dataesr/scientific-paragraphs-categorization huggingface dataset:
HYDRUS-MODFLOW coupling The sequential coupling of MODFLOW and HYDRUS involves exchanging information about the recharge rates and the position of the groundwater table. During each MODFLOW time step, the HYDRUS package is called to solve the 1D Richards equation for each defined soil profile. Due to the variability and nonlinearity of flow in the unsaturated zone, the HYDRUS solution is generally performed using much smaller time steps than the MODFLOW solution. HYDRUS time steps are adjusted automatically within a user-defined range according to the performance of the iterative solver. The initial condition for each soil profile is specified by users for the first MODFLOW time step. The atmospheric boundary condition at the top of each profile is used to represent precipitation, irrigation and/or evaporation as described above.
Pressure head and water content distributions in each HYDRUS profile are updated next to reflect the new position of the water table and the vertical flux in the considered HYDRUS profile at the end of the previous MODFLOW time step  (Beegum et al. , 2018). Note that the HYDRUS package for MODFLOW does not consider full coupling of saturated and unsaturated flow, such as in the VSF  (Thoms et al. , 2006) , VS2DI  (Healy, 2008) , or SUTRA  (Voss and Provost, 2010)  codes. Rather, the HYDRUS package is now merely a tool to facilitate and improve estimations of infiltration and evapotranspiration fluxes, which otherwise could be achieved using the standalone HYDRUS-1D code or other codes solving the 1D Richards equation, as implemented for example by  Contractor and Jenson (2000)  and  Kamps et al.
Values of the soil hydraulic parameters are listed in Table  1. The first set, with a relatively high saturated conductivity (400 m/d), is denoted as medium sand in the database of VS2DTI and VS2DHI codes  (Hsieh et al. , 1999)  and was used e. g. by  Dan et al. (2012). While such a high hydraulic conductivity is not typical for vertical flow in the vadose zone (except macropore flow), we include this set as an example of highly permeable material. The second set, denoted here as fine sand, is reported by  Carsel and Parrish (1988)  as an average set of parameters for sand according to the USDA textural division. Similarly, the third set represents an average set of parameters for a loamy sand according to the USDA classification  (Carsel and Parrish, 1988 ). In the following section, we report results obtained for each of these three soil types.
A warm-up period for the vadose zone simulations was also necessary, but it is much shorter, on the order of a single year. As a basic check, we compared the steady-state results obtained with the HYDRUS package with a constant infiltration rate to the results obtained with the RCH MODFLOW package. This package allows one to input a user-specified rate of recharge at the water table. While the results are not shown here, the agreement in terms of both the groundwater table elevation and the saltwater-freshwater interface position was very good (differences in the position of the groundwater table and the fresh-salt interface in the center of the domain were less that 1. 5 cm, while the elevations agreed with the Ghyben-Herzberg formula  (Verruijt, 1968) ). Simulations were performed for a 5-year period using the available weather data from  Gdańsk (2011 Gdańsk ( -2015)) , starting from the initial steady-state conditions.
We used either daily, weekly, or monthly averages of the precipitation and evapotranspiration rates. Regardless of the temporal resolution of the weather data, we used a constant MODFLOW time step of 1 day and allowed the time steps in HYDRUS to vary within a range from 10 -5 to 1 day. All simulations assumed a ponding depth of zero and a minimum soil water pressure head at the soil surface of -10000 m. The input data for the groundwater model were partly prepared using the ModelMuse interface  (Winston, 2009). Since ModelMuse does not support HYDRUS for MODFLOW, all data required by this package were prepared using spreadsheet software.
CONCLUSIONS Based on preliminary evaluations carried out in this study, we consider the HYDRUS package for MODFLOW to be a promising tool for integrating vadose zone processes in a saturated groundwater flow model accounting for saltwater intrusion. This package allows calculations of spatially and temporally variable recharge rates using the physically-based Richards equation and an appropriate set of water retention and hydraulic conductivity functions characterizing vadose zone flow processes, and accounting for interactions with groundwater. Our simulations indicated that for the particular system considered in this study, changes in soil hydraulic parameters had a moderate influence on the final position of the groundwater table and the freshwater-saltwater interface. This may explained by the fact that we considered only sandy soils, which are characterized by low water holding capacities and relatively high hydraulic conductivities.
Acknowledgements. This work has been supported by the  National Science Centre, Poland , in the framework of project  2015/17/B/ST10/03233  "Groundwater recharge on outwash plain". Weather data were obtained from a station operated by the  Department of Hydraulic Engineering, Faculty of Civil and Environmental Engineering, Gdańsk University of Technology. The authors would like to thank two reviewers for their detailed and insightful remarks, which helped to improve the manuscript.
If the induced SET pulse has a sufficient width and amplitude, it can propagate until the memory element and be sampled by memory elements, causing spurious effects in the application. Consequently, it is fundamental to investigate and characterize the susceptibility of a device to these effects. Thus, in the last decades researchers put a lot of effort in estimating devices and applications sensibility with respect to SEUs and SETs  [6] -  [10]. Considering the new generation of UltraScale+ devices, due to the reduced technology size, higher sensibility is expected. However, since these devices have been released recently, not many studies have been dedicated to them. Thus, in this paper we propose an analytical computation-based strategy, based on software tools to produce an estimation of the design susceptibility to radiation effects on Xilinx UltraScale+ devices.
A. SEU Estimation The VERIPlace Tool is a software tool developed in house by Politecnico di Torino, which is capable of 1) performing design application error rate prediction for SRAM-based FPGA w. r. t. SEUs in the configuration memory induced by radiation effects; 2) improving design reliability without introducing hardware resource overhead. It has been used in previous radiation test for error rate prediction and the effectiveness of its hardening strategy has also been verified  [17]. The application error rate is calculated as the probability of application generating an error at the output w. r. t. certain number of SEU accumulated in the configuration memory. For the purpose of this work we modified the VERIPlace algorithm in order to include the estimation of the SET probability and the correlation with the radiation particle energy used during the radiation campaigns.
The algorithm continues with connecting netlist of the design to the timing information reported in SDF file. It means that starting from the extracted path, the algorithms calculates the delay regarding each path based on used segments in the routing for the same module_ID and creates the database including TMR nodes with their delay information. The last phase of the algorithm is dedicated to evaluate the sensitivity of the implemented design regarding SET phenomena in TMR. Therefore, this phase takes into account the source SET duration which has been defined by the user, the frequency of the implemented design and the databased generated by the tool in terms of the extracted paths. Based on this data, the algorithm calculates if the considered SET at the extracted node generates the error in the outputs or not.
E. Error Rate Prediction and Comparison In order to evaluate the possibility to predict the application error rate (AEP), that means when the implemented circuit on the FPGA will have at least one erroneous data output, we performed the analysis of the implemented circuit using the VERIPlace tool. The results provided by the tool are illustrated in Fig. 9. Please note that since the circuit under test has been designed without effectively applying a TMR approach, we compute the AEP without considering any mitigation techniques applied to the mapped circuit. In order to make a feasible comparison we needed to modify the calculation of both the experimental and the VERIPlace tool analyses. In details, we extract from the radiation test data the number of scrub cycles and the relative CRAM events provoking an error on the output of the FF Chains, these events were classified during the radiation experiments as burst errors.
We assessed the robustness of our results by running the EEG preprocessing with two different versions of Matlab as well as Python. Results: A simple linear regression between peak power or frequency of the alpha waves and the status or age of the participants did not allow to identify any statistically significant relationship. The non-parametric normative model (which took account the non-linear effect of age on the alpha profiles) suggested that participants with ASD displayed more variability than control participants for both frequency and amplitude of the alpha peak (p < 0. 05). Independent of the status of the individual, we also observed weak associations (uncorrected p < 0. 05) between the alpha frequency, and the volumes of several cortical and subcortical structures (in particular the striatum), but which did not survive correction for multiple testing and changed between analysis pelines.
