Search Results (66)

Against the backdrop of global climate change and carbon neutrality strategies, land use carbon emissions have become a prominent topic amid regional efforts toward low-carbon transformation. However, existing studies on land-use carbon emissions have predominantly focused on humid and economically developed regions, while the unique carbon metabolism pathways of arid oasis–desert ecosystems, which are characterized by extremely low environmental carrying capacity and high sensitivity to land-use disturbance, remain largely unexplored. This study takes the oasis urban cluster in the Tarim Basin in southern Xinjiang Uygur Autonomous Region as the research object. This region belongs to a typical oasis–desert composite ecosystem, with a simple structure and low environmental carrying capacity (reflected by sparse vegetation cover < 20%, annual precipitation < 100 mm, extremely limited water resources, and high sensitivity to land disturbance). Its carbon metabolism pathway (i.e., the dynamic balance between carbon sources and sinks induced by land-use change) is fundamentally different from that in humid areas, and thus merits dedicated investigation. This study selects the period from 2000 to 2020 as the research period, which completely covers the acceleration period of urbanization and agricultural expansion in the Tarim Basin oasis urban cluster since the advancement of China’s Western Development Initiative. The data have a temporal resolution of 5 years (samples in 2000, 2005, 2010, 2015, 2020) and a spatial resolution of 30 m for land use and prefecture level for socio-economic indicators. Based on this, to fill the above-mentioned research gap, a research framework integrating the carbon emission coefficient accounting method, landscape pattern index, spatial autocorrelation analysis and geographic detector is adopted. Specifically, this study aims to systematically quantify the spatio-temporal evolution of land use carbon emissions and identify the most robust driving factors in the Tarim Basin oasis urban cluster by integrating multiple models, an approach that has not been previously applied to arid oasis regions. The research results show: (1) Based on the carbon emission coefficient method, total carbon emissions increased from 1.4455 million tons to 22.364 million tons, following a ‘slow-then-fast’ trajectory. In terms of temporal evolution, the study period can be further divided into three sub-stages: 2000–2005 (slow diffusion, with emission center skewed toward the northern energy-intensive zone), 2005–2015 (rapid restructuring, characterized by a ‘unipolar surge’ in Aksu and spread to the central oasis belt), and 2015–2020 (high-intensity stabilization, forming a cross-regional emission belt). Meanwhile, the land use structure has undergone a significant transformation. Construction land and cultivated land have continued to expand, while ecological land has significantly shrunk, resulting in a complex transformation pattern of oasis–desert ecotone. (2) The overall landscape became increasingly fragmented and diversified, the integrity of ecological space was damaged, and the regional carbon sink function was weakened. (3) The spatial autocorrelation analysis indicates that the spatial distribution of carbon emissions shows a heterogeneous pattern, forming a high-emission concentration area centered around Aksu-Bayingol. However, the global Moran’s I index is negative (such as −0.171 in 2020, p > 0.05), suggesting that carbon emissions have not formed a significant spatial clustering. (4) Carbon emissions are dominated by human and economic factors, and the interaction of factors is significant. The geographic detector identifies population density (average q value 0.904) and the proportion of construction land (average q value 0.858) as the key determinants of spatial variation in carbon emissions, reflecting the sensitive response of the human-nature system of arid zones to the urbanization process. These findings not only clarify the spatio-temporal features and driving forces of land use carbon emissions in the Tarim Basin oasis urban cluster, but also provide a replicable analytical framework for carbon-emission research in other arid and semi-arid regions worldwide. Based on these findings, we discuss the unique driving mechanisms of carbon emissions in arid regions, conclude that construction land expansion and population density are the dominant factors, and recommend a three-tier zoning governance system (carbon source control zone, carbon sink enhancement zone, coordinated development zone) for low-carbon spatial planning in arid areas. Full article

This work presents a nonlinear dynamic model and an energy-based control strategy for a coaxial vertical take-off and landing Unmanned Aerial Vehicle (UAV) equipped with independently tilting propulsion units. The proposed model captures the full six-degree-of-freedom motion of the vehicle and explicitly incorporates the forces and moments produced by the coaxial thrust-vectoring propulsion system, as well as the additional force components induced by the two-degree-of-freedom thrust vectoring mechanism. To regulate the vehicle during hover, cruise, and transition maneuvers, a passivity-based control framework formulated in terms of unit quaternions is developed. The control law simultaneously stabilizes the translational and rotational subsystems without relying on model linearization. In order to map the virtual control forces and torques into physically realizable actuator commands, a nonlinear control allocation procedure is introduced. This allocation scheme enables independent angular positioning of the propulsion units while computing the corresponding motor angular velocities. The effectiveness of the proposed modeling and control framework is assessed through three-dimensional dynamic simulations and numerical experiments, demonstrating accurate trajectory tracking, autonomous UAV landing capabilities, and smooth transitions between flight regimes for thrust-vectored UAV platforms. Full article

Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft. Full article

Accurate identification of arable land, as the foundation of the high-standard farmland construction, impacts the crop layout, accurate management of water and fertilizers, and intelligent control. Due to the 10-m resolution limitation of Sentinel-2 imagery, there is feature overlap within individual pixels of the satellite imagery. This leads to fragmented semantic features during farmland identification, and adjacent plots often appear unclear and intertwined. To address these issues, a Hierarchical Agricultural Segmentation Network (HASNet) was proposed. Built upon the classic encoder-decoder structure, this HASNet model incorporates an expanded feature enhancer (DFE) module to recover weak features and reconstruct cropland features (e.g., edges and shapes) that are obscured by mixed pixels. It also introduces a lightweight strip spatial attention (LSSA) mechanism to capture long-range features unique to farmland. Furthermore, it used a pyramid decoding module (PDM) to refine cropland parcel boundaries. Taking a farm in Xinjiang Uygur Autonomous Region, a semantic segmentation dataset of cultivated land was constructed based on Sentinel-2 imagery. Through accuracy comparisons, visualizations, and inferences, HASNet achieved an MIoU of 88.52% and a Kappa coefficient of 87.82%, outperforming mainstream models such as Unetformer and MPFUnet. Ablation experiments confirmed the effectiveness of the DFE, LSSA, and PDM modules in feature capture and edge refinement. The large-scale image sliding inference experiment prevented the seam effect and demonstrated its practicality. In summary, HASNet provides low-cost technical and theoretical support for the intelligent monitoring of high-standard farmland. Full article

While drones play important roles in areas such as communication and logistics delivery, they have certain limitations in emergency rescue scenarios due to their inability to carry passengers. Building on mature drone technologies such as autonomous flight and environmental perception, unmanned passenger Electric Vertical Take-off and Landing (eVTOL) aircraft are designed with a manned cabin, enabling them to operate without an onboard pilot while rapidly transporting injured people. Consequently, eVTOLs can play a significant role in emergency rescue that cargo-only drones cannot fulfill, as they are capable of rapidly reaching emergency scenes, effectively overcoming the delays caused by traditional ground traffic congestion. Despite their potential, eVTOLs still face several critical obstacles, including signal disruption, limited coverage of dispatching centers, mutual authentication among entities, and concerns related to security and privacy preservation. As a remedy, this paper presents a lightweight authentication protocol leveraging drone assistance to overcome these challenges for unmanned eVTOL emergency rescue. In scenarios where an unmanned eVTOL experiences signal blockage due to dense urban high-rise structures, neighboring drones can serve as a transmission relay to assist the unmanned eVTOL and the dispatch center (DC) in completing mutual authentication and session key negotiation, thereby enabling the unmanned eVTOL to safely complete its mission. To enhance security, physical unclonable functions (PUFs) are integrated into unmanned eVTOLs, drones, and the DC, safeguarding sensitive data against side-channel and physical capture attacks while preserving the confidentiality of unmanned eVTOL identities to mitigate privacy risks. Our protocol achieves provable security in the random oracle model while exhibiting strong resistance to various well-known attacks. Comparative analysis with the existing drone authentication and drone-assisted emergency rescue authentication protocols reveals that our protocol not only provides stronger security guarantees but also maintains a low computational overhead. Full article

Deploying unmanned aerial vehicles (UAVs) cooperatively with legged robots for disaster response and inspection requires autonomous docking on miniature walking platforms. This study addresses the problem of landing a foldable quadrotor onto the back of a trotting wheel-legged robot ($300\times 180$ mm) and subsequently taking off while carrying it as a payload. Four tightly coupled challenges distinguish this task from conventional mobile-platform landing: (i) an extremely small landing surface, (ii) gait-induced periodic vibrations at $2.5$ Hz, (iii) continuous platform translation at $0.3$–$0.8$ m/s, and (iv) surface docking that requires simultaneous position and attitude matching rather than mere point tracking. The proposed framework comprises four components: (1) a novel single-servo crank-rocker folding mechanism that reduces the folded body footprint by 48.5% and the maximum linear dimension from 590 mm to 309 mm (↓47.6%) compared with the prior dual-servo design; (2) a staged Continuous Fast Nonsingular Terminal Sliding Mode (CFNTSM) controller combined with a Gait-Frequency-Aware Finite-time Extended Observer (GFA-FEO); (3) a Feature-wise Linear Modulation Soft Actor-Critic (FiLM-SAC) residual reinforcement-learning policy conditioned on physical states and mission phase, with an adaptive trust weight $\lambda \left(t\right)$; and (4) a payload-adaptive takeoff strategy with parameter hot-switching to handle the twofold mass increase. Extensive Monte Carlo simulations and ablation studies across three experiment groups demonstrate that the proposed hierarchical framework achieves sub-centimetre (<10 mm) position accuracy and <3° attitude matching on a walking platform. Quantitatively, the full method reduces docking RMSE by 42% relative to the model-based CFNTSM + GFA-FEO controller without residual RL (4.2 vs. 7.2 mm) and reduces post-lock takeoff RMSE by 63% through FEO hot-switching (16.2 vs. 44.2 mm). Full article

Maneuverability and performance of UAVs are strongly influenced by the adopted propulsion layout. Electrification has enabled modern UAVs to achieve unprecedented maneuverability, including hovering and VTOL (Vertical Take Off and Landing) capabilities, allowing the adoption of complex propulsion layouts otherwise impossible to manage with conventional fossil powered machines. Despite significant advancements in lithium-based cell technologies, the energy densities achieved by current storage systems remain insufficient to ensure extended operational autonomy. Hybrid systems represent an effective compromise, combining the high energy density of conventional fuels with agile power management of electric storage systems. In this work, the authors investigate the design, modelling, and control of an innovative autonomous compound helicopter equipped with a hybrid propulsion system. For this purpose, a comprehensive digital twin has been developed, capable of simulating the interactions among the vehicle, propulsion system, and energy management systems under a predefined mission profile. Full article

This study analyzes how dynamic disturbances influence the decisions made during the human supervision of autonomous unmanned aerial vehicles. While previous research has primarily focused on control algorithms and system stability, the effect of disturbances originating from system dynamics on operator intervention behavior has been less extensively investigated. To examine this problem, a hardware-in-the-loop (HIL) experimental framework was developed, which is based on a previously validated unmanned aerial vehicles (UAVs) test platform and was adapted in this study to enable the investigation of human supervisory decision-making. Participants observed the behavior of an autonomously operating system under controlled disturbances and were provided with the possibility to intervene by activating an emergency landing mechanism. The results indicate that the disturbance intensity had a significant effect on intervention decisions, while the reaction times did not show notable differences. This finding suggests that supervisory behavior is primarily determined by the evaluation of the system state rather than by timing characteristics. It also identifies that subjective risk perception plays a decisive role in the formation of intervention decisions, indicating the presence of an implicit decision threshold for participant behavior. The research findings offer a novel approach to the interpretation of human–UAV interaction by emphasizing the role of system dynamics in shaping user decisions. The presented method may provide a foundation for the development of predictive and adaptive supervisory systems that take into account the characteristics of human decision-making, thereby contributing to the design of safer and more efficient autonomous systems. Full article

