Abstract
This work proposes an innovative methodology for the optimal placement of grid-forming converters (GFM) in converter-dominated grids while accounting for multiple stability classes. A heuristic-based methodology is proposed to solve an optimisation problem whose objective function encompasses up to 4 stability indices obtained through the simulation of a shortlist of disturbances. The proposed methodology was employed in a modified version of the 39-bus test system, using DigSILENT Power Factory as the simulation engine. First, the GFM placement problem is solved individually for the different stability classes to highlight the underlying physical phenomena that explain the optimality of the solutions and evidence the need for a multi-class approach. Second, a multi-class approach that combines the different stability indices through linear scalarisation (weights), using the normalised distance of each index to its limit as a way to define its importance, is adopted. For all the proposed fitness function formulations, the method successfully converged to a balanced solution among the various stability classes, thereby enhancing overall system stability.

Abstract
This work studies the influence of an Erbium-Doped Fiber Amplifier (EDFA) on the phase variation of light in an optical fiber. To this end, the state of polarization (SOP) was measured as a function of optical power by adjusting the EDFA amplification, for two different laser output powers (2 dBm and 5 dBm). Results show that phase variation correlates with changes in optical power in both cases.

Abstract
A fast response to faults in large-scale photovoltaic power plants (PVPPs), which can occur on hundreds of components like photovoltaic panels and inverters, is fundamental for maximizing energy generation and reliable system operation. This work proposes using a Graph Neural Network (GNN) combined with a digital twin for synthetic fault data scenario generation for fault location in PVPPs. It shows that GNN can adapt to system changes without requiring model retraining, thus offering a scalable solution for the real operating PVPPs, where some parts of the system may be disconnected for maintenance. The results for a real PVPP show the GNN outperforms baseline models, especially in larger topologies, achieving up to twice the accuracy in a fault location task. The GNN's adaptability to topology changes was tested on the simulated reconfigured systems. A decrease in performance was observed, and its value depends on the complexity of the original training topology. It can be mitigated by using several system reconfigurations in the training set.

Abstract
Industry 4.0 promotes a paradigm shift in the orchestration, oversight, and optimization of value chains across product and service life cycles. For instance, leveraging large-scale data from sensors and devices, coupled with Machine Learning techniques can enhance decision-making and facilitate various improvements in industrial settings, including predictive maintenance. However, ensuring data quality remains a significant challenge. Malfunctions in sensors or external factors such as electromagnetic interference have the potential to compromise data accuracy, thereby undermining confidence in related systems. Neglecting data quality not only compromises system outputs but also contributes to the proliferation of bad data, such as data duplication, inconsistencies, or inaccuracies. To consider these problems is crucial to fully explore the potential of data in Industry 4.0. This paper introduces an extensible system designed to ingest, organize, and monitor data generated by various sources, focusing on industrial settings. This system can serve as a foundation for enhancing intelligent processes and optimizing operations in smart manufacturing environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Este capítulo abordou o desenvolvimento de competências empreendedoras no ambiente acadêmico, a partir do Paradigma da Educação OnLIFE e seus fundamentos teóricos. A Educação OnLIFE, um paradigma que emerge e vai se constituindo na conexão com a vida online e offline, é analisada em relação ao desenvolvimento de competências empreendedoras, destacando sua relevância e pertinência no contexto educacional moderno. Inicialmente, o capítulo define e identifica as competências empreendedoras essenciais, discutindo suas características. Em seguida, é apresentada uma concepção de educação empreendedora OnLIFE, enfatizando a flexibilidade curricular para permitir que os estudantes sejam co-criadores dos ambientes ecologicamente conectados, desenvolvendo trajetórias de aprendizado de acordo com seus interesses e necessidades de uma sociedade Onlife. O capítulo também propõe uma rede de Competências para Educação Empreendedora, que se conflua em dimensões da formação do empreendedor no paradigma da Educação OnLIFE: (1) Competências Empreendedoras, (2) Competências Comportamentais, (3) Competências Técnicas, (4) Competências digitais, (5) Competências para o Século XXI, (6) Competências Verdes e Azuis, e (7) Competências para a Educação OnLIFE. Assim, aprofundam-se os conceitos da Educação OnLIFE, explicando como esse paradigma inovador pode problematizar a práxis para o ensino e a aprendizagem do empreendedorismo.

Abstract
Interoperability among diverse devices, from traditional substation control rooms to modern inverters managing components like Distributed Energy Resources (DERs), is a primary challenge in modern power systems. It is essential for streamlining decision-making and control processes through effective communication, ultimately enhancing energy management efficiency. This paper introduces the open-source Legacy Protocol Converter (LPC) grounded in the IEEE 2030.5 standard, which incorporates advanced features for improved adaptability. The LPC bridges legacy equipment using standard protocols such as Message Queuing Telemetry Transport (MQTT) and Modbus with a light-weight asynchronous Neural Autonomic Transport System (NATS) communication system. In light of the limitations inherent in traditional synchronous RESTful systems-specifically those compliant with IEEE 2030.5 that are incapable of facilitating multiple endpoints-the adoption of asynchronous NATS is implemented. This approach can notably enhance communication flexibility and performance. The implementation is containerized for efficient service orchestration and supports the reusability of solutions. The LPC is engineered for seamless integration of DERs with Energy Management System (EMS), aggregation platforms, and Hardware-in-the-loop (HIL) testing environments. In this paper, the LPC has been tested and further developed in various use cases such as multi-physics optimization involving HIL and fast frequency services, e.g., virtual inertia and load shedding, each in a different architectural setup. The findings validate the applicability of LPC not only for devices within modern power systems, but also for heat pumps in the thermal energy sector, facilitating sector coupling. Moreover, the paper provides additional insights into LPC's functionality, reaffirming its efficacy as a scalable, robust, and user-friendly solution for bridging legacy systems through the enhanced IEEE 2030.5 standard designed for the monitoring and control of DERs.