dataesr/scientific-paragraphs-categorization huggingface dataset:
The proposed system would optimize the ability to analyze any alert generated by an event such as rain. The two main phases of our Intelligent REWS are: (1) building the predictive model and (2) near real-time detection and prediction. In the first phase, the historical data generated by Germany's REWS  [3]  were analyzed to extract knowledge about the previous incidents that occurred in the past. The historical databases contain raw unlabeled data (i. e. , time series) corresponding to the gamma dose rate monitoring at each probe. The data used in this work comprise the past ten years' minute-by-minute gamma dose rate real data for over a thousand probes. In  [4] , we already proposed an unsupervised machine learning model that helps us automatically determine the reasons behind the incidents.
Context and Problem Statement In this research, we deal with univariate time-series which are unlabeled as shown in Figure  1. Incidents caused by the same event may not have a recognizable temporal trace or characteristics but more common behavior. For example, a particular event may cause peaks of increasing amplitudes that decrease during a longer period of time; another may cause an abrupt increase and maintain its amplitude during a period of time, and so on. Note that incidents caused by the same event can last for a varying length of time and reach different amplitudes. In the first phase, we were able to collect nearly 300 innocent incidents from 45 different locations in Germany. Choosing different locations allowed us to have diverse shapes of observations representing the innocent incidents, thus gaining a higher quality dataset to build our investigation upon.
The incoming readings are analyzed to explore the thresholds in real-time. Once an incident is detected, the data preprocessing model used in phase one is also applied to deal with the scale and length issues. Notice that we start analyzing an incident even if the incident is not yet finished. As we aim to match unlabeled incidents without any human intervention as quickly as possible, our research is in the field of supervised machine learning time-series classification. After investigating the classification algorithms presented in the literature, we noticed several shortcomings that prevented us from relying on a specific algorithm for our online classification model. After going through the classification algorithms introduced in the literature, we noticed that no specific algorithm could perfectly fit our data since our data have unique characteristics and behavior.
State of the Art In this section, we briefly recall the main time-series classification algorithms mentioned in the literature. We compare these techniques based on applicability and effectiveness. In addition to conducting a literature study, we also apply these different techniques to our dataset to test their performance. For time-series data, there exist several algorithms that consider the time factor, which is essential in our study. A common problematic solution that could happen when dealing with time-series data is to treat each value in the sequence as a separate feature. This is the core difference between time-series data and tabular data. In time-series data, the order of the data is essential and critical. In contrast, in tabular data, the order is ignored and scrambling the order of the features will not affect the prediction process.
• Frequency-based algorithms: classifiers that follow this type of algorithm rely on the frequency of the extracted features from the time-series data. Random interval spectral ensemble known as RISE, is a straightforward classifier that is similar to a time-series forest  [12]. Therefore, this algorithm constructs decision trees, and the classification takes place upon the majority of votes. • Shapelet-based algorithms: the main objective of the shapelet-based algorithm is to identify, for a particular class, the bag of shapelets with discriminatory power. Each shapelet is an interval extracted from a time series and it should follow the same order. During classification, the Shapelet-based algorithm transforms the incoming datasets into "K" shapelets that are yet to be compared by the "K" shapelets extracted for each class in the training phase  [13].
Extracted incidents cannot directly enter into the classification phase. Preprocessing treatment for the raw incidents should be done. Incidents coming from different probes have different characteristics in terms of length, scale and level. During the preprocessing phase, the data of the incidents are normalized using the z-normalization technique and padded using the zero-padding technique. This preprocessing phase will not affect the shape of the incident; it will only standardize the incidents to become similar to the training dataset that the classifiers have trained over. This will help classifiers identify or predict the class of these unknown incidents. The classification phase is divided into two phases: the voting phase and the counselor's decision. In the voting phase, four classifiers from the state of the art are implemented separately.
Experimentation In order to compare the different approaches of the state of the art, as well as to see the benefit of our proposed model, we decided to evaluate systematically different experiments and evaluations on the labeled time-series data. After investigating all of the mentioned algorithms in the state of the art, we attempted to implement each classifier based on its best practice for selecting the optimal parameters and then apply it over our labeled data. First, the data we have are split into two sets, a training dataset (90% of the dataset) and a testing dataset (forms the rest 10%). When splitting the data between training and testing, we guarantee that the training dataset is balanced and presented well in all three classes so that the classifier will be trained well. All the implementations are performed using Python libraries.
