dataesr/scientific-paragraphs-categorization huggingface dataset:
We took advantage of the new annotationBlock element which codes several annotation levels, a function that is commonly required in spoken language annotations. The annotationBlock contains two major elements: the u element which contains the transcription in orthographic form and the spanGrp elements which contain tier elements that annotate the utterance described in the u element. A spanGrp element contains as many span elements as required. All span elements have the same type of content, as indicated in the father spanGrp element. Figure  1  provides an example of conversion from a CLAN file to illustrate how a production annotated on different levels (orthography, morphosyntax, dependencies) is represented in TEI with a first main utterance element u to which two spanGrp are linked, one for each annotation level, in our case one spanGrp for morphosyntax and one spanGrp for dependencies (see Figure  3 ).
Advanced structures Although the presentation described above can represent the data of many corpora and tools, a single-level annotation structure within the spanGrp elements is insufficient to represent the complex organisation that can be constructed with the ELAN and Praat tools. ELAN is a tool used by many researchers to describe data of greater complexity than the data presented in the ISO/TEI guidelines. As the goal of the TEICORPO project was to convert all types of structure used in the spoken language community, including ELAN and Praat, it was necessary to extend the description method presented in section 2. 2. 2. In ELAN and Praat, the multi-tiered annotations can be organized in a structured manner. These tools take advantage of the partition presentation of the data, so that the relationship between a parent tier and a child tier can be precisely organized.
<annotationBlock end="#T153" start="#T1" who="locuteur" xml:id="au4763"> <u> <seg>$L1</seg> </u> <span from="#T21" to="#T23" xml:id="s72"> petites <spanGrp type="syllabe"> <span from="#T21" to="#T22" xml:id="s73"> p@ <spanGrp type="phone"> <span from="#T21" to="#T2973" xml:id="s74">p</span> <span from="#T2973" to="#T22" xml:id="s75">@</span> </spanGrp> </span> <span from="#T22" to="#T23" xml:id="s76"> tit <spanGrp type="phone"> <span from="#T22" to="#T2974" xml:id="s77">t</span> <span from="#T2974" to="#T2975" xml:id="s78">i</span> <span from="#T2975" to="#T23" xml:id="s79">t</span> </spanGrp> </span> </spanGrp> </span> The spanGrp and span offer a generic representation of data coming from relatively unconstrained representations produced by partition software. The name of the tiers used in the ELAN and Praat tools is given in the content of the type attribute.
These names are not used to provide structural information, the structure being represented only by the spanGrp and span hierarchy. However, the organization into spanGrp and span is not always sufficient to represent all the details of the tier organization of each software feature. This is the case for some of the ELAN structures, which can specify the nature of span elements further than in the TEI feature. For example, the timediv ELAN property specifies that only contiguous temporal division is allowed, whereas the incl property allows non-contiguous elements. It was therefore necessary to include the type of organization in the header of the TEI document, using the note structure. The note element here points to a case where dedicated tags do not currently exist in the TEI, so we used the note element as the best solution not to lose the information.
Exporting to research tools In the TEICORPO approach, no modification is made to the original format and conversion remains as lossless as possible. This allows for all types of corpora to be stored for long-term preservation purposes. It also allows the corpora to be used with other editing tools, some of which are suited to specific processing. For example: Praat for phonetics/phonology; Transcriber/CLAN for raw transcription; ELAN for gesture and visual coding. However, a large proportion of scientific research and applications done using corpora requires further processing of the data. For example, although querying or using raw language forms is possible, many research investigations and tools use words, part of speech, grammatical or semantic information. This requires further processing starting with the original raw corpus form.
