## 4 Conclusions  
This study successfully demonstrated the feasibility and efficacy of a low-cost data acquisition system for measuring instantaneous acoustic power in thermoacoustic engines, with particular emphasis on the data acquisition process. While previous studies with low-cost systems focused on low-frequency acquisition, mainly for temperature sampling, this study demonstrates the feasibility of extending these applications to dynamic acquisition. The system, based on Arduino and employing cost-effective pressure sensors, was thoroughly tested and compared against a high-cost, professional data acquisition system. The low-cost system demonstrated satisfactory performance in terms of pressure resolution and timing accuracy, with no data loss during single sensor measurement, confirming its utility for experimental setups in thermoacoustics. In addition, this paper provides valuable details for the design and fabrication of the thermoacoustic engine used in this study and how the expected output power of such an engine could be measured using the two-pressure and RC load techniques.  
The proposed system facilitates the acquisition, monitoring, and storage of acoustic wave power data in real time. Moreover, this study validates the low-cost system's capability to accurately capture and process dynamic signals. Comparisons with a high-performance B&K system demonstrate that the low-cost system achieves satisfactory accuracy with an error margin less than 2% for pressure amplitude and frequency measurements. This level of accuracy is adequate for most experimental setups in thermoacoustics, where precise, yet budget-friendly solutions are required. Thus, the system provides a valuable tool for the study of complex phenomena in thermoacoustic engines, such as phase relationships and power flows, which are essential for improving the efficiency and understanding of these devices.  
Although tailored for thermoacoustic engines, the flexibility of the system's design and the general approach of using Python algorithms for signal processing suggest that it can be adapted to other applications for dynamic measurements. This could include studies on acoustic properties of materials, noise analysis, and other engineering applications where cost-effective data acquisition is required.  
Conceptualization, C.I.; methodology, C.I.; software, C.I. and L.S.; validation, C.I. and F.P.; formal analysis, C.I.; investigation, C.I., L.S., J.L.O., F.P., and J.V.; resources, C.I. and J.V.; data curation, C.I. and L.S.; writing--original draft preparation, C.I.; writing--review and editing, C.I., J.V., and F.P.; visualization, C.I. and J.V.; supervision, F.P. and J.V.; project administration, J.V. and J.L.O.; funding acquisition, J.V. and J.L.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministerio de Ciencia e Innovacion, call: \"Proyectos de Generacion de Conocimiento 2022, Investigacion Orientenda Tipo B\", grant number PID2022-138700 OB-I00.  
**Institutional Review Board Statement:** Not applicable.  
Figure 13: Comparison between low-cost (Arduino) and high-cost (B&K) acquisition systems and wave parameter results obtained from data post-processing.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data supporting the reported results will be made available by the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.