For the best environment performance and for easy implementation, we installed Anaconda, in which we used the Jupyter notebook for writing and testing the code. Anaconda provides us with an isolated environment containing all the needed libraries to perform our tests. Going deeply into the libraries dedicated to time-series data, several methods are defined to handle this type of data. The traditional machine learning algorithms implemented for tabular classifiers cannot be applied in our case of time-series data because these neglect the time factor essential in our data. Thus, in addition to Sklearn, pandas, numpy, and other libraries, we installed and used the Sktime library which contains the time-series classifiers. In Table  1 , we found the evaluation results for the four times series classifiers of the state of the art.
However, after we investigated the results, we uncovered the reason for such an outcome. The data that we have are very challenging because they are very similar to each other, which makes their shapes very similar; this is why the model was confused. Even though the classification model was able to identify the rain class, it failed in the other two classes (calibration and stormy rain). To start evaluating our online classification module, we first tested incoming incidents. These incidents are preprocessed online after being extracted and then prepared to be classified with the classification algorithms. Then, the classification algorithms will work individually in parallel on the incoming incident, trying to classify it as soon as possible. The classification algorithms presented in Table  2  will return their predictions for the incoming incidents as soon as possible.
When those approaches failed to classify our data when properly testing each approach alone, we proposed our counselor classification model for using all the classification algorithms simultaneously and voting for the one with the best outcome. Displaying the obtained matching percentages through the experimentation of various algorithms, we were able to highlight how our model comparatively gave the best results. Furthermore, the experts expressed positive and hopeful thoughts upon inspecting the results, which motivated us to publish this contribution in an article. As future work, the next step would be to improve the quality of the overall framework by exploring the evaluation with more datasets to automate the evaluation as well.
In the past twenty years, green analytical chemistry has gained more and more attention. However, the environmental impacts of analytical methods have never been quantified. The purpose of this work is to apply life cycle assessment (LCA) to the preparation of one sample using SBSE and SPE techniques and to show that LCA is a suitable framework to quantitatively assess the environmental impacts of a sample preparation. The amounts of consumables, chemicals and energy needed to prepare a sample with both techniques were determined with the literature and lab measurements. We converted this data into environmental impacts through the use of a life cycle inventory database (ecoinvent 3. 7. 1) and a life cycle impact assessment method (ReCiPe 2016 Midpoint). The results of the LCA (baseline scenario) showed that the SBSE induces less overall environmental impacts than the SPE because it uses less chemicals to prepare one sample.
) have been excluded from the study because of lack of data. Also, it is common that LCA case studies exclude infrastructures in the system boundaries. Resource use and emissions related to the background processes were retrieved from the ecoinvent 3. 7. 1 database  [12]. The study has been made in the French context: all the data used are considered most accurate for the French situation. When French data is not available, European data have been selected. The environmental impacts are then characterized using the ReCiPe 2016 Midpoint (H)  [13]  life cycle impact assessment method ( Table  1  ) selected as one of the most up-to-date method for the European context. We used Activity Browser  [14]  , a graphical user interface for the LCA software Brightway2 to determine the impacts of each background processes from ecoinvent 3.
The data used for the calculation of environmental impacts come from generic ecoinvent datasets that might not give exact estimations of the impacts of resources and processes needed to achieve the manufacturing of high purity chemicals or the quality of consumables needed for the specificity of analytical chemistry. It has been shown that fine chemicals manufacturing for pharmaceutical use had much higher impacts than basic chemicals production  [19]  , we can suppose that the same results would be obtained with the high purity chemicals used in analytical chemistry. Some impacts could thus have been underestimated because further steps might be needed to get high purity and quality materials (example: the activated silica of the SPE cartridge, or high purity nitrogen). Also, the number of reuses of the vial and the vial cap, or the amount of chemicals used for the washing step are based on field experience but could vary depending on the laboratory practices.
