dataesr/scientific-paragraphs-categorization huggingface dataset:
Range and Standard Deviation Transformation Using Legacy Databases Estimated clay contents after boxcox transformation ( were transformed using the Focused-Legacy and No-Focused-Legacy databases following equations (  3 ) and (  4 ). This process using the Focused-Legacy and No-Focused-Legacy databases allowed increasing the performance of predictions with a decrease in bias (respectively -187 g/kg and -288 g/kg) and SEP (respectively 219 g/kg and 311 g/kg) (Table  2 ). Finally, estimated clay contents after boxcox transformation ( ) were transformed using the Entire-Legacy database following equations (  3 ) and (  4 ). This process allowed obtaining predictions with a bias of -245 g/kg and a SEP of 269 g/kg (Table  2 ).
The raw clay predicted map was obtained from the spectral index following equation (1) (Figure  2a ). And the clay maps of using the REF, Focused-Legacy and No-Focused-Legacy databases were also obtained (Figure  2b, c  and d ). The estimations at field-scale had low accuracies because of high bias (Table  2 ). Nevertheless, these maps restricted to the Kamech catchment, showed the close successions of clay-rich and poor-clay areas whatever the soil dataset used for standardisation.
DISCUSSION The clay spectral index proposed by  Levin et al (2007)  and used without calibration data to estimate relative soil property values, offered accurate estimations (r = 0. 71) but highly biased (bias = -414 g/kg). The standardisation of the estimated clay contents using the REF dataset allows a significant increasing of model performances (r = 0. 76 and SEP = 116 g/kg), compared to the model without standardisation. These performances are comparable to those obtained by  Lagacherie et al. (2008)  also using a clay spectral index and Hymap airborne data (R 2 = 0. 6 and RMSE = 130 g/kg). Nevertheless, the model performances remain lower than those obtained by  Gomez et al. (2015)  (R 2 val = 0. 75 and SEP = 86 g/kg) using PLSR technique to estimate clay content from the same datasets than in this study (the VNIR/SWIR AISA-DUAL images and the REF soil data).
The standardisation of the estimated clay contents using legacy soil data allows a slightly increasing of model performances (accuracy increasing and bias decreasing), compared to the model without standardisation. Nevertheless, the model performances remain lower than using the REF dataset, whatever the legacy soil dataset used for the standardisation. The location of the legacy samples is more important than the number of these legacy samples for standardisation. Indeed, the use of a small legacy soil database (~80 samples, Focused-Legacy database) with all samples located over the study area, provides better results than bigger legacy soil database (~150 samples, Entire-Legacy database) including samples located out of the study area. Much of the performances limitations are due to bias of clay measurements that affect the legacy soil databases.
This means that these legacy soil databases need to be standardized themselves before using them for such approach, as some attempts has been already done in that objective  (Baume et al, 2011 , Ciampalini, 2013)  The standardisation applied in our study is composed of a boxcox transformation for normalization of the predicted data, and a scaling and centering of the normalized data. This standardisation process is comparable to one of the most widely used transfer methods for correcting predicted values which is the univariate slope and bias correction (SBC)  (Bouveresse et al. , 1996). Transfer methods have been developed to enable a calibration model to be effectively transferred between two "systems" (e. g. , two spectroscopic instruments), thus eliminating the need for a full recalibration  (Fearn 2001; Feudale et al.
, 2002). In our case, we do not have a calibration model which has to be adjusted to different "systems", but we have a model which has to be calibrated with an approximate system (legacy soil dataset). In future, this work should be extended to other clay spectral indexes, such as those proposed by  Chabrillat et al, 2002 and Lagacherie et al. , 2008. As well, this  work would be extended to other topsoil properties, such as iron or CaCO3 using iron and carbonate indexes (e. g. ,  Madeira et al. , 1997; Lagacherie et al. , 2008). This may confirm the independence of our actual observations from the selected spectral index and the topsoil property.
CONCLUSION This paper shows a procedure to estimate relative soil property values from a spectral index applied to VNIR/SWIR AISA-DUAL hyperspectral airborne data, and standardize it with legacy soil data. The maps accuracy depends on the legacy soil data. The more the legacy soil data are focused on the study area, the more the accuracy is high and the soil patterns are mapped. With the coming availability of a next generation of hyperspectral VNIR/SWIR satellite data for the entire globe (such as the French HYPerspectral X Imagery -HYPXIM -, the Spaceborne Hyperspectral Applicative Land and Ocean Mission -SHALOM -, the PRecursore IperSpettrale della Missione Applicativa -PRISMA -, the Environmental Mapping and Analysis Program -EnMAPand the Hyperspectral Infrared Imager -HyspIRI -), this study may open new way toward accessible and cheap methods for the delivery of soil properties maps to the geoscience community.
ACKNOWLEDGEMENTS This research was supported by the  French National Research Agency (ANR)  through the  ALMIRA  project ( ANR-12-TMED-0003 ). The authors are indebted to UMR LISAH (IRD, France) and to CNCT ( Centre National de Cartographie et de Télédétection, Tunisia ), for providing the AISA-DUAL hyperspectral images for this study. This hyperspectral data acquisition was granted by  IRD (Institut de Recherche pour le Developpement) ,  INRA  ( Institut National de la Recherche Agronomique ) and the  French National Research Agency (ANR)  ( ANR-O8-BLAN-C284-01 ).
Motivation: Imaging mass spectrometry (IMS) is a maturating technique of molecular imaging. Confidence in the reproducible quality of IMS data is essential for its integration into routine use. However, the predominant method for assessing quality is visual examination, a time consuming, unstandardized and non-scalable approach. So far, the problem of assessing the quality has only been marginally addressed and existing measures do not account for the spatial information of IMS data. Importantly, no approach exists for unbiased evaluation of potential quality measures. Results: We propose a novel approach for evaluating potential measures by creating a goldstandard set using collective expert judgements upon which we evaluated image-based measures. To produce a gold standard, we engaged 80 IMS experts, each to rate the relative quality between 52 pairs of ion images from MALDI-TOF IMS datasets of rat brain coronal sections.
Motivation 1. 1. 1 The need for quality measures in IMS IMS recently emerged as an analytical chemistry technique for untargeted and label-free molecular imaging of tissue sections, agar plates and cell cultures that can localize hundreds of molecules simultaneously with a high molecular specificity and sensitivity, and with cellular spatial resolution  (Spengler, 2015). IMS was introduced for biological analysis in 1997 and rapidly commercialized so that it now provides a capable tool for molecular imaging directly from tissue sections with the increasing potential for routine application. Matrix assisted laser desorption ionization (MALDI) is probably the most commonly encountered ionization technique used for IMS. IMS produces a dataset which can be viewed as a collection of mass spectra acquired at pixels of the analyzed area, or as a hyperspectral imaging dataset with thousands to millions of channels (ion images), each representing the spatial distribution of an ion with a particular mass to charge ratio (mz) value.
, 2015)  and hundreds of serial sections for 3D images  (Oetjen et al. , 2013; Trede et al. , 2012)  that makes it impractical for an expert to evaluate the data produced. Moreover, the optimization of experimental settings with the increase of considered parameters leads to the explosion of possible combinations of settings and, correspondingly, of datasets to be generated and evaluated. A quantitative measure for objective, unbiased and automated evaluation of the quality of IMS data would enable optimizing experimental steps, continuous monitoring of instrument performance, evaluating the suitability of data for analysis and reporting results across instruments and laboratories. In this study, we propose using image-based quality measures and address the key question of their evaluation through creating a gold-standard dataset.
1. 1. 2 Creating gold standard using collective expert knowledge For knowledge-intensive tasks, it is often the expert judgements that is the most important measure of quality of results, against which automatic methods can be evaluated. Moreover, it is assumed that collective judgments sourced from a crowd of experts are more reliable than judgement of any single expert  (Gwet, 2012). Expertsannotated ground truth datasets are called 'gold-standard' and are used to train and test algorithms in a wide range of domains including natural language processing  (Carletta, 1996) , content analysis  (Krippendorff, 2012)  and clinical decision support systems  (Berner, 2003). However, creating an annotated gold-standard data by involving experts is a challenging task. Particular attention should be paid to the experimental design and inter-annotator reliability  (Gwet, 2012).
, 2010). By examining the properties of spectra (e. g. peak shape or intensity), defects can be automatically detected  (Mutton et al. , 2011). 1. 2. 2. 2 Quality of tandem mass spectra. A particular problem needing estimation of quality of individual mass spectra is the matching of tandem mass spectra against databases. Here, the filtering out of low-quality spectra that are unlikely to return a database match can increases the matching efficiency. Heuristic approaches have been developed which typically examine the number of peaks and relative intensities of peaks detected  (Bern et al. , 2004; Ma et al. , 2003). Machine learning approaches have been used to recognize 'good' spectra by training a support vector machine on sets of labelled 'good' and 'bad' spectra  (Bern et al. , 2004). Typically this labelling is achieved by running database matching on a subset of the spectra with 'good' being manually annotated or those for which a match was found and 'bad' being those that were not matched  (Ma et al.
, 2003; Nesvizhskii et al. , 2006). 1. 2. 2. 3 Quality of IMS data. Currently, no approach exist that could automatically assess the quality of IMS data and would account for specific properties of IMS data which are: complex spectra representing mixtures of analytes analyzed without separation, limited-to-no capabilities for tandem mass spectrometry, repetition of acquisition of spectra coming from a region of similar chemical composition and spatial imaging information. Karlsson et al. (2014)  recently reported on optimizing the experimental settings for MALDI IMS through using a variance-based measure of quality of spectra. Considering three measures of variance aided in minimizing the unwanted variance in MALDI IMS and to discover subtle changes in protein expression in various subregions of the brain.
Selection of ion images 2. 1. 1. 1 MALDI IMS data. A 12 mm thick cryo-sections of wild-type mouse brain were prepared on conductive-coated slides that then underwent a washing procedure with either ethanol or propanol. Sinapinic acid matrix was applied with the ImagePrep (Bruker Daltonics, Bremen, Germany) either using the manufacturer's standard protocol or with an extended incubation time. MALDI IMS datasets were collected on an UltraFlex (Bruker Daltonics, Bremen, Germany) in linear positive-ion mode and in the mass range 2-20 kDa. A 75-mm raster size was selected and each spectrum was the sum of 100 or 600 laser shots at 2000 Hz repetition rate using either a tightly focused laser beam (with 5% random walk) or a static defocused beam that covered an equivalent area. In total 10 datasets were collected with different combinations of instrument parameters.
2. 1. 1. 2 Selection of ion images for rating. We aimed to select 52 different and representative ion images to be used in the survey. (For explanations on why 52 images were needed, please see Section 2. 1. 2. ) In order to avoid biasing the images selected with the authors' own opinions, a semi-automated selection procedure was used: For each dataset, peaks in the mean spectrum were determined from gradient turning points as per  Coombes et al. (2005). The 10 most intense peaks were taken from each dataset and merged into a single aggregate list for all datasets, with any values within 1 Da averaged. Ion images were produced from each dataset for every peak in the aggregate list with a summation window of 65 Da around the peak centroid. The intensities of each image I were scaled to [0, 1].
Interface design. A web-based survey was deployed with three sections: an introduction with instructions; a series of pairs of ion images; and an exit questionnaire asking for a feedback both on the task and the survey design. Screenshots of the sections can be found in the GitHub project repository. The welcome page introduced raters to the survey and its broad aims, described what they are expected to do and how long it can take and provided contact details for the project. The instruction To encourage raters to treat the survey as if they were flicking through a dataset, they were instructed to spend at most 10 s per pair. Image pairs were then presented one-by-one for rating using the following slider mechanic: the two images were displayed side-byside with a movable slider bar underneath (see Fig.
