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In remote sensing image fusion, the conventional Convolutional Neural Networks (CNNs) extract local features of the image through layered convolution, which is limited by the receptive field and struggles to capture global features. Transformer utilizes self-attention to capture long-distance dependencies in images, which has a global receptive field, but the computational cost for high-resolution images is excessively high. In response to the above issues, this paper draws inspiration from the FusionNet network, harnessing the local detail acquisition capability of CNNs and the global data procuring capacity of Transformer. It presents a novel method for remote sensing image sharpening named Guided Filtering-Cross Stage Partial Network-Transformer, abbreviated as GF-CSTNet. This solution unifies the strengths of Guided Filtering (GF), Cross Stage Partial Network (CSPNet), and Transformer. Firstly, this method utilizes GF to enhance the acquired remote sensing image data. The CSPNet and Transformer structures are then combined to further enhance fusion performance by leveraging their respective advantages. Subsequently, a Rep-Conv2Former method is designed to streamline attention and extract diverse receptive field features through a multi-scale convolution modulator block. Simultaneously, a reparameterization module is constructed to integrate the multiple branches generated during training into a unified branch during inference, thereby optimizing the model's inference speed. Finally, a residual learning module incorporating attention has been devised to augment the modeling and feature extraction capabilities of images. Experimental results obtained from the GaoFen-2 and WorldView-3 datasets demonstrate the effectiveness of the proposed GF-CSTNet approach. It effectively extracts detailed information from images while avoiding the problem of spectral distortion. [ABSTRACT FROM AUTHOR]

AbstractA novel square hierarchical structure axially varying thickness (SHSAVT) structure, drawing inspiration from origami, has been introduced. And a systematic numerical analysis has been conducted to assess the crashworthiness of this innovative structure when subjected to axial impacts. The findings of the crashworthiness analysis demonstrate that SHSAVT structures with k values greater than zero exhibit a remarkable capacity for energy absorption (EA) while maintaining the same mass, and they notably reduce the initial peak crushing force (IPCF). Additionally, it was observed that as the hierarchy level increases, the action force experiences less fluctuation, resulting in increased EA, thus enhancing the overall crashworthiness of the structure while reducing IPCF. Moreover, when compared to structures with the same wall thickness, SHSAVT with k values greater than zero demonstrated a substantial 54.46% decrease in maximum IPCF and a significant 112.27% increase in maximum crushing force efficiency (CFE). Furthermore, higher k values correlated with lower IPCF and higher CFE, SHSAVT with k values greater than zero as superior in terms of IPCF and CFE performance. [ABSTRACT FROM AUTHOR]