Antonio Robles-Gómez

Distance learning imposes specific challenges to provide quality education. Technology is a great opportunity for educational improvement. In this work we analyze some challenges and we have shown that with the proper use of technology it is possible to overcome them. We introduce here a set of remote laboratories currently being used for control engineering education in our university, which contribute to teach several control engineering topics like perception systems and the use of sensors and actuators, cybersecurity and penetration testing of IoT smart sensors and similar devices that can be found in industry, computer system architecture and low-level programming of microprocessors to implement fast control algorithms. The value of this work lies not only in the utility of remote labs as learning tools but also in their implementation following IEEE standards and integrating modern devices and communication protocols with industrial application.

Massive Open Online Courses (MOOCs) have established a new paradigm in education, enabling asynchronous, remote learning. Although MOOCs offer diverse educational content to students, comprehensive and realistic education requires hands‐on training. Therefore, we present our integration of remote laboratories within a MOOC focused on Industry 4.0, together with a mixed‐methods analysis combining demographic data, questionnaire responses, platform logs, and remote laboratory server records. The course incorporated seven distinct laboratories in two categories: remote Arduino‐based laboratories, which connected physical devices to the MOOC, and a virtual infrastructure based on JupyterHub to support practical activities. Our findings indicate that the majority of participants in our MOOC were actively employed, with a mean age of 46.9 years, and males representing 88%. The students were mainly motivated by personal and professional growth, and 66% of these learners had no prior remote laboratory experience. Learners reported generally positive perceptions of the laboratory experience, including high levels of interest, usefulness, and self‐efficacy, although engagement varied substantially across participants. Behavioral analyses revealed that students who ultimately pursued certification showed markedly higher participation and involvement in the remote laboratories than those who did not. At the same time, the course exhibited the substantial dropout patterns commonly reported in MOOCs, indicating that the inclusion of remote laboratories does not by itself eliminate persistence challenges. Our work provides an illustrative example of integrating remote laboratories into a MOOC and offers an analysis of enrolled students, delivering valuable findings for future researchers.

The continuous evolution of techniques for generating manipulated audio, known as voice deepfakes, and the widespread availability of tools that produce convincing forgeries have created an urgent need for reliable detection methods. This work considers the dimensionality of Mel‐Frequency Cepstral Coefficients (MFCCs) as a core design variable for practical, deployable systems. The aim is to identify the smallest number of coefficients that preserve detection performance across heterogeneous models while reducing computational cost, a critical factor for mobile and edge deployment. This study evaluates a hybrid setting on the ASVspoof 2019 Logical Access dataset, in which the same feature family serves as input to five traditional machine learning algorithms (Random Forest, k‐Nearest Neighbours, Linear Support Vector Classification, Extreme Gradient Boosting and Support Vector Machine with radial basis function kernel) and five deep learning models (Convolutional Neural Network, Recurrent Neural Network, Convolutional Recurrent Neural Network, Xception and ResNet). Results indicate that deep models reach near‐peak performance with a small number of coefficients, whereas classical methods require a larger number to achieve stable performance (except Linear Support Vector Classification, which consistently underperforms). Accordingly, 32 coefficients are considered an effective operating point for hybrid deployments. Overall, the results provide evidence to guide the selection of the number of MFCC coefficients in voice deepfake detection, aiming for efficient, reproducible and explainable systems.

The growing complexity of corporate, cloud, and industrial environments has increased the difficulty of acquiring digital evidence, particularly volatile data such as memory and transient network artifacts. Manual forensic procedures and agent-based solutions often introduce operational risks, scalability constraints, and legal challenges in critical infrastructures. This paper proposes an agentless and automated framework for the remote acquisition of digital evidence in heterogeneous networks. The solution is defined as code and orchestrated using Ansible, enabling reproducible, traceable, and minimally intrusive acquisition without requiring permanent software installation on target systems. It supports the collection of volatile memory, system artifacts, and network evidence across on-premise, cloud (AWS), and industrial control system (ICS) environments. The framework is validated through experimental evaluation and a comparative analysis with an agent-based forensic platform (Velociraptor), focusing on scalability, acquisition time, integrity, and operational impact. Compliance with international forensic standards and recent European regulations is also discussed. The results indicate that agentless automation is a viable and flexible approach for digital forensic acquisition in modern hybrid environments.