ESCUELA POLITÉCNICA SUPERIOR

Permanent URI for this collectionhttps://hdl.handle.net/10641/5230

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Thermodynamic Coupling Forming Performance of Short Fiber-Reinforced PEEK by Additive Manufacturing
(2024-07) Sun, Qili; Wen, Xiaomu; Yin, Guangzhong; Jia, Zijian; Yang, Xiaomei; Escuela Politécnica Superior
Exploiting Waste towards More Sustainable Flame-Retardant Solutions for Polymers : A Review
(2024-05) Ma, De Xin; Yin, Guang Zhong; Ye, Wen; Jiang, Yan; Wang, Na; Wang, De Yi; Escuela Politécnica Superior
Application of natural language processing techniques to network traffic processing for classification using deep learning models
(2025-12) Maitin, Ana M.; Arranz-Luque, Carlos; Alba, Emilio; García-Tejedor, Álvaro J.; Centro de Innovación Experimental del Conocimiento (CEIEC); Universidad Francisco de Vitoria; Escuela Politécnica Superior
Background: The rapid growth of encrypted network traffic has increased the need for effective and unbiased Network Traffic Classification (NTC). Traditional techniques struggle with encrypted data, limited feature availability, and high traffic volume, reducing their reliability in real-world scenarios. Methods: We propose a novel pre-processing methodology that analyzes raw network traffic into a textual format (nt2txt), enabling the application of Natural Language Processing (NLP) and Deep Learning techniques. This approach eliminates bias from protocol metadata, structures the data into fixed-size semi-flows, and uses rigorous data-splitting to prevent flow overlap between training and testing. An LSTM-based model is then trained to classify traffic using only payload data. Results: This work provides a scalable, protocol-agnostic framework for encrypted traffic classification, demonstrating the effectiveness of NLP techniques in improving model performance and reducing dataset bias. Our methodology achieved 88,87 ± 0,04% accuracy on a blind external dataset, outperforming similar LSTM and hybrid CNN-LSTM models. Metrics such as Cohen’s Kappa and Matthew’s Correlation Coefficient further confirm the robustness and generalizability of our approach.
State-of-the-Art AI for Smarter Cities: A Global Research Showcase : Developed for Smart City Expo World Congress. 4-6 NOV 2025 Barcelona (Spain)
(2025-11-28) Ondiviela García, José Antonio; Fernández-Anez, Victoria; Escuela Politécnica Superior
Mechanical Properties and Thermal Decomposition Mechanism of Glycidyl Azide Polyol Energetic Thermoplastic Elastomer Binder with RDX Composite
(2024-09) Sun, Qili; Yang, Xiao Mei; Yin, Guang Zhong; Escuela Politécnica Superior
Navigating Passenger Satisfaction : A Structural Equation Modeling–Artificial Neural Network Approach to Intercity Bus Services
(2024-06) Rahnama, Shaghayegh; Cortez, Adriana; Monzon, Andres; Escuela Politécnica Superior
The phenomenon of passenger satisfaction is an important issue for public transport services and transport companies. Clarifying the relationship between influencing attributes and passenger satisfaction significantly improves service satisfaction. This study examines passenger satisfaction with intercity buses and, in particular, the role of digital information channels (websites and mobile apps) in promoting sustainable travel choices on the Madrid–Bilbao route. This study analyzed data from 459 passengers to identify the key factors influencing the bus choice for intercity bus travel. Punctuality, safety, and ticket price are the most important determinants. We use a combined structural equation modeling (SEM) and artificial neural network (ANN) approach to capture the intricate relationships between service attributes and information channels. The results show that information channels, travel experience, and ticket prices significantly impact passenger satisfaction, which bus operators should improve. Also, inserting the SEM result as input for the ANN showed that ticket price is the most significant predictor of satisfaction, followed by information channels (84%) and travel experience (65%). This approach provides valuable insights for improving the passenger experience. This study emphasizes integrating digital transformation strategies into public transport systems to promote sustainable mobility goals.
