dataesr/scientific-paragraphs-categorization huggingface dataset:
Consensus Classifier In this work, our strategy was to propose a complete pipeline of different classifiers in accordance with Ye et al. but dedicated to clinical microbiology in a routine setting  [3]. Our experiments showed that Kraken 2 is generally a more precise classifier than Kaiju (Figure  1 ). However, the Kaiju protein database was more complete and allowed more reads to be classified. Therefore, we built an ensemble classifier as follows: each read was classified with Kraken 2 results, and as a fallback, reads not classified with Kraken 2 were classified using results from Kaiju (Figure  1 ). Sunbeam, by comparison, also provides an extensible pipeline for analyzing metagenomic sequencing data is based on Kraken 1, which demands much more computational resources and only refers to databases built for Kraken v1  [4].
Potential Limitations The aim of this work was to describe optimal quality procedures and filtering steps for a taxonomic classifier pipeline to be used on a routine basis for metagenomic analysis of clinical data, taking into account both accuracy and cost constraints (compute resource, process time). This optimized pipeline resulted in a combination of classifiers previously described by Ye et al. [3]  improved with specific quality steps and thresholds to be applied. While the scope of this work was not to present a new classifier for all types of datasets, the presented optimized pipeline is a good candidate for additional analyses on different groups of microorganisms and interlaboratory collaborations to further describe its sensitivity and specificity.
Open boundary conditions (OBCs) are based on the 2D characteristic method  (Blayo and Debreu, 2005). The atmospheric pressure component is added, hypothesizing pure isostatic response at open boundaries (inverse barometer approximation). As in the operational IBI configuration  (Sotillo et al. , 2015 (Sotillo et al. , , 2021)) , river freshwater inputs are imposed partly as daily OBC in the domain locations for 33 main rivers and partly as a climatological coastal runoff to close the water budget from land. For the 33 main rivers explicitly considered, flow-rate data are based on a combination of daily observations, simulated data (from SMHI E-HYPE hydrological model) and climatology (monthly climatological data from GRDC and French "Banque Hydro" dataset). The tidal forcing is prescribed from the FES2014 dataset  (Carrere et al.
47 to +4. 14 locally. Temporal evolution of simulated sea surface temperature is also compared to in situ observations (drifting buoys) available on the Coriolis project portal (http://www. coriolis. eu. org, last access: 7 April 2022) in Fig. 5  (the locations of the observations used for the comparison are shown in Fig. 3a ). Among the full observational dataset, we select only data which have almost full time series during the 7 simulated days (33 drifting buoys), and with an hourly period   (see B1 and B2 examples in Fig. 5a, b ). Despite this selection, the high density of drifting buoy observations allows us to evaluate the simulated SST over the entire domain. For all the buoys represented in Fig. 3a , statistics for all the experiments (Table  2 ) are computed and are summarized in the Taylor diagram in Fig.
OCE-ifs and OCE-aro have been compared to the available in situ profile measurements (Argo floats, CTD profiles, mooring, gliders and drifting buoys, from the CORA 5. 2 database,  Szekely et al. , 2019)  for the ocean mixed layer (OML) temperature (i. e. around 13 m depth) through root mean square errors (RMSEs, Fig. 8 ) to further examine the mixed layer representation. It shows in fact that the two ocean-only simulations have quite similar skill scores on average over the domain and along the simulation period, with very slightly lower RMSE for OCE-ifs than for OCE-aro (Fig. 8b ), but some large improvements are found locally when using AROME forcing, notably along the Cornwall coast in the Celtic Sea (Fig. 8a ). Differences between daily-averaged (last simulated day) ocean mixed layer depth (MLD) simulated by the three simulations (OA, OCE-ifs and OCE-aro) are represented in Fig.
