Search Results (66)

In modern battlefields, the rapid advancement of Counter-UAV (C-UAV) technologies has made single-UAV missions increasingly difficult. This highlights the need for distributed swarm systems that can operate reliably under such threats. Among various swarm coordination methods, hierarchical leader–follower structures have been actively studied for battlefield environments with high risk of agent loss and limited communication. The Virtual Leader-based Formation System (VLFS), which follows this structure, enables formation through a virtual leader. It also introduces a novel collision avoidance approach that allows followers to avoid obstacles during formation flight. However, the conventional VLFS suffers from long convergence time with severe oscillations. In addition, it does not consider inter-UAV collisions and has demonstrated avoidance only in simple obstacle environments. To address these limitations, this paper proposes the VLFS-RF method, which directly integrates a repulsive function into the VLFS. The proposed method consists of four control modes that perform formation tracking, inter-UAV collision avoidance, and obstacle avoidance simultaneously according to the situation. Software-In-The-Loop (SITL) simulations were conducted in a ROS-Gazebo environment using V-shaped and hexagonal formations. The results show that the formation tracking error is reduced by approximately 59% compared to the conventional VLFS. In addition, inter-UAV collisions are prevented during initial convergence, and obstacles are successfully avoided in narrow passages and gaps between two obstacles. These results demonstrate that VLFS-RF is a practical formation control method for UAV swarms in complex environments. Full article

Hydraulic short-circuit (HSC) has gained widespread attention as a novel approach to enhancing the flexibility of pumped-storage power plants (PSPPs). This paper investigates the flow and structural characteristics of bifurcated pipes in PSPPs, conducting numerical simulations under multiple operating conditions under pumping, generating, and HSC modes. Computational fluid dynamics (CFD) simulations indicate that the flow pattern deteriorates significantly under the HSC mode, with energy loss increasing notably as the flow split ratio (FSR) rises, though peaking at only 1.2% of total energy. Driven by secondary flow, a pair of counter-rotating Dean vortices develops from the upstream main pipe to the generating branch as the FSR increases. The entropy production rate reveals the energy dissipation mechanisms in the main flow region, namely, the shear interaction between high-velocity outflow and low-velocity vortex flow, along with the viscous dissipation within the Dean vortices. Furthermore, fluid–structure interaction (FSI) simulation results confirm that the structural reliability of the bifurcated pipe is ensured under the HSC mode, as the dominant load stems from the high static pressure of the upstream reservoir, with fluid impact loads playing a relatively insignificant role. This study provides a theoretical foundation for the practical operation of hydraulic short-circuit with respect to the performance and safety of a bifurcated pipe. Full article

The explosive growth of the Internet of Things (IoT) has brought an array of resource-constrained devices to domains such as smart homes, industrial automation, and healthcare, raising substantial cybersecurity challenges. Lightweight wireless protocols, such as Thread, Zigbee, and Z-Wave, are integral to IoT connectivity, but the degree to which their embedded cryptographic mechanisms satisfy formal cybersecurity certification schemes remains underexplored. This work draws primarily on recent peer-reviewed publications and major conference proceedings to rigorously evaluate Thread, Zigbee, and Z-Wave against the Common Criteria (CC) Functional Requirements for Cryptography (FCS) as specified in CC:2022 and the EU cybersecurity certification scheme on Common Criteria (EUCC). The assessment focuses on essential CC cryptographic components, including key generation (FCS_CKM.1), secure key distribution (FCS_CKM.2), agreement protocols (FCS_CKM_EXT.7), cryptographic operations (FCS_COP.1), and random bit generators (FCS_RBG.1). The analysis reveals that Thread demonstrates the strongest alignment with CC requirements by leveraging Advanced Encryption Standard—Counter with CBC-MAC mode (AES-CCM) authenticated encryption and Elliptic Curve Diffie-Hellman (ECDH)-based key exchange within a decentralized trust framework. Zigbee matches this cryptographic strength at the primitive level, but its dependency on a centralized Trust Center for key management complicates full compliance with key lifecycle and distribution controls. Z-Wave, especially through its S2 Security framework, improves by incorporating authenticated ECDH exchanges, though proprietary constraints and limited protocol transparency remain obstacles to independent assurance. This comparative study concludes that while all three protocols provide a baseline of robust cryptographic security, only Thread currently aligns with CC and EUCC certification schemes. Zigbee and Z-Wave will require additional protocol hardening and enhancement of cryptographic key lifecycle management to achieve comparable assurance levels. Ensuring conformance with formal cybersecurity standards is imperative for building trust and resilience across critical IoT infrastructures. Full article

