dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical Weather Model ARPEGE In Lebanon, a physical forecasting model used by civil aviation meteo service at BRHIA is ARPEGE. The prognostic variables of the model for the atmospheric part are: the horizontal components of the wind, the temperature, the specific humidities of the water vapor and four categories of hydrometeors (liquid droplets, ice crystals, rain, snow) and the turbulent kinetic energy etc. In addition, the outputs of the ARPEGE model are graphs, vertical sections, time diagrams, weather maps etc. A post-processing of data makes the recovered data as a possible process in term of graphs, maps of weather etc. Unfortunately, in some cases, the forecasts provided by ARPEGE have been erroneous and biased due to several factors: • character of the chaotic equations; • errors resulting from the measurements of the initial state of the atmosphere; • errors induced by the discretization of the atmosphere (horizontal and vertical dimensions of the mesh); • error related to the spatio-temporal iterative process; • problem related to the complication of modeling physical phenomena of the atmosphere like the convection.
These various sources of error enforce us to try to find an efficient and reliable alternative to reduce the minimum possible errors and the biases issuing form the ARPEGE outputs. In the absence of satellite data, we propose to build a forecasting model based on a statistical approach to improve the quality of existing models such as time series analysis approach that provides the methodology ARIMA. In order to validate the proposed methodology, we will focus on the case of Lebanon in collaboration with meteorological service in order to access a vast database related to Beirut meteorological station. These data that have been recorded over several years will be used to test the reliability of the proposed model.
ARIMA Sequential The data of daily temperature of Beirut city for a period of 11 years from 01/01/2006 to 31/12/2016 were taken from the meteorological service at Beirut Rafic Hariri International Airport (BRHIA). ARIMA model takes the temperature time series data as an input. The three mandatory p, d and q parameters that have to be selected, represent the order of the ARIMA model. p: is the number of autoregressive terms, d is the number of non-seasonal differences and q is the number of moving average model coefficients. ARIMA model is considered to be a promoter approach in weather forecasting. It is a great statistical technique for modeling time series, temperature and other meteorological variables. 1) Data collecting The meteorological service (Met-Service) of Beirut Rafic Hariri International Airport (BRHIA) has 20 climatological stations spread all over Lebanon.
These stations are equipped with a multitude of sensors to measure several meteorological variables: temperature, humidity, precipitation, wind speed, wind direction and vapor pressure. Among these stations, 17 are automatic and autonomous weather observation stations, while 3 are manual observation stations that require the intervention of technicians to send data. The BRHIA climate department represents the data warehouse that collects all data from other meteorological stations via a Local Area Network (LAN); these data have been arc-hived as text format. At the end of the day, it connects to the other meteorological stations in order to fetch and save daily temperature report in text files. 2) Data Base Oracle Oracle database collects and organizes data from text files into tables; this mission was executed through PL/SQL procedural language packages and libraries.
At the end of each day, a schedule job procedure connects to the data warehouse available in the climate department, opens each text temperature data file, reads values of temperature parameter to finally insert them into the database; these libraries also have the capability to generate daily CSV files. 3) ARIMA methodology The realization of the ARIMA proposed model has been achieved after the execution of a sequence of processes illustrated in Figure  2. a) Data processing During this phase, null data is checked, in case there would be null data in the series, they would be replaced by the average values of the series itself related to a month where it has been found in a specific year. The data used in this article is: minimum daily temperature. b) Seasonality verification The result in Figure  3  shows that there is a seasonality in the series.
(  1 ) can be restated as: 𝑇 = 𝑘 𝑣 (𝑟𝑥) 2 Λ, (2) where r is the infiltration-excess runoff in units of L m -2 day -1 for each storm and x is the distance from the drainage divide in units of meters. The cumulative value of T that results from the frequency distribution of storm events that occur in a month can then be written as: ∑ 𝑇 = 𝑘 𝑣 𝑥 2 Λ ∑ 𝑟 2. (3) Eq. (  3 ) is used in a relation that allows for estimates of the hillslope-length averaged Sediment Yield (Y) to the slope base, or: 𝑌 = ∑ 𝐶 𝑏 𝑥 𝑏 = 𝑘 𝑣 𝑥 𝑏 Λ 𝑏 ∑ 𝑟 2 (4) where the unit of Y is kg m -2 day -1 and the subscript b denotes an evaluation at the hillslope base. Eq. (  4 ) does not consider fractions of sediment stored within the hillside during soil transfer, and PESERA does not model soil transfer through the catchment network.
