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Environment perception and navigation are the core technologies for automated driving of deep space exploration rovers. Due to the special circumstances of the deep space environment, the rover's environment perception and navigation are particularly challenging comparing to the corresponding technologies in autonomous car driving on Earth. The paper presents a review of the progresses of environment perception, rover localization and path planning, from engineering application and scientific research perspectives. Moreover, the future prospects of intelligent environment perception and long-range rover navigation are discussed.

In a planetary-rover exploration mission, stereovision-based 3D reconstruction has been widely applied to topographic mapping of the planetary surface using stereo cameras onboard the rover. In this study, we propose an enhanced topographic mapping method based on multiple stereo images taken at the same rover location with changing illumination conditions. Key steps of the method include dense matching of stereo images, 3D point-cloud generation, point-cloud co-registration, and fusion. The final point cloud has more complete coverage and more details of the terrain than that conventionally generated from a single stereo pair. The effectiveness of the proposed method is verified by experiments using the Yutu-2 rover, in which two data sets were acquired by the navigation cameras at two locations and under changing illumination conditions. This method, which does not involve complex operations, has great potential for application in planetary-rover and lander missions.

SiC/B4C composite was obtained using the reaction sintering method with Si infiltration, which exhibited excellent mechanical properties. The dynamic compressive response was investigated using a Split Hopkinson pressure bar at high strain rates ranging from 0.4×103 to 1.2×103 s-1. The results show that the dynamic strength of the SiC/B4C composite obtains a peak value at a strain rate of 1000/s, while its strain increased continuously with increasing strain rate. The dynamic loading mode of SiC/B4C composite exhibited three deformation regions, including an inelastic deformation region, rapid loading region and failure region. The dynamic failure mode of SiC/B4C composite depended upon the strain rate.

With the development of information technology in modern military confrontation, specific emitter identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Specific emitter identification requires a historical reconnaissance signal as the matching template. In order to avoid being intercepted by enemy electronic reconnaissance equipment, modern radar often has multiple sets of working parameters, such as pulse width and signal bandwidth, which change when performing different tasks and training. At this time, the collected fingerprint features cannot fully match the fingerprint template in the radar database, making the traditional specific emitter identification algorithm ineffective. Therefore, when the working parameters of enemy radar change, that is, when there is no such variable working parameter signal template in our radar database, it is a bottleneck problem in the current electronic reconnaissance field to realize the specific emitter identification. In order to solve this problem, this paper proposes a network model based on metric learning. By learning deep fingerprint features and learning a deep nonlinear metric between different sample signals, the same individual sample signals under different working parameters can be associated. Even if there are no samples under a certain kind of working parameter signal, it can still be associated with the original individual through this network model, so as to achieve the purpose of specific emitter identification. As opposed to the situation in which the traditional specific emitter identification algorithm cannot be associated with the original individual when the signal samples of changing working parameters are not collected, the algorithm proposed in this paper can better solve the problem of changing working parameters and zero samples.

The quasi-static and dynamic compressive mechanical behaviors of two kinds of fiber reinforced SiC-matrix composites including 2D-C/SiC and 2D-SiC/SiC were investigated. Their compressive behaviors of materials at room temperature and strain rate from 10-4 to 104 /s were studied. The fracture surfaces and damage morphology were observed by scanning electron microscopy (SEM). The results showed that the dynamic failure strengths of the two kinds of fiber reinforced SiC-matrix composites obey the Weibull distribution. The Weibull modulus of the two materials were 13.70 (2D-C/SiC) and 5.66 (2D-SiC/SiC), respectively. It was found that the two kinds of fiber reinforced ceramic matrix composites presented a transition from brittle to tough with the decrease of strain rate. The 2D-SiC/SiC materials demonstrated a more HYPERLINK "http://dict.cnki.net/dict_result.aspx?searchword=%e6%98%be%e8%91%97%e7%9a%84&tjType=sentence&style=&t=remarkable"significant strain rate sensitivity and smoother fracture surface compared to the 2D-C/SiC composites, implying that the former composites present brittle features. This was because the SiC/SiC composites possessed high bonding strength in interface of fiber/fiber and fiber/matrix is very strong.

