dataesr/scientific-paragraphs-categorization huggingface dataset:
Procedure The participants were tested individually in a sound attenuated booth equipped with headphones, a mouse, and a button box with stickers JA 'yes' / NEE 'no' on the buttons. The participants first signed a consent form and filled in a language background questionnaire, before doing the lexical decision task. The lexical decision task was presented with PsychoPy  (Peirce, 2007). The participants were instructed to indicate as fast as possible with the button-box, using their dominant hand, whether the word they heard over the headphones was a real word in French or not. The instructions insisted that the participant did not need to know the exact meaning of the word in order to press the 'yes' button but that they had to be certain that the word occurred in French. The next trial initiated 1000 ms after the participant's answer or 3500 ms after the onset of the preceding stimulus in case the participant did not react.
Results One participant in condition AB and one participant in condition BB were removed from the dataset since their accuracy on the experimental words in the lexical decision task was below chance level  (43. 75 % and 33. 33 %, respectively). We analysed all the data from this study using the software R (R Development  Core Team, 2007). All the trials to which the participants did not react were discarded (ten out of the 5664 experimental word trials). Accuracies were analysed by means of a linear mixed effects model for logistic regression  (Jaeger, 2008) , for which the dependent variable was the probability of a correct response. Reaction times (RTs; measured from word offset) to correct trials within 2. 5 standard deviations from the targets' grand mean (345 ms; discarding 52 data points out of 1768; 3 % of the data) were analysed by means of mixed effects regression models  (Baayen, Davidson, & Bates, 2008).
96) were retained in the final models. Predictors which were significant in an interaction, but not as main effects were kept in the models as well. Once the fixed effect structure was finalized, random slopes on item and participant were tested for all fixed effects. A random slope was kept in the final model exclusively when supported by likelihood ratio tests (i. e. p<0. 05). Finally, following  Baayen (2008) , to ensure no significant effect was driven by outliers, the final RT model was refitted: RTs with residual standard errors more than 2. 5 standard deviation units were excluded from the dataset of the final statistical model (49 data points were removed out of 1716; 3 % of the dataset). No predictor lost significance as a result of this refitting of the model 2. The p values reported were obtained with the lmerTest package version 2.
We also found a main effect of Voicing (see Table  3 ), without an interaction of Voicing with Token: participants were slower at processing devoiced targets, independently of whether the targets were token A or token B (cf. Figure  3 ). That is, contrary to the Accuracy data, which showed that the participants were only sensitive to devoicing for the tokens A, the RT data show that the participants were sensitive to the devoicing manipulation for both tokens A and tokens B. L2 listeners were thus sensitive to L1   irrelevant information. Interestingly, the significant main effect of Token without an interaction of Voicing and Token indicates that the tokens B were processed significantly faster (i. e. were easier to comprehend for the participants) than the tokens A, independently of whether the tokens were voiced or not.
Acknowledgments We gratefully acknowledge support from  DFG  grant  220199  to JB. We are also grateful to two anonymous reviewers for their useful suggestions. We thank  Felix Golcher  for his advice on the statistical modelling of the data. We thank  Miriam Oschkinat  for her support during data acquisition and  Yulia Guseva  for her help with the preparation of the manuscript. We also thank all participants who took part in the study.
V. IMPLEMENTATION The complete design of a WT model in a typical EMT software package (EMTP  [31]  is used in this paper) is based on hierarchical (subnetworks containing subnetworks) blocks with masking. It consists of the aggregated FSC WT, the aggregated LV/MV WT transformer, the equivalent PI The aggregated and single unit model per unit parameters are same for both FSC WT and WT transformer when the base power for the aggregated unit is selected as S agg = N S single ( 25 ) where S single is the single unit base power, N is the number of units in aggregation. The FSC WT control offers both AVR and AQR usage at GSC. When AQR usage at GSC is selected, the reactive power reference for the aggregated WT model is produced by the proportional voltage control of WPC. When AVR usage at GSC is selected, the reactive power regulator of the WPC adjusts the voltage reference value of the aggregated WT model (see Fig.
