dataesr/scientific-paragraphs-categorization huggingface dataset:
The STM measurements were performed in the constant current mode with a tip which was mechanically cut from a Pt/Ir (90/10 chemical composition) wire. All bias voltages are given with respect to a grounded tip. The STM images were processed with WSxM software. [24] Additionally, the LEED patterns were acquired using a commercial LEED setup (Specs ErLEED 150) and simulated with LEEDPat4. 2 software. [25] Furthermore, the XPS measurements were performed at the I09 beam line at the Diamond Light Source (Oxfordshire, UK). Sample preparation and measurements were performed in situ under UHV conditions. As confirmed by the LEED measurements at the I09 beam line, the structural arrangement of both networks (close-packed and porous) was successfully reproduced. The analysis chamber contained a VG Scienta EW4000 HAXPES hemispherical photoelectron analyzer.
The C 1s and N 1s core level spectra were acquired using a photon energy of 500 eV, while the Ni 2p 3/2 spectra were acquired with a photon energy of 1. 02 keV. The BE scale of the XPS data was calibrated to the Au 4f 7/2 peak at 84. 0 eV. For all the spectra a linear background was subtracted. The XPS spectra were fitted with Voigt profiles (convolution of a Gaussian and a Lorentzian).
Acknowledgements This work was supported by the  Netherlands Organization for Scientific Research (NWO)  ( Vici grant   680-47-633  and  Veni grant   722. 012. 010 ), by the  Foundation for Fundamental Research on Matter (FOM) , part of  NWO , and the  Zernike Institute for Advanced Materials of the University of Groningen. University of Strasbourg ,  CNRS ,  Institut Universitaire de France (IUF) , and the  International Center for Frontier Research in Chemistry (icFRC)  and the  Ministry of Education and Research  (scholarship to N. M. ) are acknowledged for financial support to the  Strasbourg group.
GO causes GSH depletion and ROS increase. In the next experiment, the ratio between GSH and GSSG inside HeLa cells before and after GO treatment was measured to confirm the intracellular reaction between GO and GSH. As shown in Figure  5a , the intracellular GSH/GSSG ratio decreased after GO treatment. This means that GO reacted with endogenous GSH after GO penetration into HeLa cells, leading to a decrease of the ratio between GSH and GSSG. Meanwhile, we measured the content of intracellular ROS to further show the change of ROS-associated oxidative stress, caused by GO. The ROS level significantly increased after GO treatment (Figure  5b ). These data evidence that GO provoke an intracellular depletion of GSH and an increase of ROS, leading to a certain degree of cytotoxicity. GSH is the main reductive substance that regulates the balance of the intracellular reductive/oxidative processes.
ACKNOWLEDGMENTS We gratefully acknowledge the  Centre National de la Recherche Scientifique (CNRS)  through the International Research Project MULTIDIM between I2CT Unit and  Okayama University , the  International Center for Frontier Research in Chemistry (icFRC) , and financial support from the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ). We wish to thank  Cathy Royer  and  Valérie Demais  for help with TEM analyses at the " Plateforme Imagerie in vitro " at the  Center of Neurochemistry (INCI, Strasbourg, France ).
X-ray Diffraction (XRD) Measurements Samples were investigated with X-ray diffraction at room temperature (prepared thin films) and at 260 K (single crystals). Powder XRD was carried out using a Bruker D8 Advance diffractometer equipped with a Lynxeye XE detector, using a nonmonochromated (Kα) Cu source in a Bragg-Brentano geometry, between 10 and 70°2θ. Single-crystal diffraction was performed on a Bruker D8 Venture diffractometer, equipped with a Photon 100 detector, using Mo Kα radiation. Samples were not cooled below 260 K due to the potential of crystal cracking or phase transformation. Diffraction data were reduced to correct with Apex 3 and the associated tools; crystal structures were solved with ShelXT and refined with ShelXL using Olex2. The refinements were stabilized using restraints where necessary.
