dataesr/scientific-paragraphs-categorization huggingface dataset:
The wave propagation is forced at the northern borders of the domain by the characteristics of the three-point waves at the northern border of the domain, in which the Anemoc database is available. At these points, the significant height of the H s waves, their T p period, the propagation direction and the directional range at the peak are known with a time step of 1 h. The time step of the model (one hour), fixed by the data of the Anemoc base, allows taking into account the tide. The change in water level over a year was calculated with the harmonic constituents of the tide using the FES2004 software  [47]. The significant wave heights of the Anemoc database are compared with those of the result of the simulation by the Swan model at the three points. The comparison is for a 20-day duration.
The strong attenuation of the wave heights results from bathymetric breaking. The simultaneous occurrence probabilities of a significant wave height Hs and a tidal water level Htidal are determined at different points on the site using the results of the Swan model forced by the Anemoc database. The results are expressed by defining classes of the significant wave height of the waves and of the tidal water level. Figure  14  illustrates the most frequent classes at the point PS5. At low tide, the waves above 2 m no longer exist. At high tides, the highest waves do not exceed 3. 5 m. The most frequent classes are centered around an average tide and wave heights between 0. 5 and 2 m. The laws of probabilities in Figure  13a  show that large wave heights are more frequently recorded on the Artha breakwater (P6 a P7) and at the end of the Socoa breakwater (P5) than on Sainte-Barbe and Socoa.
The water depth in these last points is the explanation. In the same way, we traced in Figure  13b  the probabilities of annual occurrence at the points PS1 a PS10 on the cross-shore profile in front of Trinka breakwater. The strong attenuation of the wave heights results from bathymetric breaking. The simultaneous occurrence probabilities of a significant wave height H s and a tidal water level H tidal are determined at different points on the site using the results of the Swan model forced by the Anemoc database. The results are expressed by defining classes of the significant wave height of the waves and of the tidal water level. Figure  14  illustrates the most frequent classes at the point PS5. At low tide, the waves above 2 m no longer exist. At high tides, the highest waves do not exceed 3.
In the same way, we traced in Figure  13b  the probabilities of annual occurrence at the points PS1 a PS10 on the cross-shore profile in front of Trinka breakwater. The strong attenuation of the wave heights results from bathymetric breaking. The simultaneous occurrence probabilities of a significant wave height Hs and a tidal water level Htidal are determined at different points on the site using the results of the Swan model forced by the Anemoc database. The results are expressed by defining classes of the significant wave height of the waves and of the tidal water level. Figure  14     • Bayonne The implemented simulations allow us to calculate the wave propagation from offshore to onshore, using a model that is refined on the North breakwater of the port of Bayonne that is presented in Figure  15a.
Second, the comparison between the two simulations was made on 13 observation points located at the dike foot (see Figure  15b ), on the north side, the most exposed to waves. Although significant differences are observed offshore, the differences in calculated power are weaker on the dike. In addition, the highest calculated power is given by the simulation forced by the Homere database. As foreseen, the results present great differences between months in summer and months in winter. The ratio is up to 1 to 6. The results indicate that the annual average power is between 15 and 18 kW/m for the most offshore points and for the two databases. The power then drops sharply for the two points close to the coast. This average power of 15 to 18 kW/m is available over a length of approximately 650 m.
We considered only GPS observations since most of the stations recorded only this constellation during most of the period considered. The orbits and clock offset products have been generated beforehand by the GFZ Analysis Center in preparation of the IGS Repro3 campaign  (Männel et al. 2020 ). These products are consistent with ITRF2014  (Altamimi et al. 2016). Regarding the models used, we kept a consistency between the two software configurations. The same antenna eccentricities are used for both processings based on station site logs. Once the two daily coordinate sets are obtained, we select the intersection of both to get equivalent time series with the same daily coordinates. Indeed, some daily data were not properly computed by one or the other software. Stations STG0, SBL0 and PDB0 are completely excluded because of a lack of observations.
