Abstract
Reconfigurable Intelligent Surfaces (RISs) are expected to play a pivotal role in future indoor ultra high data rate wireless communications as well as highly accurate three-dimensional localization and sensing, mainly due to their capability to provide flexible, cost- and power-efficient coverage extension, even under blockage conditions. However, when considering beyond millimeter wave frequencies where there exists GHz-level available bandwidth, realistic models of indoor RIS-parameterized channels verified by field-trial measurements are unavailable. In this article, we first present and characterize three RIS prototypes with unit cells of half-wavelength intercell spacing, which were optimized to offer a specific nonspecular reflection with 1-, 2-, and 3-bit phase quantization at 304 GHz. The designed static RISs were considered in an indoor channel measurement campaign carried out with a 304 GHz channel sounder. Channel measurements for two setups, one focusing on the transmitter-RIS-receiver path gain and the other on the angular spread of multipath components, are presented and compared with both state-of-the-art theoretical models as well as full-wave simulation results. The article is concluded with a list of challenges and research directions for RIS design and modeling of RIS-parameterized channels at THz frequencies.

Abstract
Human Activity Recognition (HAR) is the identification and classification of static and dynamic human activities, which find applicability in domains like healthcare, entertainment, security, and cyber-physical systems. Traditional HAR approaches rely on wearable sensors, vision-based systems, or ambient sensing, each with inherent limitations such as privacy concerns or restricted sensing conditions. Instead, Radio Frequency (RF)-based HAR relies on the interaction of RF signals with people to infer activities. Reconfigurable Intelligent Surfaces (RISs) are significant for this use-case by allowing dynamic control over the wireless environment, enhancing the information extracted from RF signals. We present an Hand Gesture Recognition (HGR) approach using our own 6.5GHz RIS design, which we use to gather a dataset for HGR classification for three different hand gestures. By employing two Convolutional Neural Networks (CNNs) models trained on data gathered under random and optimized RIS configuration sequences, we achieved classification accuracies exceeding 90%.

Abstract
Wi-Fi 6E will enable dense communications with low latency and high throughput, meeting the demands of ever growing network traffic and supporting emergent services such as ultra HD or multi-video streaming, and augmented or virtual reality. However, the 6GHz band suffers from higher path loss and signal attenuation, and poor performance in NLoS conditions. Reconfigurable Intelligent Surfaces (RISs) can address these challenges by providing low-cost directional communications with increased spectral and energy efficiency. However, RIS designs for the WiFi-6E range are under-explored in literature. We present the implementation of an 8x8 RIS tuned for 6.5GHz designed for scalability. We characterize the response of the unit cell, and evaluate the RIS in an anechoic chamber, measuring the far field radiation patterns for several digital beamsteering configurations in a horizontal plane, demonstrating effective signal steering.

Abstract
Thanks to the vast amount of available resources and unique propagation properties, terahertz (THz) frequency bands are viewed as a key enabler for achieving ultrahigh communication performance and precise sensing capabilities in future wireless systems. Recently, the European Telecommunications Standards Institute (ETSI) initiated an Industry Specification Group (ISG) on THz which aims at establishing the technical foundation for subsequent standardization of this technology, which is pivotal for its successful integration into future networks. Starting from the work recently finalized within this group, this article provides an industrial perspective on potential use cases and frequency bands of interest for THz communication systems. We first identify promising frequency bands in the 100 GHz-1 THz range, offering over 500 GHz of available spectrum that can be exploited to unlock the full potential of THz communications. Then, we present key use cases and application areas for THz communications, emphasizing the role of this technology and its advantages over other frequency bands. We discuss their target requirements and show that some applications demand multi-Tb/s data rates, latency below 0.5 ms, and sensing accuracy down to 0.5 cm. Additionally, we identify the main deployment scenarios and outline other enabling technologies crucial for overcoming the challenges faced by THz systems. Finally, we summarize past and ongoing standardization efforts focusing on THz communications, while also providing an outlook toward the inclusion of this technology as an integral part of the future sixth generation (6G) and beyond communication networks.

Abstract

This study presents the first application-ready demonstration of radio-frequency (RF) switches based on memristors fabricated through a combination of electrochemical exfoliation and liquid-liquid interfacial assembly (EC-LL). This 2D layer fabrication method yields uniform, low-defect bilayer MoS2 nanosheet networks without relying on high-temperature processes or hazardous gases typical of chemical vapor deposition (CVD), offering a low-cost and environmentally friendly route towards CMOS-compatible integration. Remarkably, the resulting devices exhibit robust unipolar resistive switching which simplifies biasing requirements and reduces power consumption. Reproducibility with retention of 104 sec, and endurance of 100 cycles is reported. RF measurements confirm reliable operation at millimeter wave (mmWave) frequencies across 10–110 GHz, demonstrating low insertion loss (0.42–0.9 dB), isolation >18 dB, and an intrinsic cut-off frequency of ~5.4 THz. Integration into Reconfigurable Intelligent Surface Unit Cells (RIS-UCs) further showcases the technology’s utility in next-generation mmWave communication systems, including 5G/6G and satellite applications. Simulations of a 24×24-element RIS panel confirm high gain (>21.6 dBi) and efficient beam steering (-60º, 60º degrees) over the 26.8–29.1 GHz band, while the ultra-low switching energy (~330 pJ per unit cell) enables zero static power consumption—critical for scalable and sustainable 6G infrastructure. This work establishes a new benchmark by delivering the first solution-processed, application-suitable 2D material in solid-state RF switches combining non-volatility, high-frequency operation, and CMOS integration potential. It marks a significant step toward reconfigurable, energy-efficient wireless communication platforms.

Abstract
Chest CT scans are essential in diagnosing lung abnormalities, including lung cancer, but their utility in training deep learning models is often pushed back by limited data availability, high labeling costs, and privacy concerns. To address these challenges, this study explores the use of score-based diffusion models for the conditional generation of lung CT scans slices. Two generation scenarios are explored: one limited to lung segmentation masks and another incorporating both lung and nodule segmentation mappings to guide the synthesis process. The proposed methods are custom U-Net architecture models trained to predict the scores in Variance Preserving (VP) and Variance Exploding (VE) Stochastic Differential Equations (SDEs), composing the primary ground for comparison in conditional sample generation. The results demonstrate the VP SDEs model's superiority in generating high-fidelity images, as evidenced by high SSIM (0.894) and PSNR (28.6) values, as well as low domain-specific FID (173.4), MMD (0.0133) and ECS (0.78) scores. The generated images consistently followed the conditional mapping guidance during the generation process, effectively producing realistic lung and nodule structures, highlighting their potential for data augmentation in medical imaging tasks. While the models achieved notable success in generating accurate 2D lung CT scan slices given simple conditional image region mappings, future work surrounds the extension of these methods to 3D conditional generation and the use of richer conditional mappings to account for broader anatomical variations. Nevertheless, this study holds promise for improvement in computer-aided systems through the support in deep learning model training for lung disease diagnosis and classification.