dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of Diffuse Transmission Spectroscopy data To clarify whether surface and bulk bubbles follow the same average growth law, we measure the average bubble radius in the bulk of the foam, in parallel with the surface observations, for all the investigated liquid fractions. Using the ISS set-up, the diffuse intensity transmitted through the foam sample is measured during the course of coarsening (cf. section 2. 2). This allows the determination of the evolution of the photon transport mean free path ℓ * (cf. section 5), from which we deduce the average bulk radius along the coarsening, for any given liquid fraction. In the ISS set-up, the foam sample is contained in a cell of rectangular cuboid shape with thickness L = 11. 3 mm, and lateral dimensions a = 14. 1 mm and b = 9. 0 mm, delimited by transparent walls on three sides and an opaque diffusing wall at the bottom due to the surface of the piston used for generating the foam.
Conclusion Extracting the bubble size distribution from images of a foam surface is difficult and full of pitfalls. We have used three different procedures: manual analysis, automatic analysis with a customized Python script and machine learning analysis. Remarkably, once the various pitfalls were identified as described in the paper, all the three procedures yielded identical results within error bars. Additional ground experiments were performed to compare bubble size distributions at the surface and in the bulk of wet foams right after their production. The size distributions were found to be, to a good approximation, the same. To investigate whether the coarsening dynamics of the foam structure in the bulk is the same as at the sample surface, we have analyzed the transmission of diffuse light through the sample in microgravity.
Acknowledgements We acknowledge funding by  ESA  and  CNES  (via the projects " Hydrodynamics of Wet Foams "), as well as  NASA  via grant number  80NSSC21K0898. Marina Pasquet  and  Nicolo Galvani  benefited from CNES PhD grants. The authors are grateful to the  BUSOC team  for their invaluable help during the ISS experiments. We also want to warmly thank  Marco Braibanti  from  ESA  and  Olaf Schoele-Schulz  from  Airbus  for their continuing support.
The normal modes of vibration and their frequency were computed with the Gaussian package at the DFT level using the M062X functional curing for self-interaction issues  [52]  and a 6-31G(d,p) basis set (Figure  8 ). As the alkyl chains have no impact on the reorganization energies, they were replaced by hydrogen atoms. The normal mode contributions to relaxation energies were computed with the MOMAP package  [53]  that follows a previously reported methodology. [54] he DFT calculations yield the following total reorganization energies:  (322 meV) and [5]C-Bu-Cbz (266 meV). The results globally show that the number of carbazole units slightly increase the reorganization energy and conserve the dependency on the nanohoop size compared to the corresponding CPP analogues. Thus, as the size of the nanohoop increases, the reorganization energy decreases, as previously shown for CPPs by Houk, Yavuz and co-workers.
It is evident that other families of nanohoops and other nanohoop sizes should be investigated to go deeper into the establishment of design rules. Such studies are mandatory to reach high-performance devices based on nanohoops. In the light of recent works that have shown their potential in phosphorescent OLEDs,  [23]  nanohoops can become a new class of promising organic semiconductors. Deposition Numbers 871414 (for [8]CPP) and 871415 (for [10]CPP) contain the supplementary crystallographic data for this paper. These data are provided free of charge by the joint Cambridge Crystallographic Data Centre and Fachinformationszentrum Karlsruhe Access Structures service.
Acknowledgements This project received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 699648  (FRODO). We thank  GENCI  (no. AD0100805032R1 ) for computing time, the  ANR  (no. 19-CE05-0024  and  ANR-22-CE07-0041 ) for funding and for a postdoctoral fellowship (F. L. ), the  Region Bretagne  ( DIADEM  project) and the  ADEME  ( ECOELEC  project) for PhD grants (F. L. and C. B. , respectively),  Nanorennes (Rennes)  for access to clean-room facilities and  CRMPO (Rennes)  for mass analyses and variable temperatures NMR studies. We also thank  Dr J. F. Bergamini  for designing the graphical abstract. The work in Mons was supported by the  Marie Curie ITN  project  UHMob  ( GA-811284 ) and the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F.
