Impact of Atmospheric Turbulence and Pointing Error on Earth Observation

Antonio Jurado-Navas; Antonio M. Mercado-Mart\'inez; Beatriz Soret; Celia S\'anchez-de-Miguel; Miguel Castillo-V\'azquez

arxiv: 2605.22268 · v1 · pith:ZPLZZASXnew · submitted 2026-05-21 · 💻 cs.NI · cs.AI· cs.CV

Impact of Atmospheric Turbulence and Pointing Error on Earth Observation

Celia S\'anchez-de-Miguel , Antonio M. Mercado-Mart\'inez , Beatriz Soret , Antonio Jurado-Navas , Miguel Castillo-V\'azquez This is my paper

Pith reviewed 2026-05-22 02:37 UTC · model grok-4.3

classification 💻 cs.NI cs.AIcs.CV

keywords atmospheric turbulencepointing jitterEarth observationvessel detectionYOLOv8RetinaNetimage simulationAI robustness

0 comments

The pith

Atmospheric turbulence and pointing jitter sharply reduce YOLOv8 recall in satellite vessel detection while RetinaNet stays steadier.

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

Earth observation images from satellites suffer distortions from atmospheric turbulence along the vertical path and from platform vibrations that create pointing errors. This paper builds an enhanced simulator to insert those physical effects into images so that AI detection models can be tested under realistic conditions rather than ideal ones. In the vessel detection case study the results show YOLOv8 recall falling from 91 percent with no distortion to 60 percent under weak turbulence and below 40 percent under strong turbulence or jitter. RetinaNet, by contrast, holds near 75 percent recall across the same range of degradations. A reader would care because operational satellite systems for maritime surveillance will encounter these effects daily, and models trained only on clean data will underperform when deployed.

Core claim

The paper introduces an enhanced image simulator that incorporates vertical-path atmospheric turbulence and satellite pointing jitter arising from platform and sensor vibrations. When this simulator is used to degrade images for a vessel detection task, YOLOv8 recall decreases from 91 percent under ideal conditions to 60 percent with weak turbulence and falls below 40 percent under strong turbulence or jitter, whereas RetinaNet maintains approximately 75 percent recall across the degraded conditions.

What carries the argument

Enhanced image simulator that adds vertical-path atmospheric turbulence and satellite pointing jitter to Earth observation images to produce physically realistic distortions for testing AI detectors.

If this is right

Training datasets for Earth observation AI models should include realistic turbulence and jitter effects to avoid overestimating performance in operational settings.
Model architecture choice affects robustness, with RetinaNet demonstrating greater resilience than YOLOv8 under the simulated degradations.
Maritime surveillance applications using AI detectors will experience reduced reliability unless physical distortions are modeled during training.
The simulator offers a repeatable method for generating degraded imagery to evaluate and improve future detection models.

Where Pith is reading between the lines

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

The observed difference in robustness between the two models suggests that other common detectors should be tested with the same simulator to map sensitivity across architectures.
Preprocessing methods that reduce turbulence effects before detection could be combined with the simulator to quantify possible performance gains.
Similar degradation modeling may be needed for other Earth observation tasks such as land cover classification or change detection that also rely on high-resolution satellite imagery.
Standard public benchmarks without these effects likely overestimate the real-world accuracy of AI models in satellite-based applications.

Load-bearing premise

The simulator reproduces the statistical and visual effects of real vertical-path atmospheric turbulence and satellite pointing jitter on Earth observation imagery.

What would settle it

Compare the statistical distribution of distortions in the simulated images against actual satellite images captured under documented levels of atmospheric turbulence and known pointing jitter.

Figures

Figures reproduced from arXiv: 2605.22268 by Antonio Jurado-Navas, Antonio M. Mercado-Mart\'inez, Beatriz Soret, Celia S\'anchez-de-Miguel, Miguel Castillo-V\'azquez.

