Robust Optimal Design with Latin Hypercube Sampling Method for Remote Sensing Satellite in LEO Orbit

Robust Optimal Design with Latin Hypercube Sampling Method for Remote Sensing Satellite in LEO Orbit | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Robust Optimal Design with Latin Hypercube Sampling Method for Remote Sensing Satellite in LEO Orbit Saman Javadi Kouchaksaraei, Alireza Toloei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7236843/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract The design of remote sensing satellites in Low Earth Orbit (LEO) presents significant challenges due to inherent orbital uncertainties such as altitude, inclination, right ascension of the ascending node (RAAN), and elevation angle. To address these issues during early-stage design, this study proposes an integrated Robust Design Optimization (RDO) framework that combines the Teaching-Learning-Based Optimization (TLBO) algorithm with Latin Hypercube Sampling (LHS). The primary objective is to minimize the satellite's total mass while ensuring stable performance under environmental perturbations. Unlike conventional approaches that treat uncertainty quantification and optimization separately, the proposed method embeds stochastic behavior directly into the design loop, capturing the interaction between uncertain parameters and design decisions. The framework is validated using real-world case studies, including Aqua, VRSS1, and CloudSat satellites, showing a significant reduction in design error compared to classical optimization. Results confirm that the TLBO-LHS integration enhances design robustness, reduces system mass, and improves mission reliability. This methodology offers a scalable and practical solution for the robust design of other complex space systems affected by uncertainty. Physical sciences/Engineering Physical sciences/Mathematics and computing robust design remote sensing satellite optimization Uncertainty orbital mechanic Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 02 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviews received at journal 29 Nov, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviews received at journal 28 Sep, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers invited by journal 25 Aug, 2025 Editor assigned by journal 25 Aug, 2025 Editor invited by journal 20 Aug, 2025 Submission checks completed at journal 17 Aug, 2025 First submitted to journal 17 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7236843","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":506195282,"identity":"8740f728-5f7c-4a3b-a559-8e1a39244304","order_by":0,"name":"Saman Javadi Kouchaksaraei","email":"","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":false,"prefix":"","firstName":"Saman","middleName":"Javadi","lastName":"Kouchaksaraei","suffix":""},{"id":506195283,"identity":"d5a79a73-fcec-4a79-afbc-6a8aa910f0a4","order_by":1,"name":"Alireza Toloei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYBAC+wPMDQwMBjYMBhC+BGEtbAyMIC1pJGthOAzTQgRgY29sfPCh4Ly9uUQC44cfDBb5hLXwHGw2nGFwO3HnjARmyR4GCcsGglokEtukeQxuJxjcSGCQBvqFsAPBWv4YnLMHamH+TbwWBoMDjBtuJLARaQvILz0GyYk7ex62WfYYEKOFvfnggx9/7OzN2ZMP3/hRUUd8YAMBOE5J0TAKRsEoGAWjACcAAJyrMs/evZPfAAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":true,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Toloei","suffix":""}],"badges":[],"createdAt":"2025-07-28 19:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7236843/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7236843/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90416186,"identity":"188cff4a-43dc-4722-bb6b-8ab876e5e795","added_by":"auto","created_at":"2025-09-02 13:15:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1443768,"visible":true,"origin":"","legend":"","description":"","filename":"Reviewers.Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7236843/v1_covered_46d2ce7c-4925-49aa-b4d3-d3a0c2928897.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Optimal Design with Latin Hypercube Sampling Method for Remote Sensing Satellite in LEO Orbit","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"robust design, remote sensing satellite, optimization, Uncertainty, orbital mechanic","lastPublishedDoi":"10.21203/rs.3.rs-7236843/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7236843/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe design of remote sensing satellites in Low Earth Orbit (LEO) presents significant challenges due to inherent orbital uncertainties such as altitude, inclination, right ascension of the ascending node (RAAN), and elevation angle. To address these issues during early-stage design, this study proposes an integrated Robust Design Optimization (RDO) framework that combines the Teaching-Learning-Based Optimization (TLBO) algorithm with Latin Hypercube Sampling (LHS). The primary objective is to minimize the satellite's total mass while ensuring stable performance under environmental perturbations. Unlike conventional approaches that treat uncertainty quantification and optimization separately, the proposed method embeds stochastic behavior directly into the design loop, capturing the interaction between uncertain parameters and design decisions. The framework is validated using real-world case studies, including Aqua, VRSS1, and CloudSat satellites, showing a significant reduction in design error compared to classical optimization. Results confirm that the TLBO-LHS integration enhances design robustness, reduces system mass, and improves mission reliability. This methodology offers a scalable and practical solution for the robust design of other complex space systems affected by uncertainty.\u003c/p\u003e","manuscriptTitle":"Robust Optimal Design with Latin Hypercube Sampling Method for Remote Sensing Satellite in LEO Orbit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 12:43:39","doi":"10.21203/rs.3.rs-7236843/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-03T04:26:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T03:08:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T01:32:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T13:02:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226015878955843819269903820757921034623","date":"2025-11-20T05:47:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112572818270508102923250363262969581149","date":"2025-11-19T14:08:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21487856096432510445768174359992090522","date":"2025-11-19T03:48:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T03:36:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198320152864599315457820953560015940555","date":"2025-08-27T06:40:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-25T05:42:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-25T05:41:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-20T06:12:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-17T13:39:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-17T13:34:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5ccbed0-bd7a-4ebd-9f81-b62fbe06e1f6","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":53765489,"name":"Physical sciences/Engineering"},{"id":53765490,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-05-22T10:41:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 12:43:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7236843","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7236843","identity":"rs-7236843","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}