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Swarm of Unmanned Aerial Vehicles (UAVs) broaden the field of application in various fields like military surveillance, crop monitoring in agriculture, combat operations, etc. Unfortunately, they are becoming increasingly susceptible to security attacks, such as jamming, information leakage and spoofing, as they become more common and in more demand. So, there is a wider need for UAVs, which requires the design of strong security procedures to fend off such attacks and security dangers. Even though several studies focused on security aspects, many questions remain unanswered, particularly in the areas of secure UAV‐to‐UAV communication, support for perfect forward secrecy and non‐repudiation. In a battle situation, it is extremely important to close these gaps. The security requirements for the UAV communication protocol in a military setting were the focus of this study. In this paper, we present the issues faced by the UAV swarm, especially during military surveillance operations. To secure the communication link in UAV, a new protocol for UAV Swarm communication is proposed with anonymous secure messaging token‐based protocol (ASMTP). The proposed protocol secures UAV‐to‐base station communication and safeguards the metadata of the sender and receiver nodes. The proposed model maintains the confidentiality, integrity and availability of data in the UAV Swarm and achieves robustness. In addition, it provides a different strategy for the cybersecurity gaps in the swarm of UAVs during military surveillance and combat operations. [ABSTRACT FROM AUTHOR]

A collaborators' experiences learning (CEL) algorithm, based on multiagent reinforcement learning (MARL) is presented for multi-UAV cooperative path-finding, where reaching destinations and avoiding obstacles are simultaneously considered as independent or interactive tasks. In this article, we are inspired by the experience learning phenomenon to propose the multiagent experience learning theory based on MARL. A strategy for updating parameters randomly is also suggested to allow homogeneous UAVs to effectively learn cooperative strategies. Additionally, the convergence of this algorithm is theoretically demonstrated. To demonstrate the effectiveness of the algorithm, we conduct experiments with different numbers of UAVs and different algorithms. The experiments show that the proposed method can achieve experience sharing and learning among UAVs and complete the cooperative path-finding task very well in unknown dynamic environments. [ABSTRACT FROM AUTHOR]

In the context of large-scale video surveillance systems, the person re-identification from UAVs is challenging due to the myriad appearance variation of a person across different UAVs. Actually, it is challenging issue to correctly match the same person in different UAV views. This paper introduced a new person re-identification method from UAVs that involves an offline phase and an inference phase. The first allows generating the person re-identification model, whereas the second aims to re-identify a person. Our method contributions are (1) the enhancement of the person re-identification robustness, using three complementary modules, namely the amplified occlusion, the hybrid discriminative features representation, and the bi-model deep metric learning; (2) the improvement of the proposed method effectiveness through relying on a new multi-shot-person retrieval module. Referring to the experimental assessment of the proposed method, the effectiveness and the efficiency of our method were evinced through a comparison with the state-of-the-art person re-identification methods. [ABSTRACT FROM AUTHOR]

The rapid advancement of artificial intelligence (AI) has significantly changed military applications, creating new competitive advantages and shifting the global balance of power. This article examines NATO’s AI strategy and the associated interoperability challenges, with a particular focus on Turkey. NATO’s AI strategy seeks to enhance interoperability among its member states by fostering the integration of AI technologies into military capabilities. However, achieving this goal is complicated by the varying levels of AI technological advancement, divergent national AI-military strategies and differing geopolitical considerations among member countries. Using Turkey as a case study, this paper explores how the rapid development of AI-based military drones contributes to Turkey’s strategic autonomy and enhances regime resilience while also highlighting certain interoperability considerations within NATO. The analysis underlines the need for a cohesive approach to AI integration that addresses these disparities to maintain NATO’s collective defence capabilities. [ABSTRACT FROM AUTHOR]

The health monitoring of planted forests is essential for the planning, management and management of these areas, which are fundamental for the economy and ecosystem services. In this sense, this review explicitly focuses on studies related to the monitoring of commercial forests threatened by biotic and abiotic stress factors. Our objective was to examine studies related to the remotely piloted aircraft (RPAS) remote sensing data, methods and technologies used, as well as discuss scientific and technical advances that could further increase the potential of RPAS for forestry in the future. A systematic bibliometric review of articles published between 2015 and 2023 was carried out. In total, 29 scientific articles were obtained, where the results revealed the preference for multispectral sensors for monitoring biotic and abiotic stress. RPAS, with the integration of passive and active sensors, and associated artificial intelligence technologies, are excellent options for the development of monitoring the health of planted forests, as they allow the rapid and accurate acquisition of information, as well as the ability to predict potential changes. Finally, monitoring crop health and vigor through RPAS has proven to be effective and robust, as evidenced by the authors in their research. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) powered by electricity are becoming increasingly popular for civil, military, and commercial applications. Accurately controlling the brushless direct current (BLDC) motor propeller system is necessary for UAVs to perform complex maneuvers in the air. A field-oriented backstepping control (FOBSC) is proposed for enhancing the speed control performance of the propeller motor drives as per the demand of the flight controller. The FOBSC is designed to minimize input control efforts and dynamically adopts various beneficial characteristics such as high tracking accuracy, quick convergence, and reduced chattering in control input of the BLDC motor propeller drive. For improved overall performance of the propeller motor drive under internal and external disturbances, especially wind gusts, the rotor speed-based MRAS disturbance observer (MRASDO) has been developed and integrated with FOBSC. An MRAS estimator based on stator current is developed to estimate real-time rotor position and speed, removing the need for physical sensors, which is more complex for UAV applications. The sensorless control algorithm adapts variations in stator resistance and rotor magnetic flux of the BLDC motor, which enhances performance and stability and provides information on temperature rise, motor fault detection, and stator/rotor condition monitoring. The closed-loop stability of the MRAS estimator, MRASDO, and FOBSC is carried out using the Lyapunov method to guarantee reliable operation under any operational conditions. Finally, comprehensive numerical and experimental tests demonstrate that the proposed sensorless FOBSC approach is superior to sliding mode control and classical PI control. [ABSTRACT FROM AUTHOR]

Detection and mapping of Sosnowsky's hogweed (HS) using remote sensing data have proven effective, yet challenges remain in identifying, localizing, and eliminating HS in urban districts and regions. Reliable data on HS growth areas are essential for monitoring, eradication, and control measures. Satellite data alone are insufficient for mapping the dynamics of HS distribution. Unmanned aerial vehicles (UAVs) with high-resolution spatial data offer a promising solution for HS detection and mapping. This study aimed to develop a method for detecting and mapping HS growth areas using a proposed algorithm for thematic processing of multispectral aerial imagery data. Multispectral data were collected using a DJI Matrice 200 v2 UAV (Dajiang Innovation Technology Co., Shenzhen, China) and a MicaSense Altum multispectral camera (MicaSense Inc., Seattle, WA, USA). Between 2020 and 2022, 146 sites in the Moscow region of the Russian Federation, covering 304,631 hectares, were monitored. Digital maps of all sites were created, including 19 digital maps (orthophoto, 5 spectral maps, and 13 vegetation indices) for four experimental sites. The collected samples included 1080 points categorized into HS, grass cover, and trees. Student's t-test showed significant differences in vegetation indices between HS, grass, and trees. A method was developed to determine and map HS-growing areas using the selected vegetation indices NDVI > 0.3, MCARI > 0.76, user index BS1 > 0.10, and spectral channel green > 0.14. This algorithm detected HS in an area of 146.664 hectares. This method can be used to monitor and map the dynamics of HS distribution in the central region of the Russian Federation and to plan the required volume of pesticides for its eradication. [ABSTRACT FROM AUTHOR]

With FaSS-MVS, we present a fast, surface-aware semi-global optimization approach for multi-view stereo that allows for rapid depth and normal map estimation from monocular aerial video data captured by unmanned aerial vehicles (UAVs). The data estimated by FaSS-MVS, in turn, facilitate online 3D mapping, meaning that a 3D map of the scene is immediately and incrementally generated as the image data are acquired or being received. FaSS-MVS is composed of a hierarchical processing scheme in which depth and normal data, as well as corresponding confidence scores, are estimated in a coarse-to-fine manner, allowing efficient processing of large scene depths, such as those inherent in oblique images acquired by UAVs flying at low altitudes. The actual depth estimation uses a plane-sweep algorithm for dense multi-image matching to produce depth hypotheses from which the actual depth map is extracted by means of a surface-aware semi-global optimization, reducing the fronto-parallel bias of Semi-Global Matching (SGM). Given the estimated depth map, the pixel-wise surface normal information is then computed by reprojecting the depth map into a point cloud and computing the normal vectors within a confined local neighborhood. In a thorough quantitative and ablative study, we show that the accuracy of the 3D information computed by FaSS-MVS is close to that of state-of-the-art offline multi-view stereo approaches, with the error not even an order of magnitude higher than that of COLMAP. At the same time, however, the average runtime of FaSS-MVS for estimating a single depth and normal map is less than 14% of that of COLMAP, allowing us to perform online and incremental processing of full HD images at 1–2 Hz. [ABSTRACT FROM AUTHOR]

Accurate detection of transportation objects is pivotal for enhancing driving safety and operational efficiency. In the rapidly evolving domain of transportation systems, the utilization of unmanned aerial vehicles (UAVs) for low-altitude detection, leveraging remotely-sensed images and videos, has become increasingly vital. Addressing the growing demands for robust, real-time object-detection capabilities, this study introduces a lightweight, memory-efficient model specifically engineered for the constrained computational and power resources of UAV-embedded platforms. Incorporating the FasterNet-16 backbone, the model significantly enhances feature-processing efficiency, which is essential for real-time applications across diverse UAV operations. A novel multi-scale feature-fusion technique is employed to improve feature utilization while maintaining a compact architecture through passive integration methods. Extensive performance evaluations across various embedded platforms have demonstrated the model's superior capabilities and robustness in real-time operations, thereby markedly advancing UAV deployment in crucial remote-sensing tasks and improving productivity and safety across multiple domains. [ABSTRACT FROM AUTHOR]

This paper investigates the ability of a relatively low cost, commercially available uncrewed aerial vehicle (UAV), the DJI Mavic 3 Multispectral, to perform cryospheric research. The performance of this UAV, where applicable, is compared to a similar but higher cost system, the DJI Matrice 350, equipped with a Micasense RedEdge-MX Multispectral dual-camera system. The Mavic 3 Multispectral was tested at three field sites: the Lemon Creek Glacier, Juneau Icefield, AK; the Easton Glacier, Mt. Baker, WA; and Bagley Basin, Mt. Baker, WA. This UAV proved capable of mapping the spatial distribution of red snow algae on the surface of the Lemon Creek Glacier using both spectral indices and a random forest supervised classification method. The UAV was able to assess the timing of snowmelt and changes in suncup morphology on snow-covered areas within the Bagley Basin. Finally, the UAV was able to classify glacier surface features using a random forest algorithm with an overall accuracy of 68%. The major advantages of this UAV are its low weight, which allows it to be easily transported into the field, its low cost compared to other alternatives, and its ease of use. One limitation would be the omission of a blue multispectral band, which would have allowed it to more easily classify glacial ice and snow features. [ABSTRACT FROM AUTHOR]

Purpose: High resolution imagery from unmanned aerial vehicles (UAVs) has been established as an important source of information to perform precise irrigation practices, notably relevant for high value crops often present in semi-arid regions such as vineyards. Many studies have shown the utility of thermal infrared (TIR) sensors to estimate canopy temperature to inform on vine physiological status, while visible-near infrared (VNIR) imagery and 3D point clouds derived from red–green–blue (RGB) photogrammetry have also shown great promise to better monitor within-field canopy traits to support agronomic practices. Indeed, grapevines react to water stress through a series of physiological and growth responses, which may occur at different spatio-temporal scales. As such, this study aimed to evaluate the application of TIR, VNIR and RGB sensors onboard UAVs to track vine water stress over various phenological periods in an experimental vineyard imposed with three different irrigation regimes. Methods: A total of twelve UAV overpasses were performed in 2022 and 2023 where in situ physiological proxies, such as stomatal conductance (gs), leaf (Ψleaf) and stem (Ψstem) water potential, and canopy traits, such as LAI, were collected during each UAV overpass. Linear and non-linear models were trained and evaluated against in-situ measurements. Results: Results revealed the importance of TIR variables to estimate physiological proxies (gs, Ψleaf, Ψstem) while VNIR and 3D variables were critical to estimate LAI. Both VNIR and 3D variables were largely uncorrelated to water stress proxies and demonstrated less importance in the trained empirical models. However, models using all three variable types (TIR, VNIR, 3D) were consistently the most effective to track water stress, highlighting the advantage of combining vine characteristics related to physiology, structure and growth to monitor vegetation water status throughout the vine growth period. Conclusion: This study highlights the utility of combining such UAV-based variables to establish empirical models that correlated well with field-level water stress proxies, demonstrating large potential to support agronomic practices or even to be ingested in physically-based models to estimate vine water demand and transpiration. [ABSTRACT FROM AUTHOR]

