An approach to estimate tree height using PolInSAR data constructed by the Sentinel-1 dual-pol SAR data and RVoG model

We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.

Wigner Frequency Point Slice Analysis of Superposition Data for Phased-Array Ground Penetrating Radar

According to the frequency property of Phasedarray ground penetrating radar (PGPR), this paper gives a frequency point slice method based on Wigner time-frequency analysis. This method solves the problem of analysis for the PGPR's superposition data and makes detecting outcome simpler and detecting target more recognizable. At last, the analytical results of road test data of the Three Gorges prove the analytical method efficient. Key words phased-array ground penetrating radar - wigner time-frequency analysis - superposition data - object identification CLC number TN 715.7 Foundation item: Supported by the National Nature Science Foundation of China (50099620) and 863 Program Foundation of China (2001AA132050-03)Biography: ZOU Lian (1975-), male, Ph. D candidate, research direction: signal processing.

Truth Discovery from Conflicting Data: A Survey

With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and get the true information, truth discovery has been proposed and received widespread attention. Many algorithms have been proposed to adapt to different scenarios. This paper aims to investigate these algorithms and summarize them from the perspective of algorithm models and specific concepts. Some classic datasets and evaluation metrics are given in this paper. Some future directions for readers are also provided to better understand the field of truth discovery.

Research on Data Combination for Phased-Array Ground Penetrating Radar

To resolve the data combination of Phased-array Ground Penetrating Radar (PAGPR), we first build a model of PAGPR and a layered model, and then a new data combination algorithm is presented based on it. This method calculates time delay of multi-receivers, basing on the signal of the nearest receiver, then shifts other signals and adds them up, and gets one signal at last. It has been proved that this method can restrain noise, multiple waves, clutter waves and improve the precision of time location. In the end, an example is given to prove the method's efficiency.

Ground-Space Correlation of TM Data and Its Application to the Prediction of Gold Deposits

Abstract: It has been inferred and proved by the remote sensing equations under rational hypotheses in atmospheric physics that there is a linear correlation between the ground reflective brightness Wij and the total reflective brightness Rij received in different bands with a remote sensor. Nine models delineating the ground-space correlation between the ground spectra and the optimal bands of images of the typical gold deposits have been established based on the ground-space correlativity and field measurements of the ground spectra of the typical gold deposits in the Ailaoshan area. According to the 9 correlation models, TM images were inverted into ground-space correlation images that are related to the typical gold deposits within the area and then recognized by a computer. Research on the ground spectra and TM data in the Ailaoshan area shows that the correlation analysis of the ground spectra and TM data of gold deposits can be effectively applied to the prediction of gold deposits, location of prospecting targets, and extraction of imagery information of gold mineralization.

Analysis of kinematic data and determination of ground reaction force of foot in slow squat

In the present paper, the ground reaction force （GRF） acting on foot in slow squat was determined through a force measuring system, and at the same time, the kinematic data of human squat were obtained by analyzing the photographed image sequences. According to the height and body weight, six healthy volunteers were selected, three men in one group and the other three women in another group, and the fundamental parameters of subjects were recorded, including body weight, height and age, etc. Based on the anatomy characteristics, some markers were placed on the right side of joints. While the subject squatted at slow speed on the force platform, the ground reaction forces on the forefoot and heel for each foot were obtained through calibrated force platform. The analysis results show that the reaction force on heel is greater than that on forefoot, and double feet have nearly constant force. Moreover, from processing and analyzing the synchronously photographed image sequences in squat, the kinematic data of human squat were acquired, including mainly the curves of angle, angular velocity and angular acceleration varied with time for knee, hip and ankle joints in a sagittal plane. The obtained results can offer instructive reference for photographing and analyzing the movements of human bodies, diagnosing some diseases, and establishing in the future appropriate mathematical models for the human motion.

Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia

We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.

