## 3 Results
### CS Estimation for Daylight Light Sources  
As described in Sections 2.3.1 and 2.3.2, the CS estimation for daylight spectra can be traced back to the determination of the CCT from the color sensor's readouts. After converting its \(RGB\) output to chromaticity space by applying a daylight-specific transformation matrix, the method of [PERSON] et al. can be used to determine the light source's CCT, which in turn is required to reconstruct its spectral composition by adopting Equations (9)-(14). Once the daylight spectrum is known, the corresponding CS value can directly be calculated without further approximations by making use of [PERSON] et al. 's original model. In order to derive the daylight-specific transformation matrix and to determine again the relationship between the sensor's green channel output and \(E_{v}\) for daylight light sources, a dataset of spectral daylight measurements performed on the 19 August 2020 between 6:32 a.m. and 8:45 p.m. in Darmstadt, Hessen, Germany (GPS coordinates 49\({}^{\circ}\)51\({}^{\prime}\)20.772\({}^{\prime\prime}\) N, 8\({}^{\circ}\)39\({}^{\prime}\)12.528\({}^{\prime\prime}\) E) using again the CSS-45 spectroradiometer was available for training. The corresponding SPDs are depicted in Figure 11a. As in the previous section, different matrices were tested for optimal model performance. The corresponding results are shown in Table 5 for a representative subset of the training data sampled at different measurement times. From these results, it can be confirmed that the use of a simple linear \(3\times 3\) matrix again yielded the smallest overall prediction errors. Comparably good results were observed for nonlinear matrices of sizes \(3\times 7,3\times 10\), and \(3\times 14\), respectively. However, following the same argumentation as in Section 3.1, the \(3\times 3\) approach, due to its good model performance and simplicity, again appears to be the method of choice for dealing with daylight spectra. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Name/Par.** & \(HA_{374}\) & \(Xenon_{1}\) & \(CFL_{2K9}\) & \(CFL_{954}\) & \(FL_{927}\) & \(FL_{945}\) & \(LED_{HCSL}\) & \(LED_{H\!Meli}\) \\ \hline \multicolumn{10}{c}{CS\({}_{2018}\) and other parameters directly calculated from the measured artificial spectra} \\ \hline CCT in K & 3300 & 4058 & 2785 & 4390 & 2640 & 4390 & 2797 & 4870 & 4000 \\ \(E_{v}\) in lx & 750 & 750 & 750 & 750 & 750 & 750 & 750 & 750 \\ \(\text{CL}_{\text{A,2018,origin}}\) & 897 & 471 & 530 & 620 & 641 & 620 & 727 & 763 & 637 \\ \(\text{CS}_{\text{2018,origin}}\) & 0.51 & 0.40 & 0.43 & 0.45 & 0.46 & 0.45 & 0.48 & 0.49 & 0.46 \\ \hline \multicolumn{10}{c}{CS\({}_{2018}\) and other parameters estimated from the color sensor readouts} \\ \hline \(E_{v,\text{processed}}\) in lx & 673 & 790 & 766 & 878 & 878 & 748 & 738 & 694 & 815 \\ \(\text{CS}_{\text{2018,Truong}}\) & 0.49 & 0.39 & 0.44 & 0.47 & 0.46 & 0.45 & 0.46 & 0.47 & 0.44 \\ \hline \multicolumn{10}{c}{Compared parameters} \\ \hline \(|\text{ACS}_{2018}|\) & 0.0208 & 0.0126 & 0.0143 & 0.0208 & 0.0038 & 0.0083 & 0.0186 & 0.0198 & 0.0203 \\ \((Meas.-Calc.)\) & & & & & & & & \\ \(\Delta u^{\prime}v^{\prime}\cdot 10^{3}\) & 2.42 & 7.50 & 4.46 & 7.04 & 7.73 & 7.04 & 6.46 & 6.10 & 4.37 \\ \((Meas.-Calc.)\) & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison between the color sensor’s CS predictions (\(\text{CS}_{\text{2018,Truong}}\)) and [PERSON] et al.’s original model (\(\text{CS}_{\text{2018,origin}}\)) for a second set of artificial light sources not included in the training data. Again, the vertical illuminance was assumed to be 750 lx, and the \(\text{CS}_{\text{2018,origin}}\) measurement accuracy is \(\pm 8\%\). The annotation “\((Meas.-Calc.