With the development of 3D data acquisition technology, point cloud wins the favor of researchers for it's simple but effective representation and it is widely used in the fields of remote sensing, scene reconstruction, 3D modeling, etc. Considering that the data acquisition process is easily disturbed by many factors such as equipment, environment and material, raw point cloud is often corrupted with noise and so it is of great significance to explore robust and efficient denoising algorithms. This paper firstly investigates the relevant research works of point cloud denoising and divides them into traditional algorithms based on the optimization idea and denoising algorithms based on the deep learning idea according to the implementation principles. Secondly, the research progress of each kind of algorithm is discussed and a detailed analysis of representative algorithms is presented. Thirdly, the data sets, the evaluation metrics and experimental results are summarized with an in-depth comparison. Finally, the problems and possible development directions and trends of point cloud denoising are prospected.

This review aims to guide the future development of related research in the field of learning path planning through the analysis of the current research status of learning path planning. Specifically, this review first introduces the definition of learning path planning and the commonly used parameters in learning path planning methods; then, it classifies in detail according to the algorithms used to generate learning path planning and summarizes the advantages and disadvantages of various learning path planning methods. In addition, the data set and evaluation method used by the learning path planning method is introduced. Finally, the challenges faced by the learning path planning method are summarized and the future development trend is predicted.

Automatic building extraction methods based on deep learning theory have the technical characteristics of high accuracy and speed,and are of great significance in industrial applications, such as urban planning,disaster prevention and mitigation. This paper introduces the deep learning modules and the traditional remote sensing validation section in the proposed building extraction method in high-resolution remote sensing imageries, forming an operational deep-learning-theory based building extraction technical system that integrates different backbone modules, UNet + + networks,and remote sensing authenticity verification modules. The basic network is transformed through the traditional convolutional network model backbones,such as VGG,ResNet, and Inception to improve the model operational efficiency,strengthen the model feature learning capabilities,verify the effectiveness and applicability of the algorithm through authenticity validation. Taking the Massachusetts building dataset published by Mnih as the data source,a comparative analysis was carried out with the traditional non-full convolutional network model and full convolutional network model. The results show that an increasing in the depth and width of the model can substantially improve the building extraction results. The InceptionV3-UNet + + backbone model has the best performance in recall rate,accuracy,CSI,F1 score,Kappa coefficients, and total accuracy,reaching 85. 14%,90. 50%,0. 781 6,0. 877 4,0. 850 4, and 95. 57%,respectively,and its robustness is also verified on the WHU datasets. This method has significantly improved the extraction accuracy and the details of the buildings extracted, especially on complex and irregular buildings, which will facilitate the building extraction applications in real, complex, and large scene of high-resolution remote sensing imageries.

Sunspot variations are the sun's symptoms of strong magnetic perturbations. In this paper, we use long short-term memory neural network and one-dimensional convolution neural network to detect sunspot variations. Here we use three different datasets, including the yearly mean sunspot number (YSSN) from 1700 to 2020, the monthly mean sunspot number (MSSN) from 1749 to 2021 and the monthly mean sunspot areas (MSSA) from 1874 to 2021. First, based on the YSSN dataset, we obtain YSSN for 2021 and the predicted YSSN in the 25th solar cycle appears at 2025 which equals 163.4; Then, based on the MSSN dataset, we obtain MSSN for June 2021 and the predicted YSSN in the 25th solar cycle appears in October 2024 which equals 245.9; Next, based on the MSSA dataset, the predicted MSSA for June 2021 is 73.1; Finally, the latitude is divided into 13 partitions to predict the butterfly diagram, and still, neural network can reconstruct the butterfly diagram. Therefore, neural network can provide a physical perspective for sunspot investigation.

Based on a high-speed photographic system, experiments on three-dimensional free jets of liquid metal in the absence of a magnetic field and in a horizontal magnetic field have been carried out to observe the process of liquid GaInSn jet breakup and droplet formation in an oxygen-free environment with a maximum We of 400 and a maximum Ha of 30. From the results on jet morphology, surface disturbance, and breakup length, we analyzed the characteristics of jet breakup. In the absence of magnetic field, the jet shows nine different morphologies, and the surface disturbance shows two forms:expansion wave and sinusoidal wave; with the increase of We, the disturbance amplitude first decreases and then increases, and the breakup length first increases and then decreases. When a horizontal magnetic field is imposed, the jet shows four typical morphologies, with the leading edge of the jet being flattened in the direction of the vertical magnetic field line and elliptical along the magnetic field line. As the Ha number increases, the jet break length tends to increase overall, but decreases in some operating conditions. The results of this paper have enriched the phenomenon of liquid metal jets under magnetohydrodynamic effects.