At each time-step, V is determined by adding the net primary productivity (NPP) to the previous time-step's biomass. The NPP, in turn, is calculated as: 𝑁𝑃𝑃 = 𝐺𝑃𝑃 -𝛴 -𝛺, ( 10 ) where GPP is gross primary productivity, Σ is respiration, and Ω is leaf and root fall. GPP is determined as a linear function of plant water use based on available empirical data  (Lieth et al. , 1975) ; note, however, that these data are for NPP and, thus, the model will conservatively underestimate the amount of biomass generation. The variable Σ is determined as a function of temperature, and Ω contribute organic matter to the soil at a rate that is dependent on biomass; the soil organic matter, in turn, decays at an exponential rate dependent on temperature.
Terrain data Topographic information for each catchment was derived from the Landmap Digital Terrain Model, which provides photogrammetrically derived elevation-data at 5-m resolution resampled, which we resampled to 100-m to fit an affordable resolution for modelling. Data for soil characteristics of the Conwy and Ehen catchments were obtained under license from the UK National Soil Research Institute (NATMAP1000, National Soil Map of England and Wales, 2013), and of the Dee from the James Hutton Research Institute (Soil Information for Scottish Soils, SIFSS). The soil data were used to give field capacity and saturated water capacity for each dominant soil series. At a resolution of 100-m with soils data of a lower resolution (1-km) than used in PESERA, crusting and erodibility were obtained through a conversion from soil texture (percentage of sand, silt and clay)  (Baggaley et al.
, 2010 (Baggaley et al. , , 2011)). Land cover data were obtained from the UK Centre for Ecology and Hydrology under the Land Cover Map 2000 (LCM2000) based on Landsat image data. Most of each of the catchments is covered by forest or grassland, providing a permanent cover of the soil surface throughout the year. Agricultural crops consist of vegetables, forage, and other minor cultures such as root crops and oilseeds. These cultures have a harvesting cycle mainly between March and August, and specific calendars for planting and harvesting have been obtained from UK Agri-Environment Offices (Table  1 ).
Sensibility Analysis implementation Temperature and rainfall increments were examined using an experimental framework, combining temperatures ranging from 0 to 8 °C and rainfall variations from -25 to +100% compared to the baseline values (i. e. , averages of 1961/1990 dataset). It should be noted that this combination attempts to represent and explore how the PESERA models react to a systematic change of these two variables and is not used for climatic modelling of the area (Figure  4 ). Within the charts, dots indicate the points related to the effective rainfall temperature combination of the three climatic scenarios derived from UKCP09 SRES A1B simulations. The procedure is useful to observe the reaction of the model, explore values other than those from real simulated scenarios, and to test different feedbacks in the context of climate and vegetation type.
Nevertheless, Great Britain presents consistent literature on soil erosion and a rich dataset of soil erosion observations, which has been collected since the 1960s using a wide range of methodologies on various spatial and temporal scales  (Boardman, 2006 (Boardman, , 2013;; Boardman et al. , 1990; Boardman & Evans, 2006 , 2019; Brazier, 2004; Evans, 1995 Evans, , 2005;; Evans et al. , 2016 Evans et al. , , 2017)). In situ observations over long periods have been performed in several programs. For instance, studies, such as the regional Soil Survey of England and Wales  (SSEW, 1982 -1986 ), Soil Survey and Land Research Centre (SSLRC, 1996 -1998)  on the location of the National Soil Inventory (NSI), and Agricultural Development and Advisory Service  (ADAS, 1989 (ADAS, -1994)) , have constituted some of the largest efforts in quantifying soil erosion nationwide until now  (Boardman, 2002; Evans, 2005).
Recent works inventoried Great Britain erosion data to improve the understanding of the phenomenon. Benaud et al. (2020)  realised a web-based, open-access, interactive geodatabase from previous records of soil erosion, which is at now available and integrable by users (i. e. , https://piabenaud. shinyapps. io/SoilErosionMap), that undoubtedly provides excellent support to collect past and future data as support for modelling validations. For a general reference of the phenomenon, a total of 1566 individual records across Great Britain have reported soil loss averages of 1. 27 and 0. 72 t ha -1 y -1 for arable and grassland, respectively. In addition,  Graves et al. (2015)  grouped erosion values from the available national datasets for the principal soilscapes of Great Britain and determined erosion averages of 1.
