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In disaster-stricken areas, rapid restoration of communication infrastructure is critical to ensuring effective emergency response and recovery. Swarm UAVs, operating as mobile aerial base stations (MABS), offer a transformative solution for bridging connectivity gaps in environments where the traditional infrastructure has been compromised. This paper presents a novel hybrid path planning approach combining affinity propagation clustering (APC) with genetic algorithms (GA), aimed at maximizing coverage, and ensuring quality of service (QoS) compliance across diverse environmental conditions. Comprehensive simulations conducted in suburban, urban, dense urban, and high-rise urban environments demonstrated the efficacy of the APC-GA approach. The proposed method achieved up to 100% coverage in suburban settings with only eight unmanned aerial vehicle (UAV) swarms, and maintained superior performance in dense and high-rise urban environments, achieving 97% and 93% coverage, respectively, with 10 UAV swarms. The QoS compliance reached 98%, outperforming benchmarks such as GA (94%), PSO (90%), and ACO (88%). The solution exhibited significant stability, maintaining consistently high performance, highlighting its robustness under dynamic disaster scenarios. Mobility model analysis further underscores the adaptability of the proposed approach. The reference point group mobility (RPGM) model consistently achieved higher coverage rates (95%) than the random waypoint model (RWPM) (90%), thereby demonstrating the importance of group-based mobility patterns in enhancing UAV deployment efficiency. The findings reveal that the APC-GA adaptive clustering and path planning mechanisms effectively navigate propagation challenges, interference, and non-line-of-sight (NLOS) conditions, ensuring reliable connectivity in the most demanding environments. This research establishes the APC-GA hybrid as a scalable and QoS-compliant solution for UAV deployment in disaster response scenarios. By dynamically adapting to environmental complexities and user mobility patterns, it advances state-of-the-art emergency communication systems, offering a robust framework for real-world applications in disaster resilience and recovery. Full article

Intelligent unmanned clusters have played a crucial role in military reconnaissance, disaster rescue, border patrol, and other domains. Nevertheless, due to factors such as multipath propagation, electromagnetic interference, and frequency band congestion in high dynamic scenarios, unmanned cluster networks experience frequent topology changes and severe spectrum limitations, which hinder the provision of connected, elastic and autonomous network support for data interaction among unmanned aerial vehicle (UAV) nodes. To address the conflict between the demand for reliable data transmission and the limited network resources, this paper proposes an AODV routing protocol based on node energy consumption and mobility optimization (AODV-EM) from the perspective of network routing protocols. This protocol introduces two routing metrics: node energy based on node degree balancing and relative node mobility, to comprehensively account for both the balance of network node load and the stability of network links. The experimental results demonstrate that the AODV-EM protocol exhibits better performance compared to traditional AODV protocol in unmanned cluster networks with dense node distribution and high mobility, which not only improves the efficiency of data transmission, but also ensures the reliability and stability of data transmission. Full article

Peru is one of the most diverse countries from a geographical and climatic point of view, where there are three large ecosystem regions called coast, Sierra, and jungle. These characteristics result in the country having many hydrographic basins, with rivers of significant dimensions in terms of the width and length of the channel. In this sense, there is a permanent need to provide connectivity and promote trade between communities through road bridge infrastructure. Thus, Peru historically developed a road network and bridges during the Inca Empire in the Tawantinsuyu region, building a cobblestone road network and suspension bridges with rope cables made of plant fibers from vegetation called Coya-Ichu. This is how bridges in Peru have evolved to meet contemporary vehicular demands and provide structural stability and functionality throughout their useful life. This article presents the following sections: (a) an introduction to the evolution of bridges, (b) the current typology and inventory of bridges, (c) the characterization of the largest bridges, (d) a discussion on the architecture, engineering, construction, and structural health monitoring (AECSHM) of bridges in the face of climate change, earthquakes, and material degradation, and (e) conclusions. Finally, this article presents opportunities and challenges in terms of Peru’s architecture, engineering, construction, and structural health monitoring of road bridges. Special emphasis is given to the use of technologies from the era of Industry 4.0 to promote the digital construction and structural health monitoring of these infrastructures. Finally, it is concluded that the integration of technologies of sensors, the IoT (Internet of Things), AI (artificial intelligence), UAVs (Unmanned Aerial Vehicles), remote sensing, BIM (Building Information Modeling), and DfMA (Design for Manufacturing and Assembly), among others, will allow for more safe, reliable, durable, productive, cost-effective, sustainable, and resilient bridge infrastructures in Peru in the face of climate change. Full article

UAVs are widely used for multiple tasks, which in many cases require autonomous processing and decision making. This autonomous function often requires significant computational capabilities that cannot be integrated into the UAV due to weight or cost limitations, making the distribution of the workload and the combination of the results produced necessary. In this paper, a dual-stage processing architecture for object detection and tracking in Unmanned Aerial Vehicles (UAVs) is presented, focusing on efficient resource utilization and real-time performance. The proposed system delegates lightweight detection tasks to onboard hardware while offloading computationally intensive processes to a ground server. The UAV is equipped with a Raspberry Pi for onboard data processing, utilizing an Intel Neural Compute Stick 2 (NCS2) for accelerated object detection. Specifically, YOLOv5n is selected as the onboard model. The UAV transmits selected frames to the ground server, which handles advanced tracking, trajectory prediction, and target repositioning using state-of-the-art deep learning models. Communication between the UAV and the server is maintained through a high-speed Wi-Fi link, with a fallback to a 4G connection when needed. The ground server, equipped with an NVIDIA A40 GPU, employs YOLOv8x for object detection and DeepSORT for multi-object tracking. The proposed architecture ensures real-time tracking with minimal latency, making it suitable for mission-critical UAV applications such as surveillance and search and rescue. The results demonstrate the system’s robustness in various environments, highlighting its potential for effective object tracking under limited onboard computational resources. The system achieves recall and accuracy scores as high as 0.53 and 0.74, respectively, using the remote server, and is capable of re-identifying a significant portion of objects of interest lost by the onboard system, measured at approximately 70%. Full article

Tidal marshes play a globally critical role in carbon and hydrologic cycles by sequestering carbon dioxide from the atmosphere and exporting dissolved organic carbon to connected estuaries. These ecosystems provide critical habitat to a variety of fauna and also reduce coastal flood impacts. Accurate characterization of tidal marsh inundation dynamics is crucial for understanding these processes and ecosystem services. In this study, we developed remote sensing-based inundation classifications over a range of tidal stages for marshes of the Mid-Atlantic and Gulf of Mexico regions of the United States. Inundation products were derived from C-band and L-band synthetic aperture radar (SAR) imagery using backscatter thresholding and temporal change detection approaches. Inundation products were validated with in situ water level observations and radiometric modeling. The Michigan Microwave Canopy Scattering (MIMICS) radiometric model was used to simulate radar backscatter response for tidal marshes across a range of vegetation parameterizations and simulated hydrologic states. Our findings demonstrate that inundation classifications based on L-band SAR—developed using backscatter thresholding applied to single-date imagery—were comparable in accuracy to the best performing C-band SAR inundation classifications that required change detection approaches applied to time-series imagery (90.0% vs. 88.8% accuracy, respectively). L-band SAR backscatter threshold inundation products were also compared to polarimetric decompositions from quad-polarimetric Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2) and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) imagery. Polarimetric decomposition analysis showed a relative shift from volume and single-bounce scattering to double-bounce scattering in response to increasing tidal stage and associated increases in classified inundated area. MIMICS modeling similarly showed a relative shift to double-bounce scattering and a decrease in total backscatter in response to inundation. These findings have relevance to the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, as threshold-based classifications of wetland inundation dynamics will be employed to verify that NISAR datasets satisfy associated mission science requirements to map wetland inundation with classification accuracies better than 80% at 1 hectare spatial scales. Full article

