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.

Abstract
Research on Programmable Electromagnetic Surfaces has gained considerable attention as an enabling technology for 6G communications, particularly at millimeter-wave and sub-THz bands. However, RISs face challenges related to the need for high-performance reconfigurable techniques that offer compact size and reduced power consumption at high frequencies. Moreover, the experimental characterization of unit cell performance using a waveguide remains a challenging issue. This paper discusses the design and performance analysis of a 1-bit reconfigurable unit cell at the Dband using non-volatile reconfigurable technology. The efficiency analysis of the unit cell was performed using periodic boundary conditions and waveguide configurations to mitigate simulation risks and validate the proposed design at the unit cell level. All simulation configurations confirmed an operational bandwidth of 25.65 GHz across the 147.8-173.45 GHz range, with a reflection loss of less than 1 dB and a phase difference within 180 degrees +/- 20 degrees.

Abstract
This paper presents an adaptive protection strategy for multi-microgrid (MMG) systems with inverter-based resources (IBRs) in medium voltage (MV) networks, using the IEEE 33-bus test system. The approach combines overcurrent (OC) and undervoltage (UV) protections through an offline-optimized, clustering-based scheme and real-time selection of setting groups. A metaheuristic algorithm determines optimal relay settings for representative scenarios, ensuring responsive and coordinated protection. Hardware-in-the-loop validation on OPAL-RT confirms the method's effectiveness across varying loads, DER outputs, and fault conditions. Results demonstrate reliable fault isolation, smooth mode transitions, and uninterrupted supply to healthy segments. Identified limitations in high-impedance fault handling suggest future improvements.

Abstract
The interaction between humans and technology has always been a key determinant factor of adoption and efficiency. This is true whether the interaction is with hardware, software or data. In the particular case of Information Retrieval (IR), recent developments in Deep Learning and Natural Language Processing (NLP) techniques opened the door to more natural and efficient IR means, no longer based on keywords or similarity metrics but on a distributed representation of meaning. In this paper we propose an agent-based architecture to serve as an interface with industrial systems, in which agents are powered by specific Large Language Models (LLMs). Its main goal is to make the interaction with such systems (e.g. data sources, production systems, machines) natural, allowing users to execute complex tasks with simple prompts. To this end, key aspects considered in the architecture are human-centricity and context-awareness. This paper provides a high-level description of this architecture, and then focuses on the development and evaluation of one of its key agents, responsible for information retrieval. For this purpose, we detail three application scenarios, and evaluate the ability of this agent to select the appropriate data sources to answer a specific prompt. Depending on the scenario and on the underlying model, results show an accuracy of up to 80%, showing that the proposed agent can be used to autonomously select from among several available data sources to answer a specific information need. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.