22 t ha-1 y-1 in medium and light sandy loams, while  Skinner and Chambers (1996) , and similarly  Evans (1993 Evans ( , 2013)) , reported rates of 1. 5 m3 ha-1 for a four-year record in the same catchment. The available information for Northern Wales (Conwy catchment) is, for the most part, referred to as the extensive grassland of the region. For instance, James et al. (1998) estimated annual soil loss ranging 0-2. 7 t ha -1 y -1 from grassland in Clwydian Hill using plot simulations. McHug et al. (2002 McHug et al. ( , 2007) )  observed general increases in highlands of Wales and England fields and no changes in erosion for sites of northern Wales based on with data collected from  1997-1999 and 2001-2002. Much of what we know about erosion rates on agricultural land in Eastern Scotland (Dee catchment) comes from individual studies  (Davidson et al.
, 2001). Watson and Evans (2008)  obtained large distribution values with a median of about 2. 5 m 3 ha -1 from field observations in agricultural lowlands. Rickson et al. (2019)  utilized Scottish Environment Protection Agency (SEPA) surveys on 10 selected catchments and summarized previous datasets to estimate erosion rates of 0. 01-23. 0 t ha -1 yr -1 in arable areas of Scotland in addition to soil losses of 0. 13-0. 33, 0. 07-0. 12, and 0. 04-0. 07 mm yr -1 for arable land, rough grassland, and forests, respectively.
Acknowledgments, Samples, and Data We thank  Emmanuel Gabet ,  Loraine Whitmarsh ,  Nikki Baggaley , and  Elizabeth B. Kendon  for assistance in hypothesis development and data collection. The study was supported by an  ESRC/NERC   Interdisciplinary Research Studentship  to  K. Walker-Springett  ( ES/I004165/1 ) and funding from the  Climate Change Consortium of Wales for R. Ciampalini. J. A. C. and S. J. O. conceived of the study. R. C. and K. W. -S. compiled baseline data and conducted model simulations. T. C. H. and I. R. H. assisted in hypothesis development and provided expertise in climate-change impacts and soil erosion. R. C. , K. W. -S. and J. A. C. led data analysis and interpretation, assisted by all co-authors. J. A. C. drafted the paper, which was then reviewed by all co-authors.
For example, in both S. sulfolobus and T. kodakarensis, A9 was demonstrated to be catalyzed by N1-methylating enzyme Trm10  (Kempenaers et al. 2010; Krishnamohan et al. 2019) , an enzyme for which the corresponding gene (PF0678) also exists in P. furiosus (Supplemental Table  S2 ). Likewise, the detected monomethylation of C56 in M. maripaludis, P. furiosus and S. acidocaldarius was interpreted as Cm56, again based on the presence in their genomes of the gene coding for a well characterized almost ubiquitous Trm56 enzyme that is present in all archaea sequenced so far (Supplemental Table  S2 ). All these putative modifications are indicated as (XXX) *. When uncertain, the methylation is noted as a 2 ′ O-methyl and underlined. Likewise, the modifications not completely identified are underlined.
2000; Omer et al. 2000; Clouet d'Orval et al. 2001; Dennis et al. 2001). From the database maintained by Todd Lowe (provided at http://lowelab. ucsc. edu/snoRNAdb/), this enzymatic machinery in S. acidocaldarius could target sitespecific methylations to tRNA-Gly(CCC) at C50 in the Darm  (Tang et al. 2005; Zago et al. 2005)  and to tRNA-Gln (UUG) at U34 and G18 of the AC-and D-loops, respectively (see also  Ziesche et al. 2004 ), a situation indeed observed in the present study. However, in S. acidocaldarius, a few other observed monomethylated nucleotides remain orphan and await corresponding snoRNAs or stand-alone specific methyltransferases to be identified (Table  1 ; Supplemental Table  S2 , they are indicated as Xm). Likewise, in tRNAs of P. furiosus, several positions were predicted to be methylated via the Fibrillarin-C/D box machinery: tRNA-Trp(CCA) at C32, C34, C39, tRNA-Leu(UAA) at G47, tRNA-Leu(CAA) at C34, tRNA-Gln at C16, tRNA-Val and tRNA-Gly at G26 and tRNA-Asp(GUC) at U35 (http ://lowelab.
