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>

Abstract

Abstract
The use of robots is widely spread across the industry. It is paramount that the robot end-effector tracks a pre-defined trajectory with the lowest energy loss. To contribute to the solution of this problem, the robot trajectory is defined using a tracking parameter which is optimised using the Matlab (R) fminunc function and the Particle Swam Optimisation algorithm. This approach was tested for a case study with the energy loss being reduced in approximately 96.15%.

Abstract
Robotics is a multidisciplinary subject that typically involves mechanics, electronics, and computer science concepts. For this reason, robotic projects are particularly well suited to the European Project Semester framework since they allow students with different backgrounds to contribute to the overall team objective in their specific knowledge areas. This chapter briefly presents illustrative examples of robotic projects that have been developed by teams of students participating in the European Project Semester at the School of Engineering of the Polytechnic Institute of Porto. It concludes by presenting and discussing student feedback, namely on the program and the projects developed.

Abstract
Orion is a NASA spacecraft being developed for human exploration of the Moon and Mars. Crew Dragon is a commercial spacecraft used to transport astronauts to and from the International Space Station (ISS). Both spacecraft are of similar design and both spacecraft perform a water landing following re-entry. This study evaluated the ability of International Institute of Astronautical Sciences (IIAS) Citizen Astronaut Candidates (CAC) to egress a spacecraft mock-up wearing a commercially available intravehicular activity (IVA) spacesuit manufactured by Final Frontier Design (FFD) (Anderson, 2014; Barker and Bellenkes; 1996; Rubio et al., 2004). This suit is similar to those worn by astronauts traveling to the ISS on board Crew Dragon. Mobility assessment revealed that most participants had sufficient ranges of motion to perform egress tasks successfully. In some instances suited participants were unable to perform selected tasks proficiently, but in these instances this often stemmed from difficulty in achieving a stable upright position in the water. Seat ingress and egress evaluation revealed no significant problems with anthropometric accommodation across participants.