With the development of information technology for modern military confrontations, radar emitter fingerprint identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Owing to the confidentiality of military systems, most of the existing studies use simulation data for radar emitter fingerprint identification experiments and analysis. However, most of the existing modeling methods focus on the mechanism analysis of the nonlinear fingerprint characteristics of a single independent component. Its main disadvantage is that it can only represent the nonlinear fingerprint characteristics of some components in the radar emitter system but cannot fully reflect the nonlinear fingerprint characteristics of the whole radar emitter system. In this paper, a nonlinear fingerprint-level radar simulation modeling method is proposed. In contrast to the previous single component modeling method, the systematic nonlinear characteristic modeling method of this model can provide individual radar signal data under different modulation modes and working parameters, and provide experimental conditions for data support and theoretical analysis of radar emitter fingerprint identification.

Local COVID-19 outbreaks and infection control measures may affect mental health in older persons. This study aims to investigate the effects of COVID-19 outbreaks and control measures on depression risk in community-dwelling older adults in Hong Kong. With rolling cross-sectional design, telephone screenings for depressive risk were conducted among 8163 older people using Patient Health Questionnaire-2 (PHQ-2) from February to September 2020. COVID-19 outbreaks across thirty weeks were measured using real-time effective reproductive number (Rt), infected new cases, and change of infected new cases by week. Infection control measures were assessed using four policy indices, including government response, government stringency, containment and health, and economic support. Linear regressions were used to test the associations of depression risk with COVID-19 outbreaks and control measures. We found that being female and higher Rt were associated with higher depression risk in the overall sample. In older adults without pre-existing mental health issues, higher depression risk were related to older age (t=-1.974, 95%CI[-0.006, 0.000], p

Node Localization technology is one of key technologies in wireless sensor network. DV-Hop localization algorithm is a kind of range-free algorithm. In this paper, an improved DV-Hop algorithm aiming to enhance localization accuracy is proposed. To enhance localization accuracy, average per-hop distance is replaced by corrected value of global average per-hop distance and global average per-hop error. When calculating hop distance, unknown nodes use corresponding average per-hop distance expression according to different hop value. Comparison with DV-Hop algorithm, simulation results show that the improved DV-Hop algorithm can reduce the localization error and enhance the accuracy of sensor nodes localization more effectively.

It is important to ensure the integrity of the aggregation result and quality of communication among node in wireless sensor network in the condition of high-efficient privacy-preserving data aggregation mechanism. In this paper, we propose a method based on reputation model to solve this problem. During the process of data aggregation, the cluster head node will choose the node with max reputation value as collaborative node to accomplish the aggregation process. To show the efficacy and efficiency of this method, we present the simulation results. The simulation results show that our method iE-CPDA(enforced Integrity Energy-efficient Cluster-based Privacy-preserving Data Aggregation) has less communication and computation and higher accuracy than iCPDA.

In this paper, multiple actively-controlled fans generate outdoor near-surface airflow in a wind tunnel with once-through openings. The wind tunnel system consists of 6 groups of fans installed on rotatable plates in a rectangular inlet. We use a stochastic strategy to control the fans and the rotatable plates to regulate wind speed and direction that fluctuate according to the pattern of outdoor near-surface airflow. We utilize the statistics and multi-scale methods to analyze the effectiveness of the strategy for simulating the outdoor near-surface airflow. We provide comparison studies on the multi-scale entropy of wind speed, wind stability, and the standard deviation of directions between out-door wind and the tunnel generated wind. Results show that a flow field akin to the near-surface airflow in outdoor environments can be produced by the wind tunnel using the stochastic control strategy, which can be considered as a reliable experiment environment for gas pollution source localization research in outdoor near-surface breeze conditions.

