dataesr/scientific-paragraphs-categorization huggingface dataset:
We adopt values for these parameters from  Senft and Gardner (1991) ;  Gardner et al. (1993) , i. e, α = 0. 62, N = 2π 3×10 2 s -1 , m * = 2π 1×10 4 m -1 , m b = 2π 5×10 2 m -1 , and s = 2. The amplitude of the angular frequency spectra is given by, B(ω) = p -1 f f ω p , (8) and the simulation assumes that the frequency spectrum is proportional to ω -p where p = 2 and, f = 2π 20 hour -1. In the case of the angular frequency spectrum, we used 282 different wave periods between 10 minute and 8 hours. In the wavenumber spectrum, 40 different vertical wavelengths were used between 1 km and 20 km. In both cases, angular frequencies and wavenumbers were sampled uniformly. Thus, the combined 2-D spectra become a superposition of more than 10000 (≈ 282 × 40) monochromatic gravity waves. Each meteor measurement in the SIMONe 2018 data gives information mainly about its time of occurrence, its position in latitude, longitude and altitude from the ground, Doppler frequency of the meteor, and Bragg vector (k).
and vw in the MLT region  (Fritts et al. , 2012). V h = A(m, ω)c A(m, ω) , (10) where V h = √ u 2 + v 2. Considering the dispersion relation for gravity waves with medium intrinsic frequencies, we have, K h = mω N , (11) where K h = √ k 2 + l 2 is the horizontal wavenumber, and N is the Brunt-Väisälä frequency. The gravity wave polarization relations under the medium frequency approximation was used to maintain the correlation between horizontal and vertical velocities  (Fritts, 2003; Nappo, 2012) , i. e. , w = V h K h m. (12) Figure  4a  shows the dependence of horizontal wavelengths with time-period and vertical wavelengths of the simulated waves. The wave propagation angle θ was used to determine the horizontal components of wavenumbers and velocities. u = V h sin(θ); v = V h cos(θ) k = K h sin(θ); l = K h cos(θ) (13) Equation 9 was applied to the SIMONe 2018 meteor data positions and time of occurrence to obtain the three components of the velocity.
Spectral characteristics of the SIMONe 2018 campaign data Here we present the spectral analysis of SIMONe 2018 using the WCFI method. In order to estimate the spectra, we allowed horizontal displacements of up to 50 km and temporal lag of 30 min between the meteor pairs within the total radar illuminated area. If one assumes the waves statistics to be the same over a relatively large area, then the 50-km lag can be obtained from different configurations, for example: (i) 0-50 km (ii) 50-100 km, and (iii) 100-150 km. In other words, in (i) 0-50 km, (ii) 50-100 km, and (iii) 100-150 km horizontal scale cases, we selected measurement pairs to have a spatial separation between 0 and 50 km, 50 and 100 km, and 100 and 150 km, respectively. Figure  5   the temporal displacements in Figure  5  demonstrates the highest number of measurement pairs in the 100-150 km horizontal scale case.
, 2015; Hildebrand et al. , 2017). This study proposed MWE and WCFI methods to estimate the power spectra, and they were implemented on a gravity wave spectral model-simulated data, and the SIMONe 2018 campaign data. The analysis of the simulated data suggests that the WCFI method is effective in reproducing the input spectra in comparison to the MWE method (Figure  4b, c ). The Usage of the measurement pairs instead of measurements by the WCFI method, allowed us to select smaller horizontal resolution while estimating the spectra. Hence in the simulation analyses, the WCFI with ∆s = 50 km provided a better estimate of the spectra. Here, the smaller horizontal resolution (50 km) helped us to capture the energy of GWs with horizontal wavelength larger than 50 km. However, the MWE in the simulation provided a much steeper slope, and it is due to the large horizontal average (about 250 km radius) of meteors inherent to the MWE technique.
In the spectra obtained by WCFI and MWE, the gravity wave spectral slope was found to be statistically similar within the range of uncertainties around each altitude, which was somewhat unexpected. In the simulation, the MWE spectra exhibited a steeper slope than WCFI due to MWE method's limitations in capturing the energy of the small scale waves. We believe that the reason for this discrepancy with the campaign data is due to the dominance of large-scale GWs during the campaign period. It should also be noted that the four-hour high pass filtered mean zonal and mean meridional winds in Figure  2  shows the presence of highly dynamic gravity waves. These mean winds were obtained after averaging over the entire illuminated radar area with a radius of approximately 250 km using the MWE method.
In addition, we will investigate the seasonal variability of momentum flux parameters, Reynolds stress tensor terms, and power spectra by analyzing two years of multistatic SMR data from MMARIA-Germany from September 2018 to September 2020 in a separate paper. The frequency spectra of vertical winds during the campaign period demonstrate high amplitudes of diurnal tides and their evolution with the altitudes. Figure  7b  shows the highest amplitude of diurnal tide energy about 20 m 2 s -2. A deeper investigation of vertical winds and their tidal-like variability will be carried out in future studies.
The MWE method inherently used the total radar illuminated area to estimate the mean winds; hence the method was only able to acquire the energy of the gravity waves with horizontal wavelengths larger than 500 km. The introduced WCFI and MWE analysis on SIMONe 2018 data exhibited an unexpected result. In the simulation, the frequency spectra demonstrated a significant difference in power-law slopes between the spectra estimated by WCFI and MWE methods, but the campaign data analysis gave similar spectral slopes at each altitude range when using the two methods. The horizontal autocorrelation analysis of SIMONe 2018 data during a 12 hour time interval of 3-hour gravity wave presence, suggested that these waves have long horizontal wavelengths. Based on the results from the simulation and campaign observations, we speculate that the MLT region during the period of the campaign was dominated by large scale waves of horizontal wavelengths larger than 500km, which probably are secondary gravity waves.
Acknowledgements. This work was partially supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under  SPP 1788 (DynamicEarth)-CH1482/2-1  and under SPP  1788 (CoSIP)-CH1482/3-1 , and by the  WATILA Project  ( SAW-2015-IAP-1 ). Some hardware, software, and analysis work at  MIT Haystack Observatory  was supported by  NSF   Major Research Infrastructure Grant   AGS-1626041. HCA appreciate the support by the  French Ministry of Foreign  and European Affairs for the Eiffel excellence scholarship (File  N 945179K ). We also thank  Rüdiger Lange  (Salzwedel), Fred and  Claudia Bauske  (Mechelsdorf),  Frank Schütz  (Gulderup),  Dieter Keuer  (Breege), and the  IAP  personnel  T. Barth ,  F. Conte ,  N. Gudadze ,  R. Latteck ,  N. Pfeffer , and  J. Trautner  for supporting the operations of the coded CW links.
Acknowledgements The support by the  French National Research Agency  in the framework of  ANR   14-CE07-0003  " HighS-Ti" program  is highly acknowledged. Xiang WANG highly acknowledges the financial support from  China Scholarship Council (CSC). G. Dirras is grateful to  Dr D. Tingaud  and  Dr. F. Mompiou  for fruitful discussions regarding EBSD and EDS investigations.
Inhibition of fatty acid oxidation FAO is a promising target for elimination of CSCs. Etomoxir, an inhibitor of the carnitine-dependent transporter CPT1 (also known as CPT1A), which inhibits the mitochondrial import of fatty acids mediated by the carnitine shuttle, decreases intracellular ATP levels as well as the viability and resistance to chemotherapy of glioblastoma and acute myeloid leukemia cells  [120]. Silencing of Nanog or overexpression of cytochrome c oxidase subunit 6A and/or inhibition of FAO by etomoxir, sensitizes CSCs to sorafenib treatment. These data suggest that FAO inhibition or OXPHOS reestablishment to induce metabolic reprogramming of CSCs should be a powerful therapy in hepatocellular carcinoma  [121]. Unfortunately, the clinical development of etomoxir terminated because of severe hepatotoxicity and hematopoietic stem cell exhaustion associated with treatment  [120].
Acknowledgements The research leading to these results has received funding from the  CPER   2007-2013  " Structuration du Pôle de Compétitivité Fibres Grand'Est" (Competitiveness Fibre Cluster) , through local ( Conseil Général des Vosges ), regional ( Région Lorraine ), national (DRRT and FNADT) and  European (FEDER)  funds. The authors also gratefully acknowledge the financial support of  European FEDER  funds ( TALiSMAN  project,  2019-000214 ), and thank the many colleagues and PhD and post-doctoral students who participated to these works and whose names appear in the reference list.
Acknowledgments  Y. Raitses  and  I. D. Kaganovich  gratefully acknowledge partial financial support by the  AFOSR  grant  FA9550-17-1-0010  and assistance in preparation and fruitful discussions with  I. Romadanov ,  Eduardo Rodriquez , and  J. B. Simmonds. The work of  A. Smolyakov  was supported in part by  Natural Sciences and Engineering Research Council of Canada (NSERC) Canada , by the  AFOSR  grant  FA9550-18-1-0132 , and by  Compute Canada , and he acknowledges fruitful discussions with  O. Chapurin ,  S. Janhunen ,  M. Jimenez ,  O. Koshkarov ,  I Romadanov , and  D. Sydorenko. The contribution of  E. Ahedo  and  M. Merino  was supported by the  Government of Spain, National Development and Research Program , Grant No. ESP2016-75887P , and they thank  J. Navarro  and  P. Fajardo  for their contribution.
M. Keidar  and  I. Schweigert  gratefully acknowledge AFSOR grant  FA9550-19-1-0166. I. Schweigert was partly supported by  Russian Science Foundation , grant  17-19-01375. S. Tsikata  acknowledges support from  CNES  ( Centre national d'études spatiales, France)". F. Taccogna gratefully acknowledges financial support of Italian minister of university and research (MIUR)  under the project  PON  " CLOSE  to the Earth" ARS  ARS01-00141. R. Gueroult  and  N. J. Fisch  were supported by  DOE   DE-SC0016072  and  NSF   PHY-1805316. A. Bourdon  and  P. Chabert  gratefully acknowledge financial support of the  French National Research Agency (L'Agence nationale de la recherche) ANR  grant  ANR-16-CHIN-003-01  and  Safran Aircraft Engines  within the project  POSEIDON.
Continuous data monitoring and discrete sampling Since March 2016, turbidity, NO3 -, and DOC are measured at the Toulon springs. Turbidity is measured using an optical sensor (Solitax sc TLine, HACH) every 10 minutes, with a relative precision of 1%, and a range of measurement of 0. 001 NTU to 4 000 NTU. Nitrate and DOC are measured every 6 minutes using a UV-visible scanning spectrophotometer (Spectro::lyser, S::CAN), with a precision of 0. 1 mg L -1 and 0. 05 mg L -1 , respectively, and a range of measurement of 0 to 46 mg L -1 and 0 to 133 mg L -1 , respectively. To avoid measurement errors due to matrix interferences when turbidity increases, a manual calibration of the spectro::lyser was made with laboratory measurements (Lorette, 2019). Simultaneously, hydrodynamic data (rainfall and discharge) were also monitored.
Geochemical variability During February 2017 and December 2017 flood event, high-resolution monitoring of hydrodynamic (discharge and rainfall) and hydrochemical parameters (turbidity, nitrate, and dissolved organic carbon) were obtained at the outlet of the Toulon karst system. Table  1  summarizes data of the Toulon springs. Fig. 3  illustrates the temporal evolution of discharge and hydrochemical parameters from the Toulon springs.
DOCnorm over NO3 -norm: Normalized data analysis Normalized data were performed using the maximum and minimum values during the flood events to determine the relative variations of a parameter free from units. Normalized data analysis from DOCnorm vs NO3 -norm was performed on the two flood events previously presented. February 2017 (Fig. 3A ) and December 2017 (Fig. 3B ) flood events presented the same trend as the DOCnorm vs NO3 -norm. In Fig. 4 , the start of the high water flow is characterized by a slope inferior to 1 (α<1). This means that the increase of NO3 -is higher compared to the increase of DOC. It is followed by a change of slope going to the right, having α>1. This signifies that the increase of DOC is more pronounced at this time than NO3 -. Then, the slope went back to α<1.
NO3 -and DOC relationship in karst environments and contribution in terms of resource management and protection Analyses performed in this study showed complex evolution of NO3 -and DOC at the Toulon springs. Coming from a same theoretical origin (soil), the evolution of these parameters can be associated with anthropogenic nitrate input in the hydrogeological catchment. Fig. 9  presents the implication of the result of the performed Normalized Data Analysis of NO3 -and DOC. This enables to identify the infiltration process (using the slope α from DOCnorm vs. NO3 -norm) and its respective progression (with particle suspension and bacterial input). Data presented in the DOCnorm vs. NO3 -norm diagram corresponds to the increase of NO3 -and DOC during the high flow event. Through time, NO3 -and DOC are increasing at a different phase.
When NO3 -increases more than DOC, a linear regression will present a slope α lower than 1 (α<1). On the contrary, when NO3 -increases less than DOC, a linear regression will present a slope α higher than 1 (α>1). The common use of the DOCnorm-NO3 -norm diagram, and especially the analysis of the evolution of the slope with continuous data allowed distinguishing punctual infiltration from diffuse infiltration through time. This analysis was applied in both February 2017 flood and December 2017 flood events, leading to: (i) describing the precision of hydrogeological functioning of the karst system and (ii) identifying water quality hazards and the period associated with microbial pathogens. According to this analysis of high-resolution monitoring of NO3 -and DOC, the two flood events studied have a similar functioning with: (i) A flush of water close from the saturated zone, bringing particles deposed during low water period (Fig.
Conclusion In water resource management, it is important to know the water quality hazards. In this study, a tool that can be used in tracing water perturbation in a karst aquifer is proposed. First, method involves high-resolution monitoring of NO3 -and DOC. Second relies on the normalization of the data with NO3 -_norm=f(DOC_norm) reference frame. From these two steps, the infiltration process can be assessed based from the obtained slope. Applied on the data acquired during a flood event, the slope (α) showed that (i) if α>1, DOC increase is more pronounced while (ii) if α<1, contaminant of anthropogenic origin, e. g. NO3 -, is more distinct. Third and fourth steps, NO3 -and DOC parameters were complemented by (a) determining and characterizing particle size and chemistry and (b) detecting bacterial contamination (e.
Acknowledgements This work was supported by the  Région Nouvelle Aquitaine , the city of  Périgueux, Suez , the  Conseil Départemental de la Dordogne , and the  Adour-Garonne Water Agency. The authors would like to thank  Jean-Christophe Studer ,  Jerome Donnette ,  Julien Frant ,  Pierre Pinet ,  Emilien Castaing  and  Philippe Savy  for their technical and field assistance contributed. This work benefited from fruitful discussion within the  KARST national observatory network (SNO KARST) initiative  from the  INSU/CNRS.
Food Frontiers: An academically sponsored new journal Search on the Web of Science Core Collection online database clearly shows that food science and technology is a fast-growing research field. In fact, total articles in the area of food science and technology have grown from a market share of 0. 84% in 2013 to 0. 91% in 2017. China, United States, Italy, Spain, Japan, South Korea, Brazil, Germany, Turkey, and Iran are the top 10 countries that publish most papers in the area of food science and technology. China has the fastest growing rate with a market share of 17. 08% in the area of food science and technology and a 5-year growth rate of 20. 81%. The publisher Elsevier dominates the market with a share of 35. 56%, followed by Wiley, Springer Although there have been a plenty of food-related journals, the launch of this new journal, Food Frontiers, is timely and essential (Figure  1 ).
Caribbean Seaway The Caribbean region has a complex geological evolution, which started during the Jurassic with the dislocation of Pangaea  (Pindell and Kennan, 2009). Rifting between North and South America during the Jurassic and Early Cretaceous led to the opening of the proto-Caribbean seaway. To the west, subduction of the Farallon plate beneath the proto-Caribbean seaway during the Early Cretaceous formed an oceanic volcanic arc stretching from the northwestern tip of South America to the southern tip of North America  (Pindell and Kennan, 2009). Emplacement of the Caribbean Large Igneous Province (CLIP) starting in the Cenomanian marked a turning point in the history of the Caribbean region. This large (4 million km 3 ) basaltic oceanic plateau was formed from 94-89 Ma  (Andjić et al.
Tethys Seaway The Tethys Ocean exhibits a complex geological history. There is evidence for Late Cretaceous marine exchange between the Central Atlantic Ocean and the Tethys Ocean, which mostly occurred through narrow and deep corridors  (Stampfli, 2000; Stampfli and Borel, 2002; Nouri et al. , 2016). These corridors formed during the final break-up of the Pangaea supercontinent, which led to the opening of the Alpine Tethys Ocean during the Early Jurassic coeval with the opening of the Central Atlantic Ocean  (Stampfli and Borel, 2002). The Alpine Tethys Ocean began to close in the Early Cretaceous in response to the rotations of Africa plate and the Iberian plate  (Stampfli and Borel, 2002). During the Late Cretaceous, two deep marine corridors located on both sides of the Anatolides-Taurides permit water exchanges between the Central Atlantic Ocean and the Tethys Ocean  (Stampfli and Borel, 2002; Nouri et al.
