dataesr/scientific-paragraphs-categorization huggingface dataset:
These included hydrated Zn(II), obtained by measuring the XAS spectrum of a 50 mM aqueous solution of Zn(NO 3 ) 2 , as well as the pure Znexchanged zeolite component (Z-3 E), measured after activation at 420 °C in O 2 , as described in more detail in our previous studies  [21, 52]. Moreover, to assess the local properties of Zn ions in the sole binder component, we characterized the two Znimpregnated alumina samples (B-1Zn and B-10Zn) by XAS. XAS spectra for the latter materials were collected at RT in air, using the same data collection strategies and parameters described above for the investigated zeolite/alumina systems. We employed these reference spectra also for a linear combination fit (LCF) analysis of the XANES spectra collected for asprepared and activated ZB-type catalysts. LCF analysis was performed in the energy interval 9643-9713 eV, using Athena  [51].
Method Analysis and Discussion In this section, we analyze the contribution of the radiometric constraint to the final fused product, study the robustness of the proposed method to noise and aliasing, and discuss on the parameter selection. For these experiments, we use Bookshelves from Harvard dataset  [3]  acquired by an HS camera that captures 31 spectral bands and Washington DC which is a remote sensing data  [53]  with 93 spectral bands. Since the ground truth images are available, we evaluate the results in terms of the root mean squared error (RMSE), which accounts for spatial distortions, and the spectral angle mapper (SAM), which measures the spectral quality. The reference images are denoised with a routine provided by Naoto Yokoya (https:// openremotesensing. net/knowledgebase/hyperspectral-and-multispectral-data-fusion/, accessed on 7 March 2021)  [6]  before data generation.
Parameter Selection The convolution, downsampling and spectral operators used in the energy (2) are the same as those considered for HS and MS data simulation. In our case, we only considered Gaussian convolution kernels since we deal with the aliasing introduced by the relationship between the optics and the sensor size. For the NL weights in (8), we restrict the nonlocal interactions to a search window of radius ν nl = 7, while the radius of the 2D patches considered on each MS band is ν p = 1. The filtering parameters are set to h spt = 2. 5 and h sim = 10. The trade-off parameters in the variational formulation (2) have been optimized in terms of the lowest RMSE on Bookshelves for Harvard dataset  [3]  and on Washington DC  [53]  for remote sensing data. All experiments have been performed using the same set of parameters.
We refer to  [6, 12, 21]  for more details on these metrics. We recall that, the reference images are denoised with a routine provided by Naoto Yokoya  [6]  before data generation. We simulate the MS and HS data according to the image formation models given in  (1). Therefore, the low-resolution HS images are generated by Gaussian convolution of standard deviation σ blur = 2 followed by downsampling of factor l = 4. The high-resolution MS images are obtained by considering the spectral degradation operator to be the Nikon D700 spectral response on Harvard dataset and the Ikonos spectral response on remote sensing data (see Figure  2 ). We add white Gaussian noise to HS and MS images in order to have a SNR of 35 dB. From these noisy images, we generate P h , Ph and gh required in our model following the procedure described in Section 3.
Performance Evaluation on Data Acquired by HS Cameras We test the performance of our method on ten images from Harvard dataset  [3] , representing different indoor and outdoor scenes. These data were acquired by an HS commercial camera (Nuance FX, CRI Inc. , Santa Maria, CA, USA)) that captures 31 narrow spectral bands with wavelengths ranging from 420 nm to 720 nm with a step of 10 nm. Each image has a resolution of 1392 × 1040 pixels, but we cropped them to 512 × 512 pixels. The reference images are displayed in Figure  6 , where 5th, 10th and 25th bands are used to account for the blue, green and red channels. Since Nikon D700 spectral response is used, MS images with three spectral bands are generated. Table  1  shows the average of the quality measures over all images of the Harvard dataset (Figure  6 ) after removing boundaries of 5 pixels.
