基于re3data的中英科学数据仓储平台对比研究

以re3data为数据获取源,选取中英两国406个科学数据仓储为研究对象,从分布特征、责任类型、仓储许可、技术标准及质量标准等5个方面、11个指标对两国科学数据仓储的建设情况进行对比分析,试图为我国数据仓储的可持续发展提出建议:广泛联结国内外异质机构,推进多学科领域的交流与合作,有效扩充仓储许可权限与类型,优化技术标准的应用现况,提高元数据使用的灵活性。

Application of 9-component S-wave 3D seismic data to study sedimentary facies and reservoirs in a biogasbearing area:A case study on the Pleistocene Qigequan Formation in Taidong area,Sanhu Depression,Qaidam Basin,NW China

To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a fourth-order isochronous stratigraphic framework was set up and then sedimentary facies and reservoirs in the Pleistocene Qigequan Formation in Taidong area of Qaidam Basin were studied by seismic geomorphology and seismic lithology.The study method and thought are as following.Firstly,techniques of phase rotation,frequency decomposition and fusion,and stratal slicing were applied to the 9-component S-wave seismic data to restore sedimentary facies of major marker beds based on sedimentary models reflected by satellite images.Then,techniques of seismic attribute extraction,principal component analysis,and random fitting were applied to calculate the reservoir thickness and physical parameters of a key sandbody,and the results are satisfactory and confirmed by blind testing wells.Study results reveal that the dominant sedimentary facies in the Qigequan Formation within the study area are delta front and shallow lake.The RGB fused slices indicate that there are two cycles with three sets of underwater distributary channel systems in one period.Among them,sandstones in the distributary channels of middle-low Qigequan Formation are thick and broad with superior physical properties,which are favorable reservoirs.The reservoir permeability is also affected by diagenesis.Distributary channel sandstone reservoirs extend further to the west of Sebei-1 gas field,which provides a basis to expand exploration to the western peripheral area.

A Semantic-Sensitive Approach to Indoor and Outdoor 3D Data Organization

Building model data organization is often programmed to solve a specific problem,resulting in the inability to organize indoor and outdoor 3D scenes in an integrated manner.In this paper,existing building spatial data models are studied,and the characteristics of building information modeling standards(IFC),city geographic modeling language(CityGML),indoor modeling language(IndoorGML),and other models are compared and analyzed.CityGML and IndoorGML models face challenges in satisfying diverse application scenarios and requirements due to limitations in their expression capabilities.It is proposed to combine the semantic information of the model objects to effectively partition and organize the indoor and outdoor spatial 3D model data and to construct the indoor and outdoor data organization mechanism of“chunk-layer-subobject-entrances-area-detail object.”This method is verified by proposing a 3D data organization method for indoor and outdoor space and constructing a 3D visualization system based on it.

2 cm以上的C-TI-RADS 3类甲状腺结节的临床特点分析

目的统计2 cm以上的中国版甲状腺影像报告及数据系统(C-TI-RADS)3类甲状腺结节的临床特点,并探究其与性别、结节成分、对侧是否有癌、是否弥漫性回声改变、甲状腺过氧化物酶抗体(thyroid peroxidase antibody,TPOAB)、甲状腺球蛋白抗体(anti-thyroglobulin antibodies,TGAB)等的相关性。方法回顾性分析2022年9月-2023年3月我科收治的甲状腺超声C-TI-RADS 3类且最大直径≥2 cm的94例甲状腺结节患者(均接受细胞病理和/或组织病理检查)的临床病理学信息。统计TBSⅠ类、良性、低风险肿瘤、恶性的比例,并比较性别、结节成分、对侧是否有癌、是否弥漫性回声改变、TPOAB、TGAB等临床特点在良性、低风险肿瘤、恶性三组中的比例有无统计学差异。结果排除7例TBSⅠ类患者,87例结节病理明确患者中,良性72例(细胞学38例、组织学34例)、低风险肿瘤5例(细胞学2例、组织学3例)、恶性10例(PTC 8例、FTC 1例、MTC 1例)。不同病理类型之间,结节成分(囊实性/实性)组间差异具有显著性,差异有统计学意义(χ^(2)=10.369,P=0.006);性别、是否弥漫性回声改变、对侧是否有癌、TPOAB、TGAB组间差异无统计学意义(P均>0.05)。进一步分析结节成分与病理类型的关系,结果表明,低风险肿瘤相对于良性结节实性比例更高,差异有统计学意义(χ^(2)=9.571,P=0.002);而恶性结节相对于低风险肿瘤(χ^(2)=2.143,P=0.143),恶性结节相对于良性结节(χ^(2)=2.165,P=0.141)囊实性比例差异均无统计学意义。结论虽然各种版本的甲状腺影像报告与数据系统均将TI-RADS 3级结节认定为良性可能,但恶性结节在C-TI-RADS 3类甲状腺结节中仍占一定比例,需要重视诸如囊实性结节、甲状腺滤泡性肿瘤、甲状腺髓样癌等超声征象不典型的甲状腺结节。在评估结节良恶性时,超声引导下细针穿刺细胞病理学检查是必要的,需要重视标本不满意或无法诊断的情况,提高诊断的准确性。

Fusion SST from Infrared and Microwave Measurement of FY-3D Meteorological Satellite

Sea surface temperature(SST)is one of the important parameters of global ocean and climate research,which can be retrieved by satellite infrared and passive microwave remote sensing instruments.While satellite infrared SST offers high spatial resolution,it is limited by cloud cover.On the other hand,passive microwave SST provides all-weather observation but suffers from poor spatial resolution and susceptibility to environmental factors such as rainfall,coastal effects,and high wind speeds.To achieve high-precision,comprehensive,and high-resolution SST data,it is essential to fuse infrared and microwave SST measurements.In this study,data from the Fengyun-3D(FY-3D)medium resolution spectral imager II(MERSI-II)SST and microwave imager(MWRI)SST were fused.Firstly,the accuracy of both MERSIII SST and MWRI SST was verified,and the latter was bilinearly interpolated to match the 5km resolution grid of MERSI SST.After pretreatment and quality control of MERSI SST and MWRI SST,a Piece-Wise Regression method was employed to correct biases in MWRI SST.Subsequently,SST data were selected based on spatial resolution and accuracy within a 3-day window of the analysis date.Finally,an optimal interpolation method was applied to fuse the FY-3D MERSI-II SST and MWRI SST.The results demonstrated a significant improvement in spatial coverage compared to MERSI-II SST and MWRI SST.Furthermore,the fusion SST retained true spatial distribution details and exhibited an accuracy of–0.12±0.74℃compared to OSTIA SST.This study has improved the accuracy of FY satellite fusion SST products in China.

Autonomous UAV 3D trajectory optimization and transmission scheduling for sensor data collection on uneven terrains

This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle(UAV),a typical Unmanned Aerial System(UAS).The ground sensors harvest renewable energy and are equipped with batteries and data buffers.The ground sensor model takes into account sensor data buffer and battery limitations.An asymptotically globally optimal method of joint UAV 3D trajectory optimization and data transmission schedule is developed.The developed method maximizes the amount of data transmitted to the UAV without losses and too long delays and minimizes the propulsion energy of the UAV.The developed algorithm of optimal trajectory optimization and transmission scheduling is based on dynamic programming.Computer simulations demonstrate the effectiveness of the proposed algorithm.

Robust Watermarking Algorithm for Medical Volume Data Based on Polar Cosine Transform and 3D-DCT

The amount of 3D data stored and transmitted in the Internet of Medical Things(IoMT)is increasing,making protecting these medical data increasingly prominent.However,there are relatively few researches on 3D data watermarking.Moreover,due to the particularity of medical data,strict data quality should be considered while protecting data security.To solve the problem,in the field of medical volume data,we proposed a robust watermarking algorithm based on Polar Cosine Transform and 3D-Discrete Cosine Transform(PCT and 3D-DCT).Each slice of the volume data was transformed by PCT to obtain feature row vector,and then the reshaped three-dimensional feature matrix was transformed by 3D-DCT.Based on the contour information of the volume data and the detail information of the inner slice,the visual feature vector was obtained by applying the per-ceptual hash.In addition,the watermark was encrypted by a multi-sensitive initial value Sine and Piecewise linear chaotic Mapping(SPM)system,and embedded as a zero watermark.The key was stored in a third party.Under the same experimental conditions,when the volume data is rotated by 80 degrees,cut 25%along the Z axis,and the JPEG compression quality is 1%,the Normalized Correlation Coefficient(NC)of the extracted watermark is 0.80,0.89,and 1.00 respectively,which are significantly higher than the comparison algorithm.

基于改进ID3算法的非结构化大数据分类优化方法

针对非结构化大数据在分类过程中,由于其数据中存在大量的冗余数据,若不能及时清洗大数据中的冗余数据,会降低数据分类精度的问题,提出一种基于改进ID3(Iterative Dichotomiser 3)算法的非结构化大数据分类优化方法。该方法针对非结构化大数据集合中冗余数据多以及维度繁杂的问题,对数据进行清洗处理,并结合有监督辨识矩阵完成数据降维;根据数据降维结果,采用改进ID3算法建立用于数据分类的决策树分类模型,通过该模型对非结构化大数据进行分类处理,从而实现数据的精准分类。实验结果表明,使用该方法对非结构化大数据分类时,分类效果好,精度高。

Added Benefit of the Early-Morning-Orbit Satellite Fengyun-3E on the Global Microwave Sounding of the Three-Orbit Constellation

The three-orbit constellation can comprehensively increase the spatial coverage of polar-orbiting satellites,but the polar-orbiting satellites currently in operation are only mid-morning-orbit and afternoon-orbit satellites.Fengyun-3E(FY-3E)was launched successfully on 5 July 2021 in China.As an early-morning-orbit satellite,FY-3E can help form a complete three-orbit observation system together with the mid-morning and afternoon satellites in the current mainstream operational system.In this study,we investigate the added benefit of FY-3E microwave sounding observations to the midmorning-orbit Meteorological Operational satellite-B(Met Op-B)and afternoon-orbit Fengyun-3D(FY-3D)microwave observations in the Chinese Meteorological Administration global forecast system(CMA-GFS).The results show that the additional FY-3E microwave temperature sounder-3(MWTS-3)and microwave humidity sounder-2(MWHS-2)data can increase the global coverage of microwave temperature and humidity sounding data by 14.8% and 10.6%,respectively.It enables the CMA-GFS to achieve nearly 100% global coverage of microwave-sounding observations at each analysis time.Furthermore,after effective quality control and bias correction,the global biases and standard deviations of the differences between observations and model simulations are also reduced.Based on the Advanced Microwave Sounding Unit A and the Microwave Humidity Sounder onboard Met Op-B,and the MWTS-2 and MWHS-2 onboard FY-3D,adding the microwave sounding data of FY-3E can further reduce the errors of analysis results and improve the global prediction skills of CMA-GFS,especially for the southern-hemisphere forecasts within 96 hours,all of which are significant at the 95% confidence level.

A Random Fusion of Mix 3D and Polar Mix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network models.These point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging applications.Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities.Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds.However,there has been a lack of focus on making the most of the numerous existing augmentation techniques.Addressing this deficiency,this research investigates the possibility of combining two fundamental data augmentation strategies.The paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named RandomFusion.Instead of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or sample.This innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or Mix3D.The crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data set.The results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation tasks.This is achieved without compromising computational efficiency.By examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point clouds.RandomFusion data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the robustness of models.The insights gained from this research can pave the way for future work aimed at developing more advanced and efficient data augmentation strategies for 3D lidar point cloud analysis.

