Search Results (269)

Although Unmanned Aircraft Systems (UASs) offer valuable services, they also introduce certain risks—particularly to individuals on the ground—referred to as third-party risk (TPR). In general, ground-level TPR tends to rise alongside the density of people who might use these services, leading current regulations to heavily restrict UAS operations in populated regions. These operational constraints hinder the ability to gather safety insights through the conventional method of learning from real-world incidents. To address this, a promising alternative is to use dynamic simulations that model UAS collisions with humans, providing critical data to inform safer UAS design. In the automotive industry, the modelling and simulation of car crashes has been well developed. For small UAS, this dynamical modelling and simulation approach has focused on the effect of the varying weight and kinetic energy of the UAS, as well as the geometry and location of the impact on a human body. The objective of this research is to quantify the effects of UAS material and shape on-ground TPR through dynamical modelling and simulation. To accomplish this objective, five camera–drone types are selected that have similar weights, although they differ in terms of airframe structure and materials. For each of these camera–drones, a dynamical model is developed to simulate impact, with a biomechanical human body model validated for impact. The injury levels and probability of fatality (PoF) results, obtained through conducting simulations with these integrated dynamical models, are significantly different for the camera–drone types. For the uncontrolled vertical impact of a 1.2 kg UAS at 18 m/s on a model of a human head, differences in UAS designs even yield an order in magnitude difference in PoF values. Moreover, the highest PoF value is a factor of 2 lower than the parametric PoF models used in standing regulation. In the same scenario for UAS types with a weight of 0.4 kg, differences in UAS designs even considered yield an order when regarding the magnitude difference in PoF values. These findings confirm that the material and shape design of a UAS plays an important role in reducing ground TPR, and that these effects can be addressed by using dynamical modelling and simulation during UAS design. Full article

Environmental assumptions, which are expectations about a system’s operating context, play a critical yet often underexplored role in the emergence of requirements technical debt (RTD). When these assumptions are incorrect, incomplete, or evolve over time, they can compromise the validity of system requirements and lead to costly rework in later stages of development. This paper investigates how environmental assumptions influence the identification of RTD through the analysis of a real-world case study in the domain of small uncrewed aerial systems (sUASs). A structured qualitative analysis of safety-related requirements and their associated assumptions was conducted to examine how deviations in these assumptions can introduce various forms of RTD. This work addresses a gap in the literature by explicitly focusing on the role of environmental assumptions in RTD identification. A classification framework is proposed, highlighting five distinct types of assumption-driven RTD. This framework serves as a foundation for supporting early detection of debt and improving the sustainability and resilience of software-intensive systems. Full article

The accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. This study employed high-resolution imagery captured by unmanned aircraft systems (UASs) throughout the shrub growing season and explored the effectiveness of transfer learning for both semantic segmentation (Attention U-Net) and instance segmentation (Mask R-CNN). It utilized pre-trained model weights from two previous studies that originally focused on tree crown delineation to improve shrub crown segmentation in non-forested areas. Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. However, fine-tuning the pre-trained models by unfreezing additional layers improved segmentation accuracy by around 30%. Fine-tuned pre-trained models show limited sensitivity to shrubs in the early growing season (April to June) and improved performance when shrub crowns become more spectrally unique in late summer (July to September). These findings highlight the value of combining pre-trained models with targeted fine-tuning to enhance model adaptability in complex remote sensing environments. The proposed framework demonstrates a scalable solution for ecological monitoring in data-scarce regions, supporting informed land management decisions and advancing the use of deep learning for long-term environmental monitoring. Full article

This study shows a comprehensive simulation to assess and enhance the throughput capacity of unmanned air system vertiports, one of the most essential elements of urban air mobility ecosystems. The framework integrates dynamic grid-based spatial management, probabilistic mission duration algorithms, and EASA-compliant operational protocols to address the infrastructural and logistical demands of high-density UAS operations. It was focused on two use cases—high-frequency food delivery utilizing small UASs and extended-range package logistics with larger UASs—and the model incorporates adaptive vertiport zoning strategies, segregating operations into dedicated sectors for battery charging, swapping, and cargo handling to enable parallel processing and mitigate congestion. The simulation evaluates critical variables such as vertiport dimensions, UAS fleet composition, and mission duration ranges while emphasizing scalability, safety, and compliance with evolving regulatory standards. By examining the interplay between infrastructure design, operational workflows, and resource allocation, the research provides a versatile tool for urban planners and policymakers to optimize vertiport layouts and traffic management protocols. Its modular architecture supports future extensions. This work underscores the necessity of adaptive, data-driven planning to harmonize vertiport functionality with the dynamic demands of urban air mobility, ensuring interoperability, safety, and long-term scalability. Full article

