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Current signal processing for synthetic aperture radar (SAR) cannot satisfy the requirements for high resolution, high squint angle, and wide swath due to the limitations of the traditional stop-and-go model. This model does not take into account the continuous motion between the satellite and the ground, leading to incomplete compensation for the corresponding phase. To address this issue, we first introduce an accurate range equation (ARE) based on the continuous satellite and ground motion, which allows for more precise slant range calculations. Second, we derive the point target spectrum according to the ARE, revealing additional phase components not considered in the traditional stop-and-go model. Third, we propose an advanced two-step processing algorithm to compensate for phase errors due to nonstop-and-go continuous motion. In the first-step processing, we eliminate range-independent components in the two-dimensional frequency domain. In the second-step processing, we compensate for residual phase errors related to range and azimuth frequency in the range-Doppler domain. We verified the proposed compensation method through simulation and compared it with the two-step compensation method used in high-resolution SAR data processing. Experimental results show that we provide an effective compensation method for high-resolution, high-squint-angle, wide-swath spaceborne SAR, which can correct azimuth shifts and solve the problem of defocusing at the edges of large scenes.

Alveolar bone resorption caused by bacteria‐induced periodontitis remains challenging due to sustained inflammation. Periodontal pathogens like Porphyromonas gingivalis launch the primed signal of NOD‐like receptor family pyrin domain‐containing 3 (NLRP3) inflammasome in macrophages; consequent overproduction of proinflammatory cytokines and reactive oxygen species (ROS) leads to tissue destruction. This provides potential targets for a new therapeutic strategy. Herein, a multifunctionalized and dual‐crosslinked hydrogel pGM/cPL@NI with NLRP3 inhibitor MCC950 loaded is prepared. Driven by the strategic functionalization of gelatin methacryloyl and ε‐poly‐lysine with phenylboronic acid and catechol, respectively, pGM/cPL@NI containing dynamic and photo‐crosslinking networks demonstrates superior mechanical strength and stimuli‐responsive behavior, as well as the overwhelmed performance in bacteria killing and ROS scavenging. Crucially, pGM/cPL@NI restores the compromised osteogenesis by specifically suppressing the proinflammatory cytokine cascade triggered by NLRP3 inflammasome activation and promoting anti‐inflammatory polarization of macrophages. Collectively, pGM/cPL@NI presents robust potential as an effective “cocktail therapy” by combining antibacterial, antioxidant, inflammation resolution, and tissue regenerative functions. The present study reveals the underlying mechanism of the bacterial‐immune‐regeneration cascade and provides an extended approach for periodontal tissue engineering.

Ship detection finds extensive applications in fisheries management, maritime rescue, and surveillance. However, detecting nearshore targets in SAR images is challenging due to land scattering interference and non-axisymmetric ship shapes. Existing SAR ship detection models struggle to adapt to oriented ship detection in complex nearshore environments. To address this, we propose an oriented-reppoints target detection scheme guided by scattering points in SAR images. Our method deeply integrates SAR image target scattering characteristics and designs an adaptive sample selection scheme guided by target scattering points. This incorporates scattering position features into the sample quality measurement scheme, providing the network with a higher-quality set of proposed reppoints. We also introduce a novel supervised guidance paradigm that uses target scattering points to guide the initialization of reppoints, mitigating the influence of land scattering interference on the initial reppoints quality. This achieves adaptive feature learning, enhancing the quality of the initial reppoints set and the performance of object detection. Our method has been extensively tested on the SSDD and HRSID datasets, where we achieved mAP scores of 89.8% and 80.8%, respectively. These scores represent significant improvements over the baseline methods, demonstrating the effectiveness and robustness of our approach. Additionally, our method exhibits strong anti-interference capabilities in nearshore detection and has achieved state-of-the-art performance.

Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm’s performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model.