T-DBSCAN - Implementing STOP logic

Question

I am attempting to implement the T-DBSCAN algorithm described in T-DBSCAN: A Spatiotemporal Density Clustering for GPS Trajectory Segmentation. I have been able to implement most of the logic between the definitions (Page 3) and the pseudo-code (page 4), but I have not been able to implement the logic to determine if a cluster is a stop as described in Definition 9 of the paper (Page 3). Specifically, I am having trouble understanding Definition 7 "Temporally Continuous", which is referenced in Definition 9:

Definition 7. Temporally continuous (TC). Let the min and max time stamps of a cluster $C$ ($\subseteq D$) $ \text{mint} $ and $ \text{maxt} $, respectively. $ C $ is known as being “temporally continuous” if, for $ \forall{p_t \in D} $ and $ mint < t < maxt $, $ p_t \in C $.

where $ D $ is the trajectory, and $ p_t $ is a point at time $ t$.

As I read Definition 7, it sounds like it means that a cluster is temporally continuous if the points in the cluster are in the trajectory, and if all the timestamps of the point in the cluster is between the minimum time and maximum timestamp in the cluster, which doesn't make sense, because all points in a cluster will always be between the minimum and maximum timestamps in the same cluster, making all clusters temporally continuous.

Would someone be willing to point me in the right direction of how to interpret this section? Definition 9 logic is also not included in the pseudo-code, so I only have the definition itself to go off of.

I've read through an existing implementation on Github which does not have this logic implemented either, which leads me to believe either this section is trivial and does not need to be implemented, or that I am not the only one who has had issues implementing this last section of the algorithm.

Thanks for any help that you can provide. I'm willing to provide anymore information that I have that will be helpful.