Ride comfort is an important requirement that passenger rail vehicles must meet. Carbody–anti-bending system is a relatively new passive method to enhance the ride comfort in passenger rail vehicles with long and light carbody. The resonance frequency of the first bending mode (FBM) of such vehicle is within the most sensitive frequency range that affects ride comfort. Anti-bending bars consist of two bars that are mounted under the longitudinal beams of the carbody chassis using vertical supports. When the carbody bends, the anti-bending bars develop moments in the neutral axis of the carbody opposing the bending of the carbody. In this way, the carbody structure becomes stiffer and the resonance frequency of the FBM can be increased beyond the upper limit of the discomfort range of frequency, improving the ride comfort. The theoretical principle of this method has been demonstrated employing a passenger rail vehicle model that includes the carbody as a free–free Euler–Bernoulli beam and the anti-bending bars as longitudinal springs jointed to the vertical supports. Also, the method feasibility has been verified in the past using an experimental scale demonstrator system. In this paper, a new model of the carbody–anti-bending bar system is proposed by including three-directional elastic elements (vertical and longitudinal direction and rotation in the vertical–longitudinal plane) to model the fastening of the anti-bending bars to the supports and the vertical motion of the anti-bending bars modelled as free–free Euler–Bernoulli beams connected to the elastic elements of the fastening. In the longitudinal direction, the anti-bending bars work as springs connected to the longitudinal elastic elements of the fastening. The modal analysis method is applied to point out the basic properties of the frequency response functions (FRFs) of the carbody–anti-bending bars system, considering the bounce and FBMs of both the carbody and the anti-bending bars. A parametric study of the FRF of the carbody shows that the vertical stiffness of the fastening should be sufficiently high enough to eliminate the influence of the modes of the anti-bending bars upon the carbody response and to reduce the anti-bending bars vibration in the frequency range of interest. Longitudinal stiffness of the elastic elements of the fastening is critical to increase the bending resonance frequency of the carbody out of the sensitive range. Longer anti-bending bars can improve the capability of the anti-bending bars to increase the bending resonance without the risk of interference effects caused by the bounce and bending modes of the anti-bending bars. Full article

Against the backdrop of the global goal of “carbon neutrality”, the advancement of electric vehicles (EVs) holds substantial importance for diminishing the reliance on fossil fuels, mitigating vehicular emissions, and fostering the transition of the automotive sector towards a sustainable, low-carbon paradigm. The wide application of electric vehicles not only reduces the dependence on non-renewable resources such as oil, but also concurrently effectuates a substantial reduction in carbon emissions within the transportation sector. In the realm of electric vehicles, ternary lithium batteries (NCM) and lithium iron phosphate batteries (LFP) are two widely used batteries. This study examines the resource utilization and environmental repercussions associated with the production of 1 kW ternary lithium batteries and lithium iron phosphate batteries, employing a life cycle assessment (LCA) framework. The importance of clean energy in reducing environmental pollution and global warming potential is revealed by introducing five different power generation types and the regional power generation structure in China into the power battery production process. The findings of the investigation indicate that lithium iron phosphate batteries exhibit pronounced superiority in terms of environmental sustainability, while ternary lithium batteries are more advantageous in terms of performance. The mitigation of environmental pollution associated with battery production can be significantly achieved by the holistic integration of clean energy sources and the systematic optimization of manufacturing processes. Specific interventions encompass enhancing the energy efficiency of the production process, incorporating renewable energy sources for power generation, and minimizing the utilization of hazardous materials. By implementing these strategies, the battery sector can advance towards a more environmentally benign and sustainable trajectory. Full article

Due to numerous distributed power sources connecting to the grid, which results in strong grid volatility and diminished power quality, the traditional energy storage configuration is limited in terms of flexibility and economy. Based on this, integrating electric vehicles (EVs) into the distribution network as energy storage devices has emerged as a promising development direction. This paper proposes a frequency-response optimization study considering the strong uncertainty model of EVs. First, from the perspective of temporal-spatial characteristics, energy storage resources, and users’ willingness to respond, the strong uncertainty model of EVs is constructed by fitting the trip chain and the access probability of their participation in energy storage. Second, the frequency optimization model is integrated and constructed according to the response capability of a single EV. Finally, examples and scenarios are analyzed to verify that the maximum and minimum frequency offsets are reduced by 69.41% and 66.69%, respectively, which significantly reduces frequency fluctuations and stabilizes the output of EV clusters. Full article

In wireless communications, traditional base stations act as the backbone for providing network connectivity to users. These base stations, however, require significant resources to construct and are therefore not suitable for remote areas and disaster scenarios. This challenge makes them unfit for deployment in remote areas or in disaster scenarios where fast network establishment is necessary. To address these challenges, cellular base stations installed on Unmanned Aerial Vehicles (UAVs) can be an alternative solution. UAVs provide quick deployment capability and can adapt to changing environmental situations, making them ideal for dynamic network scenarios. In this paper, we address the critical issue of UAV positioning to maximize the total user coverage, which can be formulated as a mixed-integer linear program. Given the complexity of larger-scale scenarios related to the number of users, we suggest a two-step method. First, we group users into clusters, and then we optimize the UAV positions with respect to these clusters. This approach introduces a trade-off between computational time efficiency and optimality, which can be tuned by adjusting the number of clusters. By varying the number of clusters, we balance computation time with the optimality of the UAV locations, allowing flexible deployment in diverse scenarios. Full article

Urban multilane roundabouts and signalized intersections are two major roadway devices used for controlling and managing traffic flow. This paper presents a comparative analysis of the saturation flow rate between urban multilane roundabouts and multilane signalized intersections using field data from the Dammam Metropolitan Area (DMA) in Saudi Arabia. The data of this study were collected at four roundabouts and four signalized intersections in Dammam metropolitan area (DMA), Saudi Arabia. A total of 7028 saturation headways at the roundabouts and 2626 saturation headways at the signalized intersections were included. The results indicated that the signalized intersections had a higher saturation flow rate at the exit lane than the roundabouts at about 1046 vehicles per hour. These findings emphasize that signalized intersections outperform roundabouts in terms of the vehicular movement rate during green lights. Moreover, when the light is green, it takes 1.82 s for a car to move through the middle lane of a traffic light intersection. This study draws a unique connection between speed fluctuations in roundabouts with energy consumption, concluding how vehicles consume more energy this way. Thus, single-lane roundabouts are recommended for optimal traffic flow management in all directions. Full article

Vehicular edge computing relies on the computational capabilities of interconnected edge devices to manage incoming requests from vehicles. This offloading process enhances the speed and efficiency of data handling, ultimately boosting the safety, performance, and reliability of connected vehicles. While previous studies have concentrated on processor characteristics, they often overlook the significance of the connecting components. Limited memory and storage resources on edge devices pose challenges, particularly in the context of deep learning, where these limitations can significantly affect performance. The impact of memory contention has not been thoroughly explored, especially regarding perception-based tasks. In our analysis, we identified three distinct behaviors of memory contention, each interacting differently with other resources. Additionally, our investigation of Deep Neural Network (DNN) layers revealed that certain convolutional layers experienced computation time increases exceeding 2849%, while activation layers showed a rise of 1173.34%. Through our characterization efforts, we can model workload behavior on edge devices according to their configuration and the demands of the tasks. This allows us to quantify the effects of memory contention. To our knowledge, this study is the first to characterize the influence of memory on vehicular edge computational workloads, with a strong emphasis on memory dynamics and DNN layers. Full article

Electric vehicles (EVs) wireless charging is enabled by inductive power transfer (IPT) technology, which eliminates the need for physical connections between the vehicle and the charging station, allowing power to be transmitted without the use of cables. However, in the present wireless charging equipment, the power transfer still needs to be improved. In this work, we present a power transfer structure using a unique “DD circular (DDC) power pad”, which mitigates the two major obstacles of wireless EV charging, due to the mitigating power of electromagnetic field (EMF) leakage emissions and the increase in misalignment tolerance. We present a DDC power pad structure, which integrates features from both double D(DD) and circular power pads. We first build a three-dimensional electromagnetic model based on the DDC structure. A detailed analysis is performed of the electromagnetic characteristics, and the device parameters regarding the power transfer efficiency, coupling coefficient, and mutual inductance are also presented to evaluate the overall performance. Then, we examine the performance of the DDC power pad under various horizontal and vertical misalignment circumstances. The coupling coefficients and mutual inductance, as two essential factors for effective power transmission under dynamic circumstances, are investigated. The findings of misalignment effects on coupling efficiency indicate that the misalignment does not compromise the DDC pad’s robust performance. Therefore, our DDC power pad structure has a better electromagnetic characteristic and a higher misalignment tolerance than conventional circular and DD pads. In general, the DDC structure we present makes it a promising solution for wireless EV charging systems and has good application prospects. Full article