Modern multi-Unmanned Aerial Vehicle (UAV) attacks pose significant challenges to existing counter-UAV frameworks due to their agility, irregular spatial formations, and increasing reliance on intelligent evasive behaviors. This paper proposes a unified interception architecture that integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for multi-target grouping, a deceptive waypoint sequencing (DWS) mechanism for adversarial evasion, and a robust sliding-mode backstepping controller augmented with extended state observers (ESOs) for precise tracking under disturbances. DBSCAN enables real-time clustering of attacking UAVs without prior knowledge of the number of formations, producing dynamic centroids that serve as tactical interception references. To counter risky attackers capable of predicting defender trajectories, a novel DWS strategy introduces centroid-relative waypoints that preserve mission objectives while reducing trajectory predictability. Lyapunov-based analysis is developed for stability, guaranteeing uniform ultimate boundedness of the tracking errors. The proposed approach achieves successful interception in both scenarios, with an interception time of 7 s and final interception error of $0.023$ m in the single-UAV case, and an interception time of 8 s with final interception error of $0.050$ m in the multiple-UAV case, whereas the PID baseline fails to achieve interception under the same conditions. Extensive simulations involving single and multi-cluster engagements demonstrate that the proposed strategy achieves fast, accurate, and deception-resilient interception, outperforming the conventional PID approach in the presence of disturbances, nonlinearities, and dynamic swarm configurations. The obtained results show the effectiveness of integrating adaptive clustering, deceptive planning, and robust nonlinear control for modern UAV–UAV defensive operations. Full article

To address the challenges posed by increased power consumption in traditional active relays and the difficulties associated with countering channel fading for Intelligent Reflecting Surfaces (IRSs), we propose a dual-mode relay (DMR). This relay can dynamically switch between two operational modes: active relaying and passive IRS reflection. The DMR allows its units (DMRUs) to select their operational modes based on channel conditions. This capability enables the transmission of composite-mode signals, which consist of both active relaying components and IRS-reflected components. This dynamic switching enhances adaptation to the wireless environment. Furthermore, under the constraint of limited transmit power, we introduce a DMR-based Adaptive Transmission (DMRAT) method. This approach explores all possible DMR operational modes and employs the Alternating Optimization (AO) algorithm in each mode to jointly optimize the beamforming matrices of both the transmitter and the DMR, along with the reflection coefficient matrix of the IRS. Consequently, this maximizes the data transmission rate for the target communication pair. The optimal DMR mode can then be determined based on the optimized data rate for the target communication across various operational modes. Simulation results demonstrate that the proposed method significantly enhances the data transmission rate for the target communication pair. Full article

This study develops a high-performance triboelectric nanogenerator (TENG) through Citrullus lanatus rind powder (CLP) which originates from watermelon waste to generate sustainable power and detect humidity. The SEM and FTIR results showed that CLP contains a natural porous structure and multiple polar functional groups which improve both the charge transfer and retention capabilities. The CLP-TENG device operated in vertical contact–separation mode with PTFE as the counter layer to generate a 255 V open-circuit voltage and 30 µA short-circuit current and 35 µW peak power output at 4 Hz and 20 MΩ load. The device successfully charged a 4.7 µF capacitor to 5 V during a 80 s period and operated low-power electronic devices to prove its ability as a sustainable power source. The device output increased with increasing operating frequency while showing operation stability throughout more than 1000 cycles and seven days of continuous operation. The device demonstrated a strong humidity detection ability through its voltage response which decreased from 250 V to 120 V when the relative humidity rose from 30% to 90%. The research proves that agricultural waste can be transformed into environmentally friendly materials which perform well in green energy systems and environmental monitoring applications. Full article

