The rapid development of spacecraft on-orbit services has increased the requirements for docking technology, especially for large-scale payloads that exceed the launch envelope. Docking technology based on astronaut extravehicular activities is one of the most promising directions for on-orbit services. In view of this, this paper designs and characterizes a handheld double-point docking mechanism for assembling large-scale payloads that is suitable for extravehicular activity (EVA) in dual-astronaut collaborative operations. It achieves the functional decoupling of docking, locking, unlocking, and separation throughout the whole process. The mechanism also has excellent design for human factors engineering, allowing astronauts to change hands, operate with one hand, and apply limited force. The mechanism adopts a dual-point probe–drogue configuration, while the misalignment tolerance design guarantees the docking accuracy and the operating range, and forms a rigid structural connection through a force amplification mechanism. Theoretical analysis and numerical simulations are implemented to estimate the dynamics, statics, and kinematics of the docking process. Corresponding experiments of the prototype are also conducted, including high–low temperature dynamics, docking tests, and kinematic tolerance experiments. The experiments validate the finite element analysis and verify the actual performance of the mechanism. The designed handheld dual-point docking mechanism was successfully applied for the first time by the Shenzhou 15 crew on China’s Space Station in March 2023. This paves a new road for spacecraft on-orbit service of large-scale payloads by EVAs, providing guidance as well as a technical foundation for the on-orbit construction of large spacecraft in the future.

The morphology and connectivity of subsurface fracture networks are critical factors controlling wellbore stability and hydraulic fracturing efficiency. Accurate characterization of the three-dimensional complexity of fractures holds significant importance for engineering safety and performance enhancement. A novel image segmentation model is established in this study. It enhances the iterative threshold method by incorporating simple linear iterative clustering superpixels, ResNet50, and a Gaussian mixture model. The model first divides complex computed tomography images into numerous superpixel images using simple linear iterative clustering superpixel segmentation. Subsequently, ResNet50 is employed to classify these superpixel images. Based on the classification results, the iterative threshold segmentation method is applied to segment different categories of superpixel images accordingly. Following preliminary image segmentation, Gaussian mixture module is used for denoising the segmented fracture images, resulting in high-precision segmented images. The two-dimensional segmented images are then reconstructed in three-dimensional space, and the three-dimensional distribution characteristics of fractures are analyzed. This study concludes that the new fracture segmentation method enables high-precision extraction of fracture regions. Compared with threshold segmentation, the morphological analysis noise value in the two-dimensional images segmented by the method proposed in this study was reduced from 0.21% to 0.08%. Fracture distribution in three-dimensional space is complex, and areas with larger fracture networks exhibit greater complexity in their three-dimensional distribution.

Two highly plastic expansive soils weathered from natural shale were investigated to determine shear strength and shear-induced volume change under a variety of hygroscopic and confinement conditions. The extents of suction and confinement were chosen to mirror the hydraulic and mechanical situations expansive soils' experience in nature. During shearing, the soil sample with less swell potential showed volumetric dilation irrespective of suction level whereas the soil with higher swelling capacity displayed mostly contraction. The effects of initial structure, confining stress, suction, capillary history, and strain level on shear strength and volume change were examined. The deviator stress increases with increasing suction, regardless of strain level and capillary history, because of the enhanced surface tension force. Capillary history affects the peak shear strength of the flocculated sample because of significant structural evolution along the capillary paths while imposing little influence on the peak strength of the laminar sample. The large-strain shear strength, however, demonstrates little sensitivity to capillary history, regardless of soil type; because the specimens at this stage are more likely experiencing complete fabric destructuring and equilibrium of diffuse double layer repulsion.

Shale hydration is a critical factor influencing wellbore stability. However, accurately quantifying hydration strain and tracking the dynamic migration of fluid imbibition fronts in tight matrices remains a significant challenge. To address this issue, we propose a high-spatial-resolution monitoring scheme based on distributed fiber optic sensing (DFOS). By applying a hydrophobic coating to alter the imbibition path, we established a one-dimensional (1D) unidirectional imbibition boundary condition and compared it with semi-immersed free imbibition. Representative shale specimens with varying geological characteristics were tested to capture the continuous spatiotemporal evolution of hydration strain under both conditions. Our results show that under the restricted 1D path, the onset of strain exhibits a regular sequential delay along the fluid invasion direction, successfully mapping the progressive advancement of the imbibition front. The comparison between specimens with and without hydrophobic coating confirms that the coating effectively alters the imbibition path, leading to pronounced spatial lag and amplitude attenuation due to gravity and viscous resistance. Furthermore, we identify a dual characteristic of hydration strain. On one hand, governed by intrinsic properties such as clay content and permeability, different specimens exhibit distinctly different strain morphologies. On the other hand, within the same specimen, strain profiles at different measurement locations display high temporal self-similarity and spatial similarity, reflecting the structural control of the rock matrix. These findings suggest that shale hydration is a highly heterogeneous process, and the morphological differences can, to some extent, reflect variations in rock structure. Overall, this study provides an important methodological reference for improving wellbore stability models and optimizing drilling fluid design in deep shale engineering.

Accurate arrival picking of acoustic emission (AE) data is essential for AE event localization and hydraulic fracture characterization in true triaxial hydraulic fracturing experiments. However, conventional arrival picking methods are highly sensitive to manually defined thresholds, whereas existing deep learning models are constrained by low signal-to-noise ratios (SNRs) and limited AE dataset sizes. To address these challenges, this study proposes an attention-based deep learning method for AE arrival picking. The proposed method introduces an attention mechanism into the PhaseNet framework to suppress noise feature transmission in the skip connections. In addition, a kernel density estimation (KDE)-based label smoothing strategy was adopted to alleviate label imbalance and account for arrival-time uncertainty. The results demonstrate that the proposed method reduced the mean absolute error (MAE) by 10.58%, 92.92%, and 98.25% compared with PhaseNet, STA/LTA, and AR-AIC, respectively. The proposed method exhibited superior picking accuracy, robustness, and computational efficiency relative to the other methods, providing a reliable foundation for AE event localization and high-precision AE monitoring in hydraulic fracturing experiments.