A singular perturbation result for a class of periodic-parabolic BVPs
(2024-01-01) Cano-Casanova, Santiago; Fernández-Rincón, Sergio; López-Gómez, Julián; Escuela Politécnica Superior
Advancements of nanotechnological strategies as conventional approach for heavy metal removal from industrial wastewater : Start-of-the-art review
(2024-01) Raturi, Sakshi; Kumari, Swati; András, Kovács; Khargotra, Rohit; Sebestyén, Viktor; Singh, Tej; Escuela Politécnica Superior
Multi-faceted growth and progression of the healthy and economical society, depends upon access to clean and safe water. Rapidly over-growing population, increased in industrialization, urbanisation, and widespread practices in agricultural have all together been contributing to the production of more rapid wastewater discharge, which has not only polluted or contaminated the water but also have played a role in killing the aquatic life. One class of harmful water pollutants that is frequently found in the environment is heavy metals. Almost every transition metal has the ability to dissolve as ions in water. Heavy metals including Pb, Cd, Hg, As, Se and others can contaminate water supplies. Conventional methods for waste-water treatment have peculiar challenges including economic feasibility, energy consumption, environmental hazards, time spent, etc. To overcome these limitations, nanotechnology have been developed, which has its greater extent of application in water treatment area. Nanoparticles have a greater probability of removing heavy metals from wastewater treatment due to their effective surface characteristics and chemical activity. This review focuses on the numerous treatment procedures that have been developed recently and also been applied practically for eradication of heavy metals from waste-water of various industries.
Co-creation and evaluation of an app to support reminiscence therapy interventions for older people with dementia
(2024-01-01) De-Rosende-Celeiro, Iván; Francisco-Gilmartín, Virginia; Bautista-Blasco, Susana; Ávila-Álvarez, Adriana; Escuela Politécnica Superior
Optimized Autonomous Drone Navigation Using Double Deep Q-Learning for Enhanced Real-Time 3D Image Capture
(2024-12) Sánchez-Soriano, Javier; Rojo-Gala, Miguel Ángel; Pérez-Pérez, Guillermo; Bemposta Rosende, Sergio; Gordo-Herrera, Natalia; Escuela Politécnica Superior
The proposed system assists in the automatic creation of three-dimensional (3D) meshes for all types of objects, buildings, or scenarios, using drones with monocular RGB cameras. All these targets are large and located outdoors, which makes the use of drones for their capture possible. There are photogrammetry tools on the market for the creation of 2D and 3D models using drones, but this process is not fully automated, in contrast to the system proposed in this work, and it is performed manually with a previously defined flight plan and after manual processing of the captured images. The proposed system works as follows: after the region to be modeled is indicated, it starts the image capture process. This process takes place automatically, with the device always deciding the optimal route and the framing to be followed to capture all the angles and details. To achieve this, it is trained using the artificial intelligence technique of Double Deep Q-Learning Networks (reinforcement learning) to obtain a complete 3D mesh of the target.
sEMG-Controlled Soft Exo-Glove for Assistive Rehabilitation Therapies
(2024) Copaci, Dorin; Cerro, David Serrano Del; Guadalupe, Janeth Arias; Lorente, Luis Moreno; Rojas, Dolores Blanco; Escuela Politécnica Superior
The movement of the human hand offers various degrees of freedom, enabling efficient performance of dynamic tasks and robust interaction with the environment in a compliant and continuous manner. However, the rigid exoskeleton used in hand rehabilitation limits the user's freedom of movement, complicating their natural interaction with the environment. In this study, we present a soft exo-glove for assistive rehabilitation actuated by Shape Memory Alloys (SMA), controlled by a surface electromyography (sEMG) hand gesture classifier. Thanks to the actuator type, the soft exo-glove enables slow, smooth, and controlled movements when activated and provides complete control transparency when the device is not active. This advantage enhances the comfort and acceptance of the exo-glove by the patient. On the other hand, the classifier, in conjunction with the control algorithm and the soft exo-glove, offers the potential to use the exo-glove in assistive hand rehabilitation therapy. For user-friendly use, an interface has been developed, enabling the acquisition of new sEMG data from new users, retraining of the classifier, and connection with the soft exo-glove for rehabilitation therapy. The main objective of this study is to demonstrate that the proposed wearable soft exo-glove, along with the control algorithm and the employed classifier, constitutes an effective solution for assistive rehabilitation tasks, as demonstrated with healthy subjects. Furthermore, this solution can be easily adapted to the users' characteristics and requirements.
