Aiming at the scarcity of Low Earth Orbit (LEO) satellite spectrum resources, this paper proposes an algorithm of interference signal feature extraction and pattern classification based on deep learning to further improve the stability of satellite–ground communication links. The algorithm can successfully predict the interference signal pattern, start–stop time, frequency change range and other parameters, and has the advantages of excellent interference detection performance, high detection accuracy and small parameter prediction error, etc. It can be applied in the field of channel monitoring of communication satellite-to-ground communication links, and realize the repeated and efficient utilization of spectrum resources. Experiments show that the precision and recall of the algorithm for detecting five kinds of interference signals are all close to 100%, the prediction error of starting and ending time is less than 4 ms, and the prediction error of starting and ending frequency is less than 6 KHz.

In this paper, the long short-term memory with dense neural network (LSTM-DNN) is first introduced to calculate marine drifting trajectory. Based on the Internet of Things technology and the LSTM-DNN algorithm, the marine drifting trajectory model is established. In this model, the information such as wind field, temperature field, ocean current motion field, and target attributes are included, and the influences of the above information on the trajectory model are studied in detail. In order to verify the proposed model, the marine experiments are carried out in the end. The results show that the predicted trajectory data matches well with the experimental trajectory data. By introducing DNN into the algorithm, computational accuracy of drifting trajectory can be significantly improved compared with the conventional LSTM-based prediction model. A detailed comparison of the two algorithms has also been given in the paper. The proposed remote sensing of marine drifting trajectory model can provide a high accurate trajectory prediction and will lead an important guidance in the marine search and rescue work.

This study investigates the properties of band gaps of circular core filling fractal phononic crystals (CCFFPCs), specifically focusing on the impact of different filling positions on the frequency of band gaps. The research demonstrates that core filling at the central positions significantly influences the formation and widening of low-frequency band gaps, while filling at corner edges predominantly affects mid-frequency band gaps, and filling at edge centers effectively opens and broadens high-frequency band gaps. These results reveal the relationship between filling positions and band gap tuning, providing a theoretical foundation for precise band gap control across a full frequency range. Moreover, this study is the first to systematically clarify the impact of core filling positions on band gap frequencies, expanding the design strategies for band gaps in fractal phononic crystals. Furthermore, this study employs genetic algorithm optimization to achieve the maximum band gap width at different frequencies, enhancing the practical value of fractal phononic crystals in engineering applications. This research deepens theoretical understanding and provides valuable guidance for optimizing their use in broadband acoustic control and energy harvesting applications.

The accurate detection of small, densely packed and arbitrarily oriented aircraft in high-resolution remote sensing imagery remains highly challenging due to significant variations in object scale, orientation and background complexity. Existing detection frameworks often struggle with insufficient representation of small objects, instability of rotated bounding box regression and inability to adapt to complex background. To address these limitations, we propose SPOD-YOLO, a novel detection framework specifically designed for small aircraft in remote sensing images. This method is based on YOLOv11, combined with the feature attention mechanism of swintransformer, through targeted improvements on cross-scale feature modelling, dynamic convolutional adaptation, and rotational geometry optimization to achieve effective detection. Additionally, we have constructed a new dataset based on satellite remote sensing images, which has high density of small aircraft with rotated bounding box annotations to provide more realistic and challenging evaluation settings. Extensive experiments on MAR20, UCAS-AOD and the constructed dataset demonstrate that our method achieves consistent performance gains over state-of-the-art approaches. SPOD-YOLO achieves an 4.54% increase in mAP50 and a 11.78% gain in mAP50:95 with only 3.77 million parameters on the constructed dataset. These results validate the effectiveness and robustness of our approach in complex remote sensing scenarios, offering a practical advancement for the detection of small objects in aerospace imagery.