twente-vortexflow: sync data card, pipelines and figures with GitHub

Syncs the twente-vortexflow card, pipelines and figures on the Hub with the reviewed state on GitHub (open-h/OpenH-RF).

Both `pipeline_chirp.yaml` and `pipeline_short_imaging_pulse.yaml` gain the grid `reconstruct.py` uses: x +/-80 mm, z 50-100 mm on a 1600 x 700 grid. Without it zea falls back to a default grid and the figures do not reproduce.

Adds the seven figures the card shows. Five were already referenced but sat at the folder root on GitHub and were never uploaded here, so the links were dead: the setup schematic and its side view, the microbubble size distribution, the reference B-mode, and the camera-to-ultrasound mapping. `assets/vortex_street.gif` is the new header loop, pairing the Photron high-speed camera view with the B-mode reconstruction frame for frame from `track_0`, so both show the same instant of the vortex street. `assets/main.png` is the release image and is not embedded in the card.

Two text fixes come with it: the reference B-mode caption said left/right where the panels are stacked top/bottom, and the reconstruction command now matches the script, which takes `ZEA_FILE` and `FRAME` at the top rather than CLI flags.

Files changed: 10.

```
+ twente-vortexflow/assets/MB_size_distribution.png
+ twente-vortexflow/assets/main.png
+ twente-vortexflow/assets/reference_bmode.png
+ twente-vortexflow/assets/reference_mapping.png
+ twente-vortexflow/assets/setup.png
+ twente-vortexflow/assets/setup_side.png
+ twente-vortexflow/assets/vortex_street.gif
~ twente-vortexflow/README.md
~ twente-vortexflow/pipeline_chirp.yaml
~ twente-vortexflow/pipeline_short_imaging_pulse.yaml
```

twente-vortexflow/README.md

@@ -19,6 +19,14 @@ size_categories:
 
 # OpenH-RF — Ultrasound-Optical Flow Phantom Chamber Data
 
+![Optical camera view beside the B-mode reconstruction of a von Karman vortex street](assets/vortex_street.gif)
+
+The Photron high-speed camera view (left) and the B-mode reconstruction (right) of
+[`AcqData_PVoltage80_TVoltage3.4.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/twente-vortexflow/data/AcqData_PVoltage80_TVoltage3.4.hdf5).
+Both come from `track_0`, frame for frame, so the optical and acoustic views show the
+same instant of the vortex street.
+
+
 ## Dataset Description
 
 Pre-beamformed ultrasound channel-capture data acquired with a curved-array transducer
@@ -30,14 +38,14 @@ data. The intended task is **blood-flow imaging and Doppler processing** (RFP ta
 
 ### Phantom
 The front and the back of the flow chamber are made from medical-grade gelatin to facilitate ultrasound transmission. A cylinder with a diameter of 6 mm is placed inside the flow chamber which generates a von Kármán vortex street. The distance between the walls of the flow chamber is about 3 cm. A schematic of the setup is shown in Figure 1. 
-![Figure 1: Ultrasound - optical flow phantom setup](setup.png)
+![Figure 1: Ultrasound - optical flow phantom setup](assets/setup.png)
 
 The elevation focus of the transducer is aligned with the optical light sheet, see Figure 2.
-![Figure 2: Alignment of the acoustical beam and the lightsheet](setup_side.png)
+![Figure 2: Alignment of the acoustical beam and the lightsheet](assets/setup_side.png)
 
 ### Contrast
 Optical scattering was facilitated by hollow glass beads (mean particle size: 9-13 micrometer, Manufacturer: Sigma-Aldrich, PubChem Substance ID: 24867590). The acoustical scatter was enhanced by adding in-house produced microbubbles. The microbubble size distribution is shown in Figure 3.
-![Figure 3: Microbubble size distribution](MB_size_distribution.png)
+![Figure 3: Microbubble size distribution](assets/MB_size_distribution.png)
 
 ### Acquisition parameters
 The acquisition settings for all six datasets are summarized in Table 1.
@@ -151,20 +159,22 @@ The pipeline applies: `Cast(float32) → Demodulate → Beamform(DAS, 100 patche
 
 To reconstruct:
 ```bash
-python reconstruct.py --input AcqData_PVoltage80_TVoltage3.4.hdf5 --frame 10
+python reconstruct.py
 ```
 
+`ZEA_FILE` and `FRAME` at the top of the script select the acquisition and frame.
+
 Reference B-mode image (AcqData_PVoltage80_TVoltage3.4.hdf5, frame 10):
 
-![Reference B-mode reconstruction](reference_bmode.png)
+![Reference B-mode reconstruction](assets/reference_bmode.png)
 
-*Left: short imaging pulse track. Right: chirp track. Two horizontal phantom wall
+*Top: short imaging pulse track. Bottom: chirp track. Two horizontal phantom wall
 reflections are visible, with a speckle-filled flow chamber between them. Near-field
 reverberation and grating-lobe artifacts at the walls and the cylinder are acquisition-induced.*
 
 Reference mapping between camera and ultrasound image (AcqData_PVoltage80_TVoltage3.4.hdf5, frame 10):
 
-![Reference images of particles in flow](reference_mapping.png)
+![Reference images of particles in flow](assets/reference_mapping.png)
 *Top: short imaging pulse track. Bottom: synchronized camera recording. The walls of the phantom and the cylinder are visible in both images. In the ultrasound image, speckle is visible in between the walls (mainly bubble induced), whereas in the camera image the contrast is induced by the hollow glass beads. Light reflection artefacts are visible in the camera image near the cylinder and the walls.* 
 
 

twente-vortexflow/pipeline_chirp.yaml

@@ -1,3 +1,8 @@
+parameters:
+    xlims: [-0.08, 0.08]   # matches reconstruct.py
+    zlims: [0.05, 0.10]
+    grid_size_x: 1600
+    grid_size_z: 700
 pipeline:
     operations:
     -   name: keras.ops.cast

twente-vortexflow/pipeline_short_imaging_pulse.yaml

@@ -1,3 +1,8 @@
+parameters:
+    xlims: [-0.08, 0.08]   # matches reconstruct.py
+    zlims: [0.05, 0.10]
+    grid_size_x: 1600
+    grid_size_z: 700
 pipeline:
     operations:
     -   name: keras.ops.cast