The locations of residues 16 or 17, either within or outside the tRNA core, may influence interactions between other residues in the T-D environment. When residue 16 is a pyrimidine, it is often observed that it forms a pair with residue 59, especially when 59 is also a pyrimidine. However, in the present archaeal tRNAs, residue 59 is always A and residue 60 is mostly U. In that case, a couple of crystal structures show A59 stacked on G15-C48 and U60 forming an H-bond between N3(U60) and the phosphate group between the two invariant G18-G19 of the D-loop (see for example PDB entry 2DU3, Supplemental Fig. S12C ;  Fukunaga and Yokoyama 2007). As stated above the modified G bearing a positively charged formamidino group (G + , archaeosine), a hallmark of archaeal tRNAs, also contributes to the global stability of the 3D-core of tRNA.
Data analysis All CID were deconvoluted using MassLynx software from Waters and manually sequenced by following the y and/or c series (w ions were also useful when sequencing was difficult or in order to confirm the sequence). Experimental masses of parents and fragments were compared to the theoretical masses obtained by the Mongo Oligo Mass Calculator (https://mods. rna. albany. edu/ masspec/Mongo-Oligo;  Cantara et al. 2011). tRNA identification was done by comparisons with the genomic sequences obtained from GtRNAdb (http://gtrnadb. ucsc. edu/; Chan and Lowe 2009, 2016). Data about nucleoside modification were obtained from Modomics  (Boccaletto et al. 2018).
Data analysis by MassSpec-Toolkit for RNAs The identification and characterization of modified tRNAs by LC-MS/MS spectrum analysis is difficult and time-consuming. To help in this process, we implemented MassSpec-Toolkit for RNAs, a Python web application (http://labex-ibmc. u-strasbg. fr/ MassSpec-Toolkit/, accessible upon request) linked to a local MongoDB database that stores user-provided RNA genomic sequences and their theoretical digestion products obtained by specific ribonucleases. For each studied species, mature tRNA sequences retrieved from GtRNAdb  (Chan and Lowe 2009, 2016)  were submitted to the application and digested in silico with RNases T1, A and/or U2, in the "RNA Digestion" module. Genomic tRNA sequences longer than 100 nt were discarded and for the remaining ones with a nonambiguous anticodon position, fragments with common U34 or G34 and/or A37 or G37 modifications were generated when appropriate.
In addition, fragment variants containing up to five additional methylations were also computed for each digestion product. Experimental data such as parent ion masses or manually reconstructed subsequences could then be compared to the theoretical ones in the "RNA Search" module. Additional criteria, like the species of interest, the ribonuclease used, as well as the presence of expected methylations or modifications at specific positions, can be specified to reduce the search space in the database. Candidate tRNAs are given a score comprised between 0 and 1 depending on the number of matching masses or subsequences they present with the list provided by the user. Besides these two main modules, the application gathers a set of "Additional Tools" under a third module that includes some of the tools present in the Mongo Oligo Mass Calculator  (Cantara et al.
ACKNOWLEDGMENTS We wish to thank the three reviewers for their constructive comments. E. W. wishes to thank  G. and D. Crahay  for their hospitality during the coronavirus lockdown. The project was supported by the  French National Program Investissement d'Avenir (Labex NetRNA ) administered by the  Agence Nationale de la Recherche  ( ANR-10-LABX-0036_NETRNA ) and  ANR-15-CE11-0021-01  [to E. W. ]) and by the EpiRNA funding from the  Region Grand Est. Received August 1, 2020; accepted September 9, 2020.
OBSERVATIONS 2. 1 TESS photometry TOI-561 was observed by TESS in two-minute cadence mode during observations of sector 8, between 2 February and 27 February 2019. The astrometric and photometric parameters of the star are listed in Table  1. Considering the download time, and the loss of 3. 26 days of data due to an interruption in communications between the instrument and the spacecraft that occurred during sector 8 1 , a total of 20. 22 days of science data were collected. The photometric observations for TOI-561 were reduced by the Science Processing Operations Center (SPOC) pipeline  (Jenkins et al. 2016; Jenkins 2020) , which detected three candidate planetary signals, with periods of 10. 8 days (TOI-561. 01), 0. 4 days (TOI-561. 02), and 16. 4 days (TOI-561. 03), respectively. The pipeline identified 55 transits of TOI-561.
stsci. edu/tess/tess_drn. html). Wright et al. 2010 ). F) Based on  Pecaut & Mamajek (2013) , assuming Gaia DR2, Johnson, 2MASS and WISE color indexes. Gaia DR2 parallax is corrected by +50 ± 7 as (with the error added in quadrature) as suggested by  Khan et al. (2019). slider implemented in the wotan package  (Hippke et al. 2019) , with a window of 1. 5 days, and masking the in-transit points to avoid modifications of the transit shape. In order to obtain an independent confirmation of the signals detected in the TESS light curve, we performed an iterative transit search on the detrended light curve using the Transit Least Squares (TLS) algorithm  (Hippke & Heller 2019). The first three significant identified signals nicely matched the TESS suggested periods ( TLS = 10. 78 d, 0. 44 d, 16.