There is a growing need for accurate bathymetric mapping in many water-related scientific disciplines. Accurate and up-to-date data are essential for both shallow and deep areas. In this article, methods and techniques for shallow water mapping have been collected and described based on the available scientific literature. The paper focuses on three survey technologies, Unmanned Aerial Systems (UASs), Airborne Bathymetry (AB), and Satellite-Derived Bathymetry (SDB), with multimedia photogrammetry and LiDAR-based approaches as processing methods. The most popular and/or state-of-the-art image and LiDAR data correction techniques are characterized. To develop good practice in shallow water mapping, the authors present examples of data acquired by all the mentioned technologies with selected correction methods. Full article

In mountainous citrus orchards, the application of conventional ground sprayers for the control of citrus red mite (Panonychus citri) is often constrained by complex terrain and low operational efficiency. The Unmanned Aerial Spraying System (UASS), due to its low-altitude, low-volume, and high-maneuverability characteristics, has emerged as a promising alternative for pest management in such challenging environments. To evaluate the spray performance and field efficacy of different UASS types in controlling P. citri, five representative UASS models (JX25, DP, T1000, E-A2021, and T20), four mainstream pesticide formulations, and four novel tank-mix adjuvants were systematically assessed in a field experiment conducted in a typical hilly citrus orchard. The results showed that T20 delivered the best overall spray deposition, with upper canopy coverage reaching 10.63%, a deposition of 3.01 μg/cm², and the highest pesticide utilization (43.2%). E-A2021, equipped with a centrifugal nozzle, produced the finest droplets and highest droplet density (120.3–151.4 deposits/cm²), but its deposition and coverage were lowest due to drift. Nonetheless, it exhibited superior penetration (dIPR 72.3%, dDPR 73.5%), facilitating internal canopy coverage. T1000, operating at higher flight parameters, had the weakest deposition. Formulation type had a limited impact, with microemulsions (MEs) outperforming emulsifiable concentrates (ECs) and suspension concentrates (SCs). All adjuvants improved spray metrics, especially Yimanchu and Silwet, which enhanced pesticide utilization to 46.8% and 46.4% for E-A2021 and DP, respectively. Adjuvant use increased utilization by 4.6–11.9%, but also raised ground losses by 1.5–4.2%, except for Yimanchu, which reduced ground loss by 2.3%. In terms of control effect, the rapid efficacy (1–7 days after application, DAA) of UASS spraying was slightly lower than that of ground sprayers—electric spray gun (ESG), while its residual efficacy (14–25 DAA) was slightly higher. The addition of adjuvants improved both rapid and residual efficacy, making it comparable to or even better than ESG. E-A2021 with 5% abamectin·etoxazole ME (5A·E) and Yimanchu achieved 97.4% efficacy at 25 DAA. Among UASSs, T20 showed the rapid control, while E-A2021 outperformed JX25 and T1000 due to finer droplets effectively targeting P. citri. In residual control (14–25 DAA), JX25 with 45% bifenazate·etoxazole SC (45B·E) was most effective, followed by T20. 5A·E and 45B·E showed better residual efficacy than abamectin-based formulations, which declined more rapidly. Adjuvants significantly extended control duration, with Yimanchu performing best. This study demonstrates that with optimized spraying parameters, nozzle types, and adjuvants, UASSs can match or surpass ground spraying in P. citri control in hilly citrus orchards, providing valuable guidance for precision pesticide application in complex terrain. Full article

Background/Objectives: Intrarenal pressure (IRP) plays a critical role in ensuring the safety of retrograde intrarenal surgery (RIRS), as elevated IRP is associated with complications such as pyelovenous backflow, infection, and renal injury. LithoVue™ Elite (LVE) is the first commercially available ureteroscope (URS) capable of providing real-time IRP measurements. Conventionally, IRP has been measured via a percutaneous nephrostomy catheter (PNC), which may not accurately reflect dynamic changes during endoscopic procedures. Recently, small ureteral access sheaths (UASs) have been increasingly used to minimize ureteral injury risk. This study aimed (1) to assess the accuracy of LVE compared with that of IRP measured by a PNC and (2) to evaluate appropriate irrigation settings suitable for small UASs using porcine kidney models and LVE. Methods: An 11/13-Fr UAS and a 10/12-Fr UAS were inserted into each model, and an automatic irrigation pump (AIP) and hand pumping (HP) with a 20-cc syringe were used. IRP was measured at various LVE tip positions (renal pelvis and upper, middle, and lower calyces) with different irrigation settings, repeated four times in each. Simultaneously, the IRP via the PNC located in the upper calyx and renal pelvis was measured. Results: LVE showed high concordance with the PNC across the upper, middle, and lower calyces (p > 0.05). However, at the renal pelvis, LVE measured IRP values that were significantly higher than the PNC by a mean of 1.93 ± 0.93 mmHg (p < 0.001). For the 11/13-Fr UAS, the IRP remained below 30 mmHg across all irrigation settings with an AIP and HP. In contrast, the 10/12-Fr UAS maintained 30 mmHg only with limited AIP settings, while HP resulted in high IRP, exceeding 100 mmHg at any location. Intergroup comparisons demonstrated that the IRP with the 10/12-Fr UAS was significantly higher than that with the 11/13-Fr UAS at any irrigation pressure setting across all URS tip positions (p < 0.05). Intragroup comparisons indicated a significant pressure difference between the upper, middle, and lower calyces and the renal pelvis in both models at all irrigation settings (p < 0.05). Conclusions: LVE provided accurate IRP measurements compared to the PNC. The IRP was significantly influenced by UAS size, irrigation setting, and URS tip position. When using small UASs, selecting appropriate irrigation settings is essential to maintain the safe threshold. Full article

Unmanned Aircraft Systems (UASs) have undergone rapid development over recent years, but have also became vulnerable to security attacks and the volatile external environment. Ensuring that the performance of UASs is safe and secure no matter how the environment changes is challenging. Runtime Verification (RV) is a lightweight formal verification technique that could be used to monitor UAS performance to guarantee safety and security, while reactive synthesis is a methodology for automatically synthesizing a correct-by-construction controller. This paper addresses the problem of the generation and design of a secure UAS controller by introducing a unified monitor and controller synthesis method based on RV and reactive synthesis. First, we introduce a novel methodological framework, in which RV monitors is applied to guarantee various UAS properties, with the reactive controller mainly focusing on the handling of tasks. Then, we propose a specification pattern to represent different UAS properties and generate RV monitors. In addition, a detailed priority-based scheduling method to schedule monitor and controller events is proposed. Furthermore, we design two methods based on specification generation and re-synthesis to solve the problem of task generation using metrics for reactive synthesis. Then, to facilitate users using our method to design secure UAS controllers more efficiently, we develop a domain-specific language (UAS-DL) for modeling UASs. Finally, we use F Prime to implement our method and conduct experiments on a joint simulation platform. The experimental results show that our method can generate secure UAS controllers, guarantee greater UAS safety and security, and require less synthesis time. Full article

The increasing adoption of unmanned aerial spraying services presents a transformative opportunity for precision agriculture, enabling targeted and efficient application of plant protection products. However, ensuring their safe and regulated integration into European farming requires a comprehensive understanding of exposure risks for operators, bystanders, and residents. Expanding scientific knowledge in this domain is crucial for establishing a dedicated risk assessment framework for unmanned aerial spraying applications. This study evaluates dermal exposure levels among operators, residents, and bystanders, comparing unmanned aerial spraying applications with conventional vehicle-based and manual handheld spraying methods based on existing risk assessment and exposure models. Results suggest that unmanned aerial sprayers reduce dermal exposure for pilots, residents, and bystanders due to their remote operation and reduced drift compared to conventional spraying methods. However, critical exposure points arise during mixing, loading, and auxiliary tasks, where dermal exposure levels exceed model estimates. These elevated exposure levels are attributed to the higher frequency and concentrated handling of plant protection products in unmanned aerial spraying operations compared to traditional spraying methods. These findings highlight the need for targeted risk mitigation strategies to enhance operator safety, such as implementing closed transfer systems, optimized handling protocols, and specialized protective equipment. Full article

