## 2 Materials and Methods
### Design of the Adaptive Self-Adjustment Procedure  
The sensitivity adjustment procedure was based on a complex analysis of time-resolved heat fluxes \(q_{\text{IN}}(t)\) and \(q_{\text{OUT}}(t)\) obtained by both experimental measurements and computational simulation of heat transfer in a studied wall. In the computational simulation, the material composition and orientation of the wall, material properties, and outdoor and indoor environmental data were used as input parameters. At first, the optimization of the computational model parameters was done to validate the capability of the model to simulate the real environment in this particular case. The computational model was optimized by a simple modification of the heat transfer coefficient \(\alpha\) on the interior surface of the wall, in order to obtain verified \(q_{\text{IN}}(t)\) and \(q_{\text{OUT}}(t)\) data through the surface temperature \(T_{s}\). For this purpose, a surface element with very low thickness (\(x=1\)-\(2\) mm) was used in spatial discretization of the computational model to meet preconditions set in Equation (1). The complex sensitivity adjustment experiment was designed using the following steps:  
1. Heat transfer coefficient \(\alpha\) of the computational model is optimized by fitting the simulated surface temperature, \(T_{s,\text{sim}}(t)\), to that determined experimentally, \(T_{s,\text{exp}}\) (\(t\));
2. Heat flux from the interior to the wall surface, \(q_{\text{IN}}(t)\), is calculated from the experimental data using Equation (2) and the optimized heat transfer coefficient \(\alpha\) from the previous step;
3. Heat flux from the wall surface element to the wall, \(q_{\text{OUT}}(t)\), is calculated from the computational data using Equation (3) and compared with \(q_{\text{IN}}(t)\) from the previous step to validate the setup and accuracy of the computational model;
4. \(q_{\text{IN}}(t)\) and \(q_{\text{OUT}}(t)\) are averaged to form a new variable, \(q_{\text{REF}}(t)\), having a sufficient robustness as it combines both experimental and computational outputs;
5. Sensitivity of heat flux sensors (thermopiles) is adjusted using raw \(U\)(t) (voltage) data and \(q(t)\) from the previous step and custom sensitivity function \(S\) is identified for each thermopile;
6. Raw data provided by the heat flux sensors in the period are adopted and transformed into heat flux using \(S\) function from the previous step;
7. The heat flux obtained in the previous step is compared with experimentally and computationally derived data to verify the adjusted sensitivity;
8. The verified heat flux obtained in the previous step is validated against the reference heat flux obtained from an independent sensor. The scheme of the designed approach is shown in Figure 2. In general, the experimental measurements can be divided into three phases that correspond to the purposes of data acquisition, processing, and application. The entire procedure can be applied repeatedly during ongoing experiments, providing a tool for an adaptive adjustment which is most effective in the scenarios with changing environmental conditions. The execution of Phase 1 is required to be carried out at least once upon the beginning of the adjusting procedure. Repeated executions of this phase are optional. However, they should be done always when environmental conditions significantly deviate from those acting during the last execution. After initialization of the adjustment procedure, the Phase 2 is carried out to obtain updated sensitivity of the heat flux sensor that will correspond with the actual environmental conditions. With the sensitivity adjusted in this way, the experiment may continue running while Phase 3 is being continuously executed in the background to verify the performance of the heat flux sensor. If some deviation is detected, i.e., substantial difference between measured heat flux and \(q(t)\) calculated from the computational model and observed quantities, a repeated execution of Phase 2 and 3 is desired.