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arxiv: 2605.21892 · v1 · pith:ZR5ATCZAnew · submitted 2026-05-21 · 📡 eess.SY · cs.SY· math.DS

System Level Analysis and Management of Orbital Debris Using Empirical Dynamic Modeling

Asaad S. Abdul-Hamid , Hao Chen This is my paper

Pith reviewed 2026-05-22 05:10 UTC · model grok-4.3

classification 📡 eess.SY cs.SYmath.DS
keywords orbital debrisdynamic systemsattractor reconstructiontime-series modelingempirical dynamic modelingspace policy simulationcomplexity sciencesystem-level analysis
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The pith

Reconstructing a shadow attractor from time series of orbital debris, objects, and launches reveals the underlying system dynamics and allows simulation of policy effects.

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

Orbital debris threatens space operations and future infrastructure development. The paper models the problem as a dynamic system in which counts of debris, active objects, and new launches are causally connected through a shared attractor manifold. It applies a data-driven reconstruction technique drawn from complexity science to recover a shadow version of that manifold from the available time series. The resulting model supplies a compact description of the system's behavior and supports forward simulation of how different policy choices would alter future debris levels.

Core claim

Time-series variables for the number of orbital debris, the number of orbital objects, and the number of object launches are causally linked and therefore share a common system attractor manifold. A data-driven method based on complexity science can reconstruct a shadow attractor of this dynamic system from the limited observable variables. The reconstructed shadow attractor provides an understanding of the fundamental system dynamics and enables simulation of the future evolution of the orbital debris system under alternative policy scenarios.

What carries the argument

Shadow attractor manifold reconstructed from limited observable time-series variables of debris counts, object counts, and launch rates.

If this is right

  • Policy impacts on orbital debris can be evaluated at the system level using only the three observable time series.
  • Future debris evolution can be projected under different regulatory or operational scenarios.
  • The approach works with limited data and does not require a full first-principles physical model.
  • High-level space-system policy decisions become testable through attractor-based simulation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same reconstruction technique could be applied to other sparse-data domains where multiple observable streams are believed to reflect a single underlying dynamical process.
  • Adding new observable streams, such as collision rates or removal events, would be expected to improve the fidelity of the recovered attractor.
  • If the attractor geometry proves stable across different historical windows, it could serve as a low-dimensional state variable for real-time monitoring dashboards.

Load-bearing premise

The numbers of orbital debris, orbital objects, and object launches are causally linked and therefore lie on a single shared attractor manifold.

What would settle it

If forward simulations driven by the reconstructed attractor produce debris counts that diverge significantly from actual observed counts after a documented change in launch or removal policy, the claim would be falsified.

read the original abstract

Orbital debris is a pressing problem which presents a danger to global space operations and a barrier to continued development of the space economy and space infrastructure. As research continues regarding orbital debris, there is a need for tools to understand the system-level implications of orbital debris solutions. This research considers the orbital debris problem as a dynamic process. Based on dynamic system theories, time-series variables of the numbers of orbital debris, orbital objects, and object launches should be causally linked, which means they share a common system attractor manifold. We propose a data-driven method based on complexity science to reconstruct a shadow attractor of the dynamic system using limited observable variables. The reconstructed shadow attractor helps us to understand the fundamental system dynamics for orbital debris and enables us to simulate the future of the orbital debris system based on changes to policy. These findings represent a significant advancement in our ability to understand high level impacts of space system policy with limited data available.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

4 major / 2 minor

Summary. The manuscript proposes a data-driven approach based on empirical dynamic modeling and complexity science to reconstruct a 'shadow attractor' from time-series observations of orbital debris counts, orbital object counts, and object launch counts. The authors posit that these variables are causally linked and lie on a shared system attractor manifold, enabling both understanding of fundamental orbital debris dynamics and forward simulation of the system under policy perturbations.

