Abstract
Interest in argument mining has resulted in an increasing number of argument annotated corpora. However, most focus on English texts with explicit argumentative discourse markers, such as persuasive essays or legal documents. Conversely, we report on the first extensive and consolidated Portuguese argument annotation project focused on opinion articles. We briefly describe the annotation guidelines based on a multi-layered process and analyze the manual annotations produced, highlighting the main challenges of this textual genre. We then conduct a comprehensive inter-annotator agreement analysis, including argumentative discourse units, their classes and relations, and resulting graphs. This analysis reveals that each of these aspects tackles very different kinds of challenges. We observe differences in annotator profiles, motivating our aim of producing a non-aggregated corpus containing the insights of every annotator. We note that the interpretation and identification of token-level arguments is challenging; nevertheless, tasks that focus on higher-level components of the argument structure can obtain considerable agreement. We lay down perspectives on corpus usage, exploiting its multi-faceted nature.

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Abstract

Abstract
Missing data can pose severe consequences in critical contexts, such as clinical research based on routinely collected healthcare data. This issue is usually handled with imputation strategies, but these tend to produce poor and biased results under the Missing Not At Random (MNAR) mechanism. A recent trend that has been showing promising results for MNAR is the use of generative models, particularly Variational Autoencoders. However, they have a limitation: the imputed values are the result of a single sample, which can be biased. To tackle it, an extension to the Variational Autoencoder that uses a partial multiple imputation procedure is introduced in this work. The proposed method was compared to 8 state-of-the-art imputation strategies, in an experimental setup with 34 datasets from the medical context, injected with the MNAR mechanism (10% to 80% rates). The results were evaluated through the Mean Absolute Error, with the new method being the overall best in 71% of the datasets, significantly outperforming the remaining ones, particularly for high missing rates. Finally, a case study of a classification task with heart failure data was also conducted, where this method induced improvements in 50% of the classifiers.

Abstract
This paper investigates the contribution of hydrogen electrolysers (HEs) as highly controllable loads in the context of the Frequency Containment Reserve (FCR), in future operation scenarios on the Iberian Peninsula (IP). The research question is whether HEs can mitigate system insecurity regarding frequency or Rate of Change of Frequency (RoCoF) in critical periods of high renewable energy penetration (i.e. low system inertia), due to the fact that these periods will coincide with high volume of green hydrogen production. The proposed simulation platform for analysis consists of a simplified dynamic model developed in MATLAB/Simulink. The results obtained illustrate how HEs can outperform conventional generators on the provision of FCR. It is seen that the reference incident of 1GW loss in the IP in a 2040 low inertia scenario does not lead to insecure values of either frequency or Rate of Change of Frequency (RoCoF). On the other hand, an instantaneous loss of inverter-based resources (IBR) generation following a short-circuit may result in RoCoF violating security thresholds. The obtained results suggest that the HEs expected to be installed in the IP in 2040 may contribute to reduce RoCoF in this case, although this mitigation may be insufficient. The existing FCR mechanism does not fully exploit the fast-ramping capability of HEs; reducing measurement acquisiton delay would not improve results.

Abstract
This work aims to identify the critical production costs, related to raw materials and labor, of ordered inflatable-based products without standardization in order to develop a quantitative model to predict these costs accurately in the early project stage, within the budget step. In order to achieve this goal, it was necessary to understand the production processes and the raw materials, as well as to study the principal theoretical aspects related to cost estimating techniques and methods, cost estimating models, model selection, and validation. Therefore, it is intended to develop a multiple linear regression model, applied to historical quantitative data, to estimate each critical variable concerning the quantity of the main raw material and the labor times for critical processes. Six models were analyzed, in which two models are identified for each critical variable such as the linear meters value of the main raw material used in the product, the main raw material cut time involved in the product and the sew time required by the product. The models were evaluated, selected, and validated, defining the best model for each critical variable. The model parameters were obtained using a train dataset and, afterwards, the results of the selected models were validated using a test dataset. The obtained results, through the proposed methodology, were evaluated and proved to be reliable for use in the early stage of product development within the budget step.