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After a merger of two massive black holes (MBHs), the remnant receives a gravitational wave (GW) recoil kick that can have a strong effect on its future evolution. The magnitude of the kick ($v_\mathrm{recoil}$) depends on the mass ratio and the alignment of the spins and orbital angular momenta, therefore on the previous evolution of the MBHs. We investigate the cosmic effect of GW recoil by running for the first time a high-resolution cosmological simulation including GW recoil that depends on the MBH spins (evolved through accretion and mergers), masses and dynamics computed self-consistently. We also run a twin simulation without GW recoil. The simulations are run down to $z=4.4$. We find that GW recoil reduces the growth of merger remnants, and can have a significant effect on the MBH-galaxy correlations and the merger rate. We find large recoil kicks across all galaxy masses in the simulation, up to a few $10^{11}\,\rm M_\odot$. The effect of recoil can be significant even if the MBHs are embedded in a rotationally supported gaseous structure. We investigate the dynamics of recoiling MBHs and find that MBHs remain in the centre of the host galaxy for low $v_\mathrm{recoil}/v_\mathrm{esc}$ and escape rapidly for high $v_\mathrm{recoil}/v_\mathrm{esc}$. Only if $v_\mathrm{recoil}$ is comparable to $v_\mathrm{esc}$ the MBHs escape the central region of the galaxy but might remain as wandering MBHs until the end of the simulation. Recoiling MBHs are a significant fraction of the wandering MBH population. Although the dynamics of recoiling MBHs may be complex, some retain their initial radial orbits but are difficult to discern from other wandering MBHs on radial orbits. Others scatter with the halo substructure or circularise in the asymmetric potential. Our work highlights the importance of including GW recoil in cosmological simulation models., Comment: submitted to A&A

We report here the results obtained from a systematic optical photometric survey aimed at finding new compact binary millisecond pulsars (also known as "spiders"): the COmpact BInary PULsar SEarch (COBIPULSE). We acquired multi-band optical images over one year around $33$ unidentified Fermi-LAT sources, selected as pulsar candidates based on their curved GeV spectra and steady $\gamma$-ray emission. We present the discovery of four optical variables coinciding with the Fermi sources 3FGL J0737.2$-$3233, 3FGL J2117.6$+$3725 (two systems in this field) and 3FGL J2221.6$+$6507, which we propose as new candidate spider systems. Indeed, they all show optical flux modulation consistent with orbital periods of $0.3548(5) \ \mathrm{d}$, $0.25328(6) \ \mathrm{d}$, $0.441961(2) \ \mathrm{d}$, and $0.165(4) \ \mathrm{d}$, respectively, with amplitudes $\gtrsim 0.3 \ \mathrm{mag}$ and colors compatible with companion star temperatures of $5000$--$6000 \ \mathrm{K}$. These properties are consistent with the "redback" sub-class of spider pulsars. If confirmed as a millisecond pulsar, 3FGL J0737.2$-$3233 will be the closest known spider to Earth ($D=659_{-20}^{+16} \ \mathrm{pc}$, from Gaia-DR3 parallax). We searched and did not find any X-ray sources matching our four candidates, placing $3\sigma$ upper limits of $\sim10^{31}$--$10^{32} \ \mathrm{erg} \ \mathrm{s}^{-1}$ ($0.3$--$10 \ \mathrm{keV}$) on their soft X-ray luminosities. We also present and discuss other multi-wavelength information on our spider candidates, from infrared to X-rays., Comment: 38 pages, 93 figures, 4 tables. This paper has been accepted for publication in ApJ

Pedestrian GPS data are key to a better understanding of micro-mobility and micro-behaviour within a neighbourhood. These data can bring new insights into walkability and livability in the context of urban sustainability. However, pedestrian open data are scarce and often lack a context for their transformation into actionable knowledge in a neighbourhood. Citizen science and public involvement practices are powerful instruments for obtaining these data and take a community-centred placemaking approach. The study shares some 3000 GPS recordings corresponding to 19 unique trajectories made and recorded by groups of participants from three distinct communities in a relatively small neighbourhood. The groups explored the neighbourhood through a number of tasks and chose different places to stop and perform various social and festive activities. The study shares not only raw data but also processed records with specific filtering and processing to facilitate and accelerate data usage. Citizen science practices and the data-collection protocols involved are reported in order to offer a complete perspective of the research undertaken jointly with an assessment of how community-centred placemaking and operative mapping are incorporated into local urban transformation actions.

In the natural and social sciences, it is common to use toy models -- extremely simple and highly idealized representations -- to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and their effect on the output. The obvious difference is that the common target of a toy and a full-scale model in the sciences is some phenomenon in the world, while the target of a surrogate model is another model. This essential difference makes toy surrogate models (TSMs) a new object of study for theories of understanding, one that is not easily accommodated under current analyses. This paper provides an account of what it means to understand an opaque ML model globally with the aid of such simple models.

Astrophysical black holes (BHs) have two fundamental properties: mass and spin. While the mass-evolution of BHs has been extensively studied, much less work has been done on predicting the distribution of BH spins. In this paper we present the spin evolution for a sample of intermediate-mass and massive BHs from the newHorizon simulation, which evolved BH spin across cosmic time in a full cosmological context through gas accretion, BH-BH mergers and BH feedback including jet spindown. As BHs grow, their spin evolution alternates between being dominated by gas accretion and BH mergers. Massive BHs are generally highly spinning. Accounting for the spin energy extracted through the Blandford-Znajek mechanism increases the scatter in BH spins, especially in the mass range $10^{5-7} \rm \ M_\odot$, where BHs had previously been predicted to be almost universally maximally spinning. We find no evidence for spin-down through efficient chaotic accretion. As a result of their high spin values, massive BHs have an average radiative efficiency of $<\varepsilon_{\rm r}^{\rm thin}> \approx 0.19$. As BHs spend much of their time at low redshift with a radiatively inefficient thick disc, BHs in our sample remain hard to observe. Different observational methods probe different sub-populations of BHs, significantly influencing the observed distribution of spins. Generally, X-ray-based methods and higher luminosity cuts increase the average observed BH spin. When taking BH spin evolution into account, BHs inject on average between 3 times (in quasar mode) and 8 times (in radio mode) as much feedback energy into their host galaxy as previously assumed., Comment: 21 pages, accepted for publication in MNRAS

Recent advances in AI have led to significant results in robotic learning, but skills like grasping remain partially solved. Many recent works exploit synthetic grasping datasets to learn to grasp unknown objects. However, those datasets were generated using simple grasp sampling methods using priors. Recently, Quality-Diversity (QD) algorithms have been proven to make grasp sampling significantly more efficient. In this work, we extend QDG-6DoF, a QD framework for generating object-centric grasps, to scale up the production of synthetic grasping datasets. We propose a data augmentation method that combines the transformation of object meshes with transfer learning from previous grasping repertoires. The conducted experiments show that this approach reduces the number of required evaluations per discovered robust grasp by up to 20%. We used this approach to generate QDGset, a dataset of 6DoF grasp poses that contains about 3.5 and 4.5 times more grasps and objects, respectively, than the previous state-of-the-art. Our method allows anyone to easily generate data, eventually contributing to a large-scale collaborative dataset of synthetic grasps., Comment: 8 pages, 9 figures. Draft version

This is a survey on the geometric classification of different varieties of algebras (nilpotent, nil-, associative, commutative associative, cyclic associative, Jordan, Kokoris, standard, noncommutative Jordan, commutative power-associative, weakly associative, terminal, Lie, Malcev, binary Lie, Tortkara, dual mock Lie, $\mathfrak{CD}$-, commutative $\mathfrak{CD}$-, anticommutative $\mathfrak{CD}$-, symmetric Leibniz, Leibniz, Zinbiel, Novikov, bicommutative, assosymmetric, antiassociative, left-symmetric, right alternative, and right commutative), $n$-ary algebras (Fillipov ($n$-Lie), Lie triple systems and anticommutative ternary), superalgebras (Lie and Jordan), and Poisson-type algebras (Poisson, transposed Poisson, Leibniz-Poisson, generic Poisson, generic Poisson-Jordan, transposed Leibniz-Poisson, Novikov-Poisson, pre-Lie Poisson, commutative pre-Lie, anti-pre-Lie Poisson, pre-Poisson, compatible commutative associative, compatible associative, compatible Novikov, compatible pre-Lie). We also discuss the degeneration level classification.

