Abstract
Worldwide, forests have been harassed by fire in recent years. Either by human intervention or other reasons, the history of the burned area is increasing considerably, harming fauna and flora. It is essential to detect an early ignition for fire-fighting authorities can act quickly, decreasing the impact of forest damage impacts. The proposed system aims to improve nature monitoring and improve the existing surveillance systems through satellite image recognition. The soil recognition via satellite images can determine the sensor modules' best position and provide crucial input information for artificial intelligence-based systems. For this, satellite images from the Sentinel-2 program are used to generate forest density maps as updated as possible. Four classification algorithms make the Tree Cover Density (TCD) map, consisting of the Gaussian Mixture Model (GMM), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), which identify zones by training known regions. The results demonstrate a comparison between the algorithms through their performance in recognizing the forest, grass, pavement, and water areas by Sentinel-2 images.

Abstract
In the power system decarbonization roadmap, novel grid management tools and market mechanisms are fundamental to solving technical problems concerning renewable energy forecast uncertainty. This work proposes a predictive algorithm for procurement of grid flexibility by the system operator (SO), which combines the SO flexible assets with active and reactive power short-term flexibility markets. The goal is to reduce the cognitive load of the human operator when analyzing multiple flexibility options and trajectories for the forecasted load/RES and create a human-in-the-loop approach for balancing risk, stakes, and cost. This work also formulates the decision problem into several steps where the operator must decide to book flexibility now or wait for the next forecast update (time-to-decide method), considering that flexibility (availability) price may increase with a lower notification time. Numerical results obtained for a public MV grid (Oberrhein) show that the time-to-decide method improves up to 22% a performance indicator related to a cost-loss matrix, compared to the option of booking the flexibility now at a lower price and without waiting for a forecast update.

Abstract
This paper investigates sampling strategies within latent spaces for music generation, focusing on (chordified) J.S. Bach Chorales and utilizing MusicVAE as the generative model. We conduct an experiment comparing three sampling and interpolation strategies within the latent space to generate chord progressions - from a discrete vocabulary of Bach's chords - to Bach's original chord sequences. Given a three-chord sequence from an original Bach chorale, we assess sampling strategies for replacing the middle chord. In detail, we adopt the following sampling strategies: (1) traditional linear interpolation, (2) k-nearest neighbors, and (3) k-nearest neighbors combined with angular alignment. The study evaluates their alignment with music theory principles of functional harmony embedding and voice-leading to mirror Bach's original chord sequences. Preliminary findings suggest that knearest neighbors and k-nearest neighbors combined with angular alignment closely align with the tonal function of the original chord, with k-nearest neighbors excelling in bass line interpolation and the combined strategy potentially enhancing voice-leading in upper voices. Linear interpolation maintains aspects of voice-leading but confines selections within defined tonal spaces, reflecting the nonlinear characteristics of the original sequences. Our study contributes to the dynamics of latent space sampling for music generation, offering potential avenues for enhancing explainable creative strategies. © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original.

Abstract
Flying Networks (FNs) have emerged as a promising solution to provide on-demand wireless connectivity when network coverage is insufficient or the communications infrastructure is compromised, such as in disaster management scenarios. Despite extensive research on Unmanned Aerial Vehicle (UAV) positioning and radio resource allocation, the challenge of ensuring reliable traffic relay through backhaul links in predictive FNs remains unexplored. This work proposes Simulated Annealing for predictive FNs (SAFnet), an innovative algorithm that optimizes network performance under positioning constraints, limited bandwidth and minimum rate requirements. Our algorithm uniquely leverages prior knowledge of the first-tier node trajectories to assign bandwidth and dynamically adjust the position of the second-tier flying relay. Building upon Simulated Annealing, our approach enhances this well-known AI algorithm with penalty functions, achieving performance levels comparable to exhaustive search while significantly reducing computational complexity.

Abstract
The concept of Industry 4.0 and the introduction of the Internet of Things (IoT) on industrial applications, known as Industrial Internet of Things (IIoT), have been changing the scenario of industrial automation. This new paradigm is expected to optimize industrial processes, increase productivity, lower costs and improve operations integration. For that, structured Machine-to-Machine (M2M) communication is key to ensure agility, interoperability and reliability, with several solutions currently available in the literature and in industry. This paper reviews the state of the art on industrial communication protocols and architectures, providing a classification and comparison of these different solutions based on their most relevant features in the context of Industry 4.0. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Progressively more advanced and complex models are proposed to address problems related to computer vision, forecasting, Internet of Things, Big Data and so on. However, these disciplines require preprocessing steps to obtain meaningful results. One of the most common problems addressed in this stage is the presence of missing values. Understanding the reason why missingness occurs helps to select data imputation methods that are more adequate to complete these missing values. Missing at Random synthetic generation presents challenges such as achieving extreme missingness rates and preserving the consistency of the mechanism. To address these shortcomings, three new methods that generate synthetic missingness under the Missing at Random mechanism are proposed in this work and compared to a baseline model. This comparison considers a benchmark covering 33 data sets and five missingness rates $(10\%, 20\%, 40\%, 60\%, 80\%)$ . Seven data imputation methods are compared to evaluate the proposals, ranging from traditional methods to deep learning methods. The results demonstrate that the proposals are aligned with the baseline method in terms of the performance and ranking of data imputation methods. Thus, three new feasible and consistent alternatives for synthetic missingness generation under Missing at Random are presented.