5-100 N), strain sweep (0. 01-100%) and frequency sweep (0. 1-100 Hz). A thin smear of silicone oil was used to cover the edge of the samples to prevent evaporation. The samples were then heated from 35 to 90 °C at 0. 05 °C/s and held for 720 s at 90 °C and cooled from 90 to 25 °C (at about 0. 35 °C/s). Computation of results was done with Rheoplus/32 software version 2. 65 (Anton Paar GmbH). The dynamic oscillation parameters evaluated were: G', G 00 , and tan⊐. The analyses were done in triplicate. The G 0 max represents the maximum G' during the heating cycle while the G 0 min represents the minimum G 0. SC represents the swelling capacity of the starches, TS is time taken for the starch granule to swell, TP is time taken by the starch gel to rupture, and T gel is the temperature at which gelatinization took place.
ACKNOWLEDGEMENTS This work was supported by  Agropolis foundation, France and African Women in Agriculture Research and Development (AWARD). Dr Dominique Dufour  is also acknowledged for facilitating the study to be conducted at  CIRAD ,  Montpelier France. The authors are grateful to grant opportunity  INV-008567  (formerly OPP1178942):  Breeding RTB Products for End User Preferences (RTBfoods) , to the  French Agricultural Research Center for International Development (CIRAD) , Montpellier, France by the  Bill & Melinda Gates Foundation 363 (BMGF) : https://rtbfoods. cirad. The authors acknowledge the review and editorial comments of  Hernán Ceballos  and proofreading of the manuscript by  Clair Hershey  to improve the quality.
CO2 Solubility The values of the solubility of CO2 in the DES (1 ChCl: 3 glycerol) with different content of water (30 wt%, 40 wt% and 50 wt%) were measured at temperatures (293. 15-373. 15) K at 10 or 20 K intervals and pressures (0. 5-3) MPa. The experimental data are given in Figure  3 , Table  1. and Table  2. Within the temperature and pressure ranges investigated in this work, the solubility of CO2 increase with increasing water content. The solubility of CO2 is 0. 008 when the percentage of water is 30% and increases to 0. 0081 when the percentage of water is 50%. With increasing temperature, the CO2 solubility decreases but the influence is low. For example, at 1. 5 MPa and 50 wt% the solubilities are 0. 0081, 0. 0078 and 0. 0076 when the temperatures are 293. 15K, 303. 15K and 313.
In both crosses, we detect additional resistance states due to the different magnetic anisotropies of the two zones P and Q, denoted as "", "#, #", and ## [Fig. 2(b) ]. These states have unique R AHE values and are accessible by cycling the applied current pulses. We found that measured Hall resistance values were stable within 1% over many hours. The resulting states are nonvolatile, holding their resistance values for at least two days. The switching currents of the irradiated areas are also reproducible within a few percent when repeating the current sweeps. Magnetooptical Kerr effect (MOKE) microscopy confirms the correspondence between the Hall resistance data and the switching of each irradiated area, allowing us to visualize the resulting magnetic configurations (Fig. 2(c) ). The boundary of non-irradiated and irradiated zones is known to pin propagating domain walls during magnetic field driven reversal  25, 26  and is found to play such a role in the SOT induced switching by Kerr microscopy visualization (not shown here).
We thank  Fabien Chevrier  and the staff of the  STnano Nanofabrication Facility  for daily support. Support from the  Fondation Jean- Marie Lehn  is gratefully acknowledged. This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie Grant  Agreement No. MaMi No. 766007 and QUSTEC No. 847471 , the  Interdisciplinary Thematic Institute QMat , as part of the  ITI 2021 2028 Program  of the  University of Strasbourg ,  CNRS  and  Inserm , by  IdEx Unistra  (No. ANR 10 IDEX 0002 ), by the  SFRI STRAT ' US  Project (No. ANR 20 SFRI 0012 ), and  EUR QMAT  under the framework of the  French Investments for the Future Program  via No. ANR-17-EURE-0024. This publication is also based upon work and has received financial support from  COST (European Cooperation in Science and Technology) Action   FIT4NANO CA19140.
warmly thank  David Huse  for very stimulating discussions on the subject. The work of J. L. L. was supported in part by the A. F. O. S. R. He thanks the  Institute for Advanced Studies  for its hospitality. T. K. acknowledges the support of the  NCN  grant  2020/37/B/ST1/00426. S. O. has been partially supported by the  ANR-15-CE40-0020-01  grant LSD.
