This data set supports the published four-component integration problem using real-world weather forecasts from the European Centre for Medium-Range Weather Forecast and a simulated linear spring--mass--damper system excited by wave elevation. Each component in the spring--mass--damper system is monitored with techniques of differing accuracies representative of marine-type health uncertainties. Weather forecast uncertainty is included using weather predictions of significant wave height and peak period up to 10 days out. As well as their exact values, different test cases include the spring, mass, and damper being modeled as noisy sensors representative of sensors onboard a vessel, as well as the spring being modeled as a visually-inspected system component reflective of human impact onboard a vessel. Complete details are given in the referenced paper; this data set represents the inputs to the machine learning classifiers discussed.
Sulkowski, B and M. Collette. (2025) A comparison of machine learning classifiers in predicting safety for a multi-component dynamic system representation of an autonomous vessel. Applied Ocean Research, 154 (104368), https://doi.org/10.1016/j.apor.2024.104368
GOES_flare_list: contains a list of more than 12,013 flare events. The list has 6 columns, flare classification, active region number, date, start time end time, emission peak time.
SHARP_data.hdf5 files contain time series of 20 physical variables derived from the SDO/HMI SHARP data files. These data are saved at a 12 minute cadence and are used to train the LSTM model.
Jiao, Z., Sun, H., Wang, X., Manchester, W., Gombosi, T., Hero, A., & Chen, Y. (2020). Solar Flare Intensity Prediction With Machine Learning Models. Space Weather, 18(7), e2020SW002440. https://doi.org/10.1029/2020SW002440 and Chen, Y., & Manchester, W. (2019). Data and Data products for machine learning applied to solar flares [Data set], University of Michigan - Deep Blue. https://doi.org/10.7302/qnsq-cs38