1  H NMR spectra were recorded on a Bruker AVA400 spectrometer operating at 399. 90 MHz, a Bruker AVA500 or a Bruker PRO500 spectrometer operating at 500. 12 MHz, or a Bruker AVA600 spectrometer operating at 599. 81 MHz. 13  C{ 1 H} NMR spectra were recorded on a Bruker AVA500 or a Bruker PRO500 spectrometer operating at 125. 76 MHz. 19 F{ 1 H} NMR spectra were recorded on a Bruker AVA500 spectrometer operating at 470. 59 MHz. Chemical shifts are reported in parts per million. 1 H and 13 C{ 1 H} NMR spectra are referenced to residual solvent resonances calibrated against an external standard, SiMe 4 (d = 0 ppm). 19 F{ 1 H} NMR spectra are referenced to an external standard, CCl 3 F (d = 0 ppm). All spectra were recorded at 298 K unless otherwise specified. All data were processed using MestReNova 12.
0. 3. Full assignments are given in the Supporting Information. Single-crystal X-ray diffraction data were collected at 120 K on an Oxford Diffraction Excalibur diffractometer using graphite-monochromated Mo Kα radiation equipped with an Eos CCD detector (λ = 0. 71073 Å) or at 120 K on a Supernova Dual Cu at Zero Atlas diffractometer using Cu Kα radiation (λ = 1. 5418 Å). ] [29]  All programs were used within the OLEX suite. 30 All non-hydrogen atoms refined with anisotropic displacement and H parameters were constrained to parent atoms and refined using a riding model unless otherwise specified. All single-crystal X-ray structures were analyzed and illustrated using Mercury 4. 3. 1. Elemental analyses were carried out by Elemental Microanalysis Ltd. , measured in duplicate. All Fourier transform infrared (FTIR) spectra were recorded using a JASCO 410 or a JASCO 460 plus spectrometer.
These properties of TDLAS are also needed for monitoring the gaseous headspace of champagne glasses, especially under the swirling conditions triggered by several revolutions of the glass per second around a vertical axis (Figure  1 ). In this article, a spectrometer initially developed to measure the concentration of gas-phase CO 2 in the headspace of champagne glasses, the so-called CO 2 -DLS  [10] , was upgraded to enable a wide range of gas-phase CO 2 concentrations in a new homemade orbital shaking setup especially designed to replicate the wine-swirling motion conducted with champagne glasses under standard tasting conditions. The first datasets recorded thanks to this setup are presented.
Instrument Control and Data Processing Laboratory software was programmed in LabVIEW to interface with the driving National Instrument NI-7852R FPGA card used to control the two lasers in both temperature and current sawtooth modulation and to control the galvanometric mirror position. The same LabVIEW software was also used to acquire the data in a customized numerical format file thanks to a National Instrument NI PXI-5105 card. The data were stored in this format to allow high speed streaming while limiting the disk footprint. Three signals were recorded simultaneously: both laser signals from the Fabry-Pérot path and from the experimental path (through the glass headspace), as well as the signal reporting the position of the galvanometric mirror. Because the position of the galvanometric mirror is synchronized with the lasers' modulation (Figure  3 ), these data were used to split the ramps from each laser into dedicated files.
These other lines, spectrally located near the recorded line, influence the line profile (Table  2  shows the line wavenumbers and strengths for both lasers). A Python program was used to adjust fitting parameters, namely the CO 2 mixing ratio ρ (ranging from 0 to 1) and the maximum of absorption on a relative scale (to obtain an absolute wavenumber scale). During the validation phase, for the first CO 2 -DLS development  [10] , the measured mixing ratio precision was estimated by analyzing the known standard CO 2 concentration. For gaseous mixtures with a concentration of CO 2 ranging from 2. 5 to 100%, the precision was found to vary between 0. 001% and 0. 4%  [10]. Table  2. List of the positions in wavenumber (σ) and absorption strength (s) of the lines contributing to the Beer-Lambert fit of the recorded transmissions for both lasers.
In order to obtain realistic results, a preliminary statistical study (data not published) was conducted to determine the magnitudes of both the radius of gyration and the angular speed of an INAO glass under standard swirling conditions. To do so, a panel of more than 80 people was recorded while swirling an INAO glass filled with 100 mL of distilled water. The panel was composed of both amateur tasters and wine experts, such as enologists, students in enology, sommeliers, and cellar masters. It is noteworthy to mention that tasters from the panel hardly followed a perfectly circular orbit when swirling the INAO glass. An elliptical trajectory was thus more realistic to describe the motion of wine swirling, with the mean angular velocity and semi-major and semi-minor axis amplitudes and their respective standard deviations (std) given in Table  3.
