dataesr/scientific-paragraphs-categorization huggingface dataset:
1997). It seems that this observation expands to vowel-tokens. A sample of the extracted data is shown in Table  2. For each individual occurrence of the vowels [a], [i], and [u], the following information is annotated. First, each identified vowel is assigned a unique identifier so that it is easily extractable from the data. Second, the language of the recording is annotated with its iso-code. For example, French is encoded as 'fra' and German is encoded as 'deu'. Third, the F1 and F2 at the middle point of the occurrence of the vowel are extracted. Then, additional metadata about the speaker ID and the recorded word are provided. Using the extracted formants, the distribution of the vowel space for each language in the data set can then be visualized and analyzed. Taking Italian as an example, we can visualize the distribution of the vowels [a], [i], and [u] in terms of formants in Figure  1.
Based on the visualisation, we acknowledge that there are obviously a few outliers in the extracted occurrences. For example, some occurrences of [u] have very different formants from the other occurrences and appear to resemble [1] or [y]. This is likely due to the segmentation accuracy, as some parts of the surrounding context might have been included by the automatic segmentation. These outliers are kept in the raw data. However, they are identified and removed during the analysis. Further details are explained in Section 3. As a summary, the recordings from Lingua Libre were downloaded. Then, they were automatically segmented using MAUS. This method allowed us to have 50 recordings for each vowel ([a], [i], and [u]) across the 10 languages of the data set. In total, we thus have 50 * 3 * 10 = 1500 tokens available for analysis.
Discussion In this paper, we have provided the first phonetic study using the crowd-sourced data from Lingua Libre to test the Inventory Size Hypothesis on a substantial amount of languages. We tested 10 languages, among which 8 can be said to be less-resourced. The data was automatically segmented using WebMAUS and analyzed using R. Two types of visualization (boxplots and scatter plots), a linear mixed model and various complementary statistical tests all fail to confirm that a larger vowel inventory correlates with smaller dispersion in production. This failure supports the results of the previous recent studies that handled a substantial amount of languages showing that the ISH is not accurate to account for phonetic variation, at least not in this form  (Hutin and Allassonnière-Tang 2022b).
Our study thus indicates that the ISH is inaccurate, which confirms the literature, but it may also need further investigations. Now that this proof of concept has proven useful, we would like to extend our methodology to more languages from more diverse language families. This will be possible in the near future since Lingua Libre is growing rapidly (in March 2022, the library contains almost 686,000 recordings, i. e. , 9000 more than in February of that same year). We would also like to extend our study to more connected speech, which is now possible since Lingua Libre was recently updated to offer the possibility to adjust the time needed for the device to detect the pause, thus allowing to read longer texts, even with prosodic pauses between phrases or even paragraphs. We would like to finish by providing some conclusions on the main topic of this paper, i.
e. , the use of crowd-sourced data from Lingua Libre to investigate scientific research questions. As summed up above, the general methodology is promising, since the results are in line with previous literature using professional corpora. The comparability of Lingua Libre and professional corpora has also been demonstrated in another study on the particular case of Polish  (Hutin and Allassonnière-Tang 2022a). On a positive note, our study shows that Lingua Libre has an impressive potential. First, it already provides great amounts of data in many languages, and as an open-access, crowd-sourced tool, it has the advantage of offering an ever-growing data set, which may help bridge the gap between well-and less-resourced languages. Second, the quality of the recordings is sufficient for extracting acoustic measures, which is ideal for phonetic research.
Finally, the documentation to access and exploit it is easily accessible and usable, which makes its exploitation feasible by any researcher. However, we also came across several difficulties, in particular regarding the metadata. First, the names of the files were not always following the same template, some following the regular "language-speaker-item" template, such as "ita-Yiyi-Brissago-Valtravaglia", others following a different template, such as "Pamputt-fra-lapin-garou". This problem made the automatic processing of files complicated. Second, linking the data to the speakers' metadata proved difficult, since the process has not yet been automatized in Lingua Libre, and we had to process it manually. Moreover, the metadata were not always accurately completed by the participants. Our suggestions for improvement would thus be to improve the uniformity of the file names and the processing of the metadata.