Most of exposure campaign studies only provide one or two weeks averaged concentration with no detail on the variability of the pollutant concentration levels. Furthermore, when investigating occupied dwellings, it is not possible to have any control on the way people are living in the dwelling. Therefore, the interaction between occupant specific behaviour and IAQ is difficult to anticipate Numerical simulation allows to detail the evaluation of the cumulative effects of data collected by laboratory or in-situ measurements. If input data are sufficiently relevant, it is possible to build a simulation that provides consistent results and allows a full control over pollution scenarios. We designed a multi-zone simulation to be as accurate and realistic as possible thanks to a type 98 coupling procedure between CONTAM and TRNSYS softwares as well as statistical data created out of PANDORA indoor emission rates database  [4].
Simulation procedure and input data The energy and airflow modelling of the house was done using the type 98 coupling process between CONTAM and TRNSYS softwares. As described in Figure  2 , zone temperatures and humidities are evaluated in TRNSYS and forwarded to CONTAM to calculate airflow rates between rooms, with the outdoor environment and the pollutant concentration in the different rooms. Multizone airflow rates are looped on TRNSYS inputs to update multizone airflow and calculate back energy and hygrothermal properties of the air in the different zones. Pollutant concentration is finally used to estimate IAQ.
Conclusion In order to perform the global assessment of IAQ in a dwelling, we previously designed a complex numerical simulation based on a type 98 coupling procedure between CONTAM and TRNSYS softwares. To be as realistic as possible, this simulation reached a certain level of complexity; this paper aims to compare accuracy of a complex simulation versus more simplified ones (Noncoupled, Monozone-coupled, and Monozone noncoupled). For balanced and single flow ventilations, the accuracy of airflow rates is very good inside ducts (<4. 5%) but it highly decreases in stairway. Any level of complexity is acceptable but monozone simulations do not provide inter-zone airflow rates. Natural ventilation relies on natural pressure and temperature variations and thus accuracy is highly affected by complexity.
RSM was applied to optimize the extraction conditions of polyphenols and tannins extracted by microwave. For this objective, a face centered composite design (FCCD) was used to determine the optimal conditions and the relevance of the three factors was evaluated: time extraction (X 1 ), solvent proportion (X 2 ) and microwave power (X 3 ). The responses measured were the following: polyphenols content (Y 1 ) and condensed tannins content (Y 2 ). The range of independent variable and their levels was presented in Table  1 , which was based on the results of preliminary experiments. The best solvent extraction was methanol. All experimental data of FCCD are shown in Table  2 ; the whole design consisted of 20 experiments point carried out in random order. For three variables (n = 3) and two levels (low (À) and high (+)), the total number of experiments was 20 determined by the expression: 2 n (2 3 = 8: full factor points) + 2n (2 * 3 = 6: axial points) + 6 (center points: six replications).
The software design expert (JMP version 11) was employed for experimental design, data analysis and model building. The experimental data were replicated three times to evaluate the statistical quality of the results. Experiments were randomized to maximize the effects of unexplained variability in the observed response due to extraneous factors. The suggested model for the quadratic model of the variables Y according to the independent variables was indicated in the following Eq. (  3 ): Y ¼ a 0 þ a 1 X 1 þ a 2 X 2 þ a 3 X 3 þ a 11 X 2 1 þ a 22 X 2 2 þ a 33 X 2 3 þ a 12 X 1 X 2 þ a 13 X 1 X 3 þ a 23 X 2 X 3 : ð3Þ where Y is the predicted response: the yield of polyphenols or condensed tannins. A 0 is the offset term, a 1 , a 2 and a 3 are the linear effect a 11 , a 22 and a 33 are the quadratic effect and a 12 , a 13 and a 23 are the interaction effect.
Evaluation of the quality of the model The evaluation of the estimated quality of the quadratic model chosen to model extraction condition of polyphenols was carried out using statistical tools: the linear regression and the adjusted regression. The linear regression of the model, permitted to evaluate graphically the quality of the established model. It is represented by the linearity of the points measured by the real model according to the points considered by the quadratic model. The goodness of the model is judged if the linear of regression coefficient is equal to or higher than 0. 80. The linear analysis of regression was established by the Experimental  design JMP 11 software are shown in Fig. 3. The coefficient of the linear regression R 2 was equal to 0. 98. This coefficient was very close to 1, therefore the choice of quadratic model to modeling extraction process of polyphenols was better.
92. This Factor was very lower than experimental factor, F exp = 267. 11 ) F theo = 2. 92, therefore the condition of Fisher Snedecor test was validated and the regression was thus significant with a confidence level of 99%. The lack of adjustment analysis permits to evaluate the variance of the residue compared to the experimental error. It is calculated from points in the center, using the «Fisher Snedecor» test. The model is validated; if the p value was lower than 0. 01. The results of this analysis, determined by software JMP 11, were represented in Table  5. The results of the lack of adjustment analysis, indicate that the p value of the experimental factor was largely lower than 0. 0001. This result showed that the error between the established and theoretical model was thus not significant for a confidence level of 99% and the result was very significant.
Determination of the polyphenols equation model After validating the established quadratic model of polyphenols, the equation of this model we can be determined. The coefficients are estimated by the method of least squares. They are said significant if the p value was lower than 0. 01. The significant effects are shown in Fig. 4. Under the linear term of three factors (time extraction, methanol proportion and microwave power), the quadratic effect of these factors and their interactions were very significant, P value <0. 0001, to improve the yields of polyphenols extracted by microwave. The equation of quadratic model used for the modeling extractions conditions of polyphenols, determined according to the results of the JMP 11 software, was represented in the following equation: Y 1 ¼ 223:10 þ 72:82 Ã X 1 þ 40:76 Ã X 2 þ 20:76 Ã X 3 þ 36:51 Ã X 12 À 29:18 Ã X 13 þ 29:97 Ã X 23 À 20:54 Ã X 2 1 þ 32:61 Ã X 2 2 þ 80:58 Ã X 2 3 : The established equation showed that the polyphenols content (Y 1 ) was significantly affected by the synergistic effect of linear term of time extraction (X 1 ) and methanol proportion (X 2 ).
Optimization extractions process of polyphenols The iso-response curves is a graphically method that permits to illustrate the result according to two factors chooses, time extraction, power extraction or methanol proportion. It is a graphical representation at two dimensions, 2D. This method permits to visualize optimal conditions and determine the best yield extract for three factors when one factor was fixed. It permits also, to compare the estimated result with the real result obtained with the experiments. The optimal condition to extract polyphenols, determined using JMP 11 Software was represented in Fig. 7. The result of iso-response curves indicated a maximum polyphenols content, 444. 3 mg GAE/g bark, under the conditions as follows: microwave power, 150 W, time extraction, 5 min and methanol proportion, 80%.
The response surface curve was a three dimensional graphical method that permits to illustrate the progression of the response according to the progress of three factors. The addition of the 3rd factor at iso-response curve, 2D, constitutes the response surface curve measured, 3D. The progression polyphenols yield according to the progress of three significant factors: time extraction, microwave power and methanol proportion, was determined using response surface curve presented in Fig. 8. The exploiting method was carried out using JMP 11 Software. The result of response surface indicated the highest polyphenols content, 481. 4 mg GAE/g bark, at 157 W during 1 min and using 80% of methanol.
Confirmation experiment To verify the quality of the result obtained using RSM, optimal conditions to extract polyphenols and condensed tannins determined by experimental data and modeling data were evaluated using the confirmation experiment. RSM gives two optimal extraction conditions, according to the tools used, isosurface curve or response surface curve. Experimental data dive one optimal. The total polyphenolic contents and condensed tannins were determined for the different extraction conditions. The result of this test is show in Table  6. The response values of the confirmation experiment trial present the same values compared to predicted optimal conditions (80. 16 mg Cya/g bark and 79 mg Cya/g bark respectively for condensed tannin; 478 mg GAE/g bark and 481 mg GAE/g bark for polyphenols contents, Table  6 ).
Stiasny number Optimal conditions to extract condensed tannins determined by experimental data and modeling data were chosen to evaluate tannin reactivity with formaldehyde. Three conditions were compared with Brazilian commercial Tannin; the first was an extraction at 182 W using 20% of methanol during 3. 7 min, the second was an extraction at 152 W using 20. 5% of methanol during 1 min and the third was an extraction at 250 W using 20% of methanol during 3 min. The Stiasny number gives us the reactivity of our extracts to formaldehyde, this information can help us to determine if the extracts can be used as adhesives  (Chupin et al. , 2013).
Acknowledgments We gratefully acknowledge the suggestions of  Dr. Hassan Moufid , Professor at Lyce´e GAUFRE ´RUDEL (33394 Blaye), and the  financial support of ''PHC Volubilis'', ''EGIDE'', ' ' Centre National de Recherche Scientifique et Technique (CNRST)  from  MOROCCO  and '' ANR-10-EQPX-16  Xyloforest''.
Introduction The Internet of Things (IoT), also known as the Internet of Objects, is a trending concept intended as a network of interconnected smart objects receiving and sending data without human intervention  [1] [2] [3]. The development of applications in IoT is strongly related to the notion of physical location and positions. Therefore, localization technologies will play an important role in the IoT and may become embedded into the infrastructure or into the object. In fact, collected data are reported for a specific IoT application, which requires dedicated data analytic tools to make sense of them and take the appropriate action. Plenty of applications are related to location-based services, which increases the importance of location information. This information can be used for target tracking, surveillance applications, guiding autonomous vehicles, etc.
It is a cost-effective solution, and its accuracy is related to the sufficiency of data, where huge RSSI databases are constructed and manipulated to achieve a good localization accuracy. This increases the complexity and the running time of fingerprint-based localization systems, making them not adapted to real-time localization and not able to deal with big sensor networks. Therefore, for a solution that uses a learned model, reducing the online complexity is extremely needed. Recently, promising indoor localization solutions were implemented based on RSSI fingerprinting combined with Machine Learning (ML) methods  [26]. Since data preparation and preprocessing are assured in the offline/training phase, only the prediction task is performed online. Therefore, to find its position, a sensor node interrogates a trained model, which performs the estimated position.
CNN is a class of deep NN that is widely used. It reduces the complexity of traditional NN and the number of weights to learn by its weight-sharing structure. This means that CNN requires less training parameters and can bring better generalization and robustness. Another reason why we use CNN is to deal with the need for large datasets required by traditional NN, to avoid overfitting problems. Another challenge in implementing positioning systems based on CNNs is that these networks have translational invariance. This feature coincides with the temporal dependency between RSSI fingerprints. Since this NN structure has high invariance in translation, it has been widely used in image processing and classification  [39] [40] [41] , achieving a spectacular success in this field. Thus, applying CNN on fingerprint images recently has arisen as an important interest in the localization community.
)  [43] [44] [45] [46]. Therefore, each radio image plane contained data received from a specific sensor. Besides robot localization, 3D radio images can be used in systems exploring different types of data. For instance, the authors in  [18]  explored RSSI data accompanied by magnetic and acceleration information. Based on CSI, 3D radio images can be generated as developed in  [20, 47] , where one CSI matrix of an antenna was considered as the red, green and blue planes of the image. Therefore, the image was constructed by combining three channels of CSI. In this paper, we deal with the issue of indoor localization in the context of the IoT as a 3D radio image-fingerprint-based location recognition problem motivated by the outstanding performance of CNN in image classification problems and based on RSSI fingerprints.
In this paper, we exploit multiple RSSI measurements like the authors in  [51] , expecting to remove the noise and improve the localization accuracy. CNN are used taking into account the correlation between different RSSI measurements. We propose to split the studied environment into region "classes" limited in space, and we construct radio images from measured RSSI fingerprints. These radio images are used as our CNN model input data to predict the real-time region index. The main contributions of this paper are summarized as follows.
• To the best of our knowledge, this is the first time that radio tensors (used as CNN localization system inputs) have been constructed based on RSSI data alone, without exploring extra information (magnetic information, acceleration, visual data, etc. ). This avoids modifications of the existing infrastructure and the increase in the cost of the proposed solution. For this, we propose to use the kurtosis values calculated from measured RSSI. By using the kurtosis, we aim to provide a statistical parameter that will give global information to local filters. This choice is justified later, empirically. Furthermore, the proposed approach is independent of the communication technology because in all of them, the RSSI can be measured.
System Model of the Proposed CNN-Based Localization Framework Our system model included two phases (Figure  1 ): an offline phase including the collection and preprocessing of data to be used as inputs for the localization CNN model and the training of the latter and an online phase introduced to find the position of each sensor node in the studied area using the trained model. First, the studied area was split into different partitions named "classes", as shown in Figure  2. Each region was labeled class q with q ∈ 1, 2,. , Q, and Q is the number of classes. We mention that the area of each class is a choice, based on the precision of localization required by the application and the availability of computing resources. RSSI values received from M deployed APs at different training points (sensor nodes) were measured at different instants.
RSSI Acquisition and Normalization At each training point, T consecutive RSSI measurements, received from M APs, were taken. N RSSI databases, called realizations, were constructed for each training point, as illustrated in Figure  3 ; where N is the number of realizations, T is the number of RSSI measurements received from each AP, and M is the number of APs. Therefore, each realization presented RSSI values received from different APs at T instants. Notice that N and T were experimentally adjusted. The most important factor in deep learning is how much data are available for training and how relevant they are. Furthermore, data are generally required to be normalized, especially when using gradient-based optimization methods, in order to accelerate the learning process and minimize the risk of algorithm divergence  [52, 53].
Radio Image Construction After collecting RSSI values and calculating the values of kurtosis corresponding to each RSSI database forming a radio tensor, 3D radio images were constructed. As the two first dimensions, we put T measured RSSI values from M APs, and we put kurtosis values in the third dimension. Thus, the size of each realization became (M × T × 2) (Figure  4 ). Constructed radio images needed to be classified and organized, so each image was labeled q, q = 1, 2,. , Q. Then, N realizations of each sensor node should belong to the associated class. Images were organized into Q folders labeled class1, class2,. , classQ, each containing the appropriate radio images.
4 GHz. The sigma of the random variable x σ took the value of two. The dimension of each RSSI fingerprint as mentioned before was (M × T), where T was varied to choose the best value, as discussed later. The number of realizations per training point was N = 72. Therefore, we obtained 72 tensors corresponding to each training sensor node, and the dimension of each tensor was (M × T × 1) with the CNNLocWoC method and (M × T × 2) when applying the CNNLocWC method. Eighty percent of tensors were considered for the training phase and 20% for validation. The presented performances were based on the validation data. Our experiments were conducted on a PC with Intel(R) Core(TM) i7-6700 CPU @3. 4 GHz. MATLAB R2018a has an advanced Neural Network Toolbox. It is a very efficient framework for us to implement our CNN models.
Fingerprint Construction We tried to construct a significant training database, which included several RSSI variations, in order to present the maximum of fluctuations and variations, since indoor environments are characterized by high dynamics of people and other structural changes. The fingerprint database can be updated periodically depending on the availability of computing and memory resources. We studied the impact of the variation of our input data's size. Thus, we studied the variation of parameter T on the localization accuracy, and we proved empirically the beneficial use of kurtosis. (A) Variation of the Parameter T As said before, we worked with RSSI databases of size (M × T). In this section, we study the impact of the variation of T on the localization accuracy taking into account the complexity and the training time.