bluuebunny/crossref_metadata_2025_split huggingface dataset:
Remote sensing extraction of aquaculture ponds in China’s coastal zone based on random forest
Authors: Zhang Peng , Gui Fukun , Feng Dejun , Zhang Guangyang 
Published: October 2024

Abstract
Aquaculture ponds are one of the most important land use types in coastal areas of China. However, the rapid expansion of aquaculture ponds threatens coastal ecosystems, which is the problem the sustainable development goals seek to address. Studying changes at its scale is crucial to exploring its drivers and understanding its impacts, which is a focus of sustainable management of aquaculture. To scientifically manage the coastal aquaculture industry, it is necessary to understand its spatial size changes. Given the problems of low extraction efficiency and low accuracy in traditional remote sensing extraction methods for large-scale areas, this study aims to use long-term sequence remote sensing images and geographical information methods to evaluate the performance of aquaculture ponds in coastal areas of China during 2019-2023. This research is based on the Google Earth Engine platform and proposes a random forest classification method for aquaculture pond extraction, taking 20 km inland of China’s coastal zone as the research area, and extracting coastal aquaculture ponds for five years from 2019 to 2023. The results show: (1) The areas of coastal breeding ponds from 2019 to 2023 were 9, 425 km², 9, 526 km², 8, 992 km², 9, 092 km², and 8, 883 km² respectively. The breeding ponds have experienced a significant decrease; (2) Breeding ponds are mainly distributed in China’s coastal plains and bay areas, with the Bohai Rim, Yangtze River Delta, and Pearl River Delta being particularly concentrated. In terms of area changes, compared with 2019, the area of China’s coastal aquaculture ponds has decreased by 5.75%, and the aquaculture area has shrunk by approximately 542 km². This change process is dynamic, going through two stages of rapid expansion and gradual shrinkage; (3) Taking Hainan Province as an example, combined with the factors affecting the distribution and area size of coastal breeding ponds obtained by the correlation analysis method, the statistical analysis concluded that the discharge of ammonia nitrogen, phosphorus, and other water pollutants is closely related to the size of the breeding ponds. The positive correlation of aquaculture output and the negative correlation of fishery output value reflects the increasing intensification of aquaculture ponds to a certain extent. This method can be applied to identify and extract coastal ponds, which is beneficial to the formulation of scientific and reasonable coastal land planning and is of great significance in promoting the sustainable development of the coastal ecological environment.
Remote sensing image fusion method based on depth feature extraction
Authors: Xiao Yunlong , Guo Xinyi 
Published: October 2024

Abstract
Remote sensing image fusion involves combining data from various remote sensing sensors or merging multiple images acquired by the same sensor but in different spectral bands to create a superior quality composite image, high resolution, multi-band, and multi-temporal remote sensing image through certain algorithms and technologies. By utilizing fusion techniques, remote sensing images of varying bands, resolutions, and phases can be combined to enhance the accuracy of ground object information. This paper introduces a remote sensing image fusion method that leverages deep learning for depth feature extraction. Swin Transformer makes full use of the characteristics of the relationship between adjacent pixels, and adaptively extracts panchromatic image (PAN) and multispectral image (MS) features through two adaptive feature extraction channels. High-quality satellite data is employed to confirm the efficacy of the suggested approach, with the findings demonstrating strong performance in ERGAS, SCC, SSIM, and PSNR, and other evaluation indexes and has good spectral information fidelity and spatial information integration degree.
Time series cube data construction and land use attribute change study based on high-resolution imagery
Authors: Zhao Jianghong , Xu Ailin , Yu Haiquan , Zhao Jifu 
Published: October 2024

Abstract
In order to better understand the changes in land use attributes, comprehend the drivers of land use changes, assess their environmental and economic impacts, and provide a scientific basis and data support for land use planning, environmental protection, and agricultural production, this study is based on Qiu County’s high-resolution image data from 2020-2022 and makes use of the latest research results of hyperspectral remote sensing technology to analyze its changes in land use attributes by establishing the time of the high-resolution image sequence cube data and solving the characteristic variables through band operations to quickly and accurately calculate a variety of remote sensing indicators and analyze the changes in their land use attributes. The results of the study show that vegetation in Qiu County will change significantly between 2020 and 2022, mainly due to seasonal and land-use changes; Water bodies will change less but more consistently; And buildings will change less, mainly focusing on villages and industrial land.
Gross error detection method for high-precision data processing of beidou satellite navigation system
Authors: Zhang Jingfa , Li Jianye , Fu Yunjie , Hong Zhijia , Song Zhenkang 
Published: October 2024

Abstract
Gross error detection is indispensable in the Beidou high-precision positioning process. In this paper, the ratio difference of cycle slip detection using the constant relationship between the observation values of any two different frequency data in the tri-frequency data is proposed. The algorithm is susceptible to abnormal changes in observations. It can detect abnormal changes of more than 0.1 weeks of observations. When the three-frequency data and three frequencies jump at the same time, the MW combination must be combined. The resolution accuracy is 1.6 weeks. Any other single-frequency or the resolution accuracy of dual-frequency simultaneous hopping can reach 0.1 weeks. Example validations were performed and found to be better than the TurbiEdit algorithm for roughness detection.
Spatial-spectral attention state-space fusion network for tree carbon estimation using multi-source data
Authors: Fu Yawen , Hu Dong , Zhou Yuxin , Sun Zhixiong , Zhang Shuyu 
Published: October 2024

Abstract
Amid rapid industrialization and urbanization, global warming and the urban heat island effect are intensifying. There is an urgent need for rapid and accurate monitoring of carbon storage in urban vegetation to assess and mitigate these environmental issues. While methods exist for estimating above-ground carbon (AGC) stocks in individual trees using deep learning, fewer studies integrate multi-source remote sensing data with these techniques. This study introduces a network that leverages spatial-spectral attention and state-space models, called the spatial-spectral attention state-space fusion network (S4FNet). S4FNet utilizes UAV hyperspectral imaging (HSI) data, light detection and ranging (LiDAR) data, and RGB data to estimate AGC storage in individual trees. This study conducted experiments at two sites, the South and North areas of Shenzhen University’s Yuehai Campus, Shenzhen City, Guangdong Province, China, and compared S4FNet with eight traditional machine learning methods and a deep learning method. The experimental results demonstrate that S4FNet achieves the best performance across all metrics, confirming its excellent capability in predicting AGC stocks using multi-source remote sensing data.
Research on on-orbit relative radiometric calibration method based on solar diffuse reflector hyperspectral camera
Authors: Zhang Wenjie , Yuan Fuxiang , Shi Haiyang 
Published: October 2024

Abstract
In the original image data obtained by optical remote sensing satellite sensors, there is stripe noise caused by inconsistent probe response, which will seriously affect the application effect and accuracy of the image. The importance of relative radiometric calibration is that it can eliminate the stripe noise in the image, thus improving the quality and clarity of the image. In this paper, a relative radiometric calibration method based on the data of a solar diffuse reflector downloaded from the satellite is proposed. The relative radiation calibration coefficient is calculated by using the least square method by obtaining the calibration image of the on-board diffuse reflector covering all the detectors. To verify the method proposed in this paper, the on-board relative radiometric calibration experiment is carried out by using the on-orbit hyperspectral camera of a terrestrial ecological carbon monitoring satellite, and the calibration effect of the calibration coefficient on the original image is tested, and the relative radiometric calibration accuracy is quantitatively analyzed. The experimental results show that the calibration coefficient can effectively eliminate the band noise of the original hyperspectral image. From the quantitative evaluation, the signal-to-noise ratio index of the corrected hyperspectral image is greater than 200, and the generalized noise index is better than 3%, which achieves a good calibration effect and meets the requirements of high-quality remote sensing image application. It provides a reliable guarantee for quantitative remote sensing applications during the satellite’s on-orbit operation.
Research on hexagonal grid off-road path planning algorithm considering multiple factors
Authors: Kong Yunbo , Chang Ming , Zhang Zhixin , Liang Rui , Zhao Xi 
Published: October 2024

