Laminar liquid jet impact has good heat transfer performance with low impact stress on targets. In order to explore the liquid film flow and heat transfer under laminar liquid jet impact on cylindrical surface, firstly the flow of liquid film was analyzed visually; then the local convective heat transfer characteristics on cylindrical surface at different impact heights and outlet Reynolds number (Re) were obtained by a combination of direct measurement and numerical simulation, followed by a comparative analysis with continuous droplets impacting on cylindrical surface. The results show that according to flow behavior of liquid film along circumferential direction, circumference can be classified into stagnation, thin liquid film, hydraulic jump, stable flow path and dripping regions. Local convective heat transfer coefficient firstly drops and subsequently increases marginally along circumferential direction, while decreases monotonically along axial direction. The effect of impact height and outlet Re on local convective heat transfer coefficient is manifested mainly in stagnation, thin liquid film and hydraulic jump regions. For outlet Re=984, as impact height rises to certain degree, there are apparent enhancements of liquid film perturbation and convective heat transfer performance. Finally, the local Nusselt number correlations in different circumferential regions were proposed.

The field of remote sensing target detection has experienced rapid development in recent years, demonstrating significant value in various applications. However, general detection algorithms still face many key challenges when dealing with directional target detection: firstly, conventional networks struggle to accurately represent features of rotated targets, particularly in modeling the slender shape characteristics of high-aspect-ratio targets; secondly, the mismatch between the static label allocation strategy and the feature space of dynamic rotating targets leads to bias in training sample selection under extreme-aspect-ratio scenarios. To address these issues, this paper proposes a single-stage Shape-Aware Dynamic Alignment Network (SADA-Net) that collaboratively enhances detection accuracy through feature representation optimization and adaptive label matching. The network’s design philosophy demonstrates greater flexibility and complementarity than that of previous models. Specifically, a Dynamic Refined Rotated Convolution Module (DRRCM) is designed to achieve rotation-adaptive feature alignment. An Anchor-Refined Feature Alignment Module (ARFAM) is further constructed to correct feature-to-spatial misalignment. In addition, a Shape-Aware Quality Assessment (SAQA) strategy is proposed to optimize sample matching quality based on target shape information. Experiment results demonstrate that SADA-Net achieves excellent performance comparable to state-of-the-art methods on three widely used remote sensing datasets (i.e., HRSC2016, DOTA, and UCAS-AOD).

Satellite remote sensing imagery has become an essential resource for large-scale surface water monitoring. Nevertheless, in karst regions, the elongated and fragmented morphology of water bodies, along with terrain shadows and vegetation interference, still leads to limitations in existing methods for small water body detection and accurate boundary delineation. To overcome the aforementioned issues, this paper proposes MESA-Net, a CNN–Mamba hybrid segmentation network for water body extraction in complex karst terrain. The network employs ResNet-18 as an encoder to extract shallow-level features. The decoder primarily consists of three modules: the Cross-Scale Adaptive Feature Fusion (CAFF) module, the Directional Gradient Histogram Edge-Guided Fusion (DGHEF) module, and the Omni-directional Global-Local Mamba Block (OGLMB). Among these, the CAFF module enhances the detection capability for small-scale water bodies by performing cross-scale feature fusion and dynamic weight allocation on the feature outputs from each level of the encoder. The OGLMB integrates an omnidirectional state space model with an 8-directional scanning mechanism and cross-attention guidance, effectively enhancing the ability to represent the structural continuity and global consistency of water bodies. The DGHEF utilizes directional gradient histograms to explicitly model multi-directional boundary information of water bodies, and combines this with a boundary guidance mechanism to enhance the representation of water body boundary features whilst suppressing spurious responses. In addition, the LJ-Water dataset has been constructed for the Lijiang River Basin in Guangxi, which is based on Sentinel-2 imagery. To validate the effectiveness and generalization capability of the method, comparative experiments were conducted on the self-built LJ-Water dataset as well as the publicly available Water-CD and LoveDA datasets. Experimental results demonstrate that MESA-Net consistently outperforms representative CNN-based, Transformer-based, and Mamba-based segmentation networks. On the LJ-Water dataset, it achieves 84.59% IoU and 91.65% F1, whilst on the Water-CD dataset, it attains 92.15% IoU and 95.91% F1, and 69.83% IoU and 82.24% F1 on the LoveDA dataset. Relative to the strongest baseline method, the proposed model achieved IoU gains of 1.51%, 2.34%, and 1.73% on the three datasets, respectively. In summary, MESA-Net demonstrates superior water segmentation performance under complex background conditions.