  A relatively new concern for the forthcoming massive spectroscopic sky
surveys is the impact of contamination from low earth orbit satellites. Several
hundred thousand of these satellites are licensed for launch in the next few
years and it has been estimated that, in some cases, up to a few percent of
spectra could be contaminated when using wide field, multi-fiber spectrographs.
In this paper, a multi-staged approach is used to assess the practicality and
limitations of identifying and minimizing the impact of satellite contamination
in a WEAVE-like stellar spectral survey. We develop a series of
convolutional-network based architectures to attempt identification, stellar
parameter and chemical abundances recovery, and source separation of stellar
spectra that we artificially contaminate with satellite (i.e. solar-like)
spectra. Our results show that we are able to flag 67% of all contaminated
sources at a precision level of 80% for low-resolution spectra and 96% for
high-resolution spectra. Additionally, we are able to remove the contamination
from the spectra and recover the clean spectra with a $<$1% reconstruction
error. The errors in stellar parameter predictions reduce by up to a factor of
2-3 when either including contamination as an augmentation to a training set or
by removing the contamination from the spectra, with overall better performance
in the former case. The presented methods illustrate several machine learning
mitigation strategies that can be implemented to improve stellar parameters for
contaminated spectra in the WEAVE stellar spectroscopic survey and others like
it.
