The integration of low earth orbit （LEO） satellite with 5G in the mobile communication system presents extensive application prospects. To ensure effective establishment of communication links between users and base stations， as well as reliable data transmission， time-frequency synchronization technology plays a crucial role. However， traditional timing and frequency synchronization algorithms face limitations when dealing with millimeter wave frequency bands， large bandwidths， high Doppler frequency shifts and change rates， and low signal-to-noise ratios in low-orbit satellite channels. These limitations result in decreased estimation accuracy. For the uplink service data channel of a 5G-based low orbit satellite communication system， a joint timing and frequency synchronization algorithm based on weighted embedded synchronization sequences is proposed to improve the accuracy of timing and frequency offset estimation. Simulation results demonstrate that the proposed timing and frequency synchronization algorithm outperforms traditional algorithms with comparable complexity in terms of synchronization performance.

Super-resolution technology has become an important tool for reconstructing high-resolution datasets and supplementing the shortage of high-resolution images with its characteristics of flexibility and low cost. Compared with natural images， remote sensing images of real scenes are complex and specific， which make super-resolution tasks more difficult. Meanwhile， for remote sensing images， traditional deep learning models can improve the resolution， but there is still a great deficiency of improvement for the details and textures of the ground objects. Therefore， based on the generative adversarial network model， this paper fuses channel-space attention to enhance the feature learning capability of the network and use an artifact suppression strategy to distinguish smooth regions from detail-rich regions， so that the network can focus more on detail-rich regions and suppress the generation of artifacts. Extensive experiments on GaoFen satellite data show that the quantitative metrics and visual quality of the method proposed in this paper are better than those of the current mainstream methods.

The palm-leaf manuscript is a treasure of human civilization， which is the scripture written or engraved on the processed leaves of palm trees. Originating in ancient India， it was gradually introduced into China in the historical period， and reached a period of great prosperity in the Tang Dynasty. Chinese ancestors had a clear understanding of the materials used to make the palm-leaf manuscript. According to our study， the “Doro” （多罗） tree in ancient books should be talipot（Corypha umbraculifera）， while the “Beiduo” （贝多） tree should be palmyra（Borassus flabellifer）. The word “Beiduo”， which appeared many times in Chinese classical poetry， has become a synonym of Buddhist classics. Material evidence of ancient palm-leaf manuscript has been unearthed in many places in Xinjiang， among which Powell’s manuscript as well as Spitzer’s manuscript unearthed in Kuqa were of great significance， with the latter being considered to be the earliest physical evidence of palm-leaf manuscript all over the world. In addition， ancient palm-leaf manuscripts were also unearthed in the Mogao Caves in Dunhuang， which was also of great importance. To date， palm-leaf manuscripts are mainly preserved in Xizang， as well as Xishuangbanna in Yunnan Province， where they have been well organized and protected.​ The characters on the palm-leaf manuscripts were carved and written， respectively， which should be adapted to their preservation conditions.

Regime shifts and their underlying mechanisms in ecosystems are a critical issue in ecological research， with profound implications for predicting ecological risks under global change. This review systematically synthesizes the theoretical advances in alternative stable state （ASS） of ecosystem multi-stability， focusing on three key dimensions： micro-macro process coupling， mechanisms of threshold response， and the role of regulatory nodes in ecosystem resilience. By integrating methodologies such as ASS theory， potential landscape modeling， and bifurcation analysis， we highlight how climate change and anthropogenic activities are driving critical ecosystems （e.g.， coral reefs， Amazon rainforest， Arctic permafrost） toward tipping points， while hysteresis effects and irreversible potentials exacerbate recovery challenges. Emerging approaches combining network theory and energy （carbon） flux analysis offer novel insights for cross-scale early warning， yet bridging micro-scale mechanisms with macro-scale patterns remains a critical challenge. This review provides a theoretical framework for ecological threshold management and underscores the urgent need for interdisciplinary approaches to address planetary-scale regime shift risks.

The convergence of broadband LEO satellite communication systems with 5G is the development trend of satellite communication. To meet the demand for high-speed data transmission, multibeam technology based on large-scale digital phased arrays is indispensable. The peak-to-average ratio problem of multibeam signals induces the non-linear distortion of onboard power amplifiers, while the non-linear distortion of power amplifiers leads to serious interference between multibeams. A digital pre-distortion structure for multibeam is proposed for low-orbit satellite communication systems using large-scale phased arrays. A pre-distortion model is developed to solve the problems of non-linearity, intermodulation distortion and multibeam interference. Through simulation experiments, it is found that the proposed structure is more advantageous than the traditional pre-distortion structure in terms of performance and complexity balance, providing a feasible solution for the implementation of multibeam and overcoming non-linearity on board.

