dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting abundances and other stellar parameters of HD 207897 are presented in Table  1. As an independent stellar classification analysis, we used the FIES and TRES data following (SPC;  Buchhave et al. 2012 Buchhave et al. , 2014)). The FIES analysis uses five spectral orders spanning a wavelength range from 5065 to 5320 Å, and the TRES analysis uses three spectral orders spanning the range from 5060 to 5300 Å. The spectra were compared to a library of synthetic templates to measure the effective temperature T eff , surface gravity log g, projected rotational velocity v sin i, and metallicity [m/H] (a solar mix of metals). We analyzed the spectra individually and calculated a weighted average of each parameter. The results were T eff = 5085 ± 50K, log g = 4. 48 ± 0. 10, v sin i < 2 km s -1 , and [m/H]= -0.
Photometry data analysis The photometry was extracted with the pre-search dataconditioned simple aperture photometry (PDC-SAP) pipeline  (Stumpe et al. 2012; Stumpe et al. 2014; Smith et al. 2012)  provided by the TESS team. We removed nans and flagged lowquality data and 5σ outliers. We then normalized and detrended the light curve with a spline-robust iterative sigma-clipping method  (Schoenberg 1946 ) using the Wotan package  (Hippke et al. 2019)   9. This method detrends the light curve by fitting the spline through minimizing the sum of squared residuals along with iteratively sigma-clipping. This step is one of the most important steps in the photometric analysis as it reduces the number of false-positive signals by removing instrumental and stellar noise. On the other hand, these detrending algorithms always include the risk of changing the transit depth or even fully remove shallow transits.
We corrected the remaining points for the charge transfer inefficiency (CTI) effect  (Santerne et al. 2012). This correction ranged from 1. 4 to 6 m s -1 with a 10 http://github. com/hippke/tls mean RV correction of 2. 1 m s -1. We then removed the nightly drift of the telescope measured from the simultaneous Th-Ar or FP reference spectra. This correction also falls within the 0. 1 to 9 m s -1 range, with a mean RV of 2. 1 m s -1. The next step was to remove a long-term drift of the zeropoint due to the instrumental effect identified in SOPHIE RV data  (Courcol et al. 2015; Hobson et al. 2018 ). To track this offset, so-called 'constant' stars were monitored each night. We combined these observations to build a time series of the RV master constant (see  Courcol et al. 2015  for more details of the method).
Stellar rotation and activity To investigate the activity of the star, we used several indicators such as bisector span, CCF FWHM, chromatic RV index (CRX), Hα, and log(R HK ) from SOPHIE spectra. We also used the Sindex from the HIRES spectra. We obtained the bisector span and CCF FWHM from the SOPHIE data reduction system. The CRX was extracted using the SERVAL  (Zechmeister et al. 2018 ) code. To compute the Hα index, which measures the flux in the Hα line, we followed the definitions of  Boisse et al. (2011). To do this, we also applied background correction to the data as appropriate for the two different calibration lamps (for further details, see  Hobson 2019). We derived the S-index from HIRES spectra following  Baliunas et al. (1995)  and  Paulson et al. (2002). We also computed the log(R HK ) index on SOPHIE spectra following the method of  Noyes et al.
Radial velocity results The combined RV data of HD 207897 are plotted in Fig. 7  after fitting a zeropoint offset for each RV dataset (see Table  2 ). We removed a linear trend of -0. 28 ± 0. 11 m s -1 yr -1 from RVs. We note that no notable differences were found in our results when this linear drift was included or excluded. However, because we saw a clear linear drift in the activity indicators such as the CCF FWHM and CRX, we decided to keep the drift. We investigated the footprints of the planet in RVs by searching for periodic signals. To do this, we used the website of the Data and Analysis Center for Exoplanets (DACE,  Delisle et al. 2016)   12  and computed periodograms for the RVs (Fig. 1 ). The RV periodogram displays a clear peak at 16. 20 d with a value lower than the 0. 1% FAP  (Baluev 2008).