**Figure 2.** Figure 2: CDF of the refractive index structure parameter at sea [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 4.** Figure 4: YOLOv8 recall over the irradiance variance [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 3.** Figure 3: Impact of the atmospheric turbulence in ship detection [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

read the original abstract

Earth Observation (EO) imagery is often degraded by atmospheric turbulence and pointing jitter; yet, these effects are rarely considered in datasets used to train AI-based detection models. Based on prior work, this paper presents an enhanced image simulator that enables the incorporation of vertical-path atmospheric turbulence and satellite pointing jitter, arising from platform and sensor vibrations, to generate physically realistic distorted images. As a case study, vessel detection is evaluated using YOLOv8 and RetinaNet on images generated by the proposed simulator under different levels of turbulence and pointing errors. Results show that YOLOv8 recall decreases from 91% under ideal conditions to 60% in the presence of weak turbulence, and falls below 40% under strong turbulence or jitter. In contrast, RetinaNet demonstrates greater robustness, maintaining approximately 75% recall across degraded conditions. These results highlight the importance of incorporating realistic physical degradations into EO training datasets to ensure reliable performance of AI-based models in operational environments, as demonstrated in maritime surveillance applications.

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.

Desk Editor's Note private letter to a colleague

Simulator shows YOLOv8 recall dropping hard under turbulence and jitter, but the numbers rest on an unvalidated model of vertical-path effects.

read the letter

The main thing to know is that their simulator produces big drops in detection recall for YOLOv8 under added turbulence and pointing errors, but the lack of validation against real measurements leaves those numbers on shaky ground. They start from existing image simulation methods and add vertical atmospheric turbulence along with jitter from the satellite platform and sensor. Then they run YOLOv8 and RetinaNet on the resulting images for vessel detection. The reported outcome is YOLOv8 going from 91 percent recall in clean conditions to 60 percent with weak turbulence and under 40 percent with strong effects or jitter. RetinaNet holds steadier near 75 percent. That comparison is useful. It shows one detector is more sensitive to these degradations than the other, which could matter when choosing models for operational Earth observation tasks like tracking ships. The weakness is clear from the abstract: everything rests on the simulator being accurate for vertical paths. They do not report any side-by-side comparison of simulated image statistics to data collected from actual satellite overpasses under known turbulence conditions. No scintillation indices, no point spread function measurements from real scenes, nothing like that. Without it, the quantitative claims could be off if the model overstates or understates the blur and intensity fluctuations. Details on the experiment are also thin in the summary. We do not see how many images went into the recall calculations or whether the drops are consistent across different scenes. This paper would fit for a reading group focused on applied computer vision in remote sensing. Readers who work on robust detectors for satellite imagery would get something out of the numbers and the reminder to include physical effects in training data. It is worth sending out for peer review. Referees can ask for the missing validation steps or simulator parameters, and the core idea of testing under realistic conditions is sound enough to justify the effort.

Referee Report

2 major / 2 minor

Summary. The manuscript presents an enhanced image simulator for generating Earth observation imagery degraded by vertical-path atmospheric turbulence and satellite pointing jitter arising from platform and sensor vibrations. As a case study in maritime vessel detection, it evaluates YOLOv8 and RetinaNet on simulator outputs, reporting that YOLOv8 recall falls from 91% under ideal conditions to 60% with weak turbulence and below 40% under strong turbulence or jitter, while RetinaNet maintains approximately 75% recall across degraded conditions. The work concludes that realistic physical degradations must be incorporated into EO training datasets to ensure reliable AI performance in operational settings.

Significance. If the simulator faithfully reproduces real vertical-path effects, the differential robustness findings could inform detector selection and dataset augmentation strategies for EO applications such as maritime surveillance. The paper correctly identifies a gap in current training data practices and provides a concrete case study demonstrating performance sensitivity to turbulence and jitter.

major comments (2)

