Abstract
PurposeSecurity in large-scale agile is a crucial aspect that should be carefully addressed to ensure the protection of sensitive data, systems and user privacy. This study aims to identify and characterize the security practices that can be applied in managing large-scale agile projects.Design/methodology/approachA qualitative study is carried out through 18 interviews with 6 software development companies based in Portugal. Professionals who play the roles of Product Owner, Scrum Master and Scrum Member were interviewed. A thematic analysis was applied to identify deductive and inductive security practices.FindingsThe findings identified a total of 15 security practices, of which 8 are deductive themes and 7 are inductive. Most common security practices in large-scale agile include penetration testing, sensitive data management, automated testing, threat modeling and the implementation of a DevSecOps approach.Originality/valueThe results of this study extend the knowledge about large-scale security practices and offer relevant practical contributions for organizations that are migrating to large-scale agile environments. By incorporating security practices at every stage of the agile development lifecycle and fostering a security-conscious culture, organizations can effectively address security challenges in large-scale agile environments.

Abstract
This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

Abstract
The deep seabed is composed of heterogeneous ecosystems, containing diverse habitats for marine life. Consequently, understanding the geological and ecological characteristics of the seabed's features is a key step for many applications. The majority of approaches commonly use optical and acoustic sensors to address these tasks; however, each sensor has limitations associated with the underwater environment. This paper presents a survey of the main techniques and trends related to seabed characterization, highlighting approaches in three tasks: classification, detection, and segmentation. The bibliography is categorized into four approaches: statistics-based, classical machine learning, deep learning, and object-based image analysis. The differences between the techniques are presented, and the main challenges for deep sea research and potential directions of study are outlined.

Abstract
The Adaptive Optics Telemetry (AOT) format has recently been proposed to standardize the telemetry data generated by adaptive optics systems. Yet its usability remains limited by the user's programming expertise and familiarity with the accompanying Python package. There is an opportunity for substantial improvement in data accessibility by offering users an alternative tool for conducting exploratory data analysis in a visual and intuitive manner. We aim to design and develop an open-source Python visualization tool for exploring AOT data. This tool should support researchers and users by offering a broad set of interactive features for the analysis and exploration of the data. We designed a prototype dashboard and performed user testing to validate its usability. We compared the prototype with existing data visualization and exploration tools to ensure we provided the necessary functionality. We made publicly available a user-friendly dashboard for analyzing and exploring AOT data.

Abstract
The co-creation of games is a research area that has shown very promising results in identifying technological requirements. It is an approach where the researcher usually adopts the role of a participant observer, guiding the dynamics of co-creation acts. This situation limits the opportunities for replicability of co-creation methods by independent facilitators, which could elucidate the quality and improvement opportunities of these methods, contributing to their more widespread application. In this paper, we present a methodology that aims to overcome this limitation, allowing the replication of co-creation workshops by different independent facilitators. This methodology was conceived in the context of collecting relevant information for the design of an educational digital platform that intends to use gamified resources for adult education in digital health data literacy. Specifically, co-creation workshops were used to gain an overview of the difficulties of different age groups in this area and their perspective on which games would best address these difficulties. The workshops were conducted in five countries with planning oriented so that each country could have a different facilitator, not requiring the presence of the researcher who designed them. The challenge of this planning was to maintain the approach of the facilitators identical in all countries, as best one could. We present here the method adopted through its planning and materials designed for information collection, which included brainstorming using card sorting and game ideation with the use of templates. The analysis of replicability by independent facilitators was done by scrutinizing the produced elements, which allowed us to observe the aspects of coherence and divergence among the various facilitators. Thus, we conclude that this approach is a good starting point to overcome current limitations and identify possible lines of improvement.

Abstract
Grapevines (Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dor & eacute;e, esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges.