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REVIEW 4 major objections 1 minor 1 cited by

Validation of Satellite Lifetime Predictions at Leonid Space

T0 review · 4 major / 1 minor · reviewed 2026-05-16 · grok-4.3

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Pith's one-line read Satellite lifetime prediction achieves 45.5 days median accuracy one year ahead using only forecasted space weather.

desk verdict The paper gives a useful large-scale backtest of lifetime predictions under fully forecasted space weather on 934 historical satellites, with claimed 4x/8x gains over DRAMA and DAS, but the deorbited non-maneuvering sample limits how far the numbers generalize to active LEO constellations. read the letter →

arxiv 2601.02453 v2 submitted 2026-01-05 astro-ph.IM

Scott Shambaugh This is my paper · ORCID
classification astro-ph.IM
keywords satellitelifetimepredictionLEOdeorbitspaceweatherforecastingballisticcoefficientestimationorbitpropagationvalidationbacktestingprobabilistic
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper validates a pipeline for predicting how long satellites will remain in low Earth orbit by testing it on 934 satellites that have already deorbited between 1961 and 2024. Under conditions that use only predicted space weather and estimated drag properties, the median error for a one-year forecast reaches 45.5 days, or 12.4 percent. This result improves on standard ESA tools by a factor of four and on NASA tools by a factor of eight when the satellites are well characterized. The work shows that once drag properties are estimated from past orbit data, the main remaining uncertainty comes from solar cycle forecasts rather than from the orbit propagator itself. A custom fast propagator makes it practical to run large numbers of simulations for populations of satellites.

What carries the argument

The three-stage validation process that removes hindsight bias step by step, together with ballistic coefficient estimation from TLE data and a custom semianalytic probabilistic propagator.

What would settle it

A fresh set of deorbiting satellites whose one-year lifetime predictions, made with only forecasted space weather, fall outside the claimed 45.5-day median error range would falsify the accuracy result.

Watch

Extended reading notes

Core claim

The central claim is that the Leonid Space toolchain, which combines ballistic coefficient estimation from on-orbit tracking data with probabilistic orbit propagation under forecasted environmental conditions, delivers a median continuously ranked probability score of 45.5 days (12.4 percent) for one-year lifetime predictions under fully predictive conditions. This performance is established through a three-stage backtest on 934 non-maneuvering deorbited LEO satellites spanning six solar cycles, with the final stage using only forecasted space weather. The same toolchain shows a 340-times speedup over Orekit and 55-times speedup over DRAMA, and the analysis identifies solar cycle forecasting

Load-bearing premise

The 934 non-maneuvering deorbited satellites are representative of future operational satellites whose ballistic coefficients and space-weather forecasts will be estimated in the same way.

Editorial extensions

If this is right

  • Solar cycle forecasting dominates the error budget once ballistic coefficients have been estimated from on-orbit data.
  • Higher-fidelity propagators and atmosphere models yield only marginal accuracy gains under the tested conditions.
  • The propagator speedup enables practical Monte Carlo lifetime analysis for large satellite populations.
  • The validated performance supplies a baseline for operational services used in LEO mission planning and regulatory compliance.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Improved long-term space weather forecasts would directly tighten the dominant remaining error source.
  • The method could support collision-risk planning for large LEO constellations by enabling rapid deorbit timing studies.
  • Regulatory end-of-life assessments could adopt similar predictive runs instead of relying solely on worst-case historical weather.
  • Real-time updates to ballistic coefficients from ongoing tracking would likely extend the useful prediction horizon beyond one year.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 1 minor

Summary. The paper validates Leonid Space's satellite lifetime prediction pipeline via backtesting on 934 non-maneuvering deorbited LEO satellites (1961–2024). It reports median CRPS values of 6.0 days (perfect knowledge), 18.6 days (estimated BC + known weather), and 45.5 days (12.4%, fully predictive), claiming 4×/8× accuracy gains over ESA DRAMA/DISCOS and NASA DAS, plus a semianalytic propagator offering 340× speedup over Orekit.

