dataesr/scientific-paragraphs-categorization huggingface dataset:
Supervised RF Classification Supervised RF classification was performed on the Sentinel-2 data. It is the most common procedure for the quantitative analysis of remote sensing imagery  [61] , and it involves the use of training data for machine-learning classification. The training data were selected with reference data, knowledge, and experience provided by the user, and the selected samples were considered representative of each class. Training data and test data in our research were selected in this step, using the RPG 2019 map. For the purpose of comparing different methods of classification, training data that were selected for PBC and OBC were as similar as possible, such as using the same area and very approximate surfaces, in order to improve the global comparability of the two methods.
Furthermore, the F-score was generated from the precision and recall; it provides a single harmonic mean of the model's precision and recall. In the hierarchical classification process, test data that were used for evaluating the classification of each step were generated as random points from the image that were used to perform classification, which is the result of the previous steps. Afterwards, the random points were labelled manually with the Graphic Parcel Register map as ground truth. However, with the aim of evaluating the performance of the proposed hierarchical classification approach by comparing with traditional direct extraction, a completely new test dataset was produced from the original Sentinel-2 image that was not classified.
Crops Phenology Monitoring This second part of the study was performed, based on the mapped winter crops from the previous step. Due to limited climate conditions in the study area, winter crop phenology monitoring was performed with Sentinel-1 C-band SAR data using the Google Earth Engine (GEE) platform. GEE is an open-source, cloud computing platform with a fast, high-performance computation and visualization system and a large data catalog which hosts a large repository of publicly available geospatial datasets, including a variety of satellite imaging systems  [67]. The platform is designed for global-scale geospatial big data storage, processing, and analyzing  [68]. The utility of GEE has been examined in different fields, for vegetation mapping and monitoring, land cover change mapping, and agriculture applications  [68].
On the contrary, the other class received a high precision index (0. 955) and a comparatively low recall (0. 797), and these indicators demonstrate that the pixels were correctly detected and labelled despite there being fewer results returned by the model. The comparably low accuracy of the two classes and the imbalance between the precision and recall indices might be explained by: (1) the similarity of the spectral behavior between winter barley and other crops and even grassland in the case of the classical direct extraction approach. (2) Since two winter crops are extracted directly from the Sentinel-2 image, the other class included not only non-vegetated urban areas, but also vegetation and other croplands which occupy a large area of our study site. Therefore, an imbalance between classes was caused, thus, more training datasets of the other class were acquired in consideration of its weak intraclass correlation.
Comparison of PBC and OBC In this work, PBC and OBC were implemented in the two steps of the classification process within the hierarchical classification structure (croplands extraction from all vegetated area, and winter croplands extraction from all croplands). In addition, the two classification models were trained by a similar dataset, and then evaluated using the same test data. The two classification methods are widely known and used, and they are always compared in different fields. OBC provides a method to the satellite image classification, and numerous studies in the remote sensing field it is demonstrated that OBC usually achieves a better classification with different data and in different landscapes over PBC by bringing complementary information other than the spectral signal and turning classification units from pixels to image objects  [80] [81] [82] [83].
Limitations and Perspectives Some limitations were revealed during the process of result analyzing. Despite the fact that the hierarchical classification approach acquired a better accuracy (0. 099 in kappa and 0. 066 in OA), this classification approach required more complicated processing steps and was more costly when one is comparing it to the direct extraction, for a slight enhancement in the results. Moreover, the confusion between winter barley and grassland was nonnegligible. For increasing classification accuracy, extra data such as SAR or Sentinel-2 time series data can be applied. Additionally, even though the three main phenological statuses were successfully extracted from Sentinel-1 backscatter time series, more field research and expert knowledge is required for identifying some others important phenological stages (e.
