dataesr/scientific-paragraphs-categorization huggingface dataset:
Photodiode, Two Stage TE Cooled; 1001-1800 nm), and a SWIR2 detector (Graded Index InGaAs. Photodiode, Two Stage TE Cooled; 1801-2500 nm). The ASD Contact Probe consisted of a halogen bulb light source with a color temperature 2901 +/-10% • K. The contact probe spot size was 10 mm in diameter. Data acquisition and calibration procedures were performed using the ASD RS 3 software (ASD Inc. , Boulder, CO, USA)  [42]. The calibration of the ASD FieldSpec ® 4 Standard-Res spectroradiometer was performed by dark acquisition, calculated referencing the dark current calibration file, and by mean of the white reference measurement, acquiring a standardized white Spectralon ® ceramic material. After this calibration stage, the spectrum was acquired, and reflectance was then computed for each sample.
For the purposes of this study, only the SWIR range of the collected spectra was investigated. Hyperspectral imaging. Hyperspectral images were acquired in the SWIR range (1000-2500 nm) using the SisuCHEMA TM XL Chemical Imaging Workstation (Specim, Spectral Imaging Ltd. , Oulu, Finland), equipped with the ImSpector TM N25E imaging spectrograph (Specim, Spectral Imaging Ltd. , Oulu, Finland). A 31 mm lens with a field of view of 50 mm was adopted. The spectral resolution was 10 nm. This device combined NIR spectroscopy with high-resolution imaging. A push-broom hyperspectral camera acquired and built the hyperspectral image of sample line by line, simultaneously acquiring all wavelengths for each line. Image data were automatically calibrated in reflectance by measuring an internal standard reference target before each sample scan.
Data acquisition and handling. The data collected by XRF analysis were used to set reference classes for SWIR investigation. ASD FieldSpec ® 4 Standard-Res spectra ". asd" data files were stacked into an ASCII text file using ViewSpec Pro (ver. 6. 2. 0; ASD Inc. , Boulder, CO, USA). The ASCII text file was then imported into the MATLAB ® environment (MATLAB R2019a ver. 9. 6. 0; The Mathworks, Inc. , Natick, MA, USA) using "fieldspec_import3. m", an ad hoc written script  [43]. Imported data files were analyzed using Eigenvector Research, Inc. PLS_toolbox (ver. 8. 6; Eigenvector Research, Inc, Wenatchee, WA, USA) within the MATLAB ® environment. Hyperspectral images were also imported into the MATLAB ® environment and subsequently analyzed with the aid of PLS_toolbox and MIA_toolbox (ver. 3.
ng 2021, 6, x FOR PEER REVIEW 8 of 22 ASD FieldSpec ® 4 Standard-Res spectra ". asd" data files were stacked into an ASCII text file using ViewSpec Pro (ver. 6. 2. 0; ASD Inc. , Boulder, CO, USA). The ASCII text file was then imported into the MATLAB ® environment (MATLAB R2019a ver. 9. 6. 0; The Mathworks, Inc. , Natick, MA, USA) using "fieldspec_import3. m", an ad hoc written script  [43]. Imported data files were analyzed using Eigenvector Research, Inc. PLS_toolbox (ver. 8. 6; Eigenvector Research, Inc, Wenatchee, WA, USA) within the MATLAB ® environment. Hyperspectral images were also imported into the MATLAB ® environment and subsequently analyzed with the aid of PLS_toolbox and MIA_toolbox (ver. 3. 0; Eigenvector Research, Inc, Wenatchee, WA, USA) and finally concatenated together in order to create a hyperspectral image mosaic.
Acquired plastic scraps were divided into two classes according to the recommended Br concentration limits  [22] : "High Br content" (>2000 mg/kg) and "Low Br content" (<2000 mg/kg), based on XRF analysis. For each plastic scrap, 5 "single spot based" spectra acquisitions in reflectance mode were carried out using the ASD FieldSpec ® device. On the same areas, the average reflectance spectra measured on circular regions of interest (ROIs) of about 10 mm diameter were extracted from the hyperspectral images (Figure  4 ). The ROIs diameter was established in respect of the average area investigated by the spectroradiometer probe. The average reflectance spectrum of each ROI was compared with the corresponding spectrum measured by the single-point device. Data pretreatments and exploratory analysis.
For each device, classification models were developed in order to perform discrimination based on bromine content first and then to identify the polymer type. Classification models for bromine content. As shown in Figure  3 , the PLS-DA method was used in order to discriminate "High Br content" and "Low Br content" classes in the 1000-2500 nm spectral range by using the portable spectrophotoradiometer and the HSI benchtop system. Two classification models were set up: one for the spectra collected by the ASD FieldSpec ® 4 Standard-Res and the other for those acquired by the SisuCHEMA XL™. For these models, about 72% of the spectral data was used as the training set, whereas the remaining percentage (28%) was used as the validation set. In more detail, 26 samples were selected to be included in the training set and 10 individuals for the validation set.
Venetian Blinds (VB) was used as a cross-validation method for assessing the optimal complexity of the models and choosing the number of Latent Variables (LVs). In this case, 3 LVs were chosen for both models. The PLS-DA model built from FieldSpec data was applied to the reflectance spectra collected on the validation set samples (10 plastic scraps). The built PLS-DA of the SisuCHEMA XL™ system spectral data was first applied to the average spectra of the ROIs selected on the validation image ("average spectra") and then to the validation image ("imaging"-validation set) and the global set ("imaging"). Classification models for polymer identification. A similar procedure, as described in the previous paragraph, was followed in order to set up the PLS-DA models for polymer classification (Figure  3 ).
SWIR Spectral Analysis Exploratory analysis. Figure  4  shows an example of the strategy adopted to perform the five punctual acquisitions by the ASD FieldSpec ® 4 Standard-Res and the reflectance spectra acquired by the SisuCHEMA XL™ HSI system in the five corresponding ROIs. Figure  5  shows the raw reflectance spectra in the SWIR range (1000-2500 nm), included in the training and validation datasets, for the two devices. Spectral signature analysis for bromine content assessment. The raw reflectance spectra of the samples averaged according to the two bromine classes (high and low content), collected with the two devices, were preprocessed utilizing the same algorithms in order to correctly compare the further classification results (Figure  6 ). The raw reflectance spectra of samples characterized by a high Br content, differently from those characterized by a low Br content, show an extra absorption peak around 1469 nm.
Similar results were also achieved by  Amigo et al. (2015)  using this approach. In this latter case, a large number of pixels were not correctly assigned to the real polymer class  [33]. Overall classification models performances. Overall classification performances of the set-up models are reported in Table  7. The set-up models to discriminate Br classes with FieldSpec and SisuCHEMA spectral data achieved a Recognition of 88. 46% and 90. 00% for the training and validation set, respectively. The models performed to identify the type of polymer achieved a Recognition of 80. 56% and 88. 89% for FieldSpec data and SisuCHEMA (global set), respectively. The classification model based on the analysis of SisuCHEMA XL™ global set spectra was not able to classify four PS samples (E31, E39, E46, and E57) that were incorrectly classified as ABS (Sensitivity = 1.
Similar results were also achieved by Amigo et al. (2015) using this approach. In this latter case, a large number of pixels were not correctly assigned to the real polymer class  [33]. Overall classification models performances. Overall classification performances of the set-up models are reported in Table  7. The set-up models to discriminate Br classes with FieldSpec and SisuCHEMA spectral data achieved a Recognition of 88. 46% and 90. 00% for the training and validation set, respectively. The models performed to identify the type of polymer achieved a Recognition of 80. 56% and 88. 89% for FieldSpec data and SisuCHEMA (global set), respectively. Table  7. Overall classification model performances. Calculations were performed applying an object-based recognition logic that was assuming a discriminating threshold of more than three out of five spectra for a punctual analysis and of 50% or more pixels correctly assigned for the hyperspectral approach, respectively.
Conclusions and Future Perspectives This study was carried out in order to test the predictive ability of SWIR working instruments to discriminate plastic scraps with high bromine content from the low ones. This goal was achieved using two different equipment: a portable spectrophotoradiometer and a benchtop HSI system. The set-up models to discriminate bromine content classes with the portable spectrophotoradiometer and HSI data achieved equal Recognition (i. e. , 88. 46% and 90% for the training and validation set, respectively). The models performed to identify the type of polymer achieved a Recognition of 80. 56% and 88. 89% for FieldSpec and SisuCHEMA data (global sets), respectively. Both the instrumentations gave useful information about BFRs added polymer scraps, for example, to evaluate the performances of the currently adopted density separation process for brominated plastics in recycling plants, especially when dealing with different polymers characterized by similar densities, such as ABS and PS.
Continuous pressure measurements permit on-the-fly changes for power control to maintain a maximal temperature, set at 80 • C with the CEM Synergy TM software (software version 0. 9, Kamp-Lintfort, Germany). Different reaction times were tested (single dynamic cycle of 5 min, 10 min and three successive dynamic cycles of 5 min each) to evaluate the heating run necessary for the butylation with high stirring speed. The butylation advancement was followed by thinlayer chromatography with cyclohexane:ethyl acetate (1:1 v/v). After the butylation was completed, the solution was neutralized with a saturated solution of sodium carbonate. The esterified compounds were extracted two times with DEE. The combined organic fractions were washed twice with Milli-Q water and dried with anhydrous sodium sulphate before filtration on a PTFE cartridge (0.
GC-MS Analyses GC-MS analyses were carried out on a Thermo Scientific™ Focus system equipped with an Al 3000 autosampler and an ITQ™ 700 Series Ion Trap Mass Spectrometer (Ther-moFisher Scientific, Illkirch-Graffenstaden, France). A ThermoGOLD™ TG-5MS fused silica capillary column (5% diphenyl; 95% dimethyl polysiloxane) of 30 m length × 0. Data treatment was performed on Xcalibur TM software (software version 4. 3, Thermo Scientific, Waltham, MA, USA). Peak identification was achieved by comparison of retention time and mass spectra with molecular commercial standards and from the NIST MS Search 2. 0 database (last access in May 2021).
Acknowledgments: This research was financially supported by  Marie Skłodowska-Curie Innovative Training Network , the ED-ARCHMAT European Joint Doctorate, H2020-MSCA-ITN-EJD ED-ARCHMAT Joint Doctorate (Project  ESR9 , Grant Agreement No. 766311 ). The authors wish to thank  Nathalie Huet , research engineer in charge of preventive conservation and furniture management at the  DRASSM (France).
Funding: This research and the  APC  were funded by  Marie Skłodowska - Curie Innovative Training Network H2020-MSCA-ITN-EJD ED-ARCHMAT Joint Doctorate ,  ESR9 , Grant Number  766311. Work in Rome was funded by  Sapienza University  project  DISCOVER  ( RM120172AE4E11D6 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Acknowledgements. The author (P. H. ) is extremely grateful to  H. Fernholz  for his most comprehensive account of the history of  Euromech  from its foundation to 2000 (Fernholz 2010), the year of  G. Batchelor 's passing. Without the information contained in his book, the present article could not have been written. M. Okrouhlik (Prague) is warmly thanked for allowing P. H. to reproduce some of the photographs included in appendix 2 of Fernholz (2010). L. Lesshafft (LadHyX) is also acknowledged for his kind assistance in the final stages of this work. Declaration of interests. The author reports no conflict of interest.
We counted starches, phytoliths and all other microremains irrespective of type at 200-400x magnification (numerical aperture = 0. 95) using an absolute counting method with a light microscope under brightfield and cross-polarised light using Axio Scope A1, Zeiss microscope with AxioVs40 V 4. 8. 2. 0 software. For 15 samples, we used a two-stage decalcification process (staged decalcification). This has been proposed as an approach to extract microremains from the outer layer of the dental calculus samples separately from the rest of the sample to elicit information on surface contamination (For more information see  Warinner et al. , 2014; Hardy et al. , 2015). In many powdery samples, this approach would have been ineffectual, and we only used this approach on samples that occurred as large chunks.
(2002)  that assumed that 100% FRE = 545 ± 70 years, and δ 15 N values of 8. 3% and 17. 0% for 100% terrestrial and aquatic diets, respectively. As a part of AMS radiocarbon dating process, the carbon and nitrogen isotope measurements on some human bone samples (n = 5) that were AMS dated were also analysed (see Supplementary Materials 4). Only samples with C and N content ≥30% and 10%, respectively (van Klinken, 1999), C/N ratios between 2. 9 and 3. 6  (DeNiro 1985)  were used for interpretation and discussion. Published data were selected using the same criteria.
Acknowledgement The authors would like to thank  Jelena Marković  ( Laboratory for Bioarchaeology, Faculty of Philosophy, University of Belgrade ) for technical support as well as  Jugoslav Pendić  ( BioSense Institute, University of Novi Sad ) and  Đurđija Nikolić  for help with Fig. 1 and photographs of dental calculus. We are also grateful to  Tamara Blagojević  ( BioSense Institute, University of Novi Sad ) for the help and  Andrej Starović  ( National Museum Belgrade ) for the access to the material from Ajmana. We thank  Roisin Murtagh  and  Tobias Deschner  for laboratory access. We are very grateful to four anonymous reviewers and to the Editor  Robin Torrence  for their encouraging, critical and constructive comments which helped to improve the quality of the article.
Population This study used the data collected from the prospective multicentric trial EDUQ-CPNE study from January 2014 to July 2019, in 4 French neurologic departments of university hospitals (Dijon, Nancy, Reims, and Tours). The aim of the study was the evaluation of psychopathological dimensions and quality of life, prospectively every 6 months over 24 months of PNES patients. All participants gave their written informed consent (Agreement of the French Ethics Committee: 2012-A01580  -43). For the present study, the inclusion criteria were subjects over 18 years of age who have experienced at least 3 months of seizures. The diagnosis of PNES has been confirmed by video-EEG. For each participant, the recruitment was performed by a neurologist and a psychiatrist or a neuropsychologist with expertise in epilepsy and PNES.
Quality of life data We evaluated the QoL every 6 months using two validated scales, one generic (SF-36) and one specific (QOLIE-31). We defined initial QoL as the quality of life at the time of diagnosis before initiating psychological care. We used the general score of physical and mental QoL according to the SF-36 which is the most commonly used scale  [17]. We used the French version of the quality of life in epilepsy inventory (QOLIE-31) wich has been showed to satisfied the established psychometric criteria for validity and reliability  [18]. This scale initially design for epileptic population has been adapted to CNEP patients replacing the word "crisis" by "seizures". Only one of the seven dimensions: the "overall QoL" was take into account in this study. Scores of the QOLIE-31 are reported as a percentage of the maximum score, with higher scores indicating a better QoL  [19].
Clinical PNES data We collected the co-occurrence of epilepsy, the duration of the illness since the beginning of the PNES (years) and the delay of diagnosis (months). We collected the number of seizures that occurred in the 6 months prior to the interview, using a seizure calendar that was delivered to patients at every follow-up interview. Patients and their families were instructed on the way to completed it. The evolution percentage of the number of seizures was defined by the difference between the number of seizures at the last interview and at the inclusion reduced in percentage.
Psychopathologic data Level of depression was assessed by Montgomery-Âsberg Depression Rating Scale (MADRS), which defined depression for scores greater than or equal to 15 out of 60  [20]. Anxiety level was assessed using Hamilton Anxiety Rating Scale (HAM-A). Scores below 17 out of 56 indicate mild anxiety, between 18 and 24 mild to moderate severity and between 25 and 30 moderate to severe anxiety and greater than 31 severe anxiety  [21]. Alexithymia was measured according to the Toronto Alexithymia Scale (TAS)  [22]. Below 51, there is no alexithymia, between 52-60, alexithymia is possible, and alexithymia is certain for score above 60. We assessed the dissociation tendency using the Dissociative Experience Scale (DES)  [23]. It evaluates the frequency of different experiences of dissociation.
Traumatic data Trauma history in childhood was explored through the Childhood Trauma Questionnaire (CTQ), which investigates forms of sexual, physical, and emotional abuse or neglect in childhood. Through detailed questioning, we also screened for a traumatic sexual, psychological/ emotional, and physical events that have occurred in adulthood. The list of traumatic life events from the Clinician Administered PTSD Scale (CAPS-4) was used to explore the trauma lifetime. Post-traumatic stress disorder (PTSD) was diagnosed using the DSM-IV criteria. The history of traumatic event was categorized by the time of occurrence: childhood trauma, adulthood trauma and lifetime trauma (traumatic event occurring both in childhood and adulthood).
Mental health care consultations After the diagnosis, the patients were referred to a psychological follow-up either to the nearest community mental health consultation centre or to a private psychiatrist/psychologist. They were addressed for routine mental health care. Clinicians had varying levels of experience and knowledge of PNES, reflecting current real-life practice. A leaflet was sent to the mental health caregivers to inform them about PNES and the therapeutic possibilities to ensure a minimum knowledge of the disorder. The number of mental health consultations for each patient was collected through cross-referenced data from both mental health caregivers and patients. This data was collected by calling the mental health structures designated at inclusion by the patients and who were informed of the study.
Rawling et al. reported a greater predictive power of psychological distress on QoL of patients with PNES than seizure frequency or demographics data  [29]. In this study, 45 subjects with PNES and 62 patients with epilepsy were recruited from outpatient neurology clinics in a hospital in United Kingdom and from membership-led organizations for individuals who experience seizures. According to the study, patients with PNES who reported lower QoL, had higher scores on measures of depression and anxiety, and considered their condition as more threatening than patients with epilepsy. Jones et al. conducted a systematic review across 14 articles including 538 individuals with PNES  [14]. Their results also stated that interpersonal and psychological factors, such as depression, anxiety, and dissociation, were negatively correlated with QoL in PNES.
In the current article, we examine if and how phraseological use develops over the course of a stay abroad. In particular, we focus on how the frequency and collocational strength of noun (N) + adjective (Adj) combinations produced by learners evolve over time. Four characteristics of this study allow us to contribute novel insights to this line of inquiry. First, we report on longitudinal data that span a period of 21 months collected at three different points in time: before the participants went abroad, at the end of an academic year spent in the target-language community, and 8 months after their return to their home country. This wide timespan allows us to both explore the impact of a longer stay abroad and investigate potential changes after the return home. Second, the 28 participants we analyzed are learners of additional-language French, making this study one of the few that has investigated a target language other than English.
The following research questions were addressed: How do the frequency and collocational strength of N+Adj combinations used in written essays evolve both after an academic year spent in a target-language community and 8 months after the return home? What factors significantly predict frequency and collocational strength of N+Adj combinations? 3. 1. Method 3. 1. 1. The LANGSNAP Corpus and Participants Data analyzed for this project came from the LANGSNAP corpus. 3 This freely available corpus is the result of a longitudinal project carried out by a research team based at the University of Southampton (see  Mitchell et al. 2017). The LANGSNAP team followed 29 additional-language learners of French enrolled in a French degree program in the United Kingdom between May 2011 and February 2013, before, during, and after an academic year they spent in France (a similar group of learners of Spanish was also followed).
For this task, the same prompt was used at pre-stay and in-stay (see 3a), whereas a different prompt was used for post-stay (3b): Pensez-vous que, de manière à inciter les gens à manger sainement, on devrait taxer les boissons sucrées et les aliments gras? 'Do you think that in order to encourage people to eat in a healthy manner, sugary beverages and fatty foods should be taxed?' For this project, we analyzed the pre-stay, in-stay, and post-stay written productions from 28 of the 29 additional-language French speakers in the LANGSNAP database; participant 122 was excluded because she did not contribute data at in-stay. Three of the 28 learners were men, and 25 were women. All participants completed an elicited imitation (EI) test at the outset of the project, whose aim was to provide a measure of overall initial proficiency.
The Dataset under Study The dataset contains 84 essays, for a total of 17,292 words. Each essay was read in order to identify all instances in which a learner used an adjective to modify a noun within the same noun phrase. In this study, we adopted a broad understanding of adjective, which means that we included many of what  Durrant and Schmitt (2009, p. 166 ) called semi-determiners. For example, même 'same', tout 'all', certain 'certain', tel 'such', and both cardinal and ordinal numbers were included when they were used adjectivally. Examples from our dataset are provided in (4).
The three models were built using the package lme4 in RStudio (RStudio Team 2020). For each of the three models, the six independent factors presented in (6) were investigated. The creators of the lme4 package in R decided not to include p values for mixed-effects models because there is trouble estimating p values with these types of model structures. For this reason, we used a model comparison to arrive at the best-fit model, which means that we began with a model that contained only the random effect and then progressively added each independent factor. When the inclusion of a given factor significantly improved the fit of the model according to an ANOVA comparison, this factor was retained in the final model. Moreover, we checked for potential significant interactions between the factor time and any other significant factors, in order to examine how factors impacting N+Adj may have evolved over time.