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With the growing maturity of unmanned aerial vehicle (UAV) technology, its applications have widened to many spheres of life. The prerequisite for a UAV to perform air tasks smoothly is an accurate localization of its own position. Traditional UAV navigation relies on the Global Navigation Satellite System (GNSS) for localization; however, this system has disadvantages of instability and susceptibility to interference. Therefore, to obtain accuracy in UAV pose estimation in GNSS-denied environments, a UAV localization method that is assisted by deep learning features of satellite imagery is proposed. With the inclusion of a top-view optical camera to the UAV, localization is achieved based on satellite imageries with geographic coordinates and a digital elevation model (DEM). By utilizing the difference between the UAV frame and satellite imagery, the convolutional neural network is used to extract deep learning features between the two images to achieve stable registration. To improve the accuracy and robustness of the localization method, a local optimization method based on bundle adjustment (BA) is proposed. Experiments demonstrate that when the UAV's relative altitude is 0.5 km, the average localization error of this method under different trajectories is within 15 m.

The increasing demands and diverse missions in earth observation lead to the complexity of satellite area coverage analysis, and satellite area coverage algorithms or applications cannot provide real-time response and online services that can handle high concurrent requests. Current analysis results cannot be visualized in real time, which increases the difficulty of data cognitive. To solve these problems, this paper proposes an efficient real-time analysis method and visualization service for satellite area coverage, which is combined with flow processing technology. First, a multi-mode judgment strategy is adopted to calculate the satellite area coverage parameters efficiently. Then, the continuous computing tasks are segmented to implement real-time data processing. Finally, based on streaming processing technology, this paper proposes an online service for real-time calculation and visualization. By using the data which contains three satellites and two ground areas to verify the algorithm, the experimental result shows that the method in this paper can not only ensure the accuracy under different sensor orientations, but can also greatly improve the computational efficiency. It can also provide real-time visualization of the analysis results in a timely and efficient manner.

To effectively guarantee the safety and efficiency of space activities, and to meet the needs of different levels of users for space object situation (SOS) knowledge, formalized and standardized SOS knowledge is needed to assist cognition. A knowledge graph (KG), as a suitable technology that can formally express knowledge and construct a standardized knowledge base, can provide knowledge generation, representation and intelligent service of SOS. In this paper, an SOS information service based on the KG (SOSKG) was constructed. Aimed at different scales of components in SOS, a multi-granularity knowledge structure was constructed, and a formalized expression of SOSKG was realized. To incorporate the complex relations into the SOSKG, a multi-level semantic relation parsing model and formalized representation were proposed from the three aspects of basic relations, spatial relations, and temporal relations. Additionally, a multi-element knowledge construction model was constructed to address the dynamic characteristics of various relations in SOSKG under a complex spatio-temporal environment. This study constructed different cases and scenarios to test the service, and the results show that the proposed service can effectively organize and express SOS knowledge in a complex spatio-temporal environment, provide an intermediate bridge between users and SOS knowledge, and promote users’ cognition of SOS knowledge.

After publication of the research paper [1], two identical figures were pointed out: Figure 18 and Figure 19. In fact, the wrong figure was Figure 19[...]

The development, deployment, and maintenance of the current space situational awareness (SSA) information system have become increasingly complex. However, researchers cannot flexibly and conveniently apply the research results to practical applications due to the lack of basic research platforms for SSA. Inspired by X as a Service (XaaS), we propose the microservice-based platform for SSA data analytics to provide a scaffold-like platform for researchers. Based on microservice, the architecture for this platform is proposed to meet the requirements of flexible development and loosely coupled deployment. To facilitate the use of the platform, the hybrid data service layer is established to provide basic data for research and the functional service layer is designed to provide services for clients and applications. Due to the massive data processing requirements, the data analysis architecture and processing model, which can easily integrate various user-defined algorithms and significantly improve the computational efficiency, are proposed based on the Lambda architecture. To verify the platform’s effectiveness, two cases are established and implemented. The results show that this platform can provide a convenient, flexible, and efficient platform for the requirements of algorithm integration, experiment, and data display from users and researchers.

