Abstract
In recent years, the increasing availability of distributed data has led to a growing interest in transfer learning across multiple nodes. However, local data may not be adequate to learn sufficiently accurate models, and the problem of learning from multiple distributed sources remains a challenge. To address this issue, Machine Learning Data Markets (MLDM) have been proposed as a potential solution. In MLDM, autonomous agents exchange relevant data in a cooperative relationship to improve their models. Previous research has shown that data exchange can lead to better models, but this has only been demonstrated with only two agents. In this paper, we present an extended evaluation of a simple version of the MLDM framework in a collaborative scenario. Our experiments show that data exchange has the potential to improve learning performance, even in a simple version of MLDM. The findings conclude that there exists a direct correlation between the number of agents and the gained performance, while an inverse correlation was observed between the performance and the data batch sizes. The results of this study provide important insights into the effectiveness of MLDM and how it can be used to improve learning performance in distributed systems. By increasing the number of agents, a more efficient system can be achieved, while larger data batch sizes can decrease the global performance of the system. These observations highlight the importance of considering both the number of agents and the data batch sizes when designing distributed learning systems using the MLDM framework.

Abstract
We live in an era in which the preservation of the environment is being widely discussed, driven by growing concerns over climate issues. One major factor contributing to this situation is the lack of attention societies give to maintaining high sustainability levels. Data plays a crucial role in understanding and assessing sustainability impacts in both urban and rural areas. However, obtaining comprehensive data on a country's sustainability is challenging due to the lack of simple and accessible sources. Existing solutions for sustainability analysis are limited by high costs and implementation difficulties, which restrict their spatial coverage. In this paper, we propose a solution using low-cost hardware and open-source technologies to collect data about the movement of people and vehicles. This solution involves low-cost video-based meters that can be flexibly deployed to various locations. Specifically, we developed a prototype using Raspberry Pi and YOLO which is able to correctly classify 91% of the vehicles by type, and 100% of the events (entering of leaving). The results indicate that this system can effectively and affordably identify and count people and vehicles, allowing for its implementations namely in remote sensitive areas such as natural parks, in which the access of people and vehicles must be controlled and monitored. © 2023 The authors and IOS Press.

Abstract
Laboratory experiments are important pedagogical tools in engineering courses. Restrictions related to the COVID-19 pandemic made it very difficult or impossible for laboratory classes to take place, resulting on a fast transition to simulation as an approach to guarantee the effectiveness of teaching. Simulation environments are powerful tools that can be adopted for remote classes and self-study. With these tools, students can perform experiments and, in some cases, make use of the laboratory facilities from outside of the University. This paper proposes and describes two free tools developed during the COVID-19 pandemic lock-down that allowed students to work from home, namely a set of simulation experiments and a Hardware-in-the-loop simulator, accessible 24/7. Two approaches in Python and C languages are presented, both in the context of Robotics courses for Engineering students. Successful results and student feedback indicate the effectiveness of the proposed approaches in institutions in Portugal and in the Netherlands.

Abstract
Due to the advancement of power electronics devices and control techniques, the modular multilevel converter (MMC) has become the most attractive converter for multiterminal direct current (MTDC) grids thanks to its most relevant features, such as modularity and scalability. Despite their advantages, conventional MMCs face a major challenge with: i) fault-tolerant operation strategy; ii) energy losses in conversion; iii) lack of DC fault handling capability. This paper provides a systematic review to identify the gaps in the literature about Intelligent Fault-Tolerant Protection Schemes for multi-terminal HVDC grids. Through the bibliometric analysis, it was possible to identify topics still to be developed within the four main clusters (Offshore wind farms, Wind turbines, Voltage Source Converters, and Wind power). The research topic opens three research paths: the first is the analysis of failures in HVDC (High Voltage Direct Current) grid equipment by the FDD (Fault Detection and Diagnosis) method; the second is failure analysis by the IFDD (Inverse Fault Detection and Diagnosis) method and the third is the possibility of interconnecting the different energy generation zones with different frequencies. © The Authors.

Abstract
Forecasting energy consumption models allow for improvements in building performance and reduce energy consumption. Energy efficiency has become a pressing concern in recent years due to the increasing energy demand and concerns over climate change. This paper addresses the energy consumption forecast as a crucial ingredient in the technology to optimize building system operations and identifies energy efficiency upgrades. The work proposes a modified multi-head transformer model focused on multi-variable time series through a learnable weighting feature attention matrix to combine all input variables and forecast building energy consumption properly. The proposed multivariate transformer-based model is compared with two other recurrent neural network models, showing a robust performance while exhibiting a lower mean absolute percentage error. Overall, this paper highlights the superior performance of the modified transformer-based model for the energy consumption forecast in a multivariate step, allowing it to be incorporated in future forecasting tasks, allowing for the tracing of future energy consumption scenarios according to the current building usage, playing a significant role in creating a more sustainable and energy-efficient building usage.

Abstract
In recent decades, many attempts have been made to automate the entire welding process, how-ever, there remain many non-automated welding operations that present a constant hazard to workers. This article presents an automated welding solution with collaborative robots, with this contribution, we intend to help companies in this sector increase productivity, improve quality, effectively reduce costs, and improve working conditions.