Study on the feature selection and classifica－tion effect of precursory data of ground tilt

Using the view point of nonlinear science and the method of selecting numerical features of pattern recognition for reference, the physical and numerical features of precursory ground tilt data are synthetically employed. The dynamic changes of data series are described with the numerical features in multi dimensional space and their distributive relations instead of an unique factor. The relationship between the ground tilt data and earthquake is examined through recognition and classification.

Data Dependent Modeling of New Contamination Cases from Urban Historic Groundwater Records

Groundwater is the water located beneath the earth＇s surface in the soil pore spaces and in the fractures of rock formations. As one of the most important natural resources, groundwater is associated with the environment, public health, welfare, and long-term economic growth, which affects the daily activities of human beings. In modern urban areas, the primary contaminants of groundwater are artificial products, such as gasoline and diesel. To protect this important water resource, a series of efforts have been exerted, including enforcement and remedial actions. Each year, the TGPC （Texas Groundwater Protection Committee） in US publishes a ＂Joint Groundwater Monitoring and Contamination Report＂ to describe historic and new contamination cases in each county, which is an important data source for the design of prevention strategies. In this paper, a DDM （data dependent modeling） approach is proposed to predict county-level NCC （new contamination cases）. A case study with contamination information from Harris County in Texas was conducted to illustrate the modeling and prediction process with promising results. The one-step prediction error is 1.5%, while the two-step error is 12.1%. The established model can be used at the county-level, state-level, and even at the country-level. Besides, the prediction results could be a reference during decision-making processes.

Improved strategy for estimating stem volume and forest biomass using moderate resolution remote sensing data and GIS

This study presents the utility of remote sensing （RS）, GIS and field observation data to estimate above ground biomass （AGB） and stem volume over tropical forest environment. Application of those data for the modeling of forest properties is site specific and highly uncertain, thus further study is encouraged. In this study we used 1460 sampling plots collected in 16 transects measuring tree diameter （DBH） and other forest properties which were useful for the biomass assessment. The study was carded out in tropical forest region in East Kalimantan, Indo- nesia. The AGB density was estimated applying an existing DBH - biomass equation. The estimate was superimposed over the modified GIS map of the study area, and the biomass density of each land cover was calculated. The RS approach was performed using a subset of sample data to develop the AGB and stem volume linear equation models. Pearson correlation statistics test was conducted using ETM bands reflectance, vegetation indices, image transform layers, Principal Component Analysis （PCA） bands, Tasseled Cap （TC）, Grey Level Co-Occurrence Matrix （GLCM） texture features and DEM data as the predictors. Two linear models were generated from the significant RS data. To analyze total biomass and stem volume of each land cover, Landsat ETM images from 2000 and 2003 were preprocessed, classified using maximum likelihood method, and filtered with the majority analysis. We found 158±16 m^3.ha^-1 of stem volume and 168±15 t.ha^-1 of AGB estimated from RS approach, whereas the field measurement and GIS estimated 157±92 m^3.ha^-1 and 167±94 t.ha^-1 of stem volume and AGB, respectively. The dynamics of biomass abundance from 2000 to 2003 were assessed from multi temporal ETM data and we found a slightly declining trend of total biomass over these periods. Remote sensing approach estimated lower biomass abundance than did the GIS and field measurement data. The earlier approach predicted 10.5 Gt and 10.3 Gt of total biomasses in 2000 and 2003, while the later estimated 11.9 Gt and 11.6 Gt of total biomasses, respectively. We found that GLCM mean texture features showed markedly strong correlations with stem volume and biomass.