This could be explained by the fact that the CNN-FUS network was not trained on images provided by hyperspectral cameras with a spatial resolution as high as Harvard dataset's. This shows that deep learning based methods are constrained by the type of data the network was trained on, which limits the performance of the network on unseen data. Nonetheless, our method is more flexible and can be easily applied on any type of images. Regarding the other methods, SFIM-HS, GLP-HS, MAPMM and FUSE perform poorly especially in terms of RMSE and ERGAS. HySure and GSA-HS show good performances in terms of RMSE but high degrees of spectral distortion as shown by the DD index. CNMF and HMWB show competitive performances in terms of SAM with low values in Q2 n. Chikusei Urban Washington DC Figure  9  shows a visual comparison of the fused images for Chikusei.
45 RMSE = 26 30 RMSEFigure 5. 4526305 Figure 5. Illustration of the robustness of the proposed method to noise. The experiments were carried out on Bookshelves. Gaussian noise was added to MS and HS data to have SNR ∈ {30, 45} dB. We display the 5th, 10th and 25th spectral bands of the HS images to account for the blue, green and red channels. RMSE values are given in magnitude of 10 -9. While the interpolated images are affected by the decrease of the SNR, we achieve similar visual and numerical results in both scenarios.
As a result, the diffusion spot could be fitted by equation (6), where the concentration [O 2 ] in each pixel x p , y p is estimated as [O 2 ](x,y)=Aexp((-(x p -X) 2 +(y p -Y) 2 )/B+C) (6) Parameters were found by fitting the equation with the raw image using the fminsearch solver (Matlab R2017a). Note that A and B are parameters, and (X, Y) is the centre of the spot. The parameter C, representing the background, was removed for further calculations. Once the image had been processed, it was possible to calculate the flux transferred by the bubble by using equation (  7 ) F O2 =U b ×∫∫ δDspot [O 2 ](x,y)dxdy (7) with x and y horizontal coordinates. Then, the mass transfer coefficient could be calculated by equation (8) as long as the surface area of the bubble was deduced from the equivalent diameter.
The plot represents the experimental chromatographic peak area vs. the targeted concentration. The red symbols correspond to the data obtained with the dilution bench while the black ones were obtained with the experimental generator equipped with a 4-stage micromixer. The flow rate was fixed at: Q = 25 NmL min -1. P pol varied from 100 to 2000 mbar and t carrier varied from 1 to 9 s. For each series of measurements, areas of the peaks obtained under conditions for the generation of variable BTEX mixtures were corrected by subtracting the average value of the peak obtained with pure nitrogen for 1 h after stabilization. This theoretically allows us to obtain a straight line passing through the origin when plotting the variations of areas as a function of pollutant concentration. These concentrations were recalculated from the exact experimental pressure data recorded using the pressure sensor.
Calibration and Reproducibility Experiments Figure  7  groups together all the results obtained from the different series of measurements made with two different BTEX cylinders at 100 or 1000 ppb respectively combined with the dilution bench or the new generation source equipped with the micromixer. The peak area of total BTEX increased perfectly linearly with the concentration of BTEX in the range 3. 7-100 ppb, the linear regression of the dataset allowed to obtain a straight line with a coefficient of determination R 2 = 0. 993. In addition, the data obtained with the new generation source agreed perfectly with those obtained by the reference method, namely the dilution bench. This confirms the ability of the new microfluidic gas mixture generator to produce a reliable linear calibration plot in a concentration range of 5 to 100 ppb, with initial standard BTEX gas mixtures of different concentrations.
5 NmL min -1 , only half of the 208 µL cell was purged. As a result, only half of the BTEX contained in the injection cell was flushed during the purge step, theoretically dividing the final concentration by 2. The small difference between the signal obtained at 25 and 50 NmL min -1 in Figure  S4  is probably due again to the poor management of the cell pressure (P cell ). Indeed, the generation of an identical concentration at a flow rate of 25 NmL min -1 and at a flow rate of 50 NmL min -1 was obviously done by doubling the P cell value. However, a different cell pressure implies that the pressure regulator should be capable of managing the exact value of the pressure downstream. The pressure regulation problems are illustrated in Figure  S6. Here, two series of data are recorded using the cell pressure sensor during the generation of 50 ppb of BTEX with P pol = 200 mbar, t carrier = 1 s and a flow rate of 25 NmL min -1.
