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.

Persistent Data Layout in File Systems

Data layout in a file system is the organization of data stored in external storages. The data layout has a huge impact on performance of storage systems. We survey three main kinds of data layout in traditional file systems： in-place update file system, log-structured file system, and copy-on-write file sys- tem. Each file system has its own strengths and weaknesses under different circumstances. We also include a recent us- age of persistent layout in a file system that combines both flash memory and byte- addressable non- volatile memory. With this survey, we conclude that persistent data layout in file systems may evolve dramatically in the era of emerging non-volatile memory.

A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive Data

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be preserved.Data owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the requester.We prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file keepers.We also provide security analysis in the context of four typical attacks.

A Complexity Analysis and Entropy for Different Data Compression Algorithms on Text Files

In this paper, we analyze the complexity and entropy of different methods of data compression algorithms: LZW, Huffman, Fixed-length code (FLC), and Huffman after using Fixed-length code (HFLC). We test those algorithms on different files of different sizes and then conclude that: LZW is the best one in all compression scales that we tested especially on the large files, then Huffman, HFLC, and FLC, respectively. Data compression still is an important topic for research these days, and has many applications and uses needed. Therefore, we suggest continuing searching in this field and trying to combine two techniques in order to reach a best one, or use another source mapping (Hamming) like embedding a linear array into a Hypercube with other good techniques like Huffman and trying to reach good results.

Refreshing File Aggregate of Distributed Data Warehouse in Sets of Electric Apparatus

Integrating heterogeneous data sources is a precondition to share data for enterprises. Highly-efficient data updating can both save system expenses, and offer real-time data. It is one of the hot issues to modify data rapidly in the pre-processing area of the data warehouse. An extract transform loading design is proposed based on a new data algorithm called Diff-Match,which is developed by utilizing mode matching and data-filtering technology. It can accelerate data renewal, filter the heterogeneous data, and seek out different sets of data. Its efficiency has been proved by its successful application in an enterprise of electric apparatus groups.

Blockchain-Enabled Federated Learning for Privacy-Preserving Non-IID Data Sharing in Industrial Internet

Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by data availability and privacy concerns.Federated learning(FL)has gained considerable attention because it allows for decentralized training on multiple local datasets.However,the training data collected by data providers are often non-independent and identically distributed(non-IID),resulting in poor FL performance.This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain technology.To overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local models.This approach can significantly improve the accuracy of FL training when there is relatively large dispersion.In addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model parameters.Finally,we evaluate the performance of the proposed scheme using commonly used open-source image datasets.The simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet.

A General Framework for Intelligent IoT Data Acquisition and Sharing in an Untrusted Environment Based on Blockchain

Traditional Io T systems suffer from high equipment management costs and difficulty in trustworthy data sharing caused by centralization.Blockchain provides a feasible research direction to solve these problems. The main challenge at this stage is to integrate the blockchain from the resourceconstrained Io T devices and ensure the data of Io T system is credible. We provide a general framework for intelligent Io T data acquisition and sharing in an untrusted environment based on the blockchain, where gateways become Oracles. A distributed Oracle network based on Byzantine Fault Tolerant algorithm is used to provide trusted data for the blockchain to make intelligent Io T data trustworthy. An aggregation contract is deployed to collect data from various Oracle and share the credible data to all on-chain users. We also propose a gateway data aggregation scheme based on the REST API event publishing/subscribing mechanism which uses SQL to achieve flexible data aggregation. The experimental results show that the proposed scheme can alleviate the problem of limited performance of Io T equipment, make data reliable, and meet the diverse data needs on the chain.

Cross-Domain Bilateral Access Control on Blockchain-Cloud Based Data Trading System

Data trading enables data owners and data requesters to sell and purchase data.With the emergence of blockchain technology,research on blockchain-based data trading systems is receiving a lot of attention.Particularly,to reduce the on-chain storage cost,a novel paradigm of blockchain and cloud fusion has been widely considered as a promising data trading platform.Moreover,the fact that data can be used for commercial purposes will encourage users and organizations from various fields to participate in the data marketplace.In the data marketplace,it is a challenge how to trade the data securely outsourced to the external cloud in a way that restricts access to the data only to authorized users across multiple domains.In this paper,we propose a cross-domain bilateral access control protocol for blockchain-cloud based data trading systems.We consider a system model that consists of domain authorities,data senders,data receivers,a blockchain layer,and a cloud provider.The proposed protocol enables access control and source identification of the outsourced data by leveraging identity-based cryptographic techniques.In the proposed protocol,the outsourced data of the sender is encrypted under the target receiver’s identity,and the cloud provider performs policy-match verification on the authorization tags of the sender and receiver generated by the identity-based signature scheme.Therefore,data trading can be achieved only if the identities of the data sender and receiver simultaneously meet the policies specified by each other.To demonstrate efficiency,we evaluate the performance of the proposed protocol and compare it with existing studies.