[Abstract / Results] Abstract and results: The headline recall figures (YOLOv8: 91% → 60% → <40%; RetinaNet ≈75%) are produced exclusively by the enhanced simulator. No validation is reported against empirical EO data, such as measured scintillation indices, PSF widths, or contrast loss from actual satellite passes under documented Cn² profiles for vertical paths. This leaves the quantitative performance gap without visible supporting evidence and makes the central claim about operational impact conditional on unverified simulator fidelity.
[Simulator Description] Simulator section: The manuscript states that the simulator builds on prior work to incorporate vertical-path turbulence and platform/sensor jitter, yet provides no direct quantitative comparison of simulated statistics (scintillation index, PSF characteristics, or contrast degradation) to measured values from real vertical-path observations. Without such grounding, the reported degradation levels may not correspond to actual satellite imagery conditions.

minor comments (2)

[Results] The abstract and results do not report the number of test images, presence or absence of error bars, or statistical significance of the recall differences. Adding these details would strengthen the presentation of the numerical findings.
[Figures / Simulator] Consider including example images or quantitative metrics (e.g., contrast loss curves) comparing ideal, weak-turbulence, and jitter-degraded cases to allow readers to visually assess the simulator outputs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The comments highlight important considerations regarding empirical grounding of the simulator. We respond to each major comment below and indicate the revisions we will make.

read point-by-point responses

Referee: [Abstract / Results] Abstract and results: The headline recall figures (YOLOv8: 91% → 60% → <40%; RetinaNet ≈75%) are produced exclusively by the enhanced simulator. No validation is reported against empirical EO data, such as measured scintillation indices, PSF widths, or contrast loss from actual satellite passes under documented Cn² profiles for vertical paths. This leaves the quantitative performance gap without visible supporting evidence and makes the central claim about operational impact conditional on unverified simulator fidelity.

Authors: We agree that direct validation against measured data from real vertical-path satellite passes would strengthen the quantitative claims. The current work focuses on the effects produced by an enhanced simulator that incorporates established physical models for vertical-path turbulence and jitter; the headline figures are therefore simulator-derived. In the revised manuscript we will add an explicit limitations paragraph in the results section that states the absence of direct empirical validation in this study, references the literature values used to set the weak/strong Cn² and jitter parameters, and outlines the data requirements for future validation. We do not claim the numbers are measured; they illustrate sensitivity under the modeled conditions. revision: partial
Referee: [Simulator Description] Simulator section: The manuscript states that the simulator builds on prior work to incorporate vertical-path turbulence and platform/sensor jitter, yet provides no direct quantitative comparison of simulated statistics (scintillation index, PSF characteristics, or contrast degradation) to measured values from real vertical-path observations. Without such grounding, the reported degradation levels may not correspond to actual satellite imagery conditions.

Authors: The simulator section already cites the prior models on which the vertical-path and jitter extensions are based. We acknowledge that the manuscript does not present side-by-side numerical comparisons (e.g., simulated vs. measured scintillation index or PSF width) for the specific parameter settings used. Obtaining matched real EO imagery with documented Cn² profiles and platform telemetry is resource-intensive and was outside the scope of this initial case study. In the revision we will expand the simulator description with additional citations to empirical vertical-path studies and will include a short table or paragraph showing that the chosen turbulence strengths produce scintillation indices and contrast losses within the ranges reported in the referenced literature. revision: partial

Circularity Check

0 steps flagged

Minor self-citation for simulator enhancement; recall metrics are direct simulation outputs with no definitional or fitted circularity

full rationale

The paper's core results consist of recall measurements obtained by running YOLOv8 and RetinaNet on images produced by an enhanced turbulence/jitter simulator. These metrics are generated outputs of the detection pipeline rather than quantities fitted to or defined in terms of the same data. The description references prior work for the simulator foundation, but this is a standard incremental citation and does not serve as the sole load-bearing justification for the reported performance gaps. No equations, parameter-fitting steps, or self-referential definitions appear that would reduce the claimed recall drops to the inputs by construction. The study therefore remains self-contained as a simulation-based evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, no explicit free parameters, axioms, or invented entities are described; the simulator is presented as an enhancement of prior work without further breakdown.

pith-pipeline@v0.9.0 · 5727 in / 1156 out tokens · 33616 ms · 2026-05-22T02:37:16.314213+00:00 · methodology

discussion (0)

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.

