dataesr/scientific-paragraphs-categorization huggingface dataset:
8 °C and 26. 5 °C in 2009, and 31. 1 °C and 27. 1 °C for 2020 can be observed (Fig. 9a ). The SST estimates around San Andrés provided by the NOAA satellite data are slightly cooler (28. 25 °C annual average, maximum 30. 2 °C, minimum 25. 9 °C). Similarly, the global time series data on SST anomalies from the NOAA website (https://www. ncdc. noaa. gov/cag/global/time-series; last consulted 12/02/ 2022), Such SST data are based on Advanced Very High Resolution Radiometer (AVHRR) or MODerate-resolution Imaging Spectroradiometer (MODIS) remote sensing data, which are comparable  (López-Gracía, 2020). These data show an increasing SST trend for the location of San Andrés, the Caribbean Islands (as defined by NOAA) and the northern hemispheres with respect to the base period average value from  1981-2010, 1910-2000 and 1901-2000  respectively for the three datasets (Fig.
Global warming, sea level rise and storm events Global warming has an important impact on the islands in the Caribbean  (Bueno et al. , 2008) , because sea level is rising  (Nerem et al. , 2018; Orejarena-Rondón et al. , 2019)  and SST are increasing  (Schmidt et al. , 2006; Glenn et al. , 2015). As shown in Figure  9a , SST increased at least by ~0. 5 °C over the past decade in the western Caribbean Sea, a trend that is reflected in the longer-term SST anomaly dataset of the Caribbean Sea or the northern hemisphere. In addition, the storm frequency and intensity of tropical cyclones affecting San Andrés Island are seemingly increasing as well in recent decades  (Ortiz Royero, 2012; Tab. 4). Although the available dataset may not yet be sufficient for providing compelling evidence for a significant increase in storm activity  (Burn and Palmer, 2015).
In fact, the question of increasing storm frequency in the Caribbean is a strong matter of debate, as the currently available data and modeling scenarios are not yet  fully conclusive (see  Vecchi and Knutson, 2008  and references therein), and the apparent increase in overall Caribbean storm frequency in the HURDAT2 dataset may be caused by combined effects of anthropogenically caused global warming and intrinsic variability in the weather-climate system  (Vecchi et al. , 2021). Nonetheless, global warming and with that increasing SST will most likely cause an increase in tropical cyclone intensity over the next century  (Reading, 1990; Knutson et al. , 2021) , although the duration of storm events may be reduced  (Knutson et et al. , 2021). San Andrés Island, being located in the southwestern part of the Caribbean Sea, is not in the area of the strongest tropical cyclone activity  (Knutson et et al.
Over the next decades storm surges of up to 5 m combined with the effects of rising sea level could cause inundation of almost half of San Andrés Island in areas at elevations between 0-6 m (Fig. 2d ). Of the 13 storm events between 1911 and 2020 summarized in Table  4 , eleven storms came closer to the island than 150 km, causing wind and storm surge damage. According to the HURDAT2 dataset the category 1 hurricane Storm No. 4 in 1911 and category 1 hurricane Hattie in 1961 passed directly over the island (Fig. 8a ). Storm No. 4 came from the eastern Caribbean and crossed the island from east to west. The locally formed hurricane Hattie was a tropical storm when it passed the island from south to north before gaining hurricane strength. The damage to people and infrastructure in 1911 was rather limited as the local population counted only about 3000 people and no well-developed tourism sector existed at that time.
This may be regarded as a conservative estimate, because the storm record over the past 20 years indicates that the frequency apparently increased to a tropical cyclone once every three years. Such an increase is not observed in the long-term record for the rest of the Caribbean Sea based on the HURDAT2 dataset  (Vecchi and Knutson (2008) ;  Vecchi et al. , 2021) , and it may be a regional phenomenon. However, it could mean that San Andrés may experience up to 26 major storm events until the end of the century, if that storm frequency would remain constant. Category 4-5 hurricanes cause the most damage, and three category 4-5 hurricanes affected San Andrés since 1911, which gives a recurrence interval of this type of event of currently ~37 years. This means there is a ~28% chance that San Andreas will be affected by a category 4-5 hurricane over the next ten years.
Acknowledgements. We thank the anthropologist of the  Centro Cultural de San Andrés of the Banco de la República  for information on the history of  San Andrés  and the present-day population and socio-economic situation of the island. Fieldwork on the island was done during a detachment to the CNRS in 2017-2018 in Colombia. We thank the associate editor  Nicolas Tribovillard  and two anonymous reviewers for their valuable comments, which helped to improve the manuscript.
Data availability The HURDAT2, SST, seismic and population data used in this study are freely available from the cited sources and internet pages. Sea surface temperature data were obtained from the NOAA webpages https://www. ndbc. noaa. gov/sta tion_page. php?station=42057 and https://www. ncdc. noaa. gov/cag/global/time-series Population data are available from https://data. worldbank. org.
This article investigates the impact of European Central Bank policies on credits considering financial and banking fragmentation. Using European data from the past decade, we estimate SVAR models to analyze the regional impact of conventional and unconventional measures on price and volume indicators of fragmentation. The risk-taking channel is studied using GVAR models to document the national consequences of this fragmentation. We find that unconventional measures increase credit in peripheral countries. Monetary policies alleviate fragmentation, but mostly in terms of price dispersion rather than credit volume. Finally, unconventional measures imply a rebalancing of European bank assets in favor of foreign currency denominated-assets.
While  Bruno and Shin (2015)  study the U. S. situation in a similar framework, we study the risk-taking channel inside the Euro monetary union in the presence of financial and banking fragmentation. Our hypothesis is that heterogeneity in the risk-taking channel in Europe could hinder European credits. We use monthly European data from 2008 to 2018 on interest rates, LTROs and quantitative easing performed by the ECB, coupled with regional Bayesian structural VAR models and national Global VAR models. First, the Bayesian structural VAR (BVAR) models are used in order to obtain regional aggregate regional impulse response functions, which provide a starting point for our analysis. The BVAR is estimated with  Sims and Zha's (1999)  approach, which allows us to circumvent the traditional overfitting problem present in VARs by reducing the number of parameters that have to be estimated.
Regional data on fragmentation Our variables are selected in the literature as reliable metrics of financial and banking fragmentation  (Claessens, 2019; ECB, 2020; Vari, 2020). Six variables are chosen to represent the degree of homogeneity in the reaction of the banking and financial sectors of the Eurozone: FINT, which is the sub-index for the equity market of the price-based financial composite indicator proposed by the ECB with a high value that corresponds to a high financial integration and country index returns; CDS which represents the five-year dispersion of banks' credit default swap; EONIA, which is the interquartile range of Eurozone countries' average short-term interbank lending rates; BOND, which is the interquartile dispersion of Eurozone ten-year sovereign bond yields; FCP is the volume of assets in foreign currency held by the Eurozone banking sector and EXL which measured the MFI's loans to non-MFI of other Eurozone member states.
Country-level data Following  Bruno and Shin (2015) , our variables can be classified into two categories representing the transmission channels from monetary policies to banks' risks, credits, and cross-border flows. The first set of variables are price indicators measuring the impact of monetary policies on financial and banking risk: SPNFC, the MFIs' lending margins on loans to nonfinancial corporations, CDS, the 5-year average banks' credit default swap, VOL, the realized volatility of daily price indices, and LTR is the 10-year interest rate for each European country. These variables are introduced in order to identify the risk-taking channel relating monetary policies and credit supply, as well as the financial markets  (Adrian et al. , 2019). In particular, lending margins have been used as a proxy for bank-pricing behavior  (Altavilla et al.
T A B L E 1 Variables of the model Variable Description Motivation and previous evidence Source Indicator proposed by the ECB in Indicators of financial integration in the euro area, ECB Statistics database, 2020. T A B L E 1 (Continued) T A B L E 1 (Continued) Variable Description Motivation and previous evidence Source EONIA ECB monetary policy variables Average interquartile range other euro area member Variable Description domestic banks to the EXL Volume of loans from Price variable measure of (Al-Eyd & Motivation and previous evidence fragmentation Indicator of banking ECB statistics a Source ECB statistics BCELR ECB marginal lending of euro area countries' states (non-MFIs), in Conventional monetary banking fragmentation Berkmen, 2013; ECB statistics facility rate, in percent. average short-term millions of euros (in policy (Bruno & (Mayordomo Bruno & Series key: interbank lending rates, log).