FADSF:A Data Sharing Model for Intelligent Connected Vehicles Based on Blockchain Technology

With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is the key to organically maximizing their efficiency.However,in the context of increasingly strict global data security supervision and compliance,numerous problems,including complex types of connected vehicle data,poor data collaboration between the IT(information technology)domain and OT(operation technology)domain,different data format standards,lack of shared trust sources,difficulty in ensuring the quality of shared data,lack of data control rights,as well as difficulty in defining data ownership,make vehicle data sharing face a lot of problems,and data islands are widespread.This study proposes FADSF(Fuzzy Anonymous Data Share Frame),an automobile data sharing scheme based on blockchain.The data holder publishes the shared data information and forms the corresponding label storage on the blockchain.The data demander browses the data directory information to select and purchase data assets and verify them.The data demander selects and purchases data assets and verifies them by browsing the data directory information.Meanwhile,this paper designs a data structure Data Discrimination Bloom Filter(DDBF),making complaints about illegal data.When the number of data complaints reaches the threshold,the audit traceability contract is triggered to punish the illegal data publisher,aiming to improve the data quality and maintain a good data sharing ecology.In this paper,based on Ethereum,the above scheme is tested to demonstrate its feasibility,efficiency and security.

A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

The dynamic landscape of the Internet of Things(IoT)is set to revolutionize the pace of interaction among entities,ushering in a proliferation of applications characterized by heightened quality and diversity.Among the pivotal applications within the realm of IoT,as a significant example,the Smart Grid(SG)evolves into intricate networks of energy deployment marked by data integration.This evolution concurrently entails data interchange with other IoT entities.However,there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem.In this paper,we introduce a hierarchical secure data-sharing platform empowered by cloud-fog integration.Furthermore,we propose a novel non-interactive zero-knowledge proof-based group authentication and key agreement protocol that supports one-to-many sharing sets of IoT data,especially SG data.The security formal verification tool shows that the proposed scheme can achieve mutual authentication and secure data sharing while protecting the privacy of data providers.Compared with previous IoT data sharing schemes,the proposed scheme has advantages in both computational and transmission efficiency,and has more superiority with the increasing volume of shared data or increasing number of participants.

Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems

In Decentralized Machine Learning(DML)systems,system participants contribute their resources to assist others in developing machine learning solutions.Identifying malicious contributions in DML systems is challenging,which has led to the exploration of blockchain technology.Blockchain leverages its transparency and immutability to record the provenance and reliability of training data.However,storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs.Additionally,current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data.However,less attention has been paid to the scenario where malicious requesters intentionally manipulate test data during evaluation to gain an unfair advantage.This paper proposes a transparent and accountable training data sharing method that securely shares data among potentially malicious system participants.First,we introduce a blockchain-based DML system architecture that supports secure training data sharing through the IPFS network.Second,we design a blockchain smart contract to transparently split training datasets into training and test datasets,respectively,without involving system participants.Under the system,transparent and accountable training data sharing can be achieved with attribute-based proxy re-encryption.We demonstrate the security analysis for the system,and conduct experiments on the Ethereum and IPFS platforms to show the feasibility and practicality of the system.

The accessible seismological dataset of a high-density 2D seismic array along Anninghe fault

The scientific goal of the Anninghe seismic array is to investigate the detailed geometry of the Anninghe fault and the velocity structure of the fault zone.This 2D seismic array is composed of 161 stations forming sub-rectangular geometry along the Anninghe fault,which covers 50 km and 150 km in the fault normal and strike directions,respectively,with~5 km intervals.The data were collected between June 2020 and June 2021,with some level of temporal gaps.Two types of instruments,i.e.QS-05A and SmartSolo,are used in this array.Data quality and examples of seismograms are provided in this paper.After the data protection period ends(expected in June 2024),researchers can request a dataset from the National Earthquake Science Data Center.

