# Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax
## Appendix B Neural Networks' Architectures
In Proceedings of the IEEE International Conference on Localization and GNSS (ICL-GNSS), Nuremberg, Germany, 4-6 June 2019; pp. 1-6. [CrossRef]
* _[PERSON] (2019)_ [PERSON]; [PERSON] The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines. _Sensors_**2019**, _19_, 5219. [CrossRef]
* _[PERSON] et al. (2010)_ [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Survey and prediction of the ionospheric scintillation using data mining techniques. _Space Weather_**2010**, \(8\), 1-10. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] _Deep Learning_; MIT Press: Cambridge, MA, USA, 2016. Available online: [[http://www.deeplearningbook.org](http://www.deeplearningbook.org)]([http://www.deeplearningbook.org](http://www.deeplearningbook.org)) (accessed on 30 December 2019). * _[PERSON] et al. (2015)_ [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Prediction of the level of ionospheric scintillation at equatorial latitudes in Brazil using a neural network. _Space Weather_**2015**, _13_, 446-457. [CrossRef]
* _[PERSON] and [PERSON] (2015)_ [PERSON] [PERSON]; [PERSON] [PERSON] modeling total electron content during geomagnetic storm conditions using empirical orthogonal functions and neural networks. _J. Geophys. Res. Space Phys._**2015**, _120_, 11000-11012. [CrossRef]
* _[PERSON] et al. (2011)_ [PERSON]; [PERSON]; [PERSON] Regional GPS TEC modeling; Attempted spatial and temporal extrapolation of TEC using neural networks. _J. Geophys. Res. Space Phys._**2011**, _116_. [CrossRef]
* _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Semi-supervised learning with deep generative models. In _Advances in Neural Information Processing Systems_; MIT Press: Cambridge, MA, USA, 2014; pp. 3581-3589. * _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Learning deep representations by mutual information estimation and maximization. _arXiv_**2018**, arXiv:1808.06670. * _[PERSON] et al. (2018)_ [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Mine: Mutual information neural estimation. _arXiv_**2018**, arXiv:1801.04062. * _[PERSON] et al. (2010)_ [PERSON]; [PERSON]; [PERSON] Estimating divergence functionals and the likelihood ratio by convex risk minimization. _IEEE Trans. Inf. Theory_**2010**, _56_, 5847-5861. [CrossRef]
* _[PERSON] et al. (2012)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Tighter variational representations of f-divergences via restriction to probability measures. _arXiv_**2012**, arXiv:1206.4664. * _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON] [PERSON] f-gan: Training generative neural samplers using variational divergence minimization. In _Advances in Neural Information Processing Systems_; MIT Press: Cambridge, MA, USA, 2016; pp. 271-279. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] On variational bounds of mutual information. _arXiv_**2019**, arXiv:1905.06922. * [PERSON] et al. (2014) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Generative adversarial nets. In _Advances in Neural Information Processing Systems_; MIT Press: Cambridge, MA, USA, 2014; pp. 2672-2680. * [PERSON] et al. (2014) [PERSON], [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Developing a multi-frequency for GNSS-based scintillation monitoring receiver. In Proceedings of the 27 th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+), Tampa, FL, USA, 8-12 September 2014; pp. 1142-1152. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Design of a configurable monitoring station for scintillations by means of a GNSS software radio receiver. _IEEE Geosci. Remote. Sens. Lett._**2018**, _15_, 325-329. [CrossRef]
* [PERSON] et al.