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Drones traditionally rely on satellite signals for positioning and altitude. However, when in a special denial environment, satellite communication is interrupted, and the traditional positioning and height determination methods face challenges. We made a dataset at the height of 80-200 m and proposed a multi-scale input network. The positioning index RDS achieved 76.3 points, and the positioning accuracy within 20 m was 81.7%. This paper proposes a method to judge the height by image alone, without the support of other sensor data. One height judgment can be made per single image. Based on the UAV image-satellite image matching positioning technology, by calculating the actual area represented by the UAV image in real space, combined with the fixed parameters of the optical camera, the actual height of the UAV flight is calculated, which is 80-200 m, and the relative error rate of height is 18.1%.

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The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat of unconventional drone and swarm attacks. In order to deal with threats, drones need to be classified by intercepting their Radio Frequency (RF) signals. With the arrival of Sixth Generation (6G) networks, it is required to develop sophisticated methods to properly categorize drone signals in order to achieve optimal resource sharing, high-security levels, and mobility management. However, deep ensemble learning has not been investigated properly in the case of 6G. It is anticipated that it will incorporate drone-based BTS and cellular networks that, in one way or another, may be subjected to jamming, intentional interferences, or other dangers from unauthorized UAVs. Thus, this study is conducted based on Radio Frequency Fingerprinting (RFF) of drones identified to detect unauthorized ones so that proper actions can be taken to protect the network's security and integrity. This paper proposes a novel method-a Composite Ensemble Learning (CEL)-based neural network-for drone signal classification. The proposed method integrates wavelet-based denoising and combines automatic and manual feature extraction techniques to foster feature diversity, robustness, and performance enhancement. Through extensive experiments conducted on open-source benchmark datasets of drones, our approach demonstrates superior classification accuracies compared to recent benchmark deep learning techniques across various Signal-to-Noise Ratios (SNRs). This novel approach holds promise for enhancing communication efficiency, security, and safety in 6G networks amidst the proliferation of drone-based applications.

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Detection of unmanned aerial vehicles (UAVs) and their classification on the basis of acoustic signals recorded in the presence of UAVs is a very important source of information. Such information can be the basis of certain decisions. It can support the autonomy of drones and their decision-making system, enabling them to cooperate in a swarm. The aim of this study was to classify acoustic signals recorded in the presence of 17 drones while they hovered individually at a height of 8 m above the recording equipment. The signals were obtained for the drones one at a time in external environmental conditions. Mel-frequency cepstral coefficients (MFCCs) were evaluated from the recorded signals. A discriminant analysis was performed based on 12 MFCCs. The grouping factor was the drone model. The result of the classification is a score of 98.8%. This means that on the basis of acoustic signals recorded in the presence of a drone, it is possible not only to detect the object but also to classify its model.

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Geoffroy's spider monkeys, an endangered, fast-moving arboreal primate species with a large home range and a high degree of fission-fusion dynamics, are challenging to survey in their natural habitats. Our objective was to evaluate how different flight parameters affect the detectability of spider monkeys in videos recorded by a drone equipped with a thermal infrared camera and examine the level of agreement between coders. We used generalized linear mixed models to evaluate the impact of flight speed (2, 4, 6 m/s), flight height (40, 50 m above ground level), and camera angle (-45°, -90°) on spider monkey counts in a closed-canopy forest in the Yucatan Peninsula, Mexico. Our results indicate that none of the three flight parameters affected the number of detected spider monkeys. Agreement between coders was "substantial" (Fleiss' kappa coefficient = 0.61-0.80) in most cases for high thermal-contrast zones. Our study contributes to the development of standardized flight protocols, which are essential to obtain accurate data on the presence and abundance of wild populations. Based on our results, we recommend performing drone surveys for spider monkeys and other medium-sized arboreal mammals with a small commercial drone at a 4 m/s speed, 15 m above canopy height, and with a -90° camera angle. However, these recommendations may vary depending on the size and noise level produced by the drone model.

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As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision-language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision-language model-based method has an acceptable prediction accuracy and low training cost.

