Abstract
It is well-known that information and communication technologies enable many tasks in the context of precision agriculture. In fact, more and more farmers and food and agriculture companies are using precision agriculture-based systems to enhance not only their products themselves, but also their means of production. Consequently, problems arising from large amounts of data management and processing are arising. It would be very useful to have an infrastructure that allows information and agricultural tasks to be efficiently shared and handled. The cloud computing paradigm offers a solution. In this study, a cloud-based software architecture is proposed with the aim of enabling a complete crop management system to be deployed and validated. Such architecture includes modules developed by using Google App Engine, which allows the information to be easily retrieved and processed and agricultural tasks to be properly defined and planned. Additionally, Google's Datastore (which ensures a high scalability degree), hosts both information that describes such agricultural tasks and agronomic data. The architecture has been validated in a system that comprises a wireless sensor network with fixed nodes and a mobile node on an unmanned aerial vehicle (UAV), deployed in an agricultural farm in the Region of Murcia (Spain). Such a network allows soil water and plant status to be monitored. The UAV (capable of executing missions defined by an administrator) is useful for acquiring visual information in an autonomous manner (under operator supervision, if needed). The system performance has been analysed and results that demonstrate the benefits of using the proposed architecture are detailed.

Abstract
Image processing has been proved to be an effective tool for analysis in various human activity areas, namely, agricultural applications. Interpreting a digital color image of fruit orchard captured in field environment is extremely challenging due to adverse weather conditions, luminance variability and the presence of dust, insects and other unavoidable image noises. The purpose of this survey is to categorize and briefly review the literature on computer analysis of fruit images in agricultural operations, which comprises more than 60 papers published in the last 10 years. With the aim to perform applied research in agricultural imaging, this paper intends to focus on advanced image processing and analysis techniques used in applications for detection and classifications of fruits, developed in the last decade. For the reviewed techniques, some performance evaluation metrics achieved in various experiments are emphasized to help the researchers when making choices and develop new computer vision applications in fruit images.

Abstract
An emerging trend in industry 4.0 is to use wireless communication infrastructure and mesh networks in applications requiring high reliability and safety. Although not a typical industrial production process, railway vehicular networks are also an industrial application which come with stringent reliability and safety requirements. Current research is focusing on using vehicular networks as an enabling technology to actively control the separation between two consecutive vehicles, enforcing a safe distance which is nevertheless much shorter than currently used to maintain vehicle separation. In this respect, we analyze a hopping strategy for Time-Slotted Channel-Hopping (TSCH), which was introduced in the IEEE 802.15.4e amendment with a view of improving the reliability of IEEE 802.15.4 networks. We define a probability framework to estimate the chance of successful hopping assuming two previously merged vehicles, and we design a zero-sum game and propose a payoff function to always place communicating nodes in a Nash equilibrium by choosing whether to hop or not, and therefore maximizing the communication throughput by mitigating jamming signals.

Abstract
Sous vide cook-chill (SVCC) is characterized by vacuum-packaging raw or partially prepared foods before pasteurization, followed by rapid chilling and storage below 3C. The application of essential oils (EOs) to food products is a suitable strategy to control pathogens and to extend their shelf life by reducing microbial levels and oxidative processes. The aim of this study was to evaluate the efficacy of Rosmarinus officinalis L. (rosemary) and Thymus vulgaris L. (thyme) EOs against Listeria monocytogenes ATCC 679, inoculated in beef processed by SVCC stored at 2 and 8C for 1, 2, 3, 7, 14, 21 and 28 days. The composition of EOs was identified by gas chromatography-mass spectrometry analysis. The main compounds identified in rosemary EO were eucalyptol (13.05%), camphor (8.93%), verbenone (8.58%), endo-borneol (7.87%) and alpha-pinene (6.78%) and in thyme EO were linalool (18.18%), thymol (7.48%), limonene (6.49%), endo-borneol (5.86%) and terpinen-4-ol (5.66%). Using the minimum inhibitory concentration (MIC) method, L. monocytogenes was inhibited at 3.9 mu L/mL to thyme EO and at 62.5 mu L/mL to rosemary EO. Beef samples of M. longissimus thoracis et lumborum were packaged in bags, inoculated and one of each EO was added at MIC values. Bags were vacuum-sealed and samples were processed at 55C/65 min for 3log(10) CFU/g reduction. A reduction of the counts of L. monocytogenes was observed in all samples at 2C. At 8C counts of L. monocytogenes were almost similar in control samples and those with thyme EO with an increase of themicrobial counts since day 7. Inversely, counts of L. monocytogenes in beef samples with rosemary EO stored at 2 and 8C decreased about 2 log(10) CFU. These results support the possibility of using rosemary EO as natural preservative due to its antimicrobial effect against L. monocytogenes. Also, our results confirm that an adequate chilling storage is essential to guarantee the safety of SVCC product regarding L. monocytogenes to avoid foodborne outbreaks.

Abstract
This paper presents a model of a system capable of addressing the training needs identified by the University of Tras-os-Montes e Alto Douro (UTAD). In addition to supporting the typical needs of distance learning/education (e.g., e-learning), it is also intended that the proposed system will complement the traditional classroom-based teaching. This model will have two modules: the physical/infrastructural module and the policies/practices module. The physical/infrastructural module will have the following (control and management) components or modules: e-learning platforms (e.g., Moodle, Sakai) with SIDE (a platform for teaching support developed at UTAD) interconnection; website with educational/training offer and e-learning events; media repository; file storage and sharing service; collaborative/cooperative platform, for the sharing of information and knowledge; scientific repository, for the storage and sharing of work and scientific research, books, conferences, etc.; forum; ticket service/helpdesk, to help solving problems that may arise; Customer Relationship Management (CRM) service (management, relationship and customer satisfaction-student, teachers, other); Learning Analytics system (collection of information about the quality of teaching, evolution and student analysis in the e-learning mode, etc.). Additionally, there will be an assessment/examination space, a contents production studio, and a training studio. The policies/practices module includes the institution policies, and including policies for the creation/dissemination of curses, implementation of course evaluation strategies and continuous improvement, training policies, recognition of the merit and effort of teachers, the training in using the system, the policies for the creation of physical spaces equipped with the necessary tools (computers, cameras, video-conferencing, etc.), among other. In line with these, UTAD has come to recognize that e-learning should be part of its strategy for its training offer and, consequently, is being adopting new policies, namely through the signing of protocols with other institutions with more experience using e-learning. To achieve these objectives, a detailed review of other models and systems that have been successfully implemented in other international reference universities will also be presented here.

Abstract
The development of interferometric methodologies for deformation monitoring that are able to deal with long time series of synthetic aperture radar (SAR) images made the detection of seasonal effects possible by decomposing the differential SAR phase. In the case of monitoring of man-made structures, particularly bridges, the use of high-resolution X-band SAR data allows the determination of three major components with significant influence on the SAR phase: the linear deformation trend, the height of structures over terrain, and the thermal expansion. In the case of stable metallic or (reinforced) concrete structures, this last effect can reach a magnitude comparable to or even exceeding the other phase components. In this review, we present two case studies that confirm the feasibility of InSAR techniques for bridge deformation monitoring and our original approach to refine the thermal expansion component.