Introduction: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking. Materials and methods: This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control. Results: Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls. Conclusion: Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring. [ABSTRACT FROM AUTHOR]

Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized various industries, finding applications in embedded systems, autonomy, control, security, and communication. Autonomous UAVs are distinguished by their ability to make informed decisions, anticipate potential scenarios, and learn from past experiences with the help of AI algorithms. This paper examines a practical monitoring system with an autonomous UAV, a charging station, and multiple targets that move randomly within a defined mission area. The mission area is divided into zones, and the UAV navigates through these zones efficiently. The primary objective is to maximize the probability of detecting targets, considering constraints such as limited battery life and charging station location. This challenge is initially framed as a search benefit maximization problem and subsequently reformulated as a Markov Decision Process (MDP) problem. To address the MDP formulation, we introduce a reinforcement learning (RL)-based approach that enables the UAV to comprehend unpredictable multi-target movements autonomously. The placement of the charging station in the proposed system is determined using the optimal median approach. The simulation results demonstrate that the proposed RL-based detection system significantly outperforms the reference systems in terms of detection rate and convergence. [ABSTRACT FROM AUTHOR]

With the increasing deployment of FANETs, efficient routing algorithms play a crucial role in ensuring reliable and optimal communication among UAVs. This paper presents a novel Clustered Learning Automata-based Routing Algorithm (called CLARA) for FANETs. The proposed algorithm is designed to enhance the performance of routing by leveraging the capabilities of learning automata. The CLARA consists of five key phases. In the first phase, actions, states, and a Q-Table are initialized to facilitate the learning process. Subsequently, in the second phase, drones within the FANET are clustered based on their geographic location and connection characteristics. This clustering enables efficient management of UAVs and improves network performance. In the third phase, the algorithm selects the next hop for data packet transmission, considering factors such as connectivity and reliability. This selection process ensures robust and efficient data delivery within the network. The fourth phase focuses on Q-Table updating and energy management, which optimizes resource allocation and prolongs network lifetime. Finally, in the fifth phase, the cluster head is selected based on its remaining energy, ensuring effective leadership within each cluster. This dynamic selection process enables the efficient distribution of responsibilities and enhances network stability. Simulation results demonstrate the superiority of the CLARA approach compared to existing methods, including OLSR, AODV, and Q-FANET. CLARA algorithm shows its superiority in the criteria of control overhead, routing overhead, computational overhead of energy consumption, network lifetime, PDR, and end-to-end delay. [ABSTRACT FROM AUTHOR]

The technological revolution has transformed the area of labor with reference to automation and robotization in various domains. The employment of robots automates these disciplines, rendering beneficial impacts as robots are cost-effective, reliable, accurate, productive, flexible, and safe. Usually, single robots are deployed to accomplish specific tasks. The purpose of this study is to focus on the next step in robot research, collaborative multi-robot systems, through flocking control in particular, improving their self-adaptive and self-learning abilities. This review is conducted to gain extensive knowledge related to swarming, or cluster flocking. The evolution of flocking laws from inception is delineated, swarming/cluster flocking is conceptualized, and the flocking phenomenon in multi-robots is evaluated. The taxonomy of flocking control based on different schemes, structures, and strategies is presented. Flocking control based on traditional and trending approaches, as well as hybrid control paradigms, is observed to elevate the robustness and performance of multi-robot systems for collective motion. Opportunities for deploying robots with flocking control in various domains are also discussed. Some challenges are also explored, requiring future considerations. Finally, the flocking problem is defined and an abstraction of flocking control-based multiple UAVs is presented by leveraging the potentials of various methods. The significance of this review is to inspire academics and practitioners to adopt multi-robot systems with flocking control for swiftly performing tasks and saving energy. [ABSTRACT FROM AUTHOR]

Aircraft carrier flight decks present high-risk mission-critical environments that need to be both efficient and safe. The concept of optimal manning, having just enough people to do the job safely and efficiently, is paramount in order to put the least amount of people at risk while not sacrificing mission effectiveness. To this end, an agent-based model, the optimal manning simulation (OMS) was developed, which specifically looks at the launch process of the flight deck in order to quantify the risk and efficiency of people working on the flight deck. OMS models different classes of crew members on the flight deck, aircraft, and resources like catapults. OMS measures safety through collisions or near-collisions of people and aircraft, as well as how long it takes to execute a launch cycle, the primary efficiency metric. Validation and sensitivity analyses provide confidence in OMS results. To demonstrate its utility, OMS is also used to predict how the future introduction of unmanned aerial vehicles could impact staffing and performance measures. [ABSTRACT FROM AUTHOR]

The use of remote sensing technology is essential for identifying and mapping tree species. In species management, remote sensing tools like Unmanned Aerial Vehicles (UAVs) are used because of their short-cycle replication, high-resolution images, and 3D capabilities. The main objectives of this research were to evaluate the ability to use UAV images and the reliability of three Nearest Neighbor (NN), Random Forest (RF), and Decision Tree (DT) algorithms, as well as the ability to differentiate deciduous tree species based on their spectral and structural characteristics. UAV images were obtained and processed, and 3D canopy crown structure features, i.e. DSM, DTM, CHM, and mean slope of the canopy crown, were prepared for object-based classification. The results showed that adding the structural feature of CHM, DSM, and slope, as combined with multispectral bands, could improve the results compared to using only multispectral bands for NN and RF algorithms. However, the DT algorithm provided the highest classification accuracy with an overall accuracy of 69.04% and a Kappa coefficient of 0.595, using spectral characteristics of the main bands, vegetation indices, and texture analysis. In contrast to the DT algorithm, which does not improve classification results by using tree structural properties, CHM shape properties in combination with their spectral properties can improve classification results. Overall, in dense deciduous forests where all trees have normal spectral reflections during the growing season, UAV images and structural features such as mean slope provide valuable information. [ABSTRACT FROM AUTHOR]