The extracted acoustic and perceptual measures are compared to normative data, considering a matching in sex and age (from a dataset of 404 speakers), leading to a deviance score for each parameter. The MonPaGe-2. 0. s screening protocol is currently based on a relatively limited set of seven descriptors linked to intelligibility, articulation, voice, speech rate, maximum phonation time, prosody and diadochokinetic rate measures. It has been shown to be quite performant in terms of sensitivity and specificity in detecting MSDs relative to neurotypical speech, even in the case of mild speech impairments  [20]. Although the first diagnostic level when assessing speech is presence versus absence of MSD, a further step would be to tease out the different subtypes of MSD. Our aim here is to investigate whether the MonPaGe-2.
Population We included 60 patients with MSD, ten from each of six different neuropathology groups: two groups of patients with mixed dysarthria associated with amyotrophic lateral sclerosis (ALS) and Wilson disease; one group of patients with flaccid (bulbar) dysarthria associated with Kennedy disease; one group of patients with hypokinetic dysarthria associated with Parkinson Disease (PD); one group of patients with ataxic dysarthria in the context of spinocerebellar ataxia (SCA), and a group with apraxia of speech (AoS) following a left-hemisphere stroke. The data were extracted from the "MoSpeeDi dataset" (https://www. unige. ch/fapse/mospeedi/mospeedi-dataset accessed on 15 February 2021). The patients in the "MoSpeeDi dataset" were all French native speakers, had a neurological diagnosis established by neurologists in the hospitals of recruitment, and presented mild or moderate acquired or progressive speech difficulty noticed by the patient and a speech and language pathologist (SLP).
No patients had a diagnosis of dementia or psychiatric disorder. The inclusion of the patients from the "MoSpeeDi dataset" was guided by the necessity to balance the severity of MSD across the six neuropathological subgroups. To avoid redundancy with the MonPaGe-2. 0. s deviance scores used in the study, an external composite perceptive severity score was used as an inclusion criterion: the composite perceptive severity score (hereafter CPSS) from a French perceptive-based speech assessment protocol  [34]. The CPSS score was computed via a perceptual rating on a 5-point scale (from normal -0 -to severely impaired -4 -) of the participant's speech on five dimensions: voice quality, segmental realization, prosody, intelligibility and naturalness of speech (maximum score = 20). Two trained SLPs assessed each dimension on recordings of about two minutes of continuous read speech.
Intelligibility The intelligibility test is an interactive task between the examiner and the participant in a face-to-face setting, in which the participant has to instruct the examiner to place testwords on a 5 × 5 grid combining icons of various shapes and colors, using a pre-learned carrier sentence ("Place the word [target_word] on the [color] [shape]", e. g. , "Place the word [dog] on the [red] [circle]"). Fifteen test words are randomly drawn from a database of four hundred and thirty-seven picturable French words, with each word having several phonological competitors (minimal pairs). Only the participant sees the 15 test words on the computer screen and the examiner has to write each target word on a paper grid. The perceptual intelligibility score is computed off-line: it corresponds to the number of test words that are understood correctly by the examiner during the interaction.
An interval of four seconds was determined by a Praat script from a boundary placed at the onset of the production and was manually adjusted to the right in order to not cut the last syllable if needed. The number of phonemes produced over this interval was used as an index of DDK rate. In order to capture difficulties in the repetition of the same syllables (AMR) vs. the repetition of a sequence of three syllables (SMR) which could be found for speakers with AoS for instance  [36] , the difference between the sequential motion rate and the alternating motion rate averaged over all CV sequences was also computed (SMRCV -AMRCV). The four DDK scores (AMRCV, AMRCCV, SMRCV and SMRCV -AMRCV) were then combined in a unique composite DDK deviance score (see below).
Automatic Classification To investigate whether the MonPaGe-2. 0. s screening protocol assesses impaired dimensions that allow discriminating between different subtypes of patients, we train vanilla decision trees for two-class automatic classification of the different pairs of MSD Brain Sci. 2022, 12, 1471 6 of 16 subtypes based on the obtained deviance scores. Decision trees are advantageous due to their interpretability, implicit feature selection and their ability to handle numerical and categorical data  [30]. Fifteen different decision trees are trained to discriminate between AoS/Parkinson, AoS/ALS, AoS/Kennedy, AoS/Wilson, AoS/SCA, Parkinson/ALS, Parkinson/Kennedy, Parkinson/Wilson, Parkinson/SCA, ALS/Kennedy, ALS/Wilson, ALS/SCA, Kennedy/Wilson, Kennedy/SCA and Wilson/SCA. The quality of each split during training is measured through the Gini impurity  [37] , which measures the frequency at which any element of the dataset will be mislabeled when it is randomly labeled.
Given the relatively small number of patients currently available in the corpus, validation is done following a leave-one-speaker-out validation strategy. To avoid overfitting, we set the maximum length of the longest path from the root to a leaf to three. Since we deal with small datasets and many two-class classifications, we decided not to tune this hyperparameter. Instead, we used the default value of three suggested in the used decision tree Python implementation  [38]. The suitability of the MonPaGe-2. 0. s DevS to discriminate between the different subtypes of MSDs is evaluated through the classification accuracy of the trained decision trees. Further, the importance of each DevS for each two-class classification is also analyzed. Feature importance refers to the usefulness of the feature at predicting the class of interest in each considered two-class classification.
Formal analyses: I. K. ; resources and data curation: C. F. and M. L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Swiss National Science Foundation (SNSF), Sinergia grants CRSII5_173711 and CRSII5_202228 and partially by a grant from the "Connaître les Syndromes Cérébelleux" Association and the program "Investissements d'Avenir" ANR-10-LABX-0083 (Labex EFL). Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of the Commission Cantonale d'Éthique de la Recherche, Project ID 2018-00212 and 2021-01587 and the CPP Sud Ouest et Outre-Mer 2, Project ID-RCB: 2019-A02553-54.
Introduction Geohazards may lead to damage or risks for human beings or infrastructure. To better understand and anticipate these hazards, it is necessary to know the triggering forcings and precursor signals. This implies regular and distributed monitoring points along the hazard-prone area. In practice, such monitoring can be difficult to implement. With smartphones, almost all citizens are now equipped with potential sensors and data transmission platforms. Participatory science programs have therefore increased in recent years. Several worldwide support platforms have also been created to support and/or structure citizen science program initiatives. These include, for example, CitSci (https://www. citsci. org/ (accessed on 6 December 2021)), Citizen Science Association (https: //citizenscience.
"Expert" Reference Dataset Survey Settings As the objective is to evaluate the quality of 3D reconstructions integrating crowdsourced photos, a first survey was carried out on 22 November 2021 in order to constitute an "expert reference dataset" serving as benchmark, which was acquired by using RTK GNSS-assisted SfM photogrammetry  [18]. For this survey, a Nikon D800 reflex camera (20 mm focal length, Nikon, Tokyo, Japan) was used (Figure  3a ). A total of 544 photos were collected, including 204 georeferenced photos from 8 stations with GNSS RTK positioning of the camera (Figure  3b ). The resolution is, on average, 2. 2 cm/pixel. The GNSS antenna was a Leica 1200+ device (Leica Geosystems, Aarau, Switzerland). It is conceivable that such monitoring could be carried out by trained site-management staff.
Data Processing The acquired photos were processed by SfM photogrammetry, using Agisoft Metashape ® (Agisoft LLC, St. Petersburg, Russia), according to the following processing chain (Figure  4a )  [18] : With such a method, the reconstruction accuracy is within 5 cm (or lower). The dense point cloud (about 65 × 10 6 points) is exported to the open-source software CloudCompare ® (CloudCompare open project, EDF R&D, Paris, France). The point cloud is then filtered with the SOR (Statistical Outlier Removal) filter proposed by CloudCompare ®. It is then subsampled to 3 cm in order to keep the computation time manageable for the next steps of this study. To limit edge effects, a common comparison zone is defined (Figure  4b ), and the point cloud is cropped according to this zone and finally meshed.