The advent of Industry 4. 0 and propelled the application of Artificial Intelligence in different industrial fields and contexts, such as predictive maintenance (PdM). Through its ability to assess the condition of equipment to detect signs of failure and anticipate them, PdM brings several potential benefits in terms of reliability, safety and maintenance costs among many other benefits. Different approaches are proposed in the literature. They are based on data, physic models or knowledge but several problems and limits persist, in particular, to override this dependence on a particular context, to utilize data and business knowledge considering the challenges of applying existing solutions to another context, difficulties associated with data analysis, and uncertainty management. In this context, the goal of this paper is also to highlight the challenges faced in the area of PdM, both for implementation and use-case.
Semi-Supervised Learning-based approaches A semi-supervised PdM would involve using a small amount of labeled data to train a model to predict the failure, and then using this model to make predictions on the rest of the data  [3]. This can be useful when there is a limited amount of labeled data available. Several different techniques can be used for semi-supervised PdM, including using a combination of labeled and unlabeled data to train a model, one-class classification to learn a model of normal equipment behavior, or using density-based anomaly detection to identify deviations from normal behavior as potential indicators of equipment failure.
The Decision Tree approach constructs regression or classification techniques in a tree structure  [1]. Deep learning (DL) is defined as a subset of ML that has networks capable of supervised learning from data that are unstructured. However, the demands of advanced prediction make it impossible for the traditional data-driven methods to handle the data complexity and growth. DL-based models have recently received great attraction as they offer several benefits such as better performance of RUL prognostics, i. e. , high prognostics accuracy and automatic feature extraction. In the context of PdM, the convolutional neural network (CNN) is predominately used for the acquisition of high-level spatial features from sensor signal data. Moreover, Long Short-Term Memory (LSTM) neural networks are specifically used for extracting sensor temporal information  [22]   [20].
Conclusion This paper presents the most recent reviews found in the literature and related works on predictive maintenance. A classification and a comparaison of the existing approaches is also proposed. Predictive maintenace stay an open domain of research. Many challenges were dressed in different surveys and reviews  [8, 16]. One of the common challenges is the lack of labeled failure data in the manufacturing industry, uncertainty management, the lack of a systematic approach to design and develop predictive maintenance systems, the extrapolation of existing solutions to complex system applications, including multiple components, and their associated faults, the fusion of large and different sources of condition monitoring data, the incorporation of external influence data, formalization and sharing of knowledge, In fact, three fundamental problems in the context of PdM are souligned in the litterature review : (1) PdM system architectures should be compatible with various industrial standards, be easy to integrate with the emerging of future techniques, satisfy the basic requirements of Pdm.
Measurements Site and Characteristics Data collection was carried out on the north coast of the state of Yucatan, Mexico. The surface of the territory is mainly flat and its elevation is 2 m above mean sea level. The climate of the site is warm semi-dry with rains in summer. Its average temperature is 26 °C with prevailing winds from the south-east  [32]. The geographical characteristics of the measurement site are presented in Table  1  and its location in Figure  1. An ultrasonic wind sensor (Gill windsonic anemometer) was used to measure the speed and direction of the wind, placed at 40 m in a mobile phone tower; see Figure  2. This sensor records the horizontal components of the wind vector to generate the scalar values of wind speed and wind direction. The technical parameters of the ultrasonic sensor used are presented in Table 2  [33].
The first variant for the SES method is given by: F 1 = y 1 F t = F t-1 + α m • (F t-1 -y t-1 ) (11) where α m = 1 N N ∑ k=1 α opt,i (12) where α opt,k is the optimal parameter of the day k and N is the number of days of the month (i. e. , N is equal to 28, 30, or 31 depending on the month). α opt,k has been calculated by using the command lsqcurvefit in the software MATLAB ®. As can be seen, this variant consists in obtaining the average of the optimal αs corresponding to a month. Then this average is implemented in the classical SES method. The second variant for the SES method is given by: F 1 = y 1 F t = F t-1 + α opt,k-1 • (F t-1 -y t-1 ) (13) In this variant, the classical SES method has been adapted by iterations of the optimal value of α opt,k-1 , in other words, it is the optimal parameter of the previous day k, the optimum value α opt,1 has been used for the first day of each month (k = 1).
