数据空间建设的实践进展与运营模式分析——基于Data Spaces Radar的案例

数据空间建设为数据要素的价值实现提供了可资借鉴的实践经验,全面解析其实践进展与运营模式,有助于破解数据流通的现实堵点、拓展数据利用的发展思路。文章首先将结构主义分析方法中的案例研究法作为主要研究方法,综合运用文献调研、比较分析等方法,以Data Spaces Radar为案例来源,从实践进展、运营模式、核心要素等维度提炼数据空间建设特征。其次,在制度与技术的双轮驱动下,数据空间建设秉持制度规范统一性、技术设计整体性和治理模式协同性的架构原则,并按照产业思维、底线思维、信任思维、链式思维、集约思维的底层逻辑,形成较为完整的运营体系。未来应着力探索和解决数据空间视域下的数据资源价值化开发路径,针对数据流通堵点、利用痛点及信任难点问题,从体制机制、政策框架与技术应用等角度为推进我国的数据要素流通利用提供有益参考。

LSTM-based lane change prediction using Waymo open motion dataset: The role of vehicle operating space

Lane change prediction is critical for crash avoidance but challenging as it requires the understanding of the instantaneous driving environment.With cutting-edge artificial intelligence and sensing technologies,autonomous vehicles(AVs)are expected to have exceptional perception systems to capture instantaneously their driving environments for predicting lane changes.By exploring the Waymo open motion dataset,this study proposes a framework to explore autonomous driving data and investigate lane change behaviors.In the framework,this study develops a Long Short-Term Memory(LSTM)model to predict lane changing behaviors.The concept of Vehicle Operating Space(VOS)is introduced to quantify a vehicle's instantaneous driving environment as an important indicator used to predict vehicle lane changes.To examine the robustness of the model,a series of sensitivity analysis are conducted by varying the feature selection,prediction horizon,and training data balancing ratios.The test results show that including VOS into modeling can speed up the loss decay in the training process and lead to higher accuracy and recall for predicting lane-change behaviors.This study offers an example along with a methodological framework for transportation researchers to use emerging autonomous driving data to investigate driving behaviors and traffic environments.

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.

INTERNET TRAFFIC DATA FLOW FORECAST BY RBF NEURAL NETWORK BASED ON PHASE SPACE RECONSTRUCTION

Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.

Reliable transmission of consultative committee for space data systems file delivery protocol in deep space communication

In consultative committee for space data systems（CCSDS） file delivery protocol（CFDP） recommendation of reliable transmission,there are no detail transmission procedure and delay calculation of prompted negative acknowledge and asynchronous negative acknowledge models.CFDP is designed to provide data and storage management,story and forward,custody transfer and reliable end-to-end delivery over deep space characterized by huge latency,intermittent link,asymmetric bandwidth and big bit error rate（BER）.Four reliable transmission models are analyzed and an expected file-delivery time is calculated with different trans-mission rates,numbers and sizes of packet data units,BERs and frequencies of external events,etc.By comparison of four CFDP models,the requirement of BER for typical missions in deep space is obtained and rules of choosing CFDP models under different uplink state informations are given,which provides references for protocol models selection,utilization and modification.

THE STATE SPACE RECONSTRUCTION TECHNOLOGY OF DIFFERENT KINDS OF CHAOTIC DATA OBTAINED FROM DYNAMICAL SYSTEM

Certain deterministic nonlinear systems may show chaotic behavior. We consider the motion of qualitative information and the practicalities of extracting a part from chaotic experimental data. Our approach based on a theorem of Takens draws on the ideas from the generalized theory of information known as singular system analysis. We illustrate this technique by numerical data from the chaotic region of the chaotic experimental data. The method of the singular-value decomposition is used to calculate the eigenvalues of embedding space matrix. The corresponding concrete algorithm to calculate eigenvectors and to obtain the basis of embedding vector space is proposed in this paper. The projection on the orthogonal basis generated by eigenvectors of timeseries data and concrete paradigm are also provided here. Meanwhile the state space reconstruction technology of different kinds of chaotic data obtained from dynamical system has also been discussed in detail.

Experiment on diffuse reflection laser ranging to space debris and data analysis

Space debris poses a serious threat to human space activities and needs to be measured and cataloged. As a new technology for space target surveillance, the measurement accuracy of diffuse reflection laser ranging （DRLR） is much higher than that of microwave radar and optoelectronic measurement. Based on the laser ranging data of space debris from the DRLR system at Shanghai Astronomical Observatory acquired in March-April, 2013, the characteristics and precision of the laser ranging data are analyzed and their applications in orbit determination of space debris are discussed, which is implemented for the first time in China. The experiment indicates that the precision of laser ranging data can reach 39 cm-228 cm. When the data are sufficient enough （four arcs measured over three days）, the orbital accuracy of space debris can be up to 50 m.