This review complements the work proposed by Matias et al. [4]  which did not present work based on the wavelet transform (WT). Therefore, the selected papers present the most recent work using WT for AF prediction, detection, and classification. Indeed, there are two main approaches for feature extraction in ECG signals:  (1)  The first approach uses the Fourier transform (FT) that describes a signal from the time domain to the frequency domain. (2)  The second approach uses the WT that describes a signal both in the frequency and time domains. The WT analysis provides an alternative to FT for the study of one-dimensional and multi-dimensional data. It also has multiple applications such as in physics, in mathematics, and in image processing. The main contributions of this article are the following: (1) covering the most recent works for the prediction and/or classification of AF using wavelets: datasets, feature extraction, preprocessing and models for prediction/classification or detection;  (2)  conducting a study to compare models based on the Fast FT (FFT) with those based on wavelets and conclude on which performs the best.
Search strategy For bibliographic search, the most used free and open source databases in the biomedical field are: (1) PubMed, 1 (2) IEEE Xplore, 2 (3) Science Direct, 3 (4) Elsevier, 4 (5) BioMedical Engineering OnLine, 5 (6) Web of Science, 6 (7) Scopus, 7 (8) ACM Digital Library. 8 All of these databases were used to search publications published between 2010 and 2021. We looked for titles and abstracts using the following keywords: ("artificial intelligence" or "machine learning") and ("ECG") and ("atrial fibrillation") and ("prediction" or "classification" or "detection") and ("wavelet"). We used the following two steps to collect the data from the sources: Step 1: searching articles related to the keywords specified above; Step 2: literature review and exclusion of articles that were not related to the research.
Selection strategy for research articles After searching for articles related to the above keywords, reviewing the appropriate bibliography and rejecting unwanted articles, some criteria were predefined to extract information from the selected publications: • Information about the article: its reference and its date of publication; • Inputs: evaluation of the inputs (data, the number and age of the patients) necessary to develop an algorithm; • Signal processing: determination of the parameters extracted from the input ECG signals, the signal duration used for training, and the equipment required for processing; • Methods: specification of the models using WT to preprocess the ECG signals, classification, AF detection/prediction and their performance evaluation, as well as the number of repetitions and the distribution of data between training and testing; • Performances: definition of the metrics used to evaluate detection, classification, and prediction.
Results of the selection At the beginning of the search, seventy-four papers were selected. 1 Free resources for biomedical literature to improve health on a global and personal level. 2 The source of reliable journals, conferences, standards, e-books, and training courses. 3 The source of medical, scientific and technical research (journals, books, and articles). 4 An editor specialized in information technology. 5 An open access database dedicated to publish research in all areas of biomedical engineering. 6 A website that provides access to multiple databases for many different academic disciplines. 7 The largest abstract and citation database of peer-reviewed literature (scientific journals, books, and conference proceedings). 8 A database for all academic databases in computer science.
Databases used in the selected papers Very expensive equipments, a medical professional, and patient protocol adherence are often required to collect ECG data. Therefore, to advance ECG-based research for various applications, including AF, researchers have developed public databases. Thirty-seven studies (89%) were based on Physionet-MIT-BIH database where eleven of them used the MIT-BIH AFDB (25 long-term ECG)  [3, [11] [12] [13] 15, 17, 20, 23, 24, 29, 33] , three studies were based on MIT-BIH PAF Prediction Challenge Database PAFDB (each record set contains two 30-min records with consecutive record names and two 5-min "continuation" records with names ending)  [20, 26, 29] , seventeen were based on the MIT-BIH Arrhythmia database  [2, [5] [6] [7] [8] 18, 19, 21, 25, 27, 28, 31, 32, [34] [35] [36] 39] , and two studies were based on the Normal Sinus Rhythm Database  [6, 20].
In addition, one study (3%) was based on Cinc/Challenge 2004. For PAF (80 ECG recording)  [14] , four studies (13. 5%) used their own database  [30, 37, 38, 40] , three studies (8%) were based on PhysioNet/Computing-in-Cardiology Challenge 2017 (8528 ECG recordings short term between 30 s and 60 s)  [6, 13, 14]. One study (3%) was based on myocardial ischemia from the European ST-T database BIDMC congestive heart Failure database (15 long-term ECG recordings for 20 hours)  [27] , another study  [9]  used the Physionet database and Abdullah et al. in Ref. [16]  used the PTB databas. Ma et al. , in Ref. [41] , trained and tested their ANN model on the AF Termination Challenge  Database and MIT-BIH Arrhythmia Database using RR intervals as an input feature for automatic classification of AF and achieve 99.