This mesh will be used as a reference model to assess the quality of reconstructions by using crowd-sourced pictures. Such a survey is carried out in a few hours and can be operated by the site managers. The duration of data processing can vary from a few hours to a few days for processing depending on the power of the computer used (here, it lasted 2 h, using an Intel(R) HD Graphics 630). with the SOR (Statistical Outlier Removal) filter proposed by CloudCompare ®. It is then subsampled to 3 cm in order to keep the computation time manageable for the next steps of this study. To limit edge effects, a common comparison zone is defined (Figure  4b ), and the point cloud is cropped according to this zone and finally meshed. This mesh will be used as a reference model to assess the quality of reconstructions by using crowd-sourced pictures.
Data Processing Using the Time-SIFT Method In the framework of an observatory, one can take advantage of the multi-date aspect of the datasets, using the Time-SIFT approach for the processing of crowd-sourced data. The Time-SIFT method, proposed by Feurer and Vinatier  [26] , is based on the properties of the SIFT (Scale Invariant Feature Transform) algorithm used for image alignment in SfM processing  [27]. This Time-SIFT method consists of performing the bundle adjustment step (to determine the extrinsic and intrinsic parameters of the cameras) by using the largest possible set of photos, which are then separated into several groups for the computation of the dense point cloud. The images of the different groups can correspond to photo sets acquired at different dates, as long as a part of the imaged area remains invariant and recognizable between these different groups.
With this method, it is therefore possible to consider "camera groups" containing fewer images and with less overlap, the density of information necessary for the correct estimation of camera models being provided by the superposition of the different groups. adjustment, these small groups of photos can be aligned with a reference dataset collected with the RTK GNSS-SfM photogrammetric approach, even if it has not been acquired at the same date (as long as part of the area remains invariant). Thus, another processing scenario was set up: -Test 6: For this test, we are working on a focused area (the screes SW of Port Ganny; see Figure  4b ). The aim is to reconstruct this area by using 330 smartphone photos, geotagged or not, collected from the foot of the cliff on 25 November 2021. For processing in Time-SIFT mode (Figure  5b ), these smartphone photos are aligned, during the bundle adjustment, with 205 additional photos acquired on 22 November 2021, using the Nikon D800 camera with RTK georeferencing.
This contributes to the appearance of "ghost structures" on the raw point clouds (Figure  6 ), which are pseudo-coherent but aberrantly placed reconstructions due to "sub-networks" of cameras that have not been connected to the main camera network  [19]. These ghosting effects are most pronounced in Tests 2 and 4 (i. e. , those involving shots from the cliff top, hence where the survey configuration favors the appearance of a This approach therefore presents a great potential for crowd-sourced photogrammetry. Indeed, it seems unrealistic to expect that crowd-sourced photographs will perfectly cover the whole area with sufficient overlap. Using the Time-SIFT method, it would be possible to focus on "hotspot areas", working on smaller groups of photos. During bundle adjustment, these small groups of photos can be aligned with a reference dataset collected with the RTK GNSS-SfM photogrammetric approach, even if it has not been acquired at the same date (as long as part of the area remains invariant).
Results As long as the geometric reliability of the RTK stations network is maintained (stations spanning a sufficiently large part of the studied area and non-alignment of the stations to avoid georeferencing ambiguities), the decrease from eight to five RTK stations (Test 1) has only a very moderate impact on the quality of the reconstruction. Indeed, when comparing the dense point cloud with the reference mesh, we obtained a mean error of -0. 5 cm and a standard deviation of 4. 6 cm (Table  3 ). Table  3. Cloud-to-mesh distances between the mesh resulting from the "expert dataset" (used as reference) and the different scenarios testing the incorporation of crowd-sourced photographs. The use of photos from different sensors (here, Nikon SLR camera and the seven smartphones tested-Table  1 ) implies different camera settings in terms of sensitivity, exposure, saturation, etc.
