## 9 Conclusions  
The main original contributions presented in this paper can be summarized as follows:  
* This paper achieved the incorporation of a static nonlinearity in the sensor models utilized in the design of a class of distributed consensus-based blind macro-calibration algorithms for sensor networks, initially formulated in [33; 34; 35; 36; 37; 43; 44; 45; 46; 47].
* This paper presents a rigorous theoretical analysis of the algorithm for offset calibration belonging to the given class of sensor nonlinearities, including a proof of stability in the B.I.B.O. sense; spacial attention is paid to: (a) the calibration algorithm based on full consensus; (b) the calibration algorithm based on a predefined micro-calibrated reference node.
* This paper achieves the derivation of the explicit formulae for corrected offsets as functions of time.
* This paper presents a rigorous theoretical analysis of the gain calibration algorithm belonging to the given class under the presence of sensor nonlinearities, including: (a) a demonstration of the instability of the gain calibration algorithm based on full consensus; (b) a proof of its B.I.B.O. stability of the calibration algorithm based on a gain reference.
* This paper demonstrates the formulation of an algorithm for the simultaneous calibration of offsets and gains, together with a proof of the B.I.B.O. stability for a case of an algorithm with references for both gain and offset.
* This paper proposes a general methodology for incorporating stochastic disturbances in sensor models together with sensor nonlinearities, including the formulation of an appropriate lemma, opening up new methodological possibilities for extending the obtained results.
* This paper provides comprehensive illustrative simulation analyses of all the theoretically derived conclusions.
* This paper presents a newly designed multichannel aerodynamic pressure-sensing instrument.
* This paper presents the functioning of the newly developed instrument under normal operating conditions.
* This paper demonstrates that the proposed algorithms can be a basis for distributed online real-time blind re-calibration of large sensor networks under normal operating conditions, applicable to complex experiments with the developed pressure-sensing instrument.  
**Author Contributions:** Conceptualization, S.S.S., M.S.S., M.V., I.J. and M.F.; methodology, S.S.S., M.S.S., I.J. and M.F.; software, S.S.S., M.S.S., I.J. and M.F.; validation, S.S.S., M.S.S., M.V., I.J. and M.F.; formal analysis, S.S.S. and M.S.S.; resources, S.S.S. and M.F.; writing--original draft preparation, S.S.S., M.S.S., M.V., I.J. and M.F.; writing--review and editing, S.S.S., I.J. and M.F.; visualization, S.S.S., I.J. and M.F.; supervision, S.S.S. and M.F.; project administration, I.J. and M.F.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Science Fund of the Republic of Serbia, Grant No. 7754287, MEMS Multisensor Instrument for Aerodynamic Pressure Measurements--MEMSAERO.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.