This paper focuses on defining an analog of differential-graded triangular matrix algebra in the context of differential-graded categories. Given two dg-categories $\mathcal{U}$ and $\mathcal{T}$ and $M \in \text{DgMod}(\mathcal{U} \otimes \mathcal{T}^{\text{op}})$, we construct the differential graded triangular matrix category $\Lambda := \left( \begin{smallmatrix} \mathcal{T} & 0 \\ M & \mathcal{U} \end{smallmatrix} \right)$. Our main result is that there is an equivalence of dg-categories between the dg-comma category $(\text{DgMod}(\mathcal{T}),\text{GDgMod}(\mathcal{U}))$ and the category $\text{DgMod}\left( \left( \begin{smallmatrix} \mathcal{T} & 0 \\ M & \mathcal{U} \end{smallmatrix} \right)\right)$. This result is an extension of a well-known result for Artin algebras (see, for example, [2,III.2]., Comment: 28 pages

The James Webb Space telescope has discovered an abundant population of broad line emitters, typical signposts for Active Galactic Nuclei (AGN). Many of these sources have red colors and a compact appearance that has led to naming them `Little Red Dots'. In this paper we develop a detailed framework to estimate the photometry of AGN embedded in galaxies extracted from the Obelisk cosmological simulation to understand the properties of color-selected Little Red Dots (cLRDs) in the context of the full AGN and massive black hole population. We find that using realistic spectral energy distributions (SEDs) and attenuation for AGN we can explain the shape of the cLRD SED as long as galaxies host a sufficiently luminous AGN that is not too much or too little attenuated. When attenuation is too low or too high, AGN do not enter the cLRD selection, because the AGN dominates over the host galaxy too much in blue filters, or it does not contribute to photometry anywhere, respectively. cLRDs are also characterized by high Eddington ratios, possibility super-Eddington, and/or high ratios between black hole and stellar mass., Comment: Submitted to A&A

This work presents the results of modelling the ion dynamics in the ART-MS (Autoresonant Trap Mass Spectrometry) device in the quasi-static approximation. This instrument utilizes an anharmonic, purely electrostatic trap for ion confinement and a radio frequency (RF) voltage source with decrementally varying frequency for selective ion extraction. The autoresonant interaction between the oscillatory motion of the ion and the RF voltage causes an increase in the amplitude of certain confined ions, allowing their selective extraction. Numerical modelling shows that the extraction of ions with a given mass occurs not only at the fundamental frequency but also at its harmonics. This effect reduces the selective properties of devices of this type because along with the main mass component for a given frequency, it is possible to enter the detector channel of ions with another mass, for which this frequency corresponds to the second or higher harmonics, even a superposition of some of these harmonics of different ions., Comment: 9 pages, 6 figures

The Monte Carlo method is a thriving and mathematically beautiful numerical technique used extensively, nowadays, to deal with many demanding problems in diverse fields. Here, we present an iterative Monte Carlo algorithm to work out very general nonlinear second-order differential equations, with Dirichlet boundary conditions. An example of its usage is, also, reported., Comment: 8 pages, 2 figures

In the period between 2009 and 2015, several very high-energy (VHE $> 100$ GeV) gamma-ray flaring events from the BL Lac object PKS 1424+240 were observed by the Cerenkov telescopes VERITAS and MAGIC. It had uncertain redshift ($z$) and using spectroscopical measurement, Paiano et al. (2017) found it to be $z=0.604$. Using four different extragalactic background light (EBL) models and the photohadronic model, nine independently observed VHE gamma-ray spectra of PKS 1424+240 are analyzed and a global $\chi^2$ fit is performed on all observations to estimate the best-fit value for the redshift for each EBL model. Confidence levels (CL) intervals for the redshift are also estimated using all the EBL models. This method is tested by comparing our analysis with the observed value. It is shown that the photohadronic scenario provides an excellent description of all the observed spectra. It is found that the EBL model of Dom\'inguez et al. (2011) is the one that provides the most restrictive limits on the redshift of PKS 1424+240, but in our analysis, $z=0.604$ lies within the $3\sigma$ CL interval of the EBL model of Saldana-Lopez et al. (2021)., Comment: 8 pages, 2 figures. To be published in MNRAS

This work presents a perception system applied to robotic manipulation, that is able to assist in navigation, household waste classification and collection in outdoor environments. This system is made up of optical tactile sensors, RGBD cameras and a LiDAR. These sensors are integrated on a mobile platform with a robot manipulator and a robotic gripper. Our system is divided in three software modules, two of them are vision-based and the last one is tactile-based. The vision-based modules use CNNs to localize and recognize solid household waste, together with the grasping points estimation. The tactile-based module, which also uses CNNs and image processing, adjusts the gripper opening to control the grasping from touch data. Our proposal achieves localization errors around 6 %, a recognition accuracy of 98% and ensures the grasping stability the 91% of the attempts. The sum of runtimes of the three modules is less than 750 ms., Comment: in Spanish language

The accumulation of litter is increasing in many places and is consequently becoming a problem that must be dealt with. In this paper, we present a manipulator robotic system to collect litter in outdoor environments. This system has three functionalities. Firstly, it uses colour images to detect and recognise litter comprising different materials. Secondly, depth data are combined with pixels of waste objects to compute a 3D location and segment three-dimensional point clouds of the litter items in the scene. The grasp in 3 Degrees of Freedom (DoFs) is then estimated for a robot arm with a gripper for the segmented cloud of each instance of waste. Finally, two tactile-based algorithms are implemented and then employed in order to provide the gripper with a sense of touch. This work uses two low-cost visual-based tactile sensors at the fingertips. One of them addresses the detection of contact (which is obtained from tactile images) between the gripper and solid waste, while another has been designed to detect slippage in order to prevent the objects grasped from falling. Our proposal was successfully tested by carrying out extensive experimentation with different objects varying in size, texture, geometry and materials in different outdoor environments (a tiled pavement, a surface of stone/soil, and grass). Our system achieved an average score of 94% for the detection and Collection Success Rate (CSR) as regards its overall performance, and of 80% for the collection of items of litter at the first attempt., Comment: Article sent for final publication

By constructing a model of spacetime having a strong curvature singularity in which causal geodesics are complete, but more generic causal curves are not, we explicitly show that some electrostatic field configurations on that background are regular everywhere, including \emph{there} where the spacetime ceases to be described by a pseudo-Riemannian manifold. Our calculations are performed using the analogue-gravity description provided by Plebanski and Tamm, according to which the characterization of the electromagnetic field on a generic curved background is equivalent to solving Maxwell's equations in flat space with a matter content verifying certain non-trivial constitutive relations. The regularity of the electric field in what could be considered the worst conceivable physical condition, opens the door to further investigation into the possibility of propagating signals capable of crossing a spacetime singularity., Comment: 9 pages, 4 figures

Gamma-ray burst (GRB), GRB 221009A, a long-duration GRB, was observed simultaneously by the Water Cherenkov Detector Array (WCDA) and the Kilometer Squared Array (KM2A) of the Large High Altitude Air Shower Observatory (LHAASO) during the prompt emission and the afterglow periods. Characteristic multi-TeV photons up to 13 TeV were observed in the afterglow phase. The observed very high-energy (VHE) gamma-ray spectra by WCDA and KM2A during different time intervals and in different energy ranges can be explained very well in the context of the photohadronic model with the inclusion of extragalactic background light models. In the photohadronic scenario, interaction of high-energy protons with the synchrotron self-Compton (SSC) photons in the forward shock region of the jet is assumed to be the source of these VHE photons. The observed VHE spectra from the afterglow of GRB 221009A are similar to the VHE gamma-ray spectra observed from the temporary extreme high-energy peaked BL Lac (EHBL), 1ES 2344+514 {\it only} during the 11th and the 12th of August, 2016. Such spectra are new and have been observed for the first time in a GRB., Comment: 7 pages, 4 figures. To be published in MNRAS Letters

