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Quality control (QC) practices are a fundamental requirement for any measurement program targeting the delivery of high-quality data. In agreement with such a need, the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) includes a number of QC steps ensuring the delivery of normalized water-leaving radiance LWN spectra at incremental accuracy levels identified as level 1.0, level 1.5, and level 2.0. Currently, the final QC step allowing for rising level 1.5 LWN spectra to level 2.0 implies the execution of an expert-based procedure, which is extremely time consuming and naturally undergoes subjective decisions on dubious cases. These limitations solicited the development of an automated procedure, so-called , mimicking the steps supporting an expert analyst during the final QC of AERONET-OC LWN spectra. applies hierarchical tests to check (i) the relative consistency of level 1.5 LWN spectra (called candidates) with respect to LWN reference spectra (called prototypes) constructed using LWN spectra formerly and independently quality controlled; (ii) the absence of any pronounced spectral feature in portions of each LWN candidate spectrum expected to exhibit a regular shape; and additionally, when applicable, (iii) the temporal consistency of the LWN candidate spectrum with respect to close-in-time spectra as a criterion to further strengthen the quality of data. performance has been verified using LWN spectra from AERONET-OC measurement sites representative of various water types embracing oligotrophic/mesotrophic waters dominated by chlorophyll-a concentration and coastal waters exhibiting increasing levels of optical complexity. has shown an acceptance rate of AERONET-OC level 1.5 LWN candidate spectra varying between approximately 89% and 93% with agreement in the range of 88%–93% with respect to the LWN spectra independently quality controlled through the expert-based procedure. The additional capability of to rank the fully quality-controlled LWN spectra combining weights depending on the various tests, anticipates the possibility to best support applications with diverse accuracy needs. Finally, acceptance rates of for LWN prototype spectra built using level 1.5 data, an alternative to fully quality-controlled level 2.0, have shown values generally increased by less than 1%. This indicates the possibility to lessen the constraint implying the existence of reference level 2.0 LWN data for the relative-consistency test at the expense of a fairly low reduction in accuracy.

During the last decade, new applications exploiting data from satellite borne lidar measurements demonstrated that these sensors can give valuable information about ocean optical properties [1,2,3]. Within this framework, COLOR (CDOM-proxy retrieval from aeOLus ObseRvations) consisted in a 18-month feasibility study approved by ESA within the Aeolus+ Innovation program. COLOR had the objective to evaluate and document the feasibility of deriving an in-water prototype product from the analysis of the signal acquired by the ESA Earth Explorer Wind Mission ADM-Aeolus (Atmospheric Dynamics Mission). In particular, COLOR project focused on the AEOLUS potential retrieval of the diffuse attenuation coefficient for downwelling irradiance (Kd [m-1]) from the ocean sub-surface backscattered component of the 355 nm received lidar signal.The core activity of the project was the characterization of the signal from the AEOLUS ground bin through two parallel and strongly interacting activities: a) Radiative transfer numerical modelling; b) AEOLUS data analysis. The main result of the project will be presented together with the discussion of the perspectives of the satellite lidar missions dedicated to ocean color. [1] M. J. Behrenfeld et al. (2019). Global satellite-observed daily vertical migrations of ocean animals», Nature, vol. 576, n. 7786, Art. n. 7786, dic. 2019, doi: 10.1038/s41586-019-1796-9.[2] Jamet, C., Ibrahim, A., Ahmad, Z., Angelini, F., Babin, M., Behrenfeld, M. J., et al. (2019). Going beyond standard ocean color observations: lidar and polarimetry. Front. Mar. Sci. 6:251. doi: 10.3389/fmars.2019.00251[3] D. Dionisi, V. E. Brando, G. Volpe, S. Colella, e R. Santoleri (2020). Seasonal distributions of ocean particulate optical properties from spaceborne lidar measurements in Mediterranean and Black sea», Remote Sens. Environ., vol. 247, pag. 111889, set. 2020, doi: 10.1016/j.rse.2020.111889.

Many scientific publications that affect machine learning have set the basis for pattern recognition and symmetry. In this paper, we revisit the concept of “Mind-life continuity” published by the authors, testing the symmetry between cognitive and electrophoretic strata. We opted for machine learning to analyze and understand the total protein profile of neurotypical subjects acquired by capillary electrophoresis. Capillary electrophoresis permits a cost-wise solution but lacks modern proteomic techniques’ discriminative and quantification power. To compensate for this problem, we developed tools for better data visualization and exploration in this work. These tools permitted us to examine better the total protein profile of 92 young adults, from 19 to 25 years old, healthy university students at the University of Lisbon, with no serious, uncontrolled, or chronic diseases affecting the nervous system. As a result, we created a graphical user interface toolbox named MODeLING.Vis, which showed specific expected protein profiles present in saliva in our neurotypical sample. The developed toolbox permitted data exploration and hypothesis testing of the biomolecular data. In conclusion, this analysis offered the data mining of the acquired neuroproteomics data in the molecular weight range from 9.1 to 30 kDa. This molecular weight range, obtained by pattern recognition of our dataset, is characteristic of the small neuroimmune molecules and neuropeptides. Consequently, MODeLING.Vis offers a machine-learning solution for probing into the neurocognitive response.