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Structural diversity (SD) characterizes the volume and physical arrangement of biotic components in an ecosystem which control critical ecosystem functions and processes. LiDAR data provides detailed 3-D spatial position information of components and has been widely used to calculate SD. However, the intensive computation of SD metrics from extensive LiDAR datasets is time-consuming and challenging for researchers who lack access to high-performance computing resources. Moreover, a lack of understanding of LiDAR data and algorithms could lead to inconsistent SD metrics. Here, we developed a SD product using the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform. This product provides SD metrics detailing height, density, openness, and complexity at a spatial resolution of 30 m, aligned to the Landsat grids, for 211 site-years for 45 Terrestrial NEON sites from 2013 to 2022. To accommodate various ecosystems with different understory heights, it includes three different cut-off heights (0.5 m, 2 m, and 5 m). This structural diversity product can enable various applications such as ecosystem productivity estimation and disturbance monitoring. [ABSTRACT FROM AUTHOR]

With the development and popularization of unmanned aerial vehicle (UAV) equipment, UAV-based remote sensing technology has developed rapidly. With UAV aerial photography platforms, the obtained low altitude remote sensing images of surfaces have high temporal and spatial resolution and can be used for monitoring and change analyses of surface geographic information. As it is impractical to carry complex and large load equipment on a UAV, it is desirable to develop embedded systems for relevant real-time processing with UAV remote sensing technology. This study combines the advantages of UAV remote sensing image processing and embedded development technology to develop a near real-time UAV remote sensing image change detection system, which realizes the fast, accurate, and automatic detection of variations in the target area. Finally, the feasibility, correctness, and practicability of the prototype system are demonstrated with experiments. [ABSTRACT FROM AUTHOR]

The accuracy of aerial work platform weighing is essential for safety. However, in practice, the same weight placed at different locations on the platform can yield varying readings, which is a phenomenon known as eccentric load. Measurement errors caused by eccentric loads can lead to missed detections and false alarms in the vehicle safety system, seriously affecting the safety of aerial work. To overcome the influence of eccentric load, the current engineering practice relies on multiple measurements at multiple points and averaging the results to eliminate the eccentric load, which greatly increases the work intensity of workers. To address the aforementioned issues, this paper proposes a three-dimensional force/torque shear force compensation scheme based on bending torque and torsional torque for pressure. The goal is to ensure that the sensor on the aerial work vehicle platform can accurately measure the anti-eccentric load under single-point measurement conditions. A three-box structure anti-eccentric load-weighing sensor for the aerial work platform was designed. Its structure has the advantages of high mechanical strength and no radial effect, ensuring the safety of aerial work, improvement of measurement sensitivity, and enabling of real-time and accurate acquisition of force/torque in three directions. In order to further improve the measurement accuracy of 3D force/torque compensation, a particle swarm optimization algorithm was adopted to optimize the 3D force/torque shear force compensation, thereby improving the safety of engineering operations. Through the verification of a self-made testing platform, the anti-eccentric load sensor designed in this study can ensure that the measurement error of objects at any position on the platform is less than 1.5%, effectively improving the safety of high-altitude platform engineering operations. [ABSTRACT FROM AUTHOR]

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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Venus cloud sample return vital to study cloud composition and search for life. • A 27 m balloon and ∼2-ton rocket can launch a 10 kg sample canister to orbit. • A low-power low-mass electrostatic cloud collector can efficiently capture sample. • Planned development in sample capture, rapid balloon inflation, and ascent vehicle. The sulfuric acid clouds of Venus are of high scientific interest due to several intriguing observations from the past decades. These observations are not fully explained with current chemical models. Many hypotheses have been posed to date, including the presence of complex organics or microbial life in the clouds. The possibility of life is primarily supported by the biologically benign temperatures in the cloud layers and is secondarily bolstered by several unresolved cloud-layer atmospheric chemical anomalies. Here we propose that a robust search for signs of life or life itself in the Venusian aerial environment requires a sample return of the cloud particles. We propose a Venus cloud sample return mission concept by performing key trade studies, and evaluating the requirements, feasibility, and technological needs. The baseline mission concept includes a Venus entry system, a balloon for sample collection and ascent in the clouds, a Venus Ascent Vehicle to deliver samples to orbit, a Venus orbiter, and an Earth return system. We present the concept of three critical systems that are the focus of our study: cloud sample collectors, an aerial platform, and a Venus Ascent Vehicle. We found an electrostatic fog collector for low-power, high-efficiency cloud sample capture. The most significant newly identified technology gaps for near-term focused development that we uncovered are the rapid inflation of a large 7000 m3 balloon and navigation and control of the ascent vehicle in a low-visibility and turbulent cloud environment. We discuss the various technologies of the sample return mission that need focused development and have synergies across multiple types of planetary missions. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is considered the core technology of the next generation of mobile communications, which can effectively improve the performance of millimeter wave (mmWave) communication systems by providing additional line of sight (LoS) path to compensate propagation losses or solve blocking problems. Aerial IRS (AIRS) deployed on aerial platforms such as drones and hot air balloons combine the high mobility/rotation characteristics of aerial platforms with the quality link characteristics provided by IRS to provide wider signal coverage than traditional ground IRS. Considering the mobility of users in the real world, it is necessary for the communication system to adjust the beamforming in real time to align the beamforming to the mobile users. However, since AIRS do not have active radio frequency chains, it is difficult to obtain angle of departure and angle of arrival at AIRS, and its deployment significantly increases the complexity of beam tracking. To solve this problem, a AIRS assisted mmWave multiple input multiple output (MIMO) system model with time-varying channels is established. Combining active beamforming at the base station, AIRS rotation angle and passive beamforming design, a cascade angle untracked Kalman filter beam tracking scheme is proposed. Simulation results show that the proposed scheme has high tracking accuracy, and AIRS rotation characteristics play an important role in improving the system reachable speed. [ABSTRACT FROM AUTHOR]

Containment leaks in process plants pose a substantial risk, leading to potential fires, explosions, and toxic releases. Given the complexity of spill detection, monitoring is essential to promptly identify LOCs and minimize consequences. For in-depth analysis, it is important to constantly monitor large areas over time to gain a global view and identify any changes. At the same time, immediate inspection provides detailed data in real-time, allowing you to respond quickly in critical situations and it is particularly useful for analysing specific details. The utilization of aerial platforms, such as unmanned aerial vehicles (UAVs or drones), can be adopted for instant inspections, significantly improving their effectiveness. Initially employed for monitoring civil structures, drones have found applications in the process industry due to their flexibility, versatility, and precision. Equipped with carefully chosen sensors, drones can provide detailed information by approaching dispersed gases, enabling accurate calculations of quantities. Additionally, drones equipped with cameras offer the capability to visualize released gases and detect small fires from a safe distance, allowing for detailed observation without direct exposure to potential dangers. This research represents a review of the state of the art with a specific focus on a gas, hydrogen, with significant potential to reduce environmental pollution and fossil fuel dependence. The wide flammability range and the low ignition heat of hydrogen pose risks of forming flammable mixtures with air during leaks, leading to spontaneous ignition. While existing studies have explored leak detection methodologies through mathematical models and laboratory-scale simulations, there is a gap in addressing real-scale hydrogen leak detection. The research proposes a meticulously designed monitoring system to safely identify hydrogen leaks, crucial in preventing potential jet fire phenomena when mixed with the surrounding air. [ABSTRACT FROM AUTHOR]

In this study, an uncrewed aerial vehicle (UAV) platform with sensing and sampling systems was developed for three-dimensional (3D) measurements of air pollutant concentrations. The sensing system of this platform contains multiple microsensors and Internet of Things devices for determining the 3D distributions of four critical air pollutants and two meteorological parameters in real time. Moreover, the sampling system comprises remote-controllable gas sampling kits, each of which contains a 1 L Tedlar bag for the 3D measurement of volatile organic compound (VOC) concentrations according to the Toxic Organics-15 (TO-15) method of the US Environmental Protection Agency. The performance of the developed UAV platform was verified in experiments where it was used to detect air pollutant emissions from a large industrial zone in Taiwan that included a traditional industrial park, a precision machinery park, and a municipal waste incineration plant. Three locations were selected as field measurement sites according to the prevailing local wind direction. The vertical distributions of four critical air pollutants, the ambient temperature, and the relative humidity were determined from data gathered at the aforementioned sites in March and May 2023. A total of 56 and 72 chemical species were qualitatively and quantitatively analyzed in these two periods, respectively. The experimental results verified the feasibility of using the proposed UAV platform for accurately evaluating the air pollutant concentration distribution and transport in an industrial zone. The sampling system can be used as the sampling part of the TO-15 method, thus extending the method to measure the 3D distribution of VOCs in an area. The UAV platform can serve as a useful tool in the management of and decision-making process for air pollution in industrial areas. [ABSTRACT FROM AUTHOR]

Aerial working platforms are widely used to transport personnel and equipment to a specified height. To ensure operation stability, their chassis are supported on the ground by a four-legged outrigger system before use. At present, leveling of an aerial working platform is manually conducted, resulting in low accuracy, low efficiency, poor robustness, and poor adaptability. In response to this, a battery-electric drive-electric control integrated aerial working chassis is proposed at first. Then the attitude and load coupling control model proposed by our team is applied to design its integrated control system. Finally, performance tests of the control system and comparisons with the traditional point-chasing method are studied. Results show that the aerial working chassis and the control system can rapidly and synchronously realize the geometry leveling and load homogenization of the chassis, thus having potential values to be promoted to special vehicles with leveling requirements. A battery-electric drive-electric control integrated aerial working chassis is proposed. The coupling control model of geometric leveling and load distribution proposed by our team is applied to chassis Performance tests of the control system and comparisons with traditional point chasing method are studied. [ABSTRACT FROM AUTHOR]

With the advancement of various aerial platforms, there is an increasing abundance of aerial images captured in various environments. However, the detection of densely packed small objects within complex backgrounds remains a challenge. To address the task of detecting multiple small objects, a multi-object detection algorithm based on Distance Intersection Over Union loss Nonminimum Suppression (DIOU-NMS) integrated with You Only Look Once version 5 (YOLOv5) is proposed. Leveraging the YOLOv5s model as the foundation, the algorithm specifically addresses the detection of abundantly and densely packed targets by incorporating a dedicated small object detection layer within the network architecture, thus effectively enhancing the detection capability for small targets using an additional up sampling operation. Moreover, conventional non-maximum suppression is replaced with DIOU-based non-maximum suppression to alleviate the issue of missed detections caused by target density. Experimental results demonstrate the effectiveness of the proposed method in significantly improving the detection performance of dense small targets in complex backgrounds. [ABSTRACT FROM AUTHOR]

