dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulations Simulations were carried out using the commercial software Lumerical FDTD. A cuboid of SiO 2 glass covered by 50 nm of ITO was chosen as the substrate. Experimentally, the refractive index of ITO depends on the specific preparation and moreover is assumed as wavelength dependent  [24] , but it was shown in previous work that the spectra can well be approximated with a refractive index of n = 1. 9  [25]. Two triangles were arranged as tip-to-tip antennas and assigned the dielectric function of Au  [26]. Each corner of the triangles was rounded with a radius of 10 nm to avoid artefacts and simulate a model that is closer to the experimental sample. A group of cross-section monitors was used to analyze the scattering field. The total field/scattering field sources were selected with the normal incidence vertical from the top of the nanoantenna.
Acknowledgements The Authors acknowledge the contribution of S. Dickreuter by implementing the nanostructure positioning software in the dark-field setup. Financial support by the  Chinese Scholarship Council (CSC)  in the  CSC -UT/INSA program , and by the  German Science Foundation (DFG)  under grant  FL 670/8-1  and the  German Excellence Strategy  is gratefully acknowledged. This work was performed within the framework of the  Graduate School NANO-PHOT  (Grant  ANR-18-EURE-0013 ).
Dispersion curves To analyze wave propagation in the PC, the dispersion curves are computed with the FEM using the ATILA software  [11]. Since the cylinders are assumed uniform and infinite along their axis, only a bi-dimensional mesh in the xy-plane is used. First, a single scatterer inside a unit cell with periodic boundary conditions applied on its faces is considered  [12]. Quadratic interpolation elements are considered in the computation. Moreover, the cylinders are supposed to be rigid i. e. acoustic waves cannot propagate inside the cylinders, and they are placed in air considered at 20 C, assumed as a non-viscous, lossless medium. Density and sound velocity in air are q air = 1. 3 kgÁm À3 and c air = 339 m. s À1 , respectively. To draw the dispersion curves (Fig. 1b ), i. e. the diagram representing the eigenmode frequencies of the PC that are associated with a specific wave vector, versus the wave vector, it is not necessary to consider all the wave vectors but it is sufficient to span a relatively small range of wave vectors covering the edges of the irreducible Brillouin zone (IBZ) (see  [3] ).
A psychoacoustic feature: Moore-Glasberg loudness From Spiousas et al. [8, 9] , one may expect that a PC introduces both spectral and sound level modifications on an acoustic signal emitted by a sound source. To estimate the potentially frequency-dependent effect of the PC on the perceived sound level, the numerical, full-band impulse responses of each configuration (FF, FS, PC) are convolved with third-octave noises (i. e. , white noise filtered with 2nd-order Butterworth filters of center frequencies corresponding to the classical third-octave bands). The resulting signals are resampled to 48 kHz, and the Moore-Glasberg loudness is computed using Matlab's "Audio Toolbox". This model takes into account the frequency masking effect and is optimized for signals such as the third-octave noises described above.
Design of the perceptual test The computation of the psychoacoustic loudness (Sects. 4. 2 and Fig. 6 ) exhibits clear differences and similarities between the PC and the other two configurations (FF and FS). To assess whether these differences are actually perceived by human listeners, a listening test is conducted, with twenty-five participants participating in a pair comparison test  1. The test takes place in a quiet room, and the test interface is implemented using Matlab on a HP laptop with an external Roland Quad Capture sound card. The stimuli are played over Prodipe Pro880 headphones delivering a level of 73 dB at the position of the ear. As differences in the computed loudness between the configurations (FF, FS, PC) are observed in the frequency range of the typical phenomena expected from a PC, the perceptual test focuses on these frequency ranges.
4(b) ]. The major axis of the ellipse corresponds to the in-plane propagation direction k 1 , while the two diffused satellites centered at ±45 • from this axis are associated to k 2 and k 3. We note that the spots corresponding to k 1 are less intense because of the dominance of the out-of-plane variants in this specific image. To get a larger scale insight on this cycloid propagation rotation, we measured the wavevector's norm and orientation with respect to P in different regions of the sample. The results are gathered in the polar diagram shown in Figure  4(c). The plotted ellipse (red solid line) is the projection onto the surface plane of a circle with radius 2π/λ b in the (111) plane (see Fig. 1 (a) and Supplemental Material  [26] ). All experimental data points fall along this ellipse, with a dispersion reflecting the large spread of cycloid propagation directions.
Combining reciprocal and real-space magnetic imaging techniques, we have shown the coexistence of antiferromagnetic domains in which the cycloid propagates along different wavevectors. In addition, owing to the nanoscale spatial resolution of scanning-NV magnetometry, we observed the formation of topological defects typical for lamellar materials at the junctions between these domains. Our work thus demonstrates that these magnetic objects, previously observed in a chiral ferromagnet  [8] , can be transposed in a multiferroic antiferromagnet, offering new opportunities in terms of robustness and electrical control towards their use in spintronic devices. The data that support this work are available in Zenodo (https://zenodo. org/record/6390870) with the identifier 10. 5281/zenodo. 6390869.
ACKNOWLEDGMENTS We thank support from the  French Agence Nationale de la Recherche (ANR)  through the project  TATOO  ( ANR-21-CE09-0033-01 ), the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 964931  (TSAR) and No. 866267  ( EX-AFONIS ), the  DARPA   TEE program  and a public grant overseen by the  ANR  as part of the ' Investissement d'Avenir' programme (LABEX NanoSaclay, ref. ANR-10-LABX-0035 , SPiCY). The  Sesame Ile de France IM-AGeSPIN  project (No. EX039175 ) is also acknowledged.
The sample resistance R S is measured with the four-point probe method to avoid additional contact resistances, and with the sample substrate disconnected from the ground. Figure  1a  shows a   [9]. These two limiting behaviour are pictured in Figure  1b : the first corresponds to the finite thickness case and the second corresponds to the semi infinite thickness case. Our data are expected to be in between the two regimes because the thickness is of the order of d. Finite element simulations with COMSOL Multiphysics have been performed to fit the data, with the sample resistivity as a free parameter. Figure  1c  show the sample geometry used for the simulations. The best fit, shown in Fig. 1b  is obtained using a resistivity of 38 Ωcm. This value is in excellent agreement with the resistivity of 40 Ωcm reported previously on single crystals of the same batch  [5].
| Statistical analysis All females who underwent LVAD implantation in the study period were extracted from the institutional database and a comparable cohort of males was identified via PS match method applied to the same database. The PS was calculated via non-parsimonious multivariable logistic regression model based on age, body mass index, implanted LVAD device type, HF etiology, INTERMACS profile, diabetes, myocardial infarction, cardiothoracic surgery history, preoperative MCS, left ventricular ejection fraction and serum creatinine. Patients were matched 1:1 using a greedy matching algorithm (nearest match without replacement) based on the PS of each patient with a caliper width of 5% of the standard deviation of the logit of the PS. Further data analysis was performed to compare PS matched females and males.
3 Therefore, the question T A B L E 2 Intraoperative and postoperative outcomes of the propensity score matched cohort is whether, by acting on as many of these pre-operative variables as possible and eliminating pre-operative differences, the outcomes of females can become comparable to those of males. The same question can also be asked in reverse: if males had the same characteristics as females, would they also have less favorable outcomes? In order to simulate a situation with no preoperative differences, we applied the statistical method of PS matching to extract a cohort of males with the same characteristics as the females recorded in our institutional database. Interestingly, the PS matching returned a cohort of patients with high incidence of preoperative ECMO (23. 9%) which is much higher compared to other international reports.
