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Multi-epoch Chandra and XMM-Newton observations of the symbiotic system R Aquarii (R Aqr) spanning 22 yr are analysed by means of a reflection model produced by an accretion disc. This methodology helps dissecting the contribution from different components in the X-ray spectra of R Aqr: the soft emission from the jet and extended emission, the heavily-extinguished plasma component of the boundary layer and the reflection contribution, which naturally includes the 6.4 keV Fe fluorescent line. The evolution with time of the different components is studied for epochs between 2000 Sep and 2022 Dec, and it is found that the fluxes of the boundary layer and that of the reflecting component increase as the stellar components in R Aqr approach periastron passage, a similar behaviour is exhibited by the shocked plasma produced by the precessing jet. Using publicly available optical and UV data we are able to study the evolution of the mass-accretion rate $\dot{M}_\mathrm{acc}$ and the wind accretion efficiency $\eta$ during periastron. These exhibit a small degree of variability with median values of $\dot{M}_\mathrm{acc}$=7.3$\times10^{-10}$ M$_\odot$ yr$^{-1}$ and $\eta$=7$\times10^{-3}$. We compare our estimations with predictions from a modified Bondi-Hoyle-Lyttleton accretion scenario., Comment: 17 pages, 15 figures, 4 tables; accepted to MNRAS

This article investigates and compares three approaches to link prediction in colaboration networks, namely, an ERGM (Exponential Random Graph Model; Robins et al. 2007), a GCN (Graph Convolutional Network; Kipf and Welling 2017), and a Word2Vec+MLP model (Word2Vec model combined with a multilayer neural network; Mikolov et al. 2013a and Goodfellow et al. 2016). The ERGM, grounded in statistical methods, is employed to capture general structural patterns within the network, while the GCN and Word2Vec+MLP models leverage deep learning techniques to learn adaptive structural representations of nodes and their relationships. The predictive performance of the models is assessed through extensive simulation exercises using cross-validation, with metrics based on the receiver operating characteristic curve. The results clearly show the superiority of machine learning approaches in link prediction, particularly in large networks, where traditional models such as ERGM exhibit limitations in scalability and the ability to capture inherent complexities. These findings highlight the potential benefits of integrating statistical modeling techniques with deep learning methods to analyze complex networks, providing a more robust and effective framework for future research in this field., Comment: 17 pages, 5 figures, 1 table

We present a detailed survey of electric, magnetic and quadrupole form factors of light and heavy spin-1 vector mesons. It complements our analogous analysis of the electromagnetic form factors of pseudoscalar and scalar mesons reported earlier. Our formalism is based upon the Schwinger-Dyson equations treatment of a vector $\times$ vector contact interaction and the Bethe-Salpeter equation description of relativistic two-body bound states. We compute the form factors, associated moments and charge radii, comparing these quantities to earlier theoretical studies and experimental results if and when possible. We also investigate the quark-mass dependence of the charge radii and find the anticipated hierarchy such that it decreases with increasing dressed quark masses. In addition, our analysis shows that the magnetic moment is independent of the mass of the light and heavy mesons. Our results agree with most measurements reported earlier, finding a negative quadrupole moment, implying the charge distribution is oblate., Comment: 14 pages, 7 figures. arXiv admin note: text overlap with arXiv:2301.11881

This study presents a comparative analysis between two scientific document classification systems. The first system employs the Scopus journal-based assignment method, adapted to a fractional model, while the second system uses the U1-F-0.8 classification, an item-by-item system based on reclassified references according to the origin of the citers. The study's results are divided into three different sections: the first involves comparisons at the Scopus area level, the second examines comparisons at the category level, and the third tests various bibliometric indicators to identify the variations between the two systems. Highlighting the characteristics of the U1-F-0.8 system, it offers a reduction in the number of categories to which each document is assigned, achieving higher values of single-category assignment compared to the All Science Journal Classification (ASJC). When reclassifying areas and categories, the U1-F-0.8 system tends to accentuate differences at the extreme values, increasing the size of the largest categories and reducing that of the smallest ones. Moreover, the U1-F-0.8 system provides more homogeneous distributions in normalised impacts and adjusts values related to excellence more uniformly.

A reference-based classification system for individual Scopus publications is presented which takes into account the categories of the papers citing those references instead of the journals in which those cited papers are published. It supports multiple assignments of up to 5 categories within the Scopus ASJC structure, but eliminates the Multidisciplinary Area and the miscellaneous categories, and it allows for the reclassification of a greater number of publications (potentially 100%) than traditional reference-based systems. Twelve variants of the system were obtained by adjusting different parameters, which were applied to the more than 3.2 million citable papers from the active Scientific Journals in 2020 indexed in Scopus. The results were analyzed and compared with other classification systems such as the original journal-based Scopus ASJC, the 2-generation-reference based M3-AWC-0.8 (\'Alvarez-Llorente et al., 2024), and the corresponding authors' assignment based AAC (\'Alvarez-Llorente et al., 2023). The different variants obtained of the classification give results that improve those used as referents in multiple scientometric fields. The variation called U1-F-0.8 seems especially promising due to its restraint in assigning multiple categories, consistency with reference classifications and the fact of applying normalization processes to avoid the overinfluence of articles that have a greater number of references.

