# Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax
## Appendix B Neural Networks' Architectures  
In this appendix, we describe the details of the various networks as well as the optimizer settings. All the software has been written using MATLAB(r) (R2019a, The MathWorks, Inc.). In our notation, a single line of a table represents a layer. The convention is the following: at the beginning of the row, we have the type of layer as well as the nonlinearity associated with the layer (if present) and the indication of presence of BatchNorm (if the BN acronym is present). We have five types of layers in our network:  
1. Input layer. It simply indicates the input of the system, the matrix \(X_{n}\). 2. 1d convolutional layer (indicated as conv_1d). Its parameters are the number of taps of the filters (Ntaps), the number of filters (Nch) as well as the stride (the decimation period at the output of the layer). 3. residual 1d convolutional layers: as standard conv_1d layers but with a residual connection (another set of filters that bypass the nonlinearity and whose output is summed to the output of the nonlinearity). flatten layer. It simply indicates that the input of this layer (a multivariate time serie) is flattened (vectorized). 5. fully connected layers, whose argument is the dimension of the output. We consider as nonlinearities the rectified linear unit (ReLu), the sigmoid (Sigm), and the hyperbolic tangent (Tanh). In the first convolutional network (\(e_{\beta}^{1}(\cdot)\)), we included skip connections of the last element of the various sequences at the two layers that are fed to the fully connected classifier \(c_{\psi}(\cdot)\). All networks are trained using Adam optimizer with default settings and learning rate 0.001 for 30 epochs with batch size equal to 64. [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Monitoring and Mitigation of Ionospheric Anomalies for GNSS-Based Safety Critical Systems: A review of up-to-date signal processing techniques. _IEEE Signal Process. Mag._**2017**, _34_, 96-110. [CrossRef]
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