Layer notation for convolutional neural networks
One paper referenced by the first paper you linked to is here. It explains in section 3 (experiments) the following notation:


  2x48x48-100C5-MP2-100C5-MP2-100C4-MP2-300N-100N-6N represents a net
  with:
  
  
  2 input images of size 48x48
  a convolutional layer with 100 maps and 5x5 filters
  a max-pooling layer over non-overlapping regions of size 2x2
  a convolutional layer with 100 maps and 4x4 filters
  a max-pooling layer over non overlapping regions of size 2x2
  a fully connected layer with 300 hidden units,
  a fully connected layer with 100 hidden units
  a fully connected layer with 6 neurons (one per class)
  


From this, the answer to your question is:


100C3 means a convolutional layer with 100 maps and 3x3 filters
MP2 means a max-pooling layer with non overlapping regions of size 2x2
200C2 means a convolutional layer with 200 maps and 2x2 filters
etc with the "C" layers (means convolutional, preceding integer is the number of features maps; final integer is the filter size)


According to the second paper you linked, the subscript _5 indicates five pairs of 300nC2−300nC2−MP2 connected layers (see section 3), and the n indicates "the number of filters in the nth convolutional layer is [300]n".  According to the accompanying model diagram (figure 3 in the linked paper), the C2 and C1 layers produce 1x1 output, meaning a scalar value. This would mean C2 is a convolutional layer with 1 map and a 2x2 filter and C1 is a convolutional layer with 1 map and 1x1 filter (though I don't fully understand what this adds).