exit code 4: CiteprocParseError: Multiple layout elements present in bibliography

hi，

After I configured this plugin, I get the following error when validating pandoc configuration.

pandocMarkdownToHTML Command failed with exit code 4: C:\Users\xxxx\AppData\Local\Pandoc\pandoc.EXE -f markdown -t html --citeproc --quiet --bibliography=F: \OneDrive\DataLibrary\Obsidian\My-Research\meta\bibtex\My-Library. json --csl=G: \AppData\Zotero\styles\gb-t-7714-2015-numeric-bilingual-no-uppercase-no-url-doi. csl CiteprocParseError: Multiple layout elements present in bibliography

Pandoc version: 3.1.2

bibliography, My-Library.json:

[
    {"id":"othmane2022PerformanceDetectingInjection","abstract":"检测 CAN 总线中制造消息注入的性能\n\nThere have been several public demonstrations of attacks on connected vehicles showing the ability of an attacker to take control of a targeted vehicle by injecting messages into their Controller Area Network (CAN) bus. In this article, using injected speed reading and Revolutions Per Minute (RPM) reading messages in in-motion vehicle, we examine the ability of the Pearson correlation and the unsupervised learning methods k-means clustering and Hidden Markov Model (HMM) to differentiate `no-attack' and `under-attack' states of the given vehicle. We found that the Pearson correlation distinguishes the two states, the k-means clustering method has an acceptable accuracy but high false positive rate and HMM detects attacks with acceptable detection rate but has a high false positive in detecting attacks from speed readings when there is no attack. The accuracy of these unsupervised learning methods are comparable to the ones of the supervised learning methods used by CAN bus Intrusion Detection System (IDS) suppliers. In addition, the article shows that studying CAN anomaly detection techniques using off-vehicle test facilities may not properly evaluate the performance of the detection techniques. The results suggest using other features besides the data content of the CAN messages and integrate knowledge about how the Electronic Control Units (ECUs) collaborate in building effective techniques for the detection of injection of fabricated message attacks.","author":[{"family":"Othmane","given":"Lotfi Ben"},{"family":"Dhulipala","given":"Lalitha"},{"family":"Abdelkhalek","given":"Moataz"},{"family":"Multari","given":"Nicholas"},{"family":"Govindarasu","given":"Manimaran"}],"call-number":"2","citation-key":"othmane2022PerformanceDetectingInjection","container-title":"IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING","DOI":"10.1109/TDSC.2020.2990192","issue":"1","issued":{"date-parts":[[2022,1,1]]},"page":"468-481","source":"6.791","title":"On the Performance of Detecting Injection of Fabricated Messages into the CAN Bus","type":"article-journal","volume":"19"}, 
    {"id":"ZhangXinPeng2015YaSuoGan","abstract":"旋转机械作为大型装备重要的组成部分,在装备高效稳定运行过程中占据重要地位,其失效或者发生故障将会对装备安全运行和效能发挥造成重大影响。因此,研究旋转机械的健康监测对于保障装备安全运行和人员生命安全具有重要意义。随着现代科技的不断发展,装备变得越来越复杂,要更为全面地评估装备健康状况就需要借助于更多类型、更多数量的运行状态数据。对于长期运行的设备来说,实时地监控装备运行状态意味着长时间的不间断连续采样,该过程会产生海量的装备状态数据,一方面会给数据存储带来巨大的压力,对于需要远距离传输状态数据到地面指挥站的装备如战斗机、直升机等,由于无线传输速率和带宽的限制,海量数据也会加大数据传输的难度和压力;另一方面,虽然数据量的增多可以提供更多装备状态信息,然而这些数据往往存在很大的冗余,因此获得的数据越多,数据冗余量也就越大,这就会造成存储和通信资源的巨大浪费。同时,数据量越大,信号处理的负担也就越重,计算代价也就越大。压缩感知作为一种新型的状态感知技术,在确保状态信息量的同时,只需采集少量的压缩观测值,可有效地缓解海量状态数据的存储和传输压力,且由于低维压缩观测值中已包涵装备运行状态信息,因此可直接用于装备的健康监测等。针对上述旋转机械健康监测中的问题,结合压缩感知的特点,本文研究了压缩感知技术在旋转机械健康监测中的应用,主要工作如下。(1)分析了压缩感知基本理论和旋转机械振动信号的稀疏表示问题,构建和分析了三类对振动信号拥有较好稀疏表示效果的字典,包括完备字典、固定的超完备字典以及基于字典学习理论的超完备字典等,并分析了不同字典对振动信号的稀疏分解性能;研究了基于稀疏表示字典的振动数据压缩方法和五种衡量振动数据压缩效果的评价指标,并基于此分析了适用于振动信号稀疏化的字典对振动数据的压缩效果。(2)提出了基于信号稀疏分解理论的轴承故障检测和诊断方法,分别构建了基于字典学习的轴承故障检测模型和故障诊断模型,使用电机驱动端深沟球轴承运行状态振动数据对所提方法进行了验证,分析了稀疏表示误差阈值和分解原子个数等对故障检测和诊断结果的影响。(3)分析了直接使用压缩感知观测值实现信号检测和分类的数学基础以及相应的检测与分类概率;分析了不同状态振动信号频域能量分布的区别,提出了基于信号频域能量分布且直接使用压缩感知观测值的轴承故障检测方法,使用电机驱动端深沟球轴承运行状态振动数据对该方法进行验证,并分析了压缩观测值数量等参数对检测结果的影响。(4)使用轴承不同状态下的振动数据训练得到可分别表示相应状态振动信号的超完备字典,在这些不同类型字典和稀疏分解理论的基础上,提出了直接从低维压缩观测值出发且无需重构原始信号的轴承故障检测方法和轴承故障诊断方法,分析了阈值、观测数量、稀疏度和压缩观测方式等因素对故障诊断结果的影响及其设置原则。(5)提出了基于压缩感知原理的振动数据修复方法。将数据丢失过程转化为压缩观测过程,根据振动数据丢失行为的规律,构建相应的压缩观测向量和压缩观测矩阵并在此基础上重构原始完整信号实现振动数据的修复。使用仿真数据和电机驱动端深沟球轴承运行状态振动数据对该方法进行验证,分析了不同丢失方式和丢失数据量对修复结果的影响。(6)提出了基于压缩感知原理的振动数据降噪方法。针对旋转机械振动数据的噪声干扰问题,利用振动信号可通过某些字典实现稀疏化而噪声信号在大多数字典上不能稀疏表示的特点,对受噪声污染的振动信号执行压缩感知和信号重构,在该转化过程中实现振动数据降噪。使用齿轮仿真数据和电机驱动端深沟球轴承运行状态振动数据对所提方法进行验证,分析了使用不同字典时的降噪效果。综上所述,本文研究了压缩感知原理及其在旋转机械健康监测领域中的应用。构建了三类适用于振动数据稀疏表示的字典,分析结果表明这些字典均对轴承振动数据具有较好的稀疏分解效果;提出和验证了基于信号稀疏分解理论的轴承故障检测和诊断方法;分析了直接使用压缩感知观测值实现信号检测和分类的数学基础以及相应的检测与分类概率,提出和验证了直接使用低维压缩感知观测值的轴承故障检测和诊断方法;提出了基于压缩感知原理的振动数据修复方法和振动数据降噪方法,并使用仿真数据和电机驱动端深沟球轴承实验数据验证了所提方法的有效性。","author":[{"literal":"张新鹏"}],"citation-key":"ZhangXinPeng2015YaSuoGan","contributor":[{"literal":"胡茑庆"}],"genre":"博士","issued":{"date-parts":[[2015]]},"language":"zh-CN","note":"18 citations(CNKI)[9-12-2022]","publisher":"国防科学技术大学","source":"CNKI","title":"压缩感知及其在旋转机械健康监测中的应用","type":"thesis","URL":"https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CDFD&dbname=CDFDLAST2017&filename=1017834243.nh&v="}
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              <text macro="secondary-contributors"/>
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            <group delimiter=". ">
              <text macro="container-contributors"/>
              <text macro="container-booklike"/>
            </group>
          </group>
          <text macro="edition"/>
          <text macro="publisher"/>
        </else-if>
        <else>
          <!-- 4.1 专著 -->
          <text macro="title"/>
          <text macro="secondary-contributors"/>
          <text macro="edition"/>
          <text macro="publisher"/>
        </else>
      </choose>
      <text macro="access"/>
    </group>
  </macro>
  <citation collapse="citation-number" after-collapse-delimiter=",">
    <sort>
      <key variable="citation-number"/>
    </sort>
    <layout vertical-align="sup" delimiter="," prefix="[" suffix="]">
      <text variable="citation-number"/>
    </layout>
  </citation>
  <bibliography entry-spacing="0" et-al-min="4" et-al-use-first="3" second-field-align="flush">
    <layout suffix="." locale="en">
      <text variable="citation-number" prefix="[" suffix="]"/>
      <text macro="entry-layout"/>
    </layout>
    <layout suffix=".">
      <text variable="citation-number" prefix="[" suffix="]"/>
      <text macro="entry-layout"/>
    </layout>
  </bibliography>
</style>

mgmeyers / obsidian-pandoc-reference-list

exit code 4: CiteprocParseError: Multiple layout elements present in bibliography #62