## 2 Sensing Field
\begin{table}
\\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \\hline \\hline
**Electronic and** & & & & & \\\\
**Mechatronic** & **Technologies** & & & & \\\\
**Technologies** & & & & & \\\\ \\hline Optomechatronic Systems & High-resolution lighting and optical models & Increase of image quality & [45; 46; 47; 48] & Combination of more procedures and models addressing the optimization of the optomechatronic systems. \\\\ \\hline  & PE Sensors & Definition of smart active object; adding a data processing unit & [49] & PE-integrated circuit in actuation boards. \\\\ \\cline{2-5} Printed Electronics (PE) & Flexible electronics & Adoption of stretchable electronic circuits suitable for harsh environments & [50] & Combination and simultaneous processing of data acquired from different production environments to find correlations that optimize quality. \\\\ \\cline{2-5}  & Pressure sensors on flexible substrate & Piezoresistive pressure sensors printed on a polymeric substrate & [51; 52] & Adoption of the same flexible substrate for the integration of other printable circuits \\\\ \\hline  & MOSFET-based gas sensors & Detection of CO and NOx gases & [53] & Reduction of gas emissions of by industrial plants by activating an automatic alerting system (sustainable production plants). \\\\ \\cline{2-5} Electronic gas sensors & & Fabrication of quantum cascade laser-based photoacoustic (QCL-PA) detectors, Gold Nanoparticle-based Field-Effect Transistor (Au-NP-FET), and III-V semiconductor-based circuits, detecting NOx gases & [54] & Integration of different technologies based on the specific sensitivity of gas detection. \\\\ \\hline  & Multiple pressure sensors monitoring fluid networks & Network monitoring system connected to SCADA units (pressure sensors monitoring water leakage); sensors monitoring water leakage). & Prediction of fluid leakage by means of AI algorithms (classification of the signals detected from different parts of the monitoring network). \\\\ \\cline{2-5} Sensors systems and networks & Sensors monitoring railway infrastructures & Multiple sensors detecting temperature or mechanical anomalies of the infrastructure & [56; 57] & Use of AI diagnostic algorithms to predict risks. \\\\ \\cline{2-5}  & Wireless sensor network & Wireless sensor network for assembly process monitoring and for process management. & [58] & Process mining addressing assembling processes enabled by AI decision-making engine. \\\\ \\cline{2-5} Smart sensor system monitoring production lines & Smart factory integrating temperature, pressure, position, force, gas, color, light, flow, nuclear, micro-production fault prediction. \\\\ \\hline \\hline \\end{tabular}
\\end{table} 
Table 2: State of the art: some electronic and mechatronic sensors adaptable for industrial applications in Industry 5.0 scenario. Concerning the industry sensing field, the analyzed state of the art finally addresses the important topics of algorithms supporting leakage detection [72, 73, 74, 75, 76, 77, 78, 79, 80, 81], the digital transformation process of Industry 4.0 [82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96], and the data processing regarding energetic consumption in production processes [97, 98, 99, 100, 101, 102, 103]. Possible improvements to integrate into an Industry 5.0 framework relate to automated AI decision-making processes which enable interventions and procedures, production simulation setting machine parameters, and auto-calibration of the machines used in manufacturing processes. In the proposed scenario, an important role is assigned to big data systems interconnected to AI engines and to data-fusion approaches. The AI-based detection algorithms could provide an important contribution to managing complex production systems by estimating many variables such as energy required for the machined production, product defects, machine failure features, and more. Some efficient AI algorithms are Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM).