Energy detection based spectrum sensing has been proposed and studied widely for primary user (PU) signal detection in the literature. With the help of multiple secondary users (SU) in the cognitive radio network, various cooperative sensing schemes are investigated to enhance the energy detection performance. However, the impacts of noise power fluctuating effects on the detection performance in multipath fading and user cooperative sensing scenarios are seldom evaluated in relevant literature. In this paper, a modified version of the classic energy detection model is presented, where the noise power uncertainty (U) is introduced into threshold setting. With a reasonable approximate derivation, we provide accurate predictions of SNRwall constraints imposed by U in Rayleigh fading environments. In addition, by employing a simple hard decision fusion rule, the collaborative gain in spectrum sensing performance is also quantified with respect to U. Our analysis and numerical results confirm that collaboration can significantly improve the spectrum sensing performance in a noise power fluctuating environment.

Accurate semantic segmentation of high-resolution images captured by unmanned aerial vehicles (UAVs) is crucial for applications in environmental monitoring, urban planning, and precision agriculture. However, challenges such as class imbalance, small-object detection, and intricate boundary details complicate the analysis of UAV imagery. To address these issues, we propose Mamba-UAV-SegNet, a novel real-time semantic segmentation network specifically designed for UAV images. The network integrates a Multi-Head Mamba Block (MH-Mamba Block) for enhanced multi-scale feature representation, an Adaptive Boundary Enhancement Fusion Module (ABEFM) for improved boundary-aware feature fusion, and an edge-detail auxiliary training branch to capture fine-grained details. The practical utility of our method is demonstrated through its application to farmland segmentation. Extensive experiments on the UAV-City, VDD, and UAVid datasets show that our model outperforms state-of-the-art methods, achieving mean Intersection over Union (mIoU) scores of 71.2%, 77.5%, and 69.3%, respectively. Ablation studies confirm the effectiveness of each component and their combined contributions to overall performance. The proposed method balances segmentation accuracy and computational efficiency, maintaining real-time inference speeds suitable for practical UAV applications.

Tourist destinations thrive on sustainable development. Electric vertical take-off and landing (eVTOL) aircraft, representing energy-efficient advancements in aviation that are pivotal to advanced air mobility (AAM), have garnered attention. Yet, the discourse on eVTOLs’ role in tourism remains scant. This study, drawing from 450 samples in the Mogan Mountain Scenic Area, introduces the AAM-tourism acceptance model (ATAM). It integrates the theory of planned behavior (TPB) and technology acceptance model (TAM) theoretical frameworks, incorporating environmental consciousness, perceived safety, hedonic motivation, and personal innovativeness, assessing their influence on tourists’ eVTOL usage intention through a structural equation model (SEM). The results reveal that environmental consciousness significantly impacts hedonic motivation and perceived usefulness, driving eVTOL adoption. Furthermore, personal innovativeness influences intention through hedonic motivation and perceived behavioral control. Therefore, environmental consciousness and hedonic motivation align deeply with eVTOL attributes, both significantly positively influencing intention to use. Thus, the study validates eVTOL’s viability in tourism and its potential for sectoral expansion. Moreover, it offers insights into how psychological factors shape eVTOL adoption, guiding the promotion of eVTOL sightseeing services and informing research on AAM acceptance across various domains.

Air logistics transportation has become one of the most promising markets for the civil drone industry. However, the large flow, high density, and complex environmental characteristics of urban scenes make tactical conflict resolution very challenging. Existing conflict resolution methods are limited by insufficient collision avoidance success rates when considering non-cooperative targets and fail to take the temporal constraints of the pre-defined 4D trajectory into consideration. In this paper, a novel reinforcement learning-based tactical conflict resolution method for air logistics transportation is designed by reconstructing the state space following the risk sectors concept and through the use of a novel Estimated Time of Arrival (ETA)-based temporal reward setting. Our contributions allow a drone to integrate the temporal constraints of the 4D trajectory pre-defined in the strategic phase. As a consequence, the drone can successfully avoid non-cooperative targets while greatly reducing the occurrence of secondary conflicts, as demonstrated by the numerical simulation results.

The open-vocabulary understanding of UAV aerial images plays a crucial role in enhancing the intelligence level of remote sensing applications, such as disaster assessment, precision agriculture, and urban planning. In this paper, we propose an innovative open-vocabulary model for UAV images, which combines vision–language methods to achieve efficient recognition and segmentation of unseen categories by generating multi-view image descriptions and feature extraction. To enhance the generalization ability and robustness of the model, we adopted Mixup technology to blend multiple UAV images, generating more diverse and representative training data. To address the limitations of existing open-vocabulary models in UAV image analysis, we leverage the GPT model to generate accurate and professional text descriptions of aerial images, ensuring contextual relevance and precision. The image encoder utilizes a U-Net with Mamba architecture to extract key point information through edge detection and partition pooling, further improving the effectiveness of feature representation. The text encoder employs a fine-tuned BERT model to convert text descriptions of UAV images into feature vectors. Three key loss functions were designed: Generalization Loss to balance old and new category scores, semantic segmentation loss to evaluate model performance on UAV image segmentation tasks, and Triplet Loss to enhance the model’s ability to distinguish features. The Comprehensive Loss Function integrates these terms to ensure robust performance in complex UAV segmentation tasks. Experimental results demonstrate that the proposed method has significant advantages in handling unseen categories and achieving high accuracy in UAV image segmentation tasks, showcasing its potential for practical applications in diverse aerial imagery scenarios.

An efficient subband adaptive beamformer with perfect reconstruction general parameter filter banks (GPFB) for wideband array antenna is proposed in this paper, which is used to enhance performance of suppressing the interference and accelerate convergence speed of weights. Instead of the conventional FFT filter banks, GPFB are utilized to partition the frequency band into several subbands without aliasing between them. With subbands running at downsampling and in manner of parallel, the loads of computational processing costs, compared with traditional fullband methods, can be alleviated. Deep suppression of the interference and convergence speed of weights are also improved.