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Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence of extensive forest fires, thereby safeguarding both forest resources and human well-being. Although drone patrols have emerged as a primary method for forest-fire prevention, the unique characteristics of forest-fire images captured from high altitudes present challenges. These include remote distances, small fire points, smoke targets with light hues, and complex, ever-changing background environments. Consequently, traditional target-detection networks frequently exhibit diminished accuracy when handling such images. In this study, we introduce a cutting-edge drone-based network designed for the detection of forest fires and smoke, named FSNet. To begin, FSNet employs the YOCO data-augmentation method to enhance image processing, thereby augmenting both local and overall diversity within forest-fire images. Next, building upon the transformer framework, we introduce the EBblock attention module. Within this module, we introduce the notion of "groups", maximizing the utilization of the interplay between patch tokens and groups to compute the attention map. This approach facilitates the extraction of correlations among patch tokens, between patch tokens and groups, and among groups. This approach enables the comprehensive feature extraction of fire points and smoke within the image, minimizing background interference. Across the four stages of the EBblock, we leverage a feature pyramid to integrate the outputs from each stage, thereby mitigating the loss of small target features. Simultaneously, we introduce a tailored loss function, denoted as L f o r e s t , specifically designed for FSNet. This ensures the model's ability to learn effectively and produce high-quality prediction boxes. We assess the performance of the FSNet model across three publicly available forest-fire datasets, utilizing mAP, Recall, and FPS as evaluation metrics. The outcomes reveal that FSNet achieves remarkable results: on the Flame, Corsican, and D-Fire datasets, it attains mAP scores of 97.2%, 87.5%, and 94.3%, respectively, with Recall rates of 93.9%, 87.3%, and 90.8%, respectively, and FPS values of 91.2, 90.7, and 92.6, respectively. Furthermore, extensive comparative and ablation experiments validate the superior performance of the FSNet model. [ABSTRACT FROM AUTHOR]

With the penetration of power electronics in more electric aircraft (MEA) power systems, the number of constant power loads continues increasing and the characteristics of negative resistance of their terminal impedances tend to impair the system stability. The variable-frequency ac (VFAC) three-stage generator (TSG) is the main power supply of the MEA VFAC power system, and it is essential to establish its small-signal impedance model for analyzing the system stability. In this article, an accurate small-signal impedance model of the VFAC-TSG is established under the synchronous d-q frame. On this basis, the terminal characteristics of the VFAC-TSG in open-loop and closed-loop modes are analyzed in detail. Furthermore, the impacts of the rotation speed and digital control delay on the terminal characteristics are demonstrated. Finally, the experimental results from a hardware-in-the-loop platform are provided to verify the accuracy of the proposed impedance model and the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Populus deltoides is a fast-growing, large tree and one of the largest North American hardwood trees. In this study, the complete chloroplast (cp) genome sequence of P. deltoides is characterized. The whole cp genome was assembled to 156,867 bp, including a large single copy (LSC) region of 85,534 bp, a small single copy (SSC) region of 16,513 bp and a pair of inverted repeats (IRs) region of 27,410 bp. The base content of the P. deltoides cp genome is A (32.0%), T (31.3%), C (18.0%), and G (18.7%), and AT bases occupy a large proportion of the cp genome. The neighbor-joining phylogenetic analysis with 20 cp genomes from the Salicaceae family showed that P. deltoides is sister to Populus davidiana. These will provide for the evolutionary and biological studies in Salicaceae family. [ABSTRACT FROM AUTHOR]