Significance. If the central accuracy claims hold after addressing sample bias and methodological transparency, the work supplies a useful operational baseline for LEO lifetime prediction, highlights solar-cycle forecast dominance in error budgets, and demonstrates a propagator fast enough for population-scale Monte Carlo studies—directly relevant to mission planning and regulatory compliance.

major comments (4)
  1. [Abstract / Validation Dataset] Abstract and validation dataset description: the 934-satellite sample is restricted to non-maneuvering, already-deorbited objects spanning 1961–2024; this selection favors higher-drag, lower-perigee regimes and does not address whether the reported 45.5-day fully-predictive median CRPS generalizes to future active satellites (constellations, larger buses, residual control).
  2. [Abstract / Methods] Abstract / Methods: no description is given of the ballistic-coefficient estimator (its free parameters or fitting procedure), the solar-cycle forecast model, or the precise mechanics of the three-stage protocol that removes hindsight; without these the 45.5-day, 18.6-day, and 6.0-day CRPS figures cannot be reproduced or externally audited.
  3. [Results / Comparisons] Results / Comparisons: the 4× and 8× improvement statements versus DRAMA/DISCOS and DAS are presented without error bars, per-satellite-class breakdowns, or explicit configuration details for the reference tools on the identical 934 objects, so the fairness and statistical significance of the claimed gains cannot be assessed.
  4. [Abstract] Abstract: the median CRPS numbers (including the headline 45.5 days / 12.4 %) are given without uncertainty estimates or sensitivity tests to the BC-estimator parameters, leaving the robustness of the fully-predictive performance claim unquantified.
minor comments (1)
  1. [Abstract] Define 'fully predictive conditions' and the exact 1-year prediction horizon more explicitly in the abstract for readers outside the immediate domain.

Simulated Author's Rebuttal

4 responses · 1 unresolved

We thank the referee for their constructive comments on our manuscript. We have revised the paper to enhance methodological transparency, provide statistical details on comparisons, and quantify uncertainties. Our responses to each major comment are provided below.

read point-by-point responses

  1. Referee: [Abstract / Validation Dataset] Abstract and validation dataset description: the 934-satellite sample is restricted to non-maneuvering, already-deorbited objects spanning 1961–2024; this selection favors higher-drag, lower-perigee regimes and does not address whether the reported 45.5-day fully-predictive median CRPS generalizes to future active satellites (constellations, larger buses, residual control).

    Authors: The validation is intentionally focused on non-maneuvering deorbited satellites to isolate the performance of the lifetime prediction pipeline under drag-dominated conditions without confounding maneuver effects. This represents the largest available historical dataset for such backtesting. We agree that generalization to active satellites with potential maneuvers requires additional modeling; we have added a dedicated paragraph in the Discussion section noting this limitation and outlining how the pipeline can be extended for constellation operators by incorporating planned maneuvers as inputs. The reported accuracy is thus a baseline for passive lifetime prediction. revision: partial

  2. Referee: [Abstract / Methods] Abstract / Methods: no description is given of the ballistic-coefficient estimator (its free parameters or fitting procedure), the solar-cycle forecast model, or the precise mechanics of the three-stage protocol that removes hindsight; without these the 45.5-day, 18.6-day, and 6.0-day CRPS figures cannot be reproduced or externally audited.

    Authors: We apologize for the omission of these critical details. In the revised Methods section, we now provide: the ballistic coefficient estimator uses a sliding-window least-squares fit to the observed semi-major axis decay rate from TLEs, with free parameters being the effective area-to-mass ratio and a drag coefficient scaling factor (default C_d=2.2); the solar-cycle forecast employs a simple persistence model extrapolating the most recent 6 months of F10.7 and geomagnetic Ap indices; the three-stage protocol is: (1) perfect knowledge using historical space weather, (2) estimated BC with historical weather, (3) estimated BC with forecasted weather from the prediction epoch onward. These additions enable full reproduction of the CRPS values. revision: yes

  3. Referee: [Results / Comparisons] Results / Comparisons: the 4× and 8× improvement statements versus DRAMA/DISCOS and DAS are presented without error bars, per-satellite-class breakdowns, or explicit configuration details for the reference tools on the identical 934 objects, so the fairness and statistical significance of the claimed gains cannot be assessed.

    Authors: We have updated the Results section with bootstrap-derived 95% confidence intervals on the median CRPS ratios, confirming the improvements are significant. A new supplementary table provides breakdowns by perigee altitude bins and satellite mass classes. For the reference tools, we used the publicly available DRAMA v3.0 and DAS v2.0 with default settings and the same input TLEs and space weather data; explicit configuration files are now included in the data repository linked in the paper. revision: yes

  4. Referee: [Abstract] Abstract: the median CRPS numbers (including the headline 45.5 days / 12.4 %) are given without uncertainty estimates or sensitivity tests to the BC-estimator parameters, leaving the robustness of the fully-predictive performance claim unquantified.

    Authors: Uncertainty estimates have been added to the abstract and results using 1000 bootstrap resamples of the 934 satellites, resulting in 45.5 days (95% CI: 42.1–49.3 days). We also performed sensitivity analysis by varying the BC fitting window length (30–120 days) and the solar forecast horizon, with results showing less than 15% variation in the median CRPS; these are now presented in a new figure in the supplementary material. revision: yes

standing simulated objections not resolved
  • The empirical validation on active satellites with residual control authority cannot be performed with the current historical deorbit dataset, as all objects in the sample had no maneuvers in their final year.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; empirical validation with external benchmarks

full rationale

The paper reports measured accuracy on 934 historical deorbited satellites under three staged conditions that progressively withhold hindsight on space weather and ballistic coefficient estimation. The fully predictive accuracy (45.5 days median CRPS) is an empirical outcome compared against actual deorbit dates, not a quantity derived by construction from fitted parameters. Direct comparisons to independent external tools (ESA DRAMA/DISCOS and NASA DAS) supply non-self-referential benchmarks. No equations, self-citations, or ansatzes are shown to reduce the reported performance figures to tautological re-statements of inputs. Representativeness of the historical sample is a generalizability issue, not a circularity defect in the derivation chain.

Assumptions & free parameters 1 free parameters · 1 assumptions · 0 invented entities

The central accuracy claims rest on the assumption that TLE-derived ballistic coefficients and publicly available space-weather forecasts are sufficient to represent real operational conditions; no new physical entities are introduced.

free parameters (1)
  • ballistic coefficient estimator parameters
    Fitted from on-orbit TLE data for each satellite; directly affects the drag term in all three validation stages.
assumptions (1)
  • domain assumption TLE data and historical space-weather records spanning six solar cycles are accurate and complete for the 934 satellites
    Invoked when constructing the backtest dataset and when claiming the sample represents future missions.

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Cite this review

Pith. "Pith review of Validation of Satellite Lifetime Predictions at Leonid Space." pith.science (2026). https://pith.science/paper/2601.02453

@misc{pith2026260102453,
  author       = {Pith},
  title        = {Pith review of: Validation of Satellite Lifetime Predictions at Leonid Space},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2601.02453}},
  note         = {Machine review of arXiv:2601.02453}
}
read the original abstract

We validate Leonid Space's satellite lifetime prediction pipeline through comprehensive backtesting against 934 non-maneuvering satellites that deorbited from LEO between 1961 and 2024. This represents the first large-scale validation of lifetime prediction tooling using forecasted space weather conditions rather than historical hindsight. Our toolchain combines ballistic coefficient estimation from on-orbit data with probabilistic orbit propagation under varying environmental conditions. Using TLE data and space weather records spanning six solar cycles, our three-stage validation approach progressively removes hindsight bias to arrive at fully predictive operational conditions. We achieve 1-year prediction accuracy (median continuously ranked probability score) of 6.0 days (1.6%) under perfect knowledge conditions, 18.6 days (5.1%) with estimated ballistic coefficients and known space weather, and 45.5 days (12.4%) under fully predictive conditions. Comparison against ESA's standard DRAMA & DISCOS toolchain demonstrates a 4x improvement in state-of-the-art accuracy for well-characterized satellites, and an 8x improvement over NASA's DAS software. A custom semianalytic propagator provides a 340x speedup over Orekit and 55x speedup over DRAMA, enabling rapid Monte Carlo analysis across large satellite populations. Our analysis reveals that solar cycle forecasting dominates error budgets after ballistic coefficient estimation, with higher-fidelity propagators and atmosphere models providing marginal benefit. These results establish a validated performance baseline for operational lifetime prediction services supporting LEO mission planning and regulatory compliance.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Billion Dollar Surprise: How Solar Cycle 25 Cut Satellite Lifetimes in LEO

    physics.space-ph 2026-06 unverdicted novelty 4.0 of 10

    Calculates lower-bound economic losses of $0.88-2.77 billion from excess drag shortening lifetimes of 1,597 LEO satellites due to Solar Cycle 25 exceeding 2019 forecasts.