To solve the safety problem existed in geographic and energy aware routing protocol (GEAR), a security geographic and energy aware routing protocol (S-GEAR) was proposed. S-GEAR obtains the preferable safety feature by adding region division, key distribute scheme based on location, reputation evaluation mechanism based on multi-point confirm to GEAR, and can withstand a variety of attacks of network layer. The simulation result shows that S-GEAR has capacity to identify malicious nodes, and has better security level than GEAR in terms of packet delivery ratio and drop ratio while there exists attacking nodes.

This study examines the mediating effects of trust on the relationships between manager’s conflict management styles (CMS) and employee attitudinal outcomes, as well as identifies the potential deviations in the areas of CMS and trust from the west in Chinese culture. One hundred sixty-nine employees in Guangzhou of China participated. As predicted, Integrating CMS of managers is found significantly correlated to trust and subordinates’ job satisfaction and turnover intention. Trust fully mediates the link between Integrating CMS and subordinates attitudinal outcomes. Results also support the expectations concerning the deviations on the impacts of the uncooperative CMS on subordinates under Chinese culture. Discussion and implications are presented.

Microbial communities of different depths (30, 60, 100, 150, 200cm) from the unexploited oilfield, gas field and control area were studied by PCR-DGGE and sequencing methods. The objectives of this study were to understand the microbial distribution in the regions of unexploited oil and gas fields, and to investigate the potential microbial indicators of oil and gas resources. The results showed that the Dice coefficients between different depths were very low (26-69.9). The microbial communities in the soil of 150 cm and 200 cm depth had greater richness (Sor = 19), diversity (Hor = 2.69) and evenness (Eor = 0. 90). The results of sequencing demonstrated that the bands from oilfield were mainly grouped into alpha-Proteobacteria, gamma-Proteobacteria, Actinobacteria, Acidobacteria with the predominance of gamma-Proteobacteria (75%). Most of the bands were related to oil-associated and hydrocarbon degrading bacteria, such as Methylophaga and Alcanivorax. While the gas field had alpha, beta, gamma, delta-Proteobacteria and Bacteroidetes, and gamma-Proteobacteria accounted for only 24%. More strains showed relativity to methanotrophs, such as Methylocystaceae. Thus, 150 cm and 200 cm were more suitable as the oil-gas exploration sampling depth. Methylocystaceae may act as potential indicators for gas resources, Methylophaga and Alcanivorax for oil.

To solve the network security response problem, a novel network response decision-making method based on the two-matrix game model was present. We analyzed the attackers and system administrators' different strategies, and then constructed the dual-benefit matrix of the attackers and defenders. On the basis of this, the two-matrix network security game model was proposed. At last, the optimal defense strategy decision-making algorithm was proposed. Using this algorithm, we could analyze benefits of both offensive and defensive, and solved the Nash Equilibrium Point which balanced the both and gave the optimal response decision-making strategy. The proposed method could make accurate response decision and had a high efficiency. Also, it occupied a lower memory which could reduce the interference to normal user when administrators manage the network.

Mitigating mental health consequences is one of the priorities for the society to advance, and the aim of this study is to investigate the mental health effects of COVID-19 in older adults and to explore risk and protective factors. Social workers recruited 722 older adults living in the community (mean age 73.6±8.4) from January 2020 to February 2021 in Hong Kong, and interviewed them for basic demographics (age, gender, education, and living status), mental health service use in recent three months (proxy for existing needs), number of people to talk to when feeling down (proxy for emotional support network), and assessed their depression using Patient Health Questionnaire-9 (PHQ-9). Impacts of COVID-19 were indicated by local daily effective reproductive number (Rt) and Nth wave since the start of the pandemic. Generalized linear models (GLM) were applied. Basic demographics were not associated with depression, existing mental health needs (β=2.99, p