dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical illustration This section presents some numerical examples illustrating the key features of the estimator of Theorem 5. 5. We impose the Dirichlet boundary condition on the whole boundary, i. e. , Γ D := ∂Ω. We consider both structured meshes and unstructured meshes. When we speak about a "structured" mesh, we mean a Cartesian partition of Ω into N × N × N cubes where each cube is first subdivided into 6 pyramids (with the basis a face and the apex the barycenter of the cube) and then each pyramid into 4 tetrahedra. The corresponding mesh size is h = √ 3/(2N ). On the other hand, the "unstructured" meshes are generated with the software pacakge MMG3D  [16] , where we simply require a maximum element size. These are typically quasi-uniform, but do not have any particular reapeting structure (every vertex patch is different).
Long-term stability The long-term stability of our setup has been characterized by monitoring over 37. 5 h both the difference frequency, set to 1. 965 GHz, and the absolute NIR frequency of Las1. The laser frequency was determined by recording its beatnote signal with a GPS-referenced OFC (FC1500, Menlo Systems), see panel (a) of Fig. 2. The beating signal was measured every second with 1 Hz resolution using a fast acquisition card. The laser frequency and the difference frequency drifts are plotted in panels (a) and (b) of Fig. 4 , respectively. An overlapping Allan deviation  43  of the difference frequency has been computed with a Python script based on the allantool 44 library and is shown in panel (c) of Fig. 4. It confirms that the difference frequency linewidth is lower than 1 Hz for a single acquisition.
A 48 kHz frequency modulation with 360 kHz depth was applied via the RF fine-tuning source, allowing lock-in detection at the second harmonic of the transmitted signal. The laser emission frequencies were measured using a high-resolution Fizeau wavemeter (WS7 from Highfinesse) with 5 MHz precision. As an example, the absorption signal of the J K A/E = 6 -1 A ← 5 -1 A methanol line, recorded at room temperature, with a step resolution of 20 kHz and 1 s per spectral point, is shown in Fig. 7. It shows an SNR of about 100 dominated by an oscillating structure that we attribute to stationary waves that develop between the THz optics and slowly evolve during the scan. The second derivative Voigt profile fit of the data yields the expected thermal broadening at 300K (Doppler width), and pressure broadening (Lorentzian width), and a line position in agreement with the parameters given in the HITRAN database  45.
Three recorded lines, fitted with a Voigt profile, are shown in Fig. 8  together with their fit residuals. The lines are essentially Doppler broadened from 1. 5 to 3. 3 MHz. Line positions, once the 83 kHz frequency shift on TX1 is taken into account, are in good agreement with positions listed in the HITRAN database  45. It is worth noting that jumping to any frequency from 100 to 1400 GHz takes only a few seconds without the need for optical realignment, making the setup particularly suitable for systematic molecular spectroscopy measurements with absolute frequency calibration. Let us emphasize that the heterodyne detection scheme generates a signal that is proportional to the power of the THz beam received. The spectra are therefore recorded in terms of transmission, which gives access to quantitative measurements of line strength.
Spectra acquisition The spectra were acquired (1) by frequency modulation of the photomixed source through the RF driver of the optical-single-side-bandmodulator; a lock-in detection chain was then set-up to detect the second harmonics of the signal detected on a Schottky diode, or (2) by heterodyne spectroscopy made possible by generating two different THz signals, with 83 kHz difference frequency; this difference frequency was filtered out and amplified from the signal received by a photodetector (RX) and a signal directly proportional to the transmission of the cell was obtained. of critical parts of the spectrometer in a particularly short time. M. M. and O. P. acknowledge "Investissements d'Avenir" LabEx PALM (ANR-10-LABX-0039-PALM) which supported a part of this work. G. D. and R. K.
also state that this work was partially supported by the IEMN Sigmacom platform, the IEMN Flagship on Ultra-High data rates (UHD). The THz testbed also received support from France 2030 programs, PEPR (Programmes et Equipements Prioritaires pour la Recherche), CPER Wavetech. The PEPR is operated by the Agence Nationale de la Recherche (ANR), under the grants ANR-22-PEEL-0006 (FUNTERA, PEPR 'Electronics'). The Contrat de Plan Etat-Region (CPER) WaveTech is supported by the Ministry of Higher Education and Research, the Hauts-de-France Regional council, the Lille European Metropolis (MEL), the Institute of Physics of the French National Centre for Scientific Research (CNRS) and the European Regional Development Fund (ERDF).
Acknowledgements We acknowledge financial support from the  Agence Nationale de la Recherche  ( METEORIT ,  ANR-22-CE30-0036 ). S. K. acknowledges additional funding from the  Agence Nationale de la Recherche  ( Equipex REFIMEVE+ ,  ANR-11-EQPX-0039 ). L. L. and S. K. acknowledge funding from the  Centre National de la Recherche Scientifique (CNRS ,  INP prematuration program ). L. D. and S. K. acknowledge funding from  Linksium Grenoble Alpes  (project number:  210019 ). L. D. , S. K. , and L. L. warmly thank  David Terrier , from the mechanical workshop, for the production Article https://doi. org/10. 1038/s41467 -023-42905- z Nature Communications | (2023) 14:7162.
Acknowledgements We thank  Giovanni S. Alberti  ( University of Genoa ),  Ralf Hiptmair  ( ETH Zürich ),  Steven Johnson  ( MIT ),  Dirk Pauly  ( Universität Duisburg-Essen ),  Luca Rondi  ( Università di Pavia ) and  Michael Taylor  ( University of North Carolina at Chapel Hill ) for useful discussions. AM acknowledges support from  GNCS-INDAM , from  PRIN  project " NA FROM-PDEs " and from  MIUR  through the  "Dipartimenti di Eccellenza" Programme  ( 2018-2022 )-Dept. of  Mathematics, University of Pavia. EAS acknowledges support from  EPSRC  grant  EP/R005591/1. Competing-interests statement. The authors declare no competing interests.
(2022)  and  Li et al. (2022)  used Sentinel-1 data, covering 5 years, to observe subsidence over several reclaimed areas in China. Other studies processed several datasets from different sensors in order to cover a longer observation period. However, due to the lifespan of the different SAR sensors, these datasets do not overlap in time and have temporal gaps. For example, the subsidence of the city of Urayasu (Japan) has been measured thanks to the ERS-1 and  ERS-2 (1993 ERS-2 ( -2006;; European Remote Sensing) ,  ALOS (2006 ALOS ( -2010;; Advanced Land Observing Satellite), and ALOS-2 (2014-2017)  In-SAR time series  (Aimaiti et al. , 2018) , leading to a gap between 2010 and 2014. In this case, the temporal profile of the deformation was too irregular to enable any modelling that could connect the measurements across the temporal gap.
6  in  Cavalié et al. , 2015). Indeed, it ranges from less than 1 mm yr -1 in the Var valley to a maximum rate of 10 mm yr -1 on the airport platform, where sediments were brought in the 1970s to build the runways. During the 2003-2011 period, InSAR data show essentially a steady subsidence. Here, we extended the time series in order to observe the behaviour of the airport platform over a longer period. This long observation period provides an opportunity to investigate new mechanisms driving vertical land motion in coastal areas. Actually, no physical process could explain a constant subsidence rate over several years. We thus processed the data from the ERS satellites between 1992 and 2001 and the data acquired by Sentinel-1 for the period 2014-2020. Then we discussed and modelled the information brought by these new datasets.
These results are useful for future physicsbased forecasting models of the coastal slope evolution. In the following sections, we shortly recount the history of NCA. Then, we present the InSAR measurement of the airport platform deformation, including a noise analysis of the generated dataset. This latest point is rarely discussed in the subsidence studies, although it is crucial to accurately ashttps://doi. org/10. 5194/nhess-23-3235-2023 Nat. Hazards Earth Syst. Sci. ,  23, [3235] [3236] [3237] [3238] [3239] [3240] [3241] [3242] [3243] [3244] [3245] [3246] 2023  sess the hazard due to the subsiding area. Finally, we model the InSAR observation to better understand the underlying physical process of the platform subsidence and discuss the potential consequences of such vertical displacements.
We, thus, selected track 22, where 65 images were acquired between November 1992 and September 2001 (while only 26 images are available for the ascending track over the same period). Envisat data have been already processed in  Cavalié et al. (2015). For Sentinel-1 data, we processed the descending track 139. We took advantage that Sentinel-1 SAR images are made of multiple bursts in order to select only those that cover the airport. As the airport is a kilometric object, only two bursts of the first subswath need to be processed. We mostly followed the same methodology as in  Cavalié et al. (2015). Ground displacement time series are generated from interferograms using the NSBAS (new small baseline subset) processing chain  (Doin et al. , 2012). As with all small baseline processing chains, the main idea consists in limiting both the temporal and spatial baseline between the images that will be combined to compute the interferograms in order to optimize the interferometric phase coherence and thus keep the highest possible number of pixels for the deformation analysis.
Surface displacements observed from 1992 to 2020 Figure  3a -c shows the averaged vertical ground velocity for the  ERS (1992 -2001 ), Envisat (2003 -2011) , and Sentinel-1 (2014-2020) periods. Note that the colour scale changes to match the maximum velocity value. This representation allows for seeing that the spatial displacement pattern is steady through the whole observation window, although the amplitude decreases in time. This is a major update compared to the 2015 study  (Cavalié et al. , 2015) , where the time window was too short to reliably measure any slowdown of the surface displacement. Thus, with this new dataset, we have the opportunity to measure subsidence rate variations over a 28-year-long period of time. To better observe the temporal variations in the airport platform subsidence, Fig.
As a result, all velocity variations observed in the InSAR data are due to noise. Assuming that the noise level is similar in the nearby deformed area, a 1σ error can be computed. With this method,  Cavalié et al. (2015)  estimated a 1σ error of 0. 25 mm yr -1 for the mean subsidence rate that occurred between 2003 and 2011. Similarly, using the time series, one can estimate the temporal dispersion of a pixel located in a stable area. Moreover, if the temporal evolution of the surface displacement is simple enough (as it is the case for NCA), one can remove a deformation model and then observe the residue dispersion. The advantage is that the 1σ error is computed directly in the subsiding area (and not for a pixel located nearby that can be affected by other local sources of noise). Figure  4  displays the InSAR signal residue (after removing the logarithmic function estimated from Eq.
1) for pixel P 1 (shown in Fig. 3a ). Overall the standard deviation in displacement σ d is ∼ 4. 2 mm. Interestingly, we see that data dispersion is very similar for the ERS, Envisat, and Sentinel-1 time series (that have been processed independently). As in  Fattahi and Amelung (2015) , if we assume that the residue is a Gaussian white noise, the uncertainty in the deformation velocity σ v can be estimated for each time series as σ v = σ d N i=1 t i -t 2 , ( 2 ) where t i is the acquisition dates, t is the mean acquisition date, and N is the number of acquisitions. σ v is estimated to be 0. 27 mm yr -1 for the ERS dataset, 0. 28 mm yr -1 for the Envisat dataset, and 0. 32 mm yr -1 for the Sentinel-1 dataset. Interestingly, the estimate for the Envisat dataset is very close to the abovementioned 1σ error of 0.
The best-fit solution allows for retrieving reasonable mechanical values of the airport sediments that have been brought to build the platform extension. Our study thus proves that the long-term InSAR data can improve our understanding of the surface processes and the subsurface material properties. Although the subsidence rate decelerates, at least for 28 years, our results show that the compaction of the sediment is still active and its future evolution is uncertain and still at stake. Indeed, if compaction bands are developing under the airport platform, creep processes could potentially lead accumulated material damage to failure. Thus, through our investigations, the data indicate that the stability of the airport platform should be monitored continuously with additional high-quality space and land observations together with submarine instrumentation on the continental slope right below the airport.
Acknowledgements. This work was supported by  CNES (Centre National d'Études Spatiales)  and focused on data provided by the  European Space Agency  (ERS, Envisat, and Sentinel-1 missions). We thank the three anonymous reviewers and the editor for their careful reading and comments that improved the original version of the paper. Financial support. This research has been supported by  CNES  through the " Var-risque " project. Review statement. This paper was edited by  Margreth Keiler  and reviewed by three anonymous referees.
Data availability. Raw data used in this study are freely available online. Sentinel-1 SAR images are from the PEPS (Plateforme d'Exploitation des Produits Sentinels) platform (https://peps. cnes. fr/rocket/#/search, CNES, 2023). ERS and Envisat SAR data were obtained free of charge through https://doi. org/10. 5194/nhess-23-3235-2023 Nat. Hazards Earth Syst. Sci. , 23, 3235-3246, 2023.