Water Architectures in the Alto Guadiana River
(2024) Santolaria Castellanos, Ana Isabel; Alderete, Jaime Ramos; Escuela Politécnica Superior
Along the Alto Guadiana River, there is a collection of architectures built at different times and with diverse uses whose raison d’être is their special relationship with water. Most of these architectural pieces are today abandoned. This research aims to underline their value, highlight their relationship with water, and reflect on the opportunities they represent, understanding that they are key pieces of the landscape. This article presents a narrative that links the Architectures of Water with the ter-ritory and time in an intimate way, a relationship that appears by walking through the landscape, giving them a new meaning. Resignification manifests itself both as a whole, considering the architectures as a collection that informs the landscape, and as individual pieces that can offer opportunities for new forms of balance between humans and nature.
Analysis of Transportation Systems for Colonies on Mars
(2024-04) de Curtò, J.; de Zarzà, I.; Escuela Politécnica Superior
The colonization of Mars poses unprecedented challenges in developing sustainable and efficient transportation systems to support inter-settlement connectivity and resource distribution. This study conducts a comprehensive evaluation of two proposed transportation systems for Martian colonies: a ground-based magnetically levitated (maglev) train and a low-orbital spaceplane. Through simulation models, we assess the energy consumption, operational and construction costs, and environmental impacts of each system. Monte Carlo simulations further provide insights into the cost variability and financial risk associated with each option over a decade. Our findings reveal that while the spaceplane system offers lower average costs and reduced financial risk, the maglev train boasts greater scalability and potential for integration with Martian infrastructural development. The maglev system, despite its higher initial cost, emerges as a strategic asset for long-term colony expansion and sustainability, highlighting the need for balanced investment in transportation technologies that align with the goals of Martian colonization. Further extending our exploration, this study introduces advanced analysis of alternative transportation technologies, including hyperloop systems, drones, and rovers, incorporating dynamic environmental modeling of Mars and reinforcement learning for autonomous navigation. In an effort to enhance the realism and complexity of our navigation simulation of Mars, we introduce several significant improvements. These enhancements focus on the inclusion of dynamic atmospheric conditions, the simulation of terrain-specific obstacles such as craters and rocks, and the introduction of a swarm intelligence approach for navigating multiple drones simultaneously. This analysis serves as a foundational framework for future research and strategic planning in Martian transportation infrastructure.
Optimizing Propellant Distribution for Interorbital Transfers
(2024-03) De Curtò, J.; De Zarzà, I.; Escuela Politécnica Superior
The advent of space exploration missions, especially those aimed at establishing a sustainable presence on the Moon and beyond, necessitates the development of efficient propulsion and mission planning techniques. This study presents a comprehensive analysis of chemical and electric propulsion systems for spacecraft, focusing on optimizing propellant distribution for missions involving transfers from Low-Earth Orbit (LEO) to Geostationary Orbit (GEO) and the Lunar surface. Using mathematical modeling and optimization algorithms, we calculate the delta-v requirements for key mission segments and determine the propellant mass required for each propulsion method. The results highlight the trade-offs between the high thrust of chemical propulsion and the high specific impulse of electric propulsion. An optimization model is developed to minimize the total propellant mass, considering a hybrid approach that leverages the advantages of both propulsion types. This research contributes to the field of aerospace engineering by providing insights into propulsion system selection and mission planning for future exploration missions to the Moon, Mars, and Venus.
