Search

Precise Orbit Determination (POD) of Satellite Laser Ranging (SLR)-tracked satellites is stepping towards millimeter-level precision, which requires further refinement of satellite perturbation models and consideration of more detailed perturbation force models that were not previously taken into accounted. Atmospheric drag is one of the main perturbative forces acting on a Low Earth Orbit (LEO) satellite, however, the atmospheric wind speed associated with atmospheric drag has not been considered in International Laser Ranging Service (ILRS) regular data processing. Therefore, in order to analyze the magnitude of atmospheric wind speed influence on POD of SLR satellites, this paper applies HWM14 (Horizontal Wind Model 14) atmospheric wind speed model to perform POD of LARES, Ajisai, Starlette, Stella, Larets satellites and analyses the results. The results show that after applying HWM14 atmospheric wind speed model, the SLR observation residual WRMS (Weighted Root Mean Square) value for LARES, Starlette, Stella, Larets satellites reach 1.04 cm, 2.13 cm, 1.73 cm, 3.36 cm, respectively, which are reduced by 0.10 cm, 0.30 cm, 0.00 cm, 0.05 cm, respectively. However, for Ajisai, one has an almost the same orbit residual WRMS value of 2.88 cm. The 3D (three-dimensional) orbit overlapping arcs error for LARES is 17.53 cm, which is reduced by 1.75 cm; for Starlette it is 19.12 cm, which is reduced by 0.96 cm; for Stella there is almost same orbit overlapping arcs error of 34.9 cm. The accuracy of orbit forecast for 1 day and 3 days for LARES reaches 147.57 cm and 163.96 cm, respectively, which is improved by 0.04 cm and 20.73 cm in the T (Tangential) direction, respectively. The accuracy of orbit forecast for 1 day and 3 days for Stella reaches 191.77 cm and 308.52 cm, respectively, which is improved by 1.95 cm and 6.74 cm in the T direction, respectively. The accuracy of orbit forecast for 1 day and 3 days for Larets reaches 6.18 m and 145.26 m, respectively, which is improved by 7.81 m and 473.61 m in the T direction, respectively. Above results display that considering HWM14 atmospheric wind speed model has a certain improvement for POD of LARES, Starlette, Stella and Larets. Therefore, the influence of atmospheric wind speed should be considered in the SLR POD and regular data processing. [ABSTRACT FROM AUTHOR]

This paper studies the unmanned aerial vehicle (UAV) based multiple intelligent reflecting surface (IRS) network, where the hovering UAV acts as a base station, and the IRS enhances signal transmission to across obstacle between users and UAV. To achieve the maximum sum rate of proposed communication scenario, a non-convex problem considering IRS association results, hovering altitude of UAV, and the phase shift design of multi-IRS is formulated. From the IRS association problem, we can find that the IRS association results are coupled to the decoding order of non-orthogonal multiple access (NOMA). To tackle this, a mathematical interference expansion scheme is developed to decouple it and transform it to convex by binary relaxation method. The non-convexity of hovering altitude optimization problem is solved by logarithm operation, approximation, and auxiliary matrices. For the phase shift optimization problem of multi-IRS, we propose a gradient approximation based initial scheme and develop a univariate optimization based approach on the basis to achieve the users sum rate improvement in multi-IRS. In the end, we compare the proposed scheme with baseline scheme to present the superiority of this work under various network settings. The internal reasons for the variation of simulation results are also analyzed. [ABSTRACT FROM AUTHOR]

Most background suppression algorithms are weakly robust due to the complexity and fluctuation of the star image's background. In this paper, a background suppression algorithm for stray lights in star images is proposed, which is named BSC-Net (Background Suppression Convolutional Network) and consist of two parts: "Background Suppression Part" and "Foreground Retention Part". The former part achieves background suppression by extracting features from various receptive fields, while the latter part achieves foreground retention by merging multi-scale features. Through this two-part design, BSC-Net can compensate for blurring and distortion of the foreground caused by background suppression, which is not achievable in other methods. At the same time, a blended loss function of smooth_L1&Structure Similarity Index Measure (SSIM) is introduced to hasten the network convergence and avoid image distortion. Based on the BSC-Net and the loss function, a dataset consisting of real images will be used for training and testing. Finally, experiments show that BSC-Net achieves the best results and the largest Signal-to-Noise Ratio (SNR) improvement in different backgrounds, which is fast, practical and efficient, and can tackle the shortcomings of existing methods. [ABSTRACT FROM AUTHOR]