The quadratic assignment problem (QAP) is one of the NP-hard combinatorial optimization problems and is known for its diverse applications in real life. The current relatively mature heuristic algorithms are usually problem-oriented to design customized algorithms and lack the ability to transfer and generalize. In order to provide a unified QAP solution strategy, this paper abstracts the flow matrix and distance matrix of QAP problem into two undirected complete graphs and constructs corresponding correlation graphs, thus transforming the assignment task of facilities and locations into node selection task on the association graph. Based on actor-critic framework, this paper proposes a new algorithm ACQAP(actor-critic for QAP). Firstly, the model uses a multi-headed attention mechanism to construct a policy network to process the node representation vectors from the graph convolutional neural network; Then, the actor-critic algorithm is used to predict the probability of each node being output as the optimal node. Finally, the model outputs an action decision sequence that satisfies the objective reward function within a feasible time. The algorithm is free from manual design and is more flexible and reliable as it is applicable to different sizes of inputs. The experimental results show that on QAPLIB instances, the algorithm has stronger transfer and generalization ability under the premise that the accuracy is comparable to the traditional heuristic algorithm, while the assignment cost for solving is less compared to the latest learning-based algorithms such as NGM, and the deviation is less than 20% in most instances.

In recent years, suburban mountain areas have become a good choice for urban residents to go outing. Intensive tourist outings and villagers' production activities bring fire safety hazards to mountains and forests. And road information is vital information for forest fire prevention emergency. However, due to the problems of occlusion, shadow, narrow and multiple branches in suburban mountainous roads, conventional urban road extraction algorithms have poor performance in suburban mountain areas. This paper proposes a road semantic segmentation model and a training method that transforms the binary into a multi-class classification problem, forcing the model to focus on learning spatial distance information to generate road results with better spatial continuity. Then, experiments were carried out on the Yajishan road dataset made by ourselves and the Massachusetts public road dataset respectively to verify the effectiveness of our model and training method. In addition, it is verified that the training method is also applicable to other commonly used semantic segmentation models such as U-Net and DeepLabV3. Finally, this paper also conducts post-processing research based on the above road extraction results to output road surface, road centerline vector data with road width information, and conducts fire truck traffic analysis application in Beijing Yaji Mountain. The research results have alleviated the problem of insufficient road information for commercial electronic maps in the suburban mountain areas with few people, and provided information technology support for forest fire emergency rescue.

Being closely related to human socioeconomic activity and its footprints, nighttime light (NTL) data shows great advantages in urbanization and socioeconomic development research, especially in densely populated cities. Based on CiteSpace software and the core databases of CNKI (China National Knowledge Infrastructure) and WOS (Web of Science), this paper tracked the hot spots of NTL data in the study of urbanization and related resource consumption and environmental effects from 2000 to 2019. The main results are as follows. 1) Urbanization was the main focus of the application of NTL data, but the researches on the resource consumption and environmental effects caused by urbanization were slightly weak, which was more obvious in Chinese literature. 2) Researches of urban expansion and urban form evolution focused on process of land expansion based on different features of NTL datasets, while in researches of population, socioeconomic development, electricity consumption and carbon emissions, NTL data usually played the role as a supporting tool to explore spatiotemporal characteristics and mechanism. 3) In regards of air pollution and urban heat island induced by urbanization, NTL datasets were usually used to represent factors related to human activities and their impacts. 4) Urbanization process and its impacts on resource and environment are complex, the improved spatial resolution and integrated multi-source data, along with new methods as machine learning, will make the urbanization related research be more precise and scientific. Finally, the paper summarizes the possible new directions of the application of NTL data in urban geography.

