The study of sedimentary mélanges holds pivotal importance in understanding orogenic processes and unveiling geodynamic mechanisms.In this study,we present findings on zircon U-Pb isotopes and whole-rock elementa...The study of sedimentary mélanges holds pivotal importance in understanding orogenic processes and unveiling geodynamic mechanisms.In this study,we present findings on zircon U-Pb isotopes and whole-rock elemental data concerning the recently uncovered Zongzhuo Formation sedimentary mélanges within the Dingri area.Field observations reveal the predominant composition of the Zongzhuo Formation,characterized by a matrix of sandstone-mudstone mixed with sand-conglomerates within native blocks exhibiting soft sediment deformation.Moreover,exotic blocks originating from littoral-neritic seas display evidence of landslide deformation.Our study identifies the depositional environment of the Zongzhuo Formation in Dingri as a slope turbidite fan,with its provenance traced back to the passive continental margin.Notably,this contrasts with the Zongzhuo Formation found in the Jiangzi-Langkazi area.Based on existing data,we conclude that the Zongzhuo Formation in the Dingri area was influenced by the Dingri-Gamba fault and emerged within a fault basin of the passive continental margin due to Neo-Tethys oceanic subduction during the Late Cretaceous period.Its provenance can be attributed to the littoral-neritic sea of the northern Tethys Himalaya region.This study holds significant implications for understanding the tectonic evolution of Tethys Himalaya and for reevaluating the activity of the Dingri-Gamba fault,as it controls the active deposition of the Zongzhuo Formation.展开更多
随着安全通信要求的提高,卫星通信信号的隐蔽性备受关注。提出了一种基于Arnold变换的抗截获波形设计方法,采用图像领域中的Arnold变换,将扩频信号在频域进行置乱后发送,使得置乱后的信号在时域上不具备扩频信号的周期特性,在频域上更...随着安全通信要求的提高,卫星通信信号的隐蔽性备受关注。提出了一种基于Arnold变换的抗截获波形设计方法,采用图像领域中的Arnold变换,将扩频信号在频域进行置乱后发送,使得置乱后的信号在时域上不具备扩频信号的周期特性,在频域上更具有随机特性,提高信号传输的安全性。经Arnold变换后的信号采用时延相关法进行检测,不能检测出明显的相关峰。根据时延相关的结果,采用峰均比(Peak-to-Average Power Ratio, PAPR)检测,分析置乱信号的检测概率。与传统扩频信号相比,设计的信号波形在相同信噪比和检测门限条件下,具有更低的检测概率,抗截获性能更好。经过仿真验证可知,接收端根据Arnold反变换,恢复出包含有重要信息的原始信号,并且经过Arnold变化后的信号几乎不会造成误码率的波动和提升。展开更多
In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classif...In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.展开更多
基金supported by the Geological Survey Project of the China Geological Survey(Grant No.DD20211547)the Basic Survey Project of the Command Center of Natural Resources Comprehensive Survey(Grant No.ZD20220508)。
文摘The study of sedimentary mélanges holds pivotal importance in understanding orogenic processes and unveiling geodynamic mechanisms.In this study,we present findings on zircon U-Pb isotopes and whole-rock elemental data concerning the recently uncovered Zongzhuo Formation sedimentary mélanges within the Dingri area.Field observations reveal the predominant composition of the Zongzhuo Formation,characterized by a matrix of sandstone-mudstone mixed with sand-conglomerates within native blocks exhibiting soft sediment deformation.Moreover,exotic blocks originating from littoral-neritic seas display evidence of landslide deformation.Our study identifies the depositional environment of the Zongzhuo Formation in Dingri as a slope turbidite fan,with its provenance traced back to the passive continental margin.Notably,this contrasts with the Zongzhuo Formation found in the Jiangzi-Langkazi area.Based on existing data,we conclude that the Zongzhuo Formation in the Dingri area was influenced by the Dingri-Gamba fault and emerged within a fault basin of the passive continental margin due to Neo-Tethys oceanic subduction during the Late Cretaceous period.Its provenance can be attributed to the littoral-neritic sea of the northern Tethys Himalaya region.This study holds significant implications for understanding the tectonic evolution of Tethys Himalaya and for reevaluating the activity of the Dingri-Gamba fault,as it controls the active deposition of the Zongzhuo Formation.
文摘随着安全通信要求的提高,卫星通信信号的隐蔽性备受关注。提出了一种基于Arnold变换的抗截获波形设计方法,采用图像领域中的Arnold变换,将扩频信号在频域进行置乱后发送,使得置乱后的信号在时域上不具备扩频信号的周期特性,在频域上更具有随机特性,提高信号传输的安全性。经Arnold变换后的信号采用时延相关法进行检测,不能检测出明显的相关峰。根据时延相关的结果,采用峰均比(Peak-to-Average Power Ratio, PAPR)检测,分析置乱信号的检测概率。与传统扩频信号相比,设计的信号波形在相同信噪比和检测门限条件下,具有更低的检测概率,抗截获性能更好。经过仿真验证可知,接收端根据Arnold反变换,恢复出包含有重要信息的原始信号,并且经过Arnold变化后的信号几乎不会造成误码率的波动和提升。
基金supported by National Natural Science Foundation of China(62371098)Natural Science Foundation of Sichuan Province(2023NSFSC1422)+1 种基金National Key Research and Development Program of China(2021YFB2900404)Central Universities of South west Minzu University(ZYN2022032).
文摘In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.
