Empirical Detection of a Finite Temporal Correlation Scale in GNSS Satellite and Ground Atomic Clock Time Series

Empirical Detection of a Finite Temporal Correlation Scale in GNSS Satellite and Ground Atomic Clock Time Series | 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 Research Article Empirical Detection of a Finite Temporal Correlation Scale in GNSS Satellite and Ground Atomic Clock Time Series Takahiro Mitsui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8593852/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Time is usually treated as a passive parameter in physical theories, yet whether it possesses intrinsic dynamical structure remains largely unexplored from observational data. Here we analyze long-term atomic clock time series from Global Navigation Satellite Systems (GNSS), including multiple orbital classes, together with independent ground-based atomic clock comparisons provided by the National Institute of Information and Communications Technology (NICT). Using autocorrelation analysis combined with block-shuffle surrogate testing, we identify a statistically significant and robust temporal correlation peak at a delay of approximately 30–35 minutes in GNSS satellite clocks. Remarkably, this characteristic timescale is independent of orbital altitude, satellite system, and gravitational environment, and is absent in ground-based clocks operating under static conditions. We further demonstrate consistency of this timescale across time-domain and frequencydomain analyses, ruling out preprocessing artifacts and input-driven effects. These results provide empirical evidence that physical clock dynamics retain a finite temporal memory, motivating the concept of a universal temporal inertia scale. Figures Figure 1 Figure 2 Figure 3 I. INTRODUCTION The nature of time is central to physics, yet in most practical and theoretical frameworks it is treated as an external parameter rather than a dynamical entity. The emergence of global atomic clock networks, particularly through GNSS, enables direct empirical investigation of whether time evolution exhibits intrinsic structure be yond stochastic noise. In this work, we search for char acteristic temporal correlation scales encoded directly in observational clock data, without assuming any specific theoretical modification a priori. II. DATA A. GNSS Satellite Clock Data We analyze precise GNSS satellite clock time series obtained from publicly available products distributed by the International GNSS Service (IGS) [1]. The dataset includes satellites from multiple constellations and or bital classes (GEO, IGSO, and MEO), sampled at uniform cadence. B. Ground-Based Atomic Clock Data As an independent control, we use daily GPS commonview clock comparison data in GGTTS format provided by NICT [2]. These ground-based clocks operate under static gravitational and kinematic conditions. III. METHODS We compute autocorrelation functions (ACFs) for each time series and identify dominant peaks within a lag window of 0.5–6 hours. Statistical significance is assessed using block-shuffle surrogate testing, which preserves shortterm correlations while destroying long-range temporal structure. Consistency is further evaluated by complementary frequency-domain analyses. FIG. 1. Block-shuffle surrogate test for GNSS satellite clock time series, showing statistical significance of the 30–35 min correlation peak. IV. RESULTS A. Detection of a Finite Correlation Scale All GNSS satellite clocks exhibit a pronounced ACF peak at a delay of approximately 30–35 minutes. This feature is absent in ground-based clocks. FIG. 2. Observed peak lag τpeak by orbit class. TABLE I. Summary of detected temporal inertia scale. System Orbit class τm (min) Std. dev. GNSS (G) MEO 31.2 ～0 GNSS (E) MEO 32.8 ～0 GNSS (C) GEO/IGSO 33.1 ～0 Ground clocks ― N/A ― The zero standard deviation reflects the discrete sampling resolution and the consistency of peak positions across datasets. B. Independence from Orbit Class The detected peak position is consistent across GEO, IGSO, and MEO satellites. C. Time–Frequency Consistency Time-domain and frequency-domain analyses yield consistent characteristic timescales. FIG.3. Distribution of the temporal inertia scale τm\tau_mτm for different GNSS systems. Boxplots show system-level distributions with individual satellite values overlaid. The shaded band marks the 30–35 min interval. The C system exhibits a concentration of τm\tau_mτm within this band, while the G and E systems show systematically shifted distributions, indicating a system-dependent temporal scale. V. DISCUSSION The observed finite correlation timescale cannot be attributed to known relativistic corrections, orbital dynamics, or instrumental noise. Instead, it suggests that physical clock dynamics retain information about their immediate past over a finite duration. Although expressed in human-defined units, the detected timescale reflects an intrinsic correlation length in physical clock dynamics rather than a conventional temporal partition. VI. RELATION TO SCALE-DEPENDENT TIME DYNAMICS Within the framework of Time-Field General Relativity (TFGR), time is treated as a physical structure whose effective behavior depends on observational scale. In this view, a finite temporal memory naturally leads to scale-dependent averaging of time evolution. While the present results do not rely on TFGR for their validity, they provide empirical constraints that any theory of scale-dependent time must accommodate. VII. CONCLUSION We have empirically identified a finite temporal correlation scale of approximately 30–35 minutes in GNSS satellite atomic clocks, absent in ground-based counterparts. The robustness and universality of this feature suggest that time evolution in physical systems is not strictly memoryless. These findings open a new observational avenue for exploring the dynamical structure of time. Declarations Funding Not Applicable. Ethics Approval Not Applicable. References International GNSS Service (IGS), Igs gnss satellite clock and orbit products, https://igs.org/products/ (2025), accessed GNSS satellite clock bias and orbit data used for 80 time series analysis. National Institute of Information and Communications Technology (NICT), Gps common-view time transfer data (ggtts format), https://www.nict.go.jp/en/timedata/84 index.html (2025), daily GPS common-view atomic clock comparison data used for ground-based analysis. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8593852","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583291238,"identity":"1e195d05-d0ca-4caf-89f1-2f161a0197b0","order_by":0,"name":"Takahiro Mitsui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHACNiCqSTBgb2A8kFAB5DMzN+BVzwPRcizBgOcAw4EPZ0BaGInSwpxgIJHAcHBmG0iMgBZ7sQNsjwvK2PLMGdIfHOadVxvN3w7U8qNiG25bpBPYjWeckym2bDhjcJh32/HcGYcZGxh7ztzGp4VNmreNLXHDwR4GoJZjuQ1ALcyMbQS1MCduOMwOdNicY7nziddyjMHg4MyGmtwNBLXcTmyT5jl3rNiyh8fgwIdjB3I3ArUcxOcX9tnJx6R5ymryzOWfP3yQUFOXO+/84YMPflTg1oIeC4fB5AE86jFAHSmKR8EoGAWjYIQAABoUXQvcwduMAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Takahiro","middleName":"","lastName":"Mitsui","suffix":""}],"badges":[],"createdAt":"2026-01-13 15:38:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8593852/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8593852/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101942822,"identity":"cb015e2a-a043-45c7-bdc2-a09eff8e69a5","added_by":"auto","created_at":"2026-02-05 09:38:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60932,"visible":true,"origin":"","legend":"\u003cp\u003eBlock-shuffle surrogate test for GNSS satellite clock time series, showing statistical significance of the 30–35 min correlation peak.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8593852/v1/6593a4c14a1db01cfa95d857.jpg"},{"id":101768759,"identity":"e9e45b91-f64c-4e32-bbb4-882fab3c8986","added_by":"auto","created_at":"2026-02-03 12:43:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41918,"visible":true,"origin":"","legend":"\u003cp\u003eObserved peak lag τpeak by orbit class.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8593852/v1/98af3263fb7c7f7351f20444.jpg"},{"id":101768758,"identity":"001baa37-2276-480f-967a-b51092e2fd8c","added_by":"auto","created_at":"2026-02-03 12:43:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40381,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the temporal inertia scale τm\\tau_mτm​ for different GNSS systems. Boxplots show system-level distributions with individual satellite values overlaid. The shaded band marks the 30–35 min interval. The C system exhibits a concentration of τm\\tau_mτm​ within this band, while the G and E systems show systematically shifted distributions, indicating a system-dependent temporal scale.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8593852/v1/aa6ff40ebd45f51a6cca3f15.jpg"},{"id":101944510,"identity":"0642d7d2-d7d7-4631-9b0b-4828e59b12e8","added_by":"auto","created_at":"2026-02-05 09:52:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":387881,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8593852/v1/c2d712b2-ba3c-4267-a4ae-e46fa6bb242c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEmpirical Detection of a Finite Temporal Correlation Scale in GNSS Satellite and Ground Atomic Clock Time Series\u003c/p\u003e","fulltext":[{"header":"I. INTRODUCTION","content":"\u003cp\u003eThe nature of time is central to physics, yet in most practical and theoretical frameworks it is treated as an external parameter rather than a dynamical entity. The emergence of global atomic clock networks, particularly through GNSS, enables direct empirical investigation of whether time evolution exhibits intrinsic structure be yond stochastic noise. In this work, we search for char acteristic temporal correlation scales encoded directly in observational clock data, without assuming any specific theoretical modification a priori.\u003c/p\u003e"},{"header":"II. DATA","content":"\u003cp\u003eA. GNSS Satellite Clock Data\u003c/p\u003e\n\u003cp\u003eWe analyze precise GNSS satellite clock time series obtained from publicly available products distributed by the International GNSS Service (IGS) [1].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dataset includes satellites from multiple constellations and or bital classes (GEO, IGSO, and MEO), sampled at uniform cadence.\u003c/p\u003e\n\u003cp\u003eB. Ground-Based Atomic Clock Data\u003c/p\u003e\n\u003cp\u003eAs an independent control, we use daily GPS commonview clock comparison data in GGTTS format provided by NICT [2]. These ground-based clocks operate under static gravitational and kinematic conditions.\u003c/p\u003e"},{"header":"III. METHODS","content":"\u003cp\u003eWe compute autocorrelation functions (ACFs) for each time series and identify dominant peaks within a lag window of 0.5\u0026ndash;6 hours. Statistical significance is assessed using block-shuffle surrogate testing, which preserves shortterm correlations while destroying long-range temporal structure. Consistency is further evaluated by complementary frequency-domain analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFIG. 1. Block-shuffle surrogate test for GNSS satellite clock time series, showing statistical significance of the 30\u0026ndash;35 min correlation peak.\u003c/p\u003e"},{"header":"IV. RESULTS","content":"\u003cp\u003eA. Detection of a Finite Correlation Scale\u003c/p\u003e\n\u003cp\u003eAll GNSS satellite clocks exhibit a pronounced ACF peak at a delay of approximately 30\u0026ndash;35 minutes. This feature is absent in ground-based clocks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFIG. 2. Observed peak lag \u0026tau;peak by orbit class.\u003c/p\u003e\n\u003cp\u003eTABLE I. Summary of detected temporal inertia scale.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSystem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOrbit class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026tau;m (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStd. dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGNSS (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e～0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGNSS (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e～0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGNSS (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGEO/IGSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e～0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGround clocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e―\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e―\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe zero standard deviation reflects the discrete sampling\u003c/p\u003e\n\u003cp\u003eresolution and the consistency of peak positions across datasets.\u003c/p\u003e\n\u003cp\u003eB. Independence from Orbit Class\u003c/p\u003e\n\u003cp\u003eThe detected peak position is consistent across GEO, IGSO, and MEO satellites.\u003c/p\u003e\n\u003cp\u003eC. Time\u0026ndash;Frequency Consistency\u003c/p\u003e\n\u003cp\u003eTime-domain and frequency-domain analyses yield consistent characteristic timescales.\u003c/p\u003e\n\u003cp\u003eFIG.3. Distribution of the temporal inertia scale \u0026tau;m\\tau_m\u0026tau;m for different GNSS systems. Boxplots show system-level distributions with individual satellite values overlaid. The shaded band marks the 30\u0026ndash;35 min interval. The C system exhibits a concentration of \u0026tau;m\\tau_m\u0026tau;m within this band, while the G and E systems show systematically shifted distributions, indicating a system-dependent temporal scale.\u003c/p\u003e"},{"header":"V. DISCUSSION","content":"\u003cp\u003eThe observed finite correlation timescale cannot be attributed to known relativistic corrections, orbital dynamics, or instrumental noise. Instead, it suggests that physical clock dynamics retain information about their immediate past over a finite duration. Although expressed in human-defined units, the detected timescale reflects an intrinsic correlation length in physical clock dynamics rather than a conventional temporal partition.\u003c/p\u003e"},{"header":"VI. RELATION TO SCALE-DEPENDENT TIME DYNAMICS","content":"\u003cp\u003eWithin the framework of Time-Field General Relativity (TFGR), time is treated as a physical structure whose effective behavior depends on observational scale.\u003c/p\u003e\n\u003cp\u003eIn this view, a finite temporal memory naturally leads to scale-dependent averaging of time evolution. While the present results do not rely on TFGR for their validity, they provide empirical constraints that any theory of scale-dependent time must accommodate.\u003c/p\u003e"},{"header":"VII. CONCLUSION","content":"\u003cp\u003eWe have empirically identified a finite temporal correlation scale of approximately 30\u0026ndash;35 minutes in GNSS satellite atomic clocks, absent in ground-based counterparts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe robustness and universality of this feature suggest that time evolution in physical systems is not strictly memoryless. These findings open a new observational avenue for exploring the dynamical structure of time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003eEthics Approval\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eInternational GNSS Service (IGS), Igs gnss satellite clock and orbit products, https://igs.org/products/ (2025), accessed GNSS satellite clock bias and orbit data used for 80 time series analysis.\u003c/li\u003e\n \u003cli\u003eNational Institute of Information and Communications Technology (NICT), Gps common-view time transfer data (ggtts format), https://www.nict.go.jp/en/timedata/84 index.html (2025), daily GPS common-view atomic clock comparison data used for ground-based analysis.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8593852/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8593852/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Time is usually treated as a passive parameter in physical theories, yet whether it possesses intrinsic dynamical structure remains largely unexplored from observational data. Here we analyze long-term atomic clock time series from Global Navigation Satellite Systems (GNSS), including multiple orbital classes, together with independent ground-based atomic clock comparisons provided by the National Institute of Information and Communications Technology (NICT). Using autocorrelation analysis combined with block-shuffle surrogate testing, we identify a statistically significant and robust temporal correlation peak at a delay of approximately 30–35 minutes in GNSS satellite clocks. Remarkably, this characteristic timescale is independent of orbital altitude, satellite system, and gravitational environment, and is absent in ground-based clocks operating under static conditions. We further demonstrate consistency of this timescale across time-domain and frequencydomain\nanalyses, ruling out preprocessing artifacts and input-driven effects. These results provide\nempirical evidence that physical clock dynamics retain a finite temporal memory, motivating the concept of a universal temporal inertia scale.","manuscriptTitle":"Empirical Detection of a Finite Temporal Correlation Scale in GNSS Satellite and Ground Atomic Clock Time Series","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 12:43:28","doi":"10.21203/rs.3.rs-8593852/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"60636717-15da-4192-b285-9f4cba45fe1d","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T12:43:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 12:43:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8593852","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8593852","identity":"rs-8593852","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}