Xinjiang is an important area connecting the east and west alongside the Silk Road. As a result of its dry weather and less rainfall, plant remains were well preserved, including hemp (Cannabis sativa). The history of Cannabis recognition and utilization in ancient Xinjiang could be roughly divided into two stages. Due to inherent advantages of the geographical environment, Xinjiang was firstly affected by the ancient civilization of Central Asia in the prehistoric period, mainly retaining the hallucinogenic tradition of Cannabis. During the historical period, the beginning of the Silk Road promoted exchanges between the Western Regions and the interior areas. The edible and fiber values of Cannabis were gradually accepted. Therefore, in terms of Cannabis usage, ancestors generally had a transformation from psychoactive agents to food crops, fiber crops, and even medicinal plants. In this study, we reviewed the Cannabis researches in Xinjiang during the past years, summarizing the ways and differences of Cannabis utilization, especially their causes in different periods. Meanwhile, we restudiedCannabis remains in Turpan with plant identification and radiocarbon dating, comprehensively sorting out the handed-down and unearthed documents, to explore not only the utilization of Cannabis medicinal value, but also to provide new clues for the spread of Chinese medicine in ancient Xinjiang.

The global ecosystem dynamics investigation(GEDI) is a full-waveform LiDAR system launched by United States in December 2018, which can provide data support for global ground elevation and forest canopy height. In order to adapt to different surface environments, GEDI L2A products provide 6 different algorithms to estimate ground elevation and forest canopy height. The choice of these algorithms will affect the extraction accuracy of surface parameters. In this paper, the digital terrain model and canopy height model obtained by airborne laser scanning data are used as reference data to evaluate the adaptability of different algorithms of the second version of GEDI L2A data under different vegetation coverage values and the impact on the accuracy of ground elevation and forest canopy height. The results show that when the vegetation coverage is less than 0. 2, the result of algorithm 4 is the best, when the coverage is greater than or equal to 0. 8, the result of algorithm 2 is the best, and the result of algorithm 1 is the best under the remaining coverage values. Comparing the optimal algorithm selected in this paper according to the vegetation coverage with the GEDI L2A default optimal algorithm, the results of this paper are generally better than those of the GEDI L2A default optimal algorithm, especially when the coverage value is less than 0. 8 and the slope is greater than or equal to 10°. The optimal algorithm selected in this paper can effectively improve the accuracy of GEDI L2A products for estimating ground elevation and forest canopy height.

Hydrous minerals play a vital role in the Earth's interior water cycle. The path of water cycle and the change of composition in the Earth's interior can be estimated by the electrical conductivity of typical hydrous minerals. In this paper, we summarized the electrical conductivities, conduction mechanisms, dehydration mechanisms and geophysical implications for typical hydrous minerals before and after dehydration under high temperatures and pressures. The electrical conductivities of most hydrous minerals before dehydration are generally distributed at 10^-4S/m, and increase significantly after dehydration. There are differences in the electrical conductivities of different hydrous minerals. The coupling of dehydration mechanisms and conduction mechanisms may determine the electrical conductivity of hydrous minerals during dehydration. The electrical conductivity of hydrous minerals after dehydration generally depends on the composition and connectivity of the fluid. The changes of electrical conductivities of hydrous minerals before and after dehydration provide experimental constraints for the high-conductivity anomalies at different depths in subduction zones. In addition, the determination of the composition of the fluid derived from the dehydration of the hydrous minerals enriched our knowledge of material circulation in subduction zones. Based on the recent progress, the potential research directions of the electrical conductivity of hydrous minerals are prospected.

Youjiang basin (Nanpanjiang basin) experienced a complex tectonic evolution of Paleozoic prototype basin-superimposed basin, and finally, it appears as a residual basin. According to the tectonic setting, sedimentary series and magmatic rocks, the evolution of Youjiang basin after Caledonian movement can be divided into six stages:intracontinental extensional basin (early rift valley) evolution stage (D²₁-D¹₂), oceanic extensional basin (rift ocean basin) evolution stage (D₂-T₁), ocean basin extinction and foreland flexure basin evolution stage (T²₁-T¹₃), fold orogeny and post collisional extension stage (T¹₃-J₁), NW trending compression orogeny stage (J₂-K²₁), and local extension stage (K³₁-E). There are a large number of Carlin-type gold deposits in the basin, and most ore bodies occur in thrust-fold belts. The Carlin-type gold deposit has multi-stage metallogenic characteristics, large-scale mineralization mainly began in the compressive background and continued to the post orogenic extension stage, and there are two concentrated metallogenic periods. The first stage was formed in the evolution stage of foreland flexure basin related to collision orogeny to post collisional extension (235-193 Ma, Carlin-type gold deposit in the central and southern part of the basin). The mineralization in this stage was controlled by metamorphic hydrothermal fluids or hydrothermal fluids relate to magmatic melting which induced by the superposition of collision orogeny of the Yangtze Block and the Indochina Block and the subduction of the Paleo-Pacific plate to the Eurasian continent. The second stage was formed in the stage of NW trending compression orogeny to local extension (148-103 Ma, Carlin-type gold deposit in the whole Youjiang basin). The mineralization in this stage was mainly affected by the magmatic hydrothermal activities during the superposition and transformation of the pre-existing structures by the NW trending compression orogeny. Magmatic or metamorphic hydrothermal activities under extensional background before the Early Triassic has the effect of initial enrichment, and it has the effect of superimposed and reformation post mineralization after the Early Cretaceous. The ore-forming fluid of Carlin-type gold deposit has the characteristics of mixed sources. It is mainly metamorphic hydrothermal solution in the central and southern part of the basin, and mostly mixed source hydrothermal solution in the central part of the basin. While, the ore-forming fluid is mainly magmatic hydrothermal solution in the northern part of Youjiang basin. Through comparative analysis, it is considered that the ages obtained by different dating methods can represent the metallogenic age to a certain extent. And the combined application of multiple methods should be used to limit the metallogenic age of Carlin-type gold deposits.