The brightness temperature map is quite uniform in case of a diffuse surface, whereas the specular case reproduces a specific light pattern as experimentally observed. To analyze in detail IR image, first considering the main and well-known heat loads used as input of photonic simulation: there are (i) the heat loads on lower divertor and antenna outboard coming from conducted particles along magnetic field lines and modeled by PFCFLUX code  [9] , (ii) the hot upper pipes exposed to fast electrons escaping from plasma  [16]  and (iii) glow electrodes not actively cooled. It is worth noting that a complex heat load pattern on the inner lower divertor is observed in the experimental image: its origin is under investigation (splitting inner-strike-point divertor, emissivity change along the poloidal direction, or both).
Finally, achieving automatized and robust IR quantitative measurements for ITER remains a major challenge and experiments in current devices such as AUG and WEST are needed to test, improve and validate the methods. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 and 2019-2020 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission and ITER Organization. CRediT authorship contribution statement M-H. Aumeunier: Conceptualization, Methodology, Formal analysis, Investigation, Writing -original draft, Writing -review & editing. J. Gerardin: Formal analysis, Investigation, Writing -review & editing. C. Talatizi: Formal analysis.
A sudden increase in plastic strain rate causes a flow stress jump, whose magnitude is proportional to 1 -. The present elastic-viscoplastic model has been implemented in the finite element software ABAQUS/Explicit. For this purpose, a user material subroutine (VUMAT) has been developed. The numerical integration of the constitutive equations is based on the radial return algorithm, see for e. g. (Simo and Hughes, 1998). 5 ), (  8 ). Other parameters are given in ̅ = 1000 s ̅ → ̅ |. = 0 ̅ → ̅ |. = 0. 5 ̅ → ̅ |. = 0. 75 ̅ → ̅ |. = 1 ̅ = 0. 001 s Effective plastic strain, N % Flow stress, O (Mpa).
Literature Review Literature review concerning sustainability of NTPs has been performed by analyzing manuscripts refer in scientific databases, mainly Web of Science, ScienceDirect and Google Scholar, revealing articles (research papers and reviews), targeting environmental/social impacts of NTPs and their relation with Industry 4. 0 technologies, LCA studies focused on a specific food NTPs, and/or analysis of specific environmental impacts associated with food NTPs such as energy, water, climate change). The selection criterion was that food related NTP articles cover any environmental/sustainability dimension within the Industry 4. 0 perspective. However, when keywords related to Food industry 4. 0, sustainability and non-thermal food processing technologies were added to searching engines, two types of manuscripts were found: (i) papers connecting NTP and Industry 4.
With this technology, customized products with complex geometric shapes can be designed and printed using various edible ingredients  [39]. The process of final product formation is presented in Figure  2. Firstly, the twodimensional (2D) model is designed in a CAD program. From 2D model, a 3D model can be created that represents the final shape of the product. Although various complex models can be created, the care should be taken with the thin and high models that can be which disintegrate during printing because they do not have a support from the bottom layers. Secondly, the created 3D model is transferred to the special computer software that cuts the model into horizontal slices (layers), such as Slic3r. Sliced model presents each layer that will be printed by a 3D printer. The software translates the model to the readable file format (i.
e. , G-code) for the printer. Finally, the model is sent to the printer where layer-by-layer, a material is added forming a final product. This product can be ready-to-eat or it can be additionally processed by heating or other processing methods in order to get a desired food product. The process of final product formation is presented in Figure  2. Firstly, the two-dimensional (2D) model is designed in a CAD program. From 2D model, a 3D model can be created that represents the final shape of the product. Although various complex models can be created, the care should be taken with the thin and high models that can be which disintegrate during printing because they do not have a support from the bottom layers. Secondly, the created 3D model is transferred to the special computer software that cuts the model into horizontal slices (layers), such as Slic3r.
ii. Microstructural characterization by SEM and AFM: Surface morphology of the films was determined by scanning electron microscopy (SEM) for thin films deposited on silicon wafers, which were diced for cross section imaging. This analysis was carried out in a Hitachi S4800 field emission microscope. Atomic force microscopy (AFM) measurements have been performed on a Park XE-150 microscope in order to characterise the column size at the top surface of the samples. After the binarization of the AFM images, average column sizes were determined using the WSxM software [  [33] ].
Acknowledgments: Work funded in part through the  Spanish Ministry of Science, Education and Universities  through grant:  PGC2018-096855-B-C41. Authors also thank the  FEDER program  through  AEI-MICINN  ( PID2019-110430GB-C21 ) and  Junta de Andalucía  ( PAIDI-2020  through project  P18-RT-3480 ) for financial support.
This gives τ D ≈ 0. 28 µs and thus 76 µs to reach the steady state or 15 pulses in our conditions (200 kHz). This is indicative that we have a weak temporal overlapping between pulses, and that we are close to the so-called "single pulse regime" discussed in the Introduction. Moreover, the thermal diffusivity D T = κ/ ρC p , where κ is the thermal conductivity, ρ the density, and C p the heat capacity, should not be drastically different when moving from LNS to LNSB. Indeed, the partial substitution of 21 mol% SiO 2 with B 2 O 3 into the glass system is not expected to change the thermal diffusivity by much (see for instance Table  8. 20 in Ref. [26] ). Additionally, the ρ and C p values, extracted from Sciglass software, are ρ(LNS, 20 • C) = 3482 kg/m 3 , C p (LNS, 20 • C) = 629. 2 J/(kg•K), ρ(LNSB, 20 • C) = 3535 kg/m 3 , and C p (LNSB, 20 • C) = 657.
56 and T gr (LNSB) ≈ 0. 52. Additionally, and for completeness, we observe that the progressive addition of B 2 O 3 shifts down the crystallization peak to lower temperatures: by about 150 B2O3 into the glass system is not expected to change the thermal diffusivity by much (see for instance Table  8. 20 in Ref. [26] ). Additionally, the ρ and C values, extracted from Sciglass software, are ρ (LNS, 20 °C) = 3482 kg/m 3 , C (LNS, 20 °C) = 629. 2 J/(kg•K), ρ(LNSB, 20 °C) = 3535 kg/m 3 , and C (LNSB, 20 °C) = 657. 8 J/(kg•K). The ρC product differs only by 6% between the two glasses. Consequently, the spatial temperature distribution profiles, for both LNS and LNSB glasses, are very similar for a given set of conditions. This is in good agreement with our experimental results where the HAV widths appear not to vary much with the chemical composition (Figure  4 ).
Large-scale land-cover classification using a supervised algorithm is a challenging task. Enormous efforts have been made to manually process and check the production of national landcover maps. This has led to complex pre-and post-processing and even the production of inaccurate mapping products from large-scale remote sensing images. Inspired by the recent success of deep learning techniques, in this study we provided a feasible automatic solution for improving the quality of national land-cover maps. However, the application of deep learning to national land-cover mapping remains limited because only small-scale noisy labels are available. To this end, a mutual transfer network MTNet was developed. MTNet is capable of learning better feature representations by mutually transferring pre-trained models from time-series of data and fine-tuning current data.
To solve these problems, pooling layers were recombined to improve the efficiency of information transmission in the network, and hierarchical sampling strategies were proposed to automatically construct training datasets  [38]. The rotation equivalence was encoded in a CNN architecture to maintain the rotation invariance  [39]. Most traditional classifiers utilize image features with clear physical meanings. These features are easy to understand and can provide stable information for classifiers. On the contrary, the feature maps in CNNs are learned from the training samples automatically, which makes it incomprehensible. To make full use of the difference in their features, a decision-based classifier that was able to combine the features from RF, SVM and CNNs was designed  [40]. As well as the features learned by traditional classifiers and CNNs, the feature maps learned by CNNs were also different  [41].