With the Time-SIFT method (Test 6), including reference survey photos (here, georeferenced Nikon D800 photographs) in the alignment step and then reconstructing the dense point cloud only from the crowd-sourced data increases the consistency of each camera group without reducing the constraints on the estimation of camera parameters. This approach allows the use of crowd-sourced data in a very satisfactory way in terms of reconstruction quality, since the standard deviation is 8. 6 cm. Table  3. Cloud-to-mesh distances between the mesh resulting from the "expert dataset" (used as reference) and the different scenarios testing the incorporation of crowd-sourced photographs. The comparison of Tests 2, 3, 4, and 5 shows that the addition of photos from a SfM RTK photogrammetry survey to the crowd-sourced photographs greatly improves (about ten times better) the quality of the reconstruction (Table  3 ).
Data Quality in Citizen Science As citizens may lack scientific expertise or technical skills, citizen data quality is widely discussed in the literature (see, for example, References  [28] [29] [30] ). Nevertheless, some studies show that a variety of citizen science projects have produced data with quality equal to or higher than the dataset collected by professionals  [29]. It depends on what is meant by "quality" (accuracy of the measurement, distribution of measurements, sufficient sample size, etc. ). In this case of geomorphological monitoring, the quality threshold depends on the amplitude of the signal to be detected. Here, we are interested in landslides or rockfalls corresponding to distance between pre-and post-event surfaces in the order of 10 cm to 10 m. The residual errors after registration with the reference reconstruction (measured here by the standard deviation) correspond to distortion or reconstruction problems (for large errors of the order of several meters) or noise (for errors of a few centimeters to a few tens of centimeters).
As mentioned in Reference  [31] , the high standard of quality that is achieved quickly, simply, and at a low cost by using crowd-sourced photogrammetry combined with the Time-SIFT method is more than suitable for characterizing most geohazards. One of the advantages of crowd-sourced photogrammetry is that, from the citizen's point of view, it is a participatory science activity without protocol. This is particularly interesting when working with tourists, who typically, at least for non-national visitors, only come to the site once, so that one cannot expect them to participate if they need to be familiar with a complex protocol. Here, as everyone knows how to take a picture, the only criterion to be considered is the relevance of the photographed area. As long as the area of interest is visible in enough images and from enough distinct points of view (the more participants there are, the easier these criteria are fulfilled), the dataset is not affected by the scientific or technical skills of the "operators".
Interactions with Citizens Involving in a photogrammetric survey requires thinking of a way to give some explanations beforehand: Why is it important to monitor this site? What will the data collected be used for? What is the best way to collect the data? Apart from using a smartphone app, on a tourist site such as the Giant's Causeway, there are several ways of informing potential participants: explanatory panels at the visitor center or at the entrance to the site, information on audio guides, information directly transmitted via the site management staff, etc. In particular, citizens must be made aware of the fact that they should not put themselves in danger in order to collect images (by approaching a cliff edge or a rockfall, for example). The results of the project could also be advertised through a website, at the visitor center with virtual exhibitions, or through the mobile app, using Augmented Reality, as it has been performed in the HeritageTogether project  [32]  Although many studies (e.
Indeed, even if they do not allow us to produce 3D models, surveillance cameras are widely used to better constrain the temporal component of environmental observations, with low costs and human efforts (see References  [34] [35] [36] ). However, it implies that a system (video sensor, data supply, and transfer modes) that is compatible with the site constraints should be designed. Their use also requires the identification of a suitable location (viewpoint and distance from the monitored area), which also needs to be non-disruptive in this preserved area. In the framework of a geo-hazard observatory and of a long-term monitoring strategy, in order to convey the results derived from this citizen science dataset in a way that can be understood by all the stakeholders involved in management (including the citizens potentially contributing to the acquisition of photographs), hazard "indicators" can be defined to serve both as evaluation and decision-making tools.