Pavement instrumentations including optic Sensors 2022, 22, 5572 2 of 18 fiber sensors, pressure sensors, strain gauges, accelerometers, wireless embedded sensors, etc. , make it possible to continuously and passively collect data for the monitoring of structures  [10]. Moreover, like GPR and FWD, these methods require the management of huge databases, and their design must be adapted to the structure. The data processing of the aforementioned methods often consists of extracting different values, such as extremum, number of positive or negative peaks, and in calculating thresholds, that are generally representative of variations in the whole structure. Therefore, it can be difficult to detect a defect located in the base layer and to prevent its propagation through the bituminous layer, or vice versa.
Recently, data were recorded from an instrumented pavement during an experimental campaign simulating high traffic running loads through an accelerated pavement testing facility (fatigue carousel) located at Univ. Gustave Eiffel, campus of Nantes (Univ. Eiffel Nantes, previously known as IFSTTAR Nantes)  [13, 14]. The tested pavement was instrumented with strain sensors and temperature sensors to evaluate, among other things, the fatigue resistance of different formulations of asphalt materials. Thus, with these available data, we seized the opportunity to check a possible implementation the OIM for the data processing of strain gauges embedded in an instrumented pavement. The aim of this paper consisted, then, of highlighting the possibility to extract some weighting functions from embedded strain sensors signals for the separate monitoring of the physical characteristics of the studied pavement.
The second objective was showing that the OIM can bring additional information compared with more conventional strain sensors signal processing. In the Section 2, the principle of the OIM and the weighting functions is briefly explained. Section 3 describes the instrumented pavement, the fatigue carousel, and the experimental setup. In Section 4, numerical modeling of the pavement behavior under the experimented conditions is proposed, which allows for the calculation and the validation of the weighting functions in Section 5. To complete this study, the data recorded from the instrumented pavement were analyzed by both extracting classic indicators and processing with the OIM, and the results were then compared and discussed.
Through the orthogonality condition given by Equations (  7 ) and  (8) , it is possible to deduce the coefficients of the matrix a jk , which allows determining p j (x) (Equation (3)) and, finally, the indicator I j. After the calculation and the validation of the weighting function, the optimized indicator I j was normalized through a reference indicator I R calculated from a reference signal ε R (x). This reference signal was obtained through numerical modeling (or a real measurement) of the considered structure. The Section 3 describes the structure of the instrumented pavement, the fatigue carousel, and the experimental procedure that was used for the acquisition of data for implementation of the OIM for embedded strain gauges.
Loading Characteristics and Data Acquisition After the construction of the pavement, a first series of spinning charging cycles w applied as a consolidation step of the pavement. The amount of cycles applied during t consolidation step was 37,800, which, considering the four arms of the carousel, cor sponds to a total amount of N = 4 × 37,800 = 151,200 loadings. The load applied by t dual-wheel load during the entire experimental test was 65 kN, which corresponds to t maximum load allowed on pavements in France. Loading wheels can be located at positions along the transverse profiles of the pavement to simulate the lateral wanderi of the traffic. In this study, only measurements recorded with dual loads located at position The strain gauges were designed for measurement in asphalt concrete and consist of a bar of fiberglass reinforced with epoxy, in which is inserted a gauge of deformations.
Loading Characteristics and Data Acquisition After the construction of the pavement, a first series of spinning charging cycles was applied as a consolidation step of the pavement. The amount of cycles applied during this consolidation step was 37,800, which, considering the four arms of the carousel, corresponds to a total amount of N = 4 × 37,800 = 151,200 loadings. The load applied by the dual-wheel load during the entire experimental test was 65 kN, which corresponds to the maximum load allowed on pavements in France. Loading wheels can be located at 11 positions along the transverse profiles of the pavement to simulate the lateral wandering of the traffic. In this study, only measurements recorded with dual loads located at position 6, which corresponds to the center of the wheel path, were considered.
