An integrated approach for coastal cliff susceptibility: The case study of Procida Island (southern Italy).

Procida Island, located in the Gulf of Naples (southern Italy), is characterized by steep cliffed coasts, articulated in a succession of headlands and small embayments with narrow pocket beaches, such as Ciraccio and Chiaia, often characterized by instability. In this study, a methodology for coastal cliff susceptibility assessment has been conceived based on hydraulic and geomorphological characteristics, which supported the construction of a Cliff Stability Index (CSI). The geomorphological characteristics are related to the whole cliff face, the cliff material resistance, and the cliff failure mechanisms. The hydraulic actions on the cliff are related to the wave impact which is exerted by the breaking waves once the wave run-up distance exceeds the beach width. The index takes into account the slope of the cliff, the rock strength, the wave energy at the cliff base produced by the broken wave and the presence of defence structures at the cliff base. The resulting index classification, obtained by addition of the partial sub-indices, has been compared with the observed coastal cliff evolution from 1954 to 2021.

Image Sensors for Wave Monitoring in Shore Protection: Characterization through a Machine Learning Algorithm.

Waves propagating on the water surface can be considered as propagating in a dispersive medium, where gravity and surface tension at the air-water interface act as restoring forces. The velocity at which energy is transported in water waves is defined by the group velocity. The paper reports the use of video-camera observations to study the impact of water waves on an urban shore. The video-monitoring system consists of two separate cameras equipped with progressive RGB CMOS sensors that allow 1080p HDTV video recording. The sensing system delivers video signals that are processed by a machine learning technique. The scope of the research is to identify features of water waves that cannot be normally observed. First, a conventional modelling was performed using data delivered by image sensors together with additional data such as temperature, and wind speed, measured with dedicated sensors. Stealth waves are detected, as are the inverting phenomena encompassed in waves. This latter phenomenon can be detected only through machine learning. This double approach allows us to prevent extreme events that can take place in offshore and onshore areas.

Signal in Space Error and Ephemeris Validity Time Evaluation of Milena and Doresa Galileo Satellites.

In August 2016, Milena (E14) and Doresa (E18) satellites started to broadcast ephemeris in navigation message for testing purposes. If these satellites could be used, an improvement in the position accuracy would be achieved. A small error in the ephemeris would impact the accuracy of positioning up to ±2.5 m, thus orbit error must be assessed. The ephemeris quality was evaluated by calculating the SISEorbit (in orbit Signal In Space Error) using six different ephemeris validity time thresholds (14,400 s, 10,800 s, 7200 s, 3600 s, 1800 s, and 900 s). Two different periods of 2018 were analyzed by using IGS products: DOYs 52-71 and DOYs 172-191. For the first period, two different types of ephemeris were used: those received in IGS YEL2 station and the BRDM ones. Milena (E14) and Doresa (E18) satellites show a higher SISEorbit than the others. If validity time is reduced, the SISEorbit RMS of Milena (E14) and Doresa (E18) greatly decreases differently from the other satellites, for which the improvement, although present, is small. Milena (E14) and Doresa (E18) reach a SISEorbit RMS of about 1 m (comparable to that of the other Galileo satellites reach with the nominal validity time) when validity time of 1800 s is used. Therefore, using this threshold, the two satellites could be used to improve single point positioning accuracy.