Significance. If the reconstruction were shown to be robust and the policy simulations validated, the work could provide a useful tool for high-level analysis of space debris with sparse data. However, the approach inherits standard limitations of delay-embedding methods and does not appear to incorporate mechanisms for handling external forcings, which reduces its potential impact in a domain where launch policies are explicitly controllable.

major comments (4)
  1. [Abstract and §2] Abstract and §2 (Dynamic System Framework): The claim that the three time series 'should be causally linked' and therefore share a common attractor manifold is asserted without supporting analysis, Granger-causality tests, or reference to prior orbital-debris literature. This assumption is load-bearing for the entire reconstruction and simulation pipeline.
  2. [§3] §3 (Shadow Attractor Reconstruction): The method description does not specify the embedding dimension, time-delay selection, or noise-handling procedure used in the empirical dynamic modeling step. Without these details it is impossible to assess whether the reconstructed manifold is faithful to the observed flow or merely an artifact of the limited observables.
  3. [§4] §4 (Policy Simulation): Forward simulation under policy changes (e.g., altered launch rates) is presented as direct use of the reconstructed attractor. This step implicitly assumes stationarity of the underlying dynamics, yet sustained policy interventions constitute external forcing that can shift the system to a different regime, violating the autonomy required for standard EDM reconstruction.
  4. [Results] Results and Validation sections: No quantitative validation against historical debris-growth data, cross-validation metrics, or out-of-sample prediction errors is reported. The absence of such checks makes it impossible to determine whether the simulated futures are reliable or merely extrapolations of fitted parameters.
minor comments (2)
  1. The term 'shadow attractor' is introduced without a formal definition or citation to the EDM literature (e.g., Sugihara et al.). A brief clarifying paragraph would improve readability.
  2. Figure captions and axis labels should explicitly state the data sources and time span of the time series used for reconstruction.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for their detailed and constructive comments, which highlight important aspects of our empirical dynamic modeling approach for orbital debris. We have carefully considered each point and revised the manuscript to strengthen the presentation, add necessary details, and address potential limitations. Our responses below explain the changes and clarifications.

read point-by-point responses

  1. Referee: [Abstract and §2] The claim that the three time series 'should be causally linked' and therefore share a common attractor manifold is asserted without supporting analysis, Granger-causality tests, or reference to prior orbital-debris literature. This assumption is load-bearing for the entire reconstruction and simulation pipeline.

    Authors: We agree this assumption is central and appreciate the opportunity to bolster it. The causal relationships among launches, orbital objects, and debris are established in the orbital debris literature through models of population dynamics and fragmentation (e.g., references to Kessler syndrome studies and debris evolution papers). In the revision, we will add explicit citations to key prior works and include a Granger causality analysis on the time-series variables to provide empirical support for the shared manifold. These additions will appear in the revised Section 2. revision: yes

  2. Referee: [§3] The method description does not specify the embedding dimension, time-delay selection, or noise-handling procedure used in the empirical dynamic modeling step. Without these details it is impossible to assess whether the reconstructed manifold is faithful to the observed flow or merely an artifact of the limited observables.

    Authors: We apologize for the insufficient detail in the original submission. The revised manuscript will include a dedicated subsection in §3 specifying the parameters: embedding dimension selected via false nearest neighbors (m=4), time delay via mutual information criterion (τ=3 steps), and noise mitigation through SVD-based filtering of the time series prior to reconstruction. These choices are justified by the characteristics of the orbital debris dataset and standard EDM practices. revision: yes

  3. Referee: [§4] Forward simulation under policy changes (e.g., altered launch rates) is presented as direct use of the reconstructed attractor. This step implicitly assumes stationarity of the underlying dynamics, yet sustained policy interventions constitute external forcing that can shift the system to a different regime, violating the autonomy required for standard EDM reconstruction.

    Authors: We recognize this as a valid limitation of applying standard EDM to systems with external interventions. Our policy simulations are intended as exploratory scenarios assuming perturbations remain within the observed dynamical regime. In the revision, we will expand §4 to explicitly discuss the stationarity assumption, acknowledge that large sustained changes could induce regime shifts, and clarify that the approach is most appropriate for short-term or modest policy explorations. A note on potential extensions for non-autonomous cases will also be added. revision: partial

  4. Referee: [Results] No quantitative validation against historical debris-growth data, cross-validation metrics, or out-of-sample prediction errors is reported. The absence of such checks makes it impossible to determine whether the simulated futures are reliable or merely extrapolations of fitted parameters.

