Search Results (32)

In both scientific and industrial fields, there has been a notable increase in attention toward High-Altitude Pseudo-Satellites (HAPSs) in recent years. This surge is driven by their distinct advantages over traditional satellites and Remotely Piloted Aircraft Systems (RPASs). These benefits are particularly evident in critical areas such as intelligent transportation systems, surveillance, remote sensing, traffic and environmental monitoring, emergency communications, disaster relief efforts, and the facilitation of large-scale temporary events. This review provides an overview of key aspects related to the propellers and propulsion systems of HAPSs. To date, propellers remain the most efficient means of propulsion for high-altitude applications. However, due to the unique operational conditions at stratospheric altitudes, propeller design necessitates specific approaches that differ from those applied in conventional applications. After a brief overview of the propulsion systems proposed in the literature or employed by HAPSs, focusing on both the technical challenges and advancements in this emerging field, this review integrates theoretical foundations, historical design approaches, and the latest multi-fidelity optimization techniques to provide a comprehensive comparison of propeller design methods for HAPSs. It identifies key trends, including the growing use of CFD-based simulations methodologies, which contribute to notable performance improvements. Additionally, the review includes a critical assessment of experimental methods for performance evaluation. These developments have enabled the design of propellers with efficiencies exceeding 85%, offering valuable insights for the next generation of high-endurance, high-altitude platforms. Full article

Advancements in remote sensing technology have remarkably enhanced the 3D Earth surface reconstruction, which is pivotal for applications such as disaster relief, emergency management, and urban planning, etc. Although satellite imagery offers a cost-effective and extensive coverage solution for 3D reconstruction, the quality of the resulted digital surface model (DSM) heavily relies on the choice of stereo image pairs. However, current approaches of stereo Earth observation still employ a post-acquisition manner without sophisticated planning in advance, causing inefficiencies and low reconstruction quality. This paper introduces a novel quality-driven planning method for satellite stereo imaging, aiming at optimizing the search of stereo pairs to achieve high-quality 3D reconstruction. Moreover, a regression model is customized and incorporated to estimate the reconstructed point cloud geopositioning quality, based on the enhanced features of possible Earth-imaging opportunities. Experiments conducted on both real satellite images and simulated constellation data demonstrate the efficacy of the proposed method in estimating reconstruction quality beforehand and searching for optimal stereo pair combinations as the final satellite imaging schedule, which can improve the stereo quality significantly. Full article

Flooding is one of the most frequent natural disasters at present, and can pose a serious threat to transmission towers. In response to the accuracy and timeliness requirements of flood emergency monitoring, a local region growth algorithm combining polarization and texture information is proposed for Synthetic Aperture Radar (SAR) image water recognition. Morphological methods and external geographic information are used to optimize the results, allowing for rapid extraction of the flood range. The method is validated using Gaofen-3 (GF-3) Fine Strip Imaging Mode II (FSII) SAR images covering Fangshan District in Beijing, China. The experimental results indicate that this method can obtain more effective water information compared to traditional threshold segmentation methods, and can also reduce the effects of noise and mountain shadows. It has good applicability and timeliness with respect to large-scale flood emergency disaster monitoring, and can help to rapidly and accurately obtain detailed information of flood-affected areas, thus providing reference for emergency rescue and disaster relief services. Full article

The growing adoption of wind energy has resulted in an increasing number of decommissioned wind turbine blades, which pose significant disposal challenges due to their size, material composition, and environmental impact. Recycling these blades has thus become essential. To this aim, this study explores the potential of using recycled wind turbine blades in post-disaster housing applications and examines the feasibility of re-purposing these durable composite materials to create robust, cost-effective, and sustainable building solutions for emergency housing. A case study of a post-earthquake relief camp in Hatay, Türkiye, affected by the 2023 earthquake, is used for analysis. First, the energy consumption of thirty traditional modular container-based post-disaster housing units is simulated with a dynamic building simulation tool. Then, the study introduces novel wind turbine blade-based housing (WTB-bH) designs developed using the same simulation tool. The energy consumption of these (WTB-bH) units is compared to that of traditional containers. The results indicate that using recycled wind turbine blades for housing not only contributes to waste reduction but also achieves 27.3% energy savings compared to conventional methods. The novelty of this study is in demonstrating the potential of recycled wind turbine blades to offer durable and resilient housing solutions in post-disaster situations and to advocate for integrating this recycling method into disaster recovery frameworks, highlighting its ability to enhance sustainability and resource efficiency in construction. Overall, the output of this study may help to present a compelling case for the innovative reuse of decommissioned wind turbine blades, providing an eco-friendly alternative to traditional waste disposal methods while addressing critical needs in post-disaster scenarios. Full article