Objective. To analyze the intervention effect of group counseling based on positive psychology on psychological crisis of college student. Method. SCL-90 mental health screening was performed on second-year students in a college by cluster stratification. Among the detected students, 210 were included into the group after brief interview and randomly assigned to the experimental group and the control group. The control group was given conventional intervention measures, and the experimental group was given group counseling according to the interview results. One week before the start, on the day of the end of the intervention and 3 months after the end of the intervention, the mental health level of all subjects was evaluated by symptom self-rating scale, general well-being scale, and adolescent mental resilience scale. Results. The scores of two groups were different at different time points during the intervention. With the extension of time, the score of the self-rating symptom scale in the experimental group decreased significantly, while the total score of the self-rating symptom scale in the control group increased, with statistical significance P < 0.05 . Before the intervention, there was no significant difference in general well-being between the two groups P > 0.05 . At 3 months after the end of intervention, the total score of the general well-being scale in the experimental group increased, while that in the control group decreased, with statistical significance P < 0.05 . Three months after the end of the intervention, the total score of adolescent mental resilience scale in the experimental group increased. Conclusion. Group counseling from the perspective of positive psychology can effectively improve the mental health status of medical students with psychological crisis and improve their mental resilience. [ABSTRACT FROM AUTHOR]

• Three sequential processes occur, including coalescence, spreading, retraction. • Spreading diameter and time are normalized by impact and stationary droplets. • Effects of Weber number, droplet volume ratio, surface wettability are clarified. • Maximum spreading coefficient elevates at larger Weber number or drop volume ratio. • An accurate theoretical model of the maximum spreading diameter is proposed. In numerous applications related to droplet impact, impacting droplets often interact with solid surfaces with adhesive droplets rather than dry surfaces, but the former is significantly less studied than the latter. Here, for the fundamental process of droplet impact on a stationary droplet on the surface, we investigate droplet dynamics and maximum spreading under various conditions. The spreading diameter and time are reasonably nondimensionalized as the spreading coefficient and dimensionless time by considering both the impacting and stationary droplets, and the complex influence of diameters of two droplets is reduced to the effect of a single droplet volume ratio. The proposed dimensionless framework provides a method to simplify influencing factors and facilitates the study of droplet dynamics under different Weber numbers, surface wettabilities, and droplet volume ratios. The maximum spreading coefficient increases with the increase of the Weber number or the droplet volume ratio, while it is nearly independent of surface wettability. A theoretical model of the maximum spreading diameter is established through energy analysis. By considering the coupled effects of various factors and modifying the viscous dissipation, the model has the merits of high prediction accuracy and broad applicability. Our findings can not only advance the basic understanding of the ubiquitous droplet impact phenomena, but also provide useful guidance for applications such as spraying cooling and inkjet printing. [Display omitted] [ABSTRACT FROM AUTHOR]

