## 4 Results and Discussion
### Descending Dataset Results  
Similar to the discussion in the previous section, 81 images acquired in descending geometry were processed from 28 January 2017 to 29 October 2022 (Figure 9) using the CPT-TPC algorithm. This approach allows the analysis of 240 interferograms defined from the available image dataset, enabling an analysis of individual pixels that show a specific quality/stability in terms of phase. The software selected as the master acquisition for the entire stack, the one obtained on 25 August 2019. Figure 8: Map of the average displacement rate along the “LoS” of the targets identified in ascending geometry. Figure 9: Interferogram distribution in descending geometry in the analyzed interval. Just as before, the Radar Targets Map (Figure 10a) and the Triangulation Map (Figure 10b) were produced for the ascending geometry. It should be noted that the latter represents cumulative displacements from the first available image, while the average velocities represent a linear speed that the model estimates over the entire acquisition interval. As can be seen (Figure 11), a target density of the same order of magnitude as that obtained in ascending geometry (about 59,132 RTs) was obtained: a high number of reflectors is identified in correspondence with the existing infrastructure and urbanized network, depending on the electromagnetic response of the existing artifacts; again, heavily vegetated environments are characterized by a reduced number of targets, making them difficult to investigate. Figure 11: Map of the average displacement rate along the “LoS” of the targets identified in descending geometry. Figure 10: (**a**) Radar Targets identified; (**b**) Triangulation Map. In detail, the analysis of the infrastructure's surrounding area revealed 29,484 targets in descending geometry, with a density of approximately 7994 RT/km\({}^{2}\). These targets exhibit average displacement velocities along the LoS up to approximately 30 mm/year maximum. This maximum displacement was recorded in the western sector of the study area. The displacement rates observed in descending geometry, as previously discussed for ascending acquisitions, confirm the occurrence of very slow kinematics. This slow movement can be attributed to a combination of factors, including the regional tectonic setting, local geological and geomorphological conditions, and anthropogenic influences such as groundwater extraction and urban development. In the urban area of San Marco dei Cavoti, a non-negligible displacement zone can also be detected in the descending geometry. These displacements along the LoS can reach a rate of up to 20 mm/year. This area of increased displacement could be associated with local geological conditions or anthropogenic factors, such as building construction or land use changes. The results obtained from both ascending and descending acquisitions provide a more comprehensive understanding of the ongoing deformation processes in the study area. These insights can inform land-use planning, infrastructure maintenance, and disaster risk reduction strategies, ensuring the long-term safety and sustainability of the area. Further investigation may be required to determine the exact cause of this observed movement and assess potential threats to the local community. San Marco dei Cavoti Village: Time Series and Analysis of Vertical and Horizontal Components with MATLAB  
The time series of six ascending (Figure 12) and six descending (Figure 13) RTs have been produced. These RTs have been chosen by considering both the presence of already inventoried landslides and by searching for the more interesting high-rate related areas. The location of these twelve points is shown in the following figures. Figure 12: Location of ascending time series (A1–A6). As shown in the graphics (Figure 14), the ascending time series located on top of already recognized landslides (A1 and A3-A6) show the presence of a trend. This is interpreted as the landslide is still active, although moving slowly.