For each time series, the corresponding velocities are determined using the trend estimation software HECTOR  (Bos et al. 2013). We model the time series as combinations of a long term linear trend and an annual+semi-annual periodic signal, along with white and power-law noise. The term trend designates hereafter the linear component. The discontinuities introduced in the trend estimation are based on the material change site logs (an antenna change is systematically considered as a discontinuity) and on a supplementary visual detection  (Sakic et al. 2019). The same discontinuities are applied for both equivalent EPOS and GINS solutions.
This statement reveals the necessity to maintain networks with a minimum of hardware discontinuities, i. e. by reducing the number of antenna changes. MGL0 station, located on the Marie-Galante Island, presents a singular behavior. It is the only station clearly uplifting, with a quasi-complete time-series of 3. 5 years, without any visible discontinuity, while the other station on Marie-Galante (MAGA) presents a subsiding trend. Unfortunately, since this station belong to the commercial ORPHEON network, we have only a few metadata that prevent us to explain clearly this behavior. We corroborate the paleo-geodesy studies carried out in the region. The coral reef records in Martinique  (Weil-Accardo et al. 2016)  and Les Saintes  (Leclerc and Feuillet 2019)  indicate also a subsidence but with a smaller order of magnitude of few tenths of a millimeter per year, which can be explained by the difference in the observation time spans (only a few years for GNSS, ca.
Acknowledgements We thank the different scientific institutes for the continuously operating GNSS acquisitions, the instrumental mainte-Estimation of  Lesser Antilles Vertical Velocity Fields Using a GNSS-PPP Software Comparison nance  and for providing publicly and freely the observations. Data of the IPGP network, operated on site by the Guadeloupe and  Martinique  seismic and volcanologic observatories, are available on volobsis. ipgp. fr. Data of the IGN and the SONEL networks are available on rgp. ign. fr and sonel. orgrespectively. ORPHEON data were provided to the authors for a scientific use in the framework of the agreement between the GEODATA company (orpheon-network. fr)and the  RESIF-RENAG  network maintained by the  CNRS-INSU  (renag. resif. fr). We acknowledge the  CNES (Centre national d'études spatiales)  for its geodetic processing software GINS.
Purpose FACE is a prospective cohort study designed to assess the effect of adding adaptive servoventilation (ASV) to standard care on morbidity and mortality in patients with chronic heart failure (HF) with preserved (HFpEF), mid-range (HFmrEF) or reduced ejection fraction (HFrEF) who have sleep-disordered breathing (SDB) with an indication for ASV. We describe the study design, ongoing data collection and baseline participant characteristics. Participants Consecutive patients with HFpEF, HFmrEF or HFrEF plus SDB with central sleep apnoea (CSA) and indication for ASV were enrolled in the study cohort between November 2009 and December 2018; the ASV group includes those treated with ASV and the control group consists of patients who refused ASV or stopped treatment early. Follow-up is based on standard clinical practice, with visits at inclusion, after 3, 12 and 24 months of follow-up.
It automatically adjusts pressure support to stabilise and reduce ventilation in patients with CSA and period breathing. Treatment with ASV has been shown to reduce respiratory events and normalise the breathing pattern. 15 16 8] [19] [20] [21]  However, more recently, the results of the large, randomised Treatment of SDB with Predominant CSA by Adaptive Servo Ventilation in Patients with HF (SERVE-HF) trial reported a neutral primary endpoint result when ASV was added to optimal medical therapy in patients with HF with reduced LVEF (HFrEF), and mortality was actually higher in patients randomised to ASV. 22  However, the indication for ASV in the SERVE-HF trial only represents about onethird of the potential ASV indications. 23 24 Other indications include treatment-emergent CSA, idiopathic CSA, opioid-induced CSA and CSA in patients with HFpEF  24  ; the latter population is currently being investigated in the FACIL-VAA study (NCT02835638).
