Datasets for Model Training, Validation, and Testing, Together with Spectral Measurements and Optical Characterization of Eight Devices

A dataset comprising 640 spectra of thermally stabilized DKSs is partitioned into training and validation sets in a 4:1 ratio. The ground-truth soliton number labels required for semi-supervised model training are first obtained through FFT-GA-based spectral analysis. These labeled datasets are then used to train a hybrid CNN-TMHA model, enabling efficient extraction of representative local and global spectral features. Finally, a testing dataset of 230 real-time acquired soliton spectra is fed into the trained model to enable rapid and accurate classification of distinct DKS states.<br>Based on the spectral measurements and optical characterization results of the eight devices, the following observations can be made:(1) As the coupling gap increases, the resonance modes progressively transition from the over-coupled regime to the critically coupled regime, and eventually to the under-coupled regime. (2) The overall symmetry of the dispersion curves about the central mode confirms that the dispersion landscape is only weakly perturbed.