The optical path corresponding to the diameter of the glass 5 mm below the INAO glass edge is 4. 6 cm. All INAO glasses had a line drawn on them to mark the 100 mL level of champagne to be poured. Champagne was carefully poured in the glass to avoid the liquid crossing the laser beam (every pouring lasted roughly 15-20 s). When the volume of 100 mL was poured and at rest in the glass, the laser beam traversed the headspace 4. 5 cm above the champagne interface. At such a distance from the champagne interface, the collar of surface bubbles and the tiny droplets of wine projected by bursting bubbles cannot impact the laser light and therefore do not affect monitoring of gas-phase CO 2. A Python script was executed on the Raspberry Pi to control the orbital shaker motor at the end of the pouring phase.
The aim of this program was to pilot five successive 5 s rotation steps, regularly triggered all along the first 5 min following the end of the pouring process. The first 5 s rotation step was triggered by the Python program 30 s after the end of the pouring process, whereas the four following ones were separated from one another by four 1 min time periods. Figure  6a  shows typical CO 2 monitoring as recorded in the headspace of an INAO glass filled with 100 mL of champagne under periodic wine-swirling conditions. Firstly, close observation of Figure  6  reveals a small peak of CO 2 rising to almost 5% (between -19 and -17 s), as seen in Figure  6b. This peak was observed for the very first time. It might correspond to gas-phase CO 2 (denser than ambient air, and initially under pressure in the headspace of the bottle before uncorking) falling from the bottleneck just before the liquid falls, as observed through infrared imaging in a previous article (see Figure  7 )  [27].
This compound was additionally identified by X-ray crystallography. Analytical data for 7b (cone conformer): colorless solid, MW 1023. 60 g/mol, m. p. > 265°C (decomp. ). 1 H-NMR (400 MHz, toluene-d8, 90 °C): δ [ppm] = 7. 85 (s, 4 H, ArH), 7. 67  (s, 4 H, ArH), 4. 84 (d, 2 JHH = 13 Hz, 4 H, ArCH2N), 3. 76 (d, 2 JHH = 13 Hz, 4 H, ArCH2N), 3. 66 (d, 2 JHH = 16 Hz, 4 H, ArCH2N), 3. 37 (d, 2 JHH = 16 Hz, 4 H, ArCH2N), 2. 39 (s, 12 H, NCH3) , 1. 98 (s, 4 H, SCH2), 1. 43 (s, 18 H, C(CH3)3), 1. 19 (s, 18 H, C(CH3)3), 1. 16 (s, 18 H, C(CH3)3). 13 C{ 1 H}-NMR (100 MHz, toluene-d8, 90°C): δ [ppm] = 152. 1 (ArCC(CH3)3), 151. 6 (ArCC(CH3)3), 145. 7 (ArCCH2), 144. 5 (ArCCH2), 131. 4 (ArCS), 127. 4 (ArCH), 62. 1 (ArCCH2N), 58. 2 (ArCCH2N), 48. 7 (SC(CH3)3), 44. 1 (NCH3), 43. 9 (NCH3), 36. 1 (SCH2), 35. 4 (ArCC(CH3)3), 35.
Graphics were produced with Ortep3 for Windows  [47]  and PovRAY. [48] position Numbers CCDC-2123379 (for 6a•2MeOH), CCDC-2123378 (for 7a. 4CHCl3), CCDC-2123377 (for 7b. CH2Cl2), CCDC-2123376 (for 13•3H2O•3. 5tBuOH) contain the supplementary crystallographic data for this paper. These data are provided free of charge by the joint Cambridge Crystallographic Data Centre and Fachinformationszentrum Karlsruhe Access Structures service. Crystallographic data for 6a. 2MeOH. C60H94N4O2S4, Mr = 1031. 63 g/mol, triclinic space group P-1, a = 14. 3561(8) Å, b = 14. 7687(9) Å, c = 15. 0754(8) Å, α = 93. 790(5)°, β = 101. 860(4)°, γ = 102. 007(4)°, V = 3039. 8(3) Å 3 , Z = 2, ρcalcd = 1. 127 g/cm 3 , T = 180(2) K, µ(Mo Kα) = 0. 199 mm -1 (λ = 0. 71073 Å), 25404 reflections measured, 12661 unique, 7962 with I > 2σ(I).
