Abstract

Abstract
Offshore wind farms are becoming the main alternative to fossil fuels and the future key to mitigating climate change by achieving energy sustainability. With favorable indicators in almost every environmental index, these structures operate under varying and dynamic environmental conditions, leading to efficiency losses and sudden failures. For these reasons, it's fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper introduces a new simulation environment for testing and training autonomous inspection techniques under a more realistic offshore wind farm scenario. Combining the Gazebo simulator with ROS, this framework can include multi-robots with different sensors to operate in a customizable simulation environment regarding some external elements (fog, wind, buoyancy...). The paper also presents a use case composed of a 3D LiDAR-based technique for autonomous wind turbine inspection with UAV, including point cloud clustering, model estimation, and the preliminary results under this simulation framework using a mixed environment (offshore simulation with a real UAV platform).

Abstract
This paper presents a digital twin demonstrator of a forest harvesters and wood processing machines. The demonstrator is a cyber-physical system that allow the emulation and identification of faults that may occur during regular machine operations. The proposed solution includes a CAN Bus communication between several electronic controller units connected to sensors and actuators. © 2023 IEEE.

Abstract
Bin picking is a challenging task involving many research domains within the perception and grasping fields, for which there are no perfect and reliable solutions available that are applicable to a wide range of unstructured and cluttered environments present in industrial factories and logistics centers. This paper contributes with research on the topic of object segmentation in cluttered scenarios, independent of previous object shape knowledge, for textured and textureless objects. In addition, it addresses the demand for extended datasets in deep learning tasks with realistic data. We propose a solution using a Mask R-CNN for 2D object segmentation, trained with real data acquired from a RGB-D sensor and synthetic data generated in Blender, combined with 3D point-cloud segmentation to extract a segmented point cloud belonging to a single object from the bin. Next, it is employed a re-configurable pipeline for 6-DoF object pose estimation, followed by a grasp planner to select a feasible grasp pose. The experimental results show that the object segmentation approach is efficient and accurate in cluttered scenarios with several occlusions. The neural network model was trained with both real and simulated data, enhancing the success rate from the previous classical segmentation, displaying an overall grasping success rate of 87.5%.

Abstract
While altruism has been studied from a variety of standpoints, none of them has proven sufficient to explain the richness of nuances detected in experimentally observed altruistic behavior. On the other hand, the recent success of behavioral economics in linking expectation formation to key behaviors in complex societies hints to social expectations having a key role in the emergence of altruism. This paper proposes an agent-based model based upon the Bush-Mosteller reinforcement learning algorithm in which agents, subject to stimuli derived from empirical and normative expectations, update their aspirations (and, consequently, their future cooperative behavior) after playing successive rounds of the Dictator Game. The results of the model are compared with experimental results. Such comparison suggests that a stimuli model based on empirical and normative expectations, such as the one presented in this work, has considerable potential for capturing the cognitive-behavioral processes that shape decision-making in contexts where cooperative behavior is relevant.

Abstract
Virtual reality and emotions have become inseparable concepts over the past few years, supported by the increasing number of studies relating them. However, these studies' methodologies are often poorly justified or dependent on the authors' subjective definition of emotion and its classification. Moreover, frequently, these studies only consider two stimuli, specifically audiovisual, despite being known the relevance of including a greater variety of sensory channels to improve the relationship between the individual and the virtual environment. So, to address these gaps, and considering the importance of multisensory stimulation, this paper aims to review the methods and instruments found in the literature regarding the analysis of the users' emotions in virtual reality. Also, we provide an overview of the main limitations of such studies. Little information can be found in the literature regarding the connection between the input stimulus and the users' emotional responses. This corroborates the difficulty in creating and evaluating immersive virtual experiences when stimulating more than two human senses, typically audiovisual. Nevertheless, we address some clues on the impact of visual, auditory, haptic, smell, and taste elements to trigger specific emotions. Also, we address the association between the research area and the method used. Finally, the main gaps and challenges are discussed. We expect that the combination of these results acts as guidelines for designing richer multisensory virtual experiences. Moreover, we intend to contribute to future research on emotions-based immersive virtual reality by providing a review of the most suitable methodologies and instruments for specific contexts.