, 2014b). Finally, the discrepancies in the literature may also result from spatial effects. Indeed, source reconstructions revealed an abnormal alpha-mu pattern in ASD, with simultaneously a decrease in occipito-parietal regions and an increase in frontal regions, resulting in an apparent absence of mu-suppression over the central regions at the scalp level  (Dumas et al. , 2014b). To quantify the impact of heterogeneity both ASD participants and methodological choices, we developed a systematic pipeline of analysis across different versions of software. Concerning clinical heterogeneity, specifically the impact of developmental variation, we used normative models using age as a clinical covariate  (Marquand et al. , 2016; Bethlehem et al. , 2018). Concerning variability of methodological choices, we control the reproducibility of manual alpha peak detection between human observers and automatic alpha peak detection between software.
, 1994) , the Autism Diagnostic Observation Scale (ADOS)  (Lord et al. , 2000) , and data from clinical reports from expert in the field. In multiplex families, only index cases were included in the present study to ensure unbiased estimate of the alpha waves' quantification. Psychiatric comorbidities according to DSM-IV-TR were screened with a semi-structured direct interview, the Schedule for Affective Disorders, and Schizophrenia for School-Age Children, Present and Lifetime Version (K-SADS-PL). Intellectual functioning of all participants was estimated with the Raven's Progressive Matrices or with the Wechsler Intelligence Scales. Participants from the control group were from the general population. All participants with a personal or a familial history of ASD were not included. Participants were TD children.
Electroencephalogram Acquisition and Data Extraction Digital 10-channel EEG (FP1/2, F7/8, T5/6, O1/2, C3/4) was recorded using a Nihon-Kohden (Inc. , Tokyo, Japan) system with the electrodes positioned according to the International 10-20 system  (Sharbrough et al. , 1994). Unipolar leads tracings were taken, regarding both sides of the ear as the reference electrode  (Sharbrough et al. , 1994). The EEG activity was acquired using a linked ears reference, sampled at 500 Hz, and filtered offline between 1 and 120 Hz. Impedance was kept below 5 k. Vigilance-controlled recordings were made according to usual clinical standards, including a 10-min resting eyes-closed state, 1 min of alternate 10 s-eyes open/eyes closed conditions, 3 min of hyperventilation, 10 min of recovery (post-hyperventilation), and 4-min photic stimulation (from 0.
5 to 60 Hz). After recording, whole raw data in Nihon Koden format were converted into an EEG EDF+ format allowing their analysis into Matlab TM. Two distinct toolboxes BIOSIG  (Vidaurre et al. , 2011)  and EEGlab  (Delorme and Makeig, 2004) , and a custom script for reading EDF+ files  (Duann and Hatz, 2012)  were used to perform the pre-processing and processing of the data (Supplementary Figure  S2 ). The final processing pipeline used the EEGlab 13. 4. 4b version with Matlab 2014b. The power line interferences were removed with a Finite Impulse Response (FIR) comb filter and electrodes with signal-to-noise ratio over three standard errors were interpolated by spherical splines. Then, FFTs on 1 s sliding windows gave spectra for all electrodes. Detection of alpha peak was automatized by computing the spectra differences between eyes closed and eyes open periods, and then, by selecting among the occipital and parietal sensors the one exhibiting the maximum peak.
Other family of functions can be used but for data with strong heterogeneity or with non-classic distribution, the obtained results can become misleading. Here we used non-parametric version using LOESS Curve Fitting (Local Polynomial Regression). LOESS is a nonparametric method that uses local weighted regression to fit a smooth curve through points in a scatter plot. The procedure was originally proposed as Locally Weighted Scatter-plot Smoother (LOWESS) by  Cleveland (1979)  and further developed by  Cleveland and Devlin (1988). The normative model was calculated by approximating the TD participants data with a polynomial function of age (smoothing kernel of 2 years). Python code is available at https://github. com/GHFC/ SoNeTAA/.
