dataesr/scientific-paragraphs-categorization huggingface dataset:
Acoustic impedance and atmospheric coupling For the first time, owing to the survey over one Martian year of the laser-induced acoustic signal amplitude, we measure seasonal variations of the acoustic impedance on Mars and understand how to correct SuperCam recordings accordingly. The laser-induced acoustic signals acquired over nearly a Martian year respond to seasonal changes at the plasma-atmosphere and microphoneatmosphere interfaces, which are caused by the temperature and pressure cycles of the Mars atmosphere. In this section, for each acoustic point, we consider the mode of the distribution described in Section 3. 1 as the recorded laser-induced acoustic amplitude. Then, in order to compare recordings acquired at various distances, sound amplitudes are corrected for the atmospheric and instrumental attenuation: the decrease of the sound amplitude as a function of the distance is fitted following Equation (2) and subsequently normalized to 2 m, the distance of the closest target of the dataset.
However, the relationship between target properties and the laser-matter coupling is still poorly understood  (Chide et al. , 2020). Therefore, this hypothesis should be investigated further and compared with the elemental composition retrieved with LIBS for each target. To that end, the atmospheric correction proposed here is a prerequisite and a critical step. This correction is also necessary to study the sound amplitude evolution as a function of the distance for the entire dataset and, therefore, to retrieve the acoustic absorption.
The attenuation points from laser sparks computed here are lower than the one computed previously on Mars  (Maurice et al. , 2022). Indeed, the former study (using only data up to L s = 104 • ) did not consider the variation of acoustic impedance, nor the turbulence scattering. In addition, the echoes were not filtered out of the time series. Therefore, for some frequency bands, not only the energy of the direct arrival may have been integrated, which explains the observed bias between their points, this study and the model. The high frequency points (> 7 kHz) tend to match for the models computed at L s = 0, 70 and 270 • (i. e. , from Northern winter to spring). Indeed, these two seasons include most of our dataset (see Fig. 3a ) and it explains why the attenuation is dominated by these atmospheric conditions.
Acknowledgements The authors thank the  Mars2020 Science and Engineering teams  for their work supporting the mission that has enabled the scientific research presented in this manuscript. The author would like to thank  Alice Le Gall  for helpful comments and discussions. This work was funded in the  US  by  NASA Mars Exploration Program  support to the Mars 2020 mission, and in France was conducted under the authority of  CNES  and  CNRS. B. C. is supported by the Director's  Postdoctoral Fellowship  from the  Los Alamos National Laboratory , grant  20210960PRD3. R. H. , A. M. , and A. S. -L. are supported by Grant  PID2019-109467GB-I00  funded by  MCIN/AEI/10. 13039/501100011033/  and by  Grupos Gobierno Vasco   IT1742-22. F. S. and S. S. are supported by  DLR. R. L. is supported by  JPL  contract  1655893.
The authors report the presence of a dust lane in the optical, and concluded that soft X-ray emission (below 3 keV) corresponds to the region where the dust lane is thinner. Piconcelli et al. (2007)  analysed XMM-Newton data from observations of 2001 and 2005. They proposed a model for the absorption that consists of a thin neutral absorber and an inner thick neutral absorber composed of clouds where X-ray photons are reflected via Compton scattering. Bianchi et al. (2009)  analysed four Suzaku and one XMM-Newton observation, taking place in 2007, which were compared to the previous XMM-Newton observations of 2001 and 2005. The authors observed an increase in the column density of the inner thick absorber during the Suzaku campaign. They invoked a third, Compton-thin, absorber in their model, that is associated with the dust lanes outside of the torus observed with Chandra and HST.
OBSERVATIONS AND DATA REDUCTION NGC 7582 was observed five times by XMM-Newton between 2001 and 2018 and three times by NuSTAR between 2012 and 2016, including a simultaneous observation between XMM-Newton and NuSTAR in 2016. We use all of the archival XMM-Newton and NuSTAR observations, except the XMM-Newton observation of 2018. The source was off-axis by more than 10 arcmin, falling on a bad pixel column. The data from this observation could not be used as they suffered from calibration issues in energy and normalisation. We present in Table  1  the observations used in this work and the associated net exposure times.
