Communication-Reliability-Aware CCAM/MaaS Framework with UAV-Assisted Sensing and a Mobile Command-and-Control Laboratory: A Romania-Specific Design

This supplementary package provides full transparency for the illustrative in-silico walkthrough presented in Sections 12–13 of the manuscript. All input values are synthetic and clearly labelled as illustrative; the package demonstrates artefact computability, not empirical validation. Table S1 defines the eighteen parameters (P01–P18) that feed into the four mathematical models: the Composite Congestion Index (CI, Eq. 7), the Operational Risk Model (Eq. 9), the Resilience Index (RI, Eq. 10), and the UAV orienteering allocation (Eq. 11). For each parameter the table reports the symbol, per-segment value, physical unit, source type, rationale, the equation in which it appears, and the recommended perturbation range for sensitivity analysis. The pivotal variable is P05 (CommDeg_norm), which takes values of 0.05–0.10 for urban segments and 0.80–0.85 for rural degraded corridors, driving the RQ2 ablation result. Table S2 documents the ten weight sets used by CI and RI across three Romanian context typologies: Urban/Plain (Timișoara), Mountain/Urban (Brașov), and Rural Degraded Corridor. Each of the thirty rows states the weight value, its calibration basis, the normalisation method applied to the corresponding input variable, the substantive interpretation, and the role of that weight in ablation and stress-test scenarios. The communication-degradation weight (w5 in CI; ω5 in RI) rises from 0.05 in the urban context to 0.35 in the rural context, which is the structural premise of IC2 and the RQ2 hypothesis. Script S1 is a self-contained Python 3.9+ script that implements the complete decision chain. It computes CI, Risk, RI, and UAV greedy orienteering for the six-segment toy network, executes the RQ2 ablation test by setting the communication weights to zero and renormalising, and exports per-segment results to CSV and JSON. Eight automated smoke tests verify internal consistency, weight-sum correctness, derivation of CommReliability, UAV endurance feasibility, and the directional prediction of RQ2 (rural segment CI values are strictly higher when the communication term is active). The key manuscript result — CI(S5) = 0.605 — is verified exactly. Running the script requires only NumPy and pandas; no external data files are needed.