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This paper proposes a novel drone detection method based on a convolutional neural network (CNN) utilizing range-Doppler map images from a frequency-modulated continuous-wave (FMCW) radar. The existing drone detection and identification techniques, which rely on the micro-Doppler signature (MDS), face challenges when a drone is small or located far away, leading to performance degradation due to signal attenuation and faint (MDS). In order to address these issues, this paper suggests a method where multiple time-series range-Doppler images from an FMCW radar are overlaid onto a single image and fed to a CNN. The experimental results, using actual data for three different drone sizes, show significant performance improvements in drone detection accuracy compared to conventional methods.

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Environmental DNA (eDNA) analysis is a powerful tool for studying biodiversity in forests and tree canopies. However, collecting representative eDNA samples from these high and complex environments remains challenging. Traditional methods, such as surface swabbing or tree rolling, are labor-intensive and require significant effort to achieve adequate coverage. This study proposes a novel approach for unmanned aerial vehicles (UAVs) to collect eDNA within tree canopies by using a surface swabbing technique. The method involves lowering a probe from a hovering UAV into the canopy and collecting eDNA as it descends and ascends through branches and leaves. To achieve this, a custom-designed robotic system was developed featuring a winch and a probe for eDNA collection. The design of the probe was optimized, and a control logic for the winch was developed to reduce the risk of entanglement while ensuring sufficient interaction force to facilitate transfer of eDNA onto the probe. The effectiveness of this method was demonstrated during the XPRIZE Rainforest Semi-Finals as 10 eDNA samples were collected from the rainforest canopy, and a total of 152 molecular operational taxonomic units (MOTUs) were identified using eDNA metabarcoding. We further investigate how the number of probe interactions with vegetation, the penetration depth, and the sampling duration influence the DNA concentration and community composition of the samples.

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Aerial drone imaging is an efficient tool for mapping and monitoring of coastal habitats at high spatial and temporal resolution. Specifically, drone imaging allows for time- and cost-efficient mapping covering larger areas than traditional mapping and monitoring techniques, while also providing more detailed information than those from airplanes and satellites, enabling for example to differentiate various types of coastal vegetation. Here, we present a systematic method for shallow water habitat classification based on drone imagery. The method includes:•Collection of drone images and creation of orthomosaics.•Gathering ground-truth data in the field to guide the image annotation and to validate the final map product.•Annotation of drone images into - potentially hierarchical - habitat classes and training of machine learning algorithms for habitat classification.As a case study, we present a field campaign that employed these methods to map a coastal site dominated by seagrass, seaweed and kelp, in addition to sediments and rock. Such detailed but efficient mapping and classification can aid to understand and sustainably manage ecologically and valuable marine ecosystems.

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OBJECTIVES: Transportation of medical samples between laboratories or hospital sites is typically performed by motorized ground transport. Due to the increased traffic congestions in urban environments, drone transportation has become an attractive alternative for fast shipping of samples. In accordance with the CLSI guidelines and the ISO 15189 standard, the impact of this transportation type on sample integrity and performance of laboratory tests must be thoroughly validated. METHODS: Blood samples from 36 healthy volunteers and bacterial spiked urine samples were subjected to a 20-40 min drone flight before they were analyzed and compared with their counterparts that stayed on the ground. Effects on stability of 30 routine biochemical and hematological parameters, immunohematology tests and flow cytometry and molecular tests were evaluated. RESULTS: No clinically relevant effects on blood group typing, flow cytometry lymphocyte subset testing and on the stability of the multicopy opacity-associated proteins (Opa) genes in bacterial DNA nor on the number of Abelson murine leukemia viral oncogene homolog 1 (abl) housekeeping genes in human peripheral blood cells were seen. For three of the 30 biochemistry and hematology parameters a statistically significant difference was found: gamma-glutamyl transferase (gamma-GT), mean corpuscular hemoglobin (MCH) and thrombocyte count. A clinically relevant effect however was only seen for potassium and lactate dehydrogenase (LDH). CONCLUSIONS: Multi-rotor drone transportation can be used for medical sample transportation with no effect on the majority of the tested parameters, including flow cytometry and molecular analyses, with the exception of a limited clinical impact on potassium and LDH.