Wheat canopy height is an important parameter for monitoring growth status. Accurately predicting the wheat canopy height can improve field management efficiency and optimize fertilization and irrigation. Changes in the growth characteristics of wheat at different growth stages affect the canopy structure, leading to changes in the quality of the LiDAR point cloud (e.g., lower density, more noise points). Multispectral data can capture these changes in the crop canopy and provide more information about the growth status of wheat. Therefore, a method is proposed that fuses LiDAR point cloud features and multispectral feature parameters to estimate the canopy height of winter wheat. Low-altitude unmanned aerial systems (UASs) equipped with LiDAR and multispectral cameras were used to collect point cloud and multispectral data from experimental winter wheat fields during three key growth stages: green-up (GUS), jointing (JS), and booting (BS). Analysis of variance, variance inflation factor, and Pearson correlation analysis were employed to extract point cloud features and multispectral feature parameters significantly correlated with the canopy height. Four wheat canopy height estimation models were constructed based on the Optuna-optimized RF (OP-RF), Elastic Net regression, Extreme Gradient Boosting, and Support Vector Regression models. The model training results showed that the OP-RF model provided the best performance across all three growth stages of wheat. The coefficient of determination values were 0.921, 0.936, and 0.842 at the GUS, JS, and BS, respectively. The root mean square error values were 0.009 m, 0.016 m, and 0.015 m. The mean absolute error values were 0.006 m, 0.011 m, and 0.011 m, respectively. At the same time, it was obtained that the estimation results of fusing point cloud features and multispectral feature parameters were better than the estimation results of a single type of feature parameters. The results meet the requirements for canopy height prediction. These results demonstrate that the fusion of point cloud features and multispectral parameters can improve the accuracy of crop canopy height monitoring. The method provides a valuable method for the remote sensing monitoring of phenotypic information of low and densely planted crops and also provides important data support for crop growth assessment and field management. Full article

A novel mechanism for seamless morphing trailing edge flaps is presented in this paper. This bio-inspired morphing concept is derived from an elephant’s trunk and is called the Elephant Trunk Mechanism (ETM). The structural flexibility of an elephant’s trunk and its ability to perform various types of deformations make it a promising choice in morphing technology for increasing the performance of continuous and smooth downward bending deformation at a trailing edge. This mechanism consists of a number of tooth-like elements attached to a solid wing box; the contractions of these tooth-like elements by external actuation forces change the trailing edge shape in the downwards direction. The main actuation forces are applied through wire ropes passing through tooth-like elements to generate the desired contractions on the flexible teeth. A static structural analysis using the Finite Element Method (FEM) is performed to examine this novel morphing concept and ensure its structural feasibility and stability. Topology optimization is also performed to find the optimum configuration with the objective of reducing the structural weight. The optimized mechanism is then attached to the flap section of a UAS-S45 wing. Finally, a skin analysis is performed to find its optimum skin material, which corresponds to the requirements of the morphing flap. The results of structural analysis and topology optimization reveal the reliability and stability of the proposed mechanism for application in the Seamless Morphing Trailing Edge (SMTE) flap. The optimization results led to significant improvements in the structural parameters, in addition to the desired weight reduction. The ETM maximum vertical displacement increased by 8.6%, while the von Mises stress decreased by 10.43%. Furthermore, the factor of safety improved from 1.3 to 1.5, thus indicating a safer design. The mass of the structure was reduced by 35.5%, achieving the primary goal of topology optimization. Full article

With the rapid development of precision agriculture spraying technology, the evaluation and detection of deposition effects have gradually become research hotspots. Rhodamine-B is often used for the quantitative elution detection of droplet deposition due to its fluorescent properties. In contrast, the method of detecting droplet deposition using water-sensitive paper (WSP) is simple to operate. However, it often faces issues with measurement accuracy due to factors such as irregular droplet diffusion and the excessive hydrophilicity of the sampler material. Based on this, the study proposes a method for correcting WSP deposition assays by using the quantitative elution of chemical colorants as a baseline reference. Experiments were conducted using a DJI T30 unmanned aerial spraying system (UASS) as the spray carrier, with four types of samplers—Ginkgo biloba leaves (GBL), Malus spectabilis leaves (MS), polyvinyl chloride (PVC) cards, and WSP—fixed at nine different angles. The deposition amounts of five concentrations of Rhodamine-B stain sprayed on the samplers were then compared. The results indicate that the correction factor can be influenced by various factors, including the environment, the type of sampler, the concentration of the sprayed colorant, and the angle of the sampler. Deposition correction coefficients for WSP with different samplers were determined to be in the ranges of 1.507 to 1.547 (WSP–GBL), 1.471 to 1.478 (WSP–MS), and 1.312 to 1.391 (WSP–PVC), respectively. The study confirmed the feasibility of the proposed fluorescence-corrected aerial spray droplet deposition method, which retains the advantages of two existing typical deposition determination methods. Additionally, pre-tests should be tailored to experimental conditions, and the choice of colorant concentration should be carefully considered. Full article

This paper discusses a precise relative navigation and separation assurance system based on the exchange of Global Navigation Satellite (GNSS) measurements via new message types for Automatic Dependent Surveillance–Light (ADS-L). A measurement-based ADS-L implementation, which transmits raw measurements from the GNSS receiver rather than aircraft state vectors and performance parameters, is utilized to improve surveillance performance and add integrity to the surveillance solution. Furthermore, recent flight tests with one manned aircraft and two UASs are discussed, and the benefits of using GNSS measurement-based ADS-L data for separation assurance as opposed to traditional methods are reviewed. Full article

Thermal heterogeneity of rivers is essential to support freshwater biodiversity. Salmon behaviorally thermoregulate by moving from patches of warm water to cold water. When implementing river restoration projects, it is essential to monitor changes in temperature and thermal heterogeneity through time to assess the impacts to a river’s thermal regime. Lightweight sensors that record both thermal infrared (TIR) and multispectral data carried via unoccupied aircraft systems (UASs) present an opportunity to monitor temperature variations at high spatial (<0.5 m) and temporal resolution, facilitating the detection of the small patches of varying temperatures salmon require. Here, we present methods to classify and filter visible wetted area, including a novel procedure to measure canopy cover, and extract and correct radiant surface water temperature to evaluate changes in the variability of stream temperature pre- and post-restoration followed by a high-intensity fire in a section of the river corridor of the South Fork McKenzie River, Oregon. We used a simple linear model to correct the TIR data by imaging a water bath where the temperature increased from 9.5 to 33.4 °C. The resulting model reduced the mean absolute error from 1.62 to 0.35 °C. We applied this correction to TIR-measured temperatures of wetted cells classified using NDWI imagery acquired in the field. We found warmer conditions (+2.6 °C) after restoration (p < 0.001) and median absolute deviation for pre-restoration (0.30) to be less than both that of post-restoration (0.85) and post-fire (0.79) orthomosaics. In addition, there was statistically significant evidence to support the hypothesis of shifts in temperature distributions pre- and post-restoration (KS test 2009 vs. 2019, p < 0.001, D = 0.99; KS test 2019 vs. 2021, p < 0.001, D = 0.10). Moreover, we used a Generalized Additive Model (GAM) that included spatial and environmental predictors (i.e., canopy cover calculated from multispectral NDVI and photogrammetrically derived digital elevation model) to model TIR temperature from a transect along the main river channel. This model explained 89% of the deviance, and the predictor variables showed statistical significance. Collectively, our study underscored the potential of a multispectral/TIR sensor to assess thermal heterogeneity in large and complex river systems. Full article

This paper presents a methodology for training a Deep Learning model aimed at flight management tasks in a fixed-wing unmanned aerial vehicle (UAV), specifically autopilot control and GPS prediction. In this formulation, sensor data and the most recent GPS signal are first processed by an LSTM to produce an initial coordinate prediction. This preliminary estimate is then merged with additional sensor inputs and passed to an MLP, which replaces the conventional autopilot algorithm by generating the control commands for real-time navigation. The approach is particularly valuable in scenarios where the aircraft must follow a predetermined route—such as surveillance operations—or maintain a remote ground link under varying GPS availability. The study focuses on Class I UAVs; however, the proposed methodology can be adapted to larger classes (II and III) by adjusting sensor configurations and network parameters. To collect training data, a small fixed-wing aircraft was instrumented to record kinematic and control inputs, which then served as inputs to the neural network. Despite the limited sensor suite and the use of an open-source flight controller (SpeedyBee), the flexibility of the proposed approach allows for easy adaptation to more complex UAVs equipped with additional sensors, potentially improving prediction accuracy. The performance of the neural network, implemented in PyTorch, was evaluated by comparing its predicted data with actual flight logs. In addition, the method has been shown to be robust to both short and prolonged GPS outages, as it relies on waypoint-based navigation along previously explored routes, ensuring reliable performance in known operational contexts. Full article