We assigned a default left/right order for the images in each pair. When showing a pair we randomized which image would be presented on the left or right. 2. 1. 2. 3 Online platform for the survey. All the surveys were pre-generated in the required format and deployed onto the LimeSurvey platform (https://www. limesurvey. org/), each with a unique URL. A custom redirect script was placed on the recruitment page of the website of the EU FP7 project 3D-MASSOMICS (http://3d-massomics. eu) which held a queue of unvisited surveys. When a potential rater followed the link within the invite email they were automatically redirected to the URL of the next unseen survey in the queue. Any surveys that were started but not completed or scored poorly on a set of three consistency control image pairs were manually returned to the queue after 1 week of inactivity.
We used the default parameters as described in Alexandrov and Bartels (2013) (r i ¼ 0:3; r d ¼ 3; x ¼ 10). In this article, we define the SC measure to be scaled as follows with the aim to have low values for a chaotic image as we assume a noisy image to be of low quality: SC ¼ 1 À SC AlexandrovBartels : (5) As the images were presented to the raters with a colour mapping from their intensity values, and this can affect the judgement of images  (Borland and Taylor, 2007) , we also explored some colour statistics after mapping the greyscale intensities using the jet colourmap in Matlab (Mathworks, USA) with eight-bit colour depth. 2. 2. 1. 8 Luminescence. Image luminescence relates the amount of visual contrast that is present in an image and depends on colour hue and saturation as well as brightness  (Webster and Mollon, 1994).
Selected ion images Given the pool of images obtained as described in Section 2. 1. 1, two images were manually selected for an internal consistency check and 50 for the online survey itself. In total, 1225 image pairs were generated for the survey. Our knowledge of MALDI-IMS suggested that images from the same mz in datasets with different sample-handling were likely to have different relative noise characteristics. Comparison of the mean spectra between the datasets (data not shown) revealed reasonable heterogeneity in peak intensities indicating that the expected variation was achieved. The mean spectra with 49 peaks used for the survey images and the images itself can be found in the project GitHub repository.
Recruited raters Based on the response times for the pilot study, we allocated one month for recruitment, but it took longer than expected to involve enough raters. We extended the recruitment time to three months with two follow-up email requests and a post on the LinkedIn IMS group. In total, 80 experts completed the survey. All recruited experts indicated some experience with MALDI IMS data, with total experience over 260 years. Nine raters reported more than ten years experience each. The distribution of experience and work background is shown graphically in Figure  3a. The majority of raters (45%) had a background of using IMS primarily for investigating biological or biochemical problems, 24% for data acquisition and the remainder encountered IMS data during technology or algorithm development.
In contrast to the survey mechanisms, which were generally agreed to be easy to understand, the task of actually rating a pair of images was not found to be straightforward. This reinforces the importance of finding a general measure that can replace subjective judgements and present route to a standardized image quality score for reporting purposes. We recorded how long each rater spent on rating each pair, see Figure  4d. The obtained learning curve is in line with our expectations: the raters were spending more time per rating when starting the survey with less time per rating being needed as the survey progressed. If this average time was extrapolated to a typical MALDI-TOF dataset with 10 000 channels then manually going through each to determine the quality would take over 20 h and highresolution IMS would take many days.
We calculated a k with each rater excluded in turn to see if individual raters performed differently to the rest of the cohort. In general, removing specific raters had a small impact (<5%) on the value of a k (see Fig. 4a ), but removing two raters caused an improvement. Removing both of these raters from the cohort increased the value of a k to 0. 55, still lower than could be called a sufficient agreement. We compared the median time each expert spent on the ratings against the change in a k when removing this expert, but no correlation was visible (data not shown). We then scored raters according to their impact on the overall agreement and sequentially removed the worst performing raters. As shown in Figure  5  (bottom), removing raters also resulted in a gradual improvement of the agreement.
