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This paper investigates ionospheric response characteristics from multiple perspectives based on globally distributed GNSS data and products, ionosonde data, FORMOSAT-7/COSMIC-2 occultation data, and Swarm satellite observations caused by the total solar eclipse of 8 April 2024 across North and Central America. The results show that both GNSS-derived TEC products have detected the ionospheric TEC degradation triggered by the total solar eclipse, with the maximum degradation exceeding 10 TECU. The TEC data from nine GNSS stations in the path of the maximum eclipse reveal that the intensity of ionospheric TEC degradation is related to the spatial location, with the maximum degradation value of the ionospheric TEC being about 14~23 min behind the moment of the maximum eclipse. Additionally, a negative anomaly of foF2 with a maximum of more than 2.7 MHz is detected by ionosonde. In the eclipse region, NmF2 and hmF2 show trends of decrease and increase, with percentages of variation of 40~70% and 4~16%, respectively. The Ne profile of the Swarm-A satellite is significantly lower than the reference value during the eclipse period, with the maximum negative anomaly value reaching 11.2 × 105 el/cm3, and it failed to show the equatorial ionization anomaly. [ABSTRACT FROM AUTHOR]

To solve the problem of traditional pollution detection methods on the prevention and control of pollution flashover of transmission line insulators,the non-contact and high-resolution hyperspectral technology is used to study the on-line pollution detection technology. At the same time,an insulator pollution level identification technology based on wavelet packet energy spectrum feature optimization is proposed to effectively extract the spectral features reflecting the pollution degree and weaken the influence of redundancy and interference information. Firstly,the spectral images of insulator samples with different pollution levels are segmented to extract the spectral mean curve of pixels in uniform pollution area. Secondly,the difference of light intensity uniformity and environmental noise of different images are preprocessed,and the differentiability among different pollution levels is improved by logarithmic transformation. Thirdly,the feature extraction of wavelet packet energy spectrum is performed on the preprocessed spectral lines. Finally,a pollution level recognition model based on the proposed features and support vector machines (SVM) is established. The experimental results show that the SVM pollution level recognition model based on wavelet energy spectrum features achieves 99.8%,and it has higher recognition accuracy than full band data or principal component analysis (PCA) feature data does.

It is very challenging to accurately segment smoke images because smoke has some adverse vision characteristics, such as anomalous shapes, blurred edges, and translucency. Existing methods cannot fully focus on the texture details of anomalous shapes and blurred edges simultaneously. To solve these problems, a Dense Multi‐scale context and Asymmetric pooling Embedding Network (DMAENet) is proposed to model the smoke edge details and anomalous shapes for smoke segmentation. To capture the feature information from different scales, a Dense Multi‐scale Context Module (DMCM) is proposed to further enhance the feature representation capability of our network under the help of asymmetric convolutions. To efficiently extract features for long‐shaped objects, the authors use asymmetric pooling to propose an Asymmetric Pooling Enhancement Module (APEM). The vertical and horizontal pooling methods are responsible for enhancing features of irregular objects. Finally, a Feature Fusion Module (FFM) is designed, which accepts three inputs for improving performance. Low and high‐level features are fused by pixel‐wise summing, and then the summed feature maps are further enhanced in an attention manner. Experimental results on synthetic and real smoke datasets validate that all these modules can improve performance, and the proposed DMAENet obviously outperforms existing state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Fire incidents near power transmission lines pose significant safety hazards to the regular operation of the power system. Therefore, achieving fast and accurate smoke detection around power transmission lines is crucial. Due to the complexity and variability of smoke scenarios, existing smoke detection models suffer from low detection accuracy and slow detection speed. This paper proposes an improved model for smoke detection in high-voltage power transmission lines based on the improved YOLOv7-tiny. First, we construct a dataset for smoke detection in high-voltage power transmission lines. Due to the limited number of real samples, we employ a particle system to randomly generate smoke and composite it into randomly selected real scenes, effectively expanding the dataset with high quality. Next, we introduce multiple parameter-free attention modules into the YOLOv7-tiny model and replace regular convolutions in the Neck of the model with Spd-Conv (Space-to-depth Conv) to improve detection accuracy and speed. Finally, we utilize the synthesized smoke dataset as the source domain for model transfer learning. We pre-train the improved model and fine-tune it on a dataset consisting of real scenarios. Experimental results demonstrate that the proposed improved YOLOv7-tiny model achieves a 2.61% increase in mean Average Precision (mAP) for smoke detection on power transmission lines compared to the original model. The precision is improved by 2.26%, and the recall is improved by 7.25%. Compared to other object detection models, the smoke detection proposed in this paper achieves high detection accuracy and speed. Our model also improved detection accuracy on the already publicly available wildfire smoke dataset Figlib (Fire Ignition Library). [ABSTRACT FROM AUTHOR]

To solve the problem of air group intention recognition under the condition of imbalance samples, a bidirectional gated recurrent unit network air group intention recognition method based on the attention mechanism is proposed. The state information of the air group is encoded into timing features. Prior information is used to encapsulate into sample labels. An improved borderline-synthetic minority oversampling method is proposed to generate a suitable sample set. The timing features of aerial group targets can be deeply extracted by bidirectional mechanism. Attention mechanism is introduced to improve the network' s ability to capture more discriminative features by assigning weights to deep information. Experiment simulation results show that proposed method has better classification effect and higher training efficiency for the intent recognition problem of the air group in the case of imbalance samples. [ABSTRACT FROM AUTHOR]

In order to realize the refined line inspection management of transmission lines' improve its operation and maintenance efficiency, realize satellite intelligent inspection, and accurately find the defects and hidden dangers of towers and transmission lines, the paper took the coordinates of transmission line towers in Kunming City, Yunnan Province as an example and proposed a method to calibrate the coordinates of transmission towers using satellite images. The method first uses the reference base - map data as the basis to match the control points and uses the digital elevation model % DEM) to perform geometric correction on the original remote sensing image. Then combined with such technologies as shadow detection and edge detection and visual interpretation, the calibrated tower coordinates are obtained. The experiment verified the geometric con'ection accuracy of the SuperView - 1 % SV1) and Gaofen -- 2 % GF2 ) sateHite images in the Kunming area, and the errors in the plane after conbection were 0.931 and 1.387 m, respectively. In addition, the experiment verified the calibration accuracy of the old tower coordinates on the two lines. The results show that the plane accuracy of the tower has increased from 13. 811 m and 8. 256 m to 5. 970 m and 5. 104 m, respectively, which meets the basic power grid requirements. This method can realize the calibration of the tower coordinates, reduce the workload of manual inspection, and improve the efficiency of line inspection. With the explosive growth of remote sensing image data, multi -- source images from the space and ground will continue to be combined, and the technology for the positioning of transmission towers based on satellite remote sensing images will have a broader development prospect. [ABSTRACT FROM AUTHOR]