Abstract
In many areas, the analysis of a cylindrical structure is necessary, and a form to analyze it is by evaluating the diameter changes. Some areas can be cited: pipelines for oil or gas distribution and radial growth of trees whose diameter changes are directly related to irrigation and the radial expansion since it depends on the water soil deficit. For some species, these radial variations can change in around 5 mm. This paper proposes and experimentally investigates a sensor based on a core diameter mismatch technique for diameter changes measurement. The sensor structure is a combination of a cylindrical piece developed using a 3D printer and a Mach-Zehnder interferometer. The pieces were developed to assist in monitoring the diameter variation. It is formed by splicing an uncoated short section of MMF (Multimode Fiber) between two standard SMFs (Singlemode Fibers) called SMF-MMF-SMF (SMS), where the MMF length is 15 mm. The work is divided into two main parts. Firstly, the sensor was fixed at two points on the first developed piece, and the diameter reduction caused dips or peaks shift of the transmittance spectrum due to curvature and strain influence. The fixation point (FP) distances used are: 5 mm, 10 mm, and 15 mm. Finally, the setup with the best sensitivity was chosen, from first results, to develop another test with an optimization. This optimization is performed in the printed piece where two supports are created so that only the strain influences the sensor. The results showed good sensitivity, reasonable dynamic range, and easy setup reproduction. Therefore, the sensor could be used for diameter variation measurement for proposed applications.

Abstract
Despite its importance, the time variable has been largely neglected in the NLP and language model literature. In this paper, we present TimeLMs, a set of language models specialized on diachronic Twitter data. We show that a continual learning strategy contributes to enhancing Twitter-based language models' capacity to deal with future and out-of-distribution tweets, while making them competitive with standardized and more monolithic benchmarks. We also perform a number of qualitative analyses showing how they cope with trends and peaks in activity involving specific named entities or concept drift. TimeLMs is available at https://github.com/cardiffnlp/timelms.

Abstract
Three-dimensional (3D) image mapping of real-world scenarios has a great potential to provide the user with a more accurate scene understanding. This will enable, among others, unsupervised automatic sampling of meaningful material classes from the target area for adaptive semi-supervised deep learning techniques. This path is already being taken by the recent and fast-developing research in computational fields, however, some issues related to computationally expensive processes in the integration of multi-source sensing data remain. Recent studies focused on Earth observation and characterization are enhanced by the proliferation of Unmanned Aerial Vehicles (UAV) and sensors able to capture massive datasets with a high spatial resolution. In this scope, many approaches have been presented for 3D modeling, remote sensing, image processing and mapping, and multi-source data fusion. This survey aims to present a summary of previous work according to the most relevant contributions for the reconstruction and analysis of 3D models of real scenarios using multispectral, thermal and hyperspectral imagery. Surveyed applications are focused on agriculture and forestry since these fields concentrate most applications and are widely studied. Many challenges are currently being overcome by recent methods based on the reconstruction of multi-sensorial 3D scenarios. In parallel, the processing of large image datasets has recently been accelerated by General-Purpose Graphics Processing Unit (GPGPU) approaches that are also summarized in this work. Finally, as a conclusion, some open issues and future research directions are presented.

Abstract
The main objective of continuing education for trainers is to promote the updating, improvement, and acquisition of new didactic and pedagogical skills that cover different fields of action, namely the design, development, and implementation of training programs in the field of research and experimentation of new approaches and methodologies applied to diversified audiences and contexts, especially in e-Learning and b-Learning environments. To fulfill these competencies, the IEFP National Centre for Trainer Qualification (CNQF), besides managing and coordinating the training and certification system for trainers in Portugal, has been developing a modular structure for the Initial Pedagogical Training of Trainers and the Continuous Pedagogical Training of the Distance Trainer (e-Trainer) to contribute to the acquisition and development of pedagogical and technical competences of trainers that will contribute to raising the standards of quality of the training provided. Technological innovation and evolution launch new challenges to Trainers requiring a great effort to adapt and master both from the point of view of pedagogical models and communication processes in learning environments and digital content. This new Continuous Pedagogical Training Referential in Digital Content for Self-Learning (e-Content) was designed in this context. It explores the pedagogical and technological dimensions of producing digital content for distance learning environments. This article presents the fundamentals of this framework, its application, and validation in a case study supported by two e-Content training courses. With this case study and in a perspective of continuous improvement, we intend to understand how the modular structure of the adopted framework influences the results obtained by the trainees of the e-Content training courses and their degree of satisfaction.

Abstract
In today's highly competitive economy, using technology to increase customer satisfaction is vital for companies. Thus, this article analyzes the relationship between order tracking and customer satisfaction, to answer the following research question: How do order tracking systems influence B2B customer satisfaction with transport companies. A qualitative study was carried out consisting of semi-structured interviews with 12 professionals in the field of logistics with experience in tracking systems in different business sectors. The study was conducted in 2021 in Portugal. According to the opinions collected in the interviews, it was possible to understand that time savings and the speed of obtaining information, among other factors, were the benefits most highlighted by customers, which contribute to satisfaction with logistics companies. Thus, it is concluded that it will be advantageous for transport and logistics companies to provide an order tracking system to their B2B customers, to contribute to the satisfaction and loyalty of these customers, as well as to improve logistics processes.

Abstract
Renewable Energy Communities (RECs) are emerging as an effective concept and model to empower the active participation of citizens in the energy transition, not only as energy consumers but also as promoters of environmentally friendly energy generation solutions, particularly through the use of photovoltaic panels. This paper aims to contribute to the management and optimization of individual and community Distributed Energy Resources (DER). The solution follows a price and source-based REC management program, in which consumers' day-ahead flexible loads (Flex Offers) are shifted according to electricity generation availability, prices, and personal preferences, to balance the grid and incentivize user participation. The heuristic approach used in the proposed algorithms allows for the optimization of energy resources in a distributed edge-and-fog approach with a low computational overhead. The simulations performed using real-world energy consumption and flexibility data of a REC with 50 dwellings show an average cost reduction, taking into consideration all the seasons of the year, of 6.5%, with a peak of 12.2% reduction in the summer, and an average increase of 32.6% in individual self-consumption. In addition, the case study demonstrates promising results regarding grid load balancing and the introduction of intra-community energy trading.