Abstract
This article proposes a hexagonal grid off-road path planning algorithm that takes into account multiple factors for the path planning problem of motor vehicles in complex off-road environments. This method first uses a hexagonal grid model to abstract and quantify the terrain factors that affect off-road vehicles. Then, the Analytic Hierarchy Process is used to determine the weight of each factor on the impact of off-road vehicle traffic. Finally, by comprehensively considering the elevation, slope, and surface coverage elements, as well as the search efficiency of the algorithm, the cost function and heuristic function of the A * algorithm are further designed and optimized. The experimental results show that the method proposed in this paper outperforms existing algorithms in terms of pathfinding results and search efficiency, thus verifying the effectiveness and feasibility of the algorithm proposed in this paper.
Wireless sensor fusion localization method for target vehicles in a collaborative environment of vehicle road trajectory information
Authors: Zhang Yusen , Qi Tianxing , Zhang Chao , Guo Tianhong , Wu Jiangong 
Published: October 2024

Abstract
Vehicle positioning technology is the foundation for achieving traffic management. Vehicle positioning errors are significant in complex environments, making it challenging to ensure positioning accuracy. To reduce positioning errors, this paper explores a wireless sensor fusion localization method for target vehicles in a collaborative environment of vehicle road trajectory information. Based on wireless sensor data, we achieve collaborative environmental perception of vehicle and road trajectory information and construct a target vehicle trajectory optimization model. Through a fuzzy neural network, data from different sensors are integrated to overcome the problem of limited positioning accuracy of a single sensor in a complex environment. The experimental results show that both the heading angle and rolling angle errors float between −0.1° and 0.1°, and the positioning error is less than 0.1 m, which proves that the method has high positioning accuracy and good application value.
Multi-reference source aircraft classification network based on unknown class prototype estimation
Authors: Guan Huihui , Wang Yuqing , Zhao Wei , Zhang Rufei , Li Nannan , Li Dongjin 
Published: October 2024

Abstract
Gathering complete aircraft types for close-set tasks is challenging and costly in fine-grained aircraft classification, resulting in encountering unknown class aircraft images in real-world models. To address this problem, we propose a network called a multi-reference source fine-grained aircraft classification network (MRSN) to explicitly and implicitly distinguish known class boundaries and embed unknown classes into the sample space. Specifically, we propose an embedding module (UCPE) to synthesize an unknown class prototype to facilitate the modeling of unknown class data distribution. Besides, we introduce a concatenated multi-reference source module (CMS) that combines distance measurement and probability estimation to classify whether testing images are of unknown class or not. Our proposed MRSN innovatively extends the open-set learning paradigm to the fine-grained classification of geospatial objects rather than simple scene classification. Besides, it shows excellent performance in our constructed dataset, which demonstrates the effectiveness and progressiveness of our method.
Cloudformer-CycleGAN: an efficient cloud removal network integrating residual learning and channel spatial attention mechanism
Authors: Fang Yongli 
Published: October 2024

Abstract
Removing clouds from remote sensing images poses a significant challenge in image analysis. Despite the widespread application of deep learning methods in the field of cloud removal, their ability to extract and integrate contextual information still needs to be strengthened, which is crucial for describing complex terrain features in remote sensing images. Therefore, we propose a new cloud removal method, Cloudformer-CycleGAN. In this method, we replace the original CNN module in CycleGAN with a Residual Group (RG) module for significant enhancement. The introduction of the Residual Group (RG) module enables the network to focus more on local features in cloud regions, significantly reducing the problem of residual clouds during cloud removal. Experimental results show that the proposed model achieves improvements in multiple image evaluation metrics, demonstrating superior performance in the issue of cloud residue, with a significant reduction in cloud residue in the images.
Measurements of Low Optical Power with Cryostat-Based Predictable Quantum Efficient Detector at Liquid Nitrogen Temperature
Authors: Korpusenko Mikhail , Sildoja Meelis-Mait , Manoocheri Farshid , Ikonen Erkki 
Published: October 2024