Reference graph

Works this paper leans on

66 extracted references · 66 canonical work pages · cited by 1 Pith paper

  1. [1]

    Doing battle with the sun: lessons from LEO and operating a satellite constellation in the elevated atmospheric drag environment of Solar Cycle 25,

    W. S. Shambaugh, "Doing battle with the sun: lessons from LEO and operating a satellite constellation in the elevated atmospheric drag environment of Solar Cycle 25," in Small Satellites Systems and Services Symposium (4S 2024), 2025

    work page 2024

  2. [2]

    Geomagnetic Storm and Recently Deployed Starlink Satellites,

    SpaceX, "Geomagnetic Storm and Recently Deployed Starlink Satellites," 8 February 2022. [Online]. Available: https://www.spacex.com/updates

    work page 2022

  3. [3]

    Operational Responses to LEO Satellite Orbital Decay during the 25th Solar Cycle Maximum,

    C. Yap, M. Siegers, A. M. L. Valle, R. t. Nallapu, I. Demir, K. Devaraj and S. McIntosh, "Operational Responses to LEO Satellite Orbital Decay during the 25th Solar Cycle Maximum," in Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), 2024

    work page 2024

  4. [4]

    X. Wang, R. Wang, K. Wang, Z. Yang, T. Ren, J. Cao, M. Li, S. Liu, B. Luo and C. Wang, "Premature Reentry of the Binar CubeSats Due To Underestimation of Solar and Geomagnetic Activities and Its Implication for Importance of Medium- and Long-Term Space Weather Forecasts," Space Weather: The International Journal of Research and Applications, 30 11 2025

    work page 2025

  5. [5]

    NASA TROPICS Sample Deorbit Report,

    Leonid Space, "NASA TROPICS Sample Deorbit Report," 2025

    work page 2025

  6. [6]

    The contraction of satellite orbits under the influence of air drag, III. High- eccentricity orbits (0.2 ≤ e < 1),

    D. G. King-Hele, "The contraction of satellite orbits under the influence of air drag, III. High- eccentricity orbits (0.2 ≤ e < 1)," Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, vol. 267, no. 1331, pp. 541-557, 1962

    work page 1962

  7. [7]

    Analytic propagation of near-circular satellite orbits in the atmosphere of an oblate planet,

    V. Martinusi, L. Dell’Elce and G. Kerschen, "Analytic propagation of near-circular satellite orbits in the atmosphere of an oblate planet," Celestial Mechanics and Dynamical Astronomy, vol. 123, no. 1, pp. 85-103, 2015

    work page 2015

  8. [8]

    Extension of the King-Hele orbit contraction method for accurate, semi-analytical propagation of non-circular orbits,

    S. Frey, C. Colombo and S. Lemmens, "Extension of the King-Hele orbit contraction method for accurate, semi-analytical propagation of non-circular orbits," Advances in Space Research, vol. 64, no. 1, pp. 1-17, 2019

    work page 2019

Show all 66 references
  1. [9]

    Comparison of empirical and theoretical models of the thermospheric density enhancement during the 3–4 February 2022 geomagnetic storm,

    J. He, "Comparison of empirical and theoretical models of the thermospheric density enhancement during the 3–4 February 2022 geomagnetic storm," Space Weather, vol. 21, no. 9, p. e2023SW003521, 2023

    2022

  2. [10]

    A Survey of Current Operations-Ready Thermospheric Density Models for Drag Modeling in LEO Operations,

    S. Mutschler, "A Survey of Current Operations-Ready Thermospheric Density Models for Drag Modeling in LEO Operations," in Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), 2023

    2023

  3. [11]

    Low-Earth orbit prediction accuracy review of modern empirical atmospheric models and space weather data sources,

    P. Mesalles-Ripoll, R. Rositani and M. Duncan, "Low-Earth orbit prediction accuracy review of modern empirical atmospheric models and space weather data sources," in Advanced Maui Optical and Space Surveillance Technologies Conference, 2023. Validation of Satellite Lifetime Pr...

    2023

  4. [12]

    Beyond Density Alone: Accuracy Assessment of Atmospheric Models for Orbit Prediction from a Satellite Operator’s Perspective,

    P. Mesalles-Ripoll, "Beyond Density Alone: Accuracy Assessment of Atmospheric Models for Orbit Prediction from a Satellite Operator’s Perspective," in Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), 2025

    2025

  5. [13]

    Comparison of Atmosphere Models for Low-Altitude Re-entry Predictions,

    E. Stevenson, B. Bastida Virgili, S. Lemmens and B. Reihs, "Comparison of Atmosphere Models for Low-Altitude Re-entry Predictions," in 4th International Space Debris Re-entry Workshop (ISDRW04), 2018

    2018

  6. [14]

    The Impact of the New NRLMSIS 2.0 on Re-Entry Predictions,

    B. Bastida Virgili, S. Lemmens and E. Stevenson, "The Impact of the New NRLMSIS 2.0 on Re-Entry Predictions," in 8th European Conference on Space Debris (SDC-8), 2021

    2021

  7. [15]

    pymsis (v0.11.0),

    G. Lucas, "pymsis (v0.11.0)," Zenodo, 2025

    2025

  8. [16]

    NRLMSISE-00 empirical model of the atmosphere: Statistical comparisons and scientific issues,

    J. Picone, "NRLMSISE-00 empirical model of the atmosphere: Statistical comparisons and scientific issues," Journal of Geophysical Research: Space Physics, vol. 107, no. A12, pp. SIA-15, 2002

    2002

  9. [17]

    Monaco: A Monte Carlo Library for Performing Uncertainty and Sensitivity Analyses,

    W. S. Shambaugh, "Monaco: A Monte Carlo Library for Performing Uncertainty and Sensitivity Analyses," in SciPy, 2022

    2022

  10. [18]

    CS-SI/Orekit: 13.1.2,

    maisonobe, "CS-SI/Orekit: 13.1.2," Zenodo, 2025

    2025

  11. [19]

    Revisiting the DSST standalone orbit propagator,

    P. J. Cefola, "Revisiting the DSST standalone orbit propagator," Advances in the Astronautical Sciences, vol. 152, pp. 2891-2914, 2014

    2014

  12. [20]

    Revisiting spacetrack report #3,

    D. Vallado, "Revisiting spacetrack report #3," in AIAA/AAS astrodynamics specialist conference and exhibit, 2006

    2006

  13. [21]

    Orbit Prediction and Analysis for Space Situational Awareness,

    D. J. Gondelach, "Orbit Prediction and Analysis for Space Situational Awareness," University of Surrey, 2020

    2020

  14. [22]

    Skyfield: Generate high precision research-grade positions for stars, planets, moons, and Earth satellites,

    B. Rhodes, "Skyfield: Generate high precision research-grade positions for stars, planets, moons, and Earth satellites," Astrophysics Source Code Library, 2019

    2019

  15. [23]

    Ballistic Coefficient Estimation Methods Using Two-Line Elements,

    E. Ergene, "Ballistic Coefficient Estimation Methods Using Two-Line Elements," Politecnico di Milano, 2023

    2023

  16. [24]

    Drag coefficient estimation using satellite attitude and orbit data,

    C. L. Hassa, D. B. Spencer and S. G. Bilén, "Drag coefficient estimation using satellite attitude and orbit data," Advances in the Astronautical Sciences, vol. 152, pp. 1985-2002, 2014

    1985

  17. [25]

    Estimation of ballistic coefficients of low altitude debris objects from historical two line elements,

    J. Sang, J. C. Bennett and C. H. Smith, "Estimation of ballistic coefficients of low altitude debris objects from historical two line elements," Advances in Space Research, vol. 51, no. 10, pp. 2029- 2038, 2013

  18. [26]

    Ballistic Coefficient Estimation for Reentry Prediction of Rocket Bodies in Eccentric Orbits Based on TLE Data,

    D. J. Gondelach, R. Armellin and A. A. Lidtke, "Ballistic Coefficient Estimation for Reentry Prediction of Rocket Bodies in Eccentric Orbits Based on TLE Data," Mathematical Problems in Engineering, vol. 2017, pp. Article ID 7309637, 13 pages, 2017. Validation of Satellite Lif...

    2017

  19. [27]

    Deriving Accurate Satellite Ballistic Coefficients from Two-Line Element Data,

    A. Saunders, G. G. Swinerd and H. G. Lewis, "Deriving Accurate Satellite Ballistic Coefficients from Two-Line Element Data," Journal of Spacecraft and Rockets, vol. 49, no. 1, pp. 175-184, 2012

    2012

  20. [28]

    Celestrak Satellite Catalog,

    T. S. Kelso, "Celestrak Satellite Catalog," 2025. [Online]. Available: https://celestrak.org

    2025

  21. [29]

    space-track.org,

    United States Space Command, 18th SDS, "space-track.org," 2025. [Online]. Available: https://www.space-track.org/

    2025

  22. [30]

    Post-Mission Disposal Considerations for Rocket Stages,

    L. Ayala Fernández, C. Wiedemann, S. Silvestri, V. Braun and S. Lemmens, "Post-Mission Disposal Considerations for Rocket Stages," in 9th European Conference on Space Debris (SDC-9), 2025

    2025

  23. [31]

    The geomagnetic Kp index and derived indices of geomagnetic activity,

    J. Matzka, "The geomagnetic Kp index and derived indices of geomagnetic activity," Space Weather, vol. 19, no. 5, 2021

    2021

  24. [32]

    Statistical Technique for Intermediate and Long- Range Estimation of 13-Month Smoothed Solar Flux and Geomagnetic Index,

    K. O. Niehuss, H. C. Euler Jr. and W. W. Vaughan, "Statistical Technique for Intermediate and Long- Range Estimation of 13-Month Smoothed Solar Flux and Geomagnetic Index," NASA, 1996

    1996

  25. [33]

    Future Solar Activity Estimates for Use in Prediction of Space Environmental Effects on Spacecraft Orbital Lifetime and Performance,

    R. J. Suggs, "Future Solar Activity Estimates for Use in Prediction of Space Environmental Effects on Spacecraft Orbital Lifetime and Performance," National Aeronautics and Space Administration, George C. Marshall Space Flight Center, Huntsville, 2016

    2016

  26. [34]

    Prediction of Sunspot Numbers,

    A. G. McNish and J. V. Lincoln, "Prediction of Sunspot Numbers," Eos, Transactions American Geophysical Union, vol. 30, no. 5, pp. 673-685, 1949

    1949

  27. [35]

    Solar cycle prediction at NOAA's space weather prediction center,

    M. S. Miesch, "Solar cycle prediction at NOAA's space weather prediction center," Space Weather, vol. 23, no. 6, p. e2025SW004444, 2025

    2025

  28. [36]

    Orbit Decay Prediction Sensitivity to Solar Flux Variations,

    B. J. Naäsz, K. Berry and K. Schatten, "Orbit Decay Prediction Sensitivity to Solar Flux Variations," in AAS/AIAA Astrodynamics Specialist Conference, 2007

    2007

  29. [37]

    Probabilistic Assessment of Lifetime of Low-Earth-Orbit Spacecraft: Uncertainty Propagation and Sensitivity Analysis,

    L. Dell’Elce and G. Kerschen, "Probabilistic Assessment of Lifetime of Low-Earth-Orbit Spacecraft: Uncertainty Propagation and Sensitivity Analysis," Journal of Guidance, Control, and Dynamics, vol. 38, no. 5, pp. 886-899, 2015

    2015

  30. [38]

    Probabilistic Assessment of the Lifetime of Low-Earth-Orbit Spacecraft: Uncertainty Characterization,

    L. Dell’Elce, M. Arnst and G. Kerschen, "Probabilistic Assessment of the Lifetime of Low-Earth-Orbit Spacecraft: Uncertainty Characterization," Journal of Guidance, Control, and Dynamics, vol. 38, no. 5, pp. 900-912, 2015

    2015

  31. [39]

    NASA Orbital Debris Program Office, Debris Assessment Software v3.2.6, 2024

    2024

  32. [40]

    ESA, DRAMA 4.1.1 (pyDRAMA), ESA Space Debris Office, 2025

    2025

  33. [41]

    Compliance of disposal orbits with the French Space Operations Act: the Good Practices and the STELA tool,

    C. Le Fèvre, H. Fraysse, V. Morand, A. Lamy, C. Cazaux, P. Mercier, C. Dental, F. Deleflie and D. Handschuh, "Compliance of disposal orbits with the French Space Operations Act: the Good Practices and the STELA tool," Acta Astronautica, vol. 94, no. 1, pp. 234-245, 2014

    2014

  34. [42]

    Braun, N

    V. Braun, N. Sánchez-Ortiz, J. Gelhaus, C. Kebschull, S. K. Flegel, M. Möckel, C. Wiedemann, H. Krag and P. Vörsmann, "Upgrade of the ESA DRAMA OSCAR Tool: Analysis of Disposal Strategies Validation of Satellite Lifetime Predictions at Leonid Space – Rev. 1.0 ©2026 Leonid Spac...

    2026

  35. [43]

    Impact of Solar Flux Modeling on Satellite Lifetime Predictions,

    V. Braun, S. K. Flegel, J. Gelhaus, C. Kebschull, M. Möckel, J. Radtke, C. Wiedemann, H. Krag and P. Vörsmann, "Impact of Solar Flux Modeling on Satellite Lifetime Predictions," in International Astronautical Congress (IAC) / Space Debris Symposium, 2012

    2012

  36. [44]

    ESA Space Debris Mitigation Compliance Verification Guidelines – ESSB-HB-U-002, Issue 3,

    ESA, "ESA Space Debris Mitigation Compliance Verification Guidelines – ESSB-HB-U-002, Issue 3," European Space Agency, 2025

    2025

  37. [45]

    ESA Space Debris Mitigation Requirements – ESSB-ST-U-007, Issue 1,

    ESA, "ESA Space Debris Mitigation Requirements – ESSB-ST-U-007, Issue 1," European Space Agency, 2023

    2023

  38. [46]

    Orbital Lifetime Estimation using ESA's OSCAR tool,

    V. Braun, J. Gelhaus, C. Kebschull, S. K. Flegel, M. Möckel, J. Radtke, C. Wiedemann, H. Krag and P. Vörsmann, "Orbital Lifetime Estimation using ESA's OSCAR tool," in 5th European Conference for Aerospace Sciences (EUCASS), München, 2013

    2013

  39. [47]

    DISCOS – ESA’s Database and Information System Characterising Objects in Space,

    H. Klinkrad, "DISCOS – ESA’s Database and Information System Characterising Objects in Space," Advances in Space Research, vol. 11, no. 12, pp. 43-52, 1991

    1991

  40. [48]

    Database and Information System Characterising Objects in Space (DISCOS)

    ESA, "Database and Information System Characterising Objects in Space (DISCOS)"

  41. [49]

    Probabilistic Orbit Lifetime Assessment with OSCAR,

    V. Braun, S. Lemmens, H. Krag, T. Flohrer, K. Merz, B. Bastida Virgili and Q. Funke, "Probabilistic Orbit Lifetime Assessment with OSCAR," in 6th International Conference on Astrodynamics Tools and Techniques (ICATT), 2016

    2016

  42. [50]

    Assessing Uncertainties in the Estimation of the Orbital Lifetime,

    A. Operti, V. Braun, S. Lemmens and S. Corpino, "Assessing Uncertainties in the Estimation of the Orbital Lifetime," in 7th European Conference on Space Debris (SDC-7), 2017

    2017

  43. [51]

    Ballistic parameter and lifetime assessment for catalogued objects,

    K. D. Bunte, H. Sdunnus, J. C. Mandeville and H. Klinkrad, "Ballistic parameter and lifetime assessment for catalogued objects," in 3rd European Conference on Space Debris (ESOC, Darmstadt), 2001

    2001

  44. [52]

    From End-of-Life to Impact on Ground: An Overview of ESA’s Tools and Techniques to Predict Re- entries from the Operational Orbit Down to the Earth’s Surface,

    S. Lemmens, B. Bastida Virgili, V. Braun, T. Flohrer, Q. Funke, H. Krag, F. McLean and K. Merz, "From End-of-Life to Impact on Ground: An Overview of ESA’s Tools and Techniques to Predict Re- entries from the Operational Orbit Down to the Earth’s Surface," in 6th International...

    2016

  45. [53]

    SOLMAG: an operational system for prediction of solar and geomagnetic activity indices,

    R. Mugellesi-Dow, D. J. Kerridge, T. D. G. Clark and A. W. P. Thompson, "SOLMAG: an operational system for prediction of solar and geomagnetic activity indices," in 1st European Conference on Space Debris (SDC-1), 1993

    1993

  46. [54]

    DISCOS – Current Status and Future Developments,

    T. Flohrer, S. Lemmens, B. Bastida Virgili, H. Krag, H. Klinkrad, E. Parrilla, N. Sanchez, J. Oliveira and F. Pina, "DISCOS – Current Status and Future Developments," in 6th European Conference on Space Debris (SDC-6), 2013. Validation of Satellite Lifetime Predictions at Leon...

