Abstract
Alto Douro Wine Region is located in the Northeast of Portugal and is classified by UNESCO as a World Heritage Site. Snaked by the Douro River, the region has been producing wines for over 2000 years, with the world-famous Porto wine standing out. The vineyards, in that region, are built in a territory marked by steep slopes and the almost inexistence of flat land and water. The vineyards that cover the great slopes rise from the Douro River and form an immense terraced staircase. All these ingredients combined make the right key for exploring precision agriculture techniques. In this study, a preliminary approach allowing to perform terrace vineyards identification is presented. This is a key-enabling task towards the achievement of important goals such as production estimation and multi-temporal crop evaluation. The proposed methodology consists in the use of Convolutional Neural Networks (CNNs) to classify and segment the terrace vineyards, considering a high-resolution dataset acquired with remote sensing sensors mounted in unmanned aerial vehicles (UAVs).

Abstract
Listeria monocytogenes has been referred to as a concern microorganism in cheese making due to its ability to survive and grow in a wide range of environmental conditions, such as refrigeration temperatures, low pH and high salt concentration at the end of the production process. Since cheese may be a potential hazard for consumers, especially high-risk consumers (e.g., pregnant, young children, the elderly, people with medical conditions), efforts of the dairy industry have been aimed at investigating new conservation techniques based on natural additives to meet consumers' demands on less processed foods without compromising the food safety. Thus, the aim of this study was to evaluate the efficacy of Myrtus communis L. (myrtle) and Rosmarinus officinalis L. (rosemary) essential oils (EO) against Listeria monocytogenes ATCC 679 spiked in sheep cheese before ripening. After the cheesemaking process, the samples were stored at 8 degrees C for 2 h, 1 d, 3 d, 14 d and 28 d. The composition of EO was identified by gas chromatography-mass spectrometry (GC-MS) analysis. Constituents such as 1,8-cineole, limonene, methyl-eugenol, alpha-pinene, alpha-terpineol, alpha-terpinolene and beta-pinene were present in both EO, accounting for 44.61% and 39.76% from the total of chemical compounds identified for myrtle and rosemary EO, respectively. According to the chemical classification, both EO were mainly composed of monoterpenes. Minimum inhibitory concentration (MIC) against L. monocytogenes was obtained at 31.25 mu L/mL to myrtle EO and at 0.40 mu L/mL to rosemary EO. Then, cheeses were inoculated with L. monocytogenes (Ca. 6 log CFU/mL) and EO was added at MIC value. The addition of rosemary and myrtle EO displayed lower counts of L. monocytogenes (p < 0.01) (about 1-2 log CFU/g) during the ripening period compared to control samples. Ripening only influences (p < 0.001) the growth of L. monocytogenes in control samples. Since rosemary and myrtle EO do not exert any negative impact on the growth of native microflora (p > 0.05), their use as natural antimicrobial additives in cheese demonstrated a potential for dairy processors to assure safety against L. monocytogenes.

Abstract
Functional organic dyes play a key role in many fields, namely in biotechnology and medical diagnosis. Herein, we report two novel 2,3- and 3,4-dihydroxyphenyl substituted rosamines (3 and 4, respectively) that were successfully synthesized through a microwave-assisted protocol. The best reaction yields were obtained for rosamine 4, which also showed the most interesting photophysical properties, specially toward biogenic amines (BAs). Several amines including n- and t-butylamine, cadaverine, and putrescine cause spectral changes of 4, in UV-Vis and fluorescence spectra, which are indicative of their potential application as an effective tool to detect amines in acetonitrile solutions. In the gas phase, the probe response is more expressive for spermine and putrescine. Additionally, we found that methanolic solutions of rosamine 4 and n-butylamine undergo a pink to yellow color change over time, which has been attributed to the formation of a new compound. The latter was isolated and identified as 5 (9-aminopyronin), whose solutions exhibit a remarkable increase in fluorescence intensity together with a shift toward more energetic wavelengths. Other 9-aminopyronins 6a, 6b, 7a, and 7b were obtained from methanolic solutions of 4 with putrescine and cadaverine, demonstrating the potential of this new xanthene entity to react with primary amines.

Abstract
Multi-temporal InSAR (MT-InSAR) techniques proved to be very effective for deformation monitoring. However, decorrelation and other noise sources, can be limiting factors in MT-InSAR. The obtained observations (PS - Persistent scatterers) are usually very demanding from a computational perspective, as they can reach hundreds of thousands of observations. To simplify and speed up the classification process, in this study we present an approach based on Convolutional Neural Networks (CNN) classification models, for the detection of MT-InSAR outlying observations. For each PS, the corresponding MT-InSAR parameters, its neighbouring scatterers parameters and its relative position are considered. Tests in two independent PS datasets, covering the regions of Bratislava city and the suburbs of Prievidza, Slovakia, were performed. The results showed that such models are robust and reduced computation time method for the evaluation of MT-InSAR outlying observations. However, the applicability of these models is limited by the deformation pattern in which such models were trained. (C) 2021 The Authors. Published by Elsevier B.V.

Abstract
In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

Abstract
Deformation monitoring is a common practice in most of dams to ensure their structural health and safety status. Systematic monitoring is frequently carried out by means of geotechnical sensors and geodetic techniques that, although very precise an accurate, can be time-consuming and economically costly. Remote sensing techniques are proved to be very effective in assessing deformation. Changes in the structure, shell or associated infrastructures of dams, including adjacent slopes, can be efficiently recorded by using satellite Synthetic Aperture Radar Inteferometry (InSAR) techniques, in particular, Muti-Temporal InSAR time-series analyses. This is a mature technology nowadays but not very common as a routine procedure for dam monitoring. Today, thanks to the availability of spaceborne satellites with high spatial resolution SAR images and short revisit times, this technology is a powerful cost-effective way to monitor millimeter-level displacements of the dam structure and its surroundings. What is more, the potential of the technique is increased since the Copernicus C-band SAR Sentinel-1 satellites are in orbit, due to the high revisit time of 6 days and the free data availability. ReMoDams is a Spanish research project devoted to provide the deformation monitoring of several embankments dams using advances time-series InSAR techniques. One of these dams is The Arenoso dam, located in the province of Cordova (southern Spain). This dam has been monitored using Sentinel-1 SAR data since the beginning of the mission in 2014. In this paper, we show the processing of 382 SLC SAR images both in ascending and descending tracks until March 2019. The results indicate that the main displacement of the dam in this period is in the vertical direction with a rate in the order of -1 cm/year in the central part of the dam body. (C) 2020 The Authors. Published by Elsevier B.V.