Maintaining the health of strategic infrastructures and bridges is crucial for effective maintenance operations. However, traditional periodical monitoring using elevating platforms is expensive and complex, leading to a search for more efficient and flexible methods. In recent years, there has been a growing adoption of noninvasive approaches such as the use of Unmanned Aerial Vehicles (UAVs) equipped with optical sensors and LiDAR technologies for rapid mapping of the territory. This study presents two methodologies for bridge inspection. The first approach integrates traditional topographic and GNSS techniques with TLS and photogrammetry using cameras mounted on UAVs. The second approach involves using a DJI Matrice 300 equipped with a LiDAR DJI Zenmuse L1 sensor for both manual and automatic flights. While the first approach resulted in a centimeter-accurate but time-consuming model, the UAV-LiDAR point cloud's georeferencing accuracy was less accurate in the case of manual flight under the bridge due to GNSS signal obstruction. However, a photogrammetric model reconstruction phase using ground control points and photos taken by the L1-embedded camera improved the overall accuracy of the workflow. This workflow can be used for flexible, low-cost mapping of bridges when medium-level accuracy (5–10 cm) is acceptable. Finally, the article presents a solution for integrating the final 3D products interactively into a Bridge Monitoring System environment. [ABSTRACT FROM AUTHOR]

With the continuous development of science and technology, UAV technology has been widely used in various fields. As an essential part, light and small UAV platforms have the advantages of being small, lightweight, and easy to operate and thus have been widely used in aerial photography, monitoring, rescue, and other fields. However, the traditional data transmission method can no longer meet the demand for the data transmission speed and stability of small and light UAV platforms. Wireless optical communication has the advantages of being high-speed and license-free, as well as having a large bandwidth strong anti-interference ability, and good confidentiality. Therefore, this paper aims to study high-speed data transmission technology for light and small UAV platforms to improve their transmission speed and stability. In this paper, the characteristics and advantages of UAV airborne laser communication are first elaborated to illustrate the advancements and importance of data transmission technology based on light and small UAV platforms. Then, the composition of a UAV airborne laser communication system is clarified to illustrate the feasibility of conducting airborne laser communication research. Then, the current domestic and international research status of people on light and small UAV airborne laser communication high-speed data transmission systems is reviewed, and its key technical features are analyzed. Finally, the prospects for its application and development trend are investigated. [ABSTRACT FROM AUTHOR]

Measuring the dynamics of vibrating cables in situ can be challenging using traditional contact-based sensors (e.g., accelerometer). This work proposes a novel computer vision–based technique using a portable unmanned aerial system (UAS) (also referred to as drones) sensing platform to enable the measurement of the dynamic displacements of a cable in a unique blast-loading experiment where other types of sensing may be impractical. The proposed technique utilizes one optical camera equipped on a UAS to record the dynamic displacement of the blast-loaded cable and measure the drift of the UAS as it hovers. Artificial targets attached to the cable are not required because the technique proves effective when tracking natural features inherent in the cable. This makes the proposed technique versatile and convenient for field implementation. This technique also allows simultaneous displacement measurement at multiple points along the cable across the entire field of view of the camera. The natural frequency, damping, and mode shape are successfully identified from the measured cable displacement time histories. Measuring the vibration of a cable is challenging with traditional techniques. A novel technique is proposed to use only one camera attached to a drone to measure the vibration of a cable directly. However, because the drone hovers in the air, there is inherent drift of the drone. The drone's drift is measured with respect to a stationary target using the camera and compensated for to measure the true motion of the cable. To test the proposed technique, a blast was detonated close to the cable to produce vibration in the cable, and the drone hovered above the cable to record its movements. Because the actual movement of the cable is unknown, finite-element simulations were performed to provide a basis for validating the proposed technique. The natural frequency of the finite-element model is compared with that obtained from the measured vibration of the cable. A good agreement was achieved with a 2.1% difference between the simulated and measured natural frequencies. [ABSTRACT FROM AUTHOR]

The evolving requirements of next-generation mobile communications networks can be met by leveraging vertically deployed Unmanned Aerial Vehicle (UAV) platforms integrated with Free Space Optical communications (FSO). This integration offers a flexible and scalable architecture capable of delivering high-rate communication without requiring licenses while aligning with the multi-gigabit paradigm. In recent times, the increasing availability of commercial aerial platforms has facilitated experimental demonstrations of UAV-enabled FSO systems, which play a crucial role in proposed backhaul networks and point-to-point communications by overcoming Line-of-Sight (LOS) challenges. These systems can be rapidly deployed to meet sudden demand scenarios. This document provides a comprehensive review of relevant field demonstrations of UAV-enabled FSO relay systems, with a particular focus on commercially available, free-flying platforms that are driving advancements in this domain. It categorizes the different platforms by considering the operational altitudes of these systems and their payload actuation capacity, which determines their adaptability to variables. The analysis aims to distill the design considerations that lead to optimal performance regarding communications throughput and other relevant metrics. Moreover, it also attempts to highlight areas where design choices have fallen short, indicating gaps in current research efforts toward the widespread adoption of UAV-enabled FSO relay systems. Finally, this work endeavors to outline effective design considerations, guidelines, and recommendations to bridge these identified gaps. It serves as a valuable reference guide for researchers involved in developing UAV-enabled FSO relay systems, enabling them to make informed decisions and pave the way for the successful implementation of such systems. [ABSTRACT FROM AUTHOR]

