Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

Designing and Implementing an Advanced Big Data Governance Platform

Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.

Application of GNSS-PPP on Dynamic Deformation Monitoring of Offshore Platforms

The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has been considered for this purpose,its monitoring accuracy is relatively low.Moreover,the influence of background noise on the dynamic monitoring accuracy of GNSS-PPP remains unclear.Hence,it is imperative to further validate the feasibility of GNSS-PPP for deformation monitoring of offshore platforms.To address these concerns,vibration table tests with different amplitudes and frequencies are conducted.The results demonstrate that GNSS-PPP can effectively monitor horizontal vibration displacement as low as±30 mm,which is consistent with GNSS-RTK.Furthermore,the spectral characteristic of background noise in GNSS-PPP is similar to that of GNSS-RTK(Real Time Kinematic).Building on this observation,an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)has been proposed to de-noise the data and enhance the dynamic monitoring accuracy of GNSS-PPP.Field monitoring application research is also undertaken,successfully extracting and analyzing the dynamic deformation of an offshore platform structure under environmental excitation using GNSS-PPP monitoring in conjunction with improved CEEMDAN de-noising.By comparing the de-noised dynamic deformation trajectories of the offshore platform during different periods,it is observed that the platform exhibits reversible alternating vibration responses under environmental excitation,with more pronounced displacement deformation in the direction of load action.The research results confirm the feasibility and potential of GNSS-PPP for dynamic deformation monitoring of offshore platforms.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

Analysis and Enlightenment of Big Data Platform for Adverse Drug Reaction Supervision in China and the United States

Objective To introduce the relevant big data platforms of FDA regulatory sciences and to provide reference for the construction of big data platform for China’s regulatory science under the“14th five-year plan”to deepen the reform of medical and health system.Methods A comparative analysis was made on China’s big data for regulatory science after studying the development process,operation mode,practical significance and characteristics of the big data platform for FDA regulatory science,which would help China to establish a perfect big database.Results and Conclusion The construction of big data platform for China’s regulatory science is not comprehensive compared with that in the United States.It is necessary to build data platforms in line with China’s national conditions through efforts in law,talents,standards,and other aspects.

BIG评分对接受去骨瓣减压术的中重度创伤性脑损伤儿童早期脑功能的预测价值

目的:探讨BIG评分(由格拉斯哥评分、国际标准化比值、碱剩余组成)对接受去骨瓣减压术(DC)的中重度创伤性脑损伤(TBI)患儿脑功能早期预后的预测价值。方法:回顾性分析2014年3月至2023年7月于我院接受DC治疗的所有中重度TBI患儿,以出院时儿童脑功能分类(PCPC)为结局,分为预后良好组(PCPC 1~2)和预后不良组(PCPC 3~6)。通过病历资料回顾,提取患儿的临床信息,并使用Logistic回归分析评估BIG评分的预测价值。结果:共纳入55例接受DC治疗的中重度TBI患儿,其中25例出院时脑功能良好,30例预后不良(包括9例死亡)。患儿入院时的高BIG评分(p < 0.001)、瞳孔对光反射差(p = 0.027),存在失血性休克(p = 0.042)及多发伤(p = 0.043)、脑水肿(p = 0.007),高血糖(p = 0.042)、高乳酸血症(p = 0.029)均与出院时脑功能不良相关。Logistic回归分析显示,入院时的高BIG评分是出院时脑功能不良的独立危险因素。ROC曲线分析确定的最佳BIG评分阈值为17.5,以此预测不良预后的敏感性为66.7%,特异性为88.0%。结论:接受DC的中重度TBI患儿出院时的总体脑功能不良比例为54.5%。入院时的BIG评分能够预测这些患儿出院时的早期脑功能预后,具有较高的敏感性和特异性。

Dynamic Analysis of a 10 MW Floating Offshore Wind Turbine Considering the Tower and Platform Flexibility

Recently,semisubmersible floating offshore wind turbine technologies have received considerable attention.For the coupled simulation of semisubmersible floating offshore wind energy,the platform is usually considered a rigid model,which could affect the calculation accuracy of the dynamic responses.The dynamic responses of a TripleSpar floating offshore wind turbine equipped with a 10 MW offshore wind turbine are discussed herein.The simulation of a floating offshore wind turbine under regular waves,white noise waves,and combined wind-wave conditions is conducted.The effects of the tower and platform flexibility on the motion and force responses of the TripleSpar semisubmersible floating offshore wind turbine are investigated.The results show that the flexibility of the tower and platform can influence the dynamic responses of a TripleSpar semisubmersible floating offshore wind turbine.Considering the flexibility of the tower and platform,the tower and platform pitch motions markedly increased compared with the fully rigid model.Moreover,the force responses,particularly for tower base loads,are considerably influenced by the flexibility of the tower and platform.Thus,the flexibility of the tower and platform for the coupled simulation of floating offshore wind turbines must be appropriately examined.

Sedimentary build-ups of pre-salt isolated carbonate platforms and formation of deep-water giant oil fields in Santos Basin,Brazil

In response to the problems of unclear distribution of deep-water pre-salt carbonate reservoirs and formation conditions of large oil fields in the Santos passive continental margin basin,based on comprehensive utilization of geological,seismic,and core data,and reconstruction of Early Cretaceous prototype basin and lithofacies paleogeography,it is proposed for the first time that the construction of pre-salt carbonate build-ups was controlled by two types of isolated platforms:inter-depression fault-uplift and intra-depression fault-high.The inter-depression fault-uplift isolated platforms are distributed on the present-day pre-salt uplifted zones between depressions,and are built on half-and fault-horst blocks that were inherited and developed in the early intra-continental and inter-continental rift stages.The late intra-continental rift coquinas of the ITP Formation and the early inter-continental rift microbial limestones of the BVE Formation are continuously constructed;intra-depression fault-high isolated platforms are distributed in the current pre-salt depression zones,built on the uplifted zones formed by volcanic rock build-ups in the early prototype stage of intra-continental rifts,and only the BVE microbial limestones are developed.Both types of limestones formed into mound-shoal bodies,that have the characteristics of large reservoir thickness and good physical properties.Based on the dissection of large pre-salt oil fields discovered in the Santos Basin,it has been found that both types of platforms could form large-scale combined structural-stratigraphic traps,surrounded by high-quality lacustrine and lagoon source rocks at the periphery,and efficiently sealed by thick high-quality evaporite rocks above,forming the optimal combination of source,reservoir and cap in the form of“lower generation,middle storage,and upper cap”,with a high degree of oil and gas enrichment.It has been found that the large oil fields are all bottom water massive oil fields with a unified pressure system,and they are all filled to the spill-point.The future exploration is recommended to focus on the inter-depression fault-uplift isolated platforms in the western uplift zone and the southern section of eastern uplift zones,as well as intra-depression fault-high isolated platforms in the central depression zone.The result not only provides an important basis for the advanced selection of potential play fairways,bidding of new blocks,and deployment of awarded exploration blocks in the Santos Basin,but also provides a reference for the global selection of deep-water exploration blocks in passive continental margin basins.

Renewable Biomass as a Platform for Preparing Green Chemistry

1 About the Special Issue Editor Qiaoguang Li is an associate professor and master’s supervisor in the Department of College of Chemistry and Chemical Engineering,Zhongkai University of Agriculture and Engineering.He received his PhD from Institute of Chemical Industry of Forestry Products,Chinese Academy of Forestry in 2018.He has been focusing his research on the chemical basis and application of natural resources.He has published nearly 30 international peer reviewed papers and applied for 10 patents.