VISCOROUTE© Software VISCOROUTE© is an analytical, multilayer pavement modeling software, based on the viscoelastic model of Huet-Sayegh, developed at Univ. Eiffel Nantes for the modeling of pavements with bituminous materials under moving wheel loads  [24]. The pavement described in Section 3. 2 is modeled as a multi-layer structure with bonded interfaces, lying on bedrock with infinite stiffness. Thus, the model also takes into account the elastic modulus of the UGM layer (E UGM ). The simulations were carried out under the conditions of temperature and speed of the experimental campaign and the model adopted a viscoelastic behavior of bituminous material integrated into the VISCOROUTE© 2. 0 software  [24] [25] [26]. After analyzing and comparing the influence of all parameters on the shape of the signal, and based on the results from mechanical characterizations of the bituminous and base layers (not shown in this paper), the Huet-Sayegh model was numerically adjusted so that the simulated signal fit the measured reference signal.
VISCOROUTE© Software VISCOROUTE© is an analytical, multilayer pavement modeling software, based on the viscoelastic model of Huet-Sayegh, developed at Univ. Eiffel Nantes for the modeling of pavements with bituminous materials under moving wheel loads  [24]. The pavement described in Section 3. 2 is modeled as a multi-layer structure with bonded interfaces, lying on bedrock with infinite stiffness. Thus, the model also takes into account the elastic modulus of the UGM layer ( ). The simulations were carried out under the conditions of temperature and speed of the experimental campaign and the model adopted a viscoelastic behavior of bituminous material integrated into the VISCOROUTE© 2. 0 software  [24] [25] [26]. After analyzing and comparing the influence of all parameters on the shape of the signal, and based on the results from mechanical characterizations of the bituminous and base layers (not shown in this paper), the Huet-Sayegh model was numerically adjusted so that the simulated signal fit the measured reference signal.
Choice of the Construction Parameters of the Optimized Indicators For the construction of the optimized indicators, a parametric study was carried out using the VISCOROUTE © software to check the influence of each pavement parameters on the shape of the modeled signal. The parametric study consists of separately applying a ±10% variation on each parameter of the model (indicated in Table  1 ) by considering the other parameters as constants. With respect to the previously proposed model and considering the values of parameters listed in Table  1  as references, each of these variations differently affects the shape of the signal and the value of the equivalent module (|E*|). The aim of this parametric study was identifying the two most influential parameters usable to determine separately the characteristics and/or the damage level for two different layers.
Data Processing through Regular Recalibration of the OIM The principle of this alternative data processing consists of re-calculating the weighting functions from the measured signals each time that one of the optimized indicators had a relative variation greater than 10%. As observed in Figure  12 , I E ∞ had a variation greater than 10% at 245,600 loadings, which makes it possible to assume that the weighting function is out of the linearity domain. Before any new calculations, the values of E ∞ and E UGM were calculated and saved from 151,200 to 207,600 loadings. Indeed, in this loadings range, the weighting functions remain in the linearity domain of the first calibration. Afterwards, the gauge signal recorded at 245,600 loadings (labeled as N ref = 245,600) was selected and used as a new reference signal for the recalibration of the weighting functions.
Conclusions Through the recent generalization of pavement instrumentation as a structural monitoring technique, the conventional data processing methods generally only allow for evaluating the global characteristics of these structures. In this paper, we successfully transposed a data-treatment method, labeled as the Optimized Indicator Method (OIM). This method consists of extracting some weighting functions, through derivatives and integrals, from a modeling of the structure to evaluate its physical characteristics independently from each other. The considered structure was a pavement, made of a bituminous layer and an Unbound Granular Material layer, instrumented with embedded longitudinal strain gauges. The first step of the study consisted of recording the gauges' signals as a function of 65 kN loadings applied through a fatigue carousel simulating high heavy traffic.
After the choice of a reference signal, the VISCOROUTE© software allowed, for numerically setting the parameters of the model of Huet-Sayegh, identifying the most influential parameters on the shape of the signal. Then, after deduction of the associated weighting functions, proceeding with the OIM of signals recorded during nearly 4 months at regular steps showed that the elastic modulus of the bituminous layer regularly dropped while the elastic modulus of the base layer remained nearly constant. After reaching a critical number of loads (688,400 loadings), the base layer seemed affected as its elastic modulus increased from 120 to 130 MPa, and the degradation of the bituminous layer continued. These results demonstrate the interest of the OIM, as the conventional methods cannot easily and separately evaluate the physical characteristics of the pavement layers.
Spectral Imaging Spectral imaging comprises multiple bands across the electromagnetic spectrum as it captures simultaneous data in the infrared, the visible spectrum, the ultraviolet, and the X-ray ranges. Spectral imaging includes multispectral (MSI) and hyperspectral (HSI) imaging. Multispectral cameras typically capture from 2 to 20 bands, while hyperspectral cameras capture hundreds, or thousands of bands, assuming a continuous spectrum or spectral signature of the object of analysis and creating the named hypercube of wavelength from 350 to 2500 nm  [42]. Hyperspectral imaging has more capability and a better resolution than multispectral imaging. Nevertheless, it adds more complexity since it works with parallel information, pixel alignment, or a larger dataset  [43]. Both MSI and HSI have been used in pavement assessment  [44].
Visible Imaging Visible imaging detects the three wavelength bands of the electromagnetic spectrum or the visible spectrum (400-700 nm): red, green and blue (typically called RGB imaging). The RGB camera can operate mounted on an UAV or in a vehicle. However, the operation mode influences the accuracy and reliability of the data. A resolution less than 0. 2 mm can be achieved when operating with the camera mounted in a vehicle and close to the road surface, although costly  [54]. Conversely, the resolution is not less than 7 mm if mounted on UAVs. Moreover, data quality is influenced by other conditioning factors such as proper illumination and atmospheric conditions (e. g. , fog, heat, and sandstorms)  [44]. The information obtained are RGB images and videos that have been used to monitor, most frequently combined with learning techniques for automatic detection.
Infrared Thermography Imaging Infrared thermography (IRT) is a non-destructive evaluation method (NDE) that is used for infrastructure condition assessment  [59, 60]. IRT is a non-contact test that does not require physical contact with the target object. The data can be collected using a camera mounted on a tripod, Unmanned Aerial Vehicles (UAVs), or on top of a vehicle. These advantages enable the inspections to be carried out rapidly and without affecting the traffic  [61] [62] [63]. Infrared thermography helps to detect surface and near-surface damage and defects by detecting the emitted radiation in the infrared range of the electromagnetic spectrum. The defects are identified by capturing the thermal contrast of the inspected surfaces (Figure  3 ). IRT can be conducted by using two different approaches depending on the source of heat, active or passive  [64].
The selection of publications to be reviewed in-depth and their relevance reflected the views of the authors. Moreover, this review does not include all the existent papers, but we considered journal articles rather than conference publications when they addressed similar topics, and measured data rather than experimental or simulated data. Original research and case study articles were preferred over review articles, especially those indexed in the Journal Citation Reports (JCR) database. Studies combining GPR with other NDTs for road pavement assessment have shown more interest in recent years. Figure  16  shows the number of publications relevant to this review topic per year since 1993. The most were published in 2016 and 2017, with 14 each, and then, in 2021, 13 were published. The majority of the publications reviewed were articles and conference papers, at 68% and 28%, respectively, as shown in Figure  17.
RSP, FWD, 3D GPR Ground-coupled antennas with a frequency bandwidth of 200-3000 MHz (antennas separation of 7. 5 cm) FWD was able to determine the modulus of each structural layer (bearing capacity evaluation), but the deflection values obtained cannot describe the level of the integrity of the pavement structure nor the existence of internal defects and extent. 3D GPR measured the thickness of each structural layer (to determine thickness deficiency) and to detect the internal damage distribution (crack rate ratio) in the pavement structure (width and weight of damage). Taylor & Francis  [200]  To demonstrate the power of integrating multisensing data in the framework of pavement conditions. The test site was an interurban motorway RSP, FWD, GPR Air-coupled antennas (frequency antenna not provided) The modeling approach presented demonstrated (i) the material moduli estimated through back-calculation (deflectometric data integrated with GPR-based thicknesses), based on the multi-layer elastic theory, showed a interquartile range of RMS varying from 2.