YOLOv8 recall decreases from 91% ... to 60% ... below 40% under strong turbulence or jitter; RetinaNet maintains ~75%

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

16 extracted references · 16 canonical work pages

[1]

Satellite edge computing for real- time and very-high resolution earth observation,

I. Leyva-Mayorga, M. Martinez-Gost, M. Moretti, A. P ´erez-Neira, M. ´A. V´azquez, P. Popovski, and B. Soret, “Satellite edge computing for real- time and very-high resolution earth observation,”IEEE Transactions on Communications, vol. 71, no. 10, pp. 6180–6194, 2023

work page 2023
[2]

Multimodal ai-enhanced ship detection for mapping fishing vessels and informing on suspicious activities,

A. Galdelli, G. Narang, R. Pietrini, M. Zazzarini, A. Fiorani, and A. N. Tassetti, “Multimodal ai-enhanced ship detection for mapping fishing vessels and informing on suspicious activities,”Pattern Recognition Letters, vol. 191, pp. 15–22, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167865525000649

work page 2025
[3]

CTS- Unet: Urban change detection by convolutional siamese concatenate network with swin transformer,

F. Heidary, M. Yazdi, P. Setoodeh, and M. Dehghani, “CTS- Unet: Urban change detection by convolutional siamese concatenate network with swin transformer,”Advances in Space Research, vol. 72, no. 10, pp. 4272–4281, 2023. [Online]. Available: https: //www.sciencedirect.com/science/article/pii/S027311772300618X

work page 2023
[4]

An efficient change detection method for disaster- affected buildings based on a lightweight residual block in high- resolution remote sensing images,

Y . Zhang, G. Yang, A. Gao, W. Lv, R. Xie, M. Huang, and S. Liu, “An efficient change detection method for disaster- affected buildings based on a lightweight residual block in high- resolution remote sensing images,”International Journal of Remote Sensing, vol. 44, no. 9, pp. 2959–2981, 2023. [Online]. Available: https://doi.org/10.1080/01431161.2023.2214274

work page doi:10.1080/01431161.2023.2214274 2023
[5]

Semantic and goal-oriented edge computing for satellite earth observation,

B. Soret, I. Leyva-Mayorga, A. M. Mercado-Mart ´ınez, M. Moretti, A. Jurado-Navas, M. Martinez-Gost, C. S ´anchez-de Miguel, A. Salas- Prendes, and P. Popovski, “Semantic and goal-oriented edge computing for satellite earth observation,”arXiv preprint arXiv:2408.15639, 2024

work page arXiv 2024
[6]

Simulating anisoplanatic turbulence by sampling intermodal and spatially correlated zernike coefficients,

N. Chimitt and S. H. Chan, “Simulating anisoplanatic turbulence by sampling intermodal and spatially correlated zernike coefficients,”Op- tical Engineering, vol. 59, no. 8, pp. 083 101–083 101, 2020

work page 2020
[7]

Ultralytics yolov8,

G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics yolov8,” 2023. [Online]. Available: https://github.com/ultralytics/ultralytics

work page 2023
[8]

Retinanet — torchvision main documentation

P. Team, “Retinanet — torchvision main documentation.” [Online]. Available: https://docs.pytorch.org/vision/main/models/retinanet.html

work page
[9]

Laser beam propagation through random media,

L. C. Andrews and R. L. Phillips, “Laser beam propagation through random media,”Laser Beam Propagation Through Random Media: Second Edition, 2005

work page 2005
[10]

Irs-assisted fso system for leo satellites constella- tions,

C. S ´anchez-de Miguel, M. Castillo-V ´azquez, T. R. Raddo, B. Soret, and A. Jurado-Navas, “Irs-assisted fso system for leo satellites constella- tions,”IEEE Open Journal of the Communications Society, vol. 7, pp. 315–327, 2026

work page 2026
[11]

Impact of receiver architecture on small satellite optical link in the presence of pointing jitter,