Data Sources For large-scale land cover mapping, data availability and corresponding observation ability are the main factors affecting the selection of data sources. Although full-wave LiDAR contain back scattering information about the detected object, it is still difficult to recognize the detected classes. In addition, LiDAR implies the highest cost among commonly used remote sensing datasets  [56, 57]. SAR is a cost-effective alternative to LiDAR and is unaffected by clouds. However, the back scattering and interferometric coherence in special areas are serious, leading to unclear structure representation  [58]. Overall, optical images are the most appropriate even though it is easily affected by clouds. As for the observation ability, low-resolution images cannot reflect the detailed information about the detected objects, which will affect the distinguishing ability on different classes, such as vegetation type.
As for the reference, the data downloaded from http://www. geodata. cn (accessed on 10 April 2018) were stored in provinces. It can be directly aligned with the mosaicked Landsat images. However, to fully utilize the label information, its classification system should be modified to adapt the representation ability of Landsat images. Referencing existing classification systems employed in  [60]  and other commonly accepted land-cover maps, the 38 second-level classes were merged into 8 first-level classes, as shown in Table  1. Then, the merged land cover map was considered as a reference. Glacier/permanent snow cover.
To fully utilize the consistency and complementarity of time series images, a mutual transfer network called MTNet was proposed in this study. First, the DL-based network was trained by the time series training set introduced in Section 3. As the training samples in the time series are similar, the purpose of this step in the training process was to learn the essential features of the training samples: the more details that can be learned, the better. Then, the trained network was used as an initialization on the current training set. Benefiting from the consistency of Landsat images, the fine-tuned process on current data cannot only make full use of the learned essential information but can also adapt to the distribution of the current classes. In addition, the initially trained MTNet can be considered as a regularization on the time series training set to resist the impact of noisy labels and thus improve the generality of the network.
Accordingly, models trained by the 2010 training set had higher accuracies than those trained by the 2005 training set. This phenomenon was not found in Hubei and Qinghai. This demonstrates that models trained using high-quality training samples are more general than models trained using poorer-quality training samples. (5) For the same study area in the same year, PSPNet-RL outperformed PSPNet, and PSPNet outperformed PSPNet-TF, while all the classification accuracies of the MTNet were higher than the others. The MTNet performed well even when the training samples were not balanced and noisy labels were included. The use of interactive training samples from time-series data significantly improved the performance of the networks. This demonstrates the efficiency of the mutual training strategy used in the MTNet.
This work allowed us to highlight the influence of the stacking sequence on the repair performance. In the second part of experimental works, the mechanical parameters of a healthy belt, textile carcass and cover rubber layer are determined in order to have material data of the bidirectional hygro-thermomechanical model. The stress distribution in a 2D plane is analyzed for the parent belt, inner textile patch and rubber cover patch of the repaired structure. The correlation between the analytical and experimental results allows us to define a strength tool to understand the load transfer from rubber to a textile rubber patch by using the bidirectional stress distribution in a bonded patch repair of the conveyor belt. Finally, a dimensionless design is proposed by means of a parametric study that consists of the determination of the influence of individual parameters on the repair performance.
Combining our STM and LEED data (Figure  3 ) we constructed a rhombic unit cell (black rhombus in Figure  3a  and d ) with dimensions a = b = 32 � 0. 2 Å, and an internal angle of α = 60°� 2°. This network has a molecular density of 0. 34 Ni-DPPyP molecules per nm  2. In addition, a rotation of 15°between the unit cell axis and the principal Au directions was observed. The porous network is incommensurate with the underlying Au substrate, i. e. , the absence of integer matrix elements in the superstructure matrix of the simulated LEED pattern (Figure  3d ) supports this affirmation. Furthermore, the two rotational domains of the network are shown in gray and green in the simulated LEED pattern (Figure  3d ). ] [39] [40]  Therefore, in addition to our STM and LEED data, we measured XP spectra for the C 1s, N 1s and Ni 2p 3/2 core levels to check both the chemical composition and possible changes of the electronic structure of Ni-DPPyP on Au(111) before and after coordination with Coatoms.