Isomorphic Structures and Operator Analysis in Mimetic Discretizations
(2024) De Curtò, J.; De Zarzà, I.; Escuela Politécnica Superior
This study presents a comprehensive examination of the structural and operatorial foundations within mimetic discretizations, with a focus on bridging the gap between discrete and continuous function spaces. By scrutinizing the mimetic gradient and divergence operators-central to the discretization of the NAVIER-STOKES equations-we study their kernel and image spaces, establishing their isomorphisms through rigorous mathematical proofs. Our methodology leverages discrete scalar and vector function spaces, delineated by grid spacing, to define linear mappings that unveil the subspace relationships and quotient space structures integral to understanding these operators' roles in computational fluid dynamics. Central to our findings is the application of the first isomorphism theorem, which facilitates a deeper insight into how mimetic discretizations reflect the continuous properties of differential operators within a discrete framework. This allows for an exploration into the algebraic and topological implications of such discretizations, notably in the context of the NAVIER-STOKES equations. Furthermore, we extend our investigation to encompass subalgebras, ideals, their quotients, and the formulation of short exact sequences that mirror the continuous interplay between gradient, divergence, and LAPLACIAN operators. Significant advances include the application of the first isomorphism theorem which confirms that our mimetic discretizations preserve key properties of differential operators, thus enhancing the accuracy and reliability of computational models. Additionally, our research introduces practical extensions into subalgebras and complex operator sequences, laying groundwork for future developments in numerical methods aimed at improving the precision of engineering simulations.
Spectral Properties of Mimetic Operators for Robust Fluid–Structure Interaction in the Design of Aircraft Wings
(2024-04) de Curtò, J.; de Zarzà, I.; Escuela Politécnica Superior
This paper presents a comprehensive study on the spectral properties of mimetic finite-difference operators and their application in the robust fluid–structure interaction (FSI) analysis of aircraft wings under uncertain operating conditions. By delving into the eigenvalue behavior of mimetic Laplacian operators and extending the analysis to stochastic settings, we develop a novel stochastic mimetic framework tailored for addressing uncertainties inherent in the fluid dynamics and structural mechanics of aircraft wings. The framework integrates random matrix theory with mimetic discretization methods, enabling the incorporation of uncertainties in fluid properties, structural parameters, and coupling conditions at the fluid–structure interface. Through spectral and localization analysis of the coupled stochastic mimetic operator, we assess the system’s stability, sensitivity to perturbations, and computational efficiency. Our results highlight the potential of the stochastic mimetic approach for enhancing reliability and robustness in the design of aircraft wings, paving the way for optimization algorithms that integrate uncertainties directly into the design process. Our findings reveal a significant impact of stochastic perturbations on the spectral radius and eigenfunction localization, indicating heightened system sensitivity. The introduction of randomized singular value decomposition (RSVD) within our framework not only enhances computational efficiency but also preserves accuracy in low-rank approximations, which is critical for handling large-scale systems. Moreover, Monte Carlo simulations validate the robustness of our stochastic mimetic framework, showcasing its efficacy in capturing the nuanced dynamics of FSI under uncertainty. This study contributes to the fields of numerical methods and aerospace engineering by offering a rigorous and scalable approach for conducting uncertainty-aware FSI analysis, which is crucial for the development of safer and more efficient aircraft.
Hybrid State Estimation : Integrating Physics-Informed Neural Networks with Adaptive UKF for Dynamic Systems
(2024-06) de Curtò, J.; de Zarzà, I.; Escuela Politécnica Superior
In this paper, we present a novel approach to state estimation in dynamic systems by combining Physics-Informed Neural Networks (PINNs) with an adaptive Unscented Kalman Filter (UKF). Recognizing the limitations of traditional state estimation methods, we refine the PINN architecture with hybrid loss functions and Monte Carlo Dropout for enhanced uncertainty estimation. The Unscented Kalman Filter is augmented with an adaptive noise covariance mechanism and incorporates model parameters into the state vector to improve adaptability. We further validate this hybrid framework by integrating the enhanced PINN with the UKF for a seamless state prediction pipeline, demonstrating significant improvements in accuracy and robustness. Our experimental results show a marked enhancement in state estimation fidelity for both position and velocity tracking, supported by uncertainty quantification via Bayesian inference and Monte Carlo Dropout. We further extend the simulation and present evaluations on a double pendulum system and state estimation on a quadcopter drone. This comprehensive solution is poised to advance the state-of-the-art in dynamic system estimation, providing unparalleled performance across control theory, machine learning, and numerical optimization domains.
Object Recognition and Conversational AI in Real-World Contexts : Enhancing Museum Experiences through Interactive Systems
(2025-11-28) Ortiz Ramírez, Adrián; Illana Sánchez, Álvaro; Salas García, Marta; Escuela Politécnica Superior
Este proyecto busca mejorar la experiencia museística de los visitantes, superando los métodos tradicionales de acceso a la información. Presenta un sistema interactivo que combina la detección de objetos en tiempo real con la generación aumentada por recuperación (Retrieval Augmented Generation) para ofrecer una guía conversacional inmersiva, personalizada y sensible al contexto. Los resultados evidencian una comprensión espacial y conversacional precisa, así como una mejora significativa en la veracidad y la relevancia de las respuestas generadas frente alas de un LLM estándar. Este proyecto demuestra el potencial del sistema para ofrecer un acceso dinámico y atractivo al patrimonio cultural.
ChatGPT assistants in online higher education and student satisfaction : a case study
(2025) Cabeza-Rodríguez, Miguel Ángel; Escuela Politécnica Superior
The perception and satisfaction of students about a virtual assistant based on OpenAI ChatGPT 3.5, integrated in 21 different subjects of the virtual campus of an online university, have been analyzed in this study. Using a mixed methodological approach, information was collected on a sample of 391 students using the validated COMUNICA questionnaire, which included four constructs: Virtual Assistant Efficiency, Learning Impact, Skill Development, and Technical and Accessibility Aspects. The analysis included descriptive statistics, inferential statistical tests, Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), complemented by a qualitative analysis of student and teacher comments. The quantitative results highlight that the female gender values the effectiveness of the assistant more than the male gender. The CFA confirmed that the factors can be grouped under a single latent variable: student satisfaction. In addition, the efficiency of the virtual assistant was found to be the most significant factor in the perception of student satisfaction, followed by the impact on learning, skill development and technical aspects. The qualitative analysis revealed mostly positive perceptions, highlighting the usefulness of the assistant in learning, an interest in extending its use to other subjects and suggestions for improvement in the accuracy of answers and functionality. It is concluded that virtual assistants have a positive impact on higher education, optimizing autonomous learning and educational interaction, although technical and design challenges persist that limit their full potential.
Big Data and I2X Communication Infrastructure for Traffic Optimization and Accident Prevention on Automated Roads
(2025) García-González, Jorge; Fernández-Andrés, Javier; Aliane, Nourdine; Sánchez-Soriano, Javier; Escuela Politécnica Superior
This paper presents a scalable big data infrastructure designed to support traffic optimization and accident prevention in automated and connected road environments. The proposed system integrates real-time data acquisition from heterogeneous sources, including multichannel roadside camera gantries and IoT-enabled vehicle telemetry. The architecture is built upon technologies such as Apache Kafka, Apache Beam, and MongoDB, enabling high-throughput data ingestion, processing, and storage. To validate its performance, two experimental use-cases were developed: one for large-scale image ingestion and another for vehicle telemetry data streaming. The system successfully handled over 48 GB of image data and more than 3.4 million telemetry messages under real-time constraints. Results show that applying data compression techniques - such as resolution reduction and transmission throttling - reduced image upload durations by up to 77%, improving ingestion efficiency without compromising system robustness. These findings demonstrate the feasibility of deploying the proposed infrastructure as a foundational layer for future intelligent traffic management systems.

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