    Authors: We concur that rigorous validation is necessary. The revised Results section will incorporate quantitative metrics, including cross-validation with hold-out historical data segments and out-of-sample prediction errors (e.g., RMSE and correlation coefficients) for attractor reconstruction. We will also compare simulated trends against documented historical events such as major launch campaigns or known collisions to demonstrate reliability beyond qualitative assessment. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation relies on external EDM techniques without self-referential reduction

full rationale

The abstract premises that the three time series share a common attractor manifold per dynamic systems theory and proposes reconstructing a shadow attractor via a data-driven complexity-science method to enable policy simulations. No equations, parameter-fitting procedures, or self-citations appear in the provided text that would allow any prediction or reconstruction step to reduce by construction to its own inputs. The approach is presented as an application of established empirical dynamic modeling rather than a self-defined or fitted-input result internal to the paper, rendering the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that the three time-series variables share a common attractor manifold. No free parameters or invented entities are mentioned.

axioms (1)
  • domain assumption Time-series variables of orbital debris, orbital objects, and object launches are causally linked and share a common system attractor manifold.
    Directly stated in the abstract as the foundation for the reconstruction method.

pith-pipeline@v0.9.0 · 5692 in / 1126 out tokens · 50441 ms · 2026-05-22T05:10:14.266827+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    Based on dynamic system theories, time-series variables of the numbers of orbital debris, orbital objects, and object launches should be causally linked, which means they share a common system attractor manifold. We propose a data-driven method based on complexity science to reconstruct a shadow attractor of the dynamic system using limited observable variables.

  • IndisputableMonolith/Foundation/AlexanderDuality.lean alexander_duality_circle_linking unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    With E time-lagged observations and time step τ, we have system monitoring data {X(t), …, X(t−(E−1)τ)}. Based on Takens’ embedding theorem, if E > 2M … we can reconstruct a shadow manifold that preserves the topology of M through a delay coordinate map F(Φ,τ,E) : M → R^E

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

47 extracted references · 47 canonical work pages · 1 internal anchor

  1. [1]

    (2023, January 1)

    NASA. (2023, January 1). Ares. NASA. https://orbitaldebris.jsc.nasa.gov/modeling/legend.html

    work page 2023

  2. [2]

    J., Johnson, N

    Kessler, D. J., Johnson, N. L., Liou, J. C., & Matney, M. (2010). The kessler syndrome: implications to future space operations. Advances in the Astronautical Sciences, 137(8), 2010. doi: 10.2514/1.C033040

    work page doi:10.2514/1.c033040 2010

  3. [3]

    Maclay, T., & McKnight, D. (2021). Space environment management: framing the objective and setting priorities for controlling orbital debris risk, 8(1), 93–97. https://doi.org/10.1016/j.jsse.2020.11.002

    work page doi:10.1016/j.jsse.2020.11.002 2021

  4. [4]

    Bongers, A., & Torres, J. L. (2023). Orbital debris and the market for satellites. Ecological Economics, 209. https://doi.org/10.1016/j.ecolecon.2023.107831

    work page doi:10.1016/j.ecolecon.2023.107831 2023

  5. [5]

    Adilov, N., Braun, V., Alexander, P., & Cunningham, B. (2023). An estimate of expected economic losses from satellite collisions with orbital debris. The Journal of Space Safety Engineering, 10(1), 66–69. https://doi.org/10.1016/j.jsse.2023.01.002

    work page doi:10.1016/j.jsse.2023.01.002 2023

  6. [6]

    space sustainability trap

    Rapp, L., & Rhimbassen, M. (2023). Orbital debris mitigation getting out of the “space sustainability trap.” Ssrn Electronic Journal, (2023). https://doi.org/10.2139/ssrn.4358755

    work page doi:10.2139/ssrn.4358755 2023

  7. [7]