Today, topological structural optimisation is a valuable computational technique for designing mechanical components with optimal mass-to-stiffness ratios. Thus, this work aims to assess the performance of the Radial Point Interpolation Method (RPIM) when compared with the well-established Finite Element Method (FEM) within the context of a vehicle suspension control arm’s structural optimisation process. Additionally, another objective of this work is to propose an optimised design for the suspension control arm. Being a meshless method, RPIM allows one to discretise the problem’s domain with an unstructured nodal distribution. Since RPIM relies on a weak form equation to establish the system of equations, it is necessary to additionally discretise the problem domain with a set of background integration points. Then, using the influence domain concept, nodal connectivity is established for each integration point. RPIM shape functions are constructed using polynomial and radial basis functions with interpolating properties. The RPIM linear elastic formulation is then coupled with a bi-evolutionary bone remodelling algorithm, allowing for non-linear structural optimisation analyses and achieving solutions with optimal stiffness/mass ratios. In this work, a vehicle suspension control arm is analysed. The obtained solutions were evaluated, revealing that RPIM allows better solutions with enhanced truss connections and a higher number of intermediate densities. Assuming the obtained optimised solutions, four models are investigated, incorporating established design principles for material removal commonly used in vehicle suspension control arms. The proposed models showed a significant mass reduction, between 18.3% and 31.5%, without losing their stiffness in the same amount. It was found that the models presented a stiffness reduction between 5.4% and 9.8%. The obtained results show that RPIM is capable of delivering solutions similar to FEM, confirming it as an alternative numerical technique. Full article

Topological structural optimization is a powerful computational tool that enhances the structural efficiency of mechanical components. It achieves this by reducing mass without significantly altering stiffness. This study combines the Natural-Neighbour Radial-Point Interpolation Method (NNRPIM) with a bio-inspired bi-evolutionary bone-remodelling algorithm. This combination enables non-linear topological optimization analyses and achieves solutions with optimal stiffness-to-mass ratios. The NNRPIM discretizes the problem using an unstructured nodal distribution. Background integration points are constructed using the Delaunay triangulation concept. Nodal connectivity is then imposed through the natural neighbour concept. To construct shape functions, radial point interpolators are employed, allowing the shape functions to possess the delta Kronecker property. To evaluate the numerical performance of NNRPIM, its solutions are compared with those obtained using the standard Finite Element Method (FEM). The structural optimization process was applied to a practical example: a vehicle’s suspension control arm. This research is divided into two phases. In the first phase, the optimization algorithm is applied to a standard suspension control arm, and the results are closely evaluated. The findings show that NNRPIM produces topologies with suitable truss connections and a higher number of intermediate densities. Both aspects can enhance the mechanical performance of a hypothetical additively manufactured part. In the second phase, four models based on a solution from the optimized topology algorithm are analyzed. These models incorporate established design principles for material removal commonly used in vehicle suspension control arms. Additionally, the same models, along with a solid reference model, undergo linear static analysis under identical loading conditions used in the optimization process. The structural performance of the generated models is analyzed, and the main differences between the solutions obtained with both numerical techniques are identified. Full article

Electric vehicles (EVs) are a sustainable means of transportation, with their onboard batteries being crucial for both performance and energy management. A modular and reconfigurable power converter topology to connect removable batteries to the main DC bus of an EV is proposed in this paper. By employing Dual Active Bridge (DAB) converters in an Input Parallel Output Series (IPOS) configuration, the proposed topology is compatible with 400 V and 800 V standards without the need for external switches. The research explored the possibility to apply a very simple control strategy based on independent linear regulators. A theoretical analysis of the IPOS DAB converter is presented and the design of independent control regulators which minimize the coupling effect between the control variables is addressed. The stability of the IPOS DAB converter could be ensured using the proposed simplistic approach, enabling us to drastically simplify the regulator design step. The dynamic performance of the system was confirmed by means of a simulation and experimentally. Full article