In this paper, we present the design, prototyping, and working of a cost-effective, energy-efficient, and scalable air quality monitoring system (AQMS), enabled by a Low-power, long-Range Wide-Area Network (LoRaWAN), an Internet of Things (IoT) technology designed to provide connectivity for massive machine-type communication applications. The growing threat of air pollution necessitates outdoor and mobile environmental monitoring systems to provide real-time, location-specific data, which is unfortunately not possible using fixed monitoring devices. For our AQMS, we have developed two custom-built sensor nodes. The first node is equipped with a Nucleo-WL55JC1 microcontroller and sensors to measure temperature, humidity, and carbon dioxide (CO₂), while the other node is equipped with an Arduino MKR WAN 1310 controller with sensors to measure carbon monoxide (CO), ammonia (NH₃), and particulate matter (PM2.5 and PM10). These sensor nodes connect to a WisGate Edge LoRaWAN gateway, which aggregates and forwards the sensor data to The Things Network (TTN) for processing and cloud storage. The final visualization is handled via the Ubidots IoT platform, allowing for real-time visualization of environmental data. Besides environmental data, we were able to acquire a received signal strength indicator, signal-to-noise ratio, as well as a frame counter, which shows the number of packets received by the gateway. We performed laboratory testing, which confirmed reliable communication, with a packet delivery rate of 98% and a minimal average latency of 2.5 s. Both nodes operated efficiently on battery power, with the Nucleo-WL55JC1 consuming an average of 20 mA in active mode, while the Arduino MKR WAN 1310 operated at 15 mA. These values ensured extended operation for remote deployment. The system’s low power consumption and modular architecture make it viable for smart city applications and large-scale deployments in resource-constrained areas. Full article

This study developed a two-dimensional multiphysics-coupled model for co-electrolysis of CO₂ and H₂O in solid oxide electrolysis cells (SOECs) using COMSOL Multiphysics, systematically investigating the influence mechanisms of key operating parameters including temperature, voltage, feed ratio, and flow configuration on co-electrolysis performance. The results demonstrate that increasing temperature significantly enhances CO₂ electrolysis, with the current density increasing over 12-fold when temperature rises from 923 K to 1423 K. However, the H₂O electrolysis reaction slows beyond 1173 K due to kinetic limitations, leading to reduced H₂ selectivity. Higher voltages simultaneously accelerate all electrochemical reactions, with CO and H₂ production at 1.5 V increasing by 15-fold and 13-fold, respectively, compared to 0.8 V, while the water–gas shift reaction rate rises to 6.59 mol/m³·s. Feed ratio experiments show that increasing CO₂ concentration boosts CO yield by 5.7 times but suppresses H₂ generation. Notably, counter-current operation optimizes reactant concentration distribution, increasing H₂ and CO production by 2.49% and 2.3%, respectively, compared to co-current mode, providing critical guidance for reactor design. This multiscale simulation reveals the complex coupling mechanisms in SOEC co-electrolysis, offering theoretical foundations for developing efficient carbon-neutral technologies. Full article

This article presents an innovative integration of Glow Discharge Detector Focal Plane Arrays (GDD FPA) with Chirped Frequency Modulated Continuous Wave (FMCW) Radar, enhancing millimeter-wave (MMW) imaging. The cost-effective FPA design using GDDs as pixel elements forms the foundation of the system. We investigate MMW effects on GDD discharge currents via basic data acquisition (DAQ) and implement a scanning mechanism with a step motor for sub-pixel imaging. The setup integrates an MMW source, optical components, a timer/counter, and an 8 × 8 FPA with 64 GDD, operating in electrical detection modes and processing signals using Fast Fourier Transform (FFT) algorithms. Recent advancements in millimeter-wave imaging have focused on improving image resolution and acquisition speed through various techniques, including lock-in amplifiers and electrical detection methods. However, these methods introduce complexity, cost, and extended acquisition times. Our approach mitigates these challenges by implementing a simplified FPA design that eliminates the need for external signal conditioning elements, providing faster and more efficient image acquisition. The primary contributions include significant improvements in the speed and automation of image acquisition achieved through a coordinated control mechanism for efficient row scanning. Compared to previous generations of GDD FPAs, this system achieves a notable reduction in image acquisition time by up to 75%, while maintaining high fidelity. These enhancements make the system particularly suitable for time-sensitive applications. Additionally, future research directions include the incorporation of 3D imaging using FMCW radar. Results from the FMCW measurements using the single GDD circuit demonstrate the system’s ability to accurately capture and process MMW radiation, even at low intensities. The combined strengths of GDD FPA and chirped FMCW radar underscore the system’s effectiveness in MMW detection, laying the groundwork for advanced MMW imaging capabilities across diverse applications. Full article

