Polar encoding, described by Arikan in IEEE Transactions on Information Theory, Vol. 55, No. 7, July 2009, was a milestone for telecommunications. A Polar code distributes information among high and low-capacity channels, showing the possibility of achieving perfect channel capacity. The high-capacity channels allow almost noiseless transmission of data. When these channels are not high noise, reliability is achieved in the signal transmission. It starts to compete against codes such a Low-Density Parity-Check (LDPC) codes. Polar code can be also considered error correcting, based on the redundancy inherent in its structure. This feature makes polar encoding also applicable to digital quantum-resistant cryptography protocols. This work explores linear decoding at a first or single trial in the case of small losses or small number of bit-flipping, and repeated transmission for medium level losses. This is distinct from Arikans successive probabilistic decoding by application of probabilistic rules. Linear decoding is done directly from solving the linear equations connecting the codewords x and the received signals y after transmission via noisy channels. Numerical examples will be shown. Along with this work, programming in Mathematica language was used. Codes are available for copy-and-paste for Mathematica users to immediately try the described formalism.