The most used method for prediction is SVM (10 times) followed by CNN (9 times), DT (5 times), and ANN (5 times). To achieve an evaluative comparison on the performance of the models, several criteria are taken into consideration: the use of the same database, the same duration for the input signal, and the use of the same AI prediction model. For detection, Ref. [12]  using DWT for feature extraction and stacking model achieved the best accuracy (99. 1%) if the length signal is 4 s. The authors in Ref. [33] , using CWT for feature extraction and CNN model, achieved the best accuracy (99. 23%) for 22 s ECG signal. For classification, the authors in Ref. [28]  using DWT for feature extraction and PNN modelachieved the best accuracy (100%) for 30 min ECG signal. For prediction, Ref. [42]  using DWT (Haar) for feature extraction and CNN model, achieved the best accuracy (98.
FFT vs WT The selected studies were analyzed, and the main results are summarized as follows: the wavelet analysis is an effective methodology for analyzing and processing non-stationary signals, recognizing patterns, compressing data, and detecting anomalies. WT allows complex information (images) to be decomposed into time (positions) and frequency (scales) and then reconstructed with high resolution. In general, the WT method allows detecting RR intervals in the ECG and then the LDA method is used to reduce the features. Finally, for classification, we can see that deep neural networks containing convolution layers give good results. Table  8  presents a comparison between the FFT approach and the wavelet decomposition approach for AF prediction. The studies use the same database. Among the papers using FFT, Ebrahimzadeh et al.
in Ref. [42]  obtained the best accuracy (98. 21%) using the expert model mixture (SVM-MLP and KNN). The results are validated by 100% as sensitivity, 95. 55% as specificity and 98. 21% as accuracy. The authors used a 300-s signal from the MIT-BIH AF Prediction Database (AFPDB). The database contains 53 two-lead signals. They calculated twenty-eight features extracted from the HRV signal (temporal, frequency, and nonlinear). Among the papers using WT, Urtunasan et al. in Ref. [20]  obtained the best accuracy (98. 7%) using the CNN model with a sensitivity of 98. 7% and a specifity of 98. 6%. They used a 30 s duration signal from the MIT-BIH AFDB. For a 300 s duration signal, the best accuracy, specificity, and precision are 92. 18%, 94. 88%, and 89. 48% respectively  [26] , using WT for denoising, compared with 90%, 89.
Conclusion In this paper, we presented a review of recent works for the detection, classification, and prediction of AF using WT with an AI approach. Indeed, tools are very promising as they speed up decisions making and improve efficiency, reliability, and accuracy. This paper completes the work presented by Matias et al. [4]. We discuss the databases used for such models, the procedures for the preprocessing, and the process of features extraction and selection. The most used database is the MITBIH fibrillation: it was used in 31 of the 37 selected studies. For the classification, SVM provides the best results for accuracy (around 99. 83%); for prediction KNN provides the best results for accuracy (around 99. 3%); for detection, CNN provides the best results for accuracy (around 99. 23%).
Data selected from articles. NP= Not published. MIT-BIH AF database = AFDB; MIT-BIH paroxysmal AF database = PAFDB; Long Term AF Database = LTAFDB; MIT-BIH Normal Sinus Rhythm Database = NSRDB; MIT-BIH ECG arrhythmia database = ADB; BIDMC database = Beth Israel Deaconess Medical Center; PTB Diagnostic ECG Database = PTB.
ADB 47 [19] 2019 Urtnasan et al. AFDB/PAFDB/NSRDB 19 804 short-term ECG [20] 2019 Alarsan et al. ADB 47 [21] 2019 Attia et al. [22] Own database 210 414 patients with 1 million ECG 2019 Wang et al. [23] AFDB 23 2018 Xiaoyan et al. [3] AFDB 25 2018 Runnan et al. AFDB 25 [11] 2018 Narin et al. [24] AFDB 100 2018 Anwar et al. [25] ADB 47 2017 Y. Xin et al. [26] PAFDB 100 2017 Lassoued et al. ADB 47 [7] 2017 Peng et al. [27] ADB 47 2017 Kaya et al. [28] ADB 47 2017 Saraswat et al. LTAFDB-MITBIH NP [29] Fantasia Database(MFD) 2017 Filos et al. [30] Own dataset 21 PAF and 17 healthy patients 2016 Dewangan et al. ADB 116 137 [31] 2016 Daqrouq et al. ADB 47 [32] 2015 Rodenas et al. AFDB 23 [33] 2015 Sharmila et al. Physionet Database NP [9] 2015 Thomas et al. ADB 47 [34] 2013 Barmase et al. ADB 47 [35] 2013 Luz et al.
Study Region: Forty-one river basins in Brazil and neighboring countries in South America. Study Focus: In large river basins, on countrywide or continental scales, it is often difficult to have consistent and accurate long time series of spatially distributed precipitation data available. However, these are needed to calibrate hydrological models and to run hydrological simulations continuously in real-time streamflow forecasting. In this study, we assess two real-time precipitation products based on rain gauges and satellite data (TRMM-MERGE and CPC-NOAA) for their use in streamflow forecasting in the hydropower sector in Brazil. To take advantage of each precipitation data source and derive a unique dataset, a methodology is proposed to combine, extend, and validate the datasets. We consider the discharges at the river basin outlets as an independent and robust reference for hydrological applications.
Observed discharges are used to quantify precipitation uncertainties and to weight the blending, while discharges obtained from hydrological modeling are used to validate the final precipitation product. New Hydrological Insights for the Region: The proposed blending method, which uses the uncertainty of the original datasets to define the weighting factors, was efficient in generating a precipitation product that performs better than each dataset separately when used to force a hydrological model. The use of the double-mass curve correlation to extend the time series of the datasets beyond their common period allowed us to produce long time series of precipitation for South American basins and hydrological applications. The study shows that it is possible to rely on river discharge data and hydrological modeling to select and combine different precipitation products in the region and presents a step-by-step methodology to do so.
The authors use precipitation-gauge stations available to first evaluate the quality of each precipitation dataset and to assess the overall performance of the hydrological model in each streamflow station. The best product is locally selected according to the performance of the hydrological simulations evaluated at discharge gauging locations. The Canadian hydrologic-land surface model H-LSM MESH was used to generate a composite precipitation dataset in the Saskatchewan River basin in Canada, which was then validated by assessing hydrological fluxes and storage at downstream gauge stations. In many parts of the world, precipitation ground data are not available, or networks are too scarce to be used in the validation of gridded precipitation products. Reliable and consistent long time series of spatially distributed precipitation over large areas are however needed for many applications, including climatological assessments and seasonal streamflow forecasting.
They are used to calibrate the hydrological models, warmup the flow forecasting models, bias correct numerical weather forecasts and apply the ensemble streamflow prediction (ESP) method, which relies on historic precipitation and temperature data as future possible climate scenarios  (Crochemore et al. , 2016; Bennett et al. , 2017; Arnal et al. , 2017; Harrigan et al. , 2018). In flow forecasting applications, these datasets also must be available in real-time or near real-time to allow operational decision making. In Brazil, gridded large-scale precipitation products are of particular importance to the real-time operations of the hydropower sector  (Schwanenberg et al. , 2015; Gibertoni et al. , 2017). The anticipation of hydrological conditions can strongly influence the centralized operation of the electrical system and energy prices  (ONS, 2016).
Given this regional context, this paper aims to present a methodology that uses a hydrological model and observed discharge time series to quantify uncertainties from the TRMM-MERGE and CPC precipitation datasets, combine  the datasets, and derive a unique precipitation dataset that can be used in (near)real-time applications. The novelty of our approach lies on using both observed and simulated discharge data at the outlet of the river basins to take advantage of the information contained in each precipitation product and derive a unique dataset. Observed discharges are used to quantify precipitation uncertainties and to weight the blending, while discharges obtained from hydrological modeling are used to validate the final precipitation product. Therefore, instead of using a reference (ground or remote sensed) precipitation dataset, which might also contain errors in the precipitation estimates, we use the discharges at the river basin outlets as an independent and more robust reference for hydrological applications at the catchment scale.