XMM-Newton XMM-Newton data from the EPIC instruments pn  (Strüder et al. 2001)  and MOS  (Turner et al. 2001)  were reduced using the Science Analysis System (SAS 20. 0;  Gabriel et al. 2004 ). We used the recent empirical calibration 1 to improve the spectral agreement between XMM-Newton and NuSTAR above 3 keV. We filtered periods of high background flaring activity in the event lists with the recommended rate threshold of 0. 4 Count s -1 for the EPIC-pn and 0. 35 Count s -1 for the EPIC-MOS detectors. The source was extracted from a circular region of 30 and the background was extracted from a circular source-free region of the same chip with a radius of 60. Source and background light-curves were extracted in the soft energy band 0. 5-3 keV and in the hard energy band 3-10 keV using the task evselect.
NuSTAR NuSTAR  (Harrison et al. 2013 ) data were reduced using the NUSTARDAS 2. 1. 1 package part of HEAsoft 6. 29 and the calibration files of CALDB 20210210. The task nupipeline was used to produce calibrated data as well as filtered event lists. Source and background regions were selected as circular regions with a radius of 80 on both instruments FPMA and FPMB. Source and background light-curves were extracted for the 3-10 keV and 10-40 keV energy bands using the task nuproducts. We use NuSTAR data only up to 40 keV as they are background dominated above this energy. To keep consistency with the XMM-Newton observations a temporal binning of 1 ks was chosen. Source and background spectra, ARF, and RMF, are extracted using the task nuproducts and grouped to at least 30 counts per energy bin.
SPECTRAL ANALYSIS In this work, the spectral analysis is performed with the X-ray Spectral Fitting Package (XSPEC; Arnaud 1996) and its Python wrapper PyXspec. We adopt the cross-sections of  Verner et al. (1996)  and the chemical abundances reported in  Wilms et al. (2000). Cash statistic (C-stat; Cash 1979) is used to account for the Poisson distribution of photons. The fitting is performed using the Bayesian X-ray Analysis package (BXA 2  ;  Buchner et al. 2014 ) bridging XSPEC with nested sampling Monte Carlo algorithm implemented in UltraNest  (Buchner 2016 (Buchner , 2019 (Buchner , 2021)). We use nested sampling for its unsupervised global parameter exploration and its naturally self-convergence criterion. We use wide log-uniform priors for normalisation and column density parameters and wide uniform priors for the other parameters.
Phenomenological model We fit the phenomenological model presented in Section 3. 1, finding good fits in all epochs. The best-fit parameters are presented in Table  4 , and the fitted spectra are shown in Figure  3  and the samples of the posterior distributions on the parameters are plotted in Figure  A1. The evolution of the fitted physical parameters is plotted in blue circles in Figure  4. The values of the cross-calibration factors are presented in Table  A1. The cross-calibration factors obtained can be up to 35% between EPIC and FPM instruments and up to 13% between EPIC cameras. As stated in the calibration technical note 4 these factors are expected to be high when using the most recent calibration files. The photon index value is usually higher when using XMM-Newton data because the data only extend between 3 and 10 keV.
ACKNOWLEDGEMENTS We thank the anonymous referee for their comments and suggestions. ML acknowledges useful discussions with  Johannes Buchner  on the UXCLUMPY model. DB/ESK/ML acknowledge financial support from the  Centre National d'Etudes Spatiales (CNES). ML acknowledges financial support through the  UK Science and Technology Facilities Council (STFC). AZ is supported by  NASA  under award number  80GSFC21M0002. This research has made use of data and/or software provided by the  High Energy Astrophysics Science Archive Research Center (HEASARC) , which is a service of the  Astrophysics Science Division at NASA/GSFC  and the  High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al.
Experiments are done on three datasets (Monster, Nigam and Minajobs), using TF-IDF and Doc2Vec as vectorization techniques, Naive Bayes, Decision Trees, Support Vector Machine (SVM), and Tranformers erchitecture (TFM) as machine learning models for top-N recommendation, and Precision, MAP and F1-score as evaluation metrics. Results show that, compared to classic job recommender systems, improvement rates can go up to 19%, 22% and 20% for systems based on Naive Bayes, go up to 34%, 37% and 34% for those based on Decision tree, go up to 33%, 34%, 34% for those based on SVM, and go up to 29%, 40% and 33% for those based on transformers architecture, respectively in the Monster, Nigam and Minajobs datasets.
That is why in this paper, we propose an extension of the conference article of Wabo et al. [7]  which introduces the beginnings of vectorization on a class-oriented document collection to get more useful keywords for job offer representation; with two machine learning models (DT and NB) and two datasets (Minajobs and Nigham). In this extension, we start with a deepening by introducing parameters that allow us to vary the size of class-oriented documents and keywords and also a combination of approaches, we add two other machine learning models (SVM and TFM) and also one more dataset (Monster). Finally we propose a more detailed and explained version of the idea previously proposed. The rest of this paper is structured as follows: the section 2 presents some work that uses vectorization in job recommender system, and the section 3 presents the Classic job recommender system based on supervised machine learning models, 4 describes how we proceed to apply vectorization on class-oriented document collection.
Decisions Tree Decision tree algorithm it is a method to build a model from a set of rules. We used the implementation of the ID3 version available in the scikit-learn python library 6. SVM consists of reducing a classification or discrimination problem to a hyperplane (feature space) in which the data are separated into several classes whose boundaries are as far away as possible from the data points (or "maximum margin"). Inspired by Tiro et al. [16]  who show that SVM provides very good performance and Prafajar et al. [6]  who show that SVM is stable. We used the sklearn implementation sklearn. svm. SVC  7.
Datasets used We use three data sets. The first, which we have named "monster", is a collection of 676 job offers for 03 profiles scanned on monster  9. These are jobs in the field of information technology. The second, which we call "nigam", is available on kaggle  10  and has already been used by Nigam et al. [4]. It contains 2,334 job offers for xxx user profiles. We have extracted the job offers in the field of computer science and information technology, which corresponds to 790 job offers for 07 user profiles specialised in information technology. The third is a subset of 1,005 job offers for 9 user profiles in the field of computer science, extracted from the minajobs 11  platform.
Evaluation protocol To assess the top-N job recommender systems considered, each dataset is split in two parts: 70% for training and 30% for testing. Recommender systems are built on the training set, and after applying them on test set, we have top-N recommendations which are evaluated using three top-N values (5, 10 and 50) and three evaluation metrics (Precision, MAP (Mean Average Precision) and F1-score)  [20]. The first evaluation metric Precision, is the ratio of the number of good recommendations on the total number of recommendations, when MAP is the mean of the means of precision's and it allows to evaluate the recommendation taking into account the position of good recommendations in the top-N list. The F1-score is a compromise between precision and recall so that the F1-score is more robust than precision and recall.
Results and comments The figure 5 presents all the best results according to parameter values of job recommender systems following all the top-N values and evaluation metrics considered. For each dataset, we have 08 sections each of 09 blocks. In the first line of each section, there is a machine learning model (DT (Decision Tree), NB (Naive bayes), SVM (Support Vecteur Machine), TFM (Transformers) ) associated with a basic vectorization technique [BASIC -(TFIDF or Doc2Vec)]. In each block of each section, the job recommender system of the first line is compared to class-oriented vectorization variants (CO, ZIPF, OWDC) which are our contribution. The green color in a cell of a block means that the job recommender system at line has the best performance according to the evaluation metric at column and is the best without exaequo.
Improvement rate in Nigam dataset The improvement rates of the Decision Tree model (DT) according to Precision metric range from 13% to 37%, according to MAP from 18% to 32% and according to F1-Score from 13% to 34%. Those of the Naive bayes model (NB) range from 4% to 22% for Precision, 6% to 11% for MAP and 4% to 20% for F1-Score. Those of the Support Vector Machine (SVM) range from 7% to 34% for Precision, 9% to 32% for MAP and 7% to 34% for F1-Score. And those of the Transformers architecture model (TFM) range from 3% to 37% for Precision, 7% to 30% for MAP and 4% to 40% for F1-Score.
Improvement rate in Minajobs dataset The improvement rates of the Decision Tree model (DT) according to Precision metric range from 13% to 34%, according to MAP from 18% to 32% and according to F1-Score from 13% to 34%. Those of the Naive bayes model (NB) range from 4% to 20% for Precision, 6% to 11% for MAP and 4% to 20% for F1-Score. Those of the Support Vector Machine (SVM) range from 6% to 42% for Precision, 9% to 32% for MAP and 6% to 34% for F1-Score. And those of the Transformers architecture model (TFM) range from 3% to 31% for Precision, 9% to 32% for MAP and 6% to 34% for F1-Score.
Improvement rate in Monster dataset The improvement rates of the Decision Tree model (DT) according to Precision metric range from 6% to 30%, according to MAP from 11% to 26% and according to F1-Score from 10% to 34%. Those of the Naive bayes model (NB) range from 0% to 17% for Precision, 0% to 11% for MAP and 3% to 19% for F1-Score. And those of the Support Vector Machine (SVM) range from 5% to 26% for Precision, 0% to 26% for MAP and 3% to 33% for F1-Score. And those of the Transformers architecture model (TFM) range from 3% to 41% for Precision, 7% to 30% for MAP and 4% to 82% for F1-Score. Use of OC, ZIPF and OWDC according to the machine learning model By carefully reading the results of the figure  6 , we can see that from the point of view of learning models decision tree (DT), Naive Bayes (NB), Support Vector Machine (SVM) and Transformers (TFM), OWDC (34/129) and ZIPF (8/30) work better in the case of decision tree, ZIPF (16/30) and OC (15/55) works better for the Naive Bayes model, OWDC(29/129) and OC (9/55) works better for the Support Vector Machine model and OWDC(45/129) and OC(6/55) works better for the Transformers architecture model.
Data availability and access The authors declare that three datasets were used in this article, The first, which they have named "monster", is a collection scanned on https://www. monster. fr/ accessed 21 October 2020. The second, which we call "nigam", is available on https://www. kaggle. com/ jsrshivam/job-recommendation-case-study accessed 15 April 2022. The third was extracted from http://minajobs. net accessed 21 October 2020. The code and the cleanned data set are available from the corresponding author by request.
Conclusion The goal in this work was the application of vectorization on class-oriented document collection to improve job recommender systems performances. To this end, we have proposed three strategies for building class-oriented documents. The first one is OC strategy, for a given class, its class-oriented document contains the most recurrent words in the job offers of this class. The second strategy is Zipf, which uses the Zipf's law to attenuate the penalty on the least recurrent words, and the last strategy is OWDC, which does not only consider the number of occurrences but makes sure that the word appears in the maximum job offers of the class. Experiments are performed on three datasets (Nigam, Monster and Minajobs), using two supervised machine learning algorithms (Decision tree, Naive bayes and SVM), two vectorization techniques of text classification (TF-IDF and Doc2Vec), considering three top-N metrics (Precision, MAP and F1-score) and three top-N values (5, 10 and 50).
Our results show that, whatever the case, the best performance is always reach by a job recommender system resulting from our contribution. Compared to classic job recommender systems, improvement rates can go up to 19%, 22% and 20% for systems based on Naive Bayes, go up to 34%, 37% and 34% for those based on Decision tree, go up to 33%, 34%, 34% for those based on SVM, and go up to 29%, 40% and 33% for those based on transformers architecture, respectively in the Monster, Nigam and Minajobs datasets.
Data collection Observations were conducted on three troops (J, L and M) during six annual field seasons between 2014 and 2019. Data collection occurred over two-to three-month periods in each field season (approx. two months in 2014, 2016 and 2017; five months in 2015; three months in 2018 and 2019). Troops J and L were followed during all years, while M, which fissioned from J troop between 2016 and 2017, was followed only in 2017 and 2019. During the study periods, baboons were usually followed daily from dawn to dusk, and data were recorded on all recognizable individuals (see below) with the application CyberTracker  [87]. The study population is habituated to the presence of observers on foot and the majority of the individuals were individually recognizable, apart from the unmarked juveniles and infants (the proportions of recognizable individuals were: 0.
The number of individuals sampled in proximity scans varied across the study years, ranging from 44 to 59 for J troop, from 41 to 74 for L troop and remaining stable at 21 for M troop. In each month, each individual was sampled 4-13 times (averaged per year) in J troop (300-701 total observations per month-averaged per year), 8-18 times in L troop (423-1037 total per month) and 13-20 times in M troop (273-427 total per month). Grooming interactions were recorded ad libitum  [90]  while moving through the troops. In this way, the ad libitum-collected data were not biased towards the individuals who were easier to find and, as such, we did not account for individual baboon observability. We collected data on the identities of the baboons grooming and the directionality of grooming events (giver to recipient).
Data analyses Data analyses were performed with R v. 4. 0. 2  [99]. Following previous studies with sparse network data  [100] [101] [102] [103] , we used multiple static networks (i. e. describing a network sampled over a particular length of time) aggregated in approx. 30-day time periods. The periods chosen for time-aggregated networks should be appropriate for the kind of questions investigated and the available data resolution  [20, 104]. We chose these periods of time because immatures undergo rapid developmental changes during their first year of life  [46] : this 30-day resolution allowed us to capture social network transformations during infancy as well as juvenile years. Proximity and grooming data were treated separately. The sampling periods were generated starting from the first day in which data were collected for a given field season; these dates were different for proximity and grooming data, thus generating different windows and numbers of periods overall.
To test our predictions, we used both linear mixed effects models (LMMs) and generalized linear mixed effects models (GLMMs) using the function (g)lmer from the package lme4. The package lmerTest was required to obtain p-values from models' summaries. The models are summarized in table 1. All models included the following fixed and random factors. The independent variables (fixed factors) of interest were age, sex and the interaction between age and sex. We then controlled as a fixed factor the mothers' relative dominance rank because rank is known to affect social bonds in primates  [39, 109, 110]. We also controlled as a fixed factor the mothers' number of offspring in the troop because the mothers' grooming time might have to be shared among her offspring  [111]. In addition, the number of maternal siblings may influence, for instance, immatures' grooming allocations in case they prefer maternal kin to other individuals  [66, 112].
The models (described in further detail below) were presented as 'full models': the models did not undergo any simplification procedure after the a priori variable selection based on the current literature (see above and table  1 )  [115, 116]. We obtained R 2 values (coefficient of determination) from the package MuMIn and the function r. squaredGLMM. We checked for multi-collinearity using variance inflation factors (VIFs) with the package car and the function vif. For LMMs, we checked residuals' homoscedasticity. For GLMMs, we verified overdispersion with the package DHARMa and the function testDispersion. In all cases, VIF values were less than or equal to 3, indicating low collinearity  [117, 118] ; LMM models' residuals were normally distributed and GLMMs' under/ overdispersion, when significant, was within the range of 0.
For this analysis, we used the proportion matrix for grooming because we were interested in similarities between networks, and proportions better represent the distribution of grooming to/from different partners, independent of the total number of events. Immatures' grooming networks may be constrained by partner availability if they tend to be in proximity to the mother and could thus choose only among her social partners. Controlling for the time spent in the mother's proximity would potentially solve this issue  [126]. However, there was no significant effect of the proportion of time spent in proximity with the mother (see electronic supplementary material, §2). Because including this variable reduced our dataset (since proximity data were not available for every grooming period), it was not included in the final models.
Acknowledgements. We are grateful to all the field assistants that worked at the Tsaobis Baboon Project between 2014 and 2019 for their invaluable help in data collection. We also thank the  Ministry of Environment and Tourism  for research permission in Namibia, the  Gobabeb Research and Training Centre  for affiliation, the Tsaobis beneficiaries for permission to work at Tsaobis, the Snyman and Wittreich families, and  Johan Venter  for permission to work on their land.
Data accessibility. Research data and R scripts are available through the Dryad Digital Repository: Roatti, Vittoria; Cowlishaw, Guy; Huchard, Elise; Carter, Alecia (2023), Social network inheritance and differentiation in wild baboons, Dryad, Dataset, https://doi. org/10. 5061/dryad. ffbg79cz1 [129]. The data are provided in the electronic supplementary material  [166].