In the case of spoken language corpora, the nature of the information inserted in the transcription has to be taken into account. This corresponds to what Schmidt (2011) calls micro-structure. This micro-structure is integrated in Schmidt's approach in which the TEI document can contain standardized information about words, specific spoken language information, and sometimes even part of speech information. This approach was not adopted in TEICORPO for several reasons. First, we had to deal with a large variety of coding approaches which makes it difficult to conduct work similar to that done in CHILDES (MacWhinney 2000, see https://childes. talkbank. org/). Secondly, there was no consensus about the way tokenization should be performed, as many researchers consider tokenization as a choice with multiple possibilities, each with different consequences on the resulting data and grammatical analyses.
In other words, tokenization is part of the linguistic analysis, so it should not be frozen at the level of data conservation. For these reasons we decided firstly to make it possible to include in a corpus both the original raw language material and the modified tokenized and analyzed forms. This is easy to do using the TEI, as any analyzed material can be inserted in spanGrp/span elements, without modifying the original u element information. Secondly, we decided to design a second category of tools aimed at processing or making it possible to process the spoken language corpus, and to use powerful tools in corpus analysis. This part of the TEICORPO library is described in the Applications section below.
In each case, some options are available to specify how to produce the: -timeline: at the beginning, before or after the speaker's production, timeline precision -line number -spoken language annotations: an export without any specific annotation is useful for automatic tools which only need an orthographic lexical transcription. Other features are available in both types of interface (command line and web service). TEICORPO allows the user to exclude some tiers, for example adult tiers in acquisition research where the user wants to study child production only, or comment tiers which are not necessary for some studies.
Export to specialized software Another kind of export concerns textometric software. TEICONVERT makes spoken language data available for TXM  (Heiden, 2010 , see http://textometrie. ens-lyon. fr), Le Trameur  (Fleury & Zimina 2014 , see http://www. tal. univ-paris3. fr/trameur/), and Iramuteq (see http://iramuteq. org/ and de Souza et al. 2018), providing a dedicated TEI export for these tools. For example, for the TXM software, the export includes a text element made of utterance elements including age and speaker attributes. Figure  7  presents an example for the TXM software. <TEI file="/corpusformat/exemple/lily-4-00-02. tei_corpo. xml"> <teiHeader/> <text> <u age="28" end="2875. 100" start="2873. 395" who="MOT"> <w age="28" loc="MOT">you</w> <w age="28" loc="MOT">have</w> <w age="28" loc="MOT">to</w> <w age="28" loc="MOT">rest</w> <w age="28" loc="MOT">now</w> <w age="28" loc="MOT">.
Using an automatic grammatical analyzer Many researchers in linguistics wish to use automatic grammatical analyzers on corpora in order to improve the querying and analysis of a text, or to implement other types of linguistic research. A present difficulty with these grammatical analyzers is that most often they run only on raw orthographic material, excluding other information. Moreover, their result is not always in a format that can be used with traditional spoken language software such as CLAN, ELAN, Praat, Transcriber or of course TEI. TEICORPO provides a way to solve this problem by running analyzers and putting the results from the analysis back in TEI format. Once the TEI format has been enriched with grammatical information, it is possible to use the results and convert them back to ELAN or Praat and use the grammatical information in these spoken language software packages.
TreeTagger TreeTagger 1   (Schmid 1994; 1995)  is a tool for annotating text with part-of-speech and lemma information. The software is freely available for research, education and evaluation. It is available in 25 languages, provides high-quality results, and can be easily improved by enriching the training set, as was done for instance by  Benzitoun, Fort, and Sagot (2012)  in the PERCEO project. They defined a syntactic model suitable for spoken language corpora, using the training feature of TreeTagger and an iterative process including manual corrections to improve the results of the automatic tool. website 4  to use them with TEICORPO. Using the analyzer is similar to using TreeTagger. The "-model" and "-syntaxformat" can be used in a similar way to specify the grammatical model to be used and the output format.
A command line example is: java -cp "teicorpo. jar:directory_for_SNLP/*" fr. ortolang. teicorpo. TeiSNLPsyntaxformat svalue -model filename. tei_corpo. xml The "directory_for_SNLP" is the name of the location on a computer where all the CoreNLP jar files can be Note that using the CoreNLP software is heavy on the memory resources of the computer and it is necessary to indicate to the Java software that it should use large amount of memory (for example to insert parameter -mx5g before parameter -cp to indicate that 5Gb of memory will be used for a full English analysis). The -model parameter can take three values: english (use the full English grammar), french (use the full French grammar), or the name of a CoreNLP parameter file which specifies any type of analysis that is available in CoreNLP.
An example is presented below in the ELAN tool (see Figure  13 ). The original utterance was "si c'est comme ça je m'en vais (if that's how it is, I'm leaving)". It is displayed at the top in the pink line. The analysis into words (line of numbers), into lemmas (third line), into parts of speech (POS: fourth line) and into orthographic words (final line) is displayed below. So for example, word 3 has the lemma "être" (to be), the POS VER:pres (verb in the present form), and it is the word "est" (is). Export can be done towards a textometric software (see Figure  14 ). In this case, instead of using a partition representation, the information from the grammatical analysis is inserted at the word level in an XML structure. For example, in the case below, the TXM export includes Treetagger annotations in PoS adding lemma and pos attributes to the word element w.
Comparison with other software suites The additional functionalities available in the TEICORPO suite are close to those available in the Weblicht web services  (Hinrichs & Vogel 2010). To a certain extent, the two suites of tools (Weblicht and TEICORPO) have the same purpose and functionalities. They can import data from various formats, run similar processes on the data, and export the data for scientific uses. In some cases, the services could complement each other or TEICORPO could be integrated in the Weblicht services. This is for example the case for handling the CHILDES format which in more functional in TEICORPO than in Weblicht (at the time of writing). A major difference between the two suites is in the way they can be used and in the type of data they target. TEICORPO is intended to be used not as an independent tool, but as a utility tool that helps researchers to go from one type of data to another.
Conclusion TEICORPO is a functional tool created by the CORLI network and ORTOLANG that converts files created by software specialized in editing spoken language data to the TEI. The result is fully compatible with the most recent developments of the TEI, especially those that concern spoken language material. The TEI files can also be converted back to the original formats or to other formats used in spoken language editing to take advantage of their functionalities. This makes the TEI a useful pivot format. Moreover, TEICORPO allows conversion to tools dedicated to corpus exploration and browsing. TEICORPO exists as a command line interface as well as a web service. It can thus be used by novice users as well as by advanced users or by developers of linguistic software. The tool is free and open source so it can be further used and developed in other projects.
TEICORPO is intended to be a part of a large set of tools using the TEI for linguistic corpus research. It can be used in parallel with or as a complement to other tools such as Weblicht or the Exmaralda tools (see  Schmidt et al. 2017). A specificity of TEICORPO is that it is more suitable to process extended forms of TEI data (especially forms which are not inside the main 'u' field of the TEI). TEICORPO is also linked to TEIMETA, a flexible solution to describe spoken language corpora in a web interface generated from an ODD file  (Etienne et al. under review). As the TEI enables metadata and data to be stored in the same file, sharing this format will promote metadata sharing and will keep them linked to the data during the lifecycle of the data. Further developments can be made to provide a wider coverage of different formats such as CMDI or linked data, for edition or data exploration purposes or any other external tool such as grammatical analyzers or the visualization of multi-level annotations.
Quadrature detection is achieved with phases Φ1, Φ2 and Φ4, whereby the phase is changed by either +90 or -90 degrees, as indicated. (B) Sensitivity comparison of diverse pulse programs with a tube-like protein assembly formed by 50 kDa-large subunits  (Fraga et al. , 2017)  and decameric assembly of 10x21 kDa, in orange and blue respectively. The data were obtained by integration of one-dimensional variants of the pulse sequences. (B) h C O N H h C A N H h C O c a N H w i t h I N E P T C O -c a h C O c a N H w i t h B S H C O -c a h N c o c a N H w i t h I N E P T c o -c a h c a C B c a N H h c a C B c a C O N H w i t h I N E P T c a -C O h c a C B c a C O N H w i t h B S H kept within the INEPT transfer delay, which is typically of the order of 6-7 ms. Additional semi-constant-time versions, 235 which do not have this limitations are also implemented.
A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90. 5% for the 2018-dataset and 91. 6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%/76% accuracy in 2018/2020) and first-year ice (86%/84% accuracy in 2018/2020). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data.
Typically SAR images are analyzed by ice analysts in operational centers for manual classification of ice types and drawing of ice charts. With the availability of more than 100 Sentinel-1 SAR images in the Arctic ocean per day this procedure understandingly requires a significant effort and human power. In several recent studies automated algorithms have been developed for dual-polarization C-band SAR image segmentation and ice/water classification  [4] [5] [6] [7] [8] [9] [10] ; for retrieval of ice concentration  [11, 12] ; for classification of several ice types  [13] [14] [15]. It is emphasized in these studies that a threshold based classification of ice types on single frequency dual-polarization SAR data is not possible and more advanced methods employing either full polarization, or multi-frequency, or texture analysis are required.
The main goal of our research is to develop a CNN-based algorithm for the classification of sea ice types on dual-polarization SAR data from Sentinel-1. The algorithm should combine relative simplicity and speed of the CNN approach, high accuracy and ability to classify sea ice types and to have relatively low sensitivity to noise in SAR data. Several CNNs were trained on the same dataset as in  [15]  for producing comparable results and for sensitivity experiments. Another CNN was trained on recent data from 2020 for operational sea ice type retrieval. The paper is structured as follows. Section 2 describes data used for training the CNNs. Section 3 describes the CNN architecture and training process. Section 4 presents the results of training and ice type classification. Section 5 sheds light on experiments with hyperparameter tuning and CNN sensitivity.
Data and Preprocessing The study area stretches from the Canadian Arctic Archipelago (90 • W) to Franz Josef Land (53 • E) and from 85 • N to 75 • N. It includes the Lincoln Sea, the Fram Strait, the Greenland Sea, the Barents Sea and Svalbard (see figures in Section 4. 2). Two sources of data are used in our study: satellite data are used to feed the classification algorithm and expert data are used for training and validation. The data are split into two datasets: one dataset from 2018 (hereafter referred to as 2018-dataset) is the same dataset as in  [15]  and is used for sensitivity experiments and training a CNN for comparison with the results from  [15]. Another dataset from 2020 (2020-dataset) is used for building a CNN for operational sea ice type retrieval. The data details and preprocessing steps are further described below.
The 2018-dataset contains 44 SAR scenes acquired between January 2018 and March 2018. It was used for experimenting with the CNN hyperparameters and training of a neural network for comparison with the results from  [15]. These scenes were the same scenes as used by  [15] , and were pre-processed with thermal and textural noise correction and incidence angle correction as described in detail in  [15] [16] [17]. Values of σ 0 were converted from dB to 255 gray levels in the range [-31, 0] dB for HH, [-32, -7] dB for HV and saved with 8-bits unsigned integer precision. The 2020-datasets contained 1500 Sentinel-1 SAR scenes acquired in January-February 2020. Only 255 scenes were manually selected by visual comparison with the expert data for training and validating the CNN. Visual comparison allows to identify scenes that match the best with the ice chart and were, presumably, used by an ice expert for building an ice chart (see details below).
An improved texture noise removal procedure was applied to satellite data in this dataset. As explained in  [17] , texture noise is manifested as increased variance of σ 0 proportional to the thermal noise induced by the SAR antenna. As well as the thermal noise, the texture noise is especially pronounced when the signal is low (e. g. , in areas with calm ocean surface). The new texture noise correction was implemented as follows: σ 0 C = σ 0 * w 1 + G(σ 0 ) * w 2 w 1 + w 2 (1) where σ 0 denotes radar backscatter corrected for thermal noise, G-Gaussian filter, w 1 is the weight of original signal and w 2 is the weight of smoothed signal. Our experiments showed that optimal results are achieved when the Gaussian filter has window size of 3, w 1 is equal to 0. 5 and w 2 is equal to signal to noise ratio (SNR) computed as follows: w 2 = G(σ 0 ) σ 0 N (2) where σ 0 N is thermal noise equivalent backscatter.