With the advancement of various technologies, the research and application of space object optical characteristic (SOOC), one of the main characteristics of space objects, are faced with new challenges. Current diverse structures of massive SOOC data cannot be stored and retrieved effectively. Moreover, SOOC processing and application platforms are inconvenient to build and deploy, while researchers&rsquo, innovative algorithms cannot be applied effectively, thereby limiting the promotion of the research achievements. To provide a scaffolding platform for users with different needs, this paper proposes SOOCP, a SOOC data and analysis service platform based on microservice architecture. Using the hybrid Structured Query Language (SQL)/NoSQL service, the platform provides efficient data storage and retrieval services for users at different levels. For promoting research achievements and reusing existing online services, the proposed heterogeneous function integration service assists researchers and developers in independently integrating algorithmic modules, functional modules, and existing online services to meet high concurrency requests with a unified interface. To evaluate the platform, three research cases with different requirement levels were considered. The results showed that SOOCP performs well by providing various data and function integration services for different levels of demand.

Fiber-optic laser-induced breakdown spectroscopy (FO-LIBS), which delivers the laser energy through an optical fiber cable, is more suitable for remote analysis and applying in complex environment than traditional laser-induced breakdown spectroscopy (LIBS). However, since the laser fluence is limited by optical fiber loss and attenuation, FO-LIBS suffers more from spectral interference, matrix effect, and self-absorption effect. In this work, three multivariate quantitative analytical methods, including two linear models partial least squares (PLS) and sparse partial least squares (SPLS), one nonlinear model support vector machine (SVM), were utilized to carry out the quantitative analysis of four trace metal elements (Manganese (Mn), Chromium (Cr), Nickel (Ni), and Titanium (Ti)) in pig iron. And the quantitative analytical ability of linear model and nonlinear model was studied and compared. The results show that nonlinear SVM model has the best performance. Using the SVM model, the coefficients of determination (R2) of Mn, Cr, Ni, and Ti were 0.9705, 0.9849, 0.9882, and 0.9837, respectively, and the root mean squared errors of prediction (RMSEP) of Mn, Cr, Ni, and Ti were 0.0982 wt%, 0.0185 wt%, 0.0179 wt%, and 0.0178 wt%, respectively. This work demonstrates that nonlinear quantitative analytical methods can effectively overcome those nonlinear effects and improve the quantitative analytical accuracy of FO-LIBS

Fiber-optic laser-induced breakdown spectroscopy (FO-LIBS) has been employed in many applications because of the flexibility of optical fiber cable. However, the inhomogeneous elemental distribution of plasmas can cause a self-absorption effect and, hence, significantly hinder the determination of FO-LIBS. Here, to solve this flaw, we took iron (Fe), magnesium (Mg), and zinc (Zn) elements in aluminum alloy as examples to investigate the self-absorption reduction and accuracy improvement using spatially resolved FO-LIBS. Spatially resolved FO-LIBS means the spectra were collected at different positions along the direction parallel to the surface of the sample rather than at the center of the plasma. With this method, the self-absorption effect could be improved by selecting different acquisition positions along the X-axis. The root mean square error of cross-validations (RMSECV) for Fe, Mg, and Zn were reduced from 0.388, 0.348, and 0.097 wt. % to 0.172, 0.224, and 0.024 wt. %, respectively. Generally, spatial resolution is an effective method of self-absorption reduction and accuracy improvement in FO-LIBS.

The implementation of increased space exploration missions reduces the distance between human beings and outer space. Although it is impossible for everyone to enter the remote outer space, virtual environments could provide computer-based digital spaces that we can observe, participate in, and experience. In this study, Sino-InSpace, a digital simulation platform, was developed to support the construction of virtual space environments. The input data are divided into two types, the environment element and the entity object, that are then supported by the unified time-space datum. The platform adopted the pyramid model and octree index to preprocess the geographic and space environment data, which ensured the efficiency of data loading and browsing. To describe objects perfectly, they were abstracted and modeled based on four aspects including attributes, ephemeris, geometry, and behavior. Then, the platform performed the organization of a visual scenario based on logical modeling and data modeling, in addition, it ensured smooth and flexible visual scenario displays using efficient data and rendering engines. Multilevel modes (application directly, visualization development, and scientific analysis) were designed to support multilevel applications for users from different grades and fields. Each mode provided representative case studies, which also demonstrated the capabilities of the platform for data integration, visualization, process deduction, and auxiliary analysis. Finally, a user study with human participants was conducted from multiple views (usability, user acceptance, presence, and software design). The results indicate that Sino-InSpace performs well in simulation for virtual space environments, while a virtual reality setup is beneficial for promoting the experience.