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  <Article>
    <Journal>
      <PublisherName>ijesm</PublisherName>
      <JournalTitle>International Journal of Engineering, Science and</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>volume 15,issue 5</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Multidisciplinary</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>May 2026</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2026</Year>
        <Month>05</Month>
        <Day>10</Day>
      </PubDate>
      <ArticleType>Engineering, Science and Mathematics</ArticleType>
      <ArticleTitle>ORBITAL__ampersandsign#8209;PARAMETER__ampersandsign#8209;CONDITIONED LSTM FOR ON__ampersandsign#8209;BOARD THERMAL LOAD FORECASTING IN 3U CUBESAT CONSTELLATIONS</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>1</FirstPage>
      <LastPage>14</LastPage>
      <AuthorList>
        <Author>
          <FirstName>David Santosh</FirstName>
          <LastName>Christopher</LastName>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
          <FirstName>Temesgen Hailegiorgis</FirstName>
          <LastName>Abebe</LastName>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
        </Author>
      </AuthorList>
      <DOI/>
      <Abstract>Accurate prediction of nanosatellite thermal loads in Low Earth Orbit (LEO) is essential to guarantee subsystem reliability and extend mission lifetime, especially for resource__ampersandsign#8209;constrained CubeSat constellations. Classical thermal tools based on finite__ampersandsign#8209;element or finite__ampersandsign#8209;difference models are too computationally intensive for on__ampersandsign#8209;board use and cannot adapt quickly to changing orbital conditions such as eclipse fraction, solar beta angle, or internal power dissipation. This work proposes an orbital__ampersandsign#8209;parameter__ampersandsign#8209;conditioned Long Short__ampersandsign#8209;Term Memory (LSTM) network for predictive thermal load forecasting in 3U CubeSat formations operating in sun__ampersandsign#8209;synchronous LEO orbits over the Indian Ocean Region (IOR). A physics__ampersandsign#8209;derived synthetic dataset of 5,000 scenarios is generated using a validated six__ampersandsign#8209;face lumped__ampersandsign#8209;capacitance thermal model coupled with RK45 orbital propagation, spanning altitudes from 450__ampersandsignndash;700 km, solar beta angles from __ampersandsignminus;75__ampersandsigndeg; to +75__ampersandsigndeg;, and three surface coating configurations</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>LSTM neural network; CubeSat thermal management; LEO orbital mechanics; predictive thermal control; nanosatellite formation flying; machine learning; thermal load forecasting.</Keywords>
      <URLs>
        <Abstract>https://www.ijesm.co.in/ubijournal-v1copy/journals/abstract.php?article_id=16232&amp;title=ORBITAL__ampersandsign#8209;PARAMETER__ampersandsign#8209;CONDITIONED LSTM FOR ON__ampersandsign#8209;BOARD THERMAL LOAD FORECASTING IN 3U CUBESAT CONSTELLATIONS</Abstract>
      </URLs>
      <References>
        <ReferencesarticleTitle>References</ReferencesarticleTitle>
        <ReferencesfirstPage>16</ReferencesfirstPage>
        <ReferenceslastPage>19</ReferenceslastPage>
        <References/>
      </References>
    </Journal>
  </Article>
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