Emergency events pose critical challenges to national and social stability, requiring efficient and timely responses to mitigate their impact. In the initial stages of an emergency, decision-makers face the dual challenge of minimizing transportation costs while adhering to stringent rescue time constraints. To address these issues, this study proposes a two-stage optimization model aimed at ensuring the equitable distribution of disaster relief materials across multiple distribution centers. The model seeks to minimize the overall cost, encompassing vehicle dispatch expenses, fuel consumption, and time window penalty costs, thereby achieving a balance between efficiency and fairness. To solve this complex optimization problem, a hybrid algorithm combining genetic algorithms and particle swarm optimization was designed. This hybrid approach leverages the global exploration capability of genetic algorithms and the fast convergence of particle swarm optimization to achieve superior performance in solving real-world logistics challenges. Case studies were conducted to evaluate the feasibility and effectiveness of both the proposed model and the algorithm. Results indicate that the model accurately reflects the dynamics of emergency logistics operations, while the hybrid algorithm exhibits strong local optimization capabilities and robust performance in handling diverse and complex scenarios. Experimental findings underscore the potential of the proposed approach in optimizing emergency response logistics. The hybrid algorithm consistently achieves significant reductions in total cost while maintaining fairness in material distribution. These results demonstrate the algorithm’s applicability to a wide range of disaster scenarios, offering a reliable and efficient tool for emergency planners. This study not only contributes to the body of knowledge in emergency logistics optimization but also provides practical insights for policymakers and practitioners striving to improve disaster response strategies. Full article

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications. Full article

Global society faces the pressing question of how to eliminate reliance on fossil fuels while meeting increasing energy demand. In comparison to solar and wind energy, nuclear power has been largely ignored in urban studies research. However, nuclear energy has recently regained attention through the emergence of Small Modular Reactors (SMRs), and as the stakes of decarbonization become increasingly essential. To evaluate situations in which SMRs bring value to urban energy mixes, this paper focuses on Nuclear Batteries (NBs), a specific class of SMRs, that can fit in standard shipping containers. First, we outline an evaluation framework for the use and application of NBs; second, we present use cases for NBs in real-world situations, from disaster relief to grid reinforcement; and third, we discuss the social challenges around this technology. Full article

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared on media platforms, aiding in-depth analysis of social sensing systems. This study proposed an analytical framework to extract disaster-related spatiotemporal information from multimodal social media data. Using a pre-trained multimodal neural network and a location entity recognition model, the framework integrates disaster semantics with spatiotemporal information, enhancing situational awareness. A case study of the April 2024 heavy rain event in Guangdong, China, using Weibo data, demonstrates that multimodal content correlates more strongly with rainfall patterns than textual data alone, offering a dynamic perception of disasters. These findings confirm the utility of multimodal social media data and offer a foundation for future research. The proposed framework offers valuable applications for emergency response, disaster relief, risk assessment, and witness discovery, and presents a viable approach for safety risk monitoring and early warning systems. Full article

Environmental contamination by heavy metals poses significant threats to terrestrial and aquatic ecosystems, necessitating the development of effective remediation strategies. Conventional methods for heavy metal removal exhibit limitations, including inadequate efficiency and elevated costs. In this context, microalgae have emerged as a promising bioremediation approach due to their robust metal-binding capabilities, specifically through biosorption. This review comprehensively examines the role of microalgae in addressing heavy metal pollution, with a primary focus on their effective removal from wastewater. Microalgae offer wastewater purification potential across diverse sources and capitalize on wastewater as a growth matrix, yielding valuable bioproducts, biomaterials, and bioenergy. Their versatility allows them to thrive in various wastewaters, facilitating effective contaminant removal. This study also investigates the application of microalgae in decentralized water treatment systems (DWTSs), where the decentralized nature of these systems proves advantageous in addressing heavy metal contaminants directly at the point of generation or use. This approach holds particular significance in regions where centralized systems face obstacles due to geographical constraints, inadequate infrastructure, or financial limitations. DWTSs not only provide a decentralized solution for heavy metals removal but also prove advantageous in disaster relief scenarios and rapidly growing urban areas. Full article

Unmanned aerial vehicles (UAVs) have recently been widely employed as effective wireless platforms for aiding users in various situations, particularly in hard-to-reach scenarios like post-disaster relief efforts. This study employs multiple UAVs to cover users in overlapping locations, necessitating the optimization of UAV-user association to maximize the spectral and energy efficiency of the UAV network. Hence, a connected bipartite graph is formed between UAVs and users using graph theory to accomplish this goal. Then, a maximum weighted matching-based maximum flow (MwMaxFlow) optimization approach is proposed to achieve the maximum data rate given users’ demands and the UAVs’ maximum capacities. Additionally, power control is applied using the $\mathcal{M}$-matrix theory to optimize users’ transmit powers and improve their energy efficiency. The proposed strategy is evaluated and compared with other benchmark schemes through numerical simulations. The simulation outcomes indicate that the proposed approach balances spectral efficiency and energy consumption, rendering it suitable for various UAV wireless applications, including emergency response, surveillance, and post-disaster management. Full article