Level one A (L1A) product of Gaofen-3 SAR satellite is the primary image set delivering for customer. This paper presents a complete workflow to facilitate the post-process of GF-3 L1A images for follow-up scientific research or value-added applications, where robust and precise processing is essential to generate the advanced high-level product concerning radiometric correction and geometric correction. Firstly, to eliminate the statistical bias caused by the null and zero pixel values induced in the quantization of the L1A product, an improved radiometric correction formula is derived based on the equivalent noise coefficient of Gaofen-3 images. Then, to determine the coordinates of image corners, an inverse algorithm supported by RPC parameters is proposed for geometric correction. This algorithm is robust by counting on the orbit direction, look direction, and sampling interval provided in an XML metadata file. Finally, a SAR filter operator is introduced into the resampling step of output results to improve the equivalent look number. Experimental results comparing with the radiometric values of a sentinel-1 image and the geometric accuracy of a sentinel-2 optical image, respectively, validate the accuracy and reliability of this method for L1A product processing.

High precision face recognition based on unmanned aerial vehicle (UAV) images plays an important role in emergency rescue, suspect tracking, and other scenes. Deep learning convolutional neural network is widely used in the field of target detection and recognition because of its high accuracy and less human interference, which can be well applied to UAV image face recognition tasks. This paper explores the use of convolution networks for high-precision face recognition in UAV application scenarios, uses the improved YOLOv3(you only look once) for face detection of UAV images, and inputs the prediction boxes into the classic Facenet network to determine the target identity. Through experiments, this paper compares the detection effect of the improved YOLOv3, the original YOLOv3, and the MTCNN (multi-task convolutional neural network), and also compares the face recognition effect of the three models combined with Facenet. The experimental results show that: 1) compared with the original YOLOv3, the improved YOLOv3 improves the accuracy and recall rate, reduces the number of model parameters; besides, the phenomenon of missing and wrong detection of the improved YOLOv3 for UAV image is less than that of the original YOLOv3; moreover, the AP (average precision) of improved YOLOv3 is 9.49% higher than that of MTCNN, and the detection speed is about 3 times of MTCNN; 2) compared with the original YOLOv3+Facenet and MTCNN+Facenet, the improved YOLOv3+Facenet has stronger ability to distinguish faces and higher accuracy, and has stronger robustness to occlusion and blur.

The night radiance mode of MODTRAN (moderate resolution atmospheric transmission) can simulate the lunar irradiance transfer, but the lunar-source model of MODTRAN simplifies the calculation of the lunar phase function, lunar albedo, sun-moon distances, and earth-moon distances. This simplicity can lead to some errors in the result, which affects the calibration accuracy. So, the sensitivity of MODTRAN lunar-source model to lunar model parameters is evaluated by using perturbation analysis method in this paper. The results show that the most important parameters affecting the accuracy of lunar irradiance simulation from MODTRAN are earth-moon distances, lunar phase function, and lunar albedo. This paper used lunar irradiance model MT2009 (Miller-Turner 2009) and SeaWiFS (sea-viewing wide field-of-view sensor) satellite data as benchmarks to compare and analyze lunar-source function of MODTRAN. The results showed that:1) Lunar irradiance calculated by MODTRAN were in better agreement with the values from MT2009 and SeaWiFS when wavelength was greater than 0.675μm; 2) When the absolute value of lunar phase angle was less than 30° and earth-moon distance was 390000km, the relative differences between MODTRAN and benchmarks were less than 10%; 3) When the absolute value of lunar phase angle was greater than 30° and the moon was closed to apogee, the relative differences between lunar irradiance model of MODTRAN and benchmarks were light. While lunar phase angle was between -41° and -30° or between 30° and 41° and the moon was closed to apogee, maximum relative difference was about 10%. This paper indicates that MODTRAN lunar-source model is suitable for low-light remote sensing calibration under specific conditions. More research work on MODTRAN lunar-source model will be carried out in the future.