The Human Machine Teaming (HMT) paradigm focuses on supporting partnerships between humans and autonomous machines. HMT describes requirements for transparency, augmented cognition, and coordination that enable far richer partnerships than those found in typical human-on-the-loop and human-in-the-loop systems. Autonomous, self-adaptive systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems, are often implemented using the MAPE-K feedback loop as the primary reference model. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions that occur between humans and autonomous machines as intended by HMT. In this article, we, therefore, present the MAPE-KHMT framework, which utilizes runtime models to augment the monitoring, analysis, planning, and execution phases of the MAPE-K loop to support HMT despite the different operational cadences of humans and machines. We draw on examples from our own emergency response system of interactive, autonomous, small unmanned aerial systems to illustrate the application of MAPE-KHMT in both a simulated and physical environment, and we discuss how the various HMT models are connected and can be integrated into a MAPE-K solution. [ABSTRACT FROM AUTHOR]

This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52 , n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60 , RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72 , RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), commonly referred to as "drones", have received a notable surge in recent years, particularly within military contexts. In this study, a simulation platform was developed to assess the viability of employing drone swarms as a defensive mechanism against opposing drones. The study encompasses diverse tactical approaches including the arrangement of attacking and defending drones, the role of drone launchers, and the critical factors of detection and response time. The results show the effectiveness of drone swarms, machine guns, anti-aircraft guns, laser guns and surface-to-air missiles against swarms of attacking drones. The findings provide a comprehensive insight into the potential performance of these countermeasures, laying the groundwork for the formulation of effective defence strategies against the emerging threat of drone swarms. [ABSTRACT FROM AUTHOR]

Mechanical ball milling is a prevalent technology for material preparation and also serves as a post-treatment method to modify electrode materials, thus enhancing electrochemical performances. This study explores the microstructure modification of commercial activated carbon through mechanical ball milling, proving its efficacy in increasing sodium-ion energy storage. The evolution of activated carbon's physical and chemical properties during ball milling was systematically examined. It was observed that the quantity of closed pores and the graphitization degree in activated carbon increased with extended ball milling duration. The sodium storage mechanism in activated carbon transitions to an insertion-pore filling process, significantly elevating platform capacity. Additionally, ball-milled activated carbon demonstrates remarkable long-term cycling stability (92% capacity retention over 200 cycles at 200 mA g−1) and rate performance. This research offers a novel approach to developing advanced anode materials for sodium-ion batteries. [ABSTRACT FROM AUTHOR]

Small unmanned aerial vehicles (UAVs) have gained significant interest in the last decade. More specifically these vehicles have the capacity to impact package delivery logistics in a disruptive way. This paper reviews research problems and state‐of‐the‐art solutions that facilitate package delivery. Different aerial manipulators and grippers are listed along with control techniques to address stability issues. Landing on a platform is next discussed which encompasses static and dynamic platforms. Landing on a dynamic platform presents further challenges. This includes delayed control responses and poor precision of the relative motion between the platform and the aerial vehicle. Subsequently, risks such as weather conditions, state estimation, and collision avoidance to ensure safe transit is considered. Finally, delivery UAV routing is investigated which categorizes the topic into two areas: drone operations and drone–truck collaborative operations. Additionally, we compare the solutions against design, environmental, and legal constraints. [ABSTRACT FROM AUTHOR]

Landslides are common geological hazards that often result in significant casualties and economic losses. Due to their occurrence in complex terrain areas, conventional geophysical techniques face challenges in early detection and warning of landslides. Semi-airborne electromagnetic (SAEM) technology, utilizing unmanned aerial platforms for rapid unmanned remote sensing, can overcome the influence of complex terrain and serve as an effective approach for landslide detection and monitoring. In response to the low computational efficiency of conventional semi-airborne EM 3D forward modeling, this study proposes an efficient forward modeling method. To handle arbitrarily complex topography and geologic structures, the unstructured tetrahedron mesh is adopted to discretize the earth. Based on the vector finite element formula, the Dual–Primal Finite Element Tearing and Interconnecting (FETI-DP) method is further applied to enhance modeling efficiency. It obtains a reduced order subsystem and avoids directly solving huge overall linear equations by converting the entirety problem into the interface problem. We check our algorithm by comparing it with 1D semi-analytical solutions and the conventional finite element method. The numerical experiments reveal that the FETI-DP method utilities less memory and exhibits higher computation efficiency than the conventional methods. Additionally, we compare the electromagnetic responses with the topography model and flat earth model. The comparison results indicate that the effect of topography cannot be ignored. Further, we analyze the characteristic of electromagnetic responses when the thickness of the aquifer changes in a landslide area. We demonstrate the effectiveness of the 3D SAEM method for landslide detection and monitoring. [ABSTRACT FROM AUTHOR]

Although existing anchor-based oriented object detection methods have achieved remarkable results, they require manual preset boxes, which introduce additional hyper-parameters and calculations. These methods often use more complex architectures for better performance, which makes them difficult to deploy on computationally constrained embedded platforms, such as satellites and unmanned aerial vehicles. We aim to design a high-performance algorithm that is simple, fast, and easy to deploy for aerial image detection. In this article, we propose a one-stage anchor-free rotated object detector, FCOSR, that can be deployed on most platforms and uses our well-defined label assignment strategy for the features of the aerial image objects. We use the ellipse center sampling method to define a suitable sampling region for an oriented bounding box (OBB). The fuzzy sample assignment strategy provides reasonable labels for overlapping objects. To solve the problem of insufficient sampling, we designed a multi-level sampling module. These strategies allocate more appropriate labels to training samples. Our algorithm achieves an mean average precision (mAP) of 79.25, 75.41, and 90.13 on the DOTA-v1.0, DOTA-v1.5, and HRSC2016 datasets, respectively. FCOSR demonstrates a performance superior to that of other methods in single-scale evaluation, where the small model achieves an mAP of 74.05 at a speed of 23.7 FPS on an RTX 2080-Ti GPU. When we convert the lightweight FCOSR model to the TensorRT format, it achieves an mAP of 73.93 on DOTA-v1.0 at a speed of 17.76 FPS on a Jetson AGX Xavier device with a single scale. [ABSTRACT FROM AUTHOR]

Aerial Manipulators are considered in modern applications due to their essential features that have the maneuverability of Unmanned Aerial Vehicles (UAVs) and abilities of robotics arms. One of the primary challenges encountered by aerial manipulators is their inability to maintain a consistent orientation though the duration of flight. This limits the application of aerial manipulators for movement of fragile and liquid objects that should not change their orientation during the flight. To solve this issue, this study introduced a new Aerial Robot Platform (AR-P) which can decouple all motions by configuring tilting rotors into the structure of the AR-P. The AR-P consists of a Tilted-Rotor Quadcopter (T-RQ) and three linked Aerial Robot Arm (AR-A) that have a gripper to grasp objects. The AR-P with this new mechanism of tilting parts overcomes the problem of manipulation limitations by adjusting the direction of the thrusted rotors according to the exerted forces of the grasped object. The new dynamics model of the AR-P is derived using the Lagrange-d'Alembert approach. A special Multi Input Multi Output (MIMO) Proportional Derivative (PD) controller based on Computed Torque Control (CTC) is designed for the developed AR-P. The method of nonlinear least squares is applied to obtain the optimal gains of the over-looped PD controller. The simulation results showed the ability of the developed CTC to track the required joint angle of each the T-RQ and each link of the AR-A with minimizing steady state errors and without overshooting. The implemented controller showed its robustness against external disturbances in form of wind conditions. On one hand, the controller is tested considering traditional aerial manipulator. On the other hand, the controller is tested by our new tilted aerial robot platform. The comparison results of the flight scenario between the new tilted aerial manipulator and the traditional one approved the advantage of the proposed new tilted AR-P to transport the grasped objects in fixed orientation. [ABSTRACT FROM AUTHOR]

Detecting people in images and videos captured from an aerial platform in wooded areas for search and rescue operations is a current problem. Detection is difficult due to the relatively small dimensions of the person captured by the sensor in relation to the environment. The environment can generate occlusion, complicating the timely detection of people. There are currently numerous RGB image datasets available that are used for person detection tasks in urban and wooded areas and consider the general characteristics of a person, like size, shape, and height, without considering the occlusion of the object of interest. The present research work focuses on developing a thermal image dataset, which considers the occlusion situation to develop CNN convolutional deep learning models to perform detection tasks in real-time from an aerial perspective using altitude control in a quadcopter prototype. Extended models are proposed considering the occlusion of the person, in conjunction with a thermal sensor, which allows for highlighting the desired characteristics of the occluded person. [ABSTRACT FROM AUTHOR]

This review paper aims to provide a meta-analysis of the scientific literature for heritage documentation and monitoring using geo-information sensors. The study initially introduces the main types of geomatic sensors that are currently widely used for heritage studies. Although the list provided here is indicative rather than exhaustive, it provides a general overview of the variety of sensors used for different observation scales. The study next focuses on the existing literature, based on published documents. Targeted queries were implemented to the Scopus database to extract the relevant information. Filtering was then applied to the results so as to limit the analysis on the specific thematic sub-domains that is applied for heritage documentation and monitoring. These domains include, among other close-range and underwater photogrammetry, Terrestrial Laser Scanner, Unmanned Aerial Vehicles platforms, and satellite observations. In total, more than 12,000 documents were further elaborated. The overall findings are summarized and presented here, providing further insights into the current status of the domain. [ABSTRACT FROM AUTHOR]

Video summarization deals with the generation of a condensed version of the original video by including meaningful frames or segments while eliminating redundant information. The main challenge in a video summarization task is to identify important frames or segments corresponding to human perception which varies from one genre to another. In the past two decades, several summarization techniques ranging from conventional non-learning to deep learning based mechanisms have been developed. This study provides a comprehensive survey focusing on the massive literature with scope ranging from general to domain specific methods, single view to multi-view processes, generic to user-interaction based mechanisms and conventional to deep learning-based approaches. The presented work provides general pipeline and broad classification of video summarization systems. The survey also presents genre-wise datasets description, various evaluation techniques and future recommendations. The key-points of presented work lie in its approach of analyzing literature in a systematic manner and its wide coverage by including some of the domains that have been overlooked over the time like aerial videos, medical videos and user-customization based approaches. The research work in each category is investigated, compared and analyzed on the basis of various intrinsic characteristics. The main objective of this manuscript is to guide future researchers about state-of-the-art work done in various domains of the video summarization field, so that the scope and performance of automatic video summarization systems can be enhanced further by designing new approaches or by improving different existing techniques. [ABSTRACT FROM AUTHOR]

The object of the study is the process of managing the air network of air communication platforms of the FANET class (Flying Ad-Hoc Network), which is a component of the ground-air communication network, and which is performed on rotary unmanned aerial vehicles (UAVs) of the mini class, at the stage of deployment and operational management. The scientific research is aimed at the managing process formalization of aerial communication platforms of the air communication network in the implementation of two classes of management tasks – the class of traffic management tasks and the class of communication tasks. The analysis of this subject area showed that the management tasks at the stage of deployment and operational management of the air subnet are a multi-parameter optimization task and require the formation of control solutions at the OSI physical, channel and network levels, open systems interaction model. Tasks related to the adaptive management of radio coverage in zones (geographic areas of the area), including the clustering of terrestrial subscribers (communication nodes), were not considered, and relate to processes at the transport and application levels. At the same time, the article shows the mathematical apparatus of the approach to the compensation of the deviations of the trajectory of an unmanned aerial vehicle (UAV) in the conditions of a directional obstacle, which will allow the formation of control solutions for adaptive control, directional patterns at the output of the transmission path. Such compensation is carried out using methods of algorithmic exchange of probes (messages) between the mobile base station and communication platforms with a certain periodicity – solutions at the channel and network levels, as well as the use of Multi User MIMO technologies. This technology allows for information exchange with several client devices at the same time, and not sequentially, sending probes to several spacecraft on one channel, using several transmitting and receiving antennas, and the calculation of channel coefficients allows to estimate the azimuthal angle of deviation and the angle of elevation. [ABSTRACT FROM AUTHOR]

Binary code similarity analysis is widely used in the field of vulnerability search where source code may not be available to detect whether two binary functions are similar or not. Based on deep learning and natural processing techniques, several approaches have been proposed to perform cross-platform binary code similarity analysis using control flow graphs. However, existing schemes suffer from the shortcomings of large differences in instruction syntaxes across different target platforms, inability to align control flow graph nodes, and less introduction of high-level semantics of stability, which pose challenges for identifying similar computations between binary functions of different platforms generated from the same source code. We argue that extracting stable, platform-independent semantics can improve model accuracy, and a cross-platform binary function similarity comparison model N_Match is proposed. The model elevates different platform instructions to the same semantic space to shield their underlying platform instruction differences, uses graph embedding technology to learn the stability semantics of neighbors, extracts high-level knowledge of naming function to alleviate the differences brought about by cross-platform and cross-optimization levels, and combines the stable graph structure as well as the stable, platform-independent API knowledge of naming function to represent the final semantics of functions. The experimental results show that the model accuracy of N_Match outperforms the baseline model in terms of cross-platform, cross-optimization level, and industrial scenarios. In the vulnerability search experiment, N_Match significantly improves hit@N, the mAP exceeds the current graph embedding model by 66%. In addition, we also give several interesting observations from the experiments. The code and model are publicly available at https://www.github.com/CSecurityZhongYuan/Binary-Name_Match. [ABSTRACT FROM AUTHOR]

The increasing usage of multi-rotor aerial platforms and the reliability of flights enabled researchers to add equipment and devices to them for application. The addition of lightweight manipulators, grippers, and mechanisms to fulfill specific tasks has been reported frequently recently. This work pushes the idea one step ahead and uses an Artificial Human Hand (AHH) in an uncrewed aerial vehicle for aerial manipulation, device delivery, and co-operation with human workers. This application requires an effective end-effector capable of grasping and holding objects of different shapes. The AHH is a lightweight custom-made human-inspired design actuated using Shape Memory Alloy (SMA) materials. The SMA actuators offer significantly high forces with respect to their light weights though the control of these new actuators is a challenge that has been successfully demonstrated in this paper. The control of the SMA actuators could be achieved via heat exchange on the actuator, indirectly carried out by changing the current. The benefit of using this new actuator is removing the motors and mechanical mechanisms and simplifying the design. A soft cover is developed for the AHH to add friction and make it closer to a human hand. The modeling of the structured actuators on the system through tendons is presented, and a series of experiments for handling and manipulating different objects have been conducted. The objects were chosen with different weights and shapes to show the effectiveness of the design. An analysis of a generated torque of the manipulator for different cylindrical objects has been carried out. An analysis and comparison for grasping a series of items, pressure and temperature analysis, and the weight-to-volume ratio have been presented. [ABSTRACT FROM AUTHOR]

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In this study, an unmanned aerial vehicle (UAV) platform with sensing and sampling systems was developed for three-dimensional (3D) measurements of air pollutant concentrations. The sensing system of this platform contains multiple microsensors and Internet of Things devices for determining the 3D distributions of four critical air pollutants and two meteorological parameters in real time. Moreover, the sampling system comprises remote-controllable gas sampling kits, each of which contains a Tedlar bag of 1 L for the 3D measurement of volatile organic compound concentrations according to the TO-15 method of the US Environmental Protection Agency. The performance of the developed UAV platform was verified in experiments where it was used to detect air pollutant emissions from a large industrial zone in Taiwan that included a traditional industrial park, precision machinery park, and municipal waste incineration plant. Three locations were selected as field measurement sites according to the prevailing local wind direction. The vertical distributions of four critical air pollutants, ambient temperature, and relative humidity were determined from data gathered at the aforementioned sites in March and May 2023. A total of 56 and 72 chemical species were qualitatively and quantitatively analyzed in these two periods, respectively. The experimental results verified the feasibility of using the proposed UAV platform for accurately evaluating the air pollutant concentration distribution and transport in an industrial zone. The sampling system can be used as a sampling part of the Method To-15, thus extending the method to measure the 3D distribution of VOCs in an area. The UAV platform can serve as a useful tool in the management and decision-making process of air pollution in industrial areas. [ABSTRACT FROM AUTHOR]

The quadrotor represents a class of highly nonlinear dynamic systems and its controllability features are challenging. Hence, it serves as an ideal unmanned aerial vehicle platform to validate many artificial intelligence-based research investigations. Nonetheless, most of the offline tuning approaches devote efforts to find near optimal control gains to regulate individually the decoupled motion directions. This work adopts a multi-objective self-adjusting search mechanism to actuate the motions of a quadrotor via deciding the control gains of the interacting loops simultaneously. This algorithm employs a first order low pass filter transfer function as an accepting approach for the tunning mechanism. The proposed approach is compared with a Genetic Algorithm and another nonlinear Proportional-Integral-Derivative approach to highlight the usefulness of the proposed mechanism. It was founded that quadrotor follows the desired trajectory with a small tracking error of less than 2% in the X-Y plane and less than 1 % error tracking error in the altitude Z. Also, it is recorded that MONLTA can overcome the simulated wind disturbances of 0.1 N.m as a disturbance torque. [ABSTRACT FROM AUTHOR]

The growth of disordered lithium dendrite and the notorious reaction between Li and electrolyte hamper the practical application of Li metal batteries (LMBs). Herein, an artificial solid electrolyte interphase (ASEI) constructed by a nitro‐functionalized covalent organic framework (NO2‐COF) is designed to regulate Li+ deposition and stable Li anodes. Strong electron‐withdrawing nitro groups can gather the surrounding electrons of connected monomer by the donor‐acceptor (D‐A) effect, thus regulating the electron structure of the covalent organic framework (COF) and constructing a specific cation‐oriented channel. The uniform Li+ deposition and inhibition of Li dendrites are achieved under such a high‐selective Li+ transportation channel and regulated surface electric charge. In addition, the nitro can also be reduced to NO2− and further react with Li to produce high ionic‐conductivity Li3N and LiNxOy during the charging/discharging, which contributes to the migration of Li+. As a result, NO2‐COF‐modified symmetrical batteries realize an ultra‐long cycling life of more than 6000 h under a current density of 5 mA cm−2 compared to bare Li and TpBD‐COF/Li (without nitro). The full cells coupled with LiFePO4 stably cycle 1000 times with a capacity retention of 91%. Hence, effectively optimizing electron structure by the donor‐acceptor (D‐A) effect provides a better platform to elevate the performance of LMB. [ABSTRACT FROM AUTHOR]

Spatial registration is the primary challenge affecting target tracking accuracy, especially for the aerial moving platform and sea target tracking. In this environment, it is important to account for both the errors in sensor observations and the variations in platform attitude. In order to solve the problem of complex types of errors in the tracking of sea targets by aerial moving platforms, a new spatial registration algorithm is proposed. Through separating and analyzing observation data, the influence of sensor observation error and attitude error on observation data is obtained, and a systematic error consistency matrix is established. Based on observation information from multiple platforms, accurate tracking of sea targets can be accomplished without estimating systematic error. In order to verify the effectiveness of the algorithm, we carried out simulation experiments and practical experiments on the lake, which showed that the new algorithm was more efficient than traditional algorithms. [ABSTRACT FROM AUTHOR]

Altitude-controlled balloons hold great promise for performing high-priority scientific investigations of Venus's atmosphere and geological phenomena, including tectonic and volcanic activity, as demonstrated by a number of recent Earth-based experiments. In this paper, we explore a concept of operations where multiple autonomous, altitude-controlled balloons monitor explosive volcanic activity on Venus through infrasound microbarometers, and autonomously navigate the uncertain wind field to perform follow-on observations of detected events of interest. We propose a novel autonomous guidance technique for altitude-controlled balloons in Venus's uncertain wind field, and show the approach can result in an increase of up to 63% in the number of close-up observations of volcanic events compared to passive drifters, and a 16% increase compared to ground-in-the-loop guidance. The results are robust to uncertainty in the wind field, and hold across large changes in the frequency of explosive volcanic events, sensitivity of the microbarometer detectors, and numbers of aerial platforms. • We propose a new autonomy architecture for a network of Venus aerobots. • A team of autonomous, variable-altitude balloons detects, visits volcanic events. • On-board autonomy increases eruptions visited by 63% compared to passive drifters. • Results are robust to uncertainty in winds, event frequency, detection sensitivity. [ABSTRACT FROM AUTHOR]

The current work exploits the effects of time-delays on the stability of unmanned aerial vehicles s). In this regard, the main contribution is a symbolic/numeric application of the multiplicity-induced-dominancy property in the control of unmanned aerial vehicles rotorcrafts featuring time-delays. The multiplicity-induced-dominancy property is considered to address two of the most representative aerial robotic platforms: a classical quadrotor vehicle and a quadrotor vehicle endowed with tilting rotors. The aforementioned property leads to an effective delayed feedback control design (multiplicity-induced-dominancy tuning criteria), allowing the system to meet prescribed behavior conditions based on the placement of the rightmost root of the corresponding closed-loop characteristic function/quasipolynomial. Lastly, the results of detailed numerical simulations, including the linear and non-linear dynamics of the vehicle, are presented and discussed to validate the proposal. [ABSTRACT FROM AUTHOR]

This paper presents a novel concept of a modular multirotor aerial robotic platform that can be used for specific profiles of heavy payload missions. A comprehensive mathematical model of the multirotor unmanned aerial vehicle (UAV) is presented, which is divided into the dynamic model and the control allocation scheme that describes the configuration of the aircraft. The components of the propulsion and energy module are selected, and the characterization of the propulsion units is carried out, as well as a preliminary analysis of the module parameters with regard to the considered payloads from 5 to 50 kg. The main goal is to design a modular aircraft that consists of easy-to-assemble modules and that enables the assembly of different propulsion and energy module configurations. Based on the analysis of system parameters, a modular aerial system is designed and the process of manufacturing using rapid prototyping technologies is presented. Based on the parameters obtained from the aircraft assembly CAD model and the implemented mathematical model, simulations are conducted for the two considered missions in the field of smart agriculture. For the purpose of conducting preliminary experiments, a quadrotor aircraft is built and tested in the case of attitude and remote control. [ABSTRACT FROM AUTHOR]

The Gulf of Mexico is the largest offshore fossil fuel production basin in the United States. Decisions on expanding production in the region legally depend on assessments of the climate impact of new growth. Here, we collect airborne observations and combine them with previous surveys and inventories to estimate the climate impact of current field operations. We evaluate all major on-site greenhouse gas emissions, carbon dioxide (CO2) from combustion, and methane from losses and venting. Using these findings, we estimate the climate impact per unit of energy of produced oil and gas (the carbon intensity). We find high methane emissions (0.60 Tg/y [0.41 to 0.81, 95% confidence interval]) exceeding inventories. This elevates the average CI of the basin to 5.3 g CO2e/MJ [4.1 to 6.7] (100-y horizon) over twice the inventories. The CI across the Gulf varies, with deep water production exhibiting a low CI dominated by combustion emissions (1.1 g CO2e/MJ), while shallow federal and state waters exhibit an extraordinarily high CI (16 and 43 g CO2e/MJ) primarily driven by methane emissions from central hub facilities (intermediaries for gathering and processing). This shows that production in shallow waters, as currently operated, has outsized climate impact. To mitigate these climate impacts, methane emissions in shallow waters must be addressed through efficient flaring instead of venting and repair, refurbishment, or abandonment of poorly maintained infrastructure. We demonstrate an approach to evaluate the CI of fossil fuel production using observations, considering all direct production emissions while allocating to all fossil products. [ABSTRACT FROM AUTHOR]

Aerial vehicles equipped with infrared thermal sensors facilitate quick density estimates of wildlife, but detection error can arise from the thermal sensor and viewer of the infrared video. We reviewed published research to determine how commonly these sources of error have been assessed in studies using infrared video from aerial platforms to sample wildlife. The number of annual articles pertaining to aerial sampling using infrared thermography has increased drastically since 2018, but past studies inconsistently assessed sources of imperfect detection. We illustrate the importance of accounting for some of these types of error in a case study on white‐tailed deer Odocoileus virginianus in Indiana, USA, using a simple double‐observer approach. In our case study, we found evidence of false negatives associated with the viewer of infrared video. Additionally, we found that concordance between the detections of two viewers increased when using a red‐green‐blue camera paired with the infrared thermal sensor, when altitude decreased and when more stringent criteria were used to classify thermal signatures as deer. We encourage future managers and ecologists recording infrared video from aerial platforms to use double‐observer methods to account for viewer‐induced false negatives when video is manually viewed by humans. We also recommend combining infrared video with red‐green‐blue video to reduce false positives, applying stringent verification standards to detections in infrared and red‐green‐blue video and collecting data at lower altitudes over snow when needed. [ABSTRACT FROM AUTHOR]

As "Industry 4.0" progresses, construction machinery is evolving toward large-scale, automation, and integration, resulting in the equipment becoming increasingly sophisticated, and the designs more difficult. Labor costs, transportation, and time will be huge challenges for construction machinery, and mixed reality technology is one of several possible ways to solve this challenge. The research presented in this paper develops a holographic visual verification platform for a digital prototype of construction machinery based on virtual terminal equipment, through investigating the synchronous remote collaboration of multiple terminal devices in a mixed reality scenario. These included semi-physical virtual-real fusion assembly, multi-person real-time voice communication, dynamic loading of MR model based on a cloud server, virtual imitation control, interface design, and human-computer interaction. The effectiveness of this paper's method is demonstrated through remote collaborative design cases. These included a double drum roller, loader, and milling planer welding production line, as well as tractor modeling review and virtual simulation manipulation of an aerial work platform. The experimental results show that this visual verification platform is a feasible, low-cost and scalable solution, which brings a qualitative breakthrough to the design, research and development, production and other stages in the field of construction machinery. [ABSTRACT FROM AUTHOR]

The current work is devoted to the numerical determination of the wind speed value, which can cause the overturning of the mobile elevating work platform of the scissor lift type. In the first step of the analysis, the scaled model of the real vehicle is prepared. In the second step, the model is used in the aerodynamic tunnel to determine the aerodynamic force values and moment, which act on the vehicle. The three different configurations of the work platform are considered, namely: (a) The work platform raised to the maximum height with an additional bridge extended, (b) the work platform raised to the maximum height, and (c) the work platform half raised. In each position, the direction of the wind is changed from the range from 0° to 180° with an increment equal to 15°. In the next step of the analysis, the CFD simulations are carried out. The ANSYS Fluent R22 software is used. As a model of turbulent airflow, the standard k-ε with standard wall function is adopted. The obtained experimental results are used to verify the numerical model. A very good agreement between the results of the experiment and the results of numerical simulations is obtained. As the main result of the numerical study, the values of the tipping moment and corresponding wind speed that cause the overturning of the analyzed real scissor lift are determined. It occurred that the lowest value of the wind speed is obtained for the first variant of the vehicle configuration V1crt = 22.315 m/s for the angle of the wind speed direction β = 30° and the highest one for the third variant V3crt = 34.534 m/s and β = 15°, without any persons on the work platform. The presence of human beings on the work platform is also considered. [ABSTRACT FROM AUTHOR]