The results presented thus far are based on the following idealized, but still reasonably representative, conditions on the DPs: Condition 1. The simulated DPs are idealized with no noise, Condition 2. The DPs used to make a decision are from the same crystal, and Condition 3. The DPs used to make a decision are from varied zone axes. Now, these conditions are relaxed, and the performance of the MVOF-ML framework is investigated in more realistic noisy scenarios. The same trained model using the simulation data is tested. To alleviate Condition 1, we tested the performance of the framework on more realistic noisy DPs. Three types of noise -perturbation, vacancy, and redundancy -were introduced to the ideal simulated DPs. For perturbation, the location and intensity of the Bragg disks were altered. The perturbed Bragg disks were obtained by multiplying the location and intensity values in the vector map with random numbers drawn from an 𝒩(1, 𝜎) Gaussian distribution, where 𝜎 is the standard deviation. As 𝜎 increases, the DPs contain Bragg disks at more strongly perturbed locations and/or intensities than their true values. For vacancy and redundancy, we randomly remove or add Bragg disks, analogous to "missing" points or detecting "false positives" in a DP that are not part of the structure. Any added Bragg disks are sampled from the range of the original vector maps. The data show that the accuracy decreases gradually with increasing noise, but there is no sudden or significant drop in accuracy when noise is introduced (Fig. ). When the noise level is low (noise level between 0.05 and 0.1 for all the three cases), the accuracies remain higher than 0.8. Thus, the MVOF-ML framework is relatively robust to those three types of noise.