This implies not only facilitating the linking between files and metadata, but also raising the contributors' awareness on the need for scientists to have access to accurate, clean information. A verification tool à la Common Voice could also improve the quality of both recordings and metadata. Another difficulty arose not from Lingua Libre per se but from the limits of our methodology for segmentation. We were indeed limited by MAUS to languages handled by the software. Future research would also benefit from investigating more alignment tools to allow the investigation of more diversified languages, from more language families and with more various phonemic inventories.
Conclusions All in all, since Lingua Libre proved useful to investigate a specific research topic, we hope to use it again for future research. On this same research topic, we would like to investigate more linguistic variables (such as the duration and F3, F4, F5 of the vowels, their position in the syllable and word, whether they are bearing lexical accent or not. ), as well as sociolinguistic variables (such as gender, geographical location, etc. ). But most of all, obviously, we hope that the data set will continue to grow rapidly so that we can include more and more languages to our study.
2 mm There are relatively few published studies of number counts at longer wavelengths λ 1 mm. The first single-dish 2 mm surv e y was conducted by the Goddard IRAM Superconducting Millimeter Observer (GISMO) at the IRAM 30-m telescope  (Staguhn et al. 2014 ; Magnelli et al. 2019 ). The number counts were later confirmed by the Mapping Obscuration to Reionization with ALMA (MORA) surv e y  (Zavala et al. 2021 ). We revisit the 2 mm number counts with the detections from ALMACAL. In Fig. 13  , we plot the new measurements from ALMACAL, as well as the data from  Magnelli et al. ( 2019 )  and  Zavala et al. ( 2021 ). Our new measurements are consistent with the two previous surv e ys at high flux densities and go 4 × deeper, down to S 2 mm ∼ 0. 1 mJy. We also predict the 2 mm number counts based on the AS2UDS sample, following the same method we used for 1.
We thus made the predictions based on our best-fitting Schechter functions, using the theoretical integration formula: ∞ 0 N ( > S) S d S = N 0 × S 0 × ( α + 2). ( 6 ) The resolved CIB and the predictions at 2 mm, 1. 2 mm, and 870 μm are summarized in Table  4. In Fig. 16  , we compare our results with the direct measurements from COBE /FIRAS and the most recent recalibrated Planck /HFI data  (Odegard et al. 2019 ). Both the resolved intensities and the predictions from ALMA are shown. Our results are consistent with the literature and our predictions are close to the direct measurements from FIRAS. At 870 μm, the resolved CIB for S 870 μm > 0. 2 mJy is 18. 3 ± 4. 7 Jy deg -2 , which matches the resolved intensity of 16. 4 ± 2. 7 Jy deg -2 from the secure sample of B éthermin et al. ( 2020 ).
The predicted CIB is the whole integration of the best-fitting differential number counts. The direct measurement from FIRAS on COBE is shown in orange. The black curve is the best-fitting FIRAS spectrum made by  Fixsen et al. ( 1998 ). We also show the recalibrated Planck /HFI data  (Odegard et al. 2019 ). Comparing with the direct measurements from FIRAS, ALMA currently resolved nearly one half of the CIB from 870 μm to 2 mm, but the fraction is highly uncertain due to the large uncertainties of the FIRAS spectrum at the submm/mm wavelengths. uncertainties also exist in the FIRAS measurement at this wavelength, the comparison of the calculation of the resolved fraction is not very accurate. More recently,  Odegard et al. ( 2019 )  hav e impro v ed the accuracy of the CIB direct measurement with Planck/HFI data, but the uncertainties at the longer wavelengths have not significantly impro v ed.
C O N C L U S I O N S ALMACAL is a surv e y that is e xploiting calibration data that are accumulating 'for free' with every scheduled ALMA observing project. Before the observatory shutdown due to Covid-19 in 2020 March, ALMACAL had accumulated more than 1000 h of observing time and co v ered 1001 calibrator fields. The sensitivity, total sk y co v erage, and wide frequenc y sampling make ALMACAL a promising data set to undertake blind surv e ys for DSFGs. Within the ALMACAL footprints, we detect 371 sources, including 186 DSFGs confirmed by their spectral indices. We report the number counts based on these DSFGs for ALMA band 3 to band 7 (wavelengths spanning from 3 mm to 870 μm), which are mostly in agreement with existing surveys at overlapping flux densities. In band 4 (2 mm) and 7 (870 μm), ALMACAL represents the deepest surv e y available to this date.