The study is aimed at observing the influence of microribonucleic acid- (miRNA-) 30a-50p on the pulmonary fibrosis in mice with Streptococcus pneumoniae infection through the regulation of autophagy by Beclin-1. Specific pathogen-free mice were instilled with Streptococcus pneumoniae through the trachea to establish the pulmonary fibrosis model. Then, they were divided into the miRNA-30a-50p mimics group (mimics group, n = 10) and miRNA-30a-5p inhibitors group (inhibitors group, n = 10), with the control group (n = 10) also set. Pulmonary tissue wet weight/dry weight (W/D) was detected. The content of tumor necrosis factor-α (TNF-α), interleukin- (IL-) 6, and myeloperoxidase (MPO) was determined using enzyme-linked immunosorbent assay (ELISA). Besides, the changes in the pulmonary function index dynamic lung compliance (Cdyn), plateau pressure (Pplat), and peak airway pressure (Ppeak) were monitored, and the gene and protein expression levels were measured via quantitative PCR (qPCR) and Western blotting. The expression level of miRNA-30a-5p was substantially raised in the mimics group (p < 0.05), but extremely low in the inhibitors group (p < 0.05). The mimics group had obviously raised levels of serum aminotransferase (AST), glutamic-pyruvic transaminase (GPT), alkaline phosphatase (ALP), and pulmonary tissue W/D (p < 0.05). Additionally, the expression levels of TNF-α, IL-6, and MPO were notably elevated in the mimics group, while their expression levels showed the opposite conditions in the inhibitors group (p < 0.05). According to the HE staining results, the inhibitors group had arranged orderly cells, while the mimics group exhibited lung injury, pulmonary edema, severe inflammatory response, and alveolar congestion. In the inhibitors group, Cdyn was remarkably elevated, but Pplat and Ppeak declined considerably (p < 0.05). Besides, the inhibitors group exhibited elevated messenger RNA (mRNA) levels of Beclin-1 and LC3, lowered mRNA levels of α-SMA and p62, a raised protein level of Beclin-1, and a markedly decreased protein level of p62 (p < 0.05). Silencing miRNA-30a-5p expression can promote the expression of Beclin-1 to accelerate the occurrence of autophagy, thereby treating pulmonary fibrosis in mice with Streptococcus pneumoniae infection. [ABSTRACT FROM AUTHOR]

The algorithm proposed in this paper aims at solving the problem of star map matching in high-limiting-magnitude astronomical images, which is inspired by geometric voting star identification techniques. It is a two-step star map matching algorithm relying only on angular features, and adopts a reasonable matching strategy to overcome the problem of poor real-time performance of the geometric voting algorithm when the number of stars is large. The algorithm focuses on application scenarios where there are a large number of dense stars (limiting magnitude greater than 13, average number of stars per square degree greater than 185) in the image, which is different from the sparse star identification problem of the star tracker, which is more challenging for the robustness and real-time performance of the algorithm. The proposed algorithm can be adapted to application scenarios such as unreliable brightness information, centroid positioning error, visual axis pointing deviation, and a large number of false stars, with high accuracy, robustness, and good real-time performance. [ABSTRACT FROM AUTHOR]

Green infrastructure (GI) could reduce surface runoff and provide essential ecosystem service (ES) in flood management. More frequent flooding necessitated GI intervention to promote flood regulation and mitigation service. This study presented the Supply-Demand-Intervention framework of ES for flood mitigation in Nanjing, and aimed to explore flood interventions of GI that improve ES supply and satisfy ES demand from the perspective of landscape pattern. This study quantified ES supply by runoff reduction capacity and assessed ES demand by flood risk index, respectively. Stepwise regression analysis revealed the association between landscape pattern and ES supply and demand. We found the mismatch between ES supply and demand in Nanjing, as well as the spatial heterogeneity between ES supply and demand during various design storms. Urban central areas showed the low ES supply, high ES demand and high priority rank of intervention. Current GI conditions had the difficulty in dealing with more severe floods in Nanjing. And landscape elongation, shape, fragmentation, and segmentation were found to have significant effect on ES supply and demand. More elongated and segmented landscape, with less continuity and connectedness, could promote ES supply, while less fragmentated landscape, with complex shape, could reduce ES demand. Different intervention strategies were proposed to adapt to different areas for promoting ES delivered by GI. In Gulou and Qinhuai district, it was suggested to apply well-shaped elongated cropland and water areas to segment and disperse the built-up land. These findings could provide critical implications for reasonable GI planning and management in flood mitigation and adaption. [Display omitted] • This study proposed Supply-Demand-Intervention framework of ecosystem service. • Ecosystem service supply and demand showed spatial heterogeneity and unmatched relationship. • Green infrastructure with more irregular shape promoted ecosystem service supply and satisfy demand. • More segmented and less fragmented landscape was more than essential. [ABSTRACT FROM AUTHOR]

The monitoring of space debris is important for spacecraft such as satellites operating in orbit, but the background in star images taken by ground-based telescopes is relatively complex, including stray light caused by diffuse reflections from celestial bodies such as the Earth or Moon, interference from clouds in the atmosphere, etc. This has a serious impact on the monitoring of dim and small space debris targets. In order to solve the interference problem posed by a complex background, and improve the signal-to-noise ratio between the target and the background, in this paper, we propose a novel star image enhancement algorithm, MBS-Net, based on background suppression. Specifically, the network contains three parts, namely the background information estimation stage, multi-level U-Net cascade module, and recursive feature fusion stage. In addition, we propose a new multi-scale convolutional block, which can laterally fuse multi-scale perceptual field information, which has fewer parameters and fitting capability compared to ordinary convolution. For training, we combine simulation and real data, and use parameters obtained on the simulation data as pre-training parameters by way of parameter migration. Experiments show that the algorithm proposed in this paper achieves competitive performance in all evaluation metrics on multiple real ground-based datasets. [ABSTRACT FROM AUTHOR]

Star image registration is the most important step in the application of astronomical image differencing, stacking, and mosaicking, which requires high robustness, accuracy, and real-time capability on the part of the algorithm. At present, there are no high-performance registration algorithms available in this field. In the present paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF) while providing excellent robustness, high accuracy, and good real-time performance. The test results on a large amount of simulated and real data show that the comprehensive performance of the proposed algorithm is significantly better than the four classical baseline algorithms as judged by the presence of rotation, insufficient overlapping area, false stars, position deviation, magnitude deviation, and complex sky background, making it a more ideal star image registration algorithm than current alternatives. [ABSTRACT FROM AUTHOR]

Many large landslides in reservoir areas have been recognized as the presence of multi-sliding zones owing to the distinctive deformation characteristics. Due to water-level fluctuations, the deformation mechanism of the multi-sliding zones landslide is poorly understood. Herein, a simplified model of a multi-sliding zones landslide subjected to the operation reservoir was conducted to reproduce the deformation process and obtain the deformation characteristics, pore water pressure, and earth pressure to investigate the deformation mechanism. The results suggest that the deformation exhibited by the multi-sliding zones landslide subjected to the operated reservoir take on a regional deformation characteristic with the multi-sliding zones as the boundary and correspond to the abrupt change in earth pressure and pore water pressure. According to the effective stress principle, the deformation mechanism is investigated by relying on the response relationship between effective stress and deformation. It shows that the softening effect caused by initial water infiltration, outward seepage force caused by initial water-level drawdown and the driving force are the external factors triggering the regional deformation of the multi-sliding zones landslide, while the immediate cause of the regional deformation is the rapid change in effective stress, which combined with the decreasing trend before the rapid effective stress release, is crucial for accurately assess whether the multi-sliding zones landslide is about to deform. This research provides improved insight into the deformation mechanism of the multi-sliding zones landslide. [ABSTRACT FROM AUTHOR]

Space debris detection is vital to space missions and space situation awareness. Convolutional neural networks are introduced to detect space debris due to their excellent performance. However, noisy labels, caused by false alarms, exist in space debris detection, and cause ambiguous targets for the training of networks, leading to networks overfitting the noisy labels and losing the ability to detect space debris. To remedy this challenge, we introduce label-noise learning to space debris detection and propose a novel label-noise learning paradigm, termed Co-correcting, to overcome the effects of noisy labels. Co-correcting comprises two identical networks, and the predictions of these networks serve as auxiliary supervised information to mutually correct the noisy labels of their peer networks. In this manner, the effect of noisy labels can be mitigated by the mutual rectification of the two networks. Empirical experiments show that Co-correcting outperforms other state-of-the-art methods of label-noise learning, such as Co-teaching and JoCoR, in space debris detection. Even with a high label noise rate, the network trained via Co-correcting can detect space debris with high detection probability. [ABSTRACT FROM AUTHOR]