Abstract
We have validated optical power measurements with a Predictable Quantum Efficient Detector (PQED) at liquid nitrogen temperature (77 K) at low optical power from 130 fW to 3.3 pW. Two laser wavelengths at 514 nm and 785 nm were used. The lowest measured optical power corresponds to a photon flux of 0.5·106 photons per second (785 nm). The PQED’s responsivity is linear within the relative measurement uncertainties of 8% at 0.5·106 ph/s and 1.4% at 10·106 ph/s (95% confidence level), which enables the calibration of other low photon flux detectors directly against a primary standard of optical power.
Characterization of hexagonal boron nitride quantum emitters for application in quantum radiometry
Authors: Sewidan L. , Georgieva H. , Tieben P. , Schell A. W. , López M. , Kück S. 
Published: October 2024

Abstract
We present the metrological characterization of single-photon emitters based on single point defects in hexagonal boron nitride (hBN) to be used in quantum radiometry. The characterization is performed in terms of their spectral characteristics, single-photon properties and stability of photon flux emission at room temperature. A statistical analysis of 563 emitters has been carried out showing that approximately one third of the photon emission can be attributed to single photon emitters. In addition, a relative calibration of two single-photon avalanche diode (SPAD) detectors using a selected hBN single-photon emitter is presented.
Hybrid trap detector sensitive at visible and telecom wavelengths
Authors: Vaigu Aigar , Kübarsepp Toomas , Rammo Matt , Ikonen Erkki 
Published: October 2024

Abstract
We have developed a transmission trap detector sensitive over a wide optical wavelength range. The developed device takes advantage of the smooth reflectance profile of silicon photodiodes. The detector is based on two 10 mm size photodiodes – a Si photodiode and an InGaAs photodiode. The constructed hybrid trap detector is sensitive at the wavelength range from 400 nm up to 1600 nm. The detector is designed to provide a tool for countermeasures against attacks to secure quantum communication.
Indoor Direct Comparison between High-Accuracy Pyrheliometer and Room Temperature Absolute Radiometer
Authors: Liu Wen-Chun , Yu Hsueh-Ling 
Published: October 2024

Abstract
This article proposes a practicable indoor method by directly comparing the pyrheliometer with the room temperature absolute radiometer (RTAR) to overcome the inconvenience of outdoor pyrheliometer calibration caused by the limitation of weather conditions. The RTAR serves as the primary standard for radiant power, irradiance, illuminance, and candela at the Center for Measurement Standards/ITRI. Given that the RTAR and pyrheliometer exhibit different sensitivity curves, the key to a successful comparison lies in designing an incident beam that enables both the RTAR and pyrheliometer to operate within their respective uniform sensitivity regions. While the indoor incident beam’s irradiance is considerably lower than solar radiation, the comparison results demonstrate a quite well measurement consistence between the RTAR and the pyrheliometer. As the indoor method is insusceptible to weather, the complex data acquisition procedures for the outdoor method can be simplified due to the stable incident beam indoors. Despite current documentary standards recommending outdoor calibration and comparison for pyrheliometer, the indoor method presented in this article can serve as a supplementary approach for assessing the accuracy of pyrheliometers during the intervals between two calibration or comparison events.
A peridynamic-based reconstruction and analysis method for short-fiber reinforced composites
Authors: Yao Zhiyang , Hu Yile 
Published: October 2024

Abstract
As the complex microstructure, predicting the effective elastic properties and damage mechanisms of Short-Fiber Reinforced Composites (SFRC) continues to pose a challenge. This study presents a new reconstruction and analysis method based on PeriDynamics (PD) for SFRC. The reconstruction of the surface profiles of short-fibers obtained from Micro-CT (Micro-Computed Tomography) scanning resulted in a PD computational model that accurately reflects the actual microstructure of SFRC. The results predicted the effective elastic modulus of SFRC while fiber volume fraction and distribution have impact. Micro-cracks emerge at stress concentration regions near the fiber-matrix-fiber interfaces between two adjacent fibers, and with increasing load, these cracks gradually propagate, interact, and merge with each other. Eventually, major cracks form, leading to material failure.
Study on the influence factors of the grain growth type of micro-nano silver powders prepared by liquid-phase reduction method
Authors: Ma Xiaoling , Wang Jianwei , He Huijun , Zhu Jie , Pan Xu , Ma Tianrun 
Published: October 2024