Wind The OA simulated wind field is examined in Fig. 11  and compared to in situ wind measurements available in the Coriolis database (coloured circles in Fig. 11 ). It is important to note that the wind observations are set at a height of 10 m; thus we use a 10 m diagnostic wind from AROME and not the prognostic 5 m wind values. During the first simulated day (12 October, Fig. 11a, d, g ), Storm Callum moves towards the British Islands, inducing strong wind (above 20 m s -1 ) over a wide area affecting Portugal to the United Kingdom. Locally, wind speed values reach the maximum value of 41. 5 m s -1 in the Celtic Sea. The comparison with data (circles in Fig. 11a ) shows that OA and ARO overestimate wind speed at that time. This overestimation is less important in the OCE-ifs simulation (Fig.
This work concretes our common first stage towards highresolution ocean-atmosphere coupling for both oceanic and atmospheric forecasts. Thanks to our joint work for its update, with the development and application to a new region, the AROME-NEMO coupled system now permits further apprehension of operational regional ocean-atmosphere coupling in both institutes, Météo-France and Mercator Ocean International. It shows the affordability of such a numerical prediction system regarding the computation costs (see Appendix A) that can be shared and especially through the development of common tools. Obviously, future challenges still remain for an operational implementation of such a high-resolution coupled system, in particular the insertion of a coupled data assimilation scheme and the issue of the data availability for both components, and coordinated code management with objectives of continuously improving the computing efficiency.
, 2021) , as a careful quantification of the costs-benefits ratio of coupling. Appendix A: Simulation technical environment and high-performance-computing characteristics All the developments are performed using a Vortex/Olive Python-based framework, used to run AROME operational simulations at Météo-France. This coupling system runs on the new Météo-France supercomputer Belenos (https://www. top500. org/system/179853/, last access: 7 April 2022). In total, this supercomputer has 294 912 cores on 2307 nodes and a peak performance of approximately 10. 5 PFlop s -1. Each node has a random access memory (RAM) of 256 GB minimum. Table  A1  summarizes the computational cost of the different simulations presented in this article (Table  2 ). The coupled simulation runs on 15 nodes and 424 cores corresponding to 12 nodes and 384 cores for AROME, 2 nodes and 32 cores for NEMO, and 1 node and 8 cores for XIOS.
2 % of the CPU cost of the OA coupled system). For the purpose of this comparison, we used the same number of nodes for NEMO simulations alone (OCE-aro and OCE-ifs simulations) as the one used in AROME-NEMO simulations, but it can be optimized, for example, by increasing the number of used cores by node. Code and data availability. NEMO is available at https://www. nemo-ocean. eu/ (Madec et al. , 2017; NEMO Community Ocean Model, 2022)  after a user registration on the NEMO website. The version used is NEMO_v3. 6. OASIS3-MCT was used in version OASIS3-MCT_4. 0. It can be downloaded at https://oasis. cerfacs. fr/en/ (Valcke, 2013; Craig et al. , 2017; The Oasis Coupler, 2022). The public may copy, distribute, use, prepare derivative works and publicly display OASIS3-MCT under the terms of the Lesser GNU General Public License (LGPL) as published by the Free Software Foundation, provided that this notice and any statement of authorship are reproduced on all copies.
SURFEX open-source version (Open-SURFEX) including the interface with OASIS from v8_0 is available at http://www. umr-cnrm. fr/surfex/ (Masson et al. , 2013; SURFEX, 2022)  using a CECILL-C Licence (a French equivalent of the L-GPL licence; http://www. cecill. info/licences/Licence_CeCILL-C_V1-en. txt, CeCILL-C Free Software License Agreement, 2022), but with the exception of the Gaussian grid projection, the LFI and FA I/O formats, and the dr HOOK tool. Although the operational AROME code cannot be obtained, the modified sources for cy43 are available on demand to the authors for the partners of the ACCORD consortium and are included in the new Météo-France official release based on cycle 48 (cy48t1). Outputs from all simulations discussed here are available upon request to the authors. The moored and drifting buoy data were collected and made freely available by the Coriolis project and programmes that contribute to it (http://www.
