We reduce the false positive rate dramatically by using an elasticity parameter that learns during the first iterations of the execution and adapts dynamically.
then, we restarted the execution from the last checkpoint (i.e., time step 15,000), and we recorded the datasets of the execution at each time step for a corruption-free execution.