Abstract
The textile industry is experiencing rapid advancement, reflected in the adoption of innovative and efficient manufacturing techniques. The automation of clothing sewing systems has the potential to reduce the allocation of repetitive tasks to operators, freeing them for more value-added operations. There are several machines on the market that automatically sew the bottom hem of T-shirts, a key component of the garment that fulfills both functional and aesthetic purposes. However, most of them require the fabric to be positioned manually by an operator. To address this issue, this work presents a solution to automate the process of feeding a T-shirt into a SiRUBA sewing machine using a YuMi dual-arm robot. In this scenario, the T-shirt arrives at the workstation with the main front and back pieces of cloth sewn together, seams facing out, and with no sleeves yet. This setup starts by turning the garment inside out with the aid of an automated hanger, ensuring that the seams are facing inward (as the machine requires), and then using the dual-arm robot to feed the garment into the sewing machine. With our approach, the feeding and hemming process took less than 35 seconds, with a feeding success rate of 98%. Therefore, this work can serve as a steppingstone towards more efficient automated sewing systems within the garment production industry.

Abstract
The objective of this paper is to delineate the ongoing doctoral research work that is focused on the development of a digital model intended to emulate the real-time operation of an electrolyzer that is powered by a DC/DC converter. The digital model of the converter and the proton exchange membrane (PEM) electrolyzer (EL) is presented, and it is based on an electrical equivalent model. A primary contribution of this study is the analysis of the errors resulting from the discretization process. Furthermore, the implementation and development of the digital model requires a comprehensive study of the errors and key affecting factors. Additionally, the formulation of a mechanism to reduce these errors is essential for advancing this topic. Preliminary results obtained using the digital emulator developed demonstrated its capacity to reproduce the voltage and current response applied to the electrolyzer with a reduced error compared to the continuous-time model.

Abstract
This paper presents a novel approach to classical linear regression, enabling accurate model computation from data streams or in a distributed setting while preserving data privacy in federated environments. We extend this framework to generalized linear models (GLMs), ensuring scalability and adaptability to diverse data distributions while maintaining privacy-preserving properties. To assess the effectiveness of our approach, we conduct numerical studies on both simulated and real datasets, comparing our method with conventional maximum likelihood estimation for GLMs using iteratively reweighted least squares. Our results demonstrate the advantages of the proposed method in distributed and federated settings.

Abstract
Climate change has led to the need to transition to clean technologies, which depend on an number of critical metals. These metals, such as nickel, lithium, and manganese, are essential for developing batteries. However, the scarcity of these elements and the risks of disruptions to their supply chain have increased interest in exploiting resources on the deep seabed, particularly polymetallic nodules. As the identification of these nodules must be efficient to minimize disturbance to the marine ecosystem, deep learning techniques have emerged as a potential solution. Traditional deep learning methods are based on the use of convolutional layers to extract features, while recent architectures, such as transformer-based architectures, use self-attention mechanisms to obtain global context. This paper evaluates the performance of representative models from both categories across three tasks: detection, object segmentation, and semantic segmentation. The initial results suggest that transformer-based methods perform better in most evaluation metrics, but at the cost of higher computational resources. Furthermore, recent versions of You Only Look Once (YOLO) have obtained competitive results in terms of mean average precision.

Abstract

Abstract
The evolution of autonomous driving is reshaping the automotive landscape into a highly cooperative and interconnected system, where vehicles and infrastructure exchange data to improve safety, efficiency, and responsiveness. In this context, service-based architectures are becoming essential to support the modular, scalable, and dynamic nature of automotive applications such as Cooperative Perception, demanding robust mechanisms for real-time communication, service discovery, interoperability, and secure data handling. This work aims at investigating the suitability of the Arrowhead Framework—a service-oriented architecture initially designed for industrial automation—as a middleware to enable and manage services in the context of cooperative autonomous driving. By integrating Arrowhead into a multi-dimensional co-simulation framework, encompassing the simulation of realistic vehicle models, control and communications, we evaluate its effectiveness in supporting service orchestration, system integration, and interoperability in different scenarios. In parallel, we aim to demonstrate how co- simulation environments can facilitate the rapid prototyping and deployment of distributed autonomous driving services.