Abstract
In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives.

Abstract
The close interaction between the electricity market and the end-users can assist the demand response (DR) aggregator in handling and managing various uncertain parameters simultaneously to reduce their effect on the aggregator's operation. As the DR aggregator's main responsibility is to aggregate the obtained DR from individual consumers and trade it into the wholesale market. Another responsibility of the aggregator is proposing the DR programs (DRPs) to the end-users. This article proposes a model to handle these uncertainties through the development of a novel hybrid stochastic-robust optimization approach that incorporates the uncertainties around wholesale market prices and the participation rate of consumers. The behavior of the consumers engaging in DRPs is addressed through stochastic programming. Additionally, the volatility of the electricity market prices is modeled through a robust optimization method. Two DRPs are considered in this model to include both time-based and incentive-based DRPs, i.e., time-of-use and incentive-based DR program to study three sectors of consumers, namely industrial, commercial, and residential consumers. An energy storage system is also assumed to be operated by the aggregator to maximize its profit. The proposed mixed-integer linear hybrid stochastic-robust model improves the evaluation of DR aggregator's scheduling for the probable worst-case scenario. Finally, to demonstrate the effectiveness of the proposed approach, the model is thoroughly simulated in a real case study.

Abstract
Botnets are responsible for some of the major malicious traffic on the Internet: DDoS attacks, Mail SPAM, brute force attacks, portscans, and others. Its dangerousness is due to the coordinated amount of infected hosts focusing on a single target. More contributions are in need, considering that (A) ML has been used for cyberattacks identification with better accuracy than standard NIDS equipments, (B) Botnet attacks are one of the most dangerous threats on the Internet. (C) the difficulties in getting representative datasets on some Botnets, and (D) Botnet traffic can be misunderstood by its infrastructure protocol. In this paper, we focus on the identification of Botnet traffic, preventing the communication from the Botmaster to the infected hosts and consequently the Botnet cyberattacks. CICFlowMeter and Machine Learning algorithms were used to analyse Botnet2014 public dataset on four different scenarios: all Botnet traffic on a single class, each class per Botnet traffic and the influence of the IPs address fields Botnet traffic detection. The results shows that Random Forest (RF) and Decision Tree (CART) archived similar accuracies on Botnet traffic classification. Important to say that CART obtained similar results with 10-20% of machine time. The metrics shown that the analysis per specific Botnet has higher accuracy than Any Botnet Traffic analysis. Also, the analysis with the IP addresses and L4 Ports scenario has higher accuracy but lower F1-Score that the equivalent without IP addresses or L4 Ports. At last, Feature Importance results confirms the literature, that Botnet traffic is not a single uniform protocol, but a collection of very different ways of communications between the botmaster and the infected hosts.

Abstract
During the initial phases of the COVID-19 pandemic, accurate tracking has proven unfeasible. Initial estimation methods pointed toward case numbers that were much higher than officially reported. In the CoronaSurveys project, we have been addressing this issue using open online surveys with indirect reporting. We compare our estimates with the results of a serology study for Spain, obtaining high correlations (R squared 0.89). In our view, these results strongly support the idea of using open surveys with indirect reporting as a method to broadly sense the progress of a pandemic.

Abstract

Abstract
<p>Ambient radioactivity reflects a wide range of physical processes, including atmospheric and geological processes, as well as space weather and solar conditions. Gamma radiation near the Earth’s surface comes from diverse sources, including space (cosmic radiation), the earth’s atmosphere, and solid earth. In addition to the terrestrial gamma radiation originating from the radioactive decay of primordial radionuclides present in every soil and rock, gamma radiation is also continuously produced in the atmosphere from the interaction of secondary cosmic rays and upper-atmosphere gases, as well as from the decay of airborne radon (Rn-222) progeny. Therefore the temporal variability of gamma radiation contains information on a wide range of physical processes and space-earth interactions, but disentangling the different contributions remains a challenging endeavor. Continuous monitoring of gamma radiation at sea enables to remove both the terrestrial and radon exhalation contributions, allowing to examine in detail the space and atmospheric sources of ambient gamma radiation.</p><p>Gamma radiation over the Atlantic Ocean was measured on board the ship-rigged sailing ship NRP Sagres in the framework of the SAIL (Space-Atmosphere-Ocean Interactions in the marine boundary Layer) project. The measurements were performed continuously (every 1-second) with a NaI(Tl) scintillator counting all the gamma rays from 475 keV to 3 MeV. The casing of the instrument was adapted in order to endure the harsh oceanic conditions and installed in the mizzen mast of the ship. The counts were linked to a rigorous temporal reference frame and precise positioning through GNSS.</p><p>Here preliminary results based on the gamma radiation measurements performed from January 5<sup>th</sup> to May 9<sup>th </sup>2020 are presented, corresponding to the journey of the ship from Lisboa to Cabo Verde, Rio de Janeiro, Montevideu, Cape Town, and back to Lisboa. The data exhibit a clear transition from the coastal to the marine environment, enabling to study in detail the temporal variation of gamma radiation in the marine boundary layer, as well as the interface between land and marine conditions in terms of environmental radioactivity.</p>