(2018). The broadband photometry presented in Table  1  is 13 EXOFASTv2 is available at https://github. com/jdeast/ EXOFASTv2 also included. We enforced the upper limit for the V-band extinction (A v ) from  Schlegel et al. (1998)  and  Schlafly & Finkbeiner (2011). The broadband photometry, the Gaia parallax, and A v allowed us to model the stellar spectral energy distribution, which is key to constraining the stellar radius. EXOFASTv2 interpolates a precompiled 4D grid of bolometric corrections (log g, T eff , [Fe/H], and reddening) to directly compute the broadband photometry flux. We did not set the limb darkening to let EXOFASTv2 constrain the best parameters of a quadratic limb darkening through the Claret (2017) tables for the TESS bands and the stellar atmosphere parameters (T eff , [Fe/H] index, and log g * ).
Discussion and summary We detected and characterized a sub-Neptune orbiting HD 207897 with a period of 16. 202161 ± 0. 000083 d. We used TESS photometry data along with SOPHIE, HIRES, and   APF RVs observations. We found that the planet has a radius of 2. 5 ± 0. 08 R E and a mass of either 14. 4 ± 1. 6 M E with a probability of 56% or 15. 9 ± 1. 6 M E with a probability of 46% based on bimodal results of the stellar activity period. The density accordingly translates into a high density of either 5. 1 ± 0. 7 g cm -3 or 5. 5 +0. 8 -0. 7 g cm -3. We used the same mass and radii bounds as  Otegi et al. (2020)  from the NASA Exoplanet Data Archive 14 (December 5, 2020) and plot all the sub-Neptune-sized planets (2 < R/R E < 4) with a determined semimajor axis and luminosity in The question now is how HD 207897 b and other similar planets with such a high density can exist at a close distance to their host star.
Two possible scenarios can be considered that are both consistent with the MMSN, however: Either the material from the outer region migrated and Furthermore, the brightness (K = 6. 3 mag) of HD 207897, its relatively small stellar radius, and its quite nearby distance (28 pc) would make HD 207897 b a good target for atmospheric characterization. Similarly, the close distance and high brightness for a transiting planet host,make HD 207897 b an excellent target for studying the architecture of the system through groundbased observations. For example, measuring the host star spinorbit alignment (obliquity) using the  Rossiter-McLaughlin (RM, Rossiter 1924; McLaughlin 1924)  anomaly can provide us with important information about planetary migration and evolution. Notes. The first three data were taken with a different CCD detector.
Acknowledgements. We warmly thank the  OHP  staff for their support on the observations. X. B. , I. B. and T. F. received funding from the  French Programme National de Physique Stellaire (PNPS)  and the  Programme National de Planétologie (PNP)  of  CNRS (INSU). We also acknowledge the financial support of  French embassy in Tehran. N. H. acknowledges  F. Vakili  for his constant academic and administrative support. N. H. warmly thanks  Ph. Stee ,  J. L. Beuzit ,  E. T. Givenchy  for all their help. N. H also thanks  Jason D. Eastman  and  Michael Hippke  who have written EXOFASTv2 and TLS package, respectively, for their guides. This publication makes use of The Data & Analysis Center for Exoplanets (DACE), which is a facility based at the  University of Geneva (CH)  dedicated to extrasolar planets data visualisation, exchange and analysis.
DACE is a platform of the  Swiss National Centre of Competence in Research (NCCR) PlanetS , federating the Swiss expertise in Exoplanet research. The DACE platform is available at https://dace. unige. ch. J. L-B. acknowledges financial support received from  "la Caixa" Foundation  ( ID 100010434 ) and from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  847648 , with fellowship code  LCF/BQ/PI20/11760023. This work has been carried out in the frame of the  National Centre for Competence in Research "Planets"  supported by the  Swiss National Science Foundation (SNSF). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  SPICE DUNE , grant agreement No  947634 ).
This work was supported by  FCT -Fundação para a Ciência e Tecnologia (FCT)  through national funds and by  FEDER  through  COM-PETE2020 -Programa Operacional Competitividade e Internacionalização  by these grants:  UID/FIS/04434/2019 ;  UIDB/04434/2020 ;  UIDP/04434/2020; PTDC/FIS-AST/32113/2017  and  POCI-01-0145-FEDER-032113; PTDC/FIS-AST/28953/2017  and  POCI-01-0145-FEDER-028953. V. A. , E. D. M, N. C. S. , and S. G. S. also acknowledge the support from FCT through Investigador FCT contracts nr. IF/00650/2015/CP1273/CT0001 ,  IF/00849/2015/CP1273/CT0003 ,  IF/00169/2012/CP0150/CT0002 , and  IF/00028/2014/CP1215/CT0002 , respectively, and  POPH/FSE (EC)  by  FEDER funding through the program  " Programa Operacional de Factores de Competitividade -COMPETE ". O. D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by  FCT.