Given the agreement results, we generated three gold-standard datasets based on three different agreement thresholds (see Fig. 5 ). The first dataset consisting of 595 image pairs includes pairs rated with the usually accepted agreement of 0. 6. The second agreement threshold is defined as a standard deviation of normalized slider values (386 image pairs). The third dataset consists of 244 image pairs that produced the maximal agreement of a k ¼ 0:85. Future work includes a more detailed study of features of the high-and low-agreement image pairs that will allow us to draw conclusions about the reasons for the raters to disagree on certain subsets of images. The gold-standard datasets can be found in the GitHub repository as a freely available resource to provide the community with an annotated dataset that we hope will spur further developments in this area.
The top 10 performing statistics are summarized in Table  2 ; the full table can be found in the GitHub repository. This table reveals that two measures (madSTD and SC) showed a strong linear relationship with the expert assessments for all three gold-standard datasets, and that the linearity increased with the rater agreement. This may indicate that local variation and the spatial structure are the characteristics that the expert raters use to judge when assessing the relative quality of images. The sign match accuracy shows a similar trend, increasing from >0. 7 to >0. 9; all values are >0. 5 that would be produced by simple guessing. It is noteworthy that among the top 10 performing measures only the one measure (medianSTD 5 ) operates on a small moving window (5 Â 5). All other measures operate either on a full image (SC) or on a large moving windows (at least 11 Â 11 in size); four measures operate on 51 Â 51 moving windows.
However, higher dimension PIC simulations become very computationally expensive. Computational fluid dynamics (CFD) simulations provide a fluid model alternative to PIC simulations, where fluid based equations are solved over a gridded geometry using numerical methods. Successful CFD simulations utilizing commercial packages, such as CFD-ACE+ (Computational Fluid Dynamics Advaced Computing Environment), have been performed for weakly ionized and dual frequency CCPs  [27] [28] [29]. Here the CFD-ACE+ package is used for a 2D fluid based simulation giving the main plasma parameters (electron density and temperature, plasma potential, DC bias, etc. ) of the Pocket Rocket device, for comparison with previously obtained experimental results.
SIMULATION MODEL The CFD-ACE+ package contains different modules that can be selected to analyze different problem types. This rf CCP simulation utilizes the flow, turbulence, electric, chemistry and plasma modules and the following is a brief description of the main equations and theory used by the CFD-ACE+ program. A transient simulation is used to allow the rf period behavior of the discharge to be analyzed, as well as the steady state (rf phase averaged) solution. The governing equations for the heavy particle flow parameters are mass conservation and the Navier-Stokes equation, given in Equations 1 and 2 respectively, where ρ is density, V is velocity, μ is viscosity (taken as constant 2. 09 × 10 -5 kgm -3 for argon) and S Mx represents momentum added from any defined source, such as the inlet.
A standard kturbulence model is included for all heavy species (refer to Launder and Spaulding  [30]  for full details of this model), however, the low pressure in the system results in little difference (∼2. 4%) between fully laminar and turbulent simulations. Heavy particle surface reactions are invoked, such that on collision with a wall an ion or excited neutral will return to the neutral ground state. Four volumetric gas phase reactions are used in the simulation, being elastic collisions between heavy particles and electrons, electron impact ionization, electron impact excitation and stepwise ionization, with rate coefficients and cross sectional data from the JILA database, University of Colorado, as included in the CFD-ACE+ package. The applied rf field and space charge effects from ions and electrons, are solved using Poisson's equation (Equation  5 ) for the electrostatic potential (φ) -∇.
These ions then bombard the surface of the dielectric, especially in the center where the large self bias develops. However, if the secondary electron coefficient is too low the number of ion induced secondary electrons accelerated by the sheath in the vicinity of the rf electrode to energies above the ionization energy of argon is not sufficient to create the main central plasma peak observed in experiments. Mean free path for electron-argon collisions within the sheath region, using a neutral density in the central region of the tube of 3. 5×10 22 m -3 (see Figure  4 ) and cross sectional data from the JILA database, University of Colorado, ranges from 0. 57 mm for an electron energy of 2 eV (σ = 3. 52 × 10 -20 m -2 ), to 85 μm for an electron energy of 14 eV (σ max = 23. 8 × 10 -20 m -2 ) and around 0.