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Quasi-static time series (QSTS) simulations have great potential for evaluating the grid's ability to accommodate the large-scale integration of distributed energy resources. However, as grids expand and operate closer to their limits, iterative power flow solvers, central to QSTS simulations, become computationally prohibitive and face increasing convergence issues. Neural power flow solvers provide a promising alternative, speeding up power flow computations by 3 to 4 orders of magnitude, though they are costly to train. In this paper, we envision how recently introduced grid foundation models could improve the economic viability of neural power flow solvers. Conceptually, these models amortize training costs by serving as a foundation for a range of grid operation and planning tasks beyond power flow solving, with only minimal fine-tuning required. We call for collaboration between the AI and power grid communities to develop and open-source these models, enabling all operators, even those with limited resources, to benefit from AI without building solutions from scratch., Comment: Equal contributors: A.P. and F.M.; Lead contact: A.P

One-to-one tutoring is one of the most efficient methods of teaching. Following the rise in popularity of Large Language Models (LLMs), there have been efforts to use them to create conversational tutoring systems, which can make the benefits of one-to-one tutoring accessible to everyone. However, current LLMs are primarily trained to be helpful assistants and thus lack crucial pedagogical skills. For example, they often quickly reveal the solution to the student and fail to plan for a richer multi-turn pedagogical interaction. To use LLMs in pedagogical scenarios, they need to be steered towards using effective teaching strategies: a problem we introduce as Pedagogical Steering and believe to be crucial for the efficient use of LLMs as tutors. We address this problem by formalizing a concept of tutoring strategy, and introducing StratL, an algorithm to model a strategy and use prompting to steer the LLM to follow this strategy. As a case study, we create a prototype tutor for high school math following Productive Failure (PF), an advanced and effective learning design. To validate our approach in a real-world setting, we run a field study with 17 high school students in Singapore. We quantitatively show that StratL succeeds in steering the LLM to follow a Productive Failure tutoring strategy. We also thoroughly investigate the existence of spillover effects on desirable properties of the LLM, like its ability to generate human-like answers. Based on these results, we highlight the challenges in Pedagogical Steering and suggest opportunities for further improvements. We further encourage follow-up research by releasing a dataset of Productive Failure problems and the code of our prototype and algorithm., Comment: 18 pages, 9 figures, 6 tables

Generative AI has seen major developments in recent years, enhancing the realism of synthetic images, also known as computer-generated images. In addition, generative AI has also made it possible to modify specific image characteristics through image editing. Previous work has developed methods based on generative adversarial networks (GAN) for generating realistic images, in particular faces, but also to modify specific features. However, this work has never been applied to specific animal species. Moreover, the assessment of the results has been generally done subjectively, rather than quantitatively. In this paper, we propose an approach based on methods for generating images of faces of male or female mandrills, a non-human primate. The main novelty of proposed method is the ability to edit their sex by identifying a sex axis in the latent space of a specific GAN. In addition, we have developed an assessment of the sex levels based on statistical features extracted from real image distributions. The experimental results we obtained from a specific database are not only realistic, but also accurate, meeting a need for future work in behavioral experiments with wild mandrills.

The energy transition, crucial for tackling the climate crisis, demands integrating numerous distributed, renewable energy sources into existing grids. Along with climate change and consumer behavioral changes, this leads to changes and variability in generation and load patterns, introducing significant complexity and uncertainty into grid planning and operations. While the industry has already started to exploit AI to overcome computational challenges of established grid simulation tools, we propose the use of AI Foundation Models (FMs) and advances in Graph Neural Networks to efficiently exploit poorly available grid data for different downstream tasks, enhancing grid operations. For capturing the grid's underlying physics, we believe that building a self-supervised model learning the power flow dynamics is a critical first step towards developing an FM for the power grid. We show how this approach may close the gap between the industry needs and current grid analysis capabilities, to bring the industry closer to optimal grid operation and planning.

We consider the frequency estimation of periodic signals using noisy time-of-arrival (TOA) information with missing (sparse) data contaminated with outliers. We tackle the problem from a mathematical optimization standpoint, formulating it as a linear regression with an unknown increasing integer independent variable and outliers. Assuming an upper bound on the variance of the noise, we derive an online, parallelizable, near-CRLB optimization-based algorithm amortized to a linear complexity. We demonstrate the outstanding robustness of our algorithm to noise and outliers by testing it against diverse randomly generated signals. Our algorithm handles outliers by design and yields precise estimations even with up to 20% of contaminated data., Comment: 5 pages, 4 figures

Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, FMs can find uses in electric power grids, challenged by the energy transition and climate change. In this paper, we call for the development of, and state why we believe in, the potential of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities, pioneering a new approach in leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit., Comment: Major equal contributors: H.F.H., T.B., B.G., L.S.A.M., A.P., A.V., J.W.; Significant equal contributors: J.B., A.B.M., S.C., I.F., B.H., R.J., K.K., V.M., F.M., M.D.M., O.R., H.S., L.X., E.S.Y., A.Z.; Other equal contributors: A.J.B., R.J.B., B.P.B., J.S., S.S; Lead contact: H.F.H

Creating defects in graphene by hydrogenation, either to achieve hydrogen chemisorption or partial etching, is a way to open an electronic band gap in graphene. Understanding the range of stability conditions of partially etched or hydrogenated graphene is crucial for application, as processing conditions (e.g. temperature) and quality control (characterization) conditions may result in modifying the material through partial or full dehydrogenation, and subsequent alteration of its electronic properties. This work reports a study of various dehydrogenation conditions of hydrogenated or hydrogen-etched monolayer graphene (1LG), either free-standing or supported by an interferential (SiO2/Si) substrate, using incremental annealing under nitrogen atmosphere up to 400 {\textdegree}C. Materials were investigated by Raman spectroscopy. Indeed, it has been known since 2012 that the intensity ratio of two Raman bands activated by double resonance, D over D' (ID/ID') can be used to identify the type of defects in defective graphene. It is shown that hydrogenated 1LG, characterized by a large ID/ID' ratio (~9-15), is stable provided annealing remains below 300 {\textdegree}C. On the other hand, defective 1LG resulting from hydrogen etching remains stable up to 400 {\textdegree}C, whether the 1LG is hydrogenated on one side or both sides, while a modification in the type and proportions of defects is likely. Experimental conditions for the safe use of Raman spectroscopy, otherwise able to induce specimen overheating because of the laser energy and power, are also determined and discussed.

Automatic speech recognition (ASR) techniques have become powerful tools, enhancing efficiency in law enforcement scenarios. To ensure fairness for demographic groups in different acoustic environments, ASR engines must be tested across a variety of speakers in realistic settings. However, describing the fairness discrepancies between models with confidence remains a challenge. Meanwhile, most public ASR datasets are insufficient to perform a satisfying fairness evaluation. To address the limitations, we built FairLENS - a systematic fairness evaluation framework. We propose a novel and adaptable evaluation method to examine the fairness disparity between different models. We also collected a fairness evaluation dataset covering multiple scenarios and demographic dimensions. Leveraging this framework, we conducted fairness assessments on 1 open-source and 11 commercially available state-of-the-art ASR models. Our results reveal that certain models exhibit more biases than others, serving as a fairness guideline for users to make informed choices when selecting ASR models for a given real-world scenario. We further explored model biases towards specific demographic groups and observed that shifts in the acoustic domain can lead to the emergence of new biases.

Graphene Hall effect magnetic field sensors hold great promise for the development of ultra-sensitive magnetometers. Their performance is frequently analysed using the two-channel model where electron and hole conductivities are simply added. Unfortunately, this model is unable to capture all the features of the sensor, particularly the bias current dependence of the magnetic field sensitivity. Here we present an advanced model that provides an in-depth understanding of how graphene Hall sensors operate, and demonstrate its ability to quantitatively assess their performance. First, we report the fabrication of sensors with different qualities of graphene, with the best devices achieving magnetic field sensitivities as high as 5000 ohms/T, outperforming the best silicon and narrow-gap semiconductor-based sensors. Then, we examine their performance in detail using the proposed numerical model, which combines Boltzmann formalism, with distinct Fermi levels for electrons and holes, and a new method for the introduction of substrate-induced electron-hole puddles. Importantly, the dependences of magnetic field sensitivity on bias current, disorder, substrate and Hall bar geometry are quantitatively reproduced for the first time. In addition, the model emphasizes that the performance of devices with widths of the order of the charge carrier diffusion length, is significantly affected by the bias current due to the occurrence of large and non-symmetric carrier accumulation and depletion areas near the edges of the Hall bar. The formation of these areas induces a transverse diffusion particle flux capable of counterbalancing the particle flux induced by the Lorentz force when the Hall electric field cancels out in the ambipolar regime. Finally, we discuss how sensor performance can be enhanced by Fermi velocity engineering, paving the way for future ultra-sensitive graphene Hall effect sensors., Comment: 29 pages including supplemental material, 12 figures in the main text and 7 figures in the supplemental material

This paper introduces an Electric Vehicle Charging Station (EVCS) model that incorporates real-world constraints, such as slot power limitations, contract threshold overruns penalties, or early disconnections of electric vehicles (EVs). We propose a formulation of the problem of EVCS control under uncertainty, and implement two Multi-Stage Stochastic Programming approaches that leverage user-provided information, namely, Model Predictive Control and Two-Stage Stochastic Programming. The model addresses uncertainties in charging session start and end times, as well as in energy demand. A user's behavior model based on a sojourn-time-dependent stochastic process enhances cost reduction while maintaining customer satisfaction. The benefits of the two proposed methods are showcased against two baselines over a 22-day simulation using a real-world dataset. The two-stage approach demonstrates robustness against early disconnections by considering a wider range of uncertainty scenarios for optimization. The algorithm prioritizing user satisfaction over electricity cost achieves a 20% and 36% improvement in two user satisfaction metrics compared to an industry-standard baseline. Additionally, the algorithm striking the best balance between cost and user satisfaction exhibits a mere 3% relative cost increase compared to the theoretically optimal baseline - for which the nonanticipativity constraint is relaxed - while attaining 94% and 84% of the user satisfaction performance in the two used satisfaction metrics.

As the share of variable renewable energy sources increases in the electricity mix, new solutions are needed to build a flexible and reliable grid. Energy arbitrage with battery storage systems supports renewable energy integration into the grid by shifting demand and increasing the overall utilization of power production systems. In this paper, we propose a mixed integer linear programming model for energy arbitrage on the day-ahead market, that takes into account operational and availability constraints of asset owners willing to get an additional revenue stream from their storage asset. This approach optimally schedules the charge and discharge operations associated with the most profitable trading strategy, and achieves between 80% and 90% of the maximum obtainable profits considering one-year time horizons using the prices of electricity in multiple European countries including Germany, France, Italy, Denmark, and Spain.

We introduce REmap-eu.app, a web-based data-mining visualization tool of the spatial and temporal variability of wind resources. It uses the latest open-access dataset of the daily wind capacity factor in 28 European countries between 1979 and 2019 and proposes several user-configurable visualizations of the temporal and spatial variations of the wind power capacity factor. The platform allows for a deep analysis of the distribution, the crosscountry correlation, and the drivers of low wind power events. It offers an easy-to-use interface that makes it suitable for the needs of researchers and stakeholders. The tool is expected to be useful in identifying areas of high wind potential and possible challenges that may impact the large-scale deployment of wind turbines in Europe. Particular importance is given to the visualization of low wind power events and to the potential of cross-border cooperations in mitigating the variability of wind in the context of increasing reliance on weather-sensitive renewable energy sources., Comment: visit Remap-eu.app

Yaw misalignment, measured as the difference between the wind direction and the nacelle position of a wind turbine, has consequences on the power output, the safety and the lifetime of the turbine and its wind park as a whole. We use reinforcement learning to develop a yaw control agent to minimise yaw misalignment and optimally reallocate yaw resources, prioritising high-speed segments, while keeping yaw usage low. To achieve this, we carefully crafted and tested the reward metric to trade-off yaw usage versus yaw alignment (as proportional to power production), and created a novel simulator (environment) based on real-world wind logs obtained from a REpower MM82 2MW turbine. The resulting algorithm decreased the yaw misalignment by 5.5% and 11.2% on two simulations of 2.7 hours each, compared to the conventional active yaw control algorithm. The average net energy gain obtained was 0.31% and 0.33% respectively, compared to the traditional yaw control algorithm. On a single 2MW turbine, this amounts to a 1.5k-2.5k euros annual gain, which sums up to very significant profits over an entire wind park.

Microgrids are local energy systems that integrate energy production, demand, and storage units. They are generally connected to the regional grid to import electricity when local production and storage do not meet the demand. In this context, Energy Management Systems (EMS) are used to ensure the balance between supply and demand, while minimizing the electricity bill, or an environmental criterion. The main implementation challenges for an EMS come from the uncertainties in the consumption, the local renewable energy production, and in the price and the carbon intensity of electricity. Model Predictive Control (MPC) is widely used to implement EMS but is particularly sensitive to the forecast quality, and often requires a subscription to expensive third-party forecast services. We introduce four Multistage Stochastic Control Algorithms relying only on historical data obtained from on-site measurements. We formulate them under the shared framework of Multistage Stochastic Programming and benchmark them against two baselines in 61 different microgrid setups using the EMSx dataset. Our most effective algorithm produces notable cost reductions compared to an MPC that utilizes the same uncertainty model to generate predictions, and it demonstrates similar performance levels to an ideal MPC that relies on perfect forecasts.

Since 2005 ESO has been working with its community and industry to develop an extremely large optical/infrared telescope. ESO's Extremely Large Telescope, or ELT for short, is a revolutionary ground-based telescope that will have a 39-metre main mirror and will be the largest visible and infrared light telescope in the world. To address specific topics that are needed for the science operations and calibrations of the telescope, thirteen specific working groups were created to coordinate the effort between ESO, the instrument consortia, and the wider community. We describe here the goals of these working groups as well as their achievements so far., Comment: 8 pages, 4 figures

We propose a multi-particle self-consistent Hamiltonian (derived from an N-body description) that is applicable for periodic structures such as traveling-wave tubes (TWTs), gyrotrons, free-electron lasers, or particle accelerators. We build a 1D symplectic multi-particle algorithm to simulate the nonlinear wave-particle interaction in the time domain occurring in an experimental 3-meters long helix TWT. Our algorithm is efficient thanks to a drastic reduction model. A 3D helix version of our reduction model is provided. Finally, we establish an explicit expression of the electromagnetic power in the time domain and in non-monochromatic (non-"continuous waveform") regime., Comment: 17 pages, 11 figures

Two-dimensional (2D) materials are among the most studied ones nowadays, because of their unique properties. These materials are made of, single- or few atom-thick layers assembled by van der Waals forces, hence allowing a variety of stacking sequences possibly resulting in a variety of crystallographic structures as soon as the sequences are periodic. Taking the example of few layer graphene (FLG), it is of an utmost importance to identify both the number of layers and the stacking sequence, because of the driving role these parameters have on the properties. For this purpose, analysing the spot intensities of electron diffraction patterns (DPs) is commonly used, along with attempts to vary the number of layers, and the specimen tilt angle. However, the number of sequences able to be discriminated this way remains few, because of the similarities between the DPs. Also, the possibility of the occurrence of C layers in addition to A and/or B layers in FLG has been rarely considered. To overcome this limitation, we propose here a new methodology based on multi-wavelength electron diffraction which is able to discriminate between stacking sequences up to 6 layers (potentially more) involving A, B, and C layers. We also propose an innovative method to calculate the spot intensities in an easier and faster way than the standard ones. Additionally, we show that the method is valid for transition metal dichalcogenides, taking the example of MoS2., Comment: 11 pages, 4 figures, 1 Table, Supplementary information

MOSAIC is the planned multi-object spectrograph for the 39m Extremely Large Telescope (ELT). Conceived as a multi-purpose instrument, it offers both high multiplex and multi-IFU capabilities at a range of intermediate to high spectral resolving powers in the visible and the near-infrared. MOSAIC will enable unique spectroscopic surveys of the faintest sources, from the oldest stars in the Galaxy and beyond to the first populations of galaxies that completed the reionisation of the Universe--while simultaneously opening up a wide discovery space. In this contribution we present the status of the instrument ahead of Phase B, showcasing the key science cases as well as introducing the updated set of top level requirements and the adopted architecture. The high readiness level will allow MOSAIC to soon enter the construction phase, with the goal to provide the ELT community with a world-class MOS capability as soon as possible after the telescope first light., Comment: Accepted to SPIE

The powerful combination of the cutting-edge multi-object spectrograph MOSAIC with the world largest telescope, the ELT, will allow us to probe deeper into the Universe than was possible. MOSAIC is an extremely efficient instrument in providing spectra for the numerous faint sources in the Universe, including the very first galaxies and sources of cosmic reionization. MOSAIC has a high multiplex in the NIR and in the VIS, in addition to multi-Integral Field Units (Multi-IFUs) in NIR. As such it is perfectly suited to carry out an inventory of dark matter (from rotation curves) and baryons in the cool-warm gas phases in galactic haloes at z=3-4. MOSAIC will enable detailed maps of the intergalactic medium at z=3, the evolutionary history of dwarf galaxies during a Hubble time, the chemistry directly measured from stars up to several Mpc. Finally, it will measure all faint features seen in cluster gravitational lenses or in streams surrounding nearby galactic halos, providing MOSAIC to be a powerful instrument with an extremely large space of discoveries. The preliminary design of MOSAIC is expected to begin next year, and its level of readiness is already high, given the instrumental studies made by the team., Comment: 8 pages, 4 Figures, Pre-edited version, to appear in the ESO Messenger No.182 - Quarter 1 2021 - Version identical to the Edited one