Based on the perspective of human-earth relationships, firstly, this article analyzes the theoretical connotation of “Lucid waters and lush mountains are invaluable assets” from the perspectives of philosophical meaning, geographical logic, and economic thinking. Secondly, from the three aspects of serving the rural revitalization, building a beautiful China, and building a community of human destiny, the era value of “Lucid waters and lush mountains are invaluable assets”. Subsequently, from the perspective of coordinated development of human-earth relations, from the four aspects of highlighting the participation of people, the value of excavation land, maintaining human-land balance, and the standardized human and ground behavior, it proposed the development of the transformation from “lucid waters and lush mountains” to “gold and silver mountains”. Finally, according to the key issues that have not been resolved in the practical path, it is pointed out that future research should be strengthened on the selection of transformation path and model, the valuation of ecological products, and the evaluation of transformation efficiency of the “Two Mountains”.

The production and supply of food are core components of sustainable development. Ensuring the sustainability of global food production and supply is crucial for maintaining human survival and socioeconomic stability, and it holds significant importance in advancing the “Zero Hunger” goal within the framework of global sustainable development. This paper selects the five key cereal crops, including wheat, barley, maize, oats, and rice, as the subjects of study, focusing on the Central Asian region. It analyzes the variations in yield per hectare, total production, and cultivated area for these cereals from 1992 to 2021, investigates regional disparities in food production fluctuations within Central Asia, and employs the ARIMA model to forecast future grain production in Central Asia. The results showed that: 1) From 1992 to 2021, the grain yield, total output and sown area in Central Asia showed a trend of first decreasing and then increasing, and the three changes ranged from 0.79～1.96 t/hm², (0.14～0.37)×10⁸ t and (0.14～0.23)×10⁸ hm², respectively. Grain yield and total production reached their peaks in 2011 at 1.96 t/hm², and 0.37×10⁸ t, respectively, while the cultivated grain area peaked in 1993 at 0.23×10⁸ hm². 2) The grain volatility in Central Asia is characterized by frequent fluctuations in grain production, with a significant proportion of years experiencing fluctuations exceeding 5%. The amplitude of these fluctuations is substantial, and the average fluctuation cycle is 2-4 years, indicating a short-term cyclical pattern dominated by classical rather than growth-oriented fluctuations. 3) In the coming years, Central Asia is projected to experience an upward trend in wheat, barley, maize, and oats production, while rice production is expected to decline. Compared to the year 2021, by 2030, Central Asia’s wheat, barley, maize, and oats production is estimated to increase by (410.15, 91.6, 795.26, and 8.91)×10⁴ t, respectively, representing growth rates of 20.1%, 31%, 299.2%, and 37.1%. Conversely, rice production may decrease by 15.99×10⁴ t, with a decline of 15.5%.

Astana Cemetery is located in Turpan Basin, Xinjiang. Due to the local extremely arid climate, large numbers of plant remains and documents were well preserved in Astana Cemetery, providing valuable materials for exploring the agricultural production and plant utilization of Turpan people during the Jin to Tang dynasties. Archaeobotanical method was applied on the Chinese jujube (Ziziphus jujuba Mill.) remains unearthed from the Astana Cemetery. Combined with relevant documents, the cultivation and utilization of Chinese jujube during the Jin to Tang dynasties were discussed. It showed that Chinese jujube was a common and important characteristic cash crop in ancient Turpan. The indigenous people used Chinese jujube in various ways, including eating in fresh and further processing it into dried jujube, jujube cake or dry powder of jujube. In addition, the medicinal value of Chinese jujube and the excellent characteristics of jujube wood were also recognized and utilized by ancient Turpan people.

Huanjing-1A （HJ-1A） CCD1 has four reflected solar bands in visible and near-infrared bands. The on-orbit absolute radiometric calibration frequency of HJ-1A/CCD1 is limited to the annual site calibration. Cross calibration， as a supplement means of site calibration， can achieve high-frequency on-orbit absolute radiometric calibration. In this paper， we select Terra/MODIS as the reference sensor to perform long time series cross calibration on HJ-1A/CCD1 based on Dunhuang calibration site. By limiting conditions such as the imaging angle difference （less than 20°）， the imaging time difference （less than 2 hours）， cloud cover， and imaging quality between HJ-1A/CCD1 and Terra/MODIS， a total of 147 effective cross calibration image pairs were screened out from September 2008 to December 2021. Then the 6SV v2.1 radiative transfer model was used to calculate the spectral band adjustment factor， and ultimately achieving long time series cross calibration of HJ-1A/CCD1. The results show that： 1） The cross-calibration coefficients calculated in this article are highly consistent with the officially published calibration coefficients， with an average relative difference of less than 2.25%. The calibration uncertainty is within 5.34%. 2） The long time series cross calibration results showed that after one year on-orbit operation， the gain status of HJ-1A/CCD1 was adjusted on October 20， 2009， resulting in a sudden change in the cross-calibration coefficients on the 409th day after launch. After the adjustment， the overall radiometric performance was relatively stable. 3） The radiometric performance of HJ-1A/CCD1 showed a slow and fluctuating downward trend from October 2009 to December 2021， with an annual attenuation rate of less than 3.10%. The method proposed in this paper can effectively improve the radiometric calibration frequency and accuracy， and can be used for radiometric performance monitoring over the whole life cycle of HJ-1A/CCD1.

This article takes 567 national level (168) and provincial level (511) traditional villages in Shandong Province as subjects of investigation. Firstly, a spatial econometric model is used to analyze the spatial distribution characteristics of traditional villages in Shandong Province. Secondly, the Delphi method and SPSS factor analysis method are used to screen and analyze the evaluation indicators. A total of 28 indicators including village cultural resources, village ecological and natural resources, institutional management, tourism infrastructure, and village economic vitality are obtained. Finally, the AHP entropy weight method is used to combine weights to score the selected indicators. A scoring table for tourism development potential of traditional villages in Shandong Province is constructed, and a multi-objective linear weighted function model is used for scoring. The results showed that: 1) The overall spatial distribution of traditional villages in Shandong Province is uneven, mostly distributed in mountainous areas or underdeveloped areas of cities with relatively uneven development and underdeveloped infrastructure; 2) Among various indicators, the cultural resources (0.331 2) and institutional management (0.144 0) of the village play an important role, followed by tourism infrastructure (0.143 7), village ecological and natural resources (0.143 4), and village economic vitality (0.097 7); 3) The evaluation results show that there are 135 key traditional villages (S≥7.9) in Shandong Province, accounting for only 23.81% of the total number of traditional villages in Shandong Province; there are 295 traditional villages (6.2<S<7.9), accounting for 52.03% of the total number of traditional villages in Shandong Province; there are 137 traditional villages with a focus on protection (S≤6.2), accounting for 24.16% of the total number of traditional villages in Shandong Province; 4) Based on the characteristics of various traditional villages, corresponding development strategies are proposed in this paper.

The potential of light detection and ranging (LiDAR) technology in the application of individual tree segmentation and parameter estimation in desert Haloxylon ammodendron forests has not been explored. This study uses UAV LiDAR data to extract canopy height models (CHM) at spatial resolutions of 0.1, 0.25, 0.5, and 1 m on different interpolation methods, and applies the CHM seed point segmentation algorithm to segment individual trees in three types of Haloxylon ammodendron plots with different growth conditions. This study evaluates the impact of spatial resolution and growth conditions on segmentation accuracy, and verifies the extraction accuracy of tree height and crown width with field measurement data. The results show that the inverse distance weighting interpolation has a higher segmentation accuracy in this study. Spatial resolution is a key factor affecting the results of individual tree segmentation, with the best segmentation results obtained at a resolution of 0.25 m.Class III plots had the highest segmentation accuracy, which was 27% higher than that of Class II plots and 44% higher than that of Class I sample plots. The overlapping crowns of Haloxylon ammodendron in plot I make it difficult to distinguish the crown boundaries, while the independent crowns in plot III make it easier to achieve accurate segmentation. The R² of the tree height fitting model for all three types of plots is around 0.80, with RMSE less than 0.31 m. The R² of the canopy extraction fit for the Class I and II plots is around 0.70, with a slightly higher RMSE error, and the branches in a half dead state of Haloxylon ammodendron in plot III affect the extraction accuracy of crown width. This study demonstrates that LiDAR data has great potential for individual tree segmentation in desert Haloxylon ammodendron forests, which can provide data support for desert forests carbon sink estimation in Xinjiang.