Analysis software for upper atmospheric data developed by the IUGONET project and its application to polar science

To comprehensively understand the Arctic and Antarctic upper atmosphere, it is often crucial to analyze various data that are obtained from many regions. Infrastructure that promotes such interdisciplinary studies on the upper atmosphere has been developed by a Japanese inter-university project called the Inter-university Upper atmosphere Global Observation Network （1UGONET）. The objective of this paper is to describe the infrastructure and tools developed by IUGONET. We focus on the data analysis software. It is written in Interactive Data Language （IDL） and is a plug-in for the THEMIS Data Analysis Software suite （TDAS）, which is a set of IDL libraries used to visualize and analyze satellite- and ground-based data. We present plots of upper atmospheric data provided by IUGONET as examples of applications, and verify the usefulness of the software in the study of polar science. We discuss IUGONET＇s new and unique developments, i.e., an executable file of TDAS that can run on the IDL Virtual Machine, IDL routines to retrieve metadata from the IUGONET database, and an archive of 3-D simulation data that uses the Common Data Format so that it can easily be used with TDAS.

NEW DYNAMIC SLOT ASSIGNMENT SCHEME OF SELF-ORGANIZED TDMA VHF DATA LINK

According to the analysis of the very high frequency (VHF) self organized time division multiple access (S TDMA) aviation data link, a new dynamic slot assignment scheme is proposed in this paper, which adopts variable data frame structure and can eliminate the effect of the idle slot on message delay. By using queue theory, the analysis models of the new scheme and previous scheme are presented, and the performance of message delay and that of system throughput are analyzed under two schemes. The simulation results show that the new scheme has a better performance than the previous one in the message delay and system throughput.

A Hybrid Algorithm for Satellite Data Transmission Schedule Based on Genetic Algorithm

A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission.At first,based on description of satellite data transmission request,satellite data transmission task model and satellite data transmission scheduling problem model are established.Secondly,the conflicts in scheduling are discussed.According to the meaning of possible conflict,the method to divide possible conflict task set is given.Thirdly,a hybrid algorithm which consists of genetic algorithm and heuristic information is presented.The heuristic information comes from two concepts,conflict degree and conflict number.Finally,an example shows the algorithm's feasibility and performance better than other traditional

Natural Gas Prospecting by Using Satellite MT Data in Chishui Area,Guizhou

Oil-gas remote sensing information is obtained from satellite TM data through graph-ic treatment in the light of the hydrocarbon-microseepage theory. The nine target areas (ofthree types) selected on this basis coincide well with the occurrence of natural gases and have been proved by subsequent prospecting. Plants in the target areas are characterized, as a result of hydrocarbon- microseepage, by abnormal spectral features with the absorption peaks of chlorophyll shifting toward blue light, reflectivity in the range of visible light increasing and re-flectivity in the near infrared region decreasing.

The Pursuit of Unhappiness:The Ground Theory to Explain Why People Choose to Be Unhappy Rather Than Happy

This article presents a ground theory to explain why some individuals choose to be unhappy rather than happy,supported by empirical data collected from a sample of 750 professionals in Greece’s public and private sectors.We begin by reviewing the existing literature on happiness and well-being,highlighting the debate between hedonic and eudaimonic perspectives.We then introduce our research questions and rationale,and describe our methods,sample,and psychometric tools used to measure happiness and other variables of interest.Our results indicate that various factors,including cultural influences,past experiences,and personal values,contribute to individuals’pursuit of unhappiness.We conclude with a thorough discussion of our results and their implications for future research and interventions aimed at promoting well-being.

基于扎根理论的政府开放数据价值影响因素研究

[目的/意义]政府开放数据价值是数字经济情境下个体数据行为嬗变的反馈性标志。从政府开放数据使用者视角识别其价值要素,对于促进政府数据管理水平,提升数据交易常态化具有理论和实践意义。[方法/过程]利用会议、访谈与网络数据获取原始资料,结合扎根理论,构建“三维—多层”的数据价值影响因素模型,涵盖121个初始概念、30项范畴和9项主范畴,各领域受访者共33位。[结果/结论]个体感知数据价值影响因素主要涉及需求、进路、市场化三个维度,综合考察各项主范畴,针对数据价值的内在关联以及外部机制,可以提炼出直接、间接、跨层、双元4种关系结构。这个过程在理论上弥补了传统政务体系下政府开放数据与个体需求二元割裂之不足,延展了政府开放数据的价值使用行为与价值释放路径。