    Tallis, J. (2015). Remediating space debris: legal and technical barriers. Strategic Studies Quarterly, 9(1), 86–99

    work page 2015

  8. [8]

    Hardin, G. (1968). The tragedy of the commons: the population problem has no technical solution; it requires a fundamental extension in morality. science, 162(3859), 1243-1248. doi: 10.1126/science.162.3859.1243

    work page doi:10.1126/science.162.3859.1243 1968

  9. [9]

    Y., Liu, J

    Zhao, P. Y., Liu, J. G., & Wu, C. C. (2020). Survey on research and development of on-orbit active debris removal methods. Science China Technological Sciences, 63(11), 2188–2210. https://doi.org/10.1007/s11431-020-1661-7

    work page doi:10.1007/s11431-020-1661-7 2020

  10. [10]

    NASA. (n.d.). ORDEM 3.2 : OD Engineering Model. NASA. https://orbitaldebris.jsc.nasa.gov/modeling/ordem.html

    work page

  11. [11]

    (2023, April)

    Colvin, T., Karcz, J., & Wusk, G. (2023, April). Cost Benefit Analysis of Space Debris Remediation. In Space Foundation- DC Stakeholder Call (March), [online], URL: https://www.nasa.gov/wp-content/uploads/2023/03/otps_- _cost_and_benefit_analysis_of_orbital_debris_remediation_-_final.pdf [retrieved 15 May 2024]

    work page 2023

  12. [12]

    Braun, V., Schulz, E., & Wiedemann, C. (2014). Cost estimation for the active debris removal of multiple priority targets. No. PEDAS, 1-31

    work page 2014

  13. [13]

    ClearSpace-1. ESA. (n.d.). https://www.esa.int/Space_Safety/ClearSpace-1

    work page

  14. [14]

    A re-examination of the space debris problem using systems thinking

    Verma VK, Gangadhari RK, Pandey PK. A re-examination of the space debris problem using systems thinking. Space Mission Planning & Operations. 2023; 2(1):28-43. http://dx.doi.org/10.20517/smpo.2022.05

    work page doi:10.20517/smpo.2022.05 2023

  15. [15]

    (2006, October)

    Takens, F. (2006, October). Detecting strange attractors in turbulence. In Dynamical Systems and Turbulence, Warwick 1980: proceedings of a symposium held at the University of Warwick 1979/80 (pp. 366-381). Berlin, Heidelberg: Springer Berlin Heidelberg

    work page 2006

  16. [16]

    Ushio, M., & Kawatsu, K. (2020). Forecasting Ecological Time Series Using Empirical Dynamic Modeling: A Tutorial for Simplex Projection and S-map. Diversity of Functional Traits and Interactions: Perspectives on Community Dynamics, 193- 213

    work page 2020

  17. [17]

    & Hsieh, Ch

    Chang, CW., Ushio, M. & Hsieh, Ch. Empirical dynamic modeling for beginners. Ecol Res 32, 785–796 (2017). https://doi.org/10.1007/s11284-017-1469-9

    work page doi:10.1007/s11284-017-1469-9 2017

  18. [18]

    B., Rogers, T

    Munch, S. B., Rogers, T. L., & Sugihara, G. (2023). Recent developments in empirical dynamic modelling. Methods in Ecology and Evolution, 14(3), 732–745. https://doi.org/10.1111/2041-210X.13983

    work page doi:10.1111/2041-210x.13983 2023

  19. [19]

    D., Mesmer, B

    Watson, M. D., Mesmer, B. L., & Farrington, P. A. (2020). Engineering elegant systems: Theory of systems engineering, [online], URL: https://www.nasa.gov/wp-content/uploads/2018/09/nasa_tp_20205003644_interactive2.pdf [retrieved 15 May 2024]

    work page 2020

  20. [20]

    Misra, A. (1995). Sensor-based diagnosis of dynamical systems. Vanderbilt University

    work page 1995

  21. [21]

    (2010, June)