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BACKGROUND: Reducing the time to treatment by means of cardiopulmonary resuscitation (CPR) and defibrillation is essential to increasing survival after cardiac arrest. A novel method of dispatching drones for delivery of automated external defibrillators (AEDs) to the site of a suspected out-of-hospital cardiac arrest (OHCA) has been shown to be feasible, with the potential to shorten response times compared with the emergency medical services. However, little is known of dispatchers' experiences of using this novel methodology. METHODS: A qualitative semi-structured interview study with a phenomenological approach was used. Ten registered nurses employed at an emergency medical dispatch centre in Gothenburg, Sweden, were interviewed and the data was analysed by qualitative content analysis. The purpose was to explore dispatcher nurses' experiences of deliveries of AEDs by drones in cases of suspected OHCA. RESULTS: Three categories were formed. Nurses expressed varying compliance to the telephone-assisted protocol for dispatch of AED-equipped drones. They experienced uncertainty as to how long would be an acceptable interruption from the CPR protocol in order to retrieve a drone-delivered AED. The majority experienced that collegial support was important. Technical support, routines and training need to be improved to further optimise action in cases of drone-delivered AEDs handled by dispatcher nurses. CONCLUSIONS: Although telephone-assisted routines for drone dispatch in cases of OHCA were available, their use was rare. Registered nurses showed variable degrees of understanding of how to comply with these protocols. Collegial and technical support was considered important, alongside routines and training, which need to be improved to further support bystander use of drone-delivered AEDs. As the possibilities of using drones to deliver AEDs in cases of OHCA are explored more extensively globally, there is a good possibility that this study could be of benefit to other nations implementing similar methods. We present concrete aspects that are important to take into consideration when implementing this kind of methodology at dispatch centres.

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Unmanned aerial vehicles (UAVs) and radar technology have benefitted from breakthroughs in recent decades. Both technologies have found applications independently of each other, but together, they also unlock new possibilities, especially for remote sensing applications. One of the key factors for a remote sensing system is the estimation of the flight attitude. Despite the advancements, accurate attitude estimation remains a significant challenge, particularly due to the limitations of a conventional Inertial Measurement Unit (IMU). Because these sensors may suffer from issues such as drifting, additional effort is required to obtain a stable attitude. Against that background, this study introduces a novel methodology for making an attitude estimation using radar data. Herein, we present a drone measurement system and detail its calculation process. We also demonstrate our results using three flight scenarios and outline the limitations of the approach. The results show that the roll and pitch angles can be calculated using the radar data, and we conclude that the findings of this research will help to improve the flight attitude estimation of remote sensing flights with a radar sensor.

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INTRODUCTION: Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. METHODS: A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in ten healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor. RESULTS: The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%. CONCLUSION: The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 min per scan and more accurate quantification.

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Signalized intersections are crash prone. This can be attributed to driver errors, red light running behaviour, and poor coordination of conflicting traffic. It is anticipated that overall crash risk at signalized intersection would increase when mixed traffic like motorcycles is involved. In this study, a real-time prediction model for motorcycle and non-motorcycle involved conflict risk at the signalized intersection is proposed. For example, high-resolution vehicle and motorcycle trajectory data are extracted from drone videos using advanced computer vision techniques. Additionally, conflict types including rear-end, angle, and head-on conflicts are also considered. Then, the multinomial logit approach is adopted to model the propensity of severe and slight vehicle-vehicle and vehicle-motorcycle conflicts. Furthermore, the problem of unobserved heterogeneity is addressed using the random parameters model with heterogeneity in means and variances. Results indicate that risk of vehicle-vehicle conflict is significantly associated with vehicle speed and acceleration, and conflict type, and that of vehicle-motorcycle conflict is associated with vehicle speed and acceleration, motorcycle lateral speed, conflict type, and time to green signal. Findings should shed light to the development and implementation of optimal traffic signal time plan and traffic management strategy that can mitigate the potential crash risk, especially involving motorcycles, at the signalized intersection.

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This review aims to present current knowledge on the effects of honey bee products on animals based on in vivo studies, focusing on their application in clinical veterinary practice. Honey's best-proven effectiveness is in treating wounds, including those infected with antibiotic-resistant microorganisms, as evidenced in horses, cats, dogs, mice, and rats. Propolis manifested a healing effect in numerous inflammatory and painful conditions in mice, rats, dogs, and pigs and also helped in oncological cases in mice and rats. Bee venom is best known for its effectiveness in treating neuropathy and arthritis, as shown in dogs, mice, and rats. Besides, bee venom improved reproductive performance, immune response, and general health in rabbits, chickens, and pigs. Pollen was effective in stimulating growth and improving intestinal microflora in chickens. Royal jelly might be used in the management of animal reproduction due to its efficiency in improving fertility, as shown in rats, rabbits, and mice. Drone larvae are primarily valued for their androgenic effects and stimulation of reproductive function, as evidenced in sheep, chickens, pigs, and rats. Further research is warranted to determine the dose and method of application of honey bee products in animals.

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Drones have become essential tools across various industries due to their ability to provide real-time data and perform automated tasks. However, integrating multiple sensors on a single drone poses challenges such as payload limitations and data management issues. This paper proposes a comprehensive system that leverages advanced deep learning techniques, specifically an attention-based generative adversarial network (GAN), to address data scarcity in drone-collected time-series sensor data. By adjusting sensing frequency based on operational conditions while maintaining data resolution, our system ensures consistent and high-quality data collection. The spatiotemporal The attention mechanism within the GAN enhances the generation of synthetic data, filling gaps caused by reduced sensing frequency with realistic data. This approach improves the efficiency and performance of various applications, such as precision agriculture, environmental monitoring, and surveillance. The experimental results demonstrated the effectiveness of our methodology in extending the operational range and duration of drones and providing reliable augmented data utilizing a variety of evaluation metrics. Furthermore, the superior performance of the proposed system was verified by comparing it with various comparative GAN models.

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Having a profound influence on marine and coastal environments worldwide, jellyfish hold significant scientific, economic, and public interest.1,2,3,4,5 The predictability of outbreaks and dispersion of jellyfish is limited by a fundamental gap in our understanding of their movement. Although there is evidence that jellyfish may actively affect their position,6,7,8,9,10 the role of active swimming in controlling jellyfish movement, and the characteristics of jellyfish swimming behavior, are not well understood. Consequently, jellyfish are often regarded as passively drifting or randomly moving organisms, both conceptually2,11 and in process studies.12,13,14 Here we show that the movement of jellyfish is modulated by distinctly directional swimming patterns that are oriented away from the coast and against the direction of surface gravity waves. Taking a Lagrangian viewpoint from drone videos that allows the tracking of multiple adjacent jellyfish, and focusing on the scyphozoan jellyfish Rhopilema nomadica as a model organism, we show that the behavior of individual jellyfish translates into a synchronized directional swimming of the aggregation as a whole. Numerical simulations show that this counter-wave swimming behavior results in biased correlated random-walk movement patterns that reduce the risk of stranding, thus providing jellyfish with an adaptive advantage critical to their survival. Our results emphasize the importance of active swimming in regulating jellyfish movement and open the way for a more accurate representation in model studies, thus improving the predictability of jellyfish outbreaks and their dispersion and contributing to our ability to mitigate their possible impact on coastal infrastructure and populations.

