Abstract
Digital transformation in entrepreneurship education is an activity that has been taking place in higher education institutions, namely, through digital access to resources, simulations, and serious games. These activities have contributed to greater student engagement and to fostering personalized learning. Despite the recognized success of these activities, entrepreneurship education is still seen as an isolated and internally implemented activity, with few synergies with other institutions and external stakeholders. This study presents a proposal for an innovative technological platform that enables entrepreneurial projects to include students from various higher education institutions helping to build businesses worldwide. The proposed approach also involves integration with investors who can invest and offer mentoring services. A prototyping methodology was employed which provides benefits in terms of rapid iteration and feedback, enabling early visualization and testing of ideas, leading to improved design, functionality, and alignment with user needs. The results of this study show that the implemented solution addresses the critical success factors (CSFs) in the implementation of a crowdsourcing platform such as usability, scalability, transparency, security, monetary compensation, and social recognition. Finally, this study is mainly relevant for higher education institutions to revolutionize their higher education processes by adopting a collaborative approach that allows them to interact with several players on a global scale.

Abstract
Since the advent of agriculture, humans have considered phytopharmaceutical products to control pests and reduce losses in farming. Sometimes some of these products, such pesticides, can potentially harm the soil life. In the literature there is evidence that AI and image processing can have a positive contribution to reduce phytopharmaceutical losses, when used in variable rate sprayers. However, it is possible to improve the existing sprayer system's precision, accuracy, and mechanical aspects. This work proposes spraying solution called GraDeS solution (Grape Detection Sprayer). GraDeS solution is a sprayer with two degrees of freedom, controlled by a AI-based algorithm to precisely treat grape bunches diseases. The experiments with the designed sprayer showed two key points. First, the deep learning algorithm recognized and tracked grape bunches. Even with structure movement and bunch covering, the algorithm employs several strategies to keep track of the discovered objects. Second, the robotic sprayer can improve precision in specified areas, such as exclusively spraying grape bunches. Because of the structure's reduced size, the system can be used in medium and small robots.

Abstract
Reducing the gap between renewable energy needs and supply is crucial to achieve sustainable growth. Hydroelectric power production predictions in several Madeira Island catchment regions are shown in this article using Long Short-Term Memory, LSTM, networks. In order to foresee hydro reservoirs inflows, our models take into account the island's dynamic precipitation and flow rates and simplify the process of water moving from the cloud to the turbine. The model developed for the Socorridos Faja Rodrigues system demonstrates the proficiency of LSTMs in capturing the unexpected flow behavior through its low RMSE. When it comes to energy planning, the model built for the CTIII Paul Velho system gives useful information despite its lower accuracy when it comes to anticipating problems.

Abstract
The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures' operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine.

Abstract
As the Internet evolves from TLS 1.2 to TLS 1.3, it offers enhanced security against network eavesdropping for online communications. However, this advancement also enables malicious command and control (C2) traffic to more effectively evade malware detectors and intrusion detection systems. Among other capabilities, TLS 1.3 introduces encryption for most handshake messages and conceals the actual TLS record content type, complicating the task for state-of-the-art C2 traffic classifiers that were initially developed for TLS 1.2 traffic. Given the pressing need to accurately detect malicious C2 communications, this paper examines to what extent existing C2 classifiers for TLS 1.2 are less effective when applied to TLS 1.3 traffic, posing a central research question: is it possible to adapt TLS 1.2 detection methodologies for C2 traffic to work with TLS 1.3 flows? We answer this question affirmatively by introducing new methods for inferring certificate size and filtering handshake/protocolrelated records in TLS 1.3 flows. These techniques enable the extraction of key features for enhancing traffic detection and can be utilized to pre-process data flows before applying C2 classifiers. We demonstrate that this approach facilitates the use of existing TLS 1.2 C2 classifiers with high efficacy, allowing for the passive classification of encrypted network traffic. In our tests, we inferred certificate sizes with an average error of 1.0%, and achieved detection rates of 100% when classifying traffic based on certificate size, and over 93% when classifying TLS 1.3 traffic behavior after training solely on TLS 1.2 traffic. To our knowledge, these are the first findings to showcase specialized TLS 1.3 C2 traffic classification.

Abstract
Grapevines (Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dor & eacute;e, esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges.