dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors are happy to acknowledge useful and stimulating discussions with  H. Katz ,  C. Scarlata ,  J. Prochaska ,  A. Henry ,  A. Smith ,  M. Haehnelt ,  L. Barnes. We further kindly thank  B. Sémelin  for providing us with parts of his own Lyman-α RT code. We thank  J. Rosdahl  and  V. Mauerhofer  for their valuable contributions to improving the code. We acknowledge development of some of the metal-line modules from intern students  J. Dumoulin ,  C. Dubois ,  A. Collard. We also acknowledge  Franz Schreier  for making publicly available his python code for comparing approximations of the Voigt function. We thank the anonymous referee for her/his careful and constructive report. JB acknowledge support from the  ORAGE  project from the  Agence Nationale de la Recherche  under grant  ANR-14-CE33-0016-03.
TG is grateful to the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) of the  Univesité de Lyon  for its financial support within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR). AV acknowledges support from the  MHV-SNF  grants  PP00P2_1176808 ,  PMPDP2_175707 , and from the  European Research Council  under grant agreement ERC-stg-757258 (TRIPLE). TK was supported in part by the  National Research Foundation of Korea  (No. 2017R1A5A1070354  and No. 2018036146 ) and in part by the  Yonsei University Future-leading Research Initiative  ( RMS2-2018-22-0183 ). MT acknowledges funding from the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013 Grant Agreement no.
614199 , project ' BLACK '). The development and tests of rascas were greatly facilitated by our access to computing resources at the  Common Computing Facility (CCF)  of the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ), and at the  CC-IN2P3 Computing Centre (Lyon/Villeurbanne -France ), a partnership between  CNRS/IN2P3  and  CEA/DSM/Irfu.
Methods. The solution for this new model was based on our in-house software, LUGREAS. The gravity spectrum power, post-fit residuals after precision orbit determination (POD), lunar surface gravity anomalies, correlations between parameters, admittance and coherence with topography model, and accuracy of POD were analyzed to validate the new CEGM03 model. Results. We analyzed the tracking data of the Chang'e 5T1 mission and estimated the CEGM03 lunar gravity field model. We found that the two-way Doppler measurement accuracy reached 0. 2 mm s -1 with 10 s integration time. The error spectrum shows that the formal error for CEGM03 was at least reduced by about 2 times below the harmonic degree of 20, when compared to the CEGM02 model. The admittance and correlation of gravity and topography was also improved when compared to the correlations for the CEGM02 model.
By integrating the four-way Doppler measurement data from the SELENE mission collected over one year,  Matsumoto et al. (2010)  estimated a gravity field model with a degree and order of 100, SGM100h. Following the SELENE mission, NASA's Gravity Recovery and Interior Laboratory (GRAIL) mission was launched in 2011. It was designed to accurately map the lunar gravity from low to very high wavelengths from a polar orbit  (Zuber et al. 2012). Only using three months of primary mission phase data from GRAIL,  Zuber et al. (2013)  published a gravity field model, GL0420A, up to a degree and order of 420. Meanwhile, the Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center.
(2015)  released two lunar gravity field models up to degree 200, AIUB-GRL200A/B. Klinger et al. (2014)  provided the Graz lunar gravity field models, GrazLGM200a, which relies on the official orbit data retrieved from GNI1B. Wirnsberger et al. (2019)  presented the first completely independent Graz lunar gravity model compiled with no involvement of the GRAIL Science Team, denoted GrazLGM420b. They estimated this gravity field model based on an in-house software package, the Orbit Re-Construction Application. The Chinese lunar exploration program has been going for over 10 yr, beginning with the Chang'e 1 mission launched in 2007  (Wei et al. 2018). Regardless of the limited accuracy and coverage of tracking data from the Chang'e 1 mission, a lunar gravity field model, CEGM01, up to degree and order 50 was estimated using one year of tracking data  (Yan et al.
2010). A new gravity field model CEGM02 with degree and order of 100 was developed  (Yan et al. 2012 ) using the Chang'e 1 orbital tracking data in combination with orbital tracking data from SELENE, LO-I to LO-V, Apollo 15 and 16, Clementine, LP, and SMART-1 (hereafter historical spacecraft). These two solutions for the gravity field model were estimated using the GSFC GEODYN-II package  (Pavlis et al. 2006). The details of their derivation are given in Table  1. Moreover, based on the GRAIL mission, many local solutions for lunar gravity fields have also been derived  (Goossens et al. 2020 (Goossens et al. , 2018 (Goossens et al. , 2014)). As shown in Table  1 , the GRGM series models have already reached an unprecedented level of accuracy and resolution with the inclusion of the GRAIL data set.
In addition, the inclination of Chang'e 5T1 is about 18-68 • , which is very different from the polar orbits employed in previous lunar gravity field solutions. Another highlight of this work is that our new gravity field model was solved based on our in-house software, LUGREAS  (Ye et al. 2016 ), which we thoroughly validated via the GEODYN-II software as a reference on precision orbit determination (POD). With the help of the Chang'e 5T1 tracking data, we also derived a solution for the k 2 value. A brief summary of this paper is as follows. In Sect. 2 we describe the Chang'e 5T1 mission, focusing on technical details and orbits. In Sect. 3 we present the methods and procedures used for the gravity modeling from the Chang'e 5T1 Doppler data. In Sect. 4 we assess the accuracy of CEGM03 in multiple ways.
During the extension mission, the orbit of Chang'e 5T1 at middle inclination was a relatively stable orbit with an eccentricity of approximately 0. 02, and it was close to circular like the orbit of the Chang'e 1 spacecraft. A combination of orbit inclinations and eccentricities like this has never been tried for a lunar gravity field modeling mission. In the next chapter, we present the radio tracking data set used to estimate our lunar gravity field model.
Observation strategies and data set To support the Chang'e 5T1 mission, the Chinese Deep Space Network (CDSN) and Chinese Very long baseline interferometry Network (CVN) have upgraded in performance and provided continuous tracking for the spacecraft, as shown in Fig. 3. We should mention that there are two antennas at the Kashi station The eccentricity evolution is small (below 0. 05) in the orbiting period, and a stable orbit is maintained. (KS), named KS1 and KS2, for Chang'e 5T1. There is a small difference in coordinates between these two antennas. During whole mission, S -band radio links were used to obtain tracking data. The closed-loop S -band round-trip signal was received and coherently transponded by phase-lock-loop. The tracking data used to estimate a new lunar gravity field period are plotted in Fig.
Computing environment A flowchart with details for all LUGREAS modules is shown in Fig. 5. The prediction and batch modules are the core processing parts of this software. The prediction part focuses on force model building, an integrator configuration, and related parameter initializations, while the batch part carries out matrix allocation, storage, calculation of observation residuals, and inversion of normal equations. The numeric integrator combines dynamic and variational equations, as illustrated in the flowchart shown in Fig. 5. The solution parameters can be divided into global and arc-related parameters. The global parameters include lunar gravity field Each normal matrix for every arc is stacked to estimate the global parameters  (Kaula 1966). The whole process is iterated to obtain a converged solution.
The parameters we estimated can be conveniently divided into arc and global parameters. The arc parameters are those that influence only the data in an arc, and global parameters are those that influence all data. The arc parameters include the initial orbital elements of the Chang'e 5T1 spacecraft, three-axis empirical accelerations, and S -band two-way Doppler measurement biases. The pass-dependent biases of the two-way Doppler were estimated to account for measurement modeling errors. The global parameters include lunar gravity field coefficients up to degree and order 100, and the degree 2 tidal Love number k 2. Of note, we used the CEGM02 covariance matrix in the solution, which means we adopted the Kaula constraint indirectly. The initial orbital elements were retrieved from a reconstructed initial ephemeris of the Chang'e 5T1 provided by Beijing Aerospace Control Center.
Residuals analysis For our gravity model, 308 arcs were collected altogether. In 2015, the CDSN tracked Chang'e 5T1 for 246 days and we obtained 182 arcs, which can be used in the solution of gravity field model. In 2016, Chang'e 5 T1 was tracked for about 202 days and we achieved 126 arcs. There are two factors leading to rejected arcs. The first is a large orbital maneuver or too few observations in the arc. The second is loss of information on spacecraft outgassing or possible propulsion leaks in original data records. Figure  6  shows the post-fit residuals of the 308 arcs used in our CEGM03 solution. In Fig. 6 , we can see that the mean values of the Doppler residual RMS were about 0. 29 mm s -1 for 2015 and 2016, compared to 4 mm s -1 of the Chang'e 1 Doppler tracking data  (Yan et al.
Orbital performance In this section, we compare the performance of the CEGM03 model with other gravity field models, GRGM660, SGM100h, and CEGM02, in terms of orbit determination for Chang'e 5T1. The maximum degree and order expansion for each gravity field model is limited to degree and order 100. It should be noted that the GRAIL model is tailored to the modeling of high degree and order coefficient. The selected arcs span the period from 25 May to 25 July 2015. Figure  13  summarizes the per-arc post-fit residual RMS values, and the orbit determination software used was LUGREAS. From Fig. 13  we can see the mean RMS of post-fit residuals is about 0. 37 mm s -1 for the GRGM660 gravity field model. The corresponding post-fit residuals are about 0. 43 mm s -1 for the CEGM03 model, 0. 66 mm s -1 for SGM150, and 0.
72 mm s -1 A45, page 9 of 11 A&A 636, A45 (2020) Fig. 12. Admittance (a) and correlation between several lunar gravity field solutions and gravity induced from topography. We can see in Fig. 14  that there are significant improvements in the CEGM03 model in the along-track component direction when compared with the CEGM02 and SGM100h models. There are no improvements when comparing CEGM03 overlaps with GRGM660 overlaps. It should be noted that the SGM100h and CEGM02 models for the orbit determination have strong similar points because of the similar data used. With the Chang'e 5T1 data combining, it contributes to improving the accuracy of orbit determination for low and middle orbital spacecraft. The average values of the overlaps are listed in Table  4.
Acknowledgements. We are grateful to the  Chinese deep space network  for providing models and data to make this research possible. We appreciated the discussion with  Prof. Jinsong Ping  from the  National Astronomical Observatory of China , and we thank  Mr. Stephen C. McClure , a native English speaker and researcher, from  Wuhan University  for helping polish this paper. The work is supported by the  National Natural Science Foundation of China  ( U1831132 ,  41874010 ,  41804025 ), the  Innovation Group of Natural Fund of Hubei Province  ( 2018CFA087 ), and the  Science and Technology Development Fund of the Macau Special Administrative Region  ( 007/2016/A1 ,  119/2017/A3 ,  187/2017/A3 ). This work is also supported by the  Open Funding of Macau University of Science and Technology  ( FDCT 119/2017/A3 ).
, 2015). References for the individual data sets are given later on in the caption of Figure  7. The da  Silva et al. (1994)  climatology is used for relative humidity. Cloudiness is evaluated using the CALIOP Lidar observations aboard the satellite Calipso from the Aqua-train  (Chepfer et al. , 2010). For comparison with models, the COSP satellite simulator is used online in the model  (Bodas-Salcedo et al. , 2011; Chepfer et al. , 2008). Results are shown for the reference configurations of LMDZ: versions 5A and 5B developed for CMIP5 and version 6A developed for CMIP6. These reference configurations result from long phases (typically 2 years) of careful corrections and adjustments. The 6A version includes several other changes than those of the boundary layer scheme discussed above, that are discussed by  Hourdin et al.
In fact, the specific humidity at the surface is almost unchanged in the DXX simulations, probably due to the compensation between relative humidity and temperature mentioned above. In terms of agreement with observations, it is difficult to conclude which value of D is the best in view of the large dispersion among the available data sets (thin dashed curves). Finally, the sensible heat flux is strongly reinforced over the ETO in the D20 and SWITCH simulations as a consequence of the larger temperature contrast at the surface. Figure  7  synthetizes the results of the experiments in terms of surface fluxes by using the ETO-Anomaly index mentioned in section 1 and first introduced by  Hourdin et al. (2015). The ETOA of a quantity is defined as the difference between the averaged quantity in the red area and the average over the tropical oceans 30 • S to 30 • N (Figure  3 ).
Looking at the sensitivity experiments with the 6A version, it is clear that increasing D increases the shadowing effect of clouds (blue stars in Figure  7 ) but that this cooling effect is in part compensated by an increased downward longwave radiation reaching the surface (green crosses in Figure  7 ). Using a diffusive formulation everywhere (as was the case in version 5A) or deactivating the thermals in the regions of subsidence (versions 5B and SWITCH) produces the largest ETOA for LW CRE and -LE (red circles). For values of parameter D between 0. 07 and 0. 1, the evaporative cooling and total fluxes reach a minimum value. Those values correspond to a bias of -10 to -15 W m -2 when compared with the observed ETOA. However, the bias is probably in the range of observational error bars as suggested by comparing the ETOA of the reference HOAPS3 data set  (Andersson et al.
The red and blue large plus signs at 0 on the x axis show the individual observational estimates (same as in Figure  7 ). The regression lines are computed using all the available models for which historical coupled simulations and amip stand-alone simulations were available on IPSL extraction of the CMIP database. CMIP5 models are shown as small plus signs and the CMIP6 models as full circles. The IPSL configurations are encircled by empty markers, as shown in the lower right part of the graph. The list of CMIP models sorted as a function of the SST ETOA, together with the values of surface fluxes are given in Tables  A1  and A2. to an increased downward LW radiation because of clouds closer to the surface. As was seen for the fluxes, the bias is significantly reduced in the new 6A version, although it does not disappear.
(2015)  the fact that the correct representation of SSTs in terms of ETOA index requires a negative value of the surface fluxes, ETOA bias suggests that the inconsistency could come from the observations. The discrepancy would be reduced if using other observational data sets than the HOAPS3, which is the less favorable for the comparison with LMDZ latent heat flux. It is also possible that the tuning of the current version of LMDZ6A overfits the SSTs by pushing the negative value of the surface flux ETOA too far to compensate for a lack of oceanic cooling, due to errors in the oceanic model, or in the wind stress. These results advocate for significant efforts to constrain surface variables over tropical oceans from observations.
Previous TVB Studies Several studies with TVB are limited by the number of metrics fitted (often a single metric such as FC fit), modalities simulated, sample size, and/or are focused on pathological changes in these neural parameters lacking a full 4 investigation of the healthy brain. The following are a few examples. We are focusing here on what our present study provides beyond state-of the art. Thereby we acknowledge the value of these previous publications on which our present study builds to further extend our present scope of understanding  Falcon et al (2015 Falcon et al ( , 2016) )  investigated stroke in TVB have a sample size of 30  (31)  subjects. These authors explored global coupling, conduction velocity, and local coupling (K12, K11 and K21) of the SJHMR3D model  (8, 9). The simulated data was validated by comparing to empirical in terms of amplitude, frequency and FC.
Brain imaging data preprocessing Preprocessing of anatomical, diffusion and functional data was performed using our pipeline for derivation of individual connectomes  (49). The preprocessing steps are described in detail there, and more briefly below. Preprocessing of T1weighted images included the following: motion correction, intensity normalization, extraction of non-brain tissue, brain mask generation, and segmentation of cortical and subcortical grey matter. We used the parcellation from the Desikan-Killiany Atlas  (50)  implemented in FREESURFER to divide the cortex into 68 regions of interest. A quality check of the parcellation of individual high-resolution T1-weighted scans was performed manually. The dwMRI data was preprocessed as follows: motion and eddy current correction, linear registration of b0 image to 8 individual T1-weighted image.
We modified the weights matrix by using the common logarithm of its values and then normalizing to its highest value. Distance matrices were the mean track length of fibers between each pair of ROIs, measured in mm. In brief, the functional data was preprocessed as follows: motion correction, brain extraction, high-pass filter (100s), registration to individual T1-weighted anatomical scans to apply high-resolution parcellation mask to the fMRI. Additionally, quality of BOLD signal was analyzed with temporal signal to noise to maps  (52). BOLD signal was averaged across voxels within brain regions. Functional connectivities were computed using a Pearson's linear correlation coefficient on the BOLD data of each region pair.
Subject Specificity An important feature of TVB is to provide subject specific simulations. Since we know that structure shapes function  (67) (68) (69) (70)  in the brain one could assume that by providing a subject specific input the output fits best to its own empirical data, i. e. by using individual SCs as a structural backbone of a simulation we generate data that is closer to its own empirical counterpart than to that when using SCs of other subjects. So far, we compared simulated to empirical FCs of the same subject to optimize our parameters. To test for subject specificity of our TVB model predictions, we compared all simulated FCs of a subject to the empirical FCs of all other subjects to determine whether the best fit is achieved with the subjects own FC or the FC of another subject.
In the alpha set the frequency is continuously decreasing as a function of global coupling. However, if we explore the parameter space more to the left of the x-axis the same phenomenon, increasing frequency, should be visible. We will explain this mechanism later, when we explore bimodality. Because averaging over subjects may obscure individual features of the signal, we also extracted an optimal parameter set for each subject. Optimal was defined as the best fit to empirical FC. The 50 resulting parameter combinations were plotted in a 2D density plot (Fig 2  4  th row). Again, we observed that best fits to empirical data were distributed across many conduction speed values but in a narrow range of global coupling. Interestingly, in the delta set there were two modes of highest fit along the conduction speed axis, suggesting a large degree of between-subject variability in this metric.
Discussion In the current study, we conducted a large parameter space exploration with TVB, simulating BOLD fMRI and electrophysiological neural signals for 50 individual human brains. TVB has previously been used to study how neural networks behave in healthy and diseased states  (8, 10, 13, 18, 21, 26). Here, we assert that goodness-of-fit of simulated to empirical data converge at a particular parameter set across multiple tenets of the EEG and fMRI signal, and that there are reliable inter-individual differences in biophysical parameters that lead to optimal functioning. In order to understand the behaviour of the system at multiple time scales, we conducted two sets of simulations, one that shows neural behaviour in the delta frequency range, and a second that shows alpha oscillations.
Acknowledgements We gratefully acknowledge the  Gauss Centre for Supercomputing e. V. (www. gauss-centre. eu) for funding this project by providing computing time through the  John von Neumann Institute for Computing (NIC)  on the  GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC). Additionally, computation has also been performed on the  HPC for Research cluster of the Berlin Institute of Health. We thank  Michael Schirner  for insightful discussions. P. R. acknowledges the following funding sources:  James S. McDonnell Foundation ,  H2020   Research and Innovation Action grants VirtualBrainCloud 826421 ,  Human Brain  Project  785907  and  ERC 683049 ;  German Research Foundation   CRC 1315 ,  CRC 936  and  RI 2073/6-1 ;  Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative.
Introduction OpenAirInterface (OAI) is an open source initiative that today provides a 3GPP compliant reference implementations of key elements of 4G and 5G Radio Access Network (RAN) and core network that run on general purpose computing platforms (x86) together with Off-The-Shelf (COTS) Software Defined Radio (SDR) cards like the ETTUS Universal Software Radio Peripheral (USRP)  1. It allows users to set up a compliant 4G/5G network and inter-operate with commercial equipment. OAI distinguishes itself from other similar projects through its unique open-source license, the OAI public license v1. 1, which was created by the OAI Software Alliance (OSA) 2  in 2017. This license is a modified version of Apache v2. 0 License, with an additional clause that allows contributing parties to make patent licenses available to third parties under FRAND terms similar to 3GPP for commercial exploitation.
3GPP has also defined a new 5G Core (5GC) architecture that supports service delivery over wireless, fixed or converged networks. This new 5GC uses a cloudaligned Service-Based Architecture (SBA) that supports CP function interaction, reusability, flexible connections and service discovery that spans all functions  [39]. This new core network allows for so called standalone operation of 5G NR, i. e. it does not rely anymore on a 4G network. A first pre-5G demonstration was given at Mobile World Congress (MWC) 2018 where OAI has shown the feasibility of maintaining a throughput of 300Mbps over a 80MHz channel using the new 5G-NR LDPC channel coding. This was achieved by offloading the most computationally expensive task, the LDPC decoder, to an Field Programmable Gate Array (FPGA). Using a software-only implementation of the LDPC decoder, a throughput of 150Mbps can be supported.