Dynamic Coupling Analysis of Semisubmersible Platform Float-over Method for Docking Case

In this paper,the multi-body coupled dynamic characteristics of a semisubmersible platform and an HYSY 229 barge were investigated.First,coupled hydrodynamic analysis of the HYSY 229 barge and the semisubmersible platform was performed.Relevant hydrodynamic parameters were obtained using the retardation function method of three-dimensional frequency-domain potential flow theory.The results of the hydrodynamic analysis were highly consistent with the test findings,verifying the accuracy of the multifloating hydrodynamic coupling analysis,and key hydrodynamic parameters were solved for different water depths and the coupling effect.According to the obtained results,the hydrodynamic influence was the largest in shallow waters when the coupling effect was considered.Furthermore,the coupled motion equation combined with viscous damping,fender system,and mooring system was established,and the hydrodynamics,floating body motion,and dynamic response of the fender system were analyzed.Motion analysis revealed good agreement among the surge,sway,and yaw motions of the two floating bodies.However,when the wave period reached 10 s,the motion of the two floating bodies showed severe shock,and a relative motion was also observed.Therefore,excessive constraints should be added between the two floating bodies during construction to ensure construction safety.The numerical analysis and model test results of the semisubmersible platform and HYSY 229 barge at a water depth of 42 m and sea conditions of 0°,45°,and 90° were in good agreement,and the error was less than 5%.The maximum movement of the HYSY 229 barge reached 2.61 m in the sway direction,whereas that of the semisubmersible platform was 2.11 m.During construction,excessive constraints should be added between the two floating bodies to limit their relative movement and ensure construction safety.

Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering analysis.Firstly,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing efficiency.Then,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results.When new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in data.Finally,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic data.Through experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios.Therefore,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering techniques.It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments.This method shows great potential in real-world applications where dynamic data growth is common,and it is of significant importance for advancing the development of data analysis technology.

Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business constraints.The BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

A Coordinate-Free Approach to the Design of Generalized Griffis-Duffy Platforms

Architectural singularity belongs to the Type II singularity,in which a parallel manipulator(PM)gains one or more degrees of freedom and becomes uncontrollable.PMs remaining permanently in a singularity are beneficial for linearto-rotary motion conversion.Griffis-Duffy(GD)platform is a mobile structure admitting a Bricard motion.In this paper,we present a coordinate-free approach to the design of generalized GD platforms,which consists in determining the shape and attachment of both the moving platform and the fixed base.The generalized GD platform is treated as a combination of six coaxial single-loop mechanisms under the same constraints.Owing to the inversion,hidden in the geometric structure of these single-loop mechanisms,the mapping from a line to a circle establishes the geometric transformation between the fixed base and the moving platform based on the center of inversion,and describes the shape and attachment of the generalized GD platform.Moreover,the center of inversion not only identifies the location of rotation axis,but also affects the shape of the platform mechanism.A graphical construction of generalized GD platforms using inversion,proposed in the paper,provides geometrically feasible solutions of the manipulator design for the requirement of the location of rotation axis.

Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.

Experimental Study on Towing Characteristics of Composite Bucket Wellhead Platform

With the rapid development of large-scale development of marginal oilfields in China,simple wellhead platforms that are simple in structure and easy to install have become an inevitable choice in the process of oilfield development.However,traditional simple wellhead platforms are often discarded after a single use.In pursuit of a more costeffective approach to developing marginal oilfields,this paper proposes a new offshore oil field development facility—an integrated bucket foundation for wellhead platform.To verify the safety of its towing behavior and obtain the dynamic response characteristics of the structure,this paper takes a bucket integrated bucket foundation for wellhead platform with a diameter of 40 m as the research object.By combining physical model tests and numerical simulations,it analyzes the static stability and dynamic response characteristics of the structure during towing,complete with the effects of the draft,wave height,wave period,and towing point height,which produce the dynamic responses of the structure under different influence factors,such as roll angle,pitch angle,heave acceleration and towing force as well as the sensibility to transport variables.The results show that the integrated bucket foundation for wellhead platform is capable of self-floating towing,and its movement is affected by the local environment,which will provide a reference for actual projects.