2 ± 0. 6 m s -1. We reduced the data using the standard HARPS-N Data Reduction Software (DRS) using a G2 flux template (the closest match to the spectral type of our target) to correct for variations in the flux distribution as a function of the wavelength, and a G2 binary mask to compute the cross-correlation function (CCF,  Baranne et al. 1996; Pepe et al. 2002). All the observations were gathered with the second fibre of HARPS-N illuminated by the Fabry-Perot calibration lamp to correct for the instrumental RV drift, except for the night of May 31, 2020. This observation setting prevented us from using the second fibre to correct for Moon contamination. However, we note that the difference between the systemic velocity of the star and the Moon is always greater than 15 km s -1 , therefore preventing any contamination of the stellar CCF (as empirically found by  Malavolta et al.
2017a  and subsequently demonstrated through simulations by Roy et al. 2020), as the average full width at half maximum (FWHM) of the CCF for TOI-561 is 6. 380 ± 0. 004 km s -1. The RV data with their 1 uncertainties and the associated activity indices (see Section 3. 3 for more details) are listed in Table  2. Before proceeding with the analysis, we removed from the total dataset 5 RV measurements, with associated errors greater than 2. 5 m s -1 from spectra with S/N < 35, that may affect the accuracy of our results. The detailed procedure performed to identify these points is described in Appendix B1.
Mass, radius, and density of the star For each set of photospheric parameters, we determined the stellar mass and radius using isochrones  (Morton 2015) , with posterior sampling performed by MultiNest  (Feroz & Hobson 2008; Feroz et al. 2009 Feroz et al. , 2019)). We provided as input the parallax of the target from the Gaia DR2 catalogue, after adding an offset of +50 ± 7 as (with the error added in quadrature to the parallax error) as suggested by  Khan et al. (2019) , plus the photometry from the TICv8, 2MASS and WISE (Table  1 ). We used two evolutionary models, the MESA  Isochrones & Stellar Tracks (MIST, Dotter 2016; Choi et al. 2016; Paxton et al. 2011 ) and the Dartmouth Stellar Evolution Database  (Dotter et al. 2008). For all methods, we assumed   eff = 70 K,  log g = 0. 12,  [Fe/H] = 0.
2017) , after applying a 5- filtering, but no significant periodicity could be identified. A periodogram analysis of the ASAS-SN light curves in each band, either by taking the full dataset or by analysing each observing season individually, confirmed these results. In conclusion, if any activity is present, its signature must be below 0. 8 ppt in the short period (rotationally-induced activity, < 30 days), and 20 ppt in the long term period (magnetic cycles, > 100 days), from the RMS of TESS and ASAS-SN photometry respectively. Incidentally, the former is close to the photometric variations of the Sun during the minimum at the end of Solar Cycle 25, when the Sun also reached a log R HK very close to the one measured for TOI-561  (Collier Cameron et al. 2019; Milbourne et al. 2019). By comparing our target to the Sun, and in general by taking into account the results of  Isaacson & Fischer (2010) , it is expected that the contribution to the RVs due to the magnetic activity of our star is likely below 1-2 m s -1.
01 and. 03 occur on our target star, while candidate. 02 has too few in-transit points in the 30-minute cadence images for this kind of analysis -in any case, its planetary nature will be confirmed by the RV signal of TOI-561 in Section 6. Finally, in order to exclude the possibility that the transit-like features were caused by instrumental artefacts, we performed some additional checks on the light curve. We visually inspected the FFIs to spot possible causes (including instrumental effects) inducing transit-like features, and we could not find any. We re-extracted the short cadence light curve using the python package lightkurve 9 (Lightkurve Collaboration et al. 2018) with different photometric masks and apertures, and we corrected them by using the TESS Cotrending Basis Vectors (CBVs); the final results were in agreement with the TESSreleased PDCSAP light curve.