Ecosystem services (ES) are the bridge connecting ecosystems and social systems, and have gradually become an important basis for decision-making. To this end, this paper reviews the progresses of applying ecosystem service in decision-making by randomly selecting 1 000 Chinese papers on the topic of ecosystem service from 1997 to 2021. According to the four-element process chain from ecosystem service to decision making, this paper quantitatively and qualitatively analyzes the main types of process chains that the existing research focuses on, analyzes the content of process chains from two aspects of salience and legitimacy of ES information, and discusses the future research challenges. We found that although ecosystem services are highly sought after, there are few studies that fully integrate them into the process chain of decision-making. More than two thirds of the papers focused on one or two elements of the process chain, and 2% of the papers included a complete process chain for applying ecosystem services to decision-making. Regarding the salience of ES information, there are few studies at the national scale. In terms of legitimacy, the focus on stakeholders is weak. In terms of ecological regulatory factors, grain yield, biomass, and net primary productivity of vegetation are high-frequency considerations. In terms of socio-economic regulatory factors, food prices, GDP, and population are high-frequency considerations. In the future, in the process of applying ecosystem services to decision-making, it is necessary to further strengthen the standardization and local optimization of ecosystem service assessment methods, spatial and temporal scale selection, enhance stakeholder and public participation, and adopt scenario analysis to provide strong support for guiding practice and policy formulation.

Single image super-resolution （SISR） can improve the resolution of remote sensing images （RSIs）， thereby improving the application value of data. At present， the number of pixels of RSIs generally reaches hundreds of millions， and it is usually necessary to divide the image into patches when performing SISR. However， there is a lack of relevant research on how to effectively determine the patch size and whether different sizes affect the results. In this paper， taking a large-scale high-resolution RSIs as the experiment data， 3 typical SISR models are selected， 9 groups of SR experiments under different patch sizes are carried out， and the super-resolution （SR） results for the whole of the large-scaled RSI are analyzed comprehensively both qualitatively and quantitatively. The results show that： 1） Cutting of the patches results in stitching seams at the stitching place. In particular， when the patch size is small， a large number of stitching seams show a block effect and the inconsistency is more obvious. 2） With the increase of the patch size， the SR accuracy of the three models is improved， and the overall computational efficiency is also improved. When the test patches are larger than the training patches， the elapsed time and accuracy stabilize. 3） The feasibility and accuracy of the whole RSI input are closely related to the model. The ESPCN model has the best accuracy when inputting the whole RSI， the RDBPN model may cause the accuracy to decrease due to the non-square matrix of the RSI， and the HSENET model has high requirements for computing power and cannot calculate the whole RSI. In conclusion， this paper provides an experimental basis for the selection of patch size for RSI SR engineering applications.

In the field of autonomous driving， accurate vehicle trajectory prediction plays a crucial role. While current deep learning-based algorithms have significantly improved the accuracy of vehicle trajectory prediction， they lack interpretability regarding the decision-making process of the algorithm. To address this issue， we incorporate prior knowledge into the deep learning-based algorithm and propose a trajectory prediction algorithm based on attention mechanisms. Diverging from traditional methods that add constraints for knowledge integration， we employ a tailored model architecture that embeds insights from the social force model to replicate the decision-making processes of drivers in complex traffic scenarios， thereby enhancing the interpretability of the predictions. Knowledge-infused trajectory prediction algorithm（KIT） leverages an attention mechanism to imitate drivers’ perception of their environment and uses a multilayer perceptron network for predicting accelerations influenced by the driver’s intentions， nearby traffic， and surrounding roads. The proposed method is validated on the Argoverse dataset， and the results indicate that KIT demonstrates superior predictive performance compared to current advanced trajectory prediction methods.