Preparation of a Combined Dataset Combination of SAR data and ice charts for CNN training/testing includes several steps explained below. First, an extra layer is added to the ice chart-distance to border (D). Each pixel in this layer contains a value of distance to the nearest border between two ice types. This value is afterwards used for filtering valid records. Under assumption that the borders on the manually drawn ice charts are not very detailed, only pixels that are far enough from the border are considered as valid. Second, another extra layer is added to the ice chart-manual mask of invalid pixels. The manual mask is added in order to avoid inclusion of pixels with incorrect values of σ 0 visible on SAR images. Some SAR images contain thermal noise or radio frequency interference artifacts that can affect the CNN results (see Figure  2 ).
These features are manually identified on SAR images and ice charts are edited in image manipulation software by replacing concerned pixel with white color (see Figure  3 ). In addition, some of the pixels with obviously wrong classification by ice expert are also blanked out including cases when the border between ice types on manual ice chart is coarse and a polygon of one ice type obviously includes ice of a different type (see examples in Section 6 and Figure  9  in  [15] ). Although this manual correction has to be done only once-during the training phase, it is worth noting that it is very time consuming and was performed only for the 2018-dataset. Next, a SAR image is divided into samples of square sub-images of the same size thus generating an input data cube of size (N × K × K × 2), where N-denotes total number of samples, K-sub-image size, 2-two layers of one sub-image from HH and HV polarizations.
The sub-image size is kept relatively small and one sub-image corresponds to one label from the ice chart. In our study K = 50 as explained in Section 5. 2. SoD labels are taken from the corresponding pixels of the rasterized ice chart thus forming the output 1D vector c with size N. The SoD labels are one-hot encoded  [20] -each label c i is replaced with a binary vector y i with length equal to the number of classes C (in our case, C = 4). The k-th element of y i is set to 1 and other elements-to 0, where k equals SoD label. For example, y i = [1, 0, 0, 0] corresponds to c i = 1 (ice free) and  For the 2020-dataset yet another layer was added to the ice chart for filtering out invalid input data-CNN segment size. As shown in the results (see figures in Section 4. 1) the CNN produces very patchy ice charts with many segments often as small as just 1 pixel.
Obviously, such small details cannot be compared with the rather generalized manual ice charts. The "CNN segment size" layer contains size of individual segments produced by a CNN. For generating such layer, first, a preliminary CNN is trained on all data and applied to all scenes in the training dataset. This CNN produces a preliminary classification of ice types (Figure  4A ). Next, the neighbor pixels on the preliminary classification with the same ice type are grouped into segments and the size of each segment is calculated (Figure  4B ). Finally, the pixels that belong to segments with too small size are marked as invalid (Figure  4C ). y i = [0, 1, 0, 0] corresponds to c i = 2 (young ice). (A) (B) Values of distance to border, manual invalid mask (only for 2018-dataset) and CNN segment size (only for the 2020-dataset) are also taken from the corresponding pixels and form 1D vectors with size N.
These vectors are used to define valid data from the input and output datasets. Finally, the dataset is randomly permuted and then split into the training and validation subsets using 70/30 ratio with the same proportion of ice types in both parts. The validation dataset-2018 is used to evaluate the sensitivity to parameters described in Section 5. In this case, even though the common practice is to use a third independent test dataset to evaluate a model, the final score is presented on the same validation dataset in order to compare with performance obtained in Park et al. [15]  where no test dataset is used. On the other hand, the validation dataset-2020 is not used to tune the method and marginally used during the training, so it constitutes a valid dataset to evaluate the final score of the method.