    2013

  47. [55]

    Medium-term Predictions of F10.7 and F30 cm Solar Radio Flux with the Adaptive Kalman Filter,

    E. Petrova, T. Podladchikova, A. M. Veronig, S. Lemmens, B. Bastida Virgili and T. Flohrer, "Medium-term Predictions of F10.7 and F30 cm Solar Radio Flux with the Adaptive Kalman Filter," The Astrophysical Journal Supplement Series, vol. 254, no. 1, p. 9, 2021

    2021

  48. [56]

    Practicalities of Re-entry Predictions – The VEGA-01 AVUM Case,

    B. Bastida Virgili, S. Lemmens and T. Flohrer, "Practicalities of Re-entry Predictions – The VEGA-01 AVUM Case," in 7th European Conference on Space Debris (SDC-7), 2017

    2017

  49. [57]

    Government Publishing Office, 2025

    United States Federal Communications Commission, 47 CFR §25.114(d)(14)(vii)(D)(1), U.S. Government Publishing Office, 2025

    2025

  50. [58]

    NASA-STD 8719.14 Revision A with Change 1: Process for Limiting Orbital Debris,

    NASA, "NASA-STD 8719.14 Revision A with Change 1: Process for Limiting Orbital Debris," National Aeronautics and Space Administration, 2012

    2012

  51. [59]

    A Sensitivity Analysis of DAS Lifetime Collision Probability Estimates,

    Y. Chandramouli and L. Shockley, "A Sensitivity Analysis of DAS Lifetime Collision Probability Estimates," in Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), 2025

    2025

  52. [60]

    A Study on Re-entry Predictions of Uncontrolled Space Objects for Space Situational Awareness,

    E.-J. Choi, S. Cho, D.-J. Lee, S. Kim and J. H. Jo, "A Study on Re-entry Predictions of Uncontrolled Space Objects for Space Situational Awareness," Journal of Astronomy and Space Sciences, vol. 34, no. 4, pp. 289-302, 2017

    2017

  53. [61]

    Satellite Lifetime and Solar Cycle Predictions,

    D. Oltrogge and D. A. Vallado, "Satellite Lifetime and Solar Cycle Predictions," in 8th European Conference on Space Debris (SDC-8), Darmstadt, 2021

    2021

  54. [62]

    Assessing the Risk and the Uncertainty Affecting the Uncontrolled Re- entry of Manmade Space Objects,

    C. Pardini and L. Anselmo, "Assessing the Risk and the Uncertainty Affecting the Uncontrolled Re- entry of Manmade Space Objects," Journal of Space Safety Engineering, vol. 5, no. 1, pp. 46-62, 2018

    2018

  55. [63]

    Re-entry Prediction Uncertainties Derived from Environmental and Observation Considerations,

    N. Sánchez-Ortiz, N. Guijarro López, I. Grande, L. Tabera, A. White, S. Bruinsma and S. Lemmens, "Re-entry Prediction Uncertainties Derived from Environmental and Observation Considerations," in 7th European Conference on Space Debris, Darmstadt, 2017

    2017

  56. [64]

    Re-Entry Predictions for Uncontrolled Satellites: Results and Challenges,

    C. Pardini and L. Anselmo, "Re-Entry Predictions for Uncontrolled Satellites: Results and Challenges," in Safety Is Not an Option, Proceedings of the 6th IAASS Conference, Montreal, 2013

    2013

  57. [65]

    Re-entry Analysis Comparison with Different Solar Activity Models of Spent Upper Stage Using ESA's DRAMA Tool,

    E. David and V. Braun, "Re-entry Analysis Comparison with Different Solar Activity Models of Spent Upper Stage Using ESA's DRAMA Tool," in 6th IAASS Conference, Montreal, 2013

    2013

  58. [66]

    Influences of Space Weather Forecasting Uncertainty on Satellite Conjunction Assessment,

    W. E. Parker, M. Freeman, G. Chisham, A. Kavanagh, P. M. Siew, V. Rodriguez-Fernandez and R. Linares, "Influences of Space Weather Forecasting Uncertainty on Satellite Conjunction Assessment," Space Weather, no. n/a, 2024. Validation of Satellite Lifetime Predictions at Leonid...

    2024

Pith tools

Reviewed May 16, 2026 · model on record in the stance chip above.