Abstract
The use of Autonomous Underwater Vehicles (AUVs) is increasingly seen as a cost-effective way to carry out underwater missions. Due to their long endurance and set of sensors onboard, AUVs may collect large amounts of data, in the order of Gbytes, which need to be transferred to shore. State of the art wireless technologies suffer either from low bitrates or limited range. Since surfacing may be unpractical, especially for deep sea operations, long-range underwater data transfer is limited to the use of low bitrate acoustic communications, precluding the timely transmission of large amounts of data. The use of data mules combined with short-range, high bitrate RF or optical communications has been proposed as a solution to overcome the problem. In this paper we describe the implementation and validation of UDMSim, a simulation platform for underwater data muling oriented systems that combines an AUV simulator and the Network Simulator 3 (ns-3). The results presented in this paper show a good match between UDMSim, a theoretical model, and the experimental results obtained by using an underwater testbed when no localization errors exist. When these errors are present, the simulator is able to reproduce the navigation of AUVs that act as data mules, adjust the throughput, and simulate the signal and connection losses that the theoretical model can not predict, but that will occur in reality. UDMSim is made available to the community to support easy and faster evaluation of data muling oriented underwater communications solutions, and enable offline replication of real world experiments.

Abstract
Autonomous Underwater Vehicles (AUVs) are seen as a safe and cost-effective platforms for performing a myriad of underwater missions. These vehicles are equipped with multiple sensors which, combined with their long endurance, can produce large amounts of data, especially when used for video capturing. These data need to be transferred to the surface to be processed and analyzed. When considering deep sea operations, where surfacing before the end of the mission may be unpractical, the communication is limited to low bitrate acoustic communications, which make unfeasible the timely transmission of large amounts of data unfeasible. The usage of AUVs as data mules is an alternative communications solution. Data mules can be used to establish a broadband data link by combining short-range, high bitrate communications (e.g., RF and wireless optical) with a Delay Tolerant Network approach. This paper presents an enhanced version of UDMSim, a novel simulation platform for data muling communications. UDMSim is built upon a new realistic AUV Motion and Localization (AML) simulator and Network Simulator 3 (ns-3). It can simulate the position of the data mules, including localization errors, realistic position control adjustments, the received signal, the realistic throughput adjustments, and connection losses due to the fast SNR change observed underwater. The enhanced version includes a more realistic AML simulator and the antenna radiation patterns to help evaluating the design and relative placement of underwater antennas. The results obtained using UDMSim show a good match with the experimental results achieved using an underwater testbed. UDMSim is made available to the community to support easy and faster evaluation of underwater data muling oriented communications solutions and to enable offline replication of real world experiments.

Abstract
The ATLANTIS project aims to establish a pioneer pilot infrastructure that will allow the demonstration of key enabling robotic technologies for inspection and maintenance of offshore wind farms. The pilot will be implemented in Viana do Castelo, Portugal, and will allow for testing, validation and demonstration of technologies with a range of technology readiness level, in near-real/real environments.The demonstration of robotic technologies can promote the transition from traditional inspection and maintenance methodologies towards automated robotic strategies, that remove or reduce the need of human-in-the-loop, reducing costs and improving the safety of interventions. Eight scenarios, split into four showcases, will be used to determine the required developments for robotic integration and demonstrate the applicability in the inspection and maintenance processes. The scenarios considered were identified by end-users as key areas for robotics.

Abstract
This paper describes a system designed to collect water samples, from the surface down to a configurable depth, and with configurable profiles of vertical velocity. The design was intended for the analysis of suspended sediments, therefore the sampling can integrate water flow for a given depth profile, or at a specific depth. The system is based on a catamaran-shaped platform, from which a towfish is lowered to collect the water samples. The use of a surface vehicle ensures a permanent link between the operator and the full system, allowing for a proper mission supervision. All components can be remotely controlled from the control station, or programmed for fully autonomous operation. Although the main intended use is for the analysis of suspended sediments in rivers, it can easily be extended to collect water samples in other water bodies.

Abstract
BIM (building information modelling) has gained wider acceptance in the AEC (architecture, engineering, and construction) industry. Conversion from 3D point cloud data to vector BIM data remains a challenging and labour-intensive process, but particularly relevant during various stages of a project lifecycle. While the challenges associated with processing very large 3D point cloud datasets are widely known, there is a pressing need for intelligent geometric feature extraction and reconstruction algorithms for automated point cloud processing. Compared to outdoor scene reconstruction, indoor scenes are challenging since they usually contain high amounts of clutter. This dataset comprises the indoor point cloud obtained by scanning four different rooms (including a hallway): two office workspaces, a workshop, and a laboratory including a water tank. The scanned space is located at the Electrical and Computer Engineering department of the Faculty of Engineering of the University of Porto. The dataset is fully labelled, containing major structural elements like walls, floor, ceiling, windows, and doors, as well as furniture, movable objects, clutter, and scanning noise. The dataset also contains an as-built BIM that can be used as a reference, making it suitable for being used in Scan-to-BIM and Scan-vs-BIM applications. For demonstration purposes, a Scan-vs-BIM change detection application is described, detailing each of the main data processing steps. Dataset: https://doi.org/10.5281/zenodo.7948116 Dataset License: Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract
Precise construction progress monitoring has been shown to be an essential step towards the successful management of a building project. However, the methods for automated construction progress monitoring proposed in previous work have certain limitations because of inefficient and unrobust point cloud processing. The main objective of this research was to develop an accurate automated method for construction progress monitoring using a 4D BIM together with a 3D point cloud obtained using a terrestrial laser scanner. The proposed method consists of four phases: point cloud simplification, alignment of the as-built data with the as-planned model, classification of the as-built data according to the BIM elements, and estimation of the progress. The accuracy and robustness of the proposed methodology was validated using a known dataset. The developed application can be used for construction progress visualization and analysis. © 2023 ITMA.