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Item type:Item, Access status: Open Access , Novel metamaterial platform with piezoelectric sensors for self-sensing mechanical support(Elsevier, 2026-02-11) Slabý, Vojtěch; Bajer, Jan; Marcián, Petr; Hrstka, Miroslav; Hadaš, ZdeněkIn the last two decades, the field of mechanical engineering has seen growing interest in the development and utilisation of mechanical metamaterials. Artificially designed materials are intended to alter the mechanical properties of structures significantly, thereby enhancing their resilience, adaptability, and efficiency. This research focuses not only on strengthening structural strength and durability but also on enabling structural health monitoring and vibration mitigation. Modern computational modelling, with coupled-field analysis, allows engineers to design smart systems that integrate piezoelectric elements into complex geometric structures. These smart piezoelectric elements, here embedded in auxetic reentrant unit cells, offer valuable insights into the behaviour of the host structure under various conditions. This integration facilitates the assessment of electromechanical responses, thereby enabling the development of more intelligent and responsive structural systems. The current research focuses on developing a computational model and creating experimental prototypes for self-sensing mechanical support. This support serves as a load-bearing element of the host structure while simultaneously enabling the generation of vibrational signals in response to external stimuli. Piezoceramic elements enable the support to function as a sensor, detecting external forces or environmental vibrations. Additionally, this structure opens new possibilities for studying mechanical vibration attenuation and the temporal decay of vibrations. The combination of advanced metamaterial design, computational tools, and integrated smart materials creates a new approach for structural health monitoring and vibration attenuation. Ultimately, such a system aims to develop a better understanding of sustainable structures that can adapt to and respond to their environment while maintaining optimal structural rigidity and functionality.Item type:Item, Access status: Open Access , Influence of surface roughness on molecular flow through labyrinth seals for space applications(Elsevier, 2025-10-01) Pouzar, Josef; Košťál, David; Westerberg, Lars-Göran; Nyberg, Erik; Poláček, Tomáš; Juřík, Karel; Křupka, IvanLabyrinth seals are commonly used in space mechanisms to reduce evaporative losses of lubricant molecules and limit the transport of contaminants. Analytical models and numerical simulations for predicting mass flow through these seals typically assume smooth, idealized surfaces, neglecting the effects of realistic surface roughness. This study systematically investigates the impact of surface roughness on the transmission probability (TP) of oil molecules using Monte Carlo simulations under free molecular flow conditions. Key geometric and surface parameters including average roughness (Ra), corridor length, and seal width are varied to evaluate their influence on molecular transport. The results demonstrate that surface roughness significantly reduces TP and molecular flux, especially in narrow and elongated geometries. Furthermore, increasing surface roughness by an order of magnitude enables a reduction in channel length or an increase in gap width by approximately 35–40% while maintaining equivalent transmission probability. Based on these findings, a correction model is proposed to improve prediction accuracy and is validated against experimentally measured oil evaporative losses. This work highlights the potential of controlled surface texturing as a design strategy to both enhance sealing effectiveness and enable geometric reductions for improved compactness and manufacturability.Item type:Item, Access status: Open Access , RF Fingerprinting to Detect Beamstealing Attacks in mmWave 5G Communications(Radioengineering Society, 2026-04) Kousal, M.; Vychodil, J.; Ali, M.; Marsalek, R.5G mmWave networks rely on directional beamforming to ensure high-bandwidth connectivity, but the initial beam alignment process is vulnerable to beam-stealing attacks. In this scenario, an adversary transmits forged synchronization signals to hijack the receiver's connection, potentially leading to denial of service. This paper analyzes these threats and proposes a physical-layer detection mechanism based on radio frequency fingerprinting. Using a 60 GHz laboratory test-bed, we emulate legitimate and malicious transmission scenarios to evaluate specific hardware impairments. We investigate two primary detection metrics: power amplifier nonlinearities, analyzed via their Amplitude Modulation to Amplitude Modulation (AM/AM) characteristics, and local oscillator stability, quantified by carrier frequency offset drift. Experimental results demonstrate that these metrics can successfully distinguish among different transmitting devices based on their saturation levels and frequency stability profiles. The study confirms that lightweight radio frequency (RF) fingerprinting is a viable solution for hardening 5G beam management against spoofing.Item type:Item, Access status: Open Access , A Wideband Rectifier Design for Internet of Things (IoT) and Wireless Sensor Network Applications(Radioengineering Society, 2026-04) Abba, A. M.; Karataev, T.; Oshiga, O.; Muhammad, S.; Tiang, J. J.; Mallat, N. K.; Usman, A. D.This paper presents a compact wideband rectifier for low-power Internet of Things (IoT) and Wireless Sensor Network (WSN) applications, which enables efficient energy harvesting across multiple frequency bands. The proposed wideband rectifier operates efficiently over a broad bandwidth from 0.5-1.4 GHz covering the DTV band, LTE-700, ISM-900, and GSM-900. It employs a Coupled Three-Line Transformer (CTLT) as an impedance matching network to achieve a compact design and robust performance across a wide bandwidth. The circuit uses a voltage doubler configuration with SMS7630 diodes. It was designed, simulated, and validated through fabrication. At 0 dBm, it achieves a Power Conversion Efficiency (PCE) greater than 57% with a 3 kΩ load. The results show that the CTLT matching network can also maintain a high PCE over a wide range of load impedance from 2 to 7 kΩ. The design has a maximum efficiency of 74.2% at 0.65 GHz; it is compact with an electrical size of 0.428λ × 0.037λ, outperforming conventional rectifiers in efficiency and size. This rectifier is well-suited for powering battery-less IoT applications and WSN devices.Item type:Item, Access status: Open Access , Deep Reinforcement Learning in Multiple UAV-and-RIS Assisted Cognitive Radio System(Radioengineering Society, 2026-04) Qian, S.; Huo, L.; Qian, Y.; Shi, L.Cognitive radio (CR) systems enable dynamic spectrum sharing but face substantial challenges in optimizing the rates of secondary users (SUs), particularly in scenarios where multiple SUs compete for the limited resources of the primary user (PU). To address this issue, we propose a multi-unmanned aerial vehicle (UAV)-assisted CR system in which reconfigurable intelligent surfaces (RISs) are mounted on UAVs to enhance spectral efficiency. Furthermore, we cast this challenge as a multi-agent Markov decision process (MDP), providing a formal framework to explore the critical trade-off between independent decision-making and centralized coordination. Consequently, we leverage established deep reinforcement learning algorithms to probe this trade-off. To provide a comprehensive performance evaluation, we adopt a Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to maximize the sum rate of the proposed system. Numerical results demonstrate that the developed UAV-RIS-assisted system adopting the MAPPO algorithm can achieve a faster convergent speed and higher sum rate when compared with that adopting Independent Proximal Policy Optimization (IPPO) and MAPPO with a clipping scheme. In addition, for the MAPPO with a clipping scheme, a selected moderate clipping parameter can effectively balance the trade-off between training stability and learning efficiency.