With the rapid development of electric Vertical Take-Off and Landing (eVTOL) aircraft for urban air mobility, ensuring safe operation in complex low-altitude environments remains a major challenge. In particular, interactions with non-cooperative airspace users introduce uncertainties that are difficult to fully handle with current autonomous systems. To better understand these risks, a Monte Carlo simulation framework is developed to model random encounters between an eVTOL and uncontrolled unmanned aerial vehicles. The results show a relatively low collision probability of approximately 0.18%. However, a large proportion of encounters fall within an intermediate separation range of 100–200 m, indicating a high-frequency conflict region that still requires continuous monitoring and decision-making. Based on these observations, Fault Tree Analysis (FTA) is further applied to evaluate system-level safety under different operational architectures, incorporating revised assumptions on human reliability and system interactions. The results suggest that the inclusion of human pilots can contribute to reducing the probability of catastrophic failure compared with fully autonomous configurations, particularly in uncertain and non-cooperative scenarios. These findings suggest that, although full autonomy is a long-term goal, current intelligent systems still face limitations in dealing with uncertain and non-cooperative scenarios in urban airspace. In such situations, human operators can provide additional situational awareness and flexible decision-making, improving overall system robustness. Overall, a phased transition toward full autonomy, starting from a human–machine collaborative approach, appears to be a practical path to ensure safety, support certification, and enable the deployment of eVTOL systems. Full article

Effective port and coastal surveillance require persistent monitoring, flexible deployment, and reliable target detection in dynamic maritime environments. This paper presents a system- and deployment-oriented autonomous tethered drone architecture, integrated with AI-based perception, for persistent maritime surveillance in ports and coastal areas. Mounted on a moving maritime platform and powered through a tether, the drone provides a persistent elevated viewpoint without the endurance limitations of conventional battery-powered Unmanned Aerial Vehicles (UAVs). The system combines maritime platform integration, tethered flight operation, fail-safe and safety mechanisms, and a distributed Artificial Intelligence (AI) pipeline for real-time object detection and tracking. The perception module is based on YOLOv8m for vessel detection and BoT-SORT for multi-object tracking, enabling continuous monitoring of maritime targets in realistic operational scenarios. Field trials conducted from moving vessels in maritime environments demonstrate autonomous take-off and landing, stable surveillance operation under realistic wind and wave conditions, and effective vessel detection and tracking on real image sequences. The results show the potential of AI-enabled tethered drone surveillance as a persistent and operationally relevant tool for maritime monitoring and security. Full article

Water scarcity and increasing climate variability have strengthened the demand for effective weather-modification technologies such as cloud seeding. In Thailand, conventional manned rainmaking aircraft remain constrained by operational range, safety risks, and sustainability considerations, motivating the development of electric vertical take-off and landing unmanned aerial vehicles (eVTOL-UAVs). This paper proposes a mission-driven conceptual design and optimization framework for a cloud-seeding eVTOL-UAV, and extends it to reliability-based design optimization (RBDO) under uncertainty. The design task is formulated as a five-objective many-objective optimization problem with the following objectives: minimizing take-off weight, turn radius, and probability of failure, while maximizing endurance and climb rate, subject to stability/control and performance constraints. Ten state-of-the-art many-objective metaheuristics are benchmarked and solve the problem, and their performance is assessed using hypervolume (HV), inverted generational distance (IGD), runtime, and Friedman rank statistics. Results show that AGEMOEAII and PREA consistently provide the most competitive solution-set quality (HV/IGD) with comparable computational cost across algorithms. A deterministic–reliability comparison further demonstrates a clear robustness gap. Five representative Pareto designs from the best-performing optimizer are reported to illustrate practical trade-offs and support decision-making for sustainable, autonomous cloud-seeding operations. Full article

This paper introduces an innovative radar-based navigation approach designed to address precision navigation challenges for Vertical Take-Off and Landing (VTOL) platforms. The key innovation is a unique extension of radar altimeter functionality when used with ground navigation beacons realized by Radio Frequency (RF) repeaters. The proposed solution is intended to support helicopter and Advanced Air Mobility (AAM) autonomous operations. Full article

The topic of reusability in the launch vehicle sector is of current worldwide interest, as a shift from expendable to partially reusable configurations can be observed. Based on the work realized in a nationally funded Nucleu project, INCAS has developed a multidisciplinary optimization environment (MDO algorithm) capable of generating preliminary launcher concepts that also take into account the recovery process needed to reuse key major assemblies, such as the lower stage. The current paper analyzes a set of five key missions of interest (with different launch locations) and their influence on the preliminary definition of a family of partially reusable microlaunchers capable of inserting the same payload (100 kg) into different inclination, low Earth orbits (ranging from almost equatorial to Sun-synchronous orbits). The proposed microlauncher concepts are based on a two-stage constant-diameter architecture, where the first stage is recovered via a downrange, autonomous vertical landing mission, while the upper stage is expendable. The main scope of this paper is to quantify the impact of different key mission requirements on the characteristics of the reusable microlauncher that minimize its lift-off mass. This approach also correlates to the definition of reusable launch vehicle concepts that have reduced the associated costs of development, production, and operation. Full article

Promoting the value realization of ecological products is a central issue in practicing the concept that “lucid waters and lush mountains are invaluable assets.” This is particularly urgent for alpine regions, which are vital ecological security barriers but face stringent developmental constraints. This study takes Golog Tibetan Autonomous Prefecture in Qinghai Province as a case study. It establishes a Gross Ecosystem Product (GEP) accounting framework tailored to the characteristics of alpine ecosystems and conducts continuous empirical accounting for the period 2020–2023. The findings reveal that: (i) The total GEP of Golog is immense (reaching 655.586 billion yuan in 2023) but exhibits significant dynamic non-stationarity driven by climatic fluctuations, with a coefficient of variation as high as 11.48%. (ii) The value structure of the GEP is highly unbalanced, with regulatory services contributing over 97.6%. Water conservation and biodiversity protection are the two pillars, highlighting its role as a supplier of public ecological products and the predicament of market failure. (iii) The spatial distribution of GEP is highly heterogeneous. Maduo County, comprising 34% of the prefecture’s land area, contributes 48% of its total GEP, with its value per unit area being 1.68 times that of Gande County, revealing the spatial agglomeration of key ecosystem services. To address the dynamic, structural, and spatial constraints identified by these quantitative features, this paper proposes synergistic realization pathways centered on “monetizing regulatory services,” “precision policy regulation,” and “capacity and institution building”. The aim is to overcome the systemic bottlenecks—“difficulties in measurement, trading, coarse compensation, and weak incentives”—in alpine ecological functional zones. This provides a systematic theoretical and practical solution for fostering a virtuous cycle between ecological conservation and regional sustainable development. Full article

The design of control systems for aircraft autonomous takeoff and landing, as well as full-flight-envelope operations—including taxiing, takeoff, climb, cruise, descent, and landing—presents significant challenges. These challenges arise from the strong nonlinear coupling between airframe dynamics and landing-gear–ground interactions, phase-specific disturbances (e.g., uneven runways and aerodynamic uncertainties), and the difficulty in coordinating trajectory tracking, attitude stability, and landing gear load management across all phases. To address these issues, this paper proposes a ground-contact-aware adaptive nonlinear dynamic inversion (GCA-ANDI) control system for fixed-wing UAVs with tricycle landing gear. Unlike conventional cascade PID or pure NDI methods, which lack phase awareness or explicit ground interaction modeling, GCA-ANDI integrates real-time ground contact perception and phase-adaptive mechanisms. This approach establishes a unified control system framework that synergizes high-level trajectory tracking, attitude stabilization, and landing gear load regulation. Simulation results demonstrate that the proposed GCA-ANDI control system outperforms benchmark methods across the full flight envelope. It achieves significant improvements in tracking accuracy, effectively suppresses attitude fluctuations induced by ground interactions or aerodynamic uncertainties, balances multi-wheel load distribution, and reduces structural impact during takeoff and landing. This study enhances robustness against phase-specific disturbances and provides a theoretical and technical foundation for high-precision, safe, and reliable full-flight-envelope autonomy in aircraft. Full article