Abstract
Grid-Forming Power Inverters: Control and Applications is the first book dedicated to addressing the operation principles, grid codes, modeling, and control of grid-forming power inverters. The book initially discusses the need for this technology due to the substantial annual integration of inverter-based renewable energy resources. The key differences between the traditional grid-following and the emerging grid-forming inverter technologies are explained. Then, the book explores in detail various topics related to grid-forming power inverters, including requirements and grid standards, modeling, control, damping power system oscillations, dynamic stability under large fault events, virtual oscillator-controlled grid-forming inverters, grid-forming inverters interfacing battery energy storage, and islanded operation of grid-forming inverters. Features: • Explains the key differences between grid-following and grid-forming inverters • Explores the requirements and grid standards for grid-forming inverters • Provides detailed modeling of virtual synchronous generators • Explains various control strategies for grid-forming inverters • Investigates damping of power system oscillations using grid-forming converters • Elaborates on the dynamic stability of grid-forming inverters under large fault events • Focuses on practical applications

Abstract
The recent situation of the COVID-19 pandemic has stimulated both the discussion on the use of IT-related teaching tools and the exposure of the student population to vulnerabilities linked to cybersecurity literacy as an integral part of the educational projects of educational institutions and a component of the exercise of citizenship and social sustainability of educational communities. The study presented is based on the assumption that the use of gamification as an element or tool that promotes learning within digital environments may be feasible, and more specifically may function as a teaching element on issues related to cybersecurity for students, especially for higher education students. In order to quantify the openness of students to such a tool path, quantitative methodology was used, and a survey was carried out in two Polytechnic Institutions (PI), achieving a sample of 95 students, and seeking perceptions on positive impacts resulting from the creation of a game scenario for better learning. Results show that students, regardless of their higher education course, clearly understand what gamification is and its goals, and also that students adopt good cybersecurity practices according to their higher education course. This last result goes accordingly with the supposition that gamification can and should be used in cybersecurity literacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Point Set Registration (PSR) algorithms have very different underlying theoretical models to define a process that calculates the alignment solution between two point clouds. The selection of a particular PSR algorithm can be based on the efficiency (time to compute the alignment) and accuracy (a measure of error using the estimated alignment). In our specific context, previous work used a CPD algorithm to detect and quantify change in spatiotemporal datasets composed of moving and shape-changing objects represented by a sequence of time stamped 2D polygon boundaries. Though the results were promising, we question if the selection of a particular PSR algorithm influences the results of detection and quantification of change. In this work we review and compare several PSR algorithms, characterize test datasets and used metrics, and perform tests for the selected datasets. The results show pyCPD and cyCPD implementations of CPD to be good alternatives and that BCPD can have potential to be yet another alternative. The results also show that detection and quantification accuracy change for some of the tested PSR implementations.

Abstract

Abstract
The robot presented in this paper was developed with the main focus on participating in robotic competitions. Therefore, the subsystems here presented were developed taking into account performance criteria instead of simplicity. Nonetheless, this paper also presents background knowledge in some basic concepts regarding robot localization, navigation, color identification and control, all of which are key for a more competitive robot.

Abstract
Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot's operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.