coriolis. eu. org, Coriolis Operational Oceanography, 2022). The L3S SST satellite data were provided by GHRSST and the CMEMS Regional Data Assembly Centre. FES2014 was produced by Noveltis, Legos and CLS and distributed by Aviso + , with support from CNES (https://www. aviso. altimetry. fr/, AVISO + Satellite Altimetry  Data, 2000; Carrere et al. , 2015). Author contributions. All authors (JP, JB, CLB, GS, GF and GG) contributed to the conceptualization and methodology of the study as well as drafting, reviewing and editing of the article. GF finalized the Vortex/Olive-Swapp experimental configuration for coupled simulations and extracted the IFS forecast files. The configurations NEMO-eNEATL36 and AROME-Mercator were developed by JP and JB, who also ran the coupled and uncoupled simulations.
JP, JB, CLB, GS and GG carried out the analysis of the results. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. The authors thank Sylvie Malardel, Soline Bielli (LACy), Sébastien Riette (CNRM) and the SWAPP system team (Météo-France), who helped us in the implementation of the coupled experiment design in the Vortex/Olive-Swapp environment. Financial support. This work was funded by Mercator Ocean International. Review statement. This paper was edited by Piero Lionello and reviewed by two anonymous referees.
Code availability This paper is based on source code that is presently incorporated in the MOCAGE-1D model. The MOCAGE-1D source code, which derives from MOCAGE-3D source code, is the property of Météo-France and CERFACS and is not publicly available. This is because MOCAGE-3D includes routines protected by intellectual property rights. Note that Météo-France plans to make available the part of MOCAGE-3D and MOCAGE-1D models dealing with gaseous chemistry as a free software in the future. However, the newly developed/modified code in the MOCAGE-1D version is made available to the editors and the referees for the review process (https://zenodo. org/record/6876348, doi: 10. 5281/zenodo. 6876348). This code will be available in the 2023 release of MOCAGE-1D.
Danan Thilakanathanet al. [13]  have built a remote caring system for patients at home using ECG sensors, cloud storage providers, and mobile technology. This work consists of finding solutions that allow patients to share their health information with the medical professional team in a reliably secure and confidential manner. M. Shamim Hossain et al. [14]  have used IoT technologies for ECG health monitoring, where ECG signals have been recorded via ECG sensors at home and sent the data to smartphones or computers via Internet. Smartphones were used as a client side that allows processing the ECG signal (removes unwanted noise from the recorded signal) and detecting the R-peaks. The acquired ECG signal was then transmitted to a server for analysis and characterization; the temporal and spectral characteristics were extracted and classified using a SVM classifier.
Their classification results can be sent to the medical professional team for the analysis and diagnosis, where they transfer the medical report later to the cloud server for the patient to be informed. R. Lakshmi Devi et al. [15]  have developed a classification system of cardiac arrhythmia using IoT that enables the ECG monitoring system to analyze the signal acquired from the data acquisition system (AD8232 & Arduino). The statistical features were extracted from the HRV signals in the temporal domain, and used as inputs for a SVM classifier to identify the cardiac arrhythmia disease. The acquired ECG signal, processing, and classification tasks were done inside the system, which sends the result to a smartphone. Khalid abusalim et al. [16]  have used an IoT ECG monitoring system that allows sending the ECG data to a cloud server via wireless networks with a security system using share the health information system (A system of encryption and decryption algorithm).
The system was developed based on the storage, processing, and classification of cardiac arrhythmia from different healthy subjects available in the MIT-BIH. This study used three different types of features that were extracted from the ECG signals: time-domain, frequency-domain, and timefrequency plane. K-nearest neighbor is used as a classification approach in this study to identify and classify the cardiac arrhythmia. In this paper, we developed a clientserver model implemented as a telemedical platform for real time-remote monitoring of the cardiovascular function in arrhythmia patients. This system allows continuous recording when the ECG Sensor is attached to the patient's body. The ECG is firstly acquired using data acquisition system designed around the Raspberry Pi zero which communicates with a server through the TCP/IP secured by the Transport Layer Security in order to secure the exchange of ECG data transmitting over the internet network.
A HRV times series is then extracted from the acquired ECG signals and analyzed by Quadratic time-frequency distributions, to calculate new TF-features to differentiate between various cardiac pathologies such as Atrial Fibrillation, SupraVentricular Tachycardia, and Congestive Heart Failure by analyzing their respective HRV signals. More precisely, the TFfeatures of interest are selected by means of the Mutual Information and the Feature Selection with Adaptive Structure Learning. These features are used as inputs for a SVM classifier to identify the acquired ECG among the different classes of the implemented training database. The system alerts the medical professional when the patient is detected as affected by cardiac arrhythmia disease according to the result of classification. The professional then sends the medical report to the cloud server and the cloud then warns the patient.
Materials and Methods Our system is composed of two main blocs (see figure  1 ): a hardware block, named the client block, devoted to the acquisition and the transmission of the ECG, and a software block, called server block, dedicated to HRV extraction, analysis and classification (features extraction, features selection, and patient classification)(see figure  8(b) ). The client block consists mainly of (1) the analog shaping circuit ECG AD8232, (2) the analog-to-digital converter MCP3008, (3) the data acquisition card (Raspberry Pi zero) to acquire ECG signal. The sampling frequency is ensured by the internal analog-to-digital (ADC) converter of the MCP3008 it has been setup at 100 Hz. Regarding the server block, the Physionet Bank  [17]  is used to choose the best features and the SVM parameters that provides the high classification rate between 2 classes of cardiac arrhythmias, namely AF and SVT among NSR and CHF.
Dataset In order to select the best features and the parameter of the SVM classifier, the normal and abnormal subjects were collected from the PhysioNet research repository  [17]   The ECG-data is firstly filtered using a band pass to remove baseline and noise with high frequency. Then the filtered ECG signal is derived to highlight the slope of the R wave. Then, the signal is squared and a moving average filter is applied to provide information about the slope of R wave. Two thresholds are automatically adjusted to float over the noise.
Concerning the server block (software part) (see figure  1 ), we developed a Graphical User Interface (GUI) to record the data acquisition task. The recorder ECG signal is then analyzed: we first estimate the HRV times series, then an SPWVD is applied on this HRV signal to extract features of interest. Finally, the learned SVM is applied to classify and label the analyzed ECG as AF and SVT, NSR and CHF.
Identification of features of interest and SVM parameters learning The Physionet database is used to help us to choose the best features and to learn the SVM parameters. More precisely, 228 ECG signals divided into four classes, namely SVT, NSR, AF, and CHF. (See Data set section) are exploited. Figure  5  shows an example the SPWVD calculated for the four different classes. Clearly, the shape and the TF distribu- tion of the amplitude of the spectra are different between the four classes, which confirm that the analysis of HRV signals in TF domain is a good choice instead of the frequency domain. In addition, we can also remark that SPWVD provides a good resolution in both time and frequency domains, with less cross-terms. Figure  6  depicts the mean and standard deviation values of eight features (see Feature extraction section ), calculated from the TF distributions of HRV signals, for NSR, CHF, AF, and SVT.
65 %, Sp=98. 55 % and Acc=97. 82 %, and ii) Se=91. 30 %, Sp=95. 65 % and Acc=94. 20 % (using MI). Table  4  presents, in detail, the classification rate of AF and SVT among CHF and NSR, using the features selected by the FSASL. The global proposed procedure seems to be very efficient whatever the classified pathology. Note that, all the previous results are obtained by randomly dividing the database into a training set (90 % of ECG) and a testing set (10 % ECG) using a bootstrap strategy.
Nevertheless, no security protocol has been provided in these studies In our study, we designed a new low-energy consumption, and a very affordable price system capable of performing continuous recording of HRV times series for easy monitoring of the heart rhythm for the detection of arrhythmia. We used an electronic circuit formed by an analog shaping part and a data acquisition system. As a software part, we developed a Graphical User Interface (GUI) within a python environment to establish the connection between the client and the server through the TCP/IP secured by the Transport Layer Security, to acquire, transmit, monitor, process and classify the hearth rhythm. We developed a new approach that combines SPWVD and SVM, for analyzing and classifying HRV signals. More precisely, three features of interest were extracted from the TF-representation of HRV signal: the coefficient of variation, the skewness, and the Shannon entropy.
Client-server application The graphical user interface (client-server) is dedicated to remote monitoring of cardiac arrhythmia, display, archiving, digital signal processing, classification, and transmission of ECG-data to a remote station under a secured TCP/IP protocol. Client application is a graphical interface implemented with python environment connected with a data acquisition system to acquire the ECG signal for transmitting over internet network to the remote station (server). The client application is represented in figure  8 (a) and involves: -Connection establishment window: relating to the communication phase between the server and the client (establish and open the connection through an internet network using the server's IP address).
We propose an adaptive pre-processing module for the image degradation problem that is common in underwater images. The module is lightweight and simple to use, and ablation experiments verify its effectiveness. Moreover, ULO Tiny, a lite version of ULO, is proposed to achieve further computation reduction. Furthermore, we optimize the annotations of the URPC2019 dataset, and the modified annotations are more accurate in localization and classification. The refined annotations are available to the public for research use.
Recently, researchers have also used machine-learning methods to assist in searching efficient network architectures  [6] [7] [8] [9]. These auto-designed architectures can have a good balance between accuracy and inference time. In this paper, we propose an underwater light-weight object detector-namely, ULOfor aquatic products, i. e. , scallops, starfish, echinus, and holothurians, and this runs on edge computing devices, such as the NVIDIA Jetson Nano, with competitive performance. Moreover, we provide a lite version of ULO, ULO Tiny, which reduces the computation further. Furthermore, an adaptive pre-processing module is proposed as a plug-in block, which benefits the detection performance. Furthermore, we optimized the annotations of the URPC2019 dataset and provided more accurate locations and labels of the targets.
Code and refined labels will be available. (Code: https://github. com/wangsssky/YOLO_ Underwater, Refined annotations: https://github. com/wangsssky/Refined-training-setof-URPC2019/, accessed on 26 July 2022). The preliminary work  [10]  was presented at the conference IEEE OCEANS 2020. In this journal extension, we updated our original work with concrete improvements in both structural designs and more comprehensive experiments.
• Secondly, the adaptive pre-processing module is introduced as a light-weight and easy image enhancement method. • Thirdly, more comprehensive experiments are conducted to evaluate the proposed networks fully. The rest of the paper is organized as follows: Section 2 introduces the URPC2019 dataset for underwater object detection and our optimizations to the annotations. Section 3 provides the details of the proposed networks and modules, such as YOLO Nano Underwater, ULO, and ULO Tiny. In Section 4, we evaluate the proposed networks on the URPC2019 dataset and conduct an ablation study on the adaptive pre-processing module. Section 5 concludes this paper.
Dataset A customized version of the dataset of the underwater robot picking contest 2019 (The competition website: http://www. urpc. org. cn/index. html, accesed on 29 July 2022) (URPC2019) is used in this work. The dataset consists of 4757 underwater images with corresponding annotations. Four types of objects are included, i. e. , scallops, starfish, echinus, and holothurians, which are commonly farmed products in the ocean. Underwater optical images are often blurred due to the attenuation of light in the underwater environment  [11]. As a result, underwater targets are also more difficult to label and are prone to category errors and deviations in the boundaries of bounding boxes. Therefore, to evaluate the algorithm results more reasonably, the annotations of the dataset are optimized.
Compared to the original annotations, the refined bounding boxes are more accurate with fewer classification mistakes. Figure  1  shows some examples of the URPC2019 dataset and the comparisons between the refined and the original bounding boxes. To encourage the research of underwater object detection, we release the modified annotations to the public for academic use. For the following experiments, the images are randomly divided into the training, validation, and testing sets. The ratio of the number of training/validation and testing images are roughly set at 5:1 by convention. Specifically, there are 3600, 400, and 757 images in training, validation, and testing sets, respectively.