As the world transitions to decarbonized fuels, understanding the impact of engine oil on emissions remains crucial. Lubricant-derived particulate emissions can influence air quality and regulatory compliance in future transport. Researchers have predominantly focused on transient driving cycles to replicate real-world conditions and capture the full range of particle size. This emphasis has led to a lack of comprehensive data on oil-related particulate emissions during steady-state operations, particularly for particles smaller than 23 nm. This paper addresses this gap as upcoming regulations, such as Euro 7, are expected to impose stricter limits by extending measurement thresholds down to 10 nm. The investigation was conducted on a 1.0 L gasoline direct injection engine, assessing total particulate number (TPN) emissions using three oil formulations: a baseline oil with mid-ash content and mid-volatility, a low-ash and low-volatility oil (LoLo), and a high-ash and high-volatility oil (HiHi). A DMS500, with and without a catalytic stripper, measured particle size distribution and TPN. Two digital filters were applied to obtain particle number (PN) metrics comparable to condensation particle counters: “F1-PN > 23” with d50 = 23 nm and “F3-PN > 10” with d50 = 10 nm. Sub-23 nm particles dominated emissions, with baseline oil generally producing higher PN emissions except at low loads. Using F1-PN > 23, HiHi exhibited higher PN counts across moderate to high speeds, while F3-PN > 10 revealed lower PN emissions for HiHi at specific conditions, excluding 2250 rpm-fast idle. By a weighted arithmetic mean, HiHi’s emissions were 9.7% higher than LoLo with F1-PN > 23 and 3.6% higher with F3-PN > 10. Oil formulation did not influence nucleation mode diameter. A three-way ANOVA demonstrated that load and speed were the predominant factors affecting emissions over the entire testing map; albeit at specific operating conditions the effect of the oil is evident. This suggests that under steady-state conditions, carbon-based fuel still plays a key role in particle formation. Future work will investigate decarbonised fuels to further isolate the effect of oil on emissions. Full article

Enhancing the combustion efficiency and flame stability in conventional systems is essential for reducing carbon emissions and advancing sustainable energy solutions. In this context, electrohydrodynamic plasma actuators offer a promising active control method for modifying and regulating flame characteristics. This study presents a numerical investigation into the effects of a ring-type plasma actuator positioned on the co-flow air side of a non-premixed turbulent methane/air combustion system—an approach not previously reported in the literature. The ring-type plasma actuator was designed by placing electrodes along the perimeter of the small diameter wall of the air duct. The impact of the plasma actuator on the reacting flow field within the burner was analyzed, with a focus on its influence on the flow dynamics and flame structure. The results, visualized through velocity and temperature contours, as well as flow streamlines, provide insight into the actuator’s effect on flame behavior. Two operating modes of the plasma actuators were evaluated: co-flow mode, where the aerodynamic effect of the plasma actuators was directed downstream; and counter-flow mode, where the effects were directed upstream. The findings indicate that the co-flow actuation positively reduces the flame height and enhances the flame anchoring at the root, whereas counter-flow actuation slightly weakens the flame root. Numerical simulations further revealed that co-flow actuation marginally increases the energy release by approximately 0.13%, while counter-flow actuation reduces the energy release by around 7.8%. Full article

Plastic injection molding is a widespread industrial process in manufacturing. This article investigates the energy consumption in the injection molding process of fruit containers, proposing a new use strategy for the application of artificial intelligence algorithms. The aim is to optimize the process parameters, such as the mold temperatures, the injector temperatures, and the cycle time, to minimize energy consumption. This new use strategy, a hybrid use strategy, combines an unsupervised autoencoder with the K-Means algorithm to analyze production data and identify factors influencing energy consumption. The results show the capability of discovering different operating modes at different levels of energy requirements. An analysis of the process parameters reveals that the number of parts left to complete production, the current cycle counter, the number of shots left to complete the production, the material needed to complete the production, and the total time dedicated to production, so far, are the most relevant features for the optimization of the energy consumption per single piece. The study demonstrates the potential of common artificial intelligence algorithms if appropriately used to improve the sustainability of the plastic injection molding process. Full article