Recently, the low earth orbit (LEO) satellite communication system has received wide attention from academia and industry, due to its global coverage and flexible construction. In the LEO satellite communication system, satellites are connected to a dynamic network through the inter-satellite link (ISL). How to reduce the communication delay of the network is an urgent research problem to be solved. For the problem of cross seam link-building of the Walker polar orbit constellation, this paper proposes a star cross looping link-building strategy (SCLBS). SCLBS divides the orbits into multiple cross link-building areas on both sides of cross seam and the transmission delay can be optimized by dynamically established ISL, providing a stable link-building scheme for polar orbit cross seam. SCLBS is applied to actual business scenarios such as the airplane and ship, and the test results show that SCLBS can effectively reduce the transmission delay by 30% to 60% compared with the link-building strategies based on the nearest satellite criterion and the longest visible time criterion, and significantly improve system performance.

This article aims to study the effectiveness of momentum strategies in Chinese commodity futures market, and after judging the existence and causes of momentum crashes, puts forward effective methods to manage the risk of momentum crashes. In this paper, considering the transaction costs, the commodity futures momentum strategy can continuously obtain significant risk-adjusted returns, and further empirical discovery of the momentum crashes phenomenon in Chinese commodity futures market. The reason of momentum crashes is that the loser portfolio has the nature of option-like is more sensitive to market portfolio volatility, which in turn leads to the crashes of momentum portfolios. In order to carry out the risk management of momentum crashes, this paper proposes to construct a dynamic weighted momentum strategy based on target condition stop to manage the risk of momentum crashes. The results show that this method effectively avoids the extreme risk brought about by momentum crashes and obtains higher momentum return and Sharpe ratio.

The Yellow River Basin is a critical economic corridor for the implementation of the Belt and Road initiative. Maintaining the ecological health of the Yellow River is important for both the national social-economic development and ecological security. However, due to human activities and climate change, the Yellow River Basin has experienced ecological crises, such as degradation of vegetation in the upper reaches, sharp reduction of water and sand in the middle reaches, water shortage in the lower reaches, and retreat of the estuarine delta. This has seriously restricted the high-quality development of the Yellow River Basin and the process of ecological civilization in China. This study uses 5 190 related literature and 145 781 references in the Web of Science (WOS) database during 1990-2020 as the research sample. By scientometric and visual knowledge network methods, it analyzes the literature output pattern, keyword co-occurrence network, and future development of hydrology research in Yellow River Basin. Specifically, it quantifies and visualizes the current status, main institutions, academic journals, high-producing authors, and hot themes of the research at a multi-dimensional and deep level. Thus, this study aims at facilitating relevant researchers to get a panorama view, so as to focus the directions of ecohydrological research of the Yellow River Basin.

In this paper, a class of stochastic Poisson systems, arising from randomly perturbing a type of Lotka-Volterra systems by certain Stratonovich white noise, are considered. We give the sufficient conditions for the almost sure existence (global non-explosion) and uniqueness of the solution of the system, and further prove that the solution is positive and bounded almost surely under the proposed conditions. Numeraical experiments are performed to verify the results.

The highlights in the image will degrade the image quality to some extent. In this paper, we focus on visually removing the highlights from degraded images and generating clean images. In order to solve this problem, we present an attention-auxiliary generative adversarial networks. It mainly consists of the convolutional long short term memory network with squeeze-and-excitation (SE) block and the map-auxiliary module. Map-auxiliary can instruct the autoencoder to generate clean images. The injection of SE block and map-auxiliary module to the generator is the main contribution of this paper. And our proposed deep learning-based approach can be easily ported to handle other similar image recovery problems. Experiments prove that the network architecture is effective and makes a lot of sense.