The application of movement-detection sensors is crucial for understanding surface movement and tectonic activities. The development of modern sensors has been instrumental in earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection. There are numerous sensors currently being utilized in earthquake engineering and science. It is essential to review their mechanisms and working principles thoroughly. Hence, we have attempted to review the development and application of these sensors by classifying them based on the timeline of earthquakes, the physical or chemical mechanisms of sensors, and the location of sensor platforms. In this study, we analyzed available sensor platforms that have been widely used in recent years, with satellites and UAVs being among the most used. The findings of our study will be useful for future earthquake response and relief efforts, as well as research aimed at reducing earthquake disaster risks. Full article

Unpredictable natural disasters brought by extreme climate change compound difficulties and cause a variety of systemic risks. It is thus critical to provide possibilistic scheduling schemes that simultaneously involve emergency evacuation and relief allocation. But the existing literature seldom takes emergency evacuation and relief supplies as a joint consideration, nor do they explore the impact of an unpredictable flood disaster on the scheduling scheme. A multi-stage stochastic programming model with updating information is constructed in this study, which considers the uncertainty of supply and demand, road network, and multiple types of emergency reliefs and vehicles. In addition, a fuzzy algorithm based on the objective weighting of two-dimensional Euclidean distance is introduced, through moderating an effect analysis of the fuzzy number. Computational results show that humanitarian equity for allocating medical supplies in the fourth period under the medium and heavy flood is about 100%, which has the same as the value of daily and medical supplies within the first and third period in the heavy scenarios. Based on verifying the applicability and rationality of the model and method, the result also presents that the severity of the flood and the fairness of resources is not a simple cause-and-effect relationship, and the consideration of survivor is not the only factor for humanitarian rescue with multi-period. Specifically, paying more attention to a trade-off analysis between the survival probability, the timeliness, and the fairness of humanitarian service is essential. The work provides a reasonable scheme for updating information and responding to sudden natural disasters flexibly and efficiently. Full article

A swarm of unmanned aerial vehicles (UAVs) can be used for many applications, including disaster relief, search and rescue, and establishing communication networks, due to its mobility, scalability, and robustness to failure. However, a UAV swarm’s performance is typically limited by each agent’s stored energy. Recent works have considered the usage of thermals, or vertical updrafts of warm air, to address this issue. One challenge lies in a swarm of UAVs detecting and taking advantage of these thermals. Inspired by hawks, a swarm could take advantage of thermals better than individuals due to the swarm’s distributed sensing abilities. To determine which emergent behaviors increase survival time, simulation software was created to test the behavioral models of UAV gliders around thermals. For simplicity and robustness, agents operate with limited information about other agents. The UAVs’ motion was implemented as a Boids model, replicating the behavior of flocking birds through cohesion, separation, and alignment forces. Agents equipped with a modified behavioral model exhibit dynamic flocking behavior, including relative ascension-based cohesion and relative height-based separation and alignment. The simulation results show the agents flocking to thermals and improving swarm survival. These findings present a promising method to extend the flight time of autonomous UAV swarms. Full article

Buildings bear much of the damage from natural disasters, and determining the extent of this damage is of great importance to post-disaster emergency relief. The application of deep learning to satellite remote sensing imagery has become more and more mature in monitoring natural disasters, but there are problems such as the small pixel scale of targets and overlapping targets that hinder the effectiveness of the model. Based on the SegFormer semantic segmentation model, this study proposes the SegDetector model for difficult detection of small-scale targets and overlapping targets in target detection tasks. By changing the calculation method of the loss function, the detection of overlapping samples is improved and the time-consuming non-maximum-suppression (NMS) algorithm is discarded, and the horizontal and rotational detection of buildings can be easily and conveniently implemented. In order to verify the effectiveness of the SegDetector model, the xBD dataset, which is a dataset for assessing building damage from satellite imagery, was transformed and tested. The experiment results show that the SegDetector model outperforms the state-of-the-art (SOTA) models such as you-only-look-once (YOLOv3, v4, v5) in the xBD dataset with F1: 0.71, Precision: 0.63, and Recall: 0.81. At the same time, the SegDetector model has a small number of parameters and fast detection capability, making it more practical for deployment. Full article

Unmanned Aerial Vehicles (UAVs) play crucial roles in numerous applications, such as healthcare services. For example, UAVs can help in disaster relief and rescue missions, such as by delivering blood samples and medical supplies. In this work, we studied a problem related to the routing of UAVs in a healthcare approach known as the UAV-based Capacitated Vehicle Routing Problem (UCVRP). This is classified as an NP-hard problem. The problem deals with utilizing UAVs to deliver blood to patients in emergency situations while minimizing the number of UAVs and the total routing distance. The UCVRP is a variant of the well-known capacitated vehicle routing problem, with additional constraints that fit the shipment type and the characteristics of the UAV. To solve this problem, we developed a heuristic known as the Greedy Battery—Distance Optimizing Heuristic (GBDOH). The idea was to assign patients to a UAV in such a way as to minimize the battery consumption and the number of UAVs. Then, we rearranged the patients of each UAV in order to minimize the total routing distance. We performed extensive experiments on the proposed GBDOH using instances tested by other methods in the literature. The results reveal that GBDOH demonstrates a more efficient performance with lower computational complexity and provides a better objective value by approximately 27% compared to the best methods used in the literature. Full article