B. Transmission Performance of PAM4 Signals We switch to the PAM4 signal format with the same system configuration to explore the supported data rates against the two benchmarking FEC thresholds. The corresponding BER performances are also measured accordingly. The same DFE configuration as the NRZ-OOK case is adopted. 2 Gbaud and 2. 5 Gbaud PAM4 signals are successfully transmitted and received through the MIR FSO link to reach below the respective KP4-and HD-FEC limits, corresponding to raw data rates of 4 Gbps and 5 Gbps. We plot the BER curves for the PAM4 signals at these two baud rates in Fig. 6 (a). The optimal detector PV for 2 Gbaud PAM4 was around -1. 2 V, and for 2. 5 Gbaud around -1. 25 V. For the PAM4 cases, we observe a slightly steeper BER degradation compared with NRZ-OOK when increasing the received power beyond the optimal values.
This work was supported in part by the  Swedish Research Council (VR)  projects under Grants  2019-05197  and  2016-04510 , in part by the  EU   H2020  Project cFLOW under Grant  828893 , in part by the  COST (European Cooperation in Science and Technology)  through  COST Action  under Grant  CA19111  NEWFOCUS, and in part by the  ERDF -funded  CARAT  project under Grant  1. 1. 1. 2/VIAA/4/20/660.
Scanning electron microscopy (SEM) For the checking of the morphological aspects of all biochars, representative SEM images were obtained using a Hitachi SU 8030 Scanning Electron Microscope (SEM) Field Emission Gun (FEG). The ImageJ software as used to analyze and treat images. Figure  1  displays the XPS survey spectra of the PSCB biochar before (Figure  1a ), and after arylation (Figure  1b ) and deposition of Ag/Cu bimetallic nanocatalysts (Figure  1c ). Stepwise modification of the biochar is clearly visible. Starting with C1s and O1s centred at 285 and 532 eV, respectively, one can note the appearance of an S2p peak at ~168 eV due to the sulfonate groups from the grafted aryl groups. Sulfur atom is confirmed by a second peak at ~229 eV. Sharp Ag3d doublet with Ag3d5/2 centred at 368 eV, and Cu2p doublet with Cu2p3/2 at ~932 eV testify for the metallic state of both silver and copper in SO3H-biochar@Ag/Cu.
Quantification For quantification of extracellular labeling of GFP-GluA1, surfaces of interest were drawn by hand following the total GFP labeling. Images were processed using ImageJ software (Rasband, W. S. , ImageJ, U. S. NIH, Bethesda, Maryland, USA, http://imagej. nih. gov/ij/, 1997-2012). Briefly, quantifications were performed by first asking the user to draw sets of regions of interest: one in the background (devoid of any structure), one in the proximal and one in the distal dendritic shaft. For each of them, average intensity and area were retrieved. Results are expressed as the mean intensity fluorescence at the PM. Statistics were performed on Prism software using unpaired t test. Value are in mean +/-SEM. Asterisks notify the following significance levels: p < 0. 05 (*), p < 0.
5mM KCl, 2mMCaCl2, 200µM glycine, 20 µm bicuculline, 300 mosm/l) for 4 minutes to induce cLTP. Then original tyrode was put back in the Ludin. The videos were acquired 25 minutes after cLTP induction, using the same acquisition parameters as for basal. Cells having less than 5 vesicles / 20 µm 2 / min. were excluded for the IT analysis of the GluA1 WT condition. Analyses of the videos are performed using ImageJ program with a help of "Kymo-Tool-Set" software homemade  (Hangen et al. , 2018). Statistics were performed on Prism software using unpaired t test. Value are in mean +/-SEM. Asterisks notify the following significance levels: p < 0. 05 (*), p < 0. 01 (**), p < 0. 001 (***) and p < 0. 0001 (****).
Acknowledgments The microscopy was done in the  Bordeaux Imaging Center (BIC) , a service unit of the  CNRS-INSERM and Bordeaux University, member of the national infrastructure "France BioImaging ". We specifically thank the help of  Magalie Mondin  and  Fabrice Cordelières. We like to thank the  IINS Cell Biology facility , especially  Emeline Verdier ,  Christelle Breillat ,  Sophie Daburon  and  Nicolas Chevrier , for cell culture and plasmid production. This work is currently supported by funding from the  Ministère de l'Enseignement Supérieur et de la Recherche ,  Centre National de la Recherche Scientifique ,  ERC  Grant # 787340   Dyn-Syn-Mem , and the  Conseil Régional de Nouvelle Aquitaine.