Drones

Urban drone delivery operates as a city-scale service network jointly structured by launch nodes, flight links, pickup lockers, pedestrian access, and outlet locations. Using 3569 street blocks and 21,366 food-delivery outlets in central Nanjing, this study develops an agent-based scenario model for pre-implementation assessment that links effective consumer access, node-level demand allocation, and outlet relocation and examines outlet-side access impedance together with 81 infrastructure configurations. Under the baseline configuration, flight links potentially cover all blocks, while only 22.6% satisfy the last-mile locker-access condition. Relative to the common road-delivery baseline, the standard deviational ellipse (SDE) area of the simulated outlet distribution contracts by a mean of 25.03% across ten runs, and the mean number of blocks with outlets decreases from 3460 to 3122.8. As outlet-to-node access impedance increases, the mean share of outlets within 1 km of launch nodes rises from 31.68% to 65.07%. Across the 81 configurations, mean SDE-area changes range from −46.69% to +21.62%. Within the tested levels, the pickup-locker configuration and flight-link distance are associated with larger differences in SDE response; walking radius mainly affects effective consumer access, whereas launch-node configuration mainly alters outlet–node proximity. The study provides a spatial screening framework for comparing urban drone-delivery networks along the complete delivery chain before detailed fleet, routing, and operational design.

Non-cooperative signal recognition is essential for spectrum awareness and interference management in unmanned aerial vehicle (UAV) communications, where unknown emitters, jamming threats, and low signal-to-noise ratio (SNR) make modulation classification and parameter estimation particularly challenging. Existing approaches often treat modulation classification, parameter estimation, and semantic reasoning as isolated tasks, making it difficult to exploit the complementarity of cross-domain information. This paper proposes PeirceTriadicNet (PTN), a Peircean triadic-relation network for this problem. The Representamen branch processes observable signal representations (IQ waveforms, time–frequency maps, and power spectral densities), the Object branch embeds physical source parameters (modulation type, symbol rate, carrier-frequency offset, roll-off, bandwidth, and SNR), and the Interpretant branch incorporates modulation-family and SNR priors for semantic reasoning. The final model, PTN-ResIcon, fuses the three branch embeddings with fixed equal weights ( ). On RML-S-Full-Std, a UAV-oriented synthetic dataset that re-generates the RML2016.10a modulation set under representative UAV link conditions (Rician fading, Doppler shift, and barrage, partial-band, follower, and deceptive jamming), with 220,000 IQ frames of 11 modulation formats over $-20$ to $+18$ dB, per-frame physical-parameter and semantic annotations, and a released open-data generation script—the covered PSK/QAM OFDM video downlinks and FSK/CPM frequency-hopping control links mirror the signal families observed in real commercial drone RF links—PTN-ResIcon achieves an overall modulation-recognition accuracy of 80.67%, which is 17.6 percentage points higher than the Representamen branch alone and 24.0, 20.1, and 20.2 percentage points higher than CNN2, CNN-LSTM, and ResNet1D, respectively; the +18 dB accuracy is 91.50%. On the real DroneRFa corpus (USRP-acquired recordings of four DJI devices at three flight distances), the complete triadic configuration reaches 72.50% device-identification accuracy, demonstrating that the framework transfers from synthetic to genuine UAV signals. An SNR-biased learnable-attention variant, PTN-ResIcon-Learned, dynamically adjusts the triadic weights from the oracle SNR and branch confidences; it reaches 76.86%, showing that fixed equal-weight triadic fusion itself provides the main gain and that overly aggressive SNR gating slightly degrades generalization. In the UAV-swarm scenario of RML-S-Full-Std, PTN-ResIcon maintains an accuracy of 81.44%, demonstrating its applicability to UAV communication environments and to spectrum-awareness tasks in emergency-response missions.

The use of Sentinel-2 satellite imagery for crop monitoring is constrained by its native spatial resolution. To address this limitation, data fusion-based super-resolution techniques have emerged as a promising approach for generating imagery with enhanced spatial detail. In this study, we propose TVDSR, a Sentinel-2 and UAV image fusion model that integrates temporal and spectral information through a Bidirectional ConvLSTM-based architecture. The images used for super-resolution were constructed from spectral compositions of five vegetation indices. Model performance was evaluated using 15 acquisition dates, including 11 Sentinel-2 images acquired in 2024 and 4 acquired in 2025. Two independent dates were reserved for validation using reconstruction quality metrics. The proposed model was compared against three state-of-the-art reference models: S³-ESRGAN, DeepSent, and UAV-Net. UAV-Net achieved the best performance for the 2024 validation image (RMSE = 0.128, MAE = 0.10, PSNR = 24.06 dB, and SSIM = 0.86). However, TVDSR obtained the best results for the 2025 evaluation image (RMSE = 0.149, MAE = 0.115, PSNR = 22.60 dB, and SSIM = 0.85) and exhibited the lowest performance degradation across acquisition dates, indicating superior temporal generalization capability. Furthermore, TVDSR more consistently preserved the derived vegetation indices, reflecting a better retention of spectral information associated with crop condition, while producing reconstructions that were visually closer to the reference UAV imagery. The joint integration of temporal and spectral information constitutes an effective strategy for improving the robustness and generalization capability of Sentinel-2 image super-resolution for crop monitoring, precision agriculture, crop condition assessment, and agricultural management applications.