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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 bHLH transcription factors are important plant regulators against abiotic stress and involved in plant growth and development. In this study, SlALC, a gene coding for a prototypical DNA-binding protein in the bHLH family, was isolated, and SlALC-overexpression tomato (SlALC-OE) plants were generated by Agrobacterium-mediated genetic transformation. SlALC transgenic lines manifested higher osmotic stress tolerance than the wild-type plants, estimated by higher relative water content and lower water loss rate, higher chlorophyll, reducing sugar, starch, proline, soluble protein contents, antioxidant enzyme activities, and lower MDA and reactive oxygen species contents in the leaves. In SlALC-OE lines, there were more significant alterations in the expression of genes associated with stress. Furthermore, SlALC-OE fruits were more vulnerable to dehiscence, with higher water content, reduced lignin content, SOD/POD/PAL enzyme activity, and lower phenolic compound concentrations, all of which corresponded to decreased expression of lignin biosynthetic genes. Moreover, the dual luciferase reporter test revealed that SlTAGL1 inhibits SlALC expression. This study revealed that SlALC may play a role in controlling plant tolerance to drought and salt stress, as well as fruit lignification, which influences fruit dehiscence. The findings of this study have established a foundation for tomato tolerance breeding and fruit quality improvement. [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]

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]

A Laplace ℓ1 robust student's T-filter is presented for satellites to estimate their attitude state despite severe measurement noise and modeling error. Although the student's T-filter (STF) has the capability of handling measurement noise, it cannot address the unknown modeling error. It is further sensitive to the degree of freedom (DOF). Hence, the measurement covariance is updated by using the maximum correntropy criterion to accommodate the covariance of unknown modeling error in robust filtering design. Moreover, the Laplace distribution is derived to reduce the influence of the DOF parameter by forming a surrogate function of an optimization problem. Then, the majorization minimization approach is formulated to solve such an optimization problem and present the proposed filtering algorithm in the STF framework. Numerical simulation of applying the proposed attitude estimation scheme is performed by comparing it with the third/fifth-order cubature Kalman filters. [ABSTRACT FROM AUTHOR]

Optical tracking and measurement for flying targets is unlike the close range photography under a controllable observation environment, which brings extreme conditions like diverse target changes as a result of high maneuver ability and long cruising range. This paper first designed and realized a distributed image interpretation and measurement processing system to achieve resource centralized management, multisite simultaneous interpretation and adaptive estimation algorithm selection; then proposed a real-time interpretation method which contains automatic foreground detection, online target tracking, multiple features location, and human guidance. An experiment is carried out at performance and efficiency evaluation of the method by semisynthetic video. The system can be used in the field of aerospace tests like target analysis including dynamic parameter, transient states, and optical physics characteristics, with security control.