A. C. et P. C. acknowledge funding from the  French National Research Agency (ANR)  under contract number  ANR-18-CE31-0019  (SPlaSH). N. A. D. acknowledges the support of  FONDECYT  project  3180063. S. H. acknowledges CNES funding through the grant  837319. X. D. and G. G. acknowledge funding in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Univ. Grenoble-Alpes. This work was supported by  Fundação para a Ciência e a Tecnologia (FCT)  and  Fundo Europeu de Desenvolvimento Regional (FEDER)  via  COM-PETE2020  through the research grants  UIDB/04434/2020 ,  UIDP/04434/2020 ,  PTDC/FIS-AST/32113/2017  and  POCI-01-0145-FEDER-032113 ,  PTDC/FIS-AST/28953/2017  and  POCI-01-0145-FEDER-028953. O. D. S. D.
A. B. is supported by the  NSF   Graduate Research Fellowship , grant No. DGE 1745301. R. A. R. is supported by the  NSF   Graduate Research Fellowship , grant No. DGE 1745301. C. D. D. acknowledges the support of the  Hellman Family Faculty Fund , the  Alfred P. Sloan Foundation , the  David and Lucile Packard Foundation , and the  National Aeronautics and Space Administration  via the  TESS Guest Investigator Program  ( 80NSSC18K1583 ). J. M. A. M. is supported by the  NSF   Graduate Research Fellowship , grant No. DGE-1842400. J. M. A. M. and P. C. acknowledge the  LSSTC Data Science Fellowship Program , which is funded by  LSSTC, NSF  Cybertraining Grant No. 1829740 , the  Brinson Foundation , and the  Moore Foundation ; their participation in the program has benefited this work. This paper is partially based on observations made with the Nordic Optical Telescope, operated by the  Nordic Optical Telescope Scientific Association at the Observatorio del Roque de los Muchachos, La Palma, Spain , of the  Instituto de Astrofisica de Canarias.
The ns-3 implementation is exploited in order to conduct various performance evaluation experiments with different CCAs (under both LOS and NLOS radio conditions). We run our ns-3 simulations on a cluster of servers  1  and deploy the OAI infrastructure in the R2lab anechoic chamber  2  which is an open wireless experimentation platform. The scripts to reproduce all our scenarios/results as well as the source code of RAPID's implementation in Linux are made available to the community  [12]. • We explore the demand-aware fairness concept embodied in the design of RAPID and demonstrate why a bandwidth allocation scheme based on Jain's Fairness Index (JFI) is not adapted for flows using different CCAs. • We highlight the limitations of current CCAs over next-generation cellular networks and show that RAPID can significantly improve the overall performance in terms of goodput, delay and jitter, even in the presence of unintercepted UDP or QUIC flows.
The remainder of the paper is organized as follows. In Section 2 we present the motivations of this work and shed light on the selfinflicted bufferbloat problem and bandwidth under-utilization issue introduced by traditional CCAs in cellular networks. Section 3 presents the design and implementation details of RAPID in the ns-3 network simulator. Section 4 describes the simulation environment, evaluates the performance of our solution in some mobile edge scenarios and discusses some observed limitations. Section 5 presents the design and implementation of our solution in Linux along with the 4G OAI experimentation testbed and evaluates its performance using well-known open source tools. Section 6 explores the related work and finally, we summarize our contributions and future research directions in Section 7.
ns-3 experimentation testbed The ns-3 mmWave module paves the way for the simulation of end-to-end 5G environments. Besides providing large bandwidths in the millimeter wave band for eMBB scenarios, it also enables the simulation of low-latency communications thanks to a customizable sub-carrier spacing (i. e. , supporting various numerologies) in the RAN. With that in mind and in order to evaluate the performance of RAPID, we implement all the functional modules illustrated in Fig. 2  in a proxy device collocated with a mmWave base station in ns-3. The proxy is connected to the core network via a 10 Gbps link in order to simulate a real-world fiber backhaul. Since Cubic and BBR are the dominant CCAs today  [3] , we simulate two TCP sources based on these algorithms as illustrated in Fig. 3.