Research on Data Tampering Prevention Method for ATC Network Based on Zero Trust

The traditional air traffic control information sharing data has weak security characteristics of personal privacy data and poor effect,which is easy to leads to the problem that the data is usurped.Starting from the application of the ATC(automatic train control)network,this paper focuses on the zero trust and zero trust access strategy and the tamper-proof method of information-sharing network data.Through the improvement of ATC’s zero trust physical layer authentication and network data distributed feature differentiation calculation,this paper reconstructs the personal privacy scope authentication structure and designs a tamper-proof method of ATC’s information sharing on the Internet.From the single management authority to the unified management of data units,the systematic algorithm improvement of shared network data tamper prevention method is realized,and RDTP(Reliable Data Transfer Protocol)is selected in the network data of information sharing resources to realize the effectiveness of tamper prevention of air traffic control data during transmission.The results show that this method can reasonably avoid the tampering of information sharing on the Internet,maintain the security factors of air traffic control information sharing on the Internet,and the Central Processing Unit(CPU)utilization rate is only 4.64%,which effectively increases the performance of air traffic control data comprehensive security protection system.

A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

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.

基于ArcGIS Engine的数据文件到Shapefile转换方法及其实现

地理数据的多源性导致存储格式的多样性,使其难以在同一GIS平台下实现互操作与空间分析,这有碍于信息的共享。为此,以ArcGIS为基础平台,采用ArcGIS Engine组件进行集成二次开发,通过对数据文件进行访问和空间参考的定义、空间与属性信息的转换来实现Excel型数据文件至Shapefile型矢量文件的转换,以初步实现地理信息间的共享与操作,进而给出了直观的、可视化的数据管理与表达。用此方法实现了山西省宁武县煤炭资源的采矿权文件之转换,即Access→Excel→Shapefile文件。结果表明,其不仅体现出了针对初始文件的降维性、无损性以及可视性的转换表达,还可将记录信息重组(分解)为空间信息与属性信息,对于前者,矿权图层可与遥感影像叠合成图;后者(矿种、开采方式等)可与前者实现无缝链接,并可据以获取相应的开发利用现状信息,以便开展资源遥感监测。

基于全球研究数据注册仓储Re3data.org的医学科学数据仓储建设

目的:总结全球医学领域科学数据仓储建设现状、经验与不足,为我国医学科学数据仓储建设提供参考和借鉴。方法:利用统计分析、共现分析和社会网络分析等方法,结合可视化图表对2018年Re3data.org中收录的637个医学科学数据仓储的分布、建设等情况进行分析。结果:美国、英国和国际组织在仓储合作建设中贡献最为突出,其中美国参与建设的数据仓储占50%,独立建设仓储达241个。多数仓储收录3～6类数据,科学和统计数据是被收录最多的类型,收录数据主题丰富,64.36%的仓储提供数据质控。Dspace等7种软件被用于数据仓储建设,数据管理涉及5种唯一标识符、16种元数据。结论:欧美国家占据了数据仓储建设高地,需多方合作共促数据开放共享。开源技术降低了仓储搭建门槛,标准规范建设保障仓储运管,数据分级共享提供接口支持,政策许可可保障多方权益。

Mendeley Data平台开放科学数据实践及启示

高效的研究数据集成发现和管理工具推动研究数据的开放共享。本文以Elsevier公司Mendeley Data科研数据管理与共享平台为研究对象,从数据治理流程、集成的开放生态系统、数据存储管理和共享功能方面进行调研,分析、总结该平台开放数据实践中围绕数据标准在流程设计和系统开发方面的优势,为我国高校图书馆、研究型图书馆以及信息服务机构开展开放数据资源建设和研究数据服务提供参考借鉴。

EOS Data Dumper——EOS免费数据自动下载与重发布系统

为了更有效的利用已有数据资源,不造成科研设施的重复投资,数据共享越来越受到重视.NASA对地观测系统(EOS)提供了大量的包括MODIS在内的免费数据资源,为此,EOS Data Dumper(EDD)通过程序模拟EOS数据门户的正常下载流程,采用了先进的Web页面文本信息捕捉技术,实现定时自动下载研究区的全部EOS免费数据,并通过免费的DIAL系统,向互联网重新发布,实现复杂的基于时空的数据查询.从技术角度详细介绍了EDD的项目背景与意义、实现方案。

基于ECharts Library和File API的本地数据绘图技术

为解决web绘图每次更改图形绘制均需修改代码,且反复修改易造成代码编写错误的弊端,提出一种针对本地TXT文件,利用ECharts Library和File API简化数据处理程序的绘图技术。该绘图技术可以直接对TXT文件进行修改,简单方便,易于操作。