内蒙古地区强震动台站背景噪声与数据质量分析

计算了内蒙古地区193个强震动台站2022年连续观测数据的加速度功率谱密度,通过绘制不同时空、不同加速度计、不同频段的功率谱密度和功率谱概率密度函数图及背景噪声速度均方根值分布图,评估了该区强震动台站的背景噪声变化特征,对出现的功率谱密度异常进行了具体分析,并针对强震动台站数据异常检测及处理提出了相应的建议。研究结果显示:不同加速度计、不同时空强震动台站的背景噪声功率谱密度昼夜差异主要体现在1 Hz以上频段,且不同区域的昼夜差异变化较大;QA-2g型、JS-A2型和TDA-33M型三种加速度计在2 s频点的背景噪声功率谱密度随区域和三分向变化较小;从强震动台站2 s频点背景噪声的监控能得到可靠的观测数据异常,数据异常主要由加速度计零点漂移大、加速度计故障、脉冲干扰、系统参数有误造成,建议优化站点观测环境,进一步规范仪器安装、调试及JOPENS系统的参数配置和审核。

基于多域物理信息神经网络的复合地层隧道掘进地表沉降预测

复合地层中盾构掘进诱发地表沉降的准确预测是隧道工程安全建设与施工决策的关键问题。基于隧道施工诱发地层变形机制构建隧道收敛变形与掘进位置的联系,并将其耦合至深度神经网络(deep neural network,简称DNN)框架,建立了预测盾构掘进诱发地层变形的物理信息神经网络(physics-informed neural network,简称PINN)模型。针对隧道上覆多个地层的地质特征,提出了多域物理信息神经网络(multi-physics-informed neural network,简称MPINN)模型,实现了在统一的框架内对不同地层的物理信息分区域表达。结果表明:MPINN模型高度还原了有限差分法的计算结果,可以准确预测复合地层中隧道开挖诱发的地表沉降;由于融入了物理机制,MPINN模型对隧道施工诱发地表沉降的问题具有普适性,可应用于不同地质和几何条件下隧道诱发地表沉降的预测;基于工程实测数据,提出的MPINN模型准确预测了监测断面的地表沉降曲线,可为复合地层下盾构掘进过程中地表沉降的预测预警提供参考。

基于扎根理论的中国数据新闻受众接受行为研究

数据新闻是随着媒介技术的发展而出现的一种新型报道形态,自2011年以来各大媒体争相使用数据新闻报道新闻。然而,由于受众对数据新闻的类型特征、创新意义和新闻价值认知不足,导致数据新闻的社会接受度不高,严重影响了数据新闻的舆论影响力和社会效益。研究以扎根理论为基础,通过深度访谈收集数据新闻作品温度、媒体资质和网络环境等因素对受众接受行为的影响,继而对访谈资料进行开放式编码、主轴式编码和选择性编码,构建出影响数据新闻传播的理论模型。研究发现:环境因素、渠道因素、内容因素、有媒体因素和受众因素影响数据新闻的发展和传播。基于相关实证研究和理论探索,本文针对性提出打造智慧新闻平台、技术赋能新闻生产、构建信度指标体系、稳步推进信息定制等创新策略和建议,为数据新闻的持续健康发展提供有益借鉴。

大数据、云计算及中台技术在能源企业门户项目模板化的设计应用

在能源企业数字化转型、信息化补强工程背景下,结合能源企业信息门户新模板研发课题要求、采用大数据、云计算及中台技术,优化技术路线,行业互补,打通最后一公里,形成新型模板方案。该模板全套技术高度实现国产化,具备人机融合生产环境、网络化项目建设、数字化运维等优势,适用范围广,模板化方案可高效赋能项目建设与运营全生命周期应用,对能源企业数字化转型领域的新改扩信息化、数字化、智慧化项目补强短板和深化应用有一定效果。