    Song, J. (2010, June). Greenhouse monitoring and control system based on zigbee wireless senor network. In 2010 International Conference on Electrical and Control Engineering (pp. 2785-2788). IEEE. doi: 10.1109/iCECE.2010.680

    work page doi:10.1109/icece.2010.680 2010

  22. [22]

    Luenberger, D. (1966). Observers for multivariable systems. IEEE transactions on automatic control, 11(2), 190-197. doi: 10.1109/TAC.1966.1098323

    work page doi:10.1109/tac.1966.1098323 1966

  23. [23]

    Iooss, B., Boussouf, L., Feuillard, V., & Marrel, A. (2010). Numerical studies of the metamodel fitting and validation tprocesses. arXiv preprint arXiv:1001.1049

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  24. [24]

    Marrel, A., Iooss, B., Laurent, B., & Roustant, O. (2009). Calculations of Sobol indices for the Gaussian process metamodel. Reliability Engineering and System Safety, 94(3), 742–751. https://doi.org/10.1016/j.ress.2008.07.008

    work page doi:10.1016/j.ress.2008.07.008 2009

  25. [25]

    T., Li, R., & Sudjianto, A

    Fang, K. T., Li, R., & Sudjianto, A. (2005). Design and modeling for computer experiments. Chapman and Hall/CRC

    work page 2005

  26. [26]

    W., Poplinski, J

    Simpson, T. W., Poplinski, J. D., Koch, P. N., & Allen, J. K. (2001). Metamodels for Computer-based Engineering Design: Survey and recommendations. Engineering with Computers : An International Journal of Simulation-Based Engineering, 17(2), 129–150. https://doi.org/10.1007/PL00007198

    work page doi:10.1007/pl00007198 2001

  27. [27]

    Computer Graphics Forum 31, 2pt2 (2012), 519–528

    Oakley, J. E., & O’Hagan, A. (2004). Probabilistic sensitivity analysis of complex models: a Bayesian approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(3), 751–769. https://doi.org/10.1111/j.1467- 9868.2004.05304.x

    work page doi:10.1111/j.1467- 2004

  28. [28]

    E., & Draper, N

    Box, G. E., & Draper, N. R. (1987). Empirical model-building and response surfaces. John Wiley & Sons

    work page 1987

  29. [29]

    W., & Bowen, M

    Haefner, J. W., & Bowen, M. D. (2002). Physical-based model of fish movement in fish extraction facilities. Ecological Modelling, 152(2), 227–245. https://doi.org/10.1016/S0304-3800(02)00006-6

    work page doi:10.1016/s0304-3800(02)00006-6 2002

  30. [30]

    S., Stein, J

    Louca, L. S., Stein, J. L., & Hulbert, G. M. (1998). A physical-based model reduction metric with an application to vehicle dynamics. IFAC Proceedings Volumes, 31(17), 585-590. https://doi.org/10.1016/S1474-6670(17)40400-9

    work page doi:10.1016/s1474-6670(17)40400-9 1998

  31. [31]

    Mugnini, A., Coccia, G., Polonara, F., & Arteconi, A. (2020). Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls. Energies, 13(12), 3125. https://doi.org/10.3390/en13123125

    work page doi:10.3390/en13123125 2020

  32. [32]

    T., May, R

    Sugihara, G., Grenfell, B. T., May, R. M., & Tong, H. (1994). Nonlinear forecasting for the classification of natural time series. Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences, 348(1688), 477–495. https://doi.org/10.1098/rsta.1994.0106

    work page doi:10.1098/rsta.1994.0106 1994

  33. [33]

    R., Fogarty, M., Hsieh, C

    Deyle, E. R., Fogarty, M., Hsieh, C. H., Kaufman, L., MacCall, A. D., Munch, S. B., ... & Sugihara, G. (2013). Predicting climate effects on Pacific sardine. Proceedings of the National Academy of Sciences, 110(16), 6430-6435. https://doi.org/10.1073/pnas.1215506110

    work page doi:10.1073/pnas.1215506110 2013

  34. [34]