Constructing Three-Dimension Space Graph for Outlier Detection Algorithms in Data Mining

Outlier detection has very important applied value in data mining literature. Different outlier detection algorithms based on distinct theories have different definitions and mining processes. The three-dimensional space graph for constructing applied algorithms and an improved GridOf algorithm were proposed in terms of analyzing the existing outlier detection algorithms from criterion and theory. Key words outlier - detection - three-dimensional space graph - data mining CLC number TP 311. 13 - TP 391 Foundation item: Supported by the National Natural Science Foundation of China (70371015)Biography: ZHANG Jing (1975-), female, Ph. D, lecturer, research direction: data mining and knowledge discovery.

基于CiteSpace的用户画像知识图谱分析

为了明确用户画像研究热点和趋势,文章运用CiteSpace软件定量化分析知网1496篇用户画像文献。研究结果表明,国内对用户画像研究呈现增长态势,总体经历了研究萌芽、快速发展和质量提升三个阶段,虽然研究机构和作者群体稳定,但尚未形成核心作者群和跨专业领域的合作。研究趋势方面,大数据、数据挖掘、图书馆、机器学习、人工智能、精准营销、知识服务、协同过滤等是主要研究热点;前沿研究聚焦于信息服务、融媒体、数据采集、媒体融合和聚类。

Analysis of dynamic of two-phase flow in small channel based on phase space reconstruction combined with data reduction sub-frequency band wavelet

A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2 mm × 0.81 mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns(bubbly flow:100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.

Research on multiple elimination methods in inverse data space based on randomized singular value decomposition

Based on surfaced-related multiple elimination （SRME） , this research has derived the methods on multiples elimination in the inverse data space. Inverse data processing means moving seismic data from forward data space （FDS） to inverse data space （ IDS） . The surface-related multiples and primaries can then be sepa-rated in the IDS, since surface-related multiples wi l l form a focus region in the IDS. Muting the multiples ener-gy can achieve the purpose of multiples elimination and avoid the damage to primaries energy during the process of adaptive subtraction. Randomized singular value decomposition （ RSYD） is used to enhance calculation speed and improve the accuracy in the conversion of FDS to IDS. The synthetic shot record of the salt dome model shows that the relationship between primaries and multiples is simple and clear, and RSVD can easily eliminate multiples and save primaries energy. Compared with conventional multiples elimination methods and ordinary methods of multiples elimination in the inverse data space, this technique has an advantage of high cal-culation speed and reliable outcomes.

Spatial Structure of China's E-commerce Express Logistics Network Based on Space of Flows

The intermediate link compression characteristics of e-commerce express logistics ne tworks influence the tradition al mode of circulation of goods and economic organization,and alter the city spatial pattern.Based on the theory of space of flows,this study adopts China Smart Logistics Network relational data to build China's e-commerce express logistics network and explore its spatial structure characteristics through social network analysis(SNA),the PageRank technique,and geospatial methods.The results are as follows:the network density is 0.9270,which is close to 1;hence,indicating that e-commerce express logistics lines between Chinese cities are nearly complete and they form a typical network structure,thereby eliminating fragmented spaces.Moreover,the average minimum number of edges is 1.1375,which indicates that the network has a small world effect and thus has a high flow efficiency of logistics elements.A significant hierarchical diffusion effect was observed in dominant flows with the highest edge weights.A diamond-structured network was formed with Shanghai,Guangzhou,Chongqing,and Beijing as the four core nodes.Other node cities with a large logistics scale and importance in the network are mainly located in the 19 city agglomerations of China,revealing the fact that the development of city agglomerations is essential for promoting the separation of experience space and changing the urban spatial pattern.This study enriches the theory of urban networks,reveals the flow laws of modern logistics elements,and encourages coordinated development of urban logistics.

Direct data domain approach to space-time adaptive processing

In non-homogeneous environment, traditional space-time adaptive processing doesn＇t effectively suppress interference and detect target, because the secondary data don＇ t exactly reflect the statistical characteristic of the range cell under test. A ravel methodology utilizing the direct data domain approach to space-time adaptive processing （ STAP ） in airbome radar non-homogeneous environments is presented. The deterministic least squares adaptive signal processing technique operates on a ＂snapshot-by-snapshot＂ basis to dethrone the adaptive adaptive weights for nulling interferences and estimating signal of interest （SOI）. Furthermore, this approach eliminates the requirement for estimating the covariance through the data of neighboring range cell, which eliminates calculating the inverse of covariance, and can be implemented to operate in real-time. Simulation results illustrate the efficiency of interference suppression in non-homogeneous environment.

Modeling and Simulation Study of Space Data Link Protocol

This research paper describes the design and implementation of the Consultative Committee for Space Data Systems (CCSDS) standards REF _Ref401069962 \r \h \* MERGEFORMAT [1] for Space Data Link Layer Protocol (SDLP). The primer focus is the telecommand (TC) part of the standard. The implementation of the standard was in the form of DLL functions using C++ programming language. The second objective of this paper was to use the DLL functions with OMNeT++ simulating environment to create a simulator in order to analyze the mean end-to-end Packet Delay, maximum achievable application layer throughput for a given fixed link capacity and normalized protocol overhead, defined as the total number of bytes transmitted on the link in a given period of time (e.g. per second) divided by the number of bytes of application data received at the application layer model data sink. In addition, the DLL was also integrated with Ground Support Equipment Operating System (GSEOS), a software system for space instruments and small spacecrafts especially suited for low budget missions. The SDLP is designed for rapid test system design and high flexibility for changing telemetry and command requirements. GSEOS can be seamlessly moved from EM/FM development (bench testing) to flight operations. It features the Python programming language as a configuration/scripting tool and can easily be extended to accommodate custom hardware interfaces. This paper also shows the results of the simulations and its analysis.

Updating Strategy of Campus Space Based on Multi-source Data:A Case Study of West Campus of Yangtze University

Under the macro background of rapid urbanization and social transformation in China,campus space renewal has become an important practice and carrier for the sustainable development of schools.The study on campus space by big data and quantitative reflection of spatial distribution of applicable people in different areas of the campus can provide a certain scientific basis for campus space updating.West campus of Yangtze University is taken as research object.Based on cognitive map method,questionnaire survey method,behavior trajectory and correlation analysis method,the types and characteristics of campus space composition,campus satisfaction,usage and its relevance are analyzed.Research results show that ①the overall imageability of campus space is higher,which has a significantly positive correlation with the satisfaction of campus environment,and has no correlation with users’ behavior activities.The use frequency of non teaching areas varies greatly in different periods of time.②The correlation between the surrounding green vegetation and the image degree of campus landmarks is the most significant,and the coefficient is 0.886.③The correlation between spatial size suitability and regional image degree is the most significant,and the coefficient is 0.937.④The correlation between public activity facilities in the region and node image degree is the most significant,and the coefficient is 0.995.According to the research results,the corresponding solutions are put forward to provide scientific and quantitative reference suggestions for the renewal and transformation of the campus.

DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

Evidences of the expanding Earth from space-geodetic data over solid land and sea level rise in recent two decades

According to the space-geodetic data recorded at globally distributed stations over solid land spanning a period of more than 20-years under the International Terrestrial Reference Frame 2008,our previous estimate of the average-weighted vertical variation of the Earth＇s solid surface suggests that the Earth＇s solid part is expanding at a rate of 0.24 ± 0.05 mm/a in recent two decades.In another aspect,the satellite altimetry observations spanning recent two decades demonstrate the sea level rise（SLR） rate 3.2 ± 0.4 mm/a,of which1.8 ± 0.5 mm/a is contributed by the ice melting over land.This study shows that the oceanic thermal expansion is 1.0 ± 0.1 mm/a due to the temperature increase in recent half century,which coincides with the estimate provided by previous authors.The SLR observation by altimetry is not balanced by the ice melting and thermal expansion,which is an open problem before this study.However,in this study we infer that the oceanic part of the Earth is expanding at a rate about 0.4 mm/a.Combining the expansion rates of land part and oceanic part,we conclude that the Earth is expanding at a rate of 0.35 ± 0.47 mm/a in recent two decades.If the Earth expands at this rate,then the altimetry-observed SLR can be well explained.

Correspondence Analysis on a Space-Time Data Set for Multiple Environmental Variables

Applications of the multivariate technique called correspondence analysis for environmental studies are relatively new and are limited to spatial multivariate data set. In this paper, a procedure of applying correspondence analysis to a large space-time data set for multiple environmental variables is shown. In particular, nitrogen dioxide and carbon monoxide hourly concentrations measured during January 1999 at several monitored stations in a district of Northern Italy are analyzed. The procedure consists in transforming the continuous variables into categorical ones by the means of appropriate indicator variables, generating special contingency tables and applying correspondence analysis. The use of this classical multivariate technique allows the identification of important relationships among pollution levels and monitoring stations and/or relationships among pollution levels and observation times.

高新区转型下的国内创新城区研究进展与展望:基于CiteSpace文献计量软件

创新城区是未来城市创新化和综合化发展的重要空间形式,其因设施系统性、功能多样性和服务综合性而受到众多关注.高新区作为以高新技术产业开发为主要功能的区域,与综合性功能的创新城区还有很大差距,若将两者进行系统对比分析则有利于进一步了解国内创新城区发展基础并促进高新区向创新城区转型.本文以中国知网核心期刊作为文献来源,运用CiteSpace文献计量软件对国内2000—2021年高新区和创新城区领域文献的年份、作者、机构和关键词进行可视化分析和对比研究.结果显示,在发展演变、学界研究及研究热点上,高新区的研究较为系统丰富,创新城区的研究相对不足,且国内对二者的研究均存在一些不足.基于此,从研究脉络、研究方法、研究内容以及学科层面展开了评述,并从多尺度、分重点、全域性和多角度4个方面提出了对高新区转型和创新城区建设的研究展望.