DATA AVA I L A B I L I T Y All the data used in the work is published/available from ALMA science archive https:// almascience. eso. org/ alma-data. The catalogue is available on https://almacal. wordpress. com and https://github. com/c jhang/almacal. The code used for this paper is also available online: https://gith ub. com/cjhang/almacal.
2 is approximately 80 km, close to that of ORCA2 in the Southern Ocean. The continental discharge rates followed the climatological dataset of  Dai and Trenberth (2002). The simulations are initialized at rest via the temperature and salinity fields from the World Ocean Atlas 2018 monthly climatology (WOA18;  Garcia et al. , 2019) , run from January 1979 to December 2017, with only the last decade of model output  (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017)  being used for analysis. Our selection principles for parameters and their values were first to target the three compartments at a stake (air, ocean and ice) and their interactions, and second, to act on uncertain and important processes. Ultimately, we selected 18 parameters (Table  1 ) to investigate the sensitivity of the sea ice budget to their uncertainties.
Then the posterior distribution for test parameter sets  * can be obtained as 𝑓𝑓(𝐗𝐗 * )|𝑓𝑓(𝐗𝐗 𝑡𝑡 )～𝑁𝑁(𝜇𝜇 * , 𝐾𝐾 * ) (2) where μ * = μ + 𝐾𝐾(𝑋𝑋 * , 𝑋𝑋 𝑡𝑡 )𝐾𝐾(𝑋𝑋 𝑡𝑡 , 𝑋𝑋 𝑡𝑡 ) -1 (𝑓𝑓(𝑋𝑋 𝑡𝑡 ) -μ), (3) 𝐾𝐾 * = 𝐾𝐾(𝑋𝑋 * , 𝑋𝑋 * ) -𝐾𝐾(𝑋𝑋 * , 𝑋𝑋 𝑡𝑡 )𝐾𝐾(𝑋𝑋 𝑡𝑡 , 𝑋𝑋 𝑡𝑡 ) -1 𝐾𝐾(𝑋𝑋 𝑡𝑡 , 𝑋𝑋 * ). (4) We used the GPy implemented in Python (GPy, 2012) to build the GP emulator for each metric of interest. Once the user has selected the mean and covariance functions, the toolkit will automatically maximize the marginal likelihood by the L-BFGS method to find the optimal values of all hyperparameters in the mean and covariance functions. In general, the prior of the mean function is assumed to be zero. Thus, the only key matter remaining is how to choose the covariance function. To achieve this, we used a 10-fold cross-validation method for model selection  (Geisser, 1975).
The idea is to divide the dataset {  , } evenly into 10 parts, each time using 9 parts as the "training data" to train the emulator and 1 part as the "true data" for model validation, and so on for 10 cycles and taking the average as a proxy for model performance. Using this approach, we traversed the linear, squared exponential, exponential, Matern 3/2, Matern 5/2 covariance functions and their sums and products  (Rasmussen and Williams, 2006) , for a total of 177 different combinations, and then selected the covariance function with both the minimum RMSE and the highest correlation coefficient between the simulated and emulated values.
Observation data Daily sea ice velocity observations from  Kimura et al. (2013)  and SIC from the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4  (Meier et al. , 2021)  (hereafter referred to as CDR) were used to calculate the observed SIC budget. The ice velocity dataset KIMURA was generated from the brightness temperature of the 36-GHz channel of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) using the maximum cross correlation technique  (Kimura et al. , 2013) , and ultimately deriving a 60 km resolution product. Therefore, the KIMURA data shares the same period as AMSR-E and its successor AMSR2, covering from 2002 to the present. Following Holland and Kwok (2012), a 3 × 3 grid filter was used in the calculations to smooth out the grid-scale noise present in the satellite-derived ice drift.
Regarding the SIC satellite observations, the CDR SIC is a rