dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION In the last decades the human population has grown exponentially, reaching 7,600 million people in 2018, and as the Food and Agriculture Organization (FAO) has predicted, in 2050 the world population will be near to 10,000 million 1. This fact involves an increasing pressure over global food production and the land surface dedicated to that purpose. However, ∼35% of the world surface is already dedicated to crop production, according to FAO database 2 , and increasing the crop land surface is not an ecologically valid solution, being in fact very controversial in most of the developing countries where population demands new eco-friendly politics. Only increasing crop yields appears as a possible solution to prevent food shortage in this future scenario, although the excessive use of NPK chemical fertilizers is already negatively affecting soil fertility, soil microbial activity, and may cause the pollution or/and eutrophication of water reservoirs  (Torrent et al.
Capillary sequencing was carried out by CIMA Lab Diagnostics. Sequencing reads were searched against RDP SeqMatch  (Cole et al. , 2014)  and BLASTn databases using default parameters. For the majority of the isolates, both databases provided the same taxonomical assignment. If not, BLASTn 3  taxonomical assignment prevailed. The reference sequences selected belonged to GenBank 16S partial sequences and were used for building the phylogenetic tree. The reference sequences and the sequences of the isolates were aligned by Clustal Omega web service 4  (Madeira et al. , 2019)  with default parameters except for the number of combined iterations, max guide tree iterations, and max HMM iterations, that were shifted from default to five in all of them. The alignment tree distances resulting from Clustal Omega were used as basic data to create the circular cladogram tree in iTOL 5   (Letunic and Bork, 2019).
Taxonomic Diversity of SHA Pre-treated Cucumber Culturable Endophytic Microbiota The number of viable endophytic bacterial isolates obtained from three different plants of cucumber previously grown in the presence of 100 ppm C SHA in the nutrient solution was 72. For the taxonomic identification of the isolates, BLASTn and RDP SeqMatch databases were utilized. Both identifications were similar, with only a misleading identification in one isolate (CR329. a, Supplementary Table  S1 ). Most of the microorganisms identified (97% of the isolates) showed ≥ 97% of similarity with reference sequences in BLASTn database. The cladogram tree represents the closest classification of isolates to reference sequences in BLAST (Figure  1 ). Most of the isolates belonged to the phylum Proteobacteria (44%), followed by Actinobacteria (24%), Firmicutes (31%), and Bacteroidetes (1%) (class, order, and family distributions of isolates are shown in Supplementary Figure  S1 ).
Study Conduct This database was created in 1992. It includes incident cases of HIV-associated histoplasmosis from the three hospitals in French Guiana. Epidemiological, clinical, paraclinical, immunovirological, and therapeutic data were collected on a standardized case recording form until October 2014. Hospitalized incident cases of HIV-associated histoplasmosis were included. Sex, age, place of birth, symptoms on admission, clinical entrance examination, immunovirological status, standard biological examinations, medical imaging, mycology, pathology, treatment received, and survival data were collected during the study period. Diagnosis of histoplasmosis was performed through direct examination and culture of samples obtained by biopsy or aspiration. Diagnosis was also obtained by histopathology and cytopathology using specific stains (Gomori-Grocott); antigen detection was not available, and molecular diagnosis was not routinely available.
There were too many missing pO2 variables in our cohort to include it in the analysis. Fever >39. 8 • C was not associated with a greater risk of death, and systolic blood pressure <90 mmHg was associated with a greater risk of death in univariate analysis only. In our cohort there were only 9 patients with meningitis, which precluded the use of this variable in the analysis. Coagulation abnormalities (prothrombin time < 70%) were not associated with an increased risk of death, but it is of note that there were many missing values in our dataset. The multivariate analysis showed that liposomal amphotericin B was associated with an 85% reduction of the risk of death in patients with renal failure. Liposomal amphotericin B was also shown to have superior efficacy in treating severe disseminated histoplasmosis in a randomized controlled trial  [12].
However, the results are directly transferable to real-world problem-solving: Which filter works best for a given combination of data density, landscape type, and type of archaeological features? In general, progressive TIN (software: lasground_new) and a hybrid (software: Global Mapper) commercial filter are consistently among the best, followed by an open source slope-based one (software: Whitebox GAT). The ability of the free multiscale curvature classification filter (software: MCC-LIDAR) to remove vegetation is also commendable. Notably, our findings show that filters based on an older generation of algorithms consistently outperform newer filtering techniques. This is a reminder of the indirect path from publishing an algorithm to filter implementation in software.
• Hilltop settlement (archaeology) • Buildings (modern) • Vegetation on steep slopes • Sharp discontinuities. The additional test site (SI2) was chosen for extremely dense vegetation as an example of data with significant gaps in the coverage of the ground points. Each site is 500 × 500 m, but only the most relevant windows are shown in illustrations. AT airborne LiDAR data was acquired in 2009. These data have a nominal average density of 4 pnt/m 2 and an estimated horizontal and vertical root mean square error (RMSE) of 0. 15 and 0. 40 m, respectively. Ground point filtering is not documented  [28]. Point cloud data are available via a formal request (we have acquired the data in the non-classified XYZ format, other formats may be available). The AT test site has 3,844,873 points with an average density of 18.
79 pts/m 2 , which is significantly higher than the nominal average density. The SI data was acquired in 2014. These data have a nominal density of 5 pnt/m 2 and an estimated horizontal and vertical RMSE of 0. 09 m. Ground point filtering was performed with the proprietary filter gLidar  [29]  and is distributed via the eVode WMS (LAS or LAZ format, ISPRS classes 0-7 are populated). The SI1 test site has 2,809,344 points with an average density of 12. 28 pts/m 2 and the SI2 has 2,756,756 points with an average density of 12. 43 pts/m 2. It must be mentioned that due to the specific processing of the raw data, each point has a near-double "shadow point". The "shadow point" is within 0. 14 m horizontally and within 0. 03 m vertically from the original point, but the distance and bearing to the original is not constant.
This means that the functional point density is half of the above-mentioned, namely 6. 14 pts/m 2. The ES data was acquired in 2015, with an average density of 1 pnt/m 2 and the altimetric accuracy and precision with a RMSE of less than 0. 20 m. Ground point filtering is not documented  [30]. The point cloud data is distributed by the CNIG Download Center (LAZ format, ISPRS classes 0-7, 10, 12 are populated). The test site ES has 453,255 points with an average density of 1. 83 pts/m 2. The most important metadata are summarized in Table  1. It is not our intention to present the archaeology of the test sites in any detail, but merely to provide the key reference for each archaeological site and to illustrate the archaeological features (Figure  1 ). The test site AT is located south of Graz near the town Wildon on a rocky hill consisting of algal limestone and marlstone interspersed with layers of clay that rise above the Mur River valley  [31].
Evaluated Filters We compare all filters known to us that are implemented in an off-the-shelf free or low-cost software under active development or maintenance (Table  2 ; Appendix A). Accordingly, BCAL Lidar Tools  [37]  was not considered because its freeware standalone version has been deprecated. Also not considered were legacy Whitebox GAT  [38] , ALDPAT  [39] , and LASground  [40]. gLiDAR  [41, 42]  was rejected because it was not designed for production and is arguably also legacy program. Furthermore, the skewness balancing filter  [43] , as implemented in PDAL, was not included because it does not work as intended (in our attempts, all points below a certain height above sea level have been marked as ground and all other as non-ground). Archaeologists do not typically work with full-waveform airborne LiDAR data (but see  [44] ), therefore we do not assess post-processing methods for this specific category of airborne LiDAR data in the context of this article.
First, we manually classified a point cloud using context knowledge (AT, SI1, and SI2) and aerial photographs. The points were classified into the ground (2) and non-ground (1) using the Global Mapper software package, and only after careful inspection were the classifications accepted. The qualitative assessment method is defined as a visual examination and comparison of the datasets. Five categories of circumstances in which the algorithms are likely to fail are observed: 1. outliers (low or high, occur mainly due to sensor errors); 2. object complexity (refers to buildings that are difficult to detect due to their size, low height, or complex shape); 3. detached objects (buildings on slopes, bridges, ramps); 4. vegetation (problematic categories are vegetation on slopes and low vegetation); 5.
Quantitative assessment was carried out on small areas of 50 × 50 m within archaeological sites. If we were to apply the original method directly, the archaeology-specific accuracy would be masked by the overall accuracy (similar to how low noise was masked in the original study). The areas were selected according to two criteria: • the archaeological features must cover at least 50% of the area, and • the area represents the most difficult rather than average situation. First, we manually classified a point cloud using context knowledge (AT, SI1, and SI2) and aerial photographs. The points were classified into the ground (2) and non-ground (1) using the Global Mapper software package, and only after careful inspection were the classifications accepted. The qualitative assessment method is defined as a visual examination and comparison of the datasets.
Quantitative Assessment The results of the qualitative assessment are summarized in Figure  6. To facilitate comparison, the performance of each filter in each category was evaluated as follows. The filter with the best performance and the lowest error received nine points, the second-best eight points. the filter with The PTIN achieved the highest average, followed by the BMHF, and PMF and SBF. The most robust performers-fewest one-were PTIN and SBF, followed by SMRF and BMHF. On average, filters performed better on dense data than on medium and low density data. By far the best on dense data was PTIN, followed by MCC and SBF, WLS, SegBF, and BMHF. PTIN and BMHF performed best on medium density data, followed by CSF and SMRF. For low density data, PMF, PTIN, and WLS were the best. For vegetation removal, MCC was best, followed by PMF and then WLS and BMHF as a distant third.
As far as archaeological features are concerned, all filters handle the features embedded in the ground perfectly. Some filters (PMF, WLS, SegBF) struggle with semi-embedded features and all but two (SMRF, SegBF) erase standing features completely. An automatic solution for the handling of standing features has yet to be found. Therefore, a considerable manual classification effort is still inevitable  [13, 15]. (ht) Maximum height delta refers to the expected elevation range of the ground in the point cloud (default: 50). Default value worked best for all datasets. (st) Expected Terrain Slope, i. e. , threshold value for the inter-point slope (default: 7. 5 deg). Higher values worked best for dense datasets with steep slopes, lower values were used for low-density data sets and the lowest value for flat terrain.
Depth profile experiments (series of 30 cycles "analysis/sputtering") are carried out using the previous conditions for surface analysis (Au + , 50x50µm 2 raster size) and a 5 kV Ar + gas gun for etching (with an etching time of 20 seconds for each cycle, a DC current of 300 nA and a sputtered area of 500x500 µm 2 ). Note that the lithium ( 6 Li + and 7 Li + ) peaks appear very intense and have to be blanked during the measurement so that to detect better the smaller peaks. Data processing is performed using ToF-DR software provided by Physical Electronics. All positive polarity mass spectra are calibrated using K + (m/z 39), Ti + (m/z 55) and TiO + (m/z 64) peaks and all negative polarity spectra are calibrated using F -(m/z 19) , O - (m/z 16) and P -(m/z 31) peaks.
X-ray Photoelectron Spectroscopy (XPS). XPS are carried out using an Escalab 250 Xi spectrometer (Thermo Fischer Scientific, Waltham (Massachusetts), US) equipped with two monochromatized sources, either Al Kα (hν =1486. 6 eV) or Ag Lα (hν = 2984. 3 eV). Electrode samples are put on a sample holder using a metallic clamp and transferred through an argon-filled glove box with low H2O/O2 levels (< 5 ppm) directly connected to the spectrometer in order to prevent any moisture/air contamination. High resolved spectra are recorded using constant 20 eV and 200 eV pass energies for Al and Ag sources respectively. Analyses are performed using a 0. 15 eV step size and short time iterative scans to follow any sample degradation. Only "non degraded" spectra are taken in account and summed for data treatment with CasaXPS software.
References and Pristine electrode To make the interpretation of ToF-SIMS data easier, reference compounds from the electrolyte and the electrodes were first analyzed separately in order to establish a database with the main specific secondary ions for each species both in positive and negative polarities. Only positive spectra are shown in this paper as they present the peaks of interest for our study. Therefore, relevant compounds like LTO-powder active material, PVdF binder, carbon additives, LiPF6 electrolyte salt and LiF were analyzed. Table  1  displays a list of the main peaks detected over a 0-100 m/z range for these compounds in the positive polarity. detected for all cycled electrodes whatever the state of charge, but in lower proportion compared to the other peaks, due to its dissolution during the washing of cycled electrodes.
Data analysis Collected data were analyzed off-line by using in-house software specifically developed for the present study (Matlab R2019b, MathWorks, Inc. ). VT was obtained by integration of the flow signal over the inflation time in each lung, which can be different between each lung (see below) and different from the machine inflation time Taking care of this an important in design, like our present one, that accommodates time constants differences between lungs. The rebreathed volume was computed as the amount of air that flowed from one patient to the other(s) during inspiration and expiration (Figure  2 ). The table in the bottom left summarizes the conditions making rebreathing to or not to happen and its computation. The hatched area in the lower right graph indicates the amount of air rebreathing from patient B to patient A.
, 2013) , PENELOPE  (Salvat, 2015)  or GEANT 4  (Agostinelli et al. , 2003; Allison et al. , 2006)  have been used by several participants for the computation of coincidence-summing corrections  (Thanh et al. , 2018; García-Toraño et al. , 2017; Hurtado et al. , 2004). In all the cases the value of F C is obtained by running the code to obtain the peak efficiencies from the simulated spectra both in the presence and in the absence of coincidence emission. MCNP-CP (Berlizov, 2012) and MCNPX, PENELOPE with PENNUC (García-Toraño et al. , 2017) and GEANT 4 with the Radioactive Decay Module  (Hauf et al. , 2013)  were used, the decay being fully simulated using ENSDF (MCNP-CP and GEANT 4) or DDEP (PENNUC) decay data. Another generalist code, developed primarily for applications in reactor physics, (TRIPOLI-4 webpage), was applied for the computation of F C values for the two gamma photons of 60 Co.
MCNP and PENELOPE were also used for providing input values of the efficiencies for other software applied for the computation of F C. Thus, MCNP was used in combination with ETNA  (L� epy et al. , 2004, 2012b ) by two participants. The complete version of ETNA was used for the evaluation of the efficiencies dependence on the position of the emission point in the volume source, using the efficiency transfer from a reference geometry, for which the efficiency was computed by MCNP. One participant defined as the reference geometries the configurations a, b and c, which were considered also the target geometries. Using the efficiencies dependence on position, ETNA performs a numerical integration for obtaining F C. In the application of the software developed at CMI  (Dryak et al. , 2012)  (2 participants), the volumes of the sources were decomposed in a number of small domains.
One participant evaluated the spectra in the presence/absence of coincidences for each domain and then merged them together. F C was obtained from the ratio of the peak count rates in the combined spectra. The other participant used MCNP to compute ε, η and F C for each domain and then averaged the values. The joint emission probabilities were evaluated using a MATLAB script or numerically. SUMCOR  (Dias et al. , 2018)  coupled with MCNP code was used in 3 sets of results. In the first, the efficiencies were computed for a number of 200 (geometries a and b) or 400 (geometry c) randomly sampled points and a Semkow type procedure  (Semkow, 1990)  was applied to obtain individual F C values, that were finally averaged. In the second set, the same point source efficiencies were used for the evaluation of the LS curve, that was further applied for F C computation via a Blaauw and Gelsema (2003) type formalism.
The quasi-point source approximation using the efficiencies computed by MCNP for the complete sources was also applied in SUMCOR. PENELOPE was used for providing input values of the efficiencies for 7 sets of F C results obtained with other software, not with PENNUC. SUMCOR was applied with input from PENELOPE in the same way as with input from MCNP (3 sets of results). The complete version of ETNA for volume sources was also applied with input efficiencies for the reference point source evaluated by PENELOPE; in this case ETNA provided the values of elementary efficiencies for emission points within the volume sources and then performed a numerical integration for obtaining F C. In addition, the efficiencies for the complete volume sources were computed using the transfer method from the point source reference (with efficiencies evaluated by PENELOPE) and the FAST version of ETNA (quasi-point source approximation) was applied for obtaining F C.
PENELOPE was also used by 2 participants for the computation of the efficiencies required in the input of the TrueCoinc software  (Sudar, 2002). The standard version of GESPECOR  (Sima et al. , 2001; Sima and Arnold, 1996; Arnold and Sima, 2004, 2006)  was applied by 7 participants. In this code the joint emission probabilities of groups of photons, evaluated analytically  (Sima and Arnold, 2008) , are combined with the corresponding integrals of products of efficiencies, obtained by Monte Carlo simulation. The GESPECOR version including the angular correlations was also applied (1 set). Finally, one set of results obtained with GESPECOR deliberately applied the quasi-point source approximation. 2 participants reported results obtained using the EFFTRAN software  (Vidmar, 2005; Vidmar and Likar, 2005; Vidmar and Korun, 2006; Vidmar et al.
Finally, the Andreev recursive formulae  (Andreev et al. , 1972)  are applied, using the peak and total efficiencies multiplied by the value read off of the LS curve at a corresponding gamma-ray energy. The ISOCS procedure included in the CANBERRA software was used by one participant. The coincidence summing corrections including up to 3 photons are evaluated using decay data from an internal library  (Zhu et al. , 2009). In the case of extended sources, the effects are evaluated for points located inside of the volume source and then are averaged  (Kolotov et al. , 1996). In most of the cases, DDEP data were used  (DDEP, 2018). Exceptions were the applications of MCNP-CP, GEANT 4, TrueCoinc and ISOCS. In several calculations, only pair coincidences were included, in others all possible coincidences with γ photons were considered.
034 in the energy range from 121 to 1408 keV, in all the other cases the values of the peak efficiencies are self-consistent to a high degree. Thus, if ISC(NP) and ISC (SP) are significantly different from zero, this is due to the method applied for the evaluation of F C , and not of the efficiencies. It should be mentioned that the sets with code numbers 9, 19, 24, 26, 27 and 28 were obtained using the quasi-point source approximation, either deliberately, or because the software requires simply the efficiency values for the complete volume source. In Table  2  the magnitude C of the coincidence-summing effects obtained when the quasi-point source approximation was deliberately applied (specific versions of GES-PECOR, ETNA and SUMCOR) are compared with the results obtained  with the same software using the averaging over the volume of the sources.
Design of Transparent Antenna for 5G Figure  1  shows the stepwise design evolution of the proposed antenna, which was designed and optimized using CST Microwave Studio software simulator. The optically transparent antenna comprises transparent glass with a dielectric constant of 4. 82 and 2 mm thickness as the substrate material. Transparent conductive oxide AgHT-8 with a surface resistivity of 8 Ω/m, which is equivalent to the conductivity of 125,000 S/m, constitutes the patch radiator and ground. A feed line with a width of 3. 58 mm is used to achieve 50 Ω impedance matching. Front and rear views of the proposed antenna are shown in Figure  1a, b. A partial ground plane is used to increase the performance of the antenna and ensure the impedance matching. The optimized dimensions of the proposed transparent antenna are listed in Table  2.
Acknowledgments: This work is granted by  PHC Maghreb (Program Hubert Curien) MELINA  (Mastering Acknowledgments: This work is granted by  PHC Maghreb (Program Hubert Curien) MELINA  ( Mastering Efficient Lighting in North Africa ) by the  French Ministry of Europe and Foreign Affairs  and supported by  Campus France  and, in Morocco, by the  Ministry of National Education ,  Professional Training , Higher Education and Scientific Research, in partnership with the  National Center for Scientific and Technical Research (CNRST).
Funding: This research was funded by  PHC   Maghreb 2020 "MELINA" (Mastering Efficient Lighting in North Africa ), grant number  43981ZG-Campus France  and supported by the  French Ministry of Europe  and  Foreign Affair  and, in Morocco, by the  Ministry of National Education ,  Professional Training , Higher Education and Scientific Research, in partnership with the  National Center for Scientific and Technical Research (CNRST). Funding: This research was funded by  PHC   Maghreb 2020 "MELINA" (Mastering Efficient Lighting in North Africa ), grant number  43981ZG-Campus France  and supported by the  French Ministry of Europe  and  Foreign Affair  and, in Morocco, by the  Ministry of National Education ,  Professional Training , Higher Education and Scientific Research, in partnership with the  National Center for Scientific and Technical Research (CNRST).