To understand how ectotherms will respond to warming temperatures, we require information on thermal habitat quality at spatial resolutions and extents relevant to the organism. Measuring thermal habitat quality is either limited to small spatial extents, such as with ground‐based 3D operative temperature (Te) replicas, representing the temperature of the animal at equilibrium with its environment, or is based on microclimate derived from physical models that use land cover variables and downscale coarse climate data. We draw on aspects of both these approaches and test the ability of unoccupied aerial vehicle (UAV) data (optical RGB) to predict fine‐scale heterogeneity in sub‐canopy lizard (Anolis bicaorum) Te in tropical forest using random forest models. Anolis bicaorum is an endemic, critically endangered, species, facing significant threats of habitat loss and degradation, and work was conducted as part of a larger project. Our findings indicate that a model incorporating solely air temperature, measured at the centre of the 20 × 20 m plot, and ground‐based leaf area index (LAI) measurements, measured at directly above the 3D replica, predicted Te well. However, a model with air temperature and UAV‐derived canopy metrics performed slightly better with the added advantage of enabling the mapping of Te with continuous spatial extent at high spatial resolutions, across the whole of the UAV orthomosaic, allowing us to capture and map Te across the whole of the survey plot, rather than purely at 3D replica locations. Our work provides a feasible workflow to map sub‐canopy lizard Te in tropical environments at spatial scales relevant to the organism, and across continuous areas. This can be applied to other species and can represent species within the same community that have evolved a similar thermal niche. Such methods will be imperative in risk modelling of such species to anthropogenic land cover and climate change. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel method for an online trajectory replanning approach for fixed-wing Unmanned Aerial Vehicles (UAVs). Our method integrates moving obstacle predictions within a gradient-based optimization framework. The trajectory is represented by uniformly discretized waypoints, which serve as the optimization variables within the cost function. This cost function incorporates multiple objectives, including obstacle avoidance, kinematic and dynamic feasibility, similarity to the reference trajectory, and trajectory smoothness. To enhance prediction accuracy, we combine physics-based and pattern-based methods for predicting obstacle movements. These predicted movements are then integrated into the online trajectory replanning framework, significantly enhancing the system's safety. Our approach provides a robust solution for navigating dynamic environments, ensuring both optimal and secure UAV operation. [ABSTRACT FROM AUTHOR]

This study introduces a novel methodology for enhancing the efficiency of solar-powered unmanned aerial vehicles (UAVs) through azimuthal solar synchronization and aerodynamic neuro-optimization, leveraging the principles of slime mold neural networks. The objective is to broaden the operational capabilities of solar UAVs, enabling them to perform over extended ranges and in varied weather conditions. Our approach integrates a computational model of slime mold networks with a simulation environment to optimize both the solar energy collection and the aerodynamic performance of UAVs. Specifically, we focus on improving the UAVs' aerodynamic efficiency in flight, aligning it with energy optimization strategies to ensure sustained operation. The findings demonstrated significant improvements in the UAVs' range and weather resilience, thereby enhancing their utility for a variety of missions, including environmental monitoring and search and rescue operations. These advancements underscore the potential of integrating biomimicry and neural-network-based optimization in expanding the functional scope of solar UAVs. [ABSTRACT FROM AUTHOR]

Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of 'Calatina' olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R² values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds. [ABSTRACT FROM AUTHOR]

In the second decade of the new millennium, artificial intelligence (AI) became a catchword among senior politicians and the military officers of India. Indian military officers have raised concerns about the potential use of AI-enabled weapon systems by China and by insurgents supported by Pakistan in the subcontinent. This article portrays the complex interlinkages between AI, strategic planning about future warfare and the role of ethics in India. The article, divided into three sections, deals with the role of AI and ethics in India's grand strategy, in military strategy and in command culture. Finally, the article also offers some policy recommendations. For cultural reasons, India follows a defensive strategy. India is attempting to integrate the AI weapons within its dharmayuddha (just war) format. Indian military strategy emphasises that on no account should these intelligent machines be autonomous. Atman (self-consciousness) must not be subordinated to yantras (machines). [ABSTRACT FROM AUTHOR]

Airspeed measurement is crucial for UAV control. To achieve accurate airspeed measurements for UAVs, this paper calculates airspeed data by measuring changes in air pressure and temperature. Based on this, a data processing method based on mechanical filtering and the improved AR-SHAKF algorithm is proposed to indirectly measure airspeed with high precision. In particular, a mathematical model for an airspeed measurement system was established, and an installation method for the pressure sensor was designed to measure the total pressure, static pressure, and temperature. Secondly, the measurement principle of the sensor was analyzed, and a metal tube was installed to act as a mechanical filter, particularly in cases where the aircraft has a significant impact on the gas flow field. Furthermore, a time series model was used to establish the sensor state equation and the initial noise values. It also enhanced the Sage–Husa adaptive filter to analyze the unavoidable error impact of initial noise values. By constraining the range of measurement noise, it achieved adaptive noise estimation. To validate the superiority of the proposed method, a low-complexity airspeed measurement device based on MEMS pressure sensors was designed. The results demonstrate that the airspeed measurement device and the designed velocity measurement method can effectively calculate airspeed with high measurement accuracy and strong interference resistance. [ABSTRACT FROM AUTHOR]

: In recent years, the potential risks posed by easily moving objects have highlighted the need for intelligent surveillance systems in protected areas, primarily to ensure the safety of human lives. Among the most common of these objects are unmanned aerial vehicles (UAVs). Recent advances in deep learning techniques for recognizing audio signals have made these techniques effective in identifying moving or aerial objects, especially those powered by engines. And the growing deployment of UAVs has made their rapid recognition in various suspicious or unauthorized circumstances critical. Detecting suspicious drone flights, especially in restricted areas, remains a significant research challenge. It is vital to perform the task of determining their distance in order to quickly detect drones approaching people in such protected areas. Therefore, this paper aims to study the research question of recognizing UAV audio data from different distances. That is, recognizing drone audio at different distances was experimentally studied using Simple RNN, LSTM and GRU based deep learning models. The main objective of this study is based on finding one of the capable types of recurrent network for the task of recognizing UAV audio data at different distances. During the experimental study, the recognition abilities of Single-layer Simple RNN, LSTM and GRU recurrent network types were studied from two basic directions: with recognition accuracy curves and classification reports. As a result, LSTM and GRU based models showed high recognition ability for these types of audio signals. It was noted that UAVs can reliably predict distances greater than 10 meters based on the proposed deep learning architecture. [ABSTRACT FROM AUTHOR]