I. U. Zaman and O. Boyraz, “Impact of receiver architecture on small satellite optical link in the presence of pointing jitter,”Appl. Opt., vol. 59, no. 32, pp. 10 177–10 184, Nov 2020. [Online]. Available: https://opg.optica.org/ao/abstract.cfm?URI=ao-59-32-10177

work page 2020
[12]

Single-layer phase screen with pointing errors for free space optical communication,

X. Liu, D. Jiang, Z. Hu, Q. Zeng, and K. Qin, “Single-layer phase screen with pointing errors for free space optical communication,”IEEE Access, vol. 9, pp. 104 070–104 078, 2021

work page 2021
[13]

Impact of transmitter wavefront errors and pointing jitter on intersatellite free space optical communications,

M. B. Aldecocea, J. Bouwmeester, P. Piron, and J. Loicq, “Impact of transmitter wavefront errors and pointing jitter on intersatellite free space optical communications,” inComputational Optics 2024, D. G. Smith and A. Erdmann, Eds., vol. 13023, International Society for Optics and Photonics. SPIE, 2024, p. 1302302. [Online]. Available: https://doi.org/10...

work page doi:10.1117/12.3021617 2024
[14]

Ships-google-earth-dataset

K. Stavrakakis, “Ships-google-earth-dataset.” [Online]. Available: https: //universe.roboflow.com/k--stavrakakis/ships-google-earth

work page
[15]

WorldView-1, WorldView-2, WordView-3 datasheet

DigitalGlove, “WorldView-1, WorldView-2, WordView-3 datasheet.” [Online]. Available: https://resources.maxar.com/data-sheets/ worldview-3 [16]Non-Geostationary Satellite Communications Systems. Institution of Engineering and Technology, Dec. 2022. [Online]. Available: http://dx.doi.org/10.1049/PBTE105E

work page doi:10.1049/pbte105e 2022
[16]

Atmospheric effects on satellite–ground free space uplink and downlink optical transmissions,

N. Maharjan, N. Devkota, and B. W. Kim, “Atmospheric effects on satellite–ground free space uplink and downlink optical transmissions,” Applied Sciences, vol. 12, no. 21, p. 10944, 2022

work page 2022

[1] [1]

Satellite edge computing for real- time and very-high resolution earth observation,

I. Leyva-Mayorga, M. Martinez-Gost, M. Moretti, A. P ´erez-Neira, M. ´A. V´azquez, P. Popovski, and B. Soret, “Satellite edge computing for real- time and very-high resolution earth observation,”IEEE Transactions on Communications, vol. 71, no. 10, pp. 6180–6194, 2023

work page 2023

[2] [2]

Multimodal ai-enhanced ship detection for mapping fishing vessels and informing on suspicious activities,

A. Galdelli, G. Narang, R. Pietrini, M. Zazzarini, A. Fiorani, and A. N. Tassetti, “Multimodal ai-enhanced ship detection for mapping fishing vessels and informing on suspicious activities,”Pattern Recognition Letters, vol. 191, pp. 15–22, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167865525000649

work page 2025

[3] [3]

CTS- Unet: Urban change detection by convolutional siamese concatenate network with swin transformer,

F. Heidary, M. Yazdi, P. Setoodeh, and M. Dehghani, “CTS- Unet: Urban change detection by convolutional siamese concatenate network with swin transformer,”Advances in Space Research, vol. 72, no. 10, pp. 4272–4281, 2023. [Online]. Available: https: //www.sciencedirect.com/science/article/pii/S027311772300618X

work page 2023

[4] [4]

An efficient change detection method for disaster- affected buildings based on a lightweight residual block in high- resolution remote sensing images,

Y . Zhang, G. Yang, A. Gao, W. Lv, R. Xie, M. Huang, and S. Liu, “An efficient change detection method for disaster- affected buildings based on a lightweight residual block in high- resolution remote sensing images,”International Journal of Remote Sensing, vol. 44, no. 9, pp. 2959–2981, 2023. [Online]. Available: https://doi.org/10.1080/01431161.2023.2214274

work page doi:10.1080/01431161.2023.2214274 2023

[5] [5]

