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https://github.com/Bonface-Osoro/saleos

Sustainability Analytics for Low Earth Orbit Satellites
https://github.com/Bonface-Osoro/saleos

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Sustainability Analytics for Low Earth Orbit Satellites

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README 

        
# Sustainability Analytics for Low Earth Orbit Satellites (saleos)



Welcome to the Sustainability Analytics for Low Earth Orbit Satellites 

(`saleos`) repository.



Paper Citation

--------------

- Ogutu, O. B., Oughton, E. J., Wilson, A. R, & Rao, A. (2023). Sustainability 

assessment of Low Earth Orbit (LEO) satellite broadband mega-constellations. 

https://arxiv.org/abs/2309.02338



There is increasing concern about adverse environmental impacts produced by 

Low Earth Orbit (LEO) megaconstellations. The `saleos` codebase provides an 

open-source integrated assessment model capable of concurrently estimating 

environmental emissions, broadband capacity, and social and financial costs 

for different LEO satellite networks.



We focus on evaluating Amazon's Kuiper, Eutelsat's OneWeb and SpaceX's Starlink,

with these three LEO constellations visualized in `Figure 1`. The `saleos` 

codebase allows you to compare these LEO constellations against a representative

Geostationary Earth Orbit (GEO) operator.  



#### Figure 1 Three key LEO constellations: Kuiper, OneWeb and Starlink (Details as of December 2023).



  




Emissions produced during the launching of satellites depend on the 

utilized rocket vehicle. Most operators planning or launching LEO broadband 

satellites have used (or intend to use) SpaceX’s Falcon-9 or Falcon-Heavy, 

the European Space Agency’s Ariane rocket system, or prior to Spring 2022, 

Russia’s Soyuz-FG rocket, as detailed in `Figure 2`. 



#### Figure 2 Details of launch rocket systems.



  




## Sustainability metrics



The `saleos` codebase is capable of estimating a range of sustainability 

metrics. `Figure 3` illustrates a selection of these including the estimated 

annual emissions per subscriber (subplot b), potential mean peak capacity per 

subscriber (subplot c), and the financial costs per subscriber (subplot e/f).



#### Figure 3 Aggregate sustainability metrics for Kuiper, OneWeb, Starlink and a hypothetical GEO operator.



  




## Method



The method is based on (i) a Life Cycle Assessment (LCA) model of environmental 

emissions and other impacts,(ii) a stochastic engineering simulation model 

estimating constellation capacity using the Friss Transmission Equation, (iii) 

potential traffic demand based on different adoption scenarios, and (iv) a 

techno-economic model of the associated social and financial costs. `Figure 4` 

illustrates the integrated assessment approach.



#### Figure 4 Integrated assessment modeling approach.



  




## Required data



To use `saleos` the following model input datasets are required from `data/raw`: 

1. `life_cycle_data.xlsx`: This dataset contains estimated emissions and 

other environmental impacts per launch for major rocket vehicles. 

2. `scenarios.csv` : This file contains the past and future launch information

for different constellations, including hydrocarbon (HYC) versus hydrogen (HYD) 

fuel-based rockets. 



Using conda

-----------

The recommended installation method is to use conda, which handles packages 

and virtual environments, along with the conda-forge channel which has a host 

of pre-built libraries and packages.



Create a conda environment called saleos:



  `conda create --name saleos python=3.7 gdal`



Activate it (run this each time you switch projects):



  `conda activate saleos`



Alternatively, to install a conda environment capable of running the model, 

you can utilize the following code:



  `conda env create -f saleos.yml`



The `saleos.yml` file represents an existing virtual environment with a 

variety of packages, necessary for running the model (e.g., pandas, numpy etc.).



First, to run `saleos` you need to generate uncertain capacity and cost 

parameters since they are not deterministic.



So navigate to the `scripts` folder and run `preprocess.py`. This will produce 

two capacity and cost.csv files named `uq_parameters_capacity.csv` and 

`uq_parameters_cost.csv` stored in the path `data/processed`.



Secondly, run the whole integrated model to produce capacity, emission and 

cost results by running the simulation script (`run.py`). It should first 

produce the following intermediate results stored in the folder 

`data/processed`:



1. `interim_results_capacity.csv`

2. `interim_results_cost.csv`



Next, you can inspect the model outputs stored in the `results` folder:



1. `individual_emissions.csv`

2. `final_capacity_results.csv`

3. `final_capacity_cost.csv`



Lastly, to visualize the results, you will navigate into the `vis` folder 

and run the following `r` scripts in any order.



1. `aggregate_metrics.r`

2. `emissions.r`

3. `capacity.r`

4. `social_cost.r`

5. `cost.r`



Quick start

-----------

To quick start, execute the `setup.py` file.



  `pip install .`



Then run the scripts in the order defined in the previous section (`Using conda`).



Background and funding

----------------------



**saleos** has been developed by researchers at George Mason University, 

University of Strathclyde and Middlebury College.



## Team

- Bonface Osoro, George Mason University (Model development).

- Edward Oughton, George Mason University (Project lead and corresponding 

author).

- Andrew Wilson, University of Strathclyde / Glasgow Caledonian University 

(LCIA modeling).

- Akhil Rao, Middlebury College (Policy and economics).



Acknowledgement

---------------

EO would like to thank Geography and Geoinformation Sciences at George Mason 

University for providing start-up funding for the project. Additionall, the 

authors thank Nils Pacher and Dr. Inigo del Portillo for providing code for 

modeling the orbit of the three LEO constellations, as well as Dr. Whitney 

Lohmeyer for providing advice on the satellite capacity model.