In other words, it always maintains a high number of bytes in flight which negatively impacts the other competing flows, and in particular Vegas, as the latter is delay-based. As a result, NewReno always grabs a larger share of the total bandwidth (see Fig. 7(c )) at the cost of over 7000% increase in delay (i. e. from 1 ms to 80 ms) and with a high degree of unfairness to the other flows. Thanks to RAPID, a certain degree of fairness is maintained between the competing flows. Despite their different designs, all the 3 CCAs are imposed a similar number of bytes in flight, which allows them to obtain the same share and to finish approximately at the same time, as illustrated in Figs. 7(b ) and 7(d). Note that other additional scenarios involving the aforementioned CCAs as well as the results shown in this section can be reproduced with ns-3 using the codes and scripts that we have made publicly available  [12].
Design and implementation All the software components used in the implementation of RAPID as well as the way they interact with each other are illustrated in Fig. 8. Basically, our Linux implementation is built on top of two open-source projects, namely pepsal  [39]  and FlexRAN  [20]. Pepsal is an open-source Performance Enhancing Proxy (PEP) that splits incoming TCP connections, as such, it naturally replaces RAPID's TCP split functional module (illustrated in Fig. 2 ). FlexRAN on the other hand brings the Radio Network Information Service (RNIS), which is exploited via Advanced Message Queuing Protocol (AMQP) so that the pepsal process can periodically receive real-time radio information. The latter information is then cleaned and sent via ioctl to the RAPID kernel module which enforces the per-flow bandwidth allocation mechanism on the intercepted TCP connections.
The Fast eMBB Download is reproduced by launching two wget traffic, each simultaneously downloading a 60 MB file from two different HTTP servers that respectively use Cubic and BBR as CCAs. Similarly, the second scenario is reproduced by running two wget traffic in parallel using Cubic, followed later by a short SSH connection under BBR which displays a 500 kB text file using the Linux cat command. Figs. 9 and 10 shows the results of these experiments for 10 successive runs with 95% confidence interval. In the first scenario, we observe that RAPID provides as expected a high level of fairness between Cubic and BBR despite the respective differences of the two CCAs. In the normal OAI deployment (i. e. , without RAPID), Cubic grabs almost all the available bandwidth aggressively while BBR exploits only the remaining 25% (at least until the Cubic flow ends) as illustrated in Fig.
10 , we observe that the short SSH traffic takes on average 7 seconds in the standard deployment while it only takes 1 second with RAPID. In other words, RAPID enables 85% or 7 times reduction in the short flow completion time. This is in fact possible thanks to the low buffer occupancy resulting from RAPID's bandwidth allocation scheme and the demand awareness feature which allows RAPID to allocate enough bandwidth to the SSH flow during its lifetime. This experiment demonstrates that RAPID is effective in real-life scenarios where the user maintains short and long flows at the same time. For instance a user may want to surf on social media while a heavy software update is progressing in the background. Impact of background UDP traffic: RAPID has been designed to only intercept TCP traffic, therefore, protocols based on UDP such as QUIC or RTP are not intercepted and can affect RAPID's per-flow bandwidth allocation scheme.
Conclusion In this paper we highlight the limits of per-user flow isolation in mitigating self-inflicted bufferbloat issues in case of multiple flows per user and intermittent mmWave links. By using up-to-date RAN statistics from MEC Radio Network Information Service and analyzing the behavior of active flows, the RAPID solution prevents CCAs from exceeding the RAN capacity, thus, drastically reducing the delay increase regardless of the radio conditions while preserving good link utilization. Our experiments with 4G OAI validate the simulation results and show that RAPID not only offers significant performance improvement in MEC cellular networks, but also mitigates BBR's limitations in fastvarying radio environments, without requiring any modifications or software patches at the server. We also demonstrate that our solution does not penalize QUIC flows, although they represent only 7% of today's Internet traffic (according to Google  [47] ).
If this is the case, what is the impact of the distribution on sleep-dependent memory consolidation? To answer this question, we further studied the human EEG dataset, which had the unique feature of testing sleep after tasks with varying memory loads. Briefly, before nap EEG recordings, subjects completed a task in which five novel outdoor scenes (high visual working memory, H-WM) or two novel outdoor scenes (low visual working memory, L-WM) were required to be held in working memory for six seconds (Figure  3a ). After the delay period, subjects were then presented with a subsequent visual scene and asked whether it belonged to the previously presented set. In each case (H-WM and L-WM), trials were balanced so that the same total number of visual scenes was presented before sleep. An increase in spindle density after memory tasks and its relationship with memory consolidation is well established  (Clemens et al.
Methods Recordings. We study performance of the CNN model across three sleep datasets obtained from electrodes ranging from traditional scalp EEG to invasive intracranial depth electrodes. These datasets represent recordings from very different electrode types, which vary widely in resolution and signal-to-noise ratio. Training the CNN model in the same way over these very different recordings demonstrates the generality of the framework developed here; further, these results also represent a cross-species comparison of sleep-rhythm dynamics in NHP and human neocortex. The first dataset contains electrocorticographic (ECoG) recording from most of the lateral cortex in two macaques during natural sleeping conditions  (Yanagawa et al. , 2013). Recordings were obtained from 128 electrodes in both monkeys and sampled at 1 kHz by a Cerebus data acquisition system (Blackrock Microsystems, Salt Lake City, UT, USA).
The quality of sleep was studied by the degree of spatial synchronization in slow wave oscillations and significant increase in delta power was reported in sleep condition versus waking activity. This dataset was recorded and distributed by Laboratory for Adaptive Intelligence, BSI, RIKEN and was made freely available at http://neurotycho. org/sleep-task. The second dataset contains high-density scalp electroencephalography (EEG) recording from 20 healthy participants  (Mei et al. , 2018). Each participant participated in two separate sessions and completed a high-and low-load visual working memory task. The recordings were obtained during naps following the working memory tasks from a 64-electrode EEG skull cap and sampled at 1 kHz. The recordings were reviewed and stage 2 sleep was manually annotated by an expert to verify quality of sleep recordings.
Ultimately, sleep recordings that did not reach stage 2 sleep or were too noisy were excluded from the study. Under these criteria, four subjects were excluded (subject 12, 20, 26 and 27). In addition, the recordings were common average referenced (CAR) to remove large artifacts with potentially non-neural origin. These recordings were made freely available at the Open Science Framework through the link https://osf. io/chav7. The last dataset contains intracranial electroencephalography (iEEG) recordings from 5 epileptic patients in the Epilepsy Monitoring Unit (EMU) at London Health Sciences Centre (LHSC). Patients were implanted using depth electrodes for the sole purpose of surgical evaluation. Informed consent was collected from the patients in accordance with local Research Ethics Board (REB) guidelines.
5 to 3 seconds, consistent with the duration of sleep spindles. The detected windows are then used for training the CNN model. To additionally verify performance of the SNR algorithm, we implement this approach over one second recordings of a 90 by 90 array of LFPs generated by a spiking network model of cortical activity in the awake state, which does not contain the thalamic reticular loops and thalamocortical projections needed to generate sleep spindles. SNR values calculated from these data were uniformly below 0 dB, confirming the robustness of our approach in uniquely detecting spindlefrequency activity through a known ground truth dataset. Convolutional neural network (CNN) for sleep spindle detection. We developed a convolutional neural network (CNN) to detect spindles activities during sleep.
The model is motivated by the successful implementation of deep learning for detecting earthquakes and gravitational waves in high-noise settings  (George and Huerta, 2018; Perol et al. , 2018). If trained properly, it has the ability to detect hidden spindles that are being unnoticed and provides a great opportunity to study the spatial and temporal analysis of spindle activities across the cortex. We first tested CNN models with different architectures and selected one of the best architectures across the sleep recording datasets (Figure  1c ). Our CNN model is a one dimensional model with 5 convolutional layers  (with 32, 64, 128, 192 and 256 filters)  and 4 fully connected layers (with sizes 128, 64, 32 and 2). Each convolutional layer is followed by a maxpool and rectified linear unit layers and the output of the fifth convolutional layer is gradually flattened into 2D vectors using the fully connected layers followed by rectified linear unit layers.