Semantic and goal-oriented edge computing for satellite earth observation,

B. Soret, I. Leyva-Mayorga, A. M. Mercado-Mart ´ınez, M. Moretti, A. Jurado-Navas, M. Martinez-Gost, C. S ´anchez-de Miguel, A. Salas- Prendes, and P. Popovski, “Semantic and goal-oriented edge computing for satellite earth observation,”arXiv preprint arXiv:2408.15639, 2024

work page arXiv 2024

[6] [6]

Simulating anisoplanatic turbulence by sampling intermodal and spatially correlated zernike coefficients,

N. Chimitt and S. H. Chan, “Simulating anisoplanatic turbulence by sampling intermodal and spatially correlated zernike coefficients,”Op- tical Engineering, vol. 59, no. 8, pp. 083 101–083 101, 2020

work page 2020

[7] [7]

Ultralytics yolov8,

G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics yolov8,” 2023. [Online]. Available: https://github.com/ultralytics/ultralytics

work page 2023

[8] [8]

Retinanet — torchvision main documentation

P. Team, “Retinanet — torchvision main documentation.” [Online]. Available: https://docs.pytorch.org/vision/main/models/retinanet.html

work page

[9] [9]

Laser beam propagation through random media,

L. C. Andrews and R. L. Phillips, “Laser beam propagation through random media,”Laser Beam Propagation Through Random Media: Second Edition, 2005

work page 2005

[10] [10]

Irs-assisted fso system for leo satellites constella- tions,

C. S ´anchez-de Miguel, M. Castillo-V ´azquez, T. R. Raddo, B. Soret, and A. Jurado-Navas, “Irs-assisted fso system for leo satellites constella- tions,”IEEE Open Journal of the Communications Society, vol. 7, pp. 315–327, 2026

work page 2026

[11] [11]

Impact of receiver architecture on small satellite optical link in the presence of pointing jitter,

I. U. Zaman and O. Boyraz, “Impact of receiver architecture on small satellite optical link in the presence of pointing jitter,”Appl. Opt., vol. 59, no. 32, pp. 10 177–10 184, Nov 2020. [Online]. Available: https://opg.optica.org/ao/abstract.cfm?URI=ao-59-32-10177

work page 2020

[12] [12]

Single-layer phase screen with pointing errors for free space optical communication,

X. Liu, D. Jiang, Z. Hu, Q. Zeng, and K. Qin, “Single-layer phase screen with pointing errors for free space optical communication,”IEEE Access, vol. 9, pp. 104 070–104 078, 2021

work page 2021

[13] [13]

Impact of transmitter wavefront errors and pointing jitter on intersatellite free space optical communications,

M. B. Aldecocea, J. Bouwmeester, P. Piron, and J. Loicq, “Impact of transmitter wavefront errors and pointing jitter on intersatellite free space optical communications,” inComputational Optics 2024, D. G. Smith and A. Erdmann, Eds., vol. 13023, International Society for Optics and Photonics. SPIE, 2024, p. 1302302. [Online]. Available: https://doi.org/10...

work page doi:10.1117/12.3021617 2024

[14] [14]

Ships-google-earth-dataset

K. Stavrakakis, “Ships-google-earth-dataset.” [Online]. Available: https: //universe.roboflow.com/k--stavrakakis/ships-google-earth

work page

[15] [15]

WorldView-1, WorldView-2, WordView-3 datasheet

DigitalGlove, “WorldView-1, WorldView-2, WordView-3 datasheet.” [Online]. Available: https://resources.maxar.com/data-sheets/ worldview-3 [16]Non-Geostationary Satellite Communications Systems. Institution of Engineering and Technology, Dec. 2022. [Online]. Available: http://dx.doi.org/10.1049/PBTE105E

work page doi:10.1049/pbte105e 2022

[16] [16]

Atmospheric effects on satellite–ground free space uplink and downlink optical transmissions,

N. Maharjan, N. Devkota, and B. W. Kim, “Atmospheric effects on satellite–ground free space uplink and downlink optical transmissions,” Applied Sciences, vol. 12, no. 21, p. 10944, 2022

work page 2022