## 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
**Detection Algorithms/Models** & **Topic** & **Description** & **Reference** & **Possible Implementation of Processes in Industry 5.0 Frameworks (Proposed in This Work)** \\\\ \\hline \\multirow{6}{*}{
\\begin{tabular}{} \\end{tabular} } & Pipeline leakage detection & Laguerre fuzzy proportional-integral-derivative (PID)observation system & [72] & Automated processes enabling pipeline repair interventions. \\\\ \\cline{2-5}  & & & Thermal readings and images analyzed by machine learning algorithms & [73; 74] & Alerting security processes enabled by AI algorithms. \\\\ \\cline{2-5}  & Gas leakages data & Spatial and temporal neural network model & [75] & Alerting security processes enabled by AI algorithms. \\\\ \\cline{2-5}  & Gas leakage images & Visual background extractor algorithm applied to mid-infrared. & [76] & Alerting security processes enabled by image vision algorithms. \\\\ \\cline{2-5}  & Water leakage detection & Data processing algorithms extracting information from infrared thermography and Ground Penetrating Radar (GPR) images & [77] & Data fusion processed could extract secure information about water leakage. \\\\ \\cline{2-5}  & Water/fluid leakage & Pressure data detected in whole water network & [78] & Data processing of correlations between all the measurements acquired along the whole water network (with application to a generic fluid network). \\\\ \\cline{2-5}  & Water/liquid leakage & Computational Fluid Dynamics (CFD) model & [79] & CFD simulations are included into reverse engineering processes to optimize production of pipeline network parts. \\\\ \\cline{2-5}  & Water/fluid leakage & Denoising method & [80] & Optimization process for algorithms detecting leakage. \\\\ \\cline{2-5}  & Water/fluid leakage & Genetic algorithm modelling leak nodes of the water network & [81] & Automatic data processing optimization that simultaneously considers all information extracted from all network nodes. \\\\ \\hline \\multirow{6}{*}{Industry 4.0} & Data-driven manufacturing & Machine learning algorithms & [82; 83; 84] & Auto-calibration of machines and manufacturing processes. \\\\ \\cline{2-5}  & Integration of technologies & Digital twin approach: product, asset, system, process twins & [85; 86; 87] & Creation of front-end Human Machine Interfaces (HMI) supporting hardware and software integration and ensuring data collection in big data systems. \\\\ \\cline{2-5}  & Algorithms processing data at different steps of levels & Pre-processing, descriptive analytics, predictive analytics and prescriptive analytics & [88] & Implementation of multi-level data processing systems managed by AI decision-making processes. \\\\ \\cline{2-5}  & Algorithms processing data at different steps of levels & Pre-processing, descriptive analytics, predictive analytics and prescriptive analytics & [88] & Implementation of multi-level data processing systems managed by AI decision-making processes. \\\\ \\cline{2-5}  & & & & \\\\ \\cline{2-5}  & & & & \\\\ \\cline{2-5}  & & & & \\\\ \\cline{2-5}  & & & & \\\\ \\hline \\end{tabular}
\\end{table} 
Table 3: State of the art: detection algorithms and possible implementation of processes in an Industry 5.0 scenario.