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INTRODUCTION: Unmanned aerial vehicles (UAVs), more commonly known as drones, have rapidly become more diverse in capabilities and utilization through technology advancements and affordability. While drones have had significant positive impact on healthcare and consumer delivery particularly in remote and austere environments, Violent Non-State Actors (VNSAs) have increasingly used drones as weapons in planning and executing terrorist attacks resulting in significant morbidity and mortality. We aim to analyze drone-related attacks globally against civilians and critical infrastructure for more effective hospital and prehospital care preparedness. METHODS: We retrospectively reviewed the Global Terrorism Database (GTD) from 1970 to 2020 to analyze the worldwide prevalence of drone-related attacks against civilians and critical infrastructure. Cases were excluded if they had insufficient information regarding a drone involvement, and if attacks were conducted by the government entities. The trends in the number of attacks per month, as well as the number of fatalities and injuries, were examined using time series and trend analysis. RESULTS: The database search yielded 253 drone-related incidents, 173 of which met inclusion criteria. These incidents resulted in 92 fatalities and 215 injuries with civilian targets most commonly attacked by drones (76 events, 43.9%), followed by military (46 events, 26.5-%). The Middle East region was most affected (168 events, 97% of attacks) and the Islamic state of Iraq was the most common perpetrator (106 events, 61.2%). Almost all attacks were by explosive devices attached to drones (172 events, 99.4%). Time series with linear trend analyses suggested an upward trends of drone attacks by VNSAs, resulting in a greater number of injuries and fatalities, that became more frequent over the years. CONCLUSIONS: Overtime, there were upward trends of drone attacks, with higher lethality and morbidity. There were more injuries compared to fatalities. Most common region affected was the Middle East, and most common type of weapon employed by drone technology was explosive weapon. Investment in medical personnel training, security, and research is crucial for an effective mass-casualty incident response after the drone attacks.

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Background: Defibrillation in the critical first minutes of out-of-hospital cardiac arrest (OHCA) can significantly improve survival. However, timely access to automated external defibrillators (AEDs) remains a barrier. Objectives: The authors estimated the impact of a statewide program for drone-delivered AEDs in North Carolina integrated into emergency medical service and first responder (FR) response for OHCA. Methods: Using Cardiac Arrest Registry to Enhance Survival registry data, we included 28,292 OHCA patients ≥18 years of age between 1 January 2013 and 31 December 2019 in 48 North Carolina counties. We estimated the improvement in response times (time from 9-1-1 call to AED arrival) achieved by 2 sequential interventions: 1) AEDs for all FRs; and 2) optimized placement of drones to maximize 5-minute AED arrival within each county. Interventions were evaluated with logistic regression models to estimate changes in initial shockable rhythm and survival. Results: Historical county-level median response times were 8.0 minutes (IQR: 7.0-9.0 minutes) with 16.5% of OHCAs having AED arrival times of <5 minutes (IQR: 11.2%-24.3%). Providing all FRs with AEDs improved median response to 7.0 minutes (IQR: 6.2-7.8 minutes) and increased OHCAs with <5-minute AED arrival to 22.3% (IQR: 16.4%-30.9%). Further incorporating optimized drone networks (326 drones across all 48 counties) improved median response to 4.8 minutes (IQR: 4.3-5.2 minutes) and OHCAs with <5-minute AED arrival to 56.3% (IQR: 46.9%-64.2%). Survival rates were estimated to increase by 34% for witnessed OHCAs with estimated drone arrival <5 minutes and ahead of FR and emergency medical service. Conclusions: Deployment of AEDs by FRs and optimized drone delivery can improve AED arrival times which may lead to improved clinical outcomes. Implementation studies are needed.

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This paper presents the first data on the biodiversity of lithophytic algae from Bulgarian megaliths obtained after the application of the direct sampling method, subsequent cultivation, and processing by light microscopy. A rich algal flora was found: 90 species and 1 variety of 65 genera from Cyanoprokaryota/Cyanobacteria (29 species, 13 genera), Chlorophyta (40 species and 1 variety, 38 genera), Streptophyta (5 species, 1 genus), and Ochrophyta (16 species, 13 genera). Among them were the globally rare Pseudodictyochloris multinucleata (Chlorophyta), found for the first time in such lowland and warm habitats, and Scotiella tuberculata (Chlorophyta), for which this is the first finding in the country. Three of the recorded species are conservationally important. The low floristic similarity between the sites (0-33%) shows the diversity of the algal flora, with no common species found for all the megaliths studied. The most widespread were the strongly adaptive and competitive Stichococcus bacillaris, Apatococcus lobatus, and Chloroidium ellipsoidium (Chlorophyta). The correlations estimated between the species number and substrate temperature (18.1-49.6 °C) suggest the prospect of future research related to the impact of global warming. In addition, the study points to the safety aspects as it revealed species from nine potentially toxin-producing cyanoprokaryotic genera that could be harmful to visitors' health.