Abstract
Context. In the Milky Way the central massive black hole, Sgr A*, coexists with a compact nuclear star cluster that contains a sub-parsec concentration of fast-moving young stars called S-stars. Their location and age are not easily explained by current star formation models, and in several scenarios the presence of an intermediate-mass black hole (IMBH) has been invoked.Aims. We use GRAVITY astrometric and SINFONI, KECK, and GNIRS spectroscopic data of S2, the best known S-star, to investigate whether a second massive object could be present deep in the Galactic Centre (GC) in the form of an IMBH binary companion to Sgr A*.Methods. To solve the three-body problem, we used a post-Newtonian framework and consider two types of settings: (i) a hierarchical set-up where the star S2 orbits the Sgr A*-IMBH binary and (ii) a non-hierarchical set-up where the IMBH trajectory lies outside the S2 orbit. In both cases we explore the full 20-dimensional parameter space by employing a Bayesian dynamic nested sampling method.Results. For the hierarchical case we find the strongest constraints: IMBH masses > 2000 M-circle dot on orbits with smaller semi-major axes than S2 are largely excluded. For the non-hierarchical case, the chaotic nature of the problem becomes significant: the parameter space contains several pockets of valid IMBH solutions. However, a closer analysis of their impact on the resident stars reveals that IMBHs on semi-major axes larger than S2 tend to disrupt the S-star cluster in less than a million years. This makes the existence of an IMBH among the S-stars highly unlikely.Conclusions. The current S2 data do not formally require the presence of an IMBH. If an IMBH hides in the GC, it has to be either a low-mass IMBH inside the S2 orbit that moves on a short and significantly inclined trajectory or an IMBH with a semi-major axis > 1 ''. We provide the parameter maps of valid IMBH solutions in the GC and discuss the general structure of our results and how future observations can help to put even stronger constraints on the properties of IMBHs in the GC.

Abstract
Wildfire disturbances can profoundly impact many aspects of both ecosystem functioning and resilience. This study proposes a satellite-based approach to assess ecosystem resilience to wildfires based on post-fire trajec-tories of four key functional dimensions of ecosystems related to carbon, water, and energy exchanges: (i) vegetation primary production; (ii) vegetation and soil water content; (iii) land surface albedo; and (iv) land surface sensible heat. For each dimension, several metrics extracted from satellite image time-series, at the short, medium and long-term, describe both resistance (the ability to withstand environmental disturbances) and re-covery (the ability to pull back towards equilibrium). We used MODIS data for 2000-2018 to analyze trajectories after the 2005 wildfires in NW Iberian Peninsula. Primary production exhibited low resistance, with abrupt breaks immediately after the fire, but rapid recoveries, starting within six months after the fire and reaching stable pre-fire levels two years after. Loss of water content after the fire showed slightly higher resistance but slower and more gradual recoveries than primary production. On the other hand, albedo exhibited varying levels of resistance and recovery, with post-fire breaks often followed by increases to levels above pre-fire within the first two years, but sometimes with effects that persisted for many years. Finally, wildfire effects on sensible heat were generally more transient, with effects starting to dissipate after one year and overall rapid recoveries. Our approach was able to successfully depict key features of post-fire processes of ecosystem functioning at different timeframes. The added value of our multi-indicator approach for analyzing ecosystem resilience to wildfires was highlighted by the independence and complementarity among the proposed indicators targeting four dimensions of ecosystem functioning. We argue that such approaches can provide an enhanced characterization of ecosystem resilience to disturbances, ultimately upholding promising implications for post-fire ecosystem management and targeting different dimensions of ecosystem functioning.

Abstract

Abstract
This paper describes the work developed to estimate the impact of the Special Regime Generation, SRG, in the generation cost in Portugal. Till the beginning of 2021 the values of the feed in tariffs paid to SRG were much larger than the market price paid to Normal Regime Generation, NRG, and this gap was often considered as a burden subsidized by consumers. In order to bring rational arguments to this discussion, several MSc Thesis were developed in recent years at the Engineering Faculty of Porto University to estimate the global generation cost in the country considering the current feed in regime and also admitting that generation paid feed in tariffs was reduced. This implied the calculation of the new market price if SRG was reduced and conversely NRG was increased. The results of the simulations developed for 2017, 2018, 2019 and 2020 indicate that the impact of SRG very much depends on the market price along the year. If the market price is reduced (for instance in good hydrological years as 2020) the elimination of SRG reduces the generation cost. Conversely, if the market price is high, the elimination of SRG tends to increase the generation cost.

Abstract
The adoption of third-party machine learning (ML) cloud services is highly dependent on the security guarantees and the performance penalty they incur on workloads for model training and inference. This paper explores security/performance trade-offs for the distributed Apache Spark framework and its ML library. Concretely, we build upon a key insight: in specific deployment settings, one can reveal carefully chosen non-sensitive operations (e.g. statistical calculations). This allows us to considerably improve the performance of privacy-preserving solutions without exposing the protocol to pervasive ML attacks. In more detail, we propose Soteria, a system for distributed privacy-preserving ML that leverages Trusted Execution Environments (e.g. Intel SGX) to run computations over sensitive information in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The experimental evaluation validates that our approach reduces the runtime of ML algorithms by up to 41% when compared to previous related work. Our protocol is accompanied by a security proof and a discussion regarding resilience against a wide spectrum of ML attacks.

Abstract
With the growing connectivity demands, Unmanned Aerial Vehicles (UAVs) have emerged as a prominent component in the deployment of Next Generation On-demand Wireless Networks. However, current UAV positioning solutions typically neglect the impact of Rate Adaptation (RA) algorithms or simplify its effect by considering ideal and non-implementable RA algorithms. This work proposes the Rate Adaptation aware RL-based Flying Gateway Positioning (RARL) algorithm, a positioning method for Flying Gateways that applies Deep Q-Learning, accounting for the dynamic data rate imposed by the underlying RA algorithm. The RARL algorithm aims to maximize the throughput of the flying wireless links serving one or more Flying Access Points, which in turn serve ground terminals. The performance evaluation of the RARL algorithm demonstrates that it is capable of taking into account the effect of the underlying RA algorithm and achieve the maximum throughput in all analysed static and mobile scenarios. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.