Application of Time-Tracking Platform in the Reperfusion Treatment of Patients with Acute Ischemic Stroke in Primary Hospitals

Objective To explore the application effect of time tracking platform in improving the reperfusion treatment of patients with acute ischemic stroke in primary hospitals. Methods and Results Patients with acute ischemic stroke who carried out emergency intravenous thrombolysis and arterial thrombectomy in our hospital in 2021, 2022 and 2023 were selected. The time tracking mode was implemented, and the patients were recorded at each time node of the hospital and the whole-process digital management was conducted. Compared the mean DNT (Door to Needle Time) of intravenous thrombolysis in emergency stroke patients in 2021, 2022 and 2023, the total number of hospital cases within 4.5 h of onset, the total number of thrombolysis cases within 4.5 h of onset, the number of intravenous thrombolysis in 60 minutes of acute ischemic stroke, and the number of thrombolysis cases. The results show that from 2021 to 2023 our emergency stroke patients with intravenous thrombolysis average DNT shortened year by year, to the hospital within 4.5 h after the onset of the difference is statistically significant (all P < 0.05) conclusion through the application of stroke time tracking platform, is beneficial to shorten the treatment time of each link, can effectively reduce the hospital time delay, improve the rate of thrombolysis, improve the reperfusion of stroke centers in primary hospitals.

Quantum circuit-based proxy blind signatures:A novel approach and experimental evaluation on the IBM quantum cloud platform

This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits,aiming to enhance security while safeguarding sensitive information.The main objective of this research is to introduce a quantum proxy blind signature(QPBS)protocol that utilizes quantum logical gates and quantum measurement techniques.The QPBS protocol is constructed by the initial phase,proximal blinding message phase,remote authorization and signature phase,remote validation,and de-blinding phase.This innovative design ensures a secure mechanism for signing documents without revealing the content to the proxy signer,providing practical security authentication in a quantum environment under the assumption that the CNOT gates are securely implemented.Unlike existing approaches,our proposed QPBS protocol eliminates the need for quantum entanglement preparation,thus simplifying the implementation process.To assess the effectiveness and robustness of the QPBS protocol,we conduct comprehensive simulation studies in both ideal and noisy quantum environments on the IBM quantum cloud platform.The results demonstrate the superior performance of the QPBS algorithm,highlighting its resilience against repudiation and forgeability,which are key security concerns in the realm of proxy blind signatures.Furthermore,we have established authentic security thresholds(82.102%)in the presence of real noise,thereby emphasizing the practicality of our proposed solution.

Simulation of wave scattering over a floating platform in the ocean with a coupled CFD-IBM model

A numerical study of linear wave scattering over a floating platform has been simulated by an efficient numericalmodel in this letter.The non-hydrostatic model is used to simulate the free surface and the uneven bottom.For thesolid body modelling,the immersed boundary method(IBM)is implemented by introducing a virtual boundaryforce into the momentum equations to emulate the boundary conditions.This implementation enhances theability of the model to simulate interactions between waves and floating structures.A numerical case involvingwave interactions with a floating platform is studied to validate the numerical model.By simulating the wavepropagation,the numerical model captures the variation of the wave scattering very well,which verifies theperformance of the numerical model and the robust strategy of the IBM.

Research on the Legal Framework of Content Regulations for Network Platforms

In the era of the Internet,various network platforms have evolved into new hubs for information dissemination.Currently,China has established a platform-centered content regulation framework,wherein platforms proactively enforce content regulations in accordance with legal censorship obligations.Additionally,platform policies and user agreements augment their authority in content regulation.The platforms can achieve cost-effective and highly efficient content regulation by leveraging their strategic advantages enabled by their own technical capabilities and extensive coverage.The platform self-regulation model,however,still faces challenges.First,accurately evaluating content remains a formidable task;second,ensuring effective platform publicity through self-regulation poses difficulties;third,users may potentially face disadvantages due to the platform’s right of self-regulation;and fourth,digital copyright owners face challenges when defending digital copyright disputes under the safe harbor rule.Therefore,it is imperative to establish,review,and revise the legal framework for content regulation of network platforms in order to enhance the efficiency of their governance systems.The formulation of the legal framework for content regulation of network platforms may encompass the following aspects:rationalizing obligations pertaining to platform content regulations,enhancing supervision over platform self-regulation,and establishing a dual-track responsibility system for digital copyright content regulation.This will ensure a harmonious balance among public interests,users’personal rights and interests,and commercial benefits through regulating the content on network platforms.