Intracranial and Brain Volume Estimations Based on Magnetic Resonance Imaging For participants with ASD included in this study, MRI data were collected using the following parameters: spoiled gradient recalled echo (SPGR), 1 mm isotropic, repetition time (TR) = 25 ms, echo time (TE) = 6 ms, flip angle = 30 •. To estimate the intracranial volume, the different datasets were first reoriented to correspond with the orientation of the MNI152 atlas. The brain was removed from the skull using AFNI tools  (Cox, 1996) , and linearly normalized to the (skull-less) MNI152 atlas using FSL tools  (Jenkinson et al. , 2002; Smith et al. , 2004). We then used the affine matrix of this transformation to initialize the linear normalization of the reoriented datasets (with skull) to the MNI152 atlas (with skull).
We used the inverse of the determinant of the affine matrix produced by this transformation as an estimation of the intracranial volume, as in  Buckner et al. (2004). All steps of the process were visually inspected for accuracy using in-house software. In the cases where the skull stripping was inappropriate (the most frequent type of failure of the automatic segmentation procedure), we manually corrected the brain extraction using in-house software, and relaunched the processing pipeline. The estimation of brain volume was obtained by segmenting automatically the gray and white matter. We then labeled the frontal, parietal, occipital, temporal lobes, and subcortical structures by non-linearly warping the individual datasets into an atlas, using FSL tools  (Zhang et al. , 2001; Smith et al.
, 2004)  and our own software  (Toro et al. , 2009). Brain volume was calculated as the sum of gray and white matter in the frontal, parietal, occipital, and temporal lobes excluding ventricles and subcortical structures and the intracranial volume estimated as the inverse of the determinant of the linear transformation matrix of each participant's brain into the MNI template excluding the skull. We used a tool to visually control the accuracy of the segmentations: https://github. com/ neuroanatomy/QCApp-vsub. The participants for whom the segmentation not fulfilled this visual quality control were excluded. After quality control and coupling with EEG data, neuroanatomic analysis was done for 20 patients with ASD (Supplementary Figure  S1 ).
Statistical Analysis After quality control with Bangdiwala's test (Supplementary Figure  S3 ), we analyzed data for 26 participants with ASD (128. 11 m. o. ± 37. 6) and for 33 participants with typical development (125. 5 m. o. ± 39. 6) (Table  2  and Supplementary Figure  S1 ). We explored the statistical difference for alpha peak frequency and power between the two groups. An analysis of variance was used to control the effect on age, status (affected or not), and the interaction age * status on the frequency and the power of alpha peak across groups. Normative models were computed with Python 2. 7. We finally explored the relationship between the frequency and the power of the alpha peak with each brain neuroanatomic structures. Statistical analyses were performed with JMP Pro 11. 2. 0.
SAS TM. To check the robustness of our EEG results, we used not only two distinct versions of Matlab but also the Python language, since we observed divergence in the preliminary tests with Matlab. We first performed our analysis with Matlab 2014b with the signal processing toolbox and the 13. 4. 4b version of the EEGlab toolbox. We then compared our results to those obtained with Matlab 2013a without the signal processing toolbox, using the 12. 0. 2. 6b version of the EEGlab toolbox, and to those obtained using Python. For this later analysis, we used Python 2. 7 and the MNE Python library  (Gramfort et al. , 2013). Key steps of the pipeline were kept as identical as possible between Matlab and Python pipelines. Both versions, including the script for non-parametric normative model are available on our GitHub repository: https://github.
Alpha Waves in Patients and Typically Developing Participants The alpha peak characteristics extracted with Matlab 2014 and EEGlab 13. 4. 4b are summarized in Table  3. We did not observe any significant difference for alpha peak frequency and power between the groups. We were also unable to detect any significant variance differences between groups for both alpha frequency (F two-sided = 0. 41; p = 0. 66) and alpha peak power (F two-sided = 0. 42; p = 0. 66) (Table  3 ). An analysis of the variance (ANOVA) of the frequency of the alpha peak was performed including age, status, and interaction age * status as independent variables (F = 0. 23; R 2 = 0. 01; p = 0. 87). There was no significant effect of the status (F = 0. 16; p = 0. 69), nor of the age (F = 0. 07; p = 0. 87) and the interaction age * status on the alpha frequency (F = 0.
Alpha Waves and Structural Brain Volume Correlations in ASD We then compared the alpha characteristics (based on Matlab 2014b-EEGlab 13. 4. 4b) to the brain structure volumes. This analysis was performed only in the participant for which one had excellent to good EEG recording quality (i. e. , participants with at least one unusable rating were excluded; N = 32). We performed a simple linear regression to model the relationship between the peak frequency or the power of alpha waves and each structural brain volumes (Supplementary Figure  S4 ). There was no significant effect of each structural brain volumes on the alpha peak power, but we observed a significant effect on alpha frequency for: the left putamen  R 2 = 0. 14, p = 0. 03), the right occipital gray matter volume [alpha frequency = 21.
Variability of the Results and Processing Pipeline Methods To explore the dependence of our results to the pipelines of analysis, we re-extracted the alpha frequency and its peak power from our dataset using first Matlab 2013a with the Signal Processing toolbox, and then Python with the MNE Python library. We compared these extractions to those obtained with Matlab 2014 (Figure  4 ). No significant difference of alpha frequency and its peak power was found between groups. Only the pipeline with Matlab 2014 revealed a developmental trajectory for alpha frequency in the TD group. Furthermore, the relationship between left and right occipital gray matter volumes and the alpha waves frequency appeared to be consistent among the two versions of Matlab, independently from the use of the Signal Processing toolbox (Table  4 ).
, 2014). The functional dysconnectivity between occipital and parietal regions, which are involved in the observation of human biological motion, may result in an abnormal information processing in ASD  (Dumas et al. , 2014b; Keehn et al. , 2017). One of the aims of the study was also to further explore the developmental effect on alpha waves, i. e. , the effect on age on the alpha frequency and power, suggesting a major interaction between both. The linear method we used first with Matlab 2013 found no statistically significant difference between the ASD and TD groups, nor the effect of age. However, repeated analyses with Matlab 2014 only found a developmental trajectory for the alpha peak frequency (r 2 = 0. 1) in the TD group (Table  4 ) which were coherent with developmental differences already reported, namely an increase of the alpha frequency and power with the age of the participant  (Dustman et al.
The reproducibility crisis recently mediatized in psychology and neurosciences also shows the importance to increase sample sizes  (Smith and Nichols, 2018) , the level of statistical practices  (Kriegeskorte et al. , 2009) , and the publication of negative results  (Button et al. , 2013). Here we also showed that the choice of software also introduced another form of heterogeneity, leading to non-replicable results on the same data. In our case, the proprietary software Matlab and the EEGlab toolbox were introducing an unexpected change in spectral analyses depending on the presence of the Signal Processing toolbox. We addressed this issue in bioinformatics recently  (Kim et al. , 2018)  and neuroinformatics is not an exception to this underestimated problem. While there is not much discussion of the EEG literature, the observations that different operating system for deriving neuroanatomical volume from automated brain segmentation might be a source of heterogeneity have been already described in the MRI literature  (Gronenschild et al.
CONCLUSION The key findings of our study are the confirmation of the higher variability in ASD group thanks to the normative models' approach, and the demonstration that variation of pipeline and software could lead to large inconsistency, even on the same data. This may explain our inability to replicate previous results supporting differences in alpha between ASD and controls. This also suggests the need for more sophisticated analytic approaches, although they may require higher-density EEG recording, finer grain anatomical measures, and better experimental design. Identify novel paradigms may indeed represent the most promising development to uncover relevant biomarkers in ASD. Beside a more precise account of brain activity at both temporal and spatial scales  (Dumas et al. , 2014b) , the challenge will be to study brain activity in individuals with ASD during relevant ecological tasks such as real-time social interaction  (Dumas et al.
FUNDING This work was supported by the funding of the  Institut Pasteur , the  INSERM , the  Fondation FondaMental , the  APHP , the  DHU Protect , the  Fondation Bettencourt-Schueller , the  Fondation Cognacq Jay , the  Fondation Conny-Maeva , the  Fondation de France , and the  Labex BioPsy.