Abstract
In this work, different weight contents of as-grown carbon nanofibers (CNFs), produced by chemical vapor deposition, were melt-extruded with polypropylene (PP) and their morphologic, structure and dielectric properties examined. The morphologic analysis reveals that the CNFs are randomly distributed in the form of agglomerates within the PP matrix, whereas the structural results depicted by Raman analysis suggest that the degree of disorder of the as-received CNFs was not affected in the PP/CNF composites. The AC conductivity of PP/CNF composites at room temperature evidenced an insulator-conductor transition in the vicinity of 2 wt.%, corresponding to a remarkable rise of the dielectric permittivity up to similar to 12 at 400 Hz, with respect to the neat PP (similar to 2.5). Accordingly, the AC conductivity and dielectric permittivity of PP/CNF 2 wt.% composites were evaluated by using power laws and discussed in the framework of the intercluster polarization model. Finally, the complex impedance and Nyquist plots of the PP/CNF composites are analyzed by using equivalent circuit models, consisting of a constant phase element (CPE). The analysis gathered in here aims at contributing to the better understanding of the enhanced dielectric properties of low-conducting polymer composites filled with carbon nanofibers.

Abstract
<p>The marine boundary layer offers a unique opportunity to investigate the electrical properties of the atmosphere, as the effect of natural radioactivity in driving near surface ionization is significantly reduced over the ocean, and the concentration of aerosols is also typically lower than over land. This work addresses the temporal variability of the atmospheric electric field in the South Atlantic marine boundary layer based on measurements from the SAIL (Space-Atmosphere-Ocean Interactions in the marine boundary Layer) project. The SAIL monitoring campaign took place on board the Portuguese navy tall ship NRP Sagres during its circumnavigation expedition in 2020.  Two identical field mills (CS110, Campbell Scientific) were installed on the same mast but at different heights (about 5 and 22 meters), recording the atmospheric electric field every 1-second. Hourly averages of the atmospheric electric field are analyzed for the ship’s leg from 3<sup>rd</sup> to 25<sup>th</sup> March, between Buenos Aires (South America) and Cape Town (South Africa). The median daily curve of the electric field has a shape compatible with the Carnegie curve, but significant variability is found in the daily pattern of individual days, with only about 30% of the days exhibiting a diurnal pattern consistent with the Carnegie curve.</p>

Abstract

Abstract
Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of the power grid. However, PV power has great fluctuations due to various meteorological factors, which increase energy prices and cause difficulties in managing the grid. This article proposes an ultra-short-term PV power forecasting model based on the optimal frequency-domain decomposition and deep learning. First, the optimal frequency demarcation points for decomposition components are obtained through frequency-domain analysis. Then, the PV power is decomposed into the low-frequency and high-frequency components, which supports the rationality of decomposition results and solves the problem that the current decomposition model only uses the direct decomposition method and the decomposition components are not physical. Then, a convolutional neural network (CNN) is used to forecast the low-frequency and high-frequency components, and the final forecasting result is obtained by addition reconstruction. Based on the actual PV data in heavy rain days, the mean absolute percentage error (MAPE) of the proposed forecasting model is decreased by 52.97%, 64.07%, and 31.21%, compared with discrete wavelet transform, variational mode decomposition, and direct prediction models. In addition, compared with recurrent neural network and long-short-term memory model, the MAPE of the CNN forecasting model is decreased by 23.64% and 46.22%, and the training efficiency of the CNN forecasting model is improved by 85.63% and 87.68%. The results fully show that the proposed model in this article can improve both forecasting accuracy and time efficiency significantly.

Abstract
Autonomous Underwater Vehicles (AUVs) are seen as a safe and cost-effective platforms for performing a myriad of underwater missions. These vehicles are equipped with multiple sensors which, combined with their long endurance, can produce large amounts of data, especially when used for video capturing. These data need to be transferred to the surface to be processed and analyzed. When considering deep sea operations, where surfacing before the end of the mission may be unpractical, the communication is limited to low bitrate acoustic communications, which make unfeasible the timely transmission of large amounts of data unfeasible. The usage of AUVs as data mules is an alternative communications solution. Data mules can be used to establish a broadband data link by combining short-range, high bitrate communications (e.g., RF and wireless optical) with a Delay Tolerant Network approach. This paper presents an enhanced version of UDMSim, a novel simulation platform for data muling communications. UDMSim is built upon a new realistic AUV Motion and Localization (AML) simulator and Network Simulator 3 (ns-3). It can simulate the position of the data mules, including localization errors, realistic position control adjustments, the received signal, the realistic throughput adjustments, and connection losses due to the fast SNR change observed underwater. The enhanced version includes a more realistic AML simulator and the antenna radiation patterns to help evaluating the design and relative placement of underwater antennas. The results obtained using UDMSim show a good match with the experimental results achieved using an underwater testbed. UDMSim is made available to the community to support easy and faster evaluation of underwater data muling oriented communications solutions and to enable offline replication of real world experiments.

Abstract
A colonic polyp is a growth in the lining of the colon or rectum and can be detected through colonoscopies. The efficiency of colonoscopies depends on the number of polyps detected. However, detecting and classifying polyps is difficult, tedious, and prone to error. Knowing that this process's performance is far from perfect, the objective of this project is to help colonoscopists in the detection of polyps during the medical intervention, using Deep Learning (DL) alongside the image recognition capabilities of Convolutional Neural Networks (CNN) models that can process colonoscopy images at high speed in real-time. In this paper, were tested different state-of-the-art CNNs using a transfer learning approach, achieving an average accuracy of 95,70% in the polyp detection task. Multiple public datasets were used in this study to train, test, and evaluate the classifiers. The negative class included images representative of healthy tissue as well as other pathologies, so the models would not mistake other diseases as polyps.