Simulation of assistive devices on pathological gait through musculoskeletal models offers the potential and advantages of estimating the effect of the device in several biomechanical variables and the device characteristics ahead of manufacturing. In this study, we introduce a novel musculoskeletal modelling approach to simulate the biomechanical impact of ankle foot orthoses (AFO) on gait in individuals with spinal cord injury (SCI). Leveraging data from the Swiss Paraplegic Center, we constructed anatomically and muscularly scaled models for SCI-AFO users, aiming to predict changes in gait kinematics and kinetics. The importance of this work lies in its potential to enhance rehabilitation strategies and improve quality of life by enabling the pre-manufacturing assessment of assistive devices. Despite the application of musculoskeletal models in simulating walking aids effects in other conditions, no predictive model currently exists for SCI gait. Evaluation through RMSE showed similar results compared with other pathologies, simulation errors ranged between 0.23 to 2.3 degrees in kinematics. Moreover, the model was able to capture ankle joint muscular asymmetries and predict symmetry improvements with AFO use. However, the simulation did not reveal all the AFO effects, indicating a need for more personalized model parameters and optimized muscle activation to fully replicate orthosis effects on SCI gait., Comment: 7 pages, 3 figures, 3 Tables. Submitted to the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. To be published in the proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged 9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions to assess the area under the receiver operating characteristic curve (AUC) of validated models including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence on chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented each model from their published literature, re-training the models if necessary, and curated each cohort from primary data sources. We observed that model performance varied greatly across clinical use cases. No single predictive model emerged as a clear winner across all cohorts, but certain models excelled in specific clinical contexts. Single timepoint chest CT AI performed well in lung screening, but struggled to generalize to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively promising performance on incidentally-detected nodules. However, when applied to nodules that underwent biopsy, all models underperformed. These results underscore the strengths and limitations of 8 validated predictive models and highlight promising directions towards personalized, noninvasive lung cancer diagnosis., Comment: Submitted to Radiology: AI

As network complexity escalates, there is an increasing need for more sophisticated methods to manage and operate these networks, focusing on enhancing efficiency, reliability, and security. A wide range of Artificial Intelligence (AI)/Machine Learning (ML) models are being developed in response. These models are pivotal in automating decision-making, conducting predictive analyses, managing networks proactively, enhancing security, and optimizing network performance. They are foundational in shaping the future of networks, collectively forming what is known as Network Intelligence (NI). Prominent Standard-Defining Organizations (SDOs) are integrating NI into future network architectures, particularly emphasizing the closed-loop approach. However, existing methods for seamlessly integrating NI into network architectures are not yet fully effective. This paper introduces an in-depth architectural design for a Network Intelligence Stratum (NI Stratum). This stratum is supported by a novel end-to-end NI orchestrator that supports closed-loop NI operations across various network domains. The primary goal of this design is to streamline the deployment and coordination of NI throughout the entire network infrastructure, tackling issues related to scalability, conflict resolution, and effective data management. We detail exhaustive workflows for managing the NI lifecycle and demonstrate a reference implementation of the NI Stratum, focusing on its compatibility and integration with current network systems and open-source platforms such as Kubernetes and Kubeflow, as well as on its validation on real-world environments. The paper also outlines major challenges and open issues in deploying and managing NI.

We consider a moduli space of lattice polarized K3 surfaces with the additional information of a frame of the trascendental cohomology with respect to the lattice polarization. This moduli space is proved to be quasi-affine, and the existence of vector fields on it, called modular vector fields, is proved. A purely algebraic version of the algebra of Siegel quasi-modular forms is obtained as the algebra of global regular functions over this moduli space, with a differential structure coming from the modular vector fields. By means of trascendental considerations we are able to obtain a differential algebra of meromorphic Siegel quasi-modular forms from the previous algebra.

A nonparametric procedure to estimate the conditional probability that a nonstationary geostatistical process exceeds a certain threshold value is proposed. The method consists of a bootstrap algorithm that combines conditional simulation techniques with nonparametric estimations of the trend and the variability. The nonparametric local linear estimator, considering a bandwidth matrix selected by a method that takes the spatial dependence into account, is used to estimate the trend. The variability is modeled estimating the conditional variance and the variogram from corrected residuals to avoid the biasses. The proposed method allows to obtain estimates of the conditional exceedance risk in non-observed spatial locations. The performance of the approach is analyzed by simulation and illustrated with the application to a real data set of precipitations in the U.S., Comment: 18 pages, 5 figures

In this book, we study Statistical Physics under conditions of thermodynamic equilibrium, starting from the definition of statistical ensembles. The book is divided into five chapters: First, a brief introduction to statistical methods. Second, the statistical description of isolated systems, corresponding to microcanonical ensembles. Third, the statistical description of systems in contact with a heat reservoir at a constant temperature T, known as the canonical ensemble. Fourth, the description of systems in contact with a heat reservoir at temperature T and with a constant chemical potential {\mu}; the grand canonical ensemble. And finally, quantum statistics. In each chapter, a theoretical description, worked examples, links to simulations (see GitHub repository: https://github.com/davidalejandromiranda/StatisticalPhysics) and discussions on recent scientific reports will be presented so readers can complement their studies., Comment: Book, in Spanish language

The significance of learning constraints from data is underscored by its potential applications in real-world problem-solving. While constraints are popular for modeling and solving, the approaches to learning constraints from data remain relatively scarce. Furthermore, the intricate task of modeling demands expertise and is prone to errors, thus constraint acquisition methods offer a solution by automating this process through learnt constraints from examples or behaviours of solutions and non-solutions. This work introduces a novel approach grounded in Deep Neural Network (DNN) based on Symbolic Regression that, by setting suitable loss functions, constraints can be extracted directly from datasets. Using the present approach, direct formulation of constraints was achieved. Furthermore, given the broad pre-developed architectures and functionalities of DNN, connections and extensions with other frameworks could be foreseen.

In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term "explanation" in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanist models. In this chapter, I show that the authors' claim that these accounts can be applied to deep neural networks as they would to any natural phenomenon is mistaken. I also provide a more general argument as to why the notion of explainability as it is currently used in the XAI literature bears little resemblance to the traditional concept of scientific explanation. It would be more fruitful to use the label "understandable AI" to avoid the confusion that surrounds the goal and purposes of XAI. In the second half of the chapter, I argue for a pragmatic conception of understanding that is better suited to play the central role attributed to explanation in XAI. Following Kuorikoski & Ylikoski (2015), the conditions of satisfaction for understanding an ML system are fleshed out in terms of an agent's success in using the system, in drawing correct inferences from it.

We present photometric CCD observations of stars in four stellar trapezia ADS 15184, ADS 4728, ADS 2843, and ADS 16795. This study is performed on images obtained at the Observatorio Astron\'omico Nacional at San Pedro M\'artir (OAN), Baja California, M\'exico. In this work we utilise aperture photometry to measure the $U$, $B$, $V$, $R$ and $I$ magnitudes of some of the stars in these dynamically unstable stellar clusters (trapezia). Using the $Q=(U-B)-0.72(B-V)$ parameter we obtained the spectral type of the studied stars as well as their distance to the Sun and their reddening. Slight differences between the $Q$-derived Spectral types and those listed in SIMBAD might be due to a different value, from $0.72$, for the slope of the reddening line on the two-colour diagram., Comment: 34 pages, 12 figures, 31 tables, accepted for publication in RevMexAA, vol. 60, num. 1, April 24

Since 1987, Spain has been continuously developing several scientific projects, mainly based on Earth Sciences, in Geodesy, Geochemistry, Geology or Volcanology. The need of a geodetic reference frame when doing hydrographic and topographic mapping meant the organization of the earlier campaigns with the main goals of updating the existing cartography and of making new maps of the area. During this period of time, new techniques arose in Space Geodesy improving the classical methodology and making possible its applications to other different fields such as tectonic or volcanism. Spanish Antarctic Geodetic activities from the 1987/1988 to 2006/2007 campaigns are described as well as a geodetic and a levelling network are presented. The first network, RGAE, was designed and established to define a reference frame in the region formed by the South Shetlands Islands, the Bransfield Sea and the Antarctic Peninsula whereas the second one, REGID, was planned to control the volcanic activity in Deception Island. Finally, the horizontal and vertical deformation models are described too, as well as the strategy which has been followed when computing an experimental geoid.

