Abstract
Due to the difficult access to the maritime environment, cooperation between different robotic platforms operating in different domains provides numerous advantages when considering Operations and Maintenance (O&M) missions. The nest Uncrewed Surface Vehicle (USV) is equipped with a parallel platform, serving as a landing pad for Uncrewed Aerial Vehicle (UAV) landings in dynamic sea states. This work proposes a methodology for short term forecasting of wave-behaviour using Fast Fourier Transforms (FFT) and a low-pass Butterworth filter to filter out noise readings from the Inertial Measurement Unit (IMU) and applying an Auto-Regressive (AR) model for the forecast, showing good results within an almost 10-second window. These predictions are then used in a Model Predictive Control (MPC) approach to optimize trajectory planning of the landing pad roll and pitch, in order to increase horizontality, consistently mitigating around 80% of the wave induced motion.

Abstract
The increasing complexity of integrated energy systems with the electric power grid requires innovative control solutions for efficient management of smart buildings and distributed energy resources. Accurately predicting weather conditions and electricity demand is crucial to make such informed decisions. Machine learning has emerged as a powerful solution to enhance prediction accuracy by harnessing advanced algorithms, but often requires complex parameterizations and ongoing model updates. The Lawrence Berkeley National Laboratory's Autonomous Forecast Framework (AFF) was developed to greatly simplify this process, providing reliable and accurate forecasts with minimal user interaction, by automatically selecting the best model out of a library of candidate models. This work expands on the AFF by not only selecting the best model, but assembling a blend of multiple models into a hybrid forecast model. The validation within this work has shown that this combination of models outperformed the selected best model of the AFF 31%, while providing greater resilience to individual model's forecast error.

Abstract
Context: Mobile application testing has gained considerable attention in recent years since mobile devices have become increasingly present in our lives. Unlike traditional software, mobile application testing has to deal with peculiarities, such as screen size and densities, different operating systems, and multiple sensors that increase the complexity of testing. Objective: This paper summarizes and analyzes the current secondary studies on mobile application testing through a tertiary study. Method: We selected and analyzed 21 secondary studies related to mobile application testing. Results: We categorized 21 secondary studies according to their main and specific research topics, test objectives, and testing platforms. Furthermore, we analyze 87 gaps and challenges identified by the secondary studies to understand which gaps have already been addressed and which gaps are still uncovered. Conclusion: Based on the results, we propose a research agenda with 15 open challenges related to mobile application testing to help future research.

Abstract
Today's power system is transforming into an increasingly complex entity, consisting of numerous components, such as transmission lines, controllable loads, and especially different types of distributed renewable energy sources (DERs). With the growing integration of DERs into the grid, multiple operational challenges arise, including overvoltage, undervoltage, or increased energy losses. Resolving these issues demands the implementation of both advanced and effective control strategies. As the dynamic power system evolves by incorporating new technologies, these control strategies need to consider other different technical aspects, such as communication protocols or real-time considerations. Additionally, the rise of smart metering devices has transformed conventional power systems into cyber-physical power systems (CPPS), which can integrate the advanced control strategies into the cyber layer. Given the operating challenges and the integration of diverse technologies, it is proposed that a CPPS testbed platform constitutes an ideal solution for developing and validating technologies in future smart grids. For this purpose, this paper introduces a co-simulation framework for implementing a CPPS testbed, utilising the real-time simulator, Typhoon HIL, within a laboratory environment. Additionally, it presents a proposed optimal power flow (OPF) control strategy that emphasises two key objectives, minimisation of operating costs and power network loss. The investigation is illustrated by a modified version of IEEE 39-bus test system with the high integration of DERs. The findings indicate that adopting a CPPS testbed can be advantageous for implementing real-time research on monitoring and control in a wide area network.

Abstract
This paper examines the concept of learning factories and their role in addressing contemporary challenges in the production sector. Learning factories integrate learning and production environments, offering hands-on experiences to develop essential competencies for modern manufacturing. Originating from initiatives like the Germany's "Lernfabriken" in the late 1980s and the National Science Foundation's funding in the 1990s, learning factories have gained global prominence. They serve as platforms for research, education, and workforce development, attracting students and workers from diverse sectors. Examples from Europe, the United States, and China illustrate various approaches to leveraging learning factories for industrial advancement and skill development. Overall, learning factories play a vital role in fostering innovation, enhancing competitiveness, and driving economic growth in the manufacturing sector.

Abstract
In collaborative energy communities, optimizing energy costs is a critical aspect of sustainable management. This article explores the potential benefits of applying clustering algorithms to vary retail tariffs monthly, aiming to reduce energy bills for the community as a whole. The article compares a traditional approach of applying the same tariff to all community members throughout the year with a novel approach of dynamically changing tariffs based on monthly clustering results. A case study is conducted, wherein energy bill costs per month are analyzed under different tariff scenarios utilizing k -means clustering. Results indicate that the proposed approach yields promising reductions in energy costs, up to 8.76% (1170.18 EUR) improvement compared to the traditional method. The study contributes valuable insights into the practical application of clustering in energy community management and highlights the potential for significant cost savings through dynamic tariff adjustments.