Absolute Photoluminescence Excitation for the PL measurements was performed with a 445 nm continuous wave laser (Insaneware) through an optical fiber into an integrating sphere. A second optical fiber was used from the output of the integrating sphere to an Andor SR393i-B spectrometer equipped with a silicon charge-coupled-device camera (DU420A-BR-DD, iDus). The system was calibrated by using a calibrated halogen lamp with specified spectral irradiance, which was shone into the integrating sphere. A spectral correction factor was established to match the spectral output of the detector with the calibrated spectral irradiance of the lamp. The spectral photon density was obtained from the corrected detector signal (spectral irradiance) by division through the photon energy (hf) and the photon numbers of the excitation and emission obtained from numerical integration using Matlab.
Vibrational Properties and Electron-Phonon Interactions in the Deformation of Potential Mechanism. Normal modes of vibration were obtained by the finite-displacement method adopting the PHONOPY program to generate the displaced structures and to collect the results for the evaluation of the Hessian,  79  while the Quantum Espresso suite software  71  was used for the explicit evaluation of the forces and energies. The real-space Hessian was evaluated on a 2 × 2 × 2 supercell of the conventional cell of the 3D Cs 2 AgBiBr 6 double perovskites (320 atoms, similar to what was done in ref 21) and on 2 × 2 × 1 supercells of the cells of PeABr1 and PeABr2 (752 and 416 atoms, respectively). DFT self-consistent-field calculations were therefore performed by adopting the PBE exchangecorrelation functional, along with ultrasoft pseudopotentials and plane-wave/electron density cutoffs of 25/200.
Simulation results In order to validate the theoretical analysis, a simulation model was developed in Matlab/Simulink, as shown in Fig. 3. Switching losses are neglected and the voltage drop across the transistor is not taken into account since they are smaller than other voltages. The aim of the simulation is focused on the impact of PWM strategies and interleaved carriers between parallel converters. Table  1  shows the parameters used in the simulation. Several uncertain parameters, such as resistance R 1 and R 2 , are chosen according to the experiments carried out on the test bench, in order to improve the simulation model. As shown in Fig. 4 , the output current ripple varies with the phase shift δ. It reaches the minimum when δ equals ε, here ε equals 0. 01. Fig. 5  shows the simulation waveform of the input current.
TVA Watts Bar Unit 1 multi-cycle benchmark As part of the verification and validation activities within the VERA-CS of the US Consortium for Advanced Simulation of Light Water Reactors (CASL), a set of progression benchmark problems were created  [7, 8]. These cover a range of cases, from two-dimensional pin cells to threedimensional multi-physics reactor core modelling. The specifications of these problems were based on data provided by the Tennessee Valley Authority (TVA), which operates the Watts Bar Unit 1 (WB1), and Westinghouse. As part of the cooperation on multi-physics between US Department of Energy (DOE) and the NEA, the TVA-WB1 data and specifications have been provided as the input for an international, collaborative, multi-cycle benchmark activity co-ordinated through the EGMPEBV.
CONCLUSIONS The NEA Expert Group on Multi-physics Experimental Data, Benchmarks and Validation (EGM-PEBV) was established to address challenges in validating multi-physics modelling and simulation tools, taking into account the lack of consensus on validation of coupled tools and the availability of data to support such validation. Having reviewed the international activities on multi-physics validation, the EGMPEBV is moving forward with multiple experimental benchmarks and validation exercises, using data from test and nuclear power reactors in different member countries. The first example is the MPCMIV benchmark, which focuses on the simulation of experiments that demonstrate the ability of both traditional and novel multi-physics simulation methods to replicate fuels performance measurements conducted in the Studsvik R2 reactor.
Off-the-Shelf Relays It has also been shown that software-only implementations using off-the-shelf NFC-enabled mobile devices are effective, which simplifies the attack and allows any person with the right type of NFC-enabled mobile phone to implement a token emulator or a reader. These attacks can therefore use a standard phone as a proxy-reader and a second phone as a proxy-token and relay the data across Bluetooth, WiFi or the mobile data network  [222, 392, 539]. Even though such attack implementations incur a larger attack delay (200-500 ms), these remain effective against real systems, as was demonstrated in an attack against Google Wallet  [505]. There are an increasing number of non-mobile NFC devices, such as the Adafruit NFC breakout board, that easily connects with embedded hardware Arduino or Raspberry Pi, which could be used as readily available proxy platforms.