Image stitching is a crucial prerequisite step for unmanned aerial vehicle （UAV） remote sensing applications， while the stitched images using most of the current image stitching methods often suffer from large irregular boundaries and multiple stitching seams， which can seriously affect subsequent analysis and applications. Existing improved methods typically cannot simultaneously address these two issues， and integrating the two types of methods in sequence is a straightforward way to solve the two problems， while this often can not obtain satisfactory performance because of the inevitable error propagation problem. This paper proposes an inpainting method for the UAV image stitching task based on the denoising diffusion probability model （DDPM）. The method uniformly designs masks for irregular boundaries and stitching seams， and a diffusion model is then utilized with manifold gradient prior constraints to complete the masked regions. By doing so， both irregular boundaries and stitching seams are simultaneously eliminated， thereby improving the quality of the stitching results. Comparative experiments are conducted using four datasets established for different scenarios. The experimental results demonstrated the efficacy of the proposed method in effectively eliminating irregular boundaries and seams in the image stitching. Moreover， from patches to pictures quality （PaQ-2-PiQ） and multi-scale image quality （MUSIQ） scores increased by 4.36% and 15.37%， respectively. Furthermore， at the locations of irregular boundaries， the structural similarity （SSIM） and peak signal-to-noise ratio （PSNR） values improved by 20.22% and 33.69%， respectively. Compared with state-of-the-art methods and other conventional image stitching algorithms， the proposed method performs better in both subjective and objective quality metric scores， has good robustness and generalization， and can be widely applied to UAV image stitching scenarios.

The coordinated development of regional economy-resource-environment system (ERE) is a crucial aspect of achieving the sustainable development goals. Taking Binzhou City in Shandong Province, a typical city in the Yellow River Delta, as an example, this paper constructs a coupling coordination degree model and examines the coupled and coordinated development of its ERE system from 2000 to 2020. Furthermore, a system dynamics (SD) model of Binzhou City’s ERE is constructed based on 71 indicators, through which causal feedback relationships and flows among subsystems are illustrated. Four scenarios are set for simulation as follows: current trend continuing mode, secondary industry leading mode, resource and environment prioritizing mode, and system coordinated mode. The results show that: 1) Over the past 20 years, the level of coupled and coordinated development in Binzhou City has been increasing year by year, and the type of coupled and coordinated development has gradually transitioned from an unbalanced state to a well-coordinated state, but there is still instability in the ERE; 2) Through SD parameter calculation and multi-scenario simulation analysis of Binzhou City’s ERE, it is pointed out that continuing the current development model cannot achieve high-quality sustainable development in Binzhou City. Therefore, a coordinated and stable development model, which takes economic, resource, and environmental benefits into account, is suggested as the relatively optimal solution for Binzhou City to achieve sustainable development in the long run.

Devices made of thermoelectric materials can realize the interconversion between heat and electricity， which is based on the Seebeck effect and the Peltier effect respectively， without any moving elements. Different from conventional metallic and inorganic thermoelectric materials， thermoelectric polymer composites possess good flexibility， decent stretchability， and healing ability， thus arousing ever-increasing interests among researchers because they are abundant， inexpensive， light-weighted， low-toxic or non-toxic， and thermally less conductive. Generally， thermoelectric polymer composites comprise nanomaterials which are usually carbon nanotubes， graphene， metal organic frameworks， MXenes， black phosphorus， etc. and conductive polymers which are typically polythiophene and its derivatives， polyaniline， and polypyrrole. Apart from traditional use of generating electricity from heat for thermoelectric materials， polymer composites acting as an indispensable complement to inorganic thermoelectric materials have combined advantages of conductive polymers and nanomaterials and are finding new applications in various sensors although their performance in certain aspects remains below that of conventional metallic and inorganic thermoelectric materials. New toolkits like artificial intelligence and machine learning have been introduced as effective ways in facilitating efficient design， preparation， and performance enhancement of thermoelectric polymer composites. It is expected that thermoelectric polymer composites will make great progress and demonstrate many broader application scenarios.

Fake reviews affect online consumers’purchasing decisions. Efficiently identifying fake reviews is a pressing issue in the current development of e-commerce. Traditional methods for detecting fake reviews are often influenced by variations in review text style， syntax， and context， resulting in lower accuracy. Although large language models （LLMs） can address this accuracy issue， their training process is typically time-consuming. To tackle this problem， we propose a novel method called CF-DRI （cluster-based fine-tuning for deceptive review identification）. This method fine-tunes the pre-trained knowledge of LLMs by selecting clustered review samples， significantly enhancing the training efficiency for fake review identification. Compared to traditional methods， CF-DRI demonstrates superior performance with fewer fine-tuning samples. Experimental results on the Yelp.com dataset show that CF-DRI achieves a precision of 92.29% and a recall of 90.03% in fake review identification using only 20% of the clustered samples. This research provides new perspectives and solutions for managing fake reviews on e-commerce platforms， potentially promoting healthy industry development.