Each of these visits includes collection of primary endpoint data (occurrence of events since the last visit), assessment of disease-specific quality of life using the Minnesota Living with Heart Failure Questionnaire (MLHFQ), and evaluation of sleepiness using the   ASV therapy (PaceWave, AutosetCS; ResMed) was initiated in the hospital using standard settings. Pressure levels were adjusted based on the results of respiratory monitoring. First generation devices had a minimum expiratory positive airway pressure (EPAP) of 5 cmH 2 O, while PaceWave with auto EPAP had a minimum EPAP of 4 cmH 2 O and minimum pressure support of 3 cmH 2 O. The PaceWave ASV algorithm sets the mean minute ventilation target to 90% of the patient's own ventilation. Patients were instructed to use the ASV device for at least 5 hours each night, 7 days a week.
sample size Based on data from observational studies,  29 30  it was assumed that treatment of SDB with positive airway pressure therapies would halve the morbidity and mortality rate. Therefore, a sample size of 300 patients would be sufficient to detect a similar decrease in risk with a type 1 error (alpha) of 5% and a power of 90%. The target sample size was set at 400 to allow for drop-outs. After enrolment of patients with HFrEF was suspended, the sample size was revised to a target of at least 300 patients with HFmrEF and HFpEF. statistical analyses All primary and secondary endpoints are being assessed in the modified intention-to-treat population, which includes all enrolled patients meeting the eligibility criteria. Eventfree survival will be estimated using the Kaplan-Meier method and compared between the ASV and control groups using a two-sided log-rank test (significance p<0.
strengths And lImItAtIons strengths FACE is a prospective, observational multicentre cohort study conducted in a real-world setting. The population being studied is unselected patients with HF and SDB, either central or not controlled by CPAP. 6] [7] [8] [9]  The FACE study will provide data on patients with chronic HF with CSA/CSR that is complementary to that generated by randomised controlled trials. Other key features of the FACE study are the inclusion of patients with HFrEF, HFmrEF or HFpEF, and those with or without coexisting obstructive apnoea that is not controlled by CPAP, allowing the effects of ASV therapy in these different patient subgroups to be compared. This will help to further define the effectiveness of ASV for controlling SDB in patients with CSA/CSR who also have obstructive apnoea events, about which there is currently only limited data.
31 32 In addition, HFpEF is not well studied compared with HFrEF, but is increasing in prevalence as the population ages (accounting for approximately half of all HF cases in the community). ASV has been shown to correct SDB, improve diastolic and right ventricular function and reduce natriuretic peptide levels in patients with HFpEF,  33 34  and the FACE results will expand this knowledge. The FACE study will also provide important insights into compliance with nocturnal positive pressure ventilation therapy, and the FACE cohort will be the first group of HF patients for whom ASV data are evaluated using device remote monitoring. There are currently no trial data specifically addressing compliance with nocturnal ventilation, and determinants of compliance, in patients with HF. FACE is being conducted in a clinical practice setting across a range of centres (both private and academic), covers a range of ASV indications, and includes patients with all forms of HF (ie, HFrEF, HFmrEF and HFpEF), which should maximise the clinical relevance of the study findings.
The FACE registry is supported by unrestricted grants from  ResMed. English language medical writing assistance was provided by  Nicola Ryan , independent medical writer, funded by  ResMed, France. Authors would like to thank FACE investigators who participated in the study. The FACE study was supported by  ResMed. Competing interests J-LP, TD,  J-MD , RT and  M-PD  acted as investigators and members of the FACE study steering committee for ResMed. AP and FG acted as investigators of the  FACE study for ResMed. RT has received unrestricted research grants from  ResMed ,  Vitalaire ,  Philips  and AGPMC foundation; consultant fees copyright.
The EAD challenge promotes awareness of and addresses this key bottleneck problem by investigating methods that can accurately classify, localize and segment artefacts in endoscopy frames as critical prerequisite tasks. Using a diverse curated multi-institutional, multi-modality, multi-organ dataset of video frames, the accuracy and performance of 23 algorithms were objectively ranked for artefact detection and segmentation. The ability of methods to generalize to unseen datasets was also evaluated. The best performing methods (top 15%) propose deep learning strategies to reconcile variabilities in artefact appearance with respect to size, modality, occurrence and organ type. However, no single method outperformed across all tasks. Detailed analyses reveal the.
Unfortunately, unlike natural images  6, 7  that can be readily crowd-sourced, labelled biomedical images are notoriously scarce and difficult to annotate. The ethical requirements and subtler image features of medical images require annotators with significant domain expertise. Current literature attempt to circumvent this data limitation by adapting methods pretrained on large natural image datasets. However, this fails due to the inability of these methods to generalize on large endoscopy data samples. This is because the evaluated image datasets are often collected in-house with limited image diversity that do not fully reflect the tissue appearance and different image acquisition practices worldwide. Most works have been performed on short video clips, selected artefact type, single imaging modality and single organ datasets  [8] [9] [10] [11].
We initiated the Endoscopy Artefact Detection Challenge (EAD2019) to enable a more comprehensive and objective benchmarking of endoscopic imaging artefact detection and segmentation algorithms by creating a diverse repository of annotated endoscopy video frames  12, 13  (Suppl. Fig. 2 ). EAD is a crowd-sourcing initiative that challenges researchers and computational experts to test and build their algorithms on a common benchmark clinical endoscopy dataset. To best capture the image variation in endoscopy, the assembled image frames were sourced from multiple organs, imaging modalities, endoscope manufacturers, and patients of different ethnic backgrounds (Suppl. Fig. 3a ). Altogether the dataset captures seven prevalent artefact classes as identified by expert endoscopists  13  (Suppl. Fig.
3b ). The impact of posing the complex problem of artefact detection in diverse clinical endoscopy data through a challenge can be seen in twofold: (1) to communicate with the wider community to best address the most fundamental limitation that impedes quantification of endoscopy data worldwide, and (2) to deliver improved patient monitoring with the endoscopy procedure by image artefact quantification and the development of enhanced computer-aided endoscopy tools. In the EAD2019 competition, the developed methods from participants were evaluated on a web-based platform with standard computer vision metrics. In this paper, we present an in-depth analysis of the EAD results including the performance impact due to the dataset size and artefact types. We aim to highlight the current best approaches for handling such a diverse endoscopy imaging dataset and to direct developers and researchers to open challenges that we believe are still insufficiently addressed by current algorithms.
Materials and Challenge Tasks Dataset. The EAD2019 dataset is the first publically available dataset aiming to capture the wide visual diversity in endoscopic videos acquired in everyday clinical settings (Suppl. Fig. 2 ). Supplementary Note I and Suppl. Figs. 3, 4  provides a detailed breakdown of the dataset and its construction. Briefly, the EAD 2019 dataset identifies seven prevalent image artefact types or classes: (1) specularity, (2) saturation, (3) artefact, (4) blur, (5) contrast, (6) bubble and (7) instrument  12  , and is multi-organ, multi-modal, multi-patient and multi-ethnic, (Suppl. Fig. 3a ). The dataset contains images from multiple patients of different ethnic origins. The defined class types aim to capture the most prevalent artefact types worldwide. The training dataset for detection and out-of-sample generalization tasks contain 2192 unique video frames with bounding box annotations and class labels from all 7 classes.
A subset collection of 475 video frames additionally have binary image mask annotations for 5 classes (excluding blur and contrast) for the semantic segmentation task. The test dataset with reference annotations (unavailable to the public) contain an additional 195, 122 and 51 video frames for detection, segmentation and out-of-sample generalization tasks, respectively. To capture the natural frequency and inherent multi-class features of endoscopic artefacts, we allow labelling of artefacts in the same spatial location with multiple relevant class labels. This is unlike natural images  6  or any other endoscopic dataset aimed at finding disease  14  where class labels are considered mutually exclusive. The frames presented in this dataset were extracted from nearly 125 endoscopic videos provided by collaborating institutions.
The data for detection and segmentation sub-challenges were collected from five international institutions: John Radcliffe Hospital, Oxford, UK; ICL Cancer Institute, Nancy, France; Ambroise Paré Hospital of Boulogne-Billancourt, Paris, France; University Hospital Vaudois, Lausanne, Switzerland and the Botkin Clinical City Hospital, Moscow, Russia. Frames for out-of-sample generalization come provided by a sixth institute, Instituto Oncologico Veneto, Padova, Italy. During the challenge, the training datasets were released in two separate batches. The first batch provided the annotations for the detection sub-challenge, while the second batch supplemented the detection sub-challenge and provided the annotations for the segmentation sub-challenge. By staggering the release of the detection training dataset, participants were able to use either the first or second batch to test detection out-of-sample generalization.
Participants had a total of three months to complete their submissions. The test data (excluding the reference annotations) were kept secret and only provided one month prior to the final workshop conference. An online system evaluated of participants results with respect to (hidden) ground truth labels. The EAD2019 training dataset can be downloaded at the open access Mendeley data repository. A comprehensive companion open-source software to assist users in data preparation and evaluation of predictions. Challenge tasks. To enable detailed assessment of algorithm performance, the overall artefact detection and classification problem was subdivided into three sub-challenges: detection, segmentation and out-of-sample generalization (Fig. 1  and Suppl. Fig. 1 ). Detection targets the coarse localization of image artefacts, identification of their class type and spatial location (given by the top-left and bottom-right coordinates of a rectangular bounding box in Fig.
Performance evaluated using the detection or out-of-sample generalization dataset is differentiated by subscripts 'd' and 'g' , respectively. Teams are ordered in decreasing order of score d. The better the method, the higher the mAP and IoU, the lower the dev g. Top 5 values for each evaluation metric is shown in bold. posed score allows for moderate bias to be placed on recall (r) with behaviour intermediate between F 1 and F 2 , (Suppl. Fig. 6 ). Evaluation criteria for the out-of-sample generalization. We define out-of-sample generalization of artefact detection as the ability of an algorithm to achieve similar performance when applied to a different imaging dataset that may differ in imaging modality and acquisition protocol but contain the same set of imaging artefact classes. To assess this, participants applied their trained methods to data collected from a sixth institution whose images were neither included in the training nor test data of the detection and segmentation tasks.
Assuming that participants applied the same trained weights, the out-of-sample generalization ability was estimated as the mean deviation between the mAP score of the detection and out-of-sample generalization test datasets of each class i for deviation greater than a tolerance of. * {0 1 mAP } i d. ∑ = N dev 1 dev (1) i i g g =        | - | ≤. | - | | - | >. dev 0, i i i i i i i i i g d g d d g d g d The deviation score dev g can be either positive or negative, however, the absolute difference should be very small (ideally, dev g = 0). The best algorithm should have high mAP g and high mAP d but a very low dev g (→0). This is because the methods should be robust to perform equally well in both seen and unseen datasets. In Eq. (1), = dev 0 i g is empirically assigned to an acceptable level of mAP fluctuations (≤0.
2d ,e, Supplementary Note II). However these teams were not equally good with respect to mAP and IoU metrics where notable shuffling of team ranks could be observed, for example with "michaelqiyao", (Suppl. Fig. 9 ). By taking only consensus detections, the "Merged" detector maintains low variation across artefact classes and demonstrates jointly high rank performance across all metrics. Notably boxes used by the "Merged" detector does not all come from top-ranked teams as might be expected but rather a mixture of 7 high and low-ranked teams; 1st yangsuhui, 5th YWa, 8th swtnb, 9th Witt, 11th XiaokangWang, 12th xiaohong1 and 14th nqt52798669. The same teams that contributed the most prevalence in the detection 'test' dataset also contributed the most in the 'out-of-sample generalization' dataset.
Out-of-sample detection generalization performance. The deviation between reported mAP on the detection test and the out-of-generalization dataset obtained from a separate sixth institute was used to analyse the out-of-sample generalization performance. Submissions for the same 15 teams we analysed for detection performance (Table  2 , Fig. 3 ). It is encouraging to note that we observed a strong, significant linear correlation (Pearson's R = 0. 896) between global detection (mAP d ) and generalization (mAP g ) mAP for all teams independent of rank. The correlation between the mAP d and mAP g scores also was also observed across teams for each artefact class though not always linearly. Blur, contrast and bubbles were the classes that tested non-significant for linear correlation, (Fig. 3a ).