These compliance limits prohibited large voltage drops inside the test circuitry, minimising the risk of damage to the ARRAY system. The entire characterisation sequence was fully automatised as a LabVIEW-based program (HexDAQ version 1. 5. 1  [16] ). Including voltage ramps and settling times, the IV(CV) measurements of low-density sensors in this work took about 1. 5 h(2. 5 h), whereby the duration is proportional to the number of voltage steps (10-15 here). The duration was about twice as long for high-density sensors due to the higher number of pads. In order to quantify the potential performance benefit via annealing, silicon sensors were warmed up to 60 • C inside the probe station. After a total of 80 min at this temperature 1, the sensor's depletion voltage is expected to be minimal  [17]  and the studied sensors were cooled again to -40 • C.
JINST 18 P08024 Federal Ministry of Education and Research (grant no. 13E18CHA). We acknowledge the enduring support provided by the following funding agencies and laboratories: BMBWF and FWF (Austria); CERN; CAS, MoST, and NSFC (China); MSES and CSF (Croatia); CEA, CNRS/IN2P3 and P2IO LabEx (ANR-10-LABX-0038) (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRT (Greece); DAE and DST (India); MES (Latvia); MOE and UM (Malaysia); MOS (Montenegro); PAEC (Pakistan); FCT (Portugal); MoST (Taipei); ThEP Center, IPST, STAR, and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); STFC (United Kingdom); and DOE (USA).
Acknowledgments We thank the staff at the  Rhode Island Nuclear Science Center  for their support during the preparation and execution of the neutron irradiation of the CE silicon pad sensor prototypes. The EP-DT and the former EP-LCD group at  CERN  have developed essential infrastructure, such as the ARRAY system including the associated data acquisition software, and have co-financed the acquisition of the utilised cold-chuck probe station at  CERN. Without their input, this R&D milestone towards the realisation of this novel calorimeter would not have been possible. We thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. This work has been sponsored by the  Wolfgang Gentner Programme  of the  German.
The high-resolution mass spectrometry (HRMS) method was applied to detect mainly fluorinated biphenyl-and bicyclohexyl-derivatives  [8]. In 2022, Su et al. established a database of 1173 LCs; these molecules were used to identify their presence in 33 sediment samples, using GC-QTOF/MS techniques  [9]. The above-mentioned considerations are strong motivations to investigate the possibility of closed-loop remediation processes for LC molecules. Recycling LCs is a challenging task because LCD screens, depending on their dimension, type, year of production, etc. , contain a mixture of a large number of different LC molecules. It should be mentioned that, to protect their production secrets and to stand out from the competition, LCD panel manufacturers add a whole series of additives and use different LCs blends for each of their technologies.
Additionally, the laser spot was not fixed at one position, but random walk was employed with a typical border width of 1000 µm. For ionization, the third harmonic of a neodymium-YAG laser (355 nm) (provided by Bruker Daltonics GmbH & Co. KG, Bremen, Germany) was used. The settings were optimized as follows: "35% laser power", "small spot size" (corresponding to 80-100 µm), 100 pulses per laser shot, and a frequency of 200 Hz. Data visualization was performed with Bruker Compass DataAnalysis 5. 0 SR1 software, and molecular formula assignments were performed with Small Molecule Accurate Recognition Technology (SMART). Inorganic impurities (ions) present in the non-purified and purified LC mixtures were identified by the inductively coupled plasma atomic emission. The samples were first mixed with aqua regia (HNO 3 + 3 HCl) to conduct a liquid-liquid extraction.
GC-MS and HRMS Analysis GC-MS analysis was performed to separate the recycled LC mixtures and identify their components. Figure  4  shows the chromatograms obtained for non-purified and purified LC mixtures. 1. The chromatograms collected for the three non-purified LC mixtures, as well as for the three purified LC mixtures, were found to be almost identical to each other. The nonpurified mixtures show a low-intensity peak at ~35. 3 min that hardly appeared in the purified mixtures. The chemical structures of some molecules corresponding to the most intense peaks in the chromatograms were estimated by exploitation of the mass spectrometry results, applying an MS reference database. MS data obtained by GC-MS analysis represent integer masses. In general, there are many chemical structures with different compositions, even if the integer mass is the same.
Therefore, in order to obtain integer mass peaks, high-resolution MS (HRMS) measurements were performed on a purified LC mixture, using the FT-ICR-MS technique. The HRMS results, in terms of obtained and calculated exact masses of putative structures, were reported (see Table  1 ). A good match between these data were obtained, confirming thus the presence of these molecules in the LC mixture. 1. The chromatograms collected for the three non-purified LC mixtures, as well as for the three purified LC mixtures, were found to be almost identical to each other. The non-purified mixtures show a low-intensity peak at ~35. 3 min that hardly appeared in the purified mixtures. The chemical structures of some molecules corresponding to the most intense peaks in the chromatograms were estimated by exploitation of the mass spectrometry results, applying an MS reference database.
MS data obtained by GC-MS analysis represent integer masses. In general, there are many chemical structures with different compositions, even if the integer mass is the same. Therefore, in order to obtain integer mass peaks, high-resolution MS (HRMS) measurements were performed on a purified LC mixture, using the FT-ICR-MS technique. The HRMS results, in terms of obtained and calculated exact masses of putative structures, were reported (see Table  1 ). A good match between these data were obtained, confirming thus the presence of these molecules in the LC mixture. According to Table  1 , nearly all identified molecules show the typical chemical structure of LCs, as can be revealed by the simultaneous presence of rigid (aromatic rings,. ) and flexible (aliphatic chains,. ) parts covalently bound in the same compound.
Acknowledgments: The authors acknowledge financial support from the  Région Hauts-de-France (FEDER) , the  I-Site of Lille , the  Chevreul Institute  ( FR 2638 ), the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation , the  ENVIE 2 E du Nord company ,  MDPI , and the  University of Lille.
Characterisation of the products Infrared (IR) spectroscopy. The infrared spectra of the products were recorded using a Perkin Elmer Spectrum One infrared spectrophotometer coupled with a Universal ATR (Attenuated Total Reflectance) Sampling Accessory (crystal: Diamond/ ZnSe) which allowed easy recording of the traces for the dark polymer powders. Thermogravimetric analysis (TGA). Polymer powders were placed on a platinum crucible on a Mettler M3 balance. A Mettler TC 10A low thermal resistance furnace was used to heat the samples at 10 and 20°C/min from 50 to 650°C under air atmosphere. The oven temperature was monitored by a thermocouple located very close to the sample boat. The raw data were processed using the Mettler graphware TA72PS2 software package, exported from Qnix to MS-DOS, and re-plotted using Excel.
Electrical conductivity measurements. Sample conductivities were determined using the van der Pauw four-probe method. Discs (13 mm ˘) of compressed dry powders were prepared using standard 13 mm dies (90 kg. mm À2 pressure, under vacuum). Their thicknesses were measured accurately using a digital calliper (average of six readings). The set-up comprised a Keithley 617 programmable electrometer and a Keithley 224 current source under computer control. Scanning electron microscopy. The products were placed on aluminium stubs, using double-sided adhesive tape, and goldcoated using a Polaron E5175 gold sputter coater and an Edwards double-stage high vacuum pump. All particle size analyses were performed on a JEOL-6310 scanning electron microscope (SEM), operated at 15 kV accelerating voltage, controlled by the ISIS 200 software.
4) then aggregated Mon:Ox:HCl = 1:0. 7:1. 5 B2000-0086 PANI-HEC (3%wt) 0°C deprotonated NaoH (pH10. 4) then aggregated Mon:Ox:HCl = 1:0. 7:1. 5 X-ray diffractometry (XRD). X-ray diffraction studies of PANI powders were performed on a Bruker-AXS D8 Advance diffractometer (Scan speed 0. 02 degree. s À1 ). The XRD patterns were obtained with CuKα radiation (0. 154 nm), with the Cu anode run at 40 kVand 30 mA, and using a nickel filter. The system was controlled via Bruker XRD Commander software, and the diffractograms were exported to Excel spreadsheets and processed. Electrorheological testing. 10 and 15 wt. % suspensions of ground (pestle and mortar) powders in dry silicone oil (polydimethylsiloxane, Sigma-Aldrich) were prepared and tested using a computer-controlled Bohlin Visco88 viscometer operating in the geometry of two concentric cylinders using a Searle-type sensor system (with rotor shear rates ranging from 14 to 1100 s À1 ) and connected to a high-voltage dc power supply (from 0 to 3.