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Unmanned aerial vehicles (UAVs) are increasingly significant in modern warfare due to their versatility and capacity to perform high-risk missions without risking human lives. Beyond surveillance and reconnaissance, UAVs with jet propulsion and engagement capabilities are set to play roles similar to conventional jets. In various scenarios, military aircraft, drones, and UAVs face multiple threats while ground radar systems continuously monitor their positions. The interaction between these aerial platforms and radars causes temporal fluctuations in scattered echo power due to changes in aspect angle, impacting radar tracking accuracy. This study utilizes the potential radar cross-section (RCS) dynamics of an aircraft throughout its flight, using ground radar as a reference. Key factors influencing RCS include time, frequency, polarization, incident angle, physical geometry, and surface material, with a focus on the complex scattering geometry of the aircraft. The research evaluates the monostatic RCS case and examines the impact of attitude variations on RCS scintillation. Here, we present dynamic RCS modeling by examining the influence of flight dynamics on the RCS fluctuations of a UAV-sized aircraft. Dynamic RCS modeling is essential in creating a robust framework for operational analysis and developing effective countermeasure strategies, such as advanced active decoys. Especially in the cognitive radar concept, aircraft will desperately need more dynamic and adaptive active decoys. A methodology for calculating target aspect angles is proposed, using the aircraft's attitude and spherical position relative to the radar system. A realistic 6DoF (6 degrees of freedom) flight data time series generated by a commercial flight simulator is used to derive aircraft-to-radar aspect angles. By estimating aspect angles for a simulated complex flight trajectory, RCS scintillation throughout the flight is characterized. The study highlights the importance of maneuver parameters such as roll and pitch on the RCS measured at the radar by comparing datasets with and without these parameters. Significant differences were found, with a 32.44% difference in RCS data between full maneuver and no roll and pitch changes. Finally, proposed future research directions and insights are discussed. [ABSTRACT FROM AUTHOR]

Abstract To understand how ectotherms will respond to warming temperatures, we require information on thermal habitat quality at spatial resolutions and extents relevant to the organism. Measuring thermal habitat quality is either limited to small spatial extents, such as with ground‐based 3D operative temperature (Te) replicas, representing the temperature of the animal at equilibrium with its environment, or is based on microclimate derived from physical models that use land cover variables and downscale coarse climate data. We draw on aspects of both these approaches and test the ability of unoccupied aerial vehicle (UAV) data (optical RGB) to predict fine‐scale heterogeneity in sub‐canopy lizard (Anolis bicaorum) Te in tropical forest using random forest models. Anolis bicaorum is an endemic, critically endangered, species, facing significant threats of habitat loss and degradation, and work was conducted as part of a larger project. Our findings indicate that a model incorporating solely air temperature, measured at the centre of the 20 × 20 m plot, and ground‐based leaf area index (LAI) measurements, measured at directly above the 3D replica, predicted Te well. However, a model with air temperature and UAV‐derived canopy metrics performed slightly better with the added advantage of enabling the mapping of Te with continuous spatial extent at high spatial resolutions, across the whole of the UAV orthomosaic, allowing us to capture and map Te across the whole of the survey plot, rather than purely at 3D replica locations. Our work provides a feasible workflow to map sub‐canopy lizard Te in tropical environments at spatial scales relevant to the organism, and across continuous areas. This can be applied to other species and can represent species within the same community that have evolved a similar thermal niche. Such methods will be imperative in risk modelling of such species to anthropogenic land cover and climate change.

In recent years, the potential risks posed by easily moving objects have highlighted the need for intelligent surveillance systems in protected areas, primarily to ensure the safety of human lives. Among the most common of these objects are unmanned aerial vehicles (UAVs). Recent advances in deep learning techniques for recognizing audio signals have made these techniques effective in identifying moving or aerial objects, especially those powered by engines. And the growing deployment of UAVs has made their rapid recognition in various suspicious or unauthorized circumstances critical. Detecting suspicious drone flights, especially in restricted areas, remains a significant research challenge. It is vital to perform the task of determining their distance in order to quickly detect drones approaching people in such protected areas. Therefore, this paper aims to study the research question of recognizing UAV audio data from different distances. That is, recognizing drone audio at different distances was experimentally studied using Simple RNN, LSTM and GRU based deep learning models. The main objective of this study is based on finding one of the capable types of recurrent network for the task of recognizing UAV audio data at different distances. During the experimental study, the recognition abilities of Single-layer Simple RNN, LSTM and GRU recurrent network types were studied from two basic directions: with recognition accuracy curves and classification reports. As a result, LSTM and GRU based models showed high recognition ability for these types of audio signals. It was noted that UAVs can reliably predict distances greater than 10 meters based on the proposed deep learning architecture.

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) is crucial for effective assessment of large-scale civil infrastructure, as manual UAV control is time consuming and prone to mishaps. Autonomous navigation outdoors typically employs GPS signals to enable accurate localization and reduce long-term drifts. However, in many civil engineering applications, GPS signals are either poor or unavailable due to interference and/or multi-path effects. Current approaches for UAV localization in GPS-denied environments, track geometric features such as corners; however, these natural features can be misrepresented due to the presence of occlusions and/or image processing errors, resulting in significant localization errors that can grow with time. AprilTags can reduce localization errors, but installation on large-scale civil infrastructure can be challenging. Therefore, this paper proposes a framework that leverages the wealth of visual and geometric information encoded in a Graphics-Based Digital Twin (GBDT) of a target infrastructure to provide accurate localization of a UAV. The GBDT is comprised of a computer graphics model that faithfully represents a target structure's geometry, structural features, and visual textures. The visual details of the GBDT are exploited to design an object recognition algorithm that detects GBDTtags, which are distinctive objects (or a collection of components) on the target structure in advance; GBDTtags provide functionality similar to AprilTags. When the camera attached to a UAV detects one or more GBDTtags, the vertices of the GBDTtags are mapped from the image plane into the GBDT coordinate system, allowing for the UAV to be localized. The framework, termed herein as GBDTpose, is first validated numerically using Blender, Gazebo, and Mavros software-in-the-loop (SITL). Subsequently, field validation is carried out using the Kavita and Lalit Bahl Smart Bridge at the University of Illinois Urbana-Champaign (UIUC). Results show that localization in GPS-denied environments can be achieved with 5-50 cm accuracy without the need for physical markers being placed on the structure. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles, sometimes known as drones, are becoming increasingly common in communications networks, drawing interest from both business and academia. Their adaptable capacities and characteristics, which may enable various communication scenarios, are mostly to blame. Drones can improve accessibility, throughput, and service quality in numerous ways. Modern communication technologies have been merged with drones to serve the enormous communication needs beyond fifth-generation (B5G) and 6G networks. Based on these foundations, this work aimed to examine the possible abilities of multi-antenna drones in an indoor cognitive radio (CR) environment for uplink transmission. Using such an integrated solution, a group of multiple-antenna drones can connect with a CR BS by taking advantage of open channels without interfering with the core user's operations. This study presents an energy-efficient resource allocation method for multi-carrier NOMA CR-based systems to increase the system's overall energy efficiency while meeting several CR and NOMA limitations. Additionally, it uses the adaptive power channel assignment (APCA) protocol, which tries to reduce the amount of transmit power used by each drone while still adhering to all necessary quality of service and CR requirements. The proposed NOMA–APCA protocol can choose the minimally powered channel. Comparing the proposed NOMA–APCA protocol's performance against the traditional equal-power allocation allows us to understand better how well it performs. The suggested NOMA–APCA protocol dramatically enhances system performance, as shown by the simulation results regarding total transmit power. [ABSTRACT FROM AUTHOR]

A compact, low-profile, and ultra-wideband antenna is always desirable for airborne Unmanned Aerial Vehicles (UAV) applications. In this paper, an ultra-wideband monopole antenna is presented with the benefit of a compact and low-profile design structure. The antenna design enables efficient communication without interfering with the UAV's other components. The antenna is designed using a slotted-hexagonal-patch fed from a co-planar microstrip feed. The bow-tie-shaped slot along with a few other rectangular-shaped parasitic slots are loaded in the hexagonal patch for bandwidth enhancement. The coplanar feed with top ground offers minimum interference with the UAV structure. The non-conducting bottom plane allows seamless integration without causing any interference to the antenna. An equivalent circuit model (ECM) of the proposed antenna is also developed. The antenna structure is fabricated and the measured results are validated. A good agreement between the measured and simulated results is observed. The antenna covers an ultra-wideband ranging from 3 GHz to 13.5 GHz with a maximum gain of 4.0dBi. The size of the antenna is 0.24λ0 ×0.29λ0 ×0.01λ0. The developed antenna, therefore, demonstrates that it is a strong contender for ultra-wideband UAV-based applications. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and sophisticated deep learning (DL) models have made the application of artificial intelligence (AI) more popular. This has resulted in an increase in the number of attempts to improve high-resolution aerial monitoring using DL for identifying abnormal activity based on visual patterns in drone videos. The study introduces a one-class support vector machine (OC-SVM) oddity locator for low-altitude, limited-scope UAVs used for ethereal video surveillance. The primary goal is to improve UAV-based observation capabilities by identifying areas or things of interest without prior knowledge, hence improving tasks like queue control, vehicle following, and hazardous product identification. The framework makes use of OC-SVM because of its quick and lightweight setup, making it suitable for continuous operation on low-computational UAVs. It empowers the identification of several peculiarities necessary for low-elevation reconnaissance by using textural characteristics to recognise both large-scale and tiny structures. Examine the UAV mosaicking and change location (UMCD) dataset to demonstrate the effectiveness of the framework, which achieves excellent accuracy and outperforms traditional methods by about one fifth in a variety of metrics. The suggested model compares with current methods, demonstrating superior accuracy and performance in recognition of peculiarities. Evaluation metrics include F1-score, review, exactness, and accuracy. The model demonstrates that it always encounters an oddity with a review compromise of up to seven on ten, achieving complete accuracy. [ABSTRACT FROM AUTHOR]

Mountain search and rescue (MSAR) seeks to assist people in extreme remote environments. This method of emergency response often relies on crewed aircraft to perform aerial visual search. Many MSAR teams use low‐cost, consumer‐grade unmanned aerial vehicles (UAVs) to augment the crewed aircraft operations. These UAVs are primarily developed for aerial photography and lack many features critical (e.g., probability‐prioritized coverage path planning) to support MSAR operations. As a result, UAVs are underutilized in MSAR. A case study of a recent mountain search and recovery scenario that did not use, but may have benefited from, UAVs is provided. An overview of the mission is augmented with a subject matter expert‐informed analysis of how the mission may have benefited from current UAV technology. Lastly, mission relevant requirements are presented along with a discussion of how future UAV development can seek to bridge the gap between state‐of‐the‐art robotics and MSAR. [ABSTRACT FROM AUTHOR]

Remote survey is becoming increasingly common for the mining industry, so studying the choice of a UAV device is a very relevant issue, which will help increase the productivity of the survey service. The aim of the study is to study UAV species, compare species, and choose the optimal one based on the results of the study. Considering the above-mentioned, there is a question of choosing the type of UAV to conduct surveys in the mining industry. To date, there is no clear comparison and description of the predominant types of UAVs for surveying. The choice of a certain type of UAV for surveying raises a number of questions related to the type of work performed, weather conditions, and the terrain of the survey. The main research methods are literature analysis, factor analysis method, analysis of UAV types for performing survey using UAVs. The above helped to justify the best devices for conducting surveying. The work carried out made it possible to determine the optimal UAV device for carrying out these works.

The article presents the results of applying an improved method for automatic segmentation of RGB imagery obtained using consumer-grade UAVs, based on the Mask R-CNN neural network architecture. Blocks for the preparation and post-processing of raster and vector files have been developed for working with geospatial data. The model was trained on 7000 crowns identified in pine forest of automorphic habitats in the mixed coniferous-broadleaf forest subzone. Training was carried out using cross-validation. Additional data of 1337 crowns were used for verification. During the sequential filtering by area, confidence level, and duplicate segments, the quality of the final segmentation results improved for all age groups of pine forests. The final average precision is 0.87, recall – 0.81, F1-score – 0.83. The results demonstrate the high efficiency of the filtering algorithm in reducing segment redundancy and increasing data reliability. The Mask R-CNN automatic segmentation method is an effective tool for analyzing the characteristics of pine canopies using RGB imagery from UAV surveys. It is capable of replicating the results of visual interpretation with high accuracy. This method particularly advantageous for scaling studies to large areas where manual delineation becomes labor-intensive.

The objective of the undertaken research was to characterize and evaluate the impact of weather and lighting conditions on recording terrain anomalies in the photographs obtained during a UAV photogrammetric flight. The present work describes the use and capabilities of the UAV in the mapping of photo acquisition conditions similar to those performed during inspection flights with the use of a manned helicopter equipped with a hyperspectral camera, in the target range of visible light. The research was conducted in the southern part of Poland (between Gliwice and Katowice), where 7 routes were selected, differing from one another in terms of terrain anomalies (buildings, types of land areas, vehicles, vegetation). In the studies, which involved photogrammetric flights performed using a UAV, different seasons and times of day as well as changes in light intensity were taken into account. The flight specification was based on the main parameters with the following assumptions: taking only perpendicular (nadir) RGB photographs, flight altitude 120 m AGL, strip width 160 m, GSD ≤0.04 m and overlap ≥83%. The analysis of the photographic material obtained made it possible to correct the catalog of anomalies defined previously, since the recognition of some objects is very difficult, being usually below the orthophotomap resolution. When making and evaluating orthophotomaps, problems with mapping the shape of objects near the edges of the frame were found. When a 12 mm lens is used, these distortions are significant. It was decided that for the purpose of generating training data from orthophotomaps, only the fragments containing objects which shape would be mapped in accordance with the real one would be used. Thus, the effective width of orthophotomaps obtained from simulated flights will be approximately 100 m.

As a result of the "Arab Spring" and the transformation of the global world order, the MENA region, and the relations with North African countries therein, are on the rise both for the international and regional actors including Russia and China as well as Saudi Arabia, Egypt, Israel, Iran and Turkey, respectively. Examining Turkey’s expansive foreign policy, we can also get an idea of how Ankara wishes to increase its sphere of interest in the wider region, particularly in the Eastern Mediterranean region, by supporting a North African country. In recent years, Turkey has become one of the best-known and most important global exporters of military unmanned aerial vehicles (UAVs), commonly known as drones, in the world military equipment market. Turkish drone development and warfare has introduced many innovative military operational concepts that have achieved great success in the conflicts of recent years. The study examines how 'new types' of technologies such as UAVs can shape regional power dynamics through the case study of the Libyan civil war and drone warfare.

Land use/land cover (LULC) is a fundamental concept of the Earth’s system intimately connected to many phases of the human and physical environment. LULC mappings has been recently revolutionized by the use of high-resolution imagery from unmanned aerial vehicles (UAVs). The present study proposes an innovative approach for obtaining LULC maps using consumer-grade UAV imagery combined with two machine learning classification techniques, namely RF and SVM. The methodology presented herein is tested at a Mediterranean agricultural site located in Greece. The emphasis has been placed on the use of a commercially available, low-cost RGB camera which is a typical consumer’s option available today almost worldwide. The results evidenced the capability of the SVM when combined with low-cost UAV data in obtaining LULC maps at very high spatial resolution. Such information can be of practical value to both farmers and decision-makers in reaching the most appropriate decisions in this regard.

Interest in Unmanned Aerial Vehicles (UAVs) has been increasingly growing in recent years, especially for purposes other than those for which they were initially used (civil and military purposes). Currently, in fact, they are used for advanced monitoring and control purposes, for 3D reconstructions of the territory and cultural heritage, and for freight transport. The problem in using these systems consists of the limited flight autonomy. In fact, commercially used drones, today, are sold with a set of batteries of limited duration which do not allow flights over large areas and, therefore, detailed surveys. The present work seeks to overcome these limitations by proposing an intelligent automatic charging system (Intelligent Charging Network) created using PC Engines Alix and an experimental drone prototype using a Raspberry Pi 3 and a Navio 2 module. At the same time, an efficient Intelligent Charging Network–drone communication system and a data transmission system are proposed, which allow images acquired by the drone to be transferred directly to the server used for data storage for their subsequent processing as well as the transmission of the flight plan from the QGroundControl application to the drone. The proposed system represents technological innovation in the field of drones with potential future developments linked to the implementation of sustainable drones. [ABSTRACT FROM AUTHOR]

With traditional survey methods such as ground-based counting, camera trapping, and aerial surveys, monitoring wild deer in Nepal's Chitwan National Park is challenging due to the dense tall vegetation that often conceals them. However, the thermal signatures of wild deer contrast sharply against the cooler background, facilitating detection via thermal imaging. This study explores the use of Unmanned Aerial Vehicles (UAVs) equipped with thermal cameras to monitor wild deer. A large volume of images can be captured, where wild animals appear as small objects. Reviewing these images manually is labor-intensive and time-consuming. To address this, we developed an object detection model using modified Faster R-CNN that automatically identifies small deer objects in the thermal images. Instead of VGG 16, the Feature Pyramid Network and Residual Neural Network (ResNet152) were employed to enhance feature extraction from these images, constructing multi-scale feature maps that enrich the feature information for small object detection. Customized anchor boxes were also designed to handle the wide variation in object scale and aspect ratios. To improve species identification accuracy for small Regions of Interest, a multi-scale aggregation method was proposed, which fuses features from multiple feature maps via Multi-scale RoIAlign pooling. The model proposed in this paper was evaluated by the COCO metrics. The experimental results obtained for the detection of deer and other animals in UAV thermal images with the resolution of 640 × 512 , showing mean Average Precision of 92.3% for all objects, 78.9% for small objects, 94.6% for medium objects, and 95.8% for large objects. This research provides a valuable means for detecting small objects in thermal images and contributes significantly to the field of wildlife monitoring. [ABSTRACT FROM AUTHOR]

In the task of power line inspection, Unmanned Aerial Vehicles (UAVs) are frequently used for capturing images. With the rapid advancement of sensor technology, the spatial, radiometric, and spectral resolutions of UAV images are constantly improving, leading to an increased storage requirement for individual images. Given that UAVs usually operate with limited computational resources, transmission capability and storage space, there are significant challenges in image compression, storage and transmission. This underscores the importance of a high-performance image compression technique. To solve the above problem, we unveil a compression strategy for images that have been acquired through learning utilizing discrete Gaussian mixture-based probability distributions to increase the efficiency of image compression and the fidelity of reconstruction. In addition, to speed up decoding, we employ a parallel context model, which facilitates decoding in a highly parallel manner. Experimental evidence indicates that our approach attains performance that is at the forefront of the field while significantly expediting the decoding process (speeding up the decoding process by more than 49.78%) in our experiments, outpacing traditional coding standards and existing learned compression approaches by 5.75 dB and 1.23 dB in PSNR. [ABSTRACT FROM AUTHOR]