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now deprecated, making many existing datasets obsolete. This article introduces a novel method for translating data between tactile sensors by exploiting sensor deformation information rather than output signals. We demonstrate the approach by translating BioTac signals into the DIGIT sensor. Our framework consists of three steps: first, converting signal data into corresponding 3D deformation meshes; second, translating these 3D deformation meshes from one sensor to another; and third, generating output images using the converted meshes. Our approach enables the continued use of valuable datasets., Comment: Extended Abstract. Accepted in ICRA@40 (40th Anniversary of the IEEE International Conference on Robotics and Automation) 23-26 September, 2024 Rotterdam, Netherlands

In recent years, nanosatellites have revolutionized the space sector due to their significant economic and time-saving advantages. As a result, they have fostered the testing of advanced instruments intended for larger space science missions. The case of MELISA is presented in this work. MELISA is a magnetic measurement instrument which aims at demonstrating the in-orbit performance of AMR sensors featuring dedicated noise reduction techniques at sub-millihertz frequencies. Such low frequency ranges are relevant for future space-borne gravitational wave detectors, where the local magnetic environment of the satellite might yield a significant contribution to the overall noise budget of the observatory. The demanding magnetic noise levels required for this bandwidth, down to 0.1 mHz, make measurements arduous. To explore sensing solutions within the H2020 European Commission Programme with the involvement of ESA, the functional performance of MELISA-III will be validated in-orbit. During operations, there is the possibility to measure the low-frequency magnetic contribution stemming from orbiting the Earth's magnetic field, impeding the characterization of the intrinsic performance of the sensor. With the objective of minimizing excess noise during the in-flight operations, the present research aims to simulate the environmental magnetic conditions in LEO to identify and subtract undesired contributions to the measurements. The in-orbit long-term magnetic fluctuations are replicated using a Helmholtz coil system. A fluxgate magnetometer allows the correlation of the generated field with the payload measurements, leading to the subsequent subtraction. Proving the effect of this approach will facilitate the noise characterization of magnetic sensors in LEO, paving the way for the in-orbit validation of MELISA-III for use in magnetically demanding missions with long integration times.

We study an exactly solvable random walk model with long-range memory on arbitrary networks. The walker performs unbiased random steps to nearest-neighbor nodes and intermittently resets to previously visited nodes in a preferential way, such that the most visited nodes have proportionally a higher probability to be chosen for revisit. The occupation probability can be expressed as a sum over the eigenmodes of the standard random walk matrix of the network, where the amplitudes slowly decay as power-laws at large time, instead of exponentially. The stationary state is the same as in the absence of memory and detailed balance is fulfilled. However, the relaxation of the transient part becomes critically self-organized at late times, as it is dominated by a single power-law whose exponent depends on the second largest eigenvalue and on the resetting probability. We apply our findings to finite networks such as rings, complete graphs, Watts-Strogatz and Barab\'asi-Albert random networks, or Barbell graphs. Our study extends to network geometries a previous preferential model considering confined diffusive particles in one dimension, and could be of interest for modeling transport phenomena in complex systems, such as human mobility, epidemic spreading, or animal foraging., Comment: 15 pages, 3 figures

Precise synthesis of graphene nanoribbons (GNRs) is of great interest to chemists and materials scientists because of their unique opto-electronic properties and potential applications in carbon-based nanoelectronics and spintronics. In addition to the tunable edge structure and width, introducing curvature in GNRs is a powerful structural feature for their chemi-physical property modification. Here, we report an efficient solution synthesis of the first pyrene-based GNR (PyGNR) with curved geometry via one-pot K-region oxidation and Scholl cyclization of its corresponding well-soluble tetrahydropyrene-based polyphenylene precursor. The efficient A2B2-type Suzuki polymerization and subsequent Scholl reaction furnishes up to 35 nm long curved GNRs bearing cove- and armchair-edges. The construction of model compound, as a cutout of PyGNR, from a tetrahydropyrene-based oligophenylene precursor proves the concept and efficiency of the one-pot K-region oxidation and Scholl cyclization, which is clearly revealed by single crystal X-ray diffraction analysis. The structure and optical properties of PyGNR are investigated by Raman, FT-IR, solid-state NMR and UV-Vis analysis with the support of DFT calculations. PyGNR shows the absorption maximum at 680 nm, exhibiting a narrow optical bandgap of 1.4 eV, qualifying as a low-bandgap GNR. Moreover, THz spectroscopy on PyGNR estimates its macroscopic charge mobility of 3.6 cm2/Vs, outperforming other curved GNRs reported via conventional Scholl reaction.