Noncontact radar sensing offers a compelling solution for estimation and continuous monitoring of vital signs. Despite significant progress in single patient vital sign detection, estimations for multi patient scenarios using a single radar sensor remains challenging due to the need of disentangling the overlapping physiological signals from a single radar data cube. To address this challenge, this paper presents a technical validation of the SiViS dataset. Specifically, we validate the influence of radar placement on vital sign estimation and benchmark a reference two-stage signal processing pipeline (adaptive Minimum Variance Distortionless Response, MVDR, beamforming followed by phase-based estimation). The focus of this work is on dataset validation and characterization of placement effects, rather than proposing a novel algorithm for heart and respiration rate estimation. Our preliminary evaluations test the optimal radar position for estimation and demonstrated the feasibility of concurrently estimating these vital signs in a multi patient scenario, benchmarking our basic pipeline. In conclusion, this work advances the capability of single sensor radar systems from single patient to multi patient noncontact monitoring, thereby highlighting future directions for robust multi patient monitoring.