To deal with the problem of complex changes of topology in UAVs (Unmanned Aerial Vehicles) network and the increasing need of data transmission, the combination of self-organizing and SDN (Software Defined Network) is a usefull paradigm. However, the control node's position in SDN will influence the transmission of command between controller and relay nodes, thus determining the quality of communication. In this context, we present a network construction, named SDN-FANET (Flying Ad-Hoc Network). We formulate the problem as a classical network center location model. To solve this problem, we propose a particle swarm algorithm based on the graph theory. Simulation results show the algorithm performs better in terms of accuracy and stability, compared with several benchmarks. Particularly, with the initial particle position optimized, the proposed algorithm converges faster. [ABSTRACT FROM AUTHOR]

Due to the potential for airborne radar to capture incomplete observational information regarding unmanned aerial vehicle (UAV) trajectories, this study introduces a novel approach called Node-former, which integrates neural ordinary differential equations (NODEs) and the Informer framework. The proposed method exhibits high accuracy in trajectory prediction, even in scenarios with prolonged data interruptions. Initially, data outside the acceptable error range are discarded to mitigate the impact of interruptions on prediction accuracy. Subsequently, to address the irregular sampling caused by data elimination, NODEs are utilized to transform computational interpolation into an initial value problem (IPV), thus preserving informative features. Furthermore, this study enhances the Informer's encoder through the utilization of time-series prior knowledge and introduces an ODE solver as the decoder to mitigate fluctuations in the original decoder's output. This approach not only accelerates feature extraction for long sequence data, but also ensures smooth and robust output values. Experimental results demonstrate the superior performance of Node-former in trajectory prediction with interrupted data compared to traditional algorithms. [ABSTRACT FROM AUTHOR]

The characteristics of grain-size distribution on the surface of gravel bars have important implications for riverbed development and fish spawning. In this study, an unmanned aerial vehicle was used to sample 204 sites on the surface of a gravel bar in the upper reaches of the Yangtze River, China. Photogrammetry was used to generate digital elevation model data for the bar. Based on the calculation results of a two-dimensional river hydrodynamic model, clear distribution maps of the flow and grain-size fields were obtained. In addition, the correlation between the hydrodynamic and grain-size indicators was discussed, and a relational equation between the Froude number and median grain size was derived. Finally, the effect of topographic changes on the grain-size distribution was analyzed. The results showed that the grain size at the head of the gravel bar was larger than that at the tail, influenced by the scouring of the flow; coarsening of the gravel at the edge of the bar bend was also evident. A positive correlation was found between , flow velocity, and the Froude number. The sorting coefficient, , exhibited a negative correlation with the flow velocity and Froude number. A positive correlation was found between grain size variability indicator and topographic variability indicator . The variability in the grain-size distribution was the highest near areas with more drastic topographic variations. The bar surfaces exhibited a pattern of coarse-grained tops and fine-grained pools. The results of this study contribute to further understanding the geomorphology of gravel bars and may help investigate the spawning sites of organisms on the bar surface sensitive to bottom particle size. [ABSTRACT FROM AUTHOR]

This study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)‐based simultaneous localization and mapping (SLAM) in outdoor environments and to discuss challenges and limitations in this field. A literature search was conducted in three online databases (Web of Science, Scopus, and IEEE) for articles published before October 2022 related to UAV‐based SLAM. A scoping review was carried out to identify the key concepts and applications, and discover research gaps in the use of algorithm‐oriented and task‐oriented, open‐source studies. A total of 97 studies met the criteria after conducting a two‐step screening by a systematic method followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. Among eligible studies, 97 were classified into two main categories: algorithm‐oriented studies and task‐oriented studies. The analysis of the literature revealed that the majority of the studies were focused on the development and implementation of new algorithms and algorithms. This review highlights the significance and diversity of sensors utilized in UAVs in different tasks and applications scenarios that employ different types of sensors. The evaluation method is able to show the real results and performance of the new algorithms in the target scenarios compared with the evaluation method by the public data set and simulation platform. [ABSTRACT FROM AUTHOR]

We propose a novel framework for achieving precision landing in drone services. The proposed framework consists of two distinct decoupled modules, each designed to address a specific aspect of landing accuracy. The first module is concerned with intrinsic errors, where new error models are introduced. This includes a spherical error model that takes into account the orientation of the drone. Additionally, we propose a live position correction algorithm that employs the error models to correct for intrinsic errors in real time. The second module focuses on external wind forces and presents an aerodynamics model with wind generation to simulate the drone's physical environment. We utilize reinforcement learning to train the drone in simulation with the goal of landing precisely under dynamic wind conditions. Experimental results, conducted through simulations and validated in the physical world, demonstrate that our proposed framework significantly increases landing accuracy while maintaining a low onboard computational cost. [ABSTRACT FROM AUTHOR]

Effective management of bark beetle infestations requires prompt detection of attacked trees. Early attack is also called green attack, since tree foliage does not yet show any visible signs of tree decline. In several bark beetle systems, including mountain pine beetle and European spruce bark beetle, unpiloted aerial vehicle (UAV)-based remote sensing has successfully detected early attack. We explore the utility of remote sensing for early attack detection of southern pine beetle (SPB; Dendroctonus frontalis Zimm.), paired with detailed ground surveys to link tree decline symptoms with SPB life stages within the tree. In three of the northernmost SPB outbreaks in 2022 (Long Island, New York), we conducted ground surveys every two weeks throughout the growing season and collected UAV-based multispectral imagery in July 2022. Ground data revealed that SPB-attacked pitch pines (Pinus rigida Mill.) generally maintained green foliage until SPB pupation occurred within the bole. This tree decline behavior illustrates the need for early attack detection tools, like multispectral imagery, in the beetle's northern range. Balanced random forest classification achieved, on average, 78.8% overall accuracy and identified our class of interest, SPB early attack, with 68.3% producer's accuracy and 72.1% user's accuracy. After removing the deciduous trees and just mapping the pine, the overall accuracy, on average, was 76.9% while the producer's accuracy and the user's accuracy both increased for the SPB early attack class. Our results demonstrate the utility of multispectral remote sensing in assessing SPB outbreaks, and we discuss possible improvements to our protocol. This is the first remote sensing study of SPB early attack in almost 60 years, and the first using a UAV in the SPB literature. [ABSTRACT FROM AUTHOR]