In this work, a fully nonparametric geostatistical approach to estimate threshold exceeding probabilities is proposed. To estimate the large-scale variability (spatial trend) of the process, the nonparametric local linear regression estimator, with the bandwidth selected by a method that takes the spatial dependence into account, is used. A bias-corrected nonparametric estimator of the variogram, obtained from the nonparametric residuals, is proposed to estimate the small-scale variability. Finally, a bootstrap algorithm is designed to estimate the unconditional probabilities of exceeding a threshold value at any location. The behavior of this approach is evaluated through simulation and with an application to a real data set., Comment: 17 pages, 4 figures

Autonomous underwater robots typically require higher cost and time for demonstrations compared to other domains due to the complexity of the environment. Due to the limited capacity and payload flexibility, it is challenging to find off-the-shelf underwater robots that are affordable, customizable, and subject to environmental variability. Custom-built underwater robots may be necessary for specialized applications or missions, but the process can be more costly and time-consuming than purchasing an off-the-shelf autonomous underwater vehicle (AUV). To address these challenges, we propose a modular underwater robot, Modularis, that can serve as an open-source testbed system. Our proposed system expedites the testing of perception, planning, and control algorithms., Comment: 7 pages, 13 figures, presented at OCEANS 2023

The High Energy Stereoscopic System (H.E.S.S.) started observing the extremely powerful long-duration gamma-ray burst, GRB 221009A, after 53 hours of the triggering event. The H.E.S.S. collaboration carried out observations on the 11, 12, and 17, of October 2022 under poor atmospheric conditions, without detecting significant very high-energy photons from the source and computed the upper limits of the fluxes for the different nights. We study these flux upper limits by using the photohadronic model and show that the interaction of high-energy protons with the synchrotron seed photons in the forward shock region of the GRB jet exhibits behavior compatible with the upper limits computed by the H.E.S.S. collaboration., Comment: 8 pages, 2 figures, 1 table. Accepted for publication in ApJ

It has been demonstrated that the amount of data is crucial in data-driven machine learning methods. Data is always valuable, but in some tasks, it is almost like gold. This occurs in engineering areas where data is scarce or very expensive to obtain, such as predictive maintenance, where faults are rare. In this context, a mechanism to generate synthetic data can be very useful. While in fields such as Computer Vision or Natural Language Processing synthetic data generation has been extensively explored with promising results, in other domains such as time series it has received less attention. This work specifically focuses on studying and analyzing the use of different techniques for data augmentation in time series for classification and regression problems. The proposed approach involves the use of diffusion probabilistic models, which have recently achieved successful results in the field of Image Processing, for data augmentation in time series. Additionally, the use of meta-attributes to condition the data augmentation process is investigated. The results highlight the high utility of this methodology in creating synthetic data to train classification and regression models. To assess the results, six different datasets from diverse domains were employed, showcasing versatility in terms of input size and output types. Finally, an extensive ablation study is conducted to further support the obtained outcomes., Comment: In peer reviewing process

Wheelchairs and mobility devices have transformed our bodies into cybernic systems, enhancing our well-being by enabling individuals with reduced mobility to regain freedom. Notwithstanding, current interfaces of control primarily rely on hand operation, therefore constraining the user from performing functional activities of daily living. In this work, we propose a design of a torso-based control interface with compliant coupling support for standing mobility assistive devices. We consider the coupling between the human and robot in the interface design. The design includes a compliant support mechanism and mapping between the body movement space and the velocity space. We present experiments including multiple conditions, with a joystick for comparison with the proposed torso control interface. The results of a path-following experiment demonstrated that users could control the device naturally using the hands-free interface, and the performance was comparable with the joystick, with 10% more consumed time, an average cross error of 0.116 m and 4.9% less average acceleration. In an object-transferring experiment, the proposed interface demonstrated a clear advantage when users needed to manipulate objects during locomotion. Lastly, the torso control scored 15% less than the joystick on the system usability scale for the path-following task but 3.3% more for the object-transferring task., Comment: Accepted by IEEE/ASME Transactions on Mechatronics

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders.