Can features that are the same in every sample contribute to learning?
It has no value if it is same for all the training set.
Let's say, you are using a global health dataset for Life expectancy then country code can be a useful feature. It might contain hidden information.
But if you are doing the same analysis for one country e.g. India, keeping a feature country which has only one value e.g. India, will be of no use.
It will not show any variance, now the variance is shifted to States instead of Country compared to the last example.
In you data, I can see multiple Sensor Id. If very dataset will always have these 4 values and there is no explicit assumption for 3rd column which is dependent on 1st, you can remove the feature