2D semiconducting nanosheets of Transition Metal Dichalcogenides are attractive materials for solar energy conversion because of their unique absorption properties. Here, we propose Mo thio- and oxo-thio-complexes anchored on 2D p-WSe2 nanosheets for efficient water splitting under visible light irradiation with photocurrent density up to 2.0 mA cm-2 at -0.2 V/NHE. Besides developing high electro-catalytic activity, the Mo complexe films were shown to display ability to heal surface defects. We propose that the observed healing of surface defects arises from the strong adsorption on point defects of the 2D WSe2 substrate of Mo complexes such as (MoS4)2-, (MoOS3)2-, (Mo2S6O2)2- as shown from DFT calculations. In addition to display catalytic and healing effects, the thio-, oxo-thio Mo complexes films were shown to enhance charge carrier separation and migration for the hydrogen evolution reaction, thus representing an example of multicomponent passivation layer exhibiting multiple properties.

Mapping of the large-scale structure through cosmic time has numerous applications in the studies of cosmology and galaxy evolution. At $z > 2$, the structure can be traced by the neutral intergalactic medium (IGM) by way of observing the Ly$\alpha$, forest towards densely-sampled lines-of-sight of bright background sources, such as quasars and star forming galaxies. We investigate the scientific potential of MOSAIC, a planned multi-object spectrograph on the European Extremely Large Telescope (ELT), for the 3D mapping of the IGM at $z \gtrsim 3$. We simulate a survey of $3 \lesssim z \lesssim 4$ galaxies down to a limiting magnitude of $m_{r}\sim 25.5$ mag in an area of 1 degree$^2$ in the sky. Galaxies and their spectra (including the line-of-sight Ly$\alpha$ absorption) are taken from the lightcone extracted from the Horizon-AGN cosmological hydrodynamical simulation. The quality of the reconstruction of the original density field is studied for different spectral resolutions and signal-to-noise ratios of the spectra. We demonstrate that the minimum $S/N$ (per resolution element) of the faintest galaxies that such survey has to reach is $S/N = 4$. We show that a survey with such sensitivity enables a robust extraction of cosmic filaments and the detection of the theoretically-predicted galaxy stellar mass and star-formation rate gradients towards filaments. By simulating the realistic performance of MOSAIC we obtain $S/N(T_{\rm obs}, R, m_{r})$ scaling relations. We estimate that $\lesssim 35~(65)$ nights of observation time are required to carry out the survey with the instrument's high multiplex mode and with the spectral resolution of $R=1000~(2000)$. A survey with a MOSAIC-concept instrument on the ELT is found to enable the mapping of the IGM at $z > 3$ on Mpc scales, and as such will be complementary to and competitive with other planned IGM tomography surveys. [abridged], Comment: 28 pages, accepted to A&A; language edited

To simulate traveling-wave tubes (TWTs) in time domain and more generally the wave-particle interaction in vacuum devices, we developed the DIscrete MOdel with HAmiltonian approach (dimoha) as an alternative to current particle-in-cell (PIC) and frequency approaches. Indeed, it is based on a longitudinal N-body Hamiltonian approach satisfying Maxwell's equations. Advantages of dimoha comprise: (i) it allows arbitrary waveform (not just field envelope), including continuous waveform (CW), multiple carriers or digital modulations (shift keying); (ii) the algorithm is much faster than PIC codes thanks to a field discretization allowing a drastic degree-of-freedom reduction, along with a robust symplectic integrator; (iii) it supports any periodic slow-wave structure design such as helix or folded waveguides; (iv) it reproduces harmonic generation, reflection, oscillation and distortion phenomena; (v) it handles nonlinear dynamics, including intermodulations, trapping and chaos. dimoha accuracy is assessed by comparing it against measurements from a commercial Ku-band tapered helix TWT and against simulations from a sub-THz folded waveguide TWT with a staggered double-grating slow-wave structure. The algorithm is also tested for multiple-carriers simulations with success.