Datasets for learning spatiotemporal fields

Proof  in paper “Learning Dynamic Spatiotemporal Fields  Using Data from Mobile Sensors”:

proof

————————————————————————————-

 

Notes on the dataset:

The name for the dataset represents info about the dataset. For example, ‘Data_n8_Ns3_r1_N10000_d15.txt’  means that this is the data file which is collected using Ns=3 mobile sensors and staying at one waypoint for r=3 steps with parameters N=10000 and d=15, and there are n=8 basis functions in the fields. In this file, there are Ns=3 columns: each column means the output of one mobile sensor.

Download dataset here:

Data_n8_Ns1_r3_N10000_d15

Data_n8_Ns1_r3_N10000_d15_info

Data_n8_Ns3_r1_N10000_d15

Data_n8_Ns3_r1_N10000_d15_info

Data_n8_Ns3_r3_N10000_d15

Data_n8_Ns3_r3_N10000_d15_info

Data_n8_refSensor_N10000_d15

Data_n8_refSensor_N10000_d15_info

————————————————————————————

Notes on the learned eigenvalues:

The name for the following files represents info about the file. For example, the learned eigenvalues using data file ‘Data_n8_Ns3_r1_N10000_d15.txt’  will be named as ‘EigenData_n8_Ns3_r1_N10000_d15’. In this file, the first two columns are the real parts and imaginary parts of the learned eigenvalues,  while the last two columns are the real parts and the imaginary parts of the true eigenvalues.

Download learned eigenvalues here:

EigenData_n8_Ns1_r3_N10000_d15

EigenData_n8_Ns3_r1_N10000_d15

EigenData_n8_Ns3_r3_N10000_d15

EigenData_n8_refSensor_N10000_d15