Abstract
The preparation was carried out by liquid-phase reduction method with AgNO3 as the oxidizing agent, ascorbic acid (C6H8O6) as the reducing agent, and polyvinyl pyrrolidone (PVPK30) as the dispersing agent. The effects of silver nitrate concentration, ascorbic acid concentration, PVPK30 concentration, solution PH value before and after the reaction, and the temperature of reaction on average particle size (D50), dispersity, and particle size uniformity of silver particles were investigated by orthogonal experiment L18 (36) with six factors and three levels. The spheroid-like silver particles with good dispersity of 400-600nm and the spheroid-like silver particles with suitable particle size uniformity of 80-100nm were successfully prepared by optimal experiments. The results of the orthogonal experiment show that the molar ratio of the reducing agent to the oxidizing agent plays a decisive role in the type of grain growth. The crystal nucleus will coagulate and grow at a higher molar ratio (⩾1.05). With the increase in molar ratio, the nucleation rate is higher, the grain growth rate is faster, and the size of silver particles is smaller.
Preparation and performance study of sludge biomass bamboo charcoal
Authors: Song Yanpeng , Zhu Jiayan , Bao Yuhang , Luo Tianwen 
Published: October 2024

Abstract
The purpose of this study is to explore a new low-cost and high-efficiency biochar adsorption material. The residual sludge as solid waste and fresh bamboo with fast growth and high yield were used as raw materials, modified by the KOH chemical activation method, and pyrolyzed in a tubular furnace with nitrogen atmosphere to obtain sludge biomass bamboo charcoal. In the performance test, the H2S gas adsorption experiment in a fixed bed reactor and the heavy metal adsorption experiment in soil were designed. Using different proportions of biochar, the preparation process of biochar was optimized by adjusting the raw material ratio and activation conditions. The results showed that when dealing with H2S gas, the appropriate amount of sludge can improve the adsorption effect, while the increase of bamboo component will reduce the adsorption rate. In the soil Cr(VI) restoration experiment, it was found that the best restoration effect was achieved after adding 10% biochar and applying it for 28 days, and the restoration rate reached 63.5%.
Highly efficient hydrogenation of levulinic acid into γ-valerolactone over modified bifunctional yolk-shell catalyst
Authors: Liu Mingya , Liu Yue 
Published: October 2024

Abstract
γ-valerolactone (GVL) is a widely used chemical raw material, which is mainly prepared by hydrogenation of levulinic acid (LA). The steps of hydrogenation and molecular lactone cyclization in the reaction make it require multi-active site catalysis. In this study, the Pd@mSiO2-CeAl catalyst was successfully prepared by doping Ce and Al to modify the Pd-based yolk-shell catalyst. It was used to catalyze LA to prepare GVL. In circumstances of mild response, 100% LA conversion and 97.5% GVL selectivity were obtained. The characterization results showed that the synergistic effect of the double acid sites (Brønsted and Lewis acid sites) and the hydrogenation sites is the key to high catalytic performance. Besides, the encapsulation strategy of the silica shell provides a stable catalytic environment for the catalyst and maintains high activity after 5 cycles.
Catalytic synthesis of DHHB using solid acids
Authors: Sun Zhendong , Wu Jinbo , Feng Baicheng 
Published: October 2024

Abstract
DHHB (Diethylamino hydroxybenzoyl hexyl benzoate) is a widely regarded UV absorber with a high degree of safety, excellent efficiency and good solubility characteristics. In the conventional method of DHHB synthesis, H2SO4 is not effective as a catalyst. To address this drawback, we synthesized solid acid (sulfonic acid group-modified ion exchange resin) catalysts to replace H2SO4 by suspension polymerization modification and optimized the process. The experimental results showed that the yield of DHHB could reach 85% at 110 °C for 10 h, and the conversion of DHHB could be maintained above 90% after 10 cycles of the catalyst. SEM, FT-IR characterization and analysis of the solid acid catalysts before and after use revealed that the catalysts did not show any significant changes. The use of solid acid not only improves the yield of DHHB and reduces the energy consumption in the experimental process, but also the solid acid is simple to recover, recycled and has less waste acid. This experiment successfully explored a green and economic synthesis method for synthesizing DHHB.