioactive background that must be removed. The images were created from the data taken every 3 hours, generating in this way a vast catalog of detected events on each CCD. In this work we propose an instance segmentation and a classification model in order to study the variation of the muon rate produced by those particles. For this purpose we developed two models: a Convolutional Neural Network (CNN) for event classification, and an instance segmentation model for identifying overlapped events. The classification model demonstrated exceptional efficacy, achieving an impressive accuracy of 0.8. Furthermore, precision and recall values of 0.85 and 0.92, respectively, were achieved for the particles of interest. Within the domain of bounding box detection, our model exhibited remarkable recall and precision rates of 71 % and 69 %, respectively, further underlining its adeptness in accurately localizing objects. Our results not only illuminate the potential of these models but also contribute to a deeper understanding of muon rate variations within the experimental context of the CONNIE setup. Index Terms- muon, deeplarning, yolo V8, yolo, CONNIE, neutrine.

This paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing system which consists on training a semantic segmentation network from a set of seeds generated by a Support Vector Machine. A region growing algorithm module is applied to the seeds to progressively increase the pixel-level supervision. The proposed method performs better than an SVM, which is one of the most popular segmentation tools in remote sensing image applications.

Many remote sensing applications require high spatial resolution images, but the elevated cost of these images makes some studies unfeasible. Single-image super-resolution algorithms can improve the spatial resolution of a lowresolution image by recovering feature details learned from pairs of low-high resolution images. In this work, several configurations of ESRGAN, a state-of-the-art algorithm for image super-resolution, are tested. We make a comparison between several scenarios, with different modes of upsampling and channels involved. The best results are obtained training a model with RGB-IR channels and using progressive upsampling.