Abstract
The importance of planktonic microbial communities is well acknowledged, since they are fundamental for several natural processes of aquatic ecosystems. Microorganisms naturally control the flux of nutrients, and also degrade and recycle anthropogenic organic and inorganic contaminants. Nevertheless, climate change effects and/or the runoff of nutrients/pollutants can affect the equilibrium of natural microbial communities influencing the occurrence of microbial pathogens and/or microbial toxin producers, which can compromise ecosystem environmental status. Therefore, improved microbial plankton monitoring is essential to better understand how these communities respond to environmental shifts. The study of marine microbial communities typically involves highly cost and time-consuming sampling procedures, which can limit the frequency of sampling and data availability. In this context, we developed and validated an in situ autonomous biosampler (IS-ABS) able to collect/concentrate in situ planktonic communities of different size fractions (targeting prokaryotes and unicellular eukaryotes) for posterior genomic, metagenomic, and/or transcriptomic analysis at a home laboratory. The IS-ABS field prototype is a small size and compact system able to operate up to 150 m depth. Water is pumped by a micropump (TCS MG2000) through a hydraulic circuit that allows in situ filtration of environmental water in one or more Sterivex filters placed in a filter cartridge. The IS-ABS also includes an application to program sampling definitions, allowing pre-setting configuration of the sampling. The efficiency of the IS-ABS was tested against traditional laboratory filtration standardized protocols. Results showed a good performance in terms of DNA recovery, as well as prokaryotic (16S rDNA) and eukaryotic (18S rDNA) community diversity analysis, using either methodologies. The IS-ABS automates the process of collecting environmental DNA, and is suitable for integration in water observation systems, what will contribute to substantially increase biological surveillances. Also, the use of highly sensitive genomic approaches allows a further study of the diversity and functions of whole or specific microbial communities.

Abstract
The growing dependence of modern-day societies on electricity increases the importance of effective monitoring and maintenance of power lines. Endowing UAVs with the appropriate sensors for inspecting power lines, the costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced. However, this implies the development of algorithms to make the inspection process reliable and autonomous. Visual methods are usually applied to locate the power lines and their components, but poor light conditions or a background rich in edges may compromise their results. To overcome those limitations, we propose to address the problem of power line detection and modeling based on LiDAR. A novel approach to the power line detection was developed, the PL2DM -Power Line LiDAR-based Detection and Modeling. It is based in a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. The algorithm was validated with a real dataset, showing promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

Abstract
Zooplankton plays a key -role on Earth's ecosystem, emerging in the oceans and rivers in great quantities and diversity, making it an important and rather common topic on scientific studies. Given the numbers of different species it is not only necessary to study their numbers but also their classification. In this paper a possible solution for the zooplankton in situ detection and classification problem in real-time is proposed using a portable deep learning approach based on Convolutional Neural Networks deployed on INESC TEC's MarinEye system. For detection a Single Shot Detection model with MobileNet was used, and ZooplanktoNet for classification. System portability is guaranteed with the use of MovidiusTMNeural Compute Stick as the deep learning motor.

Abstract
For the context of a mobile tracking system, an underwater acoustic positioning system was developed, using three hydrophones to compute the direction of an acoustic source relative to an Autonomous Surface Vehicle (ASV). The paper presents an algorithm for the Direction of Arrival (DoA) of an acoustic source, which allows to estimate its position. Preliminary results will be shown in this paper relative to the detection and identification (ID) of the acoustic sources, as well as an analysis of the proposed algorithm. The solution allows the position estimation of an acoustic source, which can be used in tracking solutions. The system can be applied in an ASV or fixed buoys, as long as the baseline's hydrophones are at equal angular distances. The main objective is to track targets with the DoA algorithm as well to estimate their position, improving what was done in [1].

Abstract
Over the last few years, oil spill incidents occured with some regularity during exploration, production and transportation, causing a large economic and ecologic impact in the world community. To minimise these impacts and reduce the time response of the initial mitigation process, autonomous vehicles, such as unmanned aerial vehicles (UAV) can be used to perform oil spill monitoring and mitigation. This paper presents the design and implementation of a multirotor UAV capable of identifying and combat the oil spill, by using a release system of consortia with bacteria and nutrients. Several field tests occurred in Portugal and Spain, where the oil spill was implemented in a simulated environment, resulting in a cooperative and simultaneous manoeuvre between the vehicles.

Abstract
The overall aim of the ROSM project is the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). These solutions will be based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in-situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be used as the first line of the responder to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or during transport activities, in a fast, efficient and low-cost way. The paper will address the development of a team of autonomous vehicles able to carry, as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), the development of a multi-robot system able to provide a first line responses to oil spill incidents under unfavourable and harsh conditions with low human intervention, and then a decentralized cooperative planning with the ability to coordinate an efficient oil spill combat. Field tests have been performed in Leixoes Harbour in Porto and Medas, Portugal, with a simulated oil spill and validated the decentralized coordinated task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV).