Urban airspace, characterized by densely packed high-rise buildings, presents complex and dynamically changing environmental conditions. It brings potential risks to UAV flights, such as the risk of collision and accidental entry into no-fly zones. Currently, mainstream path planning algorithms, including the PSO algorithm, have issues such as a tendency to converge to local optimal solutions and poor stability. In this study, an improved particle swarm optimization algorithm (LGPSO) is proposed to address these problems. This algorithm redefines path planning as an optimization problem, constructing a cost function that incorporates safety requirements and operational constraints for UAVs. Stochastic inertia weights are added to balance the global and local search capabilities. In addition, asymmetric learning factors are introduced to direct the particles more precisely towards the optimal position. An enhanced Lévy flight strategy is used to improve the exploration ability, and a greedy algorithm evaluation strategy is designed to evaluate the path more quickly. The configuration space is efficiently searched using the corresponding particle positions and UAV parameters. The experiments, which involved mapping complex urban environments with 3D modeling tools, were carried out by simulations in MATLAB R2023b to assess their algorithmic performance. The results show that the LGPSO algorithm improves by 23% over the classical PSO algorithm and 18% over the GAPSO algorithm in the optimal path distance under guaranteed security. The LGPSO algorithm shows significant improvements in stability and route planning, providing an effective solution for UAV path planning in complex environments.

Information spreading dynamics on the temporal network is a hot topic in the field of network science. In this paper, we propose an information spreading model on an activity-driven temporal network, in which a node is accepting the information dependents on the cumulatively received pieces of information in its recent two steps. With a generalized Markovian approach, we analyzed the information spreading size, and revealed that network temporality might suppress or promote the information spreading, which is determined by the information transmission probability. Besides, the system exists a critical mass, below which the information cannot globally outbreak, and above which the information outbreak size does not change with the initial seed size. Our theory can qualitatively well predict the numerical simulations.

Reliable beam management in Unmanned Aerial Vehicle (UAV)-assisted Integrated Sensing and Communication (ISAC) systems needs accurate trajectory prediction and a clear sense of prediction risk. Most existing methods use deterministic future positions or raw uncalibrated uncertainty. Under high mobility and uncertainty, this leads to unreliable beam decisions. We design a control-oriented probabilistic trajectory prediction framework. It uses calibrated trajectory uncertainty as a risk signal for adaptive beam management. The framework first combines motion history and visual context to predict trajectory distributions. Split conformal calibration turns raw Gaussian uncertainty into statistically reliable risk bounds. A codebook-constrained beam management strategy adjusts beamwidth based on the calibrated spatial risk. This balances beamforming gain, coverage robustness, and switching stability. Tests on UAV data show better prediction accuracy than representative probabilistic baselines. The raw uncertainty remains under-calibrated, and conformal calibration is therefore applied to improve its reliability before beam-control decisions. Using the calibrated uncertainty for beam control improves communication coverage and cuts outages and severe misalignment in high-risk situations. Calibrated predictive uncertainty can serve as an actionable control variable for robust beam management in dynamic UAV-assisted ISAC environments.

In the task of unmanned aerial vehicle UAV-based small object detection within complex aerial scenes, objects are characterized by significant scale variation, extremely low pixel occupancy, and dense distribution. These factors severely limit the feature representation capability and fine-grained information modeling of detectors, leading to frequent false positives and missed detections. Multi-modal image fusion, which leverages complementary information from different sensing modalities, is widely regarded as an effective approach to enhance detection performance. To improve the accuracy and robustness of object detection in aerial scenes, this paper proposes YOLO-CH, a multi-modal fusion detection method based on a dual-stream YOLOv11 architecture. The method develops parallel dual-stream feature extraction branches to encode modality-specific features from visible and infrared images. A Cross-modal Feature Transformer (CFT) module is introduced within the backbone network by step, which employs a self-attention mechanism to model intra-modal and inter-modal global dependencies, achieving deep feature interaction and enhanced representation. Furthermore, to mitigate the issue where multi-scale and small object features are susceptible to background interference, we redesigned and optimized the structure of the neck to form a high-level semantic screening feature pyramid network (High-level Screening Feature Pyramid Network, HSFPN). This module utilizes high-level semantic information in a top-down manner to refine low-level detail representations, thereby improving small object discrimination. Experimental results on the VEDAI and AVMS datasets demonstrate that the proposed method outperforms baseline models in both detection accuracy and robustness, while maintaining strong adaptability and performance across different input scales.

Unmanned Aerial Vehicles (UAVs) are gradually emerging as key platforms for Integrated Sensing, Communication, and Computation (ISCC) systems in next-generation wireless networks. However, strict resource constraints and task coupling make static allocation inefficient in dynamic environments. This paper studies a UAV-driven ISCC system in which a single UAV dynamically allocates communication bandwidth, sensing resources, and computing power. Considering that sensing data in mission-critical applications is highly time-sensitive, minimizing the response time is paramount. To reduce system latency while maintaining sensing quality and energy efficiency, we propose D3PG-Light, a deployment oriented and stability-enhanced refinement of the deep reinforcement learning framework, specifically tailored for real-time resource scheduling under UAV hardware constraints. D3PG-Light incorporates an adaptive gradient stabilization mechanism, Long Short-Term Memory (LSTM), and feature fusion to enhance training stability. Simulation results based on real air–ground channel measurements show that D3PG-Light converges faster and achieves more stable learning behavior than DDPG, TD3, and the original D3PG. In particular, the proposed method reduces the 95th-percentile latency from over 100 ms to approximately 24 ms, achieves higher converged reward values, and requires fewer than 50 k model parameters. These results demonstrate the effectiveness of D3PG-Light for latency-sensitive UAV-ISCC applications.

To address the issue of missing position in flight trajectory data collected by Automatic Dependent Surveillance-Broadcast (ADS-B) systems, a flight trajectory tensor completion model based on truncated Schatten p-norm minimization is proposed. First, the low-rank characteristics of the trajectory set are validated using Singular Value Decomposition (SVD); based on this, the data is transformed into a three-dimensional tensor structure. Next, a regularization strategy combining the Schatten p-norm with a singular value truncation mechanism is introduced to construct the trajectory tensor completion model, which suppresses noise and interference from minor components while preserving the main variation patterns of the trajectories. Finally, the model is optimized and solved using the Alternating Direction Method of Multipliers (ADMM) to obtain the completed trajectories. Taking historical ADS-B trajectory data from Orly Airport to Toulouse Airport as an example, the completion results of the proposed model under different missing patterns, missing rates, and flight phases are analyzed from both qualitative and quantitative perspectives. Experimental results show that compared with other representative models, the proposed model achieves the best completion performance under different missing patterns and missing rates; the completion performance during the cruise phase is better than during the ascent and descent phases. The proposed model can serve as a preprocessing technique for flight trajectory data in air traffic, providing more complete and reliable data support for various downstream applications.