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The pose estimation is increasingly attracting attention in research fields such as constrained guidance and control, robotics, and communication technology. In the extreme environment of space, existing spacecraft pose estimation methods are not mature. In this regard, this paper introduces a spacecraft quadrilateral pose estimation method based on deep learning and keypoint filtering, specifically designed for spacecraft with coplanar features. A two-stage neural network is employed to detect and extract features from the spacecraft's solar panels, generating a heatmap of 2D keypoints. Geometric constraint equations are formulated based on the homographic relationship between the solar panels and the image plane, yielding the spacecraft's rough pose through the solution of these equations. The predicted confidence of 2D keypoints and rough pose are utilized to construct a pixel error loss function for keypoint filtering. The refined pose is obtained by optimizing this loss function. Extensive experiments are conducted using commonly used spacecraft pose estimation data sets, demonstrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The utilization of optical and synthetic aperture radar (SAR) multi-source data to obtain better land classification results has received increasing research attention. However, there is a large property and distributional difference between optical and SAR data, resulting in an enormous challenge to fuse the inherent correlation information to better characterize land features. Additionally, scale differences in various features in remote sensing images also influence the classification results. To this end, an optical and SAR Siamese semantic segmentation network, OPT-SAR-MS2Net, is proposed. This network can intelligently learn effective multi-source features and realize end-to-end interpretation of multi-source data. Firstly, the Siamese network is used to extract features from optical and SAR images in different channels. In order to fuse the complementary information, the multi-source feature fusion module fuses the cross-modal heterogeneous remote sensing information from both high and low levels. To adapt to the multi-scale features of the land object, the multi-scale feature-sensing module generates multiple information perception fields. This enhances the network's capability to learn contextual information. The experimental results obtained using WHU-OPT-SAR demonstrate that our method outperforms the state of the art, with an mIoU of 45.2% and an OA of 84.3%. These values are 2.3% and 2.6% better than those achieved by the most recent method, MCANet, respectively. [ABSTRACT FROM AUTHOR]

Epoxides are important synthetic intermediates due to their high propensity to undergo ring-opening to give a variety of products with useful functional groups. In this study, we disclose a non-halide-mediated electrochemical epoxidation of alkenes to form epoxides using water as an oxygen source. This method catalyzed by (TMP)MnCl (TMP = tetramesitylporphyrin) exhibits remarkable efficiency, enabling the epoxidation of both aromatic and aliphatic alkenes with excellent faradaic efficiencies (up to 89%). Preliminary mechanistic studies suggest the MnV＝O species generated from single electron oxidations serves as the key intermediate for the epoxidation reaction. [ABSTRACT FROM AUTHOR]

An ultra‐low quiescent current tri‐mode DC–DC boost converter is proposed in this paper, which can efficiently handle loads from 1 μA to 300 mA, providing 5‐V output from 3 to 4.2‐V input. Hysteresis current mode control is adopted to realize seamless mode switching between continuous conduction mode (CCM) and discontinuous conduction mode (DCM). In order to deal with the light load situation, this paper proposes a quasi burst mode (QBM). In this mode, most of the modules are turned off and only the QBM controller, low‐power current bias and sample and hold circuit are retained, which significantly reduces the static current of the converter, and thus improves the light load efficiency. Meanwhile, the average current of error amplifier and comparator are reduced significantly by dynamic biasing. Finally, the efficiency of the converter is greatly improved and kept at a reasonable output ripple. The proposed converter is implemented in a 0.13‐μm BCD process design and manufacturing. The test results show that the quiescent current of the converter is only 780 nA, which can achieve a peak efficiency of 95.6%, and ensure that the efficiency is maintained above 80% in the load range of 10 μA to 300 mA. [ABSTRACT FROM AUTHOR]