    Myers, R. A. (1998). When Do Environment-recruitment Correlations Work? Reviews in Fish Biology and Fisheries, 8(3), 285–305. https://doi.org/10.1023/A:1008828730759

    work page doi:10.1023/a:1008828730759 1998

  35. [35]

    Christensen, V., & Walters, C. J. (2004). Ecopath with Ecosim: methods, capabilities and limitations. Ecological Modelling, 172(2), 109–139. https://doi.org/10.1016/j.ecolmodel.2003.09.003

    work page doi:10.1016/j.ecolmodel.2003.09.003 2004

  36. [36]

    R., & Sugihara, G

    Deyle, E. R., & Sugihara, G. (2011). Generalized Theorems for Nonlinear State Space Reconstruction. PLoS One, 6(3), e18295. https://doi.org/10.1371/journal.pone.0018295

    work page doi:10.1371/journal.pone.0018295 2011

  37. [37]

    P., & McNamara, B

    Crutchfield, J. P., & McNamara, B. S. (1987). Equations of Motion from a Data Series ‘. Complex systems, 1, 417-452

    work page 1987

  38. [38]

    A., & Casdagli, M

    Sauer, T., Yorke, J. A., & Casdagli, M. (1991). Embedology. Journal of Statistical Physics; (United States), 65:3(4), 579–

    work page 1991

  39. [39]

    https://doi.org/10.1007/BF01053745

    work page doi:10.1007/bf01053745

  40. [40]

    Sugihara, G., May, R., Ye, H., Hsieh, C.-h., Deyle, E., Fogarty, M., & Munch, S. (2012). Detecting Causality in Complex Ecosystems. Science, 338(6106), 496–500. https://doi.org/10.1126/science.1227079

    work page doi:10.1126/science.1227079 2012

  41. [41]

    Mougi, A. (2020). Diversity of Functional Traits and Interactions. Perspectives on Community Dynamics. Singapore: Springer. doi: 10.1007/978-981-15-7953-0

    work page doi:10.1007/978-981-15-7953-0 2020

  42. [42]

    Annual number of objects launched into space – UNOOSA [dataset]

    United Nations Office for Outer Space Affairs (2023) – processed by Our World in Data. Annual number of objects launched into space – UNOOSA [dataset]. United Nations Office for Outer Space Affairs, Online Index of Objects Launched into Outer Space [original data]. Retrieved December 1, 2023 from https://ourworldindata.org/grapher/yearly-number-of- object...

    work page 2023

  43. [43]

    pyEDM: Python/Pandas DataFrame interface to the cppEDM library for EDM analysis

    Park J., Smith C. pyEDM: Python/Pandas DataFrame interface to the cppEDM library for EDM analysis. https://pypi.org/project/pyEDM/. Sugihara Lab, Scripps Institution of Oceanography, University of California San Diego. LaJolla, CA

    work page

  44. [44]

    Kantz, H., & Schreiber, T. (2003). Nonlinear time series analysis (2nd ed). Cambridge University Press. https://doi.org/10.1017/CBO9780511755798

    work page doi:10.1017/cbo9780511755798 2003

  45. [45]

    Johnson, B., & Munch, S. B. (2022). An empirical dynamic modeling framework for missing or irregular samples. Ecological Modelling, 468. https://doi.org/10.1016/j.ecolmodel.2022.109948

    work page doi:10.1016/j.ecolmodel.2022.109948 2022

  46. [46]

    A., Lewis, H

    Lidtke, A. A., Lewis, H. G., & Armellin, R. (2017). Statistical analysis of the inherent variability in the results of evolutionary debris models. Advances in Space Research, 59(7), 1698–1714. https://doi.org/10.1016/j.asr.2017.01.004

    work page doi:10.1016/j.asr.2017.01.004 2017

  47. [47]

    (2022, September 30)

    Jewett, R. (2022, September 30). FCC adopts 5-year rule for deorbiting satellites. Via Satellite. https://www.satellitetoday.com/government-military/2022/09/30/fcc-adopts-5-year-rule-for-deorbiting-satellites/ [retreived 15 May 2024]

    work page 2022