Abstract
Serious Games use game strategies to encourage participants to make decisions and face challenges in a training environment; the more interactive the game, the more engaged the participants are with the content. Moreover, the best way to train is to simulate and identify scenarios for decision making, recreating situations, and strategies for learning. The Serious Games for training have this purpose. A Serious Game for Training can be refined with a game narrative, a methodology centered on the player to present independent and straightforward scenarios, giving solutions through the game story. The challenge is to rethink a unique narrative according to the individual player's experience. The present systematic literature review aims to answer which are the benefits of using Design Thinking for serious game narratives; the benefits of learning theories; the Design Thinking benefits for innovative solutions; and how game design elements can create an engaging Serious Game experience.

Abstract
O processo de revisão sistemática de literatura em investigação continua a apresentar-se como um processo com um elevado custo de recursos humanos e de tempo. Com vista em otimizar este processo pretende-se estudar a performance da ferramenta de pesquisa Cognitive Search da Microsoft que contem funcionalidades de inteligência artificial (IA). Neste trabalho foi implementada uma solução de pesquisa, i.e., parametrização do serviço de pesquisa, que produz uma classificação de relevância dos artigos científicos. Uma análise qualitativa aos artigos científicos foi efetuada para analisar a performance da solução de pesquisa e habilidades de inteligência artificial da ferramenta. O tema da revisão sistemática é “how is artificial intelligence (AI) being used in Higher Education (HE) today, involving tree dimensions: learning with AI, learning about AI and learning for AI”.;The systematic review process of research literature continues to be a very time and human resource expensive process. With the objective of optimizing this process we intend to study the performance of Microsoft Cognitive Search service which contains artificial intelligence capabilities. In this work the search service tool was configured and parameterized (search solution) to produce a classification ranking of the research articles. These were manually analysed to infer on the performance of the search solution. The topic of the systematic review is “how is artificial intelligence (AI) being used in Higher Education (HE) today, involving tree dimensions: learning with AI, learning about AI and learning for AI”.

Abstract
Extreme summer temperatures in the Iberia Peninsula are analyzed from ERA5-Land reanalysis data based on an extreme value mixture model combining a Normal distribution for the bulk distribution (i.e. for the non-extreme values) and a Generalized Pareto Distribution for the extremes in the upper tail. This approach allows to treat the threshold of temperature exceedances as being one of model parameters rather than fixed a priori, enabling to take into account its corresponding uncertainty. Extreme value mixture models are estimated individually for each location, and the analysis is performed separately for two distinct periods, namely from 1981 to 2000 and from 2000 to 2019, respectively. The results show significant differences in the extreme value mixture models for the two periods, and in their corresponding 20-year return levels. The mean of the bulk distribution of summer maximum temperature increases significantly, particularly in Eastern Iberia. The largest differences in the tails of the data distribution between the two periods occur in the eastern Mediterranean area, and are characterized by a significant increase in the threshold for temperature exceedances and in their corresponding return levels.

Abstract
The atmospheric electric field is a key characteristic of the Earth system. Despite its relevance, oceanic measurements of the atmospheric electric field are scarce, as typically oceanic measurements tend to be focused on ocean properties rather than on the atmosphere above. This motivated the set-up of an innovative campaign on board the sail ship NRP Sagres focused on the measurement of the atmospheric electric field in the marine boundary layer. This paper describes the monitoring system that was developed to measure the atmospheric electric field during the planned circumnavigation expedition of the sail ship NRP Sagres.

Abstract
<p><br>The automatic classification of peaks in gamma radiation time series is relevant for both scientific and practical applications. From the practical perspective, the classification of  peaks is fundamental for  early-warning systems for radiation protection and detection of radioactive material. From the scientific point of view, peaks in gamma radiation are often driven by precipitation  and consequent  scavenging of airborne radon progeny radionuclides to the ground (mainly Pb-214 and Bi-214). Thus measurements of gamma radiation at the earth's surface have the potential to provide information on micro-physical processes occurring high above in the clouds, as the dominant source of radon progeny is thought to be associated with in-cloud processes – nucleation scavenging and interstitial aerosol collection by cloud or rain droplets. </p><p>The present study addresses the classification of peaks in high-resolution (1-minute) gamma radiation time series from the GRM (Gamma Radiation Monitoring) campaign, which is being carried out since 2015 at the Eastern North Atlantic (ENA) station of the ARM (Atmospheric Radiation Measurements) programme. In addition to the gamma time series, precipitation information from laser disdrometer measurements is considered, including rain rate, liquid water content, median drop diameter and droplet concentration. Diverse machine learning algorithms are examined with the goal to identify and classify gamma peaks driven by precipitation events, and further examine the association between precipitation characteristics and the resulting gamma radiation peak on the ground.</p><p> </p>

Abstract
<p>Radon and its progeny are well-documented sources of natural radioactivity which can be used as benchmarks for testing a novel ionisation detector. The miniaturised ionisation detector was deployed aboard the NRP Sagres on a SAIL mission in July 2021 which travelled between the Açores and Lisbon in the North Atlantic Ocean. On its voyage, the detector profiled natural background radiation and in-directly detected cosmic ray muons, providing both spectroscopic energy discrimination and count rate data. The detector was simultaneously run with a NaI(Tl) gamma ray counter and other meteorological instruments.</p><p>The small form factor and low-power detector, composed of a 1x1x0.8 cm<sup>3 </sup>CsI(Tl) microscintillator coupled to a PiN photodiode, was able to identify gamma peaks from Bi-214 and K-40, having been calibrated using laboratory gamma sources up to 1.3 MeV. This research aims to investigate the performance of the ionisation detector and behaviour of discrete gamma energies over the duration of the voyage. Additionally, we will show a comparison of the CsI(Tl) based ionisation detector against the gamma ray counter which features a larger NaI(Tl) scintillator.</p>