Relay-Resistance in EMV Relay attacks are particularly relevant in contactless-payment systems. Indeed, no PIN code or other payee-originating input is requested with such payments. Moreover, most contactless payment cards rely on ISO 14443, which is a standard available in most of today's smartphones. Consequently, performing a relay attack between a payment terminal and a payment card is as simple as uploading an app on a smartphone  [567]. Indeed, using off-the-shelf smartphones and some in-house Android software, this relay threat was exhibited in practice by  Chothia et al. [141]  against the EMV (Europay, Mastercard and Visa) contactless-payment protocol; this is the most wide-spread type for contactless payments. In their work, Chothia et al. also introduced a countermeasure to mitigate their own relay attack.
2. 3 Objectives of the experiment ALC1604 The so-called ALC1604 experiment has been implemented at the MHM URL to characterize the behavior of a full-scale HLW cell under a power monitored thermal loading (as it will be the case when waste packages will be stored in the disposal cell). The first objective is to study the influence of heat loading on the mechanical behavior of a buried steel sleeve including the occurrence of overloads resulting from rock mass thermal expansion. This experiment will also provide additional data concerning THM behavior of COx claystone mainly in terms of pore pressure increase induced by the heating phase. The goal here is (i) to confirm THM behavior identified on small-scale heating experiments and (ii) to validate and improve THM modelling.
Temperature Figure  4  shows the temperature evolution measured by the sensors installed in the peripheral boreholes ALC1616 and ALC1617. These two boreholes are located in the horizontal and vertical plane of the cell, respectively, but only ALC1616_03 and ALC1617_03 were located at the same distance from the wall. Unfortunately, ALC1617_03 was damaged from the beginning of the experiment and no data were recorded. Therefore, it was not possible to study the anisotropic thermal properties of the COx as it was done in  [3]. Nevertheless, these measurements allow to study the temperature evolution in the COx during the heating phase. Similar tendency is observed in both directions and only the temperature at ALC1616_05 is affected by the temperature variation on the GAN drift wall. The temperature increased rapidly during the first year of the heating phase followed by a smooth increase that tended to stabilize over the last year, prior to the beginning of the cooling phase.
Numerical models Two numerical models were used for the reproduction of the THM behavior of the COx due to cell excavation and heating. The first numerical model was performed by Andra as part of Task E of the DECOVALEX-2019 project  [18]. Its theoretical formulation is based on the general expression of the governing equations for a classical thermo-poro-elastic saturated medium as developed in  [19]. The anisotropy induced by the bedding is taken into account by considering a transversely isotropic elastic stiffness tensor in the mechanical law as well as in the thermal conductivity and saturated permeability tensors. The numerical code used for the numerical analysis was COMSOL Multiphysics® and its objective was the prediction of the THM response of the COx in the heating phase by using the parameters obtained from the model calibration of the small-scale TED experiment performed within Task E.
Acknowledgements DECOVALEX is an international research project comprising participants from industry, government and academia, focusing on development of understanding, models and codes in complex coupled problems in sub-surface geological and engineering applications; DECOVALEX-2019 is the current phase of the project. The authors appreciate and thank the DECOVALEX-2019 Funding Organizations Andra, BGR/ UFZ ,  CNSC, US DOE, ENSI ,  JAEA ,  IRSN ,  KAERI ,  NWMO , RWM, SÚRAO, SSM and Taipower for their financial and technical support of the work described in this report. The statements made in the report are, however, solely those of the authors and do not necessarily reflect those of the Funding Organizations. The experimental work presented in this article has received funding from the  European Atomic Energy Community 's  Seventh Framework Program  under Grant Agreement n• 269905 , the  LUCOEX  project.
Acknowledgements The authors acknowledge gratefully the contributions of their coworkers to parts of these studies:  A. Auffèves  and  G. Hornecker 53  ,  H. Thyrrestrup 25 , 52  ,  A. Hartsuiker   25  ,  E. Yüce  and  G. Csistis 52  , and  A. Gérard   48. T. S. thanks the  Laboratoire d'Excellence LANEF in Grenoble  ( ANR-10-LABX-51-01 ) for a PhD grant. G. M. T. and P. L. S. acknowledge funding from  CAPES  ( PDSE 88881. 187056/2018. 1 )and  CNPq. This work was also partly funded by the  FOM "Zap!" project , which is financially supported by  NWO ,  NWO-Nano , and  STW , as well as by  NOMOS  project, which is financed by  ANR  ( ANR-18CE24-0026 ).
The obtained results showed an energy-saving gain of 30% and up to 20% of throughput while using DAs. This work aims to develop a routing scheme that takes advantage of spatial diversity offered by DAs by selecting the beamwidth and the transmission power level intelligently and adaptively. To achieve this goal, several main contributions are presented here as follow: • we provide an optimization framework with a weighted objective function to capture the energy consumption and the throughput tradeoff. • we develop a heuristic based on Ant Colony and Q-learning algorithms to resolve the optimization problem and reduce its complexity. The problem is formulated as a Mixed Integer Linear Program (MILP). • we design a routing scheme based on two algorithms: the Directional Neighbors Discovery algorithm (DNDa) and the Ant-Q for Energy Efficient Routing over Beams (AQ-EERoB).
Related work Several studies have been conducted on WMNs equipped with either OAs or DAs. Most of these work considered optimization tools to derive and improve WMNs performance either by using software solvers such as ILOG Cplex optimizer  [13]  or by proposing heuristics to solve the considered optimization problem, which is NP-Hard. This section focuses on some recent optimization-based studies for both OAs and DAs WMNs by presenting the proposed heuristics and approaches. The network performance commonly studied in literature are throughput, energy consumption or efficiency, and end-to-end delay. In order to summarize previous work, we use the following optimization model for which the throughput, the energy consumption, and the deployment costs (Capex and Opex) of a WMN are jointly considered as follows: ⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩ Minimize 𝛼.
Conclusion & perspectives This paper addressed joint energy consumption minimization and throughput maximization in WMN using DAs to advantage of spatial diversity. As a resolution approach, we developed a routing scheme based on beam and transmission power selection while guaranteeing a trade-off between energy consumption and throughput. First, we formulated the joint energy consumption and throughput optimization problem as a Mixed Integer Linear Program (MILP). Secondly, we proposed an algorithm based on the AQ-EERoB heuristic to reduce the resolution complexity. The routing scheme we proposed allows us to jointly select optimal beams and transmission power levels, using two algorithms: DNDa (Directional Neighbors Discovery Algorithm) and AQ-EERoB (Ant-Q for Energy Efficient Routing over Beams).
75 μs (π/12) and a delay time of 0. 1 s. 19 F MAS NMR spectra were obtained using typical π/2 pulse of 2. 9 μs and a pulse space of 2 s. The chemical shift values were reported in ppm from tetramethylsilane for 29 Si, from NaF for 19 F and from a 0. 1 M AlCl 3 and NaCl solution for 27 Al and 23 Na, respectively. Spectra were simulated using the DMFIT software  (Massiot et al. , 2002)  and Gaussian-Lorenztian ratio, position, linewidth and amplitude were the fitted parameters.
This presented work was conducted in the framework of Subproject C1 of the Collaborative Research Project SFB 837 'Interaction Modeling in Mechanized Tunneling', financed by the German Research Foundation (DFG) (Grant agreement 77309832). The authors would like to thank the DFG for the support of this project. The second author has received funding dor the SATBIM project from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 702874. The second author would like to gratefully acknowledge this support. The third author thanks the support from the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 105. 08-2018. 310. Open access funding enabled and organized by Projekt DEAL.
Characterization of carbon materials The textural properties of pristine and activated biochars were determined from both N 2 and CO 2 adsorption isotherms at -196 °C and 0 °C, respectively. Around 120 mg of sample were degassed under vacuum at 150 °C. ASAP 2020 and ASAP 2420 automatic adsorption analyzers (Micromeritics, USA) were used. The results obtained from the isotherms were treated using the MicroActive software. We determined the BET area (A BET ) by application of the BET model, as well as the Gurvitch volume (V 0. 97 ). The enhanced 2D-NLDFT model  [34]  was employed using SAIEUS software (available at www. nldft. com) to evaluate the pore size distributions (PSDs) and other related parameters: surface area (S 2D-NLDFT ), ultra-micropore volume (V ultra , d p < 0. 7 nm), micropore volume (V micro , d p < 2 nm), total pore volume (V t ) and mesopore volume (V meso , d p in the range of 2 to 50 nm).
