Abstract
<p>Gamma radiation measured over the ocean is mainly due to airborne radionuclides, as gamma emission by radon degassing from the ocean is negligible. Airborne gamma-emitting elements include radon progeny (Pb-2114, Bi-214, Pb-210) and cosmogenic radionuclides such as Be-7. Radon progeny attaches readily to aerosols, thus the fate of gamma-emitting radon progeny, after its formation by radioactive decay from radon, is expected to be closely linked to that of aerosols.</p> <p>Gamma radiation measurements over the Atlantic Ocean were made on board the ship-rigged sailing ship NRP Sagres in the framework of project SAIL (Space-Atmosphere-Ocean Interactions in the marine boundary Layer). The measurements were performed continuously with a NaI(Tl) scintillator counting all gamma rays from 475 keV to 3 MeV.  </p> <p>The counts from the sensor were recorded every 1 second into a computer system which had his time reference corrected by a GNSS pulse per second (PPS) signal. The GNSS was also used to precisely position the ship. The measurements were performed over the Atlantic ocean from January to May 2020, along the ship’s round trip from Lisboa - Cape Verde – Rio de Janeiro – Buenos Aires – Cape Town – Cape Verde - Lisboa.</p> <p>The results show that the gamma radiation time series displays considerable higher counts and larger variability in January compared to the remaining period. Reanalysis data also indicate higher aerosol concentration. This work investigates in detail the association between the temporal evolution of the gamma radiation measurements obtained from the SAIL campaign over the Atlantic Ocean and co-located total aerosol concentration at 550 nm obtained every 3 hours from EAC4(ECMWF Atmospheric Composition Reanalysis 4) data.</p>

Abstract
We present MONET, a new multimodal dataset captured using a thermal camera mounted on a drone that flew over rural areas, and recorded human and vehicle activities. We captured MONET to study the problem of object localisation and behaviour understanding of targets undergoing large-scale variations and being recorded from different and moving viewpoints. Target activities occur in two different land sites, each with unique scene structures and cluttered backgrounds. MONET consists of approximately 53K images featuring 162K manually annotated bounding boxes. Each image is timestamp-aligned with drone metadata that includes information about attitudes, speed, altitude, and GPS coordinates. MONET is different from previous thermal drone datasets because it features multimodal data, including rural scenes captured with thermal cameras containing both person and vehicle targets, along with trajectory information and metadata. We assessed the difficulty of the dataset in terms of transfer learning between the two sites and evaluated nine object detection algorithms to identify the open challenges associated with this type of data. Project page: https://github.com/fabiopoiesi/monet-dataset.

Abstract
Intensive cattle farming as a means of protein production contributes with the direct emission of greenhouse gases and the indirect contamination of soil and water. The public awareness towards this issue is growing in western cultures, leading to the stagnation of meat consumption and to the willingness to adopt alternative sustainable sources of protein. A solution is to farm insects as they present a reduced environmental impact and constitute a well-known source of protein. However, for westerners, eating insects implies a cultural change as they are still seen as dirty and disgusting. In 2022, a team of five EPS@ISEP students chose to design a solution for this problem followed by the assembly and test of the corresponding proof-of-concept prototype. They decided to design a home farming kit to grow mealworms driven by ethical, sustainable and the market needs. Exploring the insect life-cycle, the kit provides protein for humans and animals, chitin for soil bacteria and frass for plants. It can also be used as an educational tool for children to learn about sustainability, social responsibility and insect life-cycles, helping to overtake the cultural barrier against insect eating from a young age. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Tourists nowadays tend to avoid tourist traps and are looking for engaging ways to explore cities in the limited time they have. Standard options to explore cities seldom offer a combination between efficiency and fun. Furthermore, a search for an exploration city app returns an unlimited supply of lookalike websites and apps, all claiming to be the best. This paper reports the development of QRioCity, an efficient and exciting way to explore cities, by the “Dragonics” student team. QRioCity offers users the option to sign up for a playful tour through the city of Porto using a public kiosk with an interactive touchscreen. There is no limit to the number of teams playing simultaneously nor there is need to provide personal data. The teams are led through the city using clues and are proposed assignments, like scanning QR codes, to earn points. At the end of the game, every team receives discount coupons for local shops or stores depending on their score, even when they play alone. This way QRioCity helps tourists enjoying the local city life while offering municipalities a chance to strengthen their local economy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Crowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non-curated nature of the crowd-originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state-of-art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream-based scenario, and identifies future research opportunities.

Abstract
Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90% values for all evaluation metrics (accuracy, precision, recall, and F-measure).