Valeria Pia Vevoto

Information

Bio

I am Valeria Pia Vevoto, a professional in the field of Electronic Engineering with a focus on Sensing and Remote Observation, with a solid foundation in electronics, sensors, IoT, and data analysis. My professional journey began at University of Sannio, Benevento, where I earned my bachelor’s degree in electronic engineering for Automation and Telecommunications on June 13, 2024. With my skills, I have contributed to the ESA Φ-lab (ESRIN) as a Visiting Researcher, implementing sensors, developing software for data acquisition, and applying machine learning to analyze correlations between satellite and in-situ measurements during my thesis activities.

Current Role

Currently, I am a second-year student in Electronic Engineering for Automation and Sensing, specializing in the Sensing track. My studies focus on electronics, sensor technologies, and signal processing, building a strong foundation in engineering principles and data acquisition systems.

Areas of Expertise

My expertise lies in several key areas of Machine Learning and sensor technologies, including electronics, IoT systems, remote sensing, data science, and sensor integration. These specializations enable me to implement and characterize sensors, develop software for data acquisition and transmission, and analyze correlations between in-situ and satellite observations. During my visiting research activities at ESA Φ-lab, I applied these skills by implementing three new sensors, developing software for the ESA-AQP 222 system to collect and transmit sensor data to the ESA webserver, comparing Sentinel-5P data with in-situ measurements from ARPA and AQP 222, and developing Machine Learning algorithms using the CatBoost library to reconstruct correlations between changes in average nitrogen dioxide levels in 2024 over the Rome area.

Additionally, my technical skills developed at university include:

• Optical and photonic sensing: experience with fiber optic sensors, Fiber Bragg Gratings (FBG), and Long Period Gratings (LPG).
• Earth observation systems: knowledge of CubeSat platforms and Synthetic Aperture Radar (SAR) for remote sensing applications.
• Sensor technologies and data acquisition.
• Laboratory experience in sensor characterization and experimental measurements.

These experiences and skills demonstrate my ability to contribute to ESA Φ-lab CIN by bridging sensor development, data acquisition, and machine learning analysis to support Earth observation and environmental monitoring projects.

Vision for the Future

Driven by a vision of a future where Earth observation and advanced sensing technologies enable precise environmental monitoring and informed decision-making, I am committed to developing innovative sensor systems, integrating IoT and machine learning, and supporting research that improves our understanding of atmospheric and environmental changes. I aim to contribute to projects that enhance sustainability, protect ecosystems, and provide accurate data for policy-making and scientific discovery.

Project as CIN Researcher

Coming Soon