This article introduces the methodologies and instrumentation for data measurement and propagation at the Back-n white neutron facility of the China Spallation Neutron Source.The Back-n facility employs backscattering...This article introduces the methodologies and instrumentation for data measurement and propagation at the Back-n white neutron facility of the China Spallation Neutron Source.The Back-n facility employs backscattering techniques to generate a broad spectrum of white neutrons.Equipped with advanced detectors such as the light particle detector array and the fission ionization chamber detector,the facility achieves high-precision data acquisition through a general-purpose electronics system.Data were managed and stored in a hierarchical system supported by the National High Energy Physics Science Data Center,ensuring long-term preservation and efficient access.The data from the Back-n experiments significantly contribute to nuclear physics,reactor design,astrophysics,and medical physics,enhancing the understanding of nuclear processes and supporting interdisciplinary research.展开更多
Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation.While performing this entire process,there is a high pos...Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation.While performing this entire process,there is a high possibility for data corruption in the mid of transmission.On the other hand,the network performance is also affected due to various attacks.To address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is proposed.Initially,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert system.Improved Random Linear Network Coding(IRLNC)is used to create an encoded packet.This encoded packet from the source and neighboring nodes is transmitted to the storage node.Finally,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)algorithm.Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics.Average residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 s.Based on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.展开更多
Purpose The LHAASO project collects trillions of cosmic ray events every year,generating about 10 PB of raw data annually,which brings big challenges for data processing platform.Method The LHAASO data processing plat...Purpose The LHAASO project collects trillions of cosmic ray events every year,generating about 10 PB of raw data annually,which brings big challenges for data processing platform.Method The LHAASO data processing platform is built to handle such a large amount of data,which is composed of some subsystems such as data transfer,data storage,high throughput computing and metadata management.Results and conclusions The platform was under construction since 2018 and has been working well since 2021.In this paper,the details of the design,implementation and performance of the data processing platform are presented.展开更多
基金supported by the National Key Research and Development Plan(No.2023YFA1606602)。
文摘This article introduces the methodologies and instrumentation for data measurement and propagation at the Back-n white neutron facility of the China Spallation Neutron Source.The Back-n facility employs backscattering techniques to generate a broad spectrum of white neutrons.Equipped with advanced detectors such as the light particle detector array and the fission ionization chamber detector,the facility achieves high-precision data acquisition through a general-purpose electronics system.Data were managed and stored in a hierarchical system supported by the National High Energy Physics Science Data Center,ensuring long-term preservation and efficient access.The data from the Back-n experiments significantly contribute to nuclear physics,reactor design,astrophysics,and medical physics,enhancing the understanding of nuclear processes and supporting interdisciplinary research.
文摘Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation.While performing this entire process,there is a high possibility for data corruption in the mid of transmission.On the other hand,the network performance is also affected due to various attacks.To address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is proposed.Initially,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert system.Improved Random Linear Network Coding(IRLNC)is used to create an encoded packet.This encoded packet from the source and neighboring nodes is transmitted to the storage node.Finally,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)algorithm.Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics.Average residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 s.Based on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.
基金supported by National Nature Science Foundation of China(GrantNos.12075268,12175255,12175258,12105300)the Chinese Academy of Science,Institute of High Energy Physics.
文摘Purpose The LHAASO project collects trillions of cosmic ray events every year,generating about 10 PB of raw data annually,which brings big challenges for data processing platform.Method The LHAASO data processing platform is built to handle such a large amount of data,which is composed of some subsystems such as data transfer,data storage,high throughput computing and metadata management.Results and conclusions The platform was under construction since 2018 and has been working well since 2021.In this paper,the details of the design,implementation and performance of the data processing platform are presented.