Acknowledgments We acknowledge high-performance computing support from  Cheyenne  (doi: 10. 5065/D6RX99HX )provided by  NCAR's Computational and Information Systems Laboratory , sponsored by the  National Science Foundation. We thank  Andrew Vande Guchte  for the initial setup of the simulations on the  NCAR Cheyenne supercomputer. J. -B. Ladant thanks the  Institut de Physique du Globe de Paris  for permission to use its premises. This work was supported by  NCAR/CISL  allocation  UMIC0063  to  J. -B. Ladant  and  C. J. Poulsen ,  Heising-Simons Foundation  Grant # 2016-05  to  C. J. Poulsen , and  U. S. National Science Foundation  ( OCE-1261562 ) to K. G. MacLeod,  E. E. Martin , and  C. J. Poulsen.
This makes the processing non-digital and different from the current procedures. However, the necessity of a side analog network for the propagation of error derivatives makes this technique still highly biologically implausible. In order to solve this limitation, it is therefore proposed an alternative solution to the use of a side network by introducing a learning technique used for energy-based models: equilibrium propagation. Experimental results show that both approaches significantly outperform conventional architectures used for pattern reconstruction. Furthermore, due to the high suitability for VLSI implementation of the equilibrium propagation learning rule, additional results on the classification of the MNIST dataset are here reported.
In the second section, a general introduction on biological algorithms is presented with particular attention on recurrent backpropagation and equilibrium propagation. In the last section, the two techniques are compared with the existing algorithms used in pattern reconstruction providing results of their compelling efficiency. Lastly, it is shown the application of a memristor-based recurrent neural network trained with equilibrium propagation used for the classification of a small subset of the MNIST dataset. The choice of using only this learning rule was mainly dictated by the fact that using a side network, as in the recurrent backpropagation approach, would at least double the required IC area.
Pattern Reconstruction In this section, a comparison between the two aforementioned training algorithms for pattern's reconstruction task is presented. For this kind of application, input units are chosen to be simultaneously output units and no hidden units are considered. Moreover, due to the construction of the gradient dynamics (13), symmetric weights were chosen for both methods. This condition also guarantees the convergence of the model (3). During the training phase, each image shown in Figure  1  is repeatedly proposed to the network by means of a constant input I until it is memorized. In the case of multiple patterns to be learnt, the previous steps are performed for each single image of the dataset for different epochs. Here patterns were shown to the network in the same order for each epoch but this choice was not restrictive since similar performances were obtained even in the case the images were proposed in a random fashion.
Pattern Classification As a second experimental result, it is now provided an application of the model introduced by Scellier and Bengio (2017) in a pattern classification task of a subset of the MNIST dataset: 5 classes and 600 patterns for each class. The model used here is still a recurrent neural network with symmetric connections, 1 hidden layer, no skip-layer and no lateral connections. Following Scellier and Bengio (2017), a hard sigmoid was chosen as activation function and the training process was performed by iterating the successive steps: 1) Fix the pattern as a constant input; 2) Run the free phase until convergence of the hidden and the outputs units may be reached and collect g(v ∞ i )g(v ∞ j ); 3) Run the weakly clamped phase until convergence and collect g(v β,∞ i )g(v β,∞ j ); 4) Update the synaptic weights according to (19).
This paper addresses the scheduling issues of body sensor data traffic originating from medical CPS over WBAN. A QoS-aware scheduling approach is proposed for WBAN in this paper. The proposed scheme is based on Enhanced Distributed Coordination Function (EDCF) via MiFi connectivity at the MAC (Media Access Control) layer. The remaining paper is organized as follows. Section 2 explains the Mobile Wi-Fi device and system architecture. Section 3 represents the related work that has been done in the QoS support area. The scheme proposed is explained in Section 4. Section 5 discusses the methodology and Section 6 evaluates the resulting analysis based on experiments. Finally, Section 7 explains future work and the conclusion based on the results of this paper.
Data Traffic Types and Prioritization In this research work, three types of data sensors with varying QoS requirements are considered. These sensors are actually biosensors and used to generate data that are acquired with the help of healthcare equipment to determine various health parameters. The three data traffic types that are cogitated in this work are ECG (Electrocardiogram), blood pressure (BP) and temperature. These sensor types generate data traffic that can be associated with different priority levels. Generally, the highest level of priority among the three traffic types is given to ECG traffic, then BP is considered less delay sensitive as compared to ECG data traffic, whereas temperature data traffic is the most delay-tolerant type of data traffic  [23]. Each of these traffic types generate data traffic with different inter-arrival times and are allocated various access categories.