前列腺小体外泄蛋白联合PSA在PSA“灰区”且PI-RADS评分3分前列腺癌诊断中的临床意义

目的:探讨前列腺小体外泄蛋白(PSEP)联合前列腺特异性抗原(PSA)在PSA“灰区”且前列腺影像报告和数据系统(PI-RADS)评分3分前列腺癌诊断中的价值。方法:收集2019~2022年行前列腺多参数核磁共振检查,并且接受前列腺穿刺活检或经尿道前列腺电切/剜除术的211例PSA“灰区”(4~10μg/L)且PI-RADS评分3分患者的临床资料。术前收集尿样,采用酶ELISA检测尿PSEP浓度。分析在PSA“灰区”且PI-RADS评分3分患者中PSEP、PSA用于诊断前列腺癌的性能及差异。结果:病理确诊前列腺癌57例(阳性组);良性前列腺组织154例(阴性组)。阳性组游离前列腺特异性抗原(fPSA)、游离前列腺特异性抗原(fPSA)与总前列腺特异性抗原(tPSA)比值(f/t PSA)、尿PSEP水平均低于阴性组(P均<0.01)。在PSA“灰区”且PI-RADS评分3分的患者中,f/t PSA,PSEP均可作为独立因子用于预测前列腺癌(P均<0.01)。其中,以f/t PSA、PSEP单参数诊断前列腺癌的受试者操作特征(ROC)曲线下面积、最佳截断值、敏感度、特异度分别为0.70、0.18、84.21%、58.44%;0.78、1.45μg/L、70.18%、77.27%。利用PSEP联合f/t PSA多参数模型预测前列腺癌的ROC曲线下面积为0.82,最佳截断值为0.31,敏感度为82.46%,特异度为75.32%,其预测性能显著优于单参数f/t PSA、PSEP(P<0.01,P=0.04)。结论:对于PSA“灰区”且PI-RADS评分3分的患者,f/t PSA,PSEP均可作为独立预测前列腺癌的有效因子;PSEP联合f/t PSA多参数模型可替代传统筛查所用的单参数f/t PSA,提高诊断性能,减少非必要穿刺活检。

A LiDAR Point Clouds Dataset of Ships in a Maritime Environment

For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.

Development of vehicle-recognition method on water surfaces using LiDAR data:SPD^(2)(spherically stratified point projection with diameter and distance)

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.

A Study of the Comets with Large Perihelion Distances C/2019 L3(ATLAS)and C/2019 O3(Palomar)

This work analyzes the photometric data of the Oort spike comets C/2019 L3(ATLAS)and C/2019 O3(Palomar)obtained between 2016 and 2023 by the ATLAS network and the Belgian Olmen Observatory.The comets Palomar and ATLAS have a typical and unusually high activity level,respectively,based on the Afρparameter corrected to phase angle zero at perihelion.The absolute magnitude of comets ATLAS and Palomar in the o-band is 4.71±0.05 and 4.16±0.02 respectively.The cometary activity of comets ATLAS and Palomar probably began at r>13 au before perihelion and will end at r>14 au after perihelion,which means that they could remain active until the second half of 2026.The nucleus of comet ATLAS has a minimum radius of 7.9 km,and the nucleus of comet Palomar could be a little larger.The c-o colors of the comets ATLAS and Palomar are redder and bluer,respectively,at perihelion than the solar twin YBP 1194.These comets showed a bluish trend in the coma color with decreasing heliocentric distance.Comet Palomar probably had two outbursts after its perihelion,each releasing about 10^(8)kg of dust.The slopes of the photometric profile of the comae of these comets were between 1and 1.5,indicating a steady state during the observation campaign.

Indoor Space Modeling and Parametric Component Construction Based on 3D Laser Point Cloud Data

In order to enhance modeling efficiency and accuracy,we utilized 3D laser point cloud data for indoor space modeling.Point cloud data was obtained with a 3D laser scanner and optimized with Autodesk Recap and Revit software to extract geometric information about the indoor environment.Furthermore,we proposed a method for constructing indoor elements based on parametric components.The research outcomes of this paper will offer new methods and tools for indoor space modeling and design.The approach of indoor space modeling based on 3D laser point cloud data and parametric component construction can enhance modeling efficiency and accuracy,providing architects,interior designers,and decorators with a better working platform and design reference.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Optimized air-ground data fusion method for mine slope modeling

Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

BDS-3卫星钟差改正数短期预报方法研究

针对RTS实时数据流产品在网络传输中存在的延迟以及数据中断等问题,文中结合BDS-3卫星钟差改正数的特点,分析实时数据流中BDS-3卫星钟差改正数的完整率和精度,提出一种基于一次差分和滑动时间窗口的残差修正钟差改正数短期预报模型。利用文中模型与一次多项式模型、基于一次差分的一次多项式模型和灰色模型进行BDS-3卫星钟差改正数预报精度的对比实验,结果表明,文中模型的预报精度最高,5 min、10 min、15 min和20 min的平均预报精度分别达到0.12 ns、0.14 ns、0.19 ns和0.20 ns,有效降低误差累积效应对预报精度的影响,对于RT-PPP的研究和应用具有重要的意义。

Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.