---
pretty_name: SoPhAr aviation - data, results and figures
license: other
tags:
- aviation
- solar-energy
- wireless-power-transfer
- decarbonization
- flight-data
---

# SoPhAr aviation: data, results and figures

Data, model results and figures for *Solar phased-array-based wireless power transfer for commercial
aviation decarbonization* (T. Wang, Y. Xu, J. Byeon, J. Jiao, J. Mohammadi, K. Kockelman, C. Claudel
and A. Bayen; under review at *Nature Sustainability*). The code is at https://github.com/BonnyWang010705/SoPhAr.

## Using the dataset with the code

Download the dataset into the root of the code repository:

```bash
python download_data.py      # run inside the cloned code repository
```

`data/`, `results/` and `figures/` then sit next to `code/`, where every script looks for them.

## Contents (4.0 GB)

### `data/` — inputs

| Path | Contents | Source |
|---|---|---|
| `flights/raw/` | 14 monthly archives, December 2024 – January 2026 | BTS TranStats, Marketing Carrier On-Time Performance |
| `flights/processed/by_month/` | one table per month (Parquet) | `code/1_flight_data/2_preprocess.py` |
| `flights/processed/flights_2025.parquet` | 2025 flight table, 7,599,787 records | `3_build_annual.py` |
| `flights/processed/flights_reference_week.parquet` (and `.csv`) | 7–13 April 2025 (Central Time), 149,739 records | `4_reference_week.py` |
| `flights/processed/flights_reference_week_matched.parquet` | flights whose origin and destination are in the airport file: the 148,814 flights analysed | `5_match_airports.py` |
| `airports/airport_data.geojson` | USA Airports | Esri, ArcGIS |
| `solar_farms/uspvdb_v3_0_20250430.geojson` | United States Large-Scale Solar Photovoltaic Database | Lawrence Berkeley National Laboratory, U.S. Geological Survey |
| `US_boundaries/` | state (1:20M) and county (1:500k) cartographic boundaries, 2023 | U.S. Census Bureau |

### `results/` — model outputs

| Path | Contents |
|---|---|
| `baseline/` | system model at 12,100 m (reference) and at 9,100 and 15,100 m: one row per eligible flight (86,084 at 12,100 m) and one row per qualified solar farm (438); Supplementary Tables 1–7 |
| `baseline/route_maps/` | route maps of the eligible flights by range class (Fig. 3b–d) |
| `optimization_1/` | flight schedule optimization at the three altitudes, with the optimized shift of each flight; Supplementary Tables 8–14 |
| `optimization_2/Optimization_2_Results_R1/` | farm-and-flight choice optimization: selected farms and flights in each of the 100 penetration scenarios (`solar_results_*` and `flight_results_*`) |
| `optimization_2/Optimization_2_summary_R1.csv` | totals of each scenario (Supplementary Table 15) |
| `optimization_2/penetration_state_totals.csv` | state totals of each scenario, for Supplementary Figs. 14–23 |
| `optimization_2/route_maps/` | route maps of the selected flights in each scenario (`flight_map_*`), for Supplementary Figs. 14–23 and Fig. 8a |
| `sensitivity/R1_Sensitivity_Tables.xlsx` | sensitivity analyses of Supplementary Notes 7–10 (Supplementary Figs. 33–37) and the model assumptions |
| `representativeness/` | weekly measures over 2025 (Supplementary Note 1 and Supplementary Fig. 1) |

### `figures/` — paper figures

| Path | Contents |
|---|---|
| `main/fig02_solar_farm_analysis/` … `main/fig06_market_penetration/` | Figs. 2–6 of the article (PNG and PDF), each with its panels in `panels/` |
| `main/fig07_solar_farm_selection/`, `main/fig08_flight_selection/` | Figs. 7–8 of the arXiv version, redrawn with the R1 results, each with its panels in `panels/` |
| `supplementary/fig01_representativeness/` | Supplementary Fig. 1 |
| `supplementary/fig02_state_rankings/` | Supplementary Fig. 2, with its panels a–f in `panels/` |
| `supplementary/fig03_selection_frequency/` | Supplementary Fig. 3 |
| `supplementary/fig04_23_penetration/` | Supplementary Figs. 4–13 (farm selection: maps and scatter plots) and 14–23 (flight selection: maps and bar charts), two images each |
| `supplementary/fig24_29_case_studies/` | Supplementary Figs. 24–29, solar farm case studies (farms 230 and 4746) |
| `supplementary/fig30_31_flight_case_study/` | Supplementary Figs. 30–31, flight case study |
| `supplementary/fig32_37_sensitivity/` | Supplementary Figs. 32–37 |

Fig. 1 (a schematic) is not included.

## Sources and licenses

The inputs are redistributed under the terms of their sources:

- BTS On-Time Performance, U.S. Department of Transportation: public domain.
- United States Large-Scale Solar Photovoltaic Database, LBNL and USGS: public domain.
- Cartographic boundary files, U.S. Census Bureau: public domain.
- USA Airports, Esri: subject to Esri's terms of use.

License of the results and figures: to be decided.

## Citation

To be added on publication.