Displacement measurement via self mixing interferometry and neural network training set

Self mixing interferometry is a simple and robust sensing method which can be used (among other things) to measure the displacement of a target along the light propagation axis. While conceptually simple, the actual use of this method is less straightforward than originally envisioned because reconstructing the target displacement from the interferometric signal is often tricky. A small neural network can do this task very well after proper training, as described in [10.1364/OE.419844]. This data set was used to train the network in that work (after data augmentation). It consists of a python dictionary with two keys: `truth` and `signal`. The `truth` part is a  195011-elements long numpy array corresponding to the displacement of the target in units of wavelength per 1.024 ms. The `signal` part is the interferometric signal corresponding to the displacement. It is arranged in a (195011,256,1) numpy array. Each segment of length 256 corresponds to the interferometric signal acquired during a 1.024 ms time window.  For instance, the displacement value in `truth[618]` corresponds to the interferometric signal segment `signal[618,:,0]`.