The secondary electron images were taken with an acceleration voltage of 3 or 5 kV to investigate the topology of the samples surface, while an acceleration tension of 10 kV was applied to carry out the chemical mapping of the materials by EDX analysis. Additionally, X-ray photoelectron spectroscopy (XPS) data of spent and fresh catalysts were recorded using an ESCAPlus OMICROM system equipped with a hemispherical electron energy analyzer, following the procedure detailed elsewhere  [35].
Acknowledgments This project recei ed funding from the  European Union 's orizon research and innovation programme under the  Marie łodows a-Curie  grant agreement No  721991. The authors also acknowledge the funding from the  Aragón Government (Ref. T22_20R ), cofunded by  FEDER   2014-2020  " Construyendo Europa desde Aragón ". ". This study was partly supported by the  French PIA  project " Lorraine Université d'Excellence" , reference  ANR-15-IDEX-04-LUE ,  PROMOTEE  project and  TALiSMAN  project, funded by  FEDER  ( 2019-000214 ). The authors gratefully thank  José Antonio Manso  (UNIZAR) and  Philippe Gadonneix (IJL)  for their help in the preparation and characterization of the samples and  Sandrine Mathieu  ( IJL ) for the SEM characterization.
This prediction uses a multitude of period models. Most of these studies focus on forecasting consumption by introducing other parameters using different predictive models depending on the nature of the input data and the sought objectives. Indeed, we note that the provided forecast horizon mainly depends on the input databases of the models. These database generally have annual, seasonal, monthly, weekly, daily or hourly resolutions. Most of the work, even based on intelligent techniques, are based on additional information. For example, the study in  [20]  uses support vector machines with monthly water demands, number of users, and total water consumption bills. Ref. [21]  discusses residential water demand management based on pricing, restriction policies, climate, weather and demographic characteristics.
[h(t -1), x(t)] + b i ) (1) A hyperbolic tangent function is applied to the input and the output data from the previous step to create a vector of a new value C(t) to be an internal state. The update of the internal state is carried out through: C(t) = tanh(W c. [h(t -1), x(t)] + b c ) (2) The forget gate f (t) is calculated with another sigmoid function that takes for its input the output h(t -1) and the input x(t): f (t) = σ W f. [h(t -1), x(t)] + b f (3) Finally, the output gate O(t) described by (  5 ) is based on the state C(t). This state is updated with a hyperbolic tangent multiplied with the output of a sigmoid: C(t) = f (t) × C(t -1) + i(t) × C(t) (4) O(t) = σ(W o. [h(t -1), x(t)] + b o ) (5) W i , W c , W f , W o , and b i , b c , b f , b o represent respectively the weights and the biases at the different levels in the LSTM memory block.
Data Collecting with Smart Meters All the data used in this study are collected in an online database from smart water meters. Smart meters are IoT devices that are appropriate to build a sustainable and advanced consumption data system  [23]. Most water distributors collect data from smart meters with a resolution of several minutes, for example every 15, 30 or 60 min, or even once a day  [5]. This implies that the capacities of smart water meters are clearly not fully exploited  [7]. This also means that the resolution of the consumption data is low. We use smart meters with the communication strategy proposed and developed in  [24]  to compress and to transmit the data with a very high resolution  [7]  according to industrial specifications. This strategy allows the dating on the server side of each liter consumed and reduces the energy consumption on the meter side.
In our AMI, we chose a maximum value of 120 bytes for L f which is the limit of the frame size. A web server receives all the transmitted frames from several smart meters. Here, a script receives, decompresses, and retrieves the data from the frames for storage in an SQL database  [10]. This process runs continuously since 2014 and allows completion of the database in real time and under real operating conditions. The database contains raw data for each individual smart meter, i. e. , the index which represents the volume of consumed water in liter and the instant when each liter has been consumed in millisecond. This instant is called a pulse or an event  [7]. It is obvious that the data collected and stored according to this platform are of high resolution and therefore precisely represent consumption habits.