https://hdl.handle.net/10481/31021 Sat, 04 Apr 2026 15:33:51 GMT 2026-04-04T15:33:51Z https://hdl.handle.net/10481/112032 Impact of Latency and Jitter on TAS Scheduling in a 5G-TSN Network: An Empirical Study Rodríguez Martín, Pablo; Adamuz Hinojosa, Óscar Ramón; Muñoz Luengo, Pablo; Caleya Sánchez, Julia; Ameigeiras Gutiérrez, Pablo José Deterministic communications are essential to meet the stringent delay and jitter requirements of Industrial Internet of Things (IIoT) services. IIoT increasingly demands wide-area wireless mobility to support Autonomous Mobile Robots (AMR) and dynamic workflows. Integrating Time-Sensitive Networking (TSN) with 5G private networks is emerging as a promising approach to fulfill these requirements. In this architecture, 5G provides wireless access for industrial devices, which connect to a TSN backbone that interfaces with the enterprise edge/cloud, where IIoT control and computing systems reside. TSN achieves bounded latency and low jitter using IEEE 802.1Qbv Time-Aware Shaper (TAS), which schedules the network traffic in precise time slots. However, the stochastic delay and jitter inherent in 5G disrupt TSN scheduling, requiring careful tuning of TAS parameters to maintain end-to-end determinism. This paper presents an empirical study evaluating the impact of 5G downlink delay and jitter on TAS scheduling using a testbed with TSN switches and a commercial 5G network. Results show that guaranteeing bounded latency and jitter requires careful setting of TAS transmission window offset between TSN switches based on the measured 5G delay bounded by a high order p-th percentile. Otherwise, excessive offset may cause additional delay or even a complete loss of determinism. This work has been financially supported by the Ministry for Digital Transformation and the Civil Service of the Spanish Government through TSI-063000-2021-28 (6G-CHRONOS) project, and by the European Union through the Recovery, Transformation and Resilience Plan - NextGeneration. Additionally, this publication is part of grant PID2022-137329OB-C43 funded by MICIU/AEI/10.13039/501100011033 and ERDF/EU, and part of FPU Grant 21/04225 funded by the Spanish Ministry of Universities. https://hdl.handle.net/10481/112032 https://hdl.handle.net/10481/112030 RIS Control through the Lens of Stochastic Network Calculus: An O-RAN Framework for Delay-Sensitive 6G Applications Adamuz Hinojosa, Óscar Ramón; Zanzi, Lanfranco; Sciancalepore, Vincenzo; Di Renzo, Marco; Costa-Perez, Xavier Reconfigurable Intelligent Surfaces (RIS) enable dynamic electromagnetic control for 6G networks, but existing control schemes lack responsiveness to fast-varying network conditions, limiting their applicability for ultra-reliable low latency communications. This work address uplink delay minimization in multi-RIS scenarios with heterogeneous per-user latency and reliability demands. We propose Delay-Aware RIS Orchestrator (DARIO), an O-RAN-compliant framework that dynamically assigns RIS devices to users within short time windows, adapting to traffic fluctuations to meet per-user delay and reliability targets. DARIO relies on a novel Stochastic Network Calculus (SNC) model to analytically estimate the delay bound for each possible user–RIS assignment under specific traffic and service dynamics. These estimations are used by DARIO to formulate a Nonlinear Integer Program (NIP), for which an online heuristic provides near-optimal performance with low computational overhead. Extensive evaluations with simulations and real traffic traces show consistent delay reductions up to 95.7% under high load or RIS availability. Funding for open access charge: Universidad de Granada / CBUA. This work is part of the project PID2022-137329OB-C43 funded by MICIU/AEI/10.13039/501100011033 and by FEDER, EU, and also part of the project C-ING-306-UGR23 funded by Consejería de Universidad, Investigación e Innovación and by ERDF Andalusia Program 2021-2027. Furthermore, it has been partially supported by the MultiX project from Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme (Grant 101192521). https://hdl.handle.net/10481/112030 https://hdl.handle.net/10481/111926 A Sub-THz Low-Cost Additive Manufactured Monolithic Geodesic H-Plane Horn Array Antenna Chen, Mingzheng; Rico Fernandez, Jose; Wang, Hairu; Segura Gómez, Cleofás; Mesa, Francisco; Quevedo Teruel, Óscar; Quevedo Teruel, Óscar A monolithic geodesic H-plane horn array antenna that operates up to 170 GHz is achieved for the first time using a low-cost additive manufacturing (AM) technique. To reach high gain and symmetric beam, a truncated geodesic H-plane horn is used to obtain a narrow beam in the H-plane, while a 1:8 power divider built on parallel-plate waveguides is constructed to narrow the beam in the E-plane. A ray-tracing and physical-optics model is developed to facilitate the design, which is capable of computing the full radiation pattern, directivity, and gain (considering conductive losses) of geodesic H-plane horn array antennas with significant time efficiency and high degree of accuracy. The adopted metal-only laser powder–bed fusion AM technique is especially suitable for fast prototyping structures with intricate shapes at a low cost. However, special adaptations are still considered in the design to ensure a successful fabrication of the prototype operating at the D-band. The prototype maintains good frequency stability from 110 to 170 GHz with a return loss better than 10 dB and a symmetric pencil beam. The measured data show a maximum realized gain of 29 dBi, a maximum aperture efficiency of 67% (calculated using realized gain), and a maximum radiation efficiency of 86%. https://hdl.handle.net/10481/111926 https://hdl.handle.net/10481/111872 A Set of Rules for Model Validation Camacho Páez, José The validation of a data- driven model is the process of assessing the model's ability to generalize to new, unseen data in the pop ulation of interest. This paper proposes a set of general rules for model validation. These rules are designed to help practitioners create reliable validation plans and report their results transparently. While no validation scheme is flawless, these rules can help practitioners ensure their strategy is sufficient for practical use, openly discuss any limitations of their validation strategy, and report clear, comparable performance metrics https://hdl.handle.net/10481/111872 https://hdl.handle.net/10481/111524 UGR-MINDVOICE: A multimodal EEG-audio dataset for overt and covert Iberian Spanish speech production Vales Cortina, Ibon; Khanday, Owais Mujtaba; Ouellet, Marc; Pérez Córdoba, José Luis; Rodríguez San Esteban, Pablo; Miccoli, Laura; Galdón Castillo, Alberto; Olivares Granados, Gonzalo; González López, José Andrés We present UGR-MINDVOICE, the University of Granada (UGR) multimodal electroencephalography (EEG) and audio dataset for overt and covert speech in Iberian Spanish intended for basic neuroscience and brain-computer interface (BCI) research. The dataset features EEG and audio recordings from 15 native Spanish speakers engaged in both overt and covert speech production tasks. This dataset is unique in its inclusion of all Spanish phonemes and a diverse set of words spanning various semantic categories and different usage frequencies. Validation of the dataset confirmed the presence of robust sensory event-related potentials, including the visual P100 and the auditory N1 (N100), indicating reliable early perceptual processing and sustained participant attention to both visual and auditory stimuli. Additionally, the EEG data were classified into rest, covert speech, and overt speech conditions with an accuracy of 81.40%, demonstrating active participant engagement in the tasks. By providing synchronised EEG and audio data for overt speech, along with EEG data for the same stimuli during covert speech, UGR-MINDVOICE constitutes a valuable resource for advancing research in basic neuroscience and brain-computer interfaces, particularly in the domain of silent speech communication. The full dataset is openly available on the Open Science Framework (OSF) (https://osf.io/6sh5d), and all accompanying code and analysis scripts are provided in a public GitHub repository (https://github.com/owaismujtaba/mind-voice) https://hdl.handle.net/10481/111524