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The WSN dataset in this study includes 6 nodes.
The validation dataset in this study highly supported the hypothesis that predictive accuracy could be obtained readily by the end of the first follow-up year (Fig. 3f).
The reason could be that the dataset used for developing DH Yam was the validation dataset in this study.
For the dataset in this study, almost 80%% of TBIs occurred within 10 min of place of residence.
The handset-based dataset in this study was collected by the MobiTrack software installed on Nokia Symbian smartphones of 183 participants or users from September 2009 to December 2010, i.e. for a period spanning about 16 months.
As shown in Additional file 1: Fig. S4, the dataset in this study is large enough to effectively eliminate the trade-off between shallow structure and structure in the target region when all seismograms and all stations are used.
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The code used to analyse the datasets in this study is available from the corresponding author on reasonable request.
The average absolute percent relative error (AAPE) for train and test datasets in this study is 2.9e-13 2.9e-13.7% for the RBFN model.
We use four datasets in this study: (1) Geo-positioning data.
The TC datasets in this study were obtained from the TC best track provided by Regional Specialized Meteorological Center RSMC -Tokyoo Typhoon CenteRegional Specialized Meteorological Center RSMC -Tokyo
Four 3D objects are used for our synthesized datasets in this study: Sphere, Caesar, Buddha and Venus.
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