Fix stereo geometry description: side-scatter arrangement, not forward-scatter

README.md

@@ -25,7 +25,7 @@ Two cases are provided:
 - **Case A (clean)**: 85 000 particles per 2048 × 2048 image (≈ 5.2 ppw at 16 × 16 windows), no noise. Sets an upper bound on PIV accuracy.
 - **Case B (noisy)**: 22 000 particles per image (≈ 1.3 ppw), Gaussian sensor noise (mean 80, std 16, SNR ≈ 8). Realistic experimental conditions.
 
-Each case contains 4 000 image pairs in both planar and stereo (± 45° forward-scatter) geometries. Ground truth is provided separately for each case — both derive from the same underlying JHTDB channel snapshots but with their respective particle counts, so finite-sample statistics are self-consistent.
+Each case contains 4 000 image pairs in both planar and stereo geometries. The stereo cameras are placed symmetrically at ± 45° from the sheet normal, in a side-scatter arrangement. Ground truth is provided separately for each case — both derive from the same underlying JHTDB channel snapshots but with their respective particle counts, so finite-sample statistics are self-consistent.
 
 Companion to the PIVtools software paper (SoftwareX, submitted). The dataset is self-contained: drop it next to a [PIVtools](https://github.com/MTT69/python-PIVtools) install and the benchmark scripts reproduce every validation figure in the paper.
 
@@ -103,7 +103,7 @@ MTT69/TurbulentChannel/
 | Case A particle count | 85 000 per image (≈ 5.2 ppw at 16 × 16 windows), no noise |
 | Case B particle count | 22 000 per image (≈ 1.3 ppw at 16 × 16 windows) |
 | Case B noise | Gaussian, mean = 80, std = 16, SNR ≈ 8 |
-| Stereo geometry | Two cameras at ±45° forward-scatter |
+| Stereo geometry | Two cameras at ± 45° from the sheet normal (side-scatter arrangement) |
 | dt | Matches JHTDB snapshot spacing (see CDL configs) |
 
 ## Ground truth