The abuse of UAVs poses a potential risk to social security, necessitating the investigation of anti-UAV methods to safeguard critical areas. However, the existing UAV countermeasures face challenges such as high environmental impact, restricted spatial deployment, and low cost-effectiveness. To address these limitations, we developed a novel anti-UAV system known as UAV Hunter, which adopts an airborne tether-net capture device with visual aids to counter unauthorized UAVs. This system employs an “Anti-UAV with UAV” scheme, comprising a ground control station and a net-capturing UAV. The operator utilizes the ground control station to determine the mission area and flight path and then controls the flight of the net-capturing UAV. During flight, the net-capturing UAV leverages its dual-mode sensor to continuously monitor the target area. Simultaneously, the onboard computer executes a UAV detection and tracking algorithm to search for unauthorized UAVs in real time. The results are relayed to the operator in real time, facilitating precise adjustments for the net-capturing UAV to launch the rope net accurately. The system successfully realizes the functions of dual-mode real-time detection and tracking, precise net capture, and efficient integrated control. Compared with existing methods, the developed system exhibits accurate recognition, rapid action, diverse application scenarios, and an enhanced human–machine interaction experience. Test results in the open environment further validate the feasibility and functional integrity of the system, demonstrating its capability to effectively capture low-altitude unauthorized UAVs. Full article

Effective permeability K_P, the ultrafiltration coefficient (K_UF), the sieving coefficient (SC), and the loss/permeation of proteins (primarily albumin) are key parameters/specifications characterizing hemofilter (HF) performance. However, there are uncertainties regarding their determination. This work aims (a) to demonstrate that the co-current flow (of blood and dialysate) can lead to beneficial unidirectional filtration (from blood/plasma to dialysate) under a fairly uniform local trans-membrane pressure (TMP), unlike the presently employed counter-current flow; (b) to study the temporal evolution of key HF performance parameters under co-current flow, particularly during the important early stage of hemocatharsis (HC). Experiments with human plasma and BSA solutions in co-current flow mode (for which a fluid mechanical model is developed) show a fairly uniform local/axial TMP, which also improves the local/axial uniformity of protein membrane fouling, particularly under (currently favored) high convective flux operation. Due to incipient membrane fouling, a significant temporal variability/decline in the effective K_P is observed, and, in turn, of other parameters (i.e., the Kuf, SC, and permeation/mass flux M_m for albumin and total proteins). A satisfactory correlation of the albumin/protein mass flux M_m with permeability K_P is obtained, indicating strong inter-dependence. In conclusion, co-current flow, allowing for a fair local TMP axial uniformity, enables the acquisition of accurate/representative data on the evolution of HF parameters, facilitating their interpretation and correlation. The new results provide a basis for exploring the clinical application of the co-current flow. Full article

With the advancement of radar technology toward multifunctionality and cognitive capabilities, traditional radar countermeasures are no longer sufficient to meet the demands of countering the advanced multifunctional radar (MFR) systems. Rapid and accurate generation of the optimal jamming strategy is one of the key technologies for efficiently completing radar countermeasures. To enhance the efficiency and accuracy of jamming policy generation, an efficient jamming policy generation method based on multi-timescale ensemble Q-learning (MTEQL) is proposed in this paper. First, the task of generating jamming strategies is framed as a Markov decision process (MDP) by constructing a countermeasure scenario between the jammer and radar, while analyzing the principle radar operation mode transitions. Then, multiple structure-dependent Markov environments are created based on the real-world adversarial interactions between jammers and radars. Q-learning algorithms are executed concurrently in these environments, and their results are merged through an adaptive weighting mechanism that utilizes the Jensen–Shannon divergence (JSD). Ultimately, a low-complexity and near-optimal jamming policy is derived. Simulation results indicate that the proposed method has superior jamming policy generation performance compared with the Q-learning algorithm, in terms of the short jamming decision-making time and low average strategy error rate. Full article