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Add Mueller matrix normalization guidance: README.md

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@@ -152,6 +152,13 @@ channel name is normalized to `eta`.
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  ## Physical Parameter Definitions
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  The target tensor follows this channel order and nominal parameter range:
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  ```text
@@ -165,6 +172,8 @@ Important encoding note:
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  - Waveplate target arrays are stored from the source `.npy` files as float32.
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  - Spectral target arrays were converted from grayscale PNG files to float32
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  values normalized to `[0, 1]`; see `target_encoding`.
 
 
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  ## Reference Label Generation
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@@ -260,6 +269,11 @@ The targets are Lu-Chipman reference outputs. Evaluation should be interpreted
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  as agreement with a physics-solver reference and related physics consistency,
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  not as proof of absolute biological ground truth.
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  ## License
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  This dataset is released under CC BY-NC 4.0.
 
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  ## Physical Parameter Definitions
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+ Mueller matrix elements are generally expected to lie within `[-1, 1]` after
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+ normalization. In measured data, small deviations outside this range may occur
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+ because of acquisition noise, calibration differences, numerical processing, or
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+ normalization error. Users should inspect the value distribution for their split
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+ and apply task-appropriate preprocessing before training, such as clipping,
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+ standardization, or normalization based on the training set.
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+
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  The target tensor follows this channel order and nominal parameter range:
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  ```text
 
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  - Waveplate target arrays are stored from the source `.npy` files as float32.
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  - Spectral target arrays were converted from grayscale PNG files to float32
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  values normalized to `[0, 1]`; see `target_encoding`.
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+ - Mueller matrix tensors are stored as measured/processed values, not forcibly
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+ clipped to `[-1, 1]`.
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  ## Reference Label Generation
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  as agreement with a physics-solver reference and related physics consistency,
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  not as proof of absolute biological ground truth.
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+ Measured Mueller matrix entries may be slightly outside the nominal `[-1, 1]`
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+ range. This is expected for real acquisition pipelines; users should decide
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+ whether to clip, standardize, or otherwise normalize values according to their
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+ training protocol.
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+
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  ## License
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  This dataset is released under CC BY-NC 4.0.