Similarly, the variants of the DES method are similar to the variants of the SES method. The first variant of the DES method is given by: S 1 = y 1 B 1 = y 2 -y 1 F 1 = y 1 S t = β m • y t + (1 -β m ) • (S t-1 + B t-1 ) B t = γ m • (S t -S t-1 ) + (1 -γ m ) • B t-1 F t = S t-1 + B t-1 (14) where β m and γ m are similarly as α m β m = 1 N N ∑ k=1 β opt,i , γ m = 1 N N ∑ k=1 γ opt,i (15) where β opt,k and γ opt,k are the optimal parameter of the day k and they have been calculated by using the command lsqcurvefit in Matlab ®. Finally, the second variant of the DES method is given by: S 1 = y 1 B 1 = y 2 -y 1 F 1 = y 1 S t = β opt,k-1 • y t + (1 -β opt,k-1 ) • (S t-1 + B t-1 ) B t = γ opt,k-1 • (S t -S t-1 ) + (1 -γ opt,k-1 ) • B t-1 F t = S t-1 + B t-1 (16) As for Equation  (13) , the optimum values β opt,1 and γ opt,1 have been used for the first day of each month (k = 1).
Results and Discussion To apply the methods developed in Section 4, the optimal constants α opt , β opt , and γ opt for each day have been calculated using MATLAB ®. Figures  5  and 6  illustrate the optimal α opt behavior from the months January-June. Figures  7  and 8  illustrate the optimal β opt and γ opt calculated from the months July-September. Figures 9 and 10 illustrate the optimal β opt and γ opt behavior from the months October-December. As can be seen in Figures 5-7 and 9, the values of the optimal constants α opt and β opt in some cases are greater than 1, which contrasts the theory of the classical SES and DES methods, since the values α and β are assumed to be between 0 and 1  [19, 40]. Similarly, the value of the optimal constant γ opt in some cases is lower than 1, when it is speculated that the value oscillates between 0 and 1 according to the classic DES method  [40] , see Figures  8  and 10.
Microstructural analysis Phase composition after sintering was determined by XRD analysis (D8 Advance Serie II, Bruker, USA) with a Bragg-Brentano θ-2θ geometry, a Cu source and in incoming beam filtrated by a Ge monochromator to remove the Kα 2 contribution. Analysis was performed on the 5-110 • angle span with a 0. 02 • step and an analysis time of 4 h. Phase identification was done with DIFRAC. EVA software and PDF-4 database. Rietveld refinement was performed to access phase proportions with MAUD (Materials Analysis Using Diffraction) software  [22] , based on Rietveld and Le Bail methods. Background noise was approximated with a 5-degree polynomial. MAX phases being highly anisotropic materials, phenomenon intensified with sintering under pressure, it is necessary to take it into account during the refinement.
Mechanical behaviour characterisation Elastic constants (Young's modulus, shear modulus and indirectly Poisson's ratio) were determined at room temperature by contact ultrasonic pulse echography in infinite mode. Transducers of 10 MHz frequency (WC37-10 and SW37-10, Ultran, State College, USA) were used in reflection configuration on 5 mm thick parts of the dense discs. The setup of this technique and equations used to calculate elastic constants from the density and thickness of the sample are presented in other works  [24]. The scatter of data was estimated around 5% of the average value. Bars of 45 × 3. 5 × 3. 5 mm 3 were cut out of the 50 mm diameter dense discs and polished to ensure the absence of surface defects which would lead to premature failure. The final step of polishing was done with a cloth disc and a 6 μm-diamond suspension.
From the elastic domain of deformation, the Young's modulus can be determined. All Stress and maximum strength values are presented with a 5% scattering. Moreover, literature showed that this type of strains lead to onset of delamination and microcracks in MAX phases  [16, 19]. To study the microstructural evolution under stress, the bending tests were coupled with an acoustic emission (AE) study. Indeed, all these mechanisms (dislocations slip, plastic deformation of grains, grains boundaries sliding, and cracks formations and propagations) are dissipating energy in the form of ultrasonic waves propagating in the material  [20]. Two wide-band sensors (200-1000 kHz) were glued on the surface of the sample (PAC Pico S/N 2346, Mistras Group Inc, USA) and on the fixture (PAC Micro 80, Mistras Group) to record the data and subtract the parasite and background noise.
An external preamplifier of 40 dB (2/4/6 AST, Mistras Group) was used and the detection threshold of the signals was set to 40 dB. Data acquisition was done on the Express-8 System (Mistras Group). To ensure recording of each signal as one hit, time window parameters were determined by Hsu-Nielsen Pencil-Lead Breakage test  [25]  and set to PDT = 300 μs , HDT = 1000 μs and HLT = 1000 μs Data was analysed with the NOESIS software (Mistras Group). The shape characteristics of the recorded signals (energy-dissipated mechanisms), amplitude in dB, energy in atto joules, duration in ms or frequency in Hz, are strongly related to the type of damage mechanisms occurring within the material during the test at microstructural stage (Fig. 1 ). The coupling of these techniques (mechanical and AE tests) with post-mortem SEM observations was performed to correlate the loading behaviour to the microstructural changes and to study the chronology of these microstructural modifications.
In the first one, it was found to be 280 GPa which is consistent with the ultrasonic measurement. It shows afterward a decrease to 266 GPa (5%) and then to 244 GPa (10%). The overall decrease of rigidity was calculated to be of 13%, most probably related to a damage occurrence within the sample at microstructural scale during the test. Recorded AE data gives us information on the chronology of internal evolution (i. e. damage) (Fig. 2a ). The first signal was recorded for a strain level of 0. 09%, then a second one, of significantly higher intensity was detected over 0. 12%. Acoustic waves detection rate afterward increased, revealing a relative quick propagation of damage within the material up to its fracture. Indeed, 2/3 of the AE of SPS_Co_1 was recorded during the last portion of the test, for a stress over 500 MPa.
10% strain. Earlier damage of SPS_Co_2 was further studied with the correlation of AE data collected during the test. As said before, Young's moduli of these two samples were close. This is coherent with their very close composition. The difference on the proportion of Ti 2 AlC and Ti 3 AlC 2 phases, which have very close Young's moduli, is indeed not enough to lead to a noticeable variation of rigidity. These one-shot tests showed a higher stress to rupture for SPS_Co_1 (530-452 MPa for SPS_Co_2), as well as a higher strain (0. 20%-0. 17%). However, SPS_Co_2 showed a higher loss of rigidity during the test, showing an earlier and easier damaging, relevant with the first loss of rigidity of -11%. Indeed, approximately 57 acoustic signals were detected in the first zone of the stress-strain curve compared to 2 for SPS_Co_1.
It decreased to 240 GPa (-15%) at around 0. 10% of deformation without any acoustic activity detected. For a higher stress (600 MPa) and a 0. 23% deformation, the Young's modulus decreased again to 219 GPa (-9%, for a -22% loss overall). This global rigidity loss seems to be the sign of the damaging of the sample but curiously, it was barely detected, and belatedly, towards the end of the test. One single hit was detected at 0. 06% of deformation and the majority of acoustic activity happens shortly before the rupture of the sample. For SPS_Co_4, data is notably different. This sample showed much lower mechanical properties, in rigidity or resistance, with a maximum strength of 409 MPa and a 0. 19% deformation. The initial elastic modulus is of 226 GPa, which is slightly superior to the value obtained by ultrasonic measurement (216 GPa) although it stays consistent.
Conclusion This study furnishes the theoretical foundation for differential evolution indicators for ultrasonic imaging of elastic variations within a heterogeneous and discontinuous background of random structure. In this vein, the wellposedness conditions for the forward and inverse scattering problems are established; in light of which, the pairwise relation between scattering solutions -associated with distinct datasets, is determined. For this purpose, twelve scenarios for microstructural transitions are investigated including (a) fracturing of inclusions, (b) evolution of discontinuity surfaces within each material component or at bimaterial interfaces, (c) elastic transformation and/or expansion of fractured inclusions, (d) conversion of microcracked damage zones, and (e) expansion of pores.
Acknowledgements This work has been funded by the  Spanish Ministry of Science and Innovation  ( RTI2018-097957-B-C33 ,  RED2018-102768-T ,  PID2020-115353RA-I00 ); the  Spanish State Research Agency by ESF Investing  in your future under grant  PRE2021-096954  and  European Union -NextGenerationEU , through the  Recovery, Transformation and Resilience Plan  ( DIN2020-011488 ); the  Community of Madrid -FEDER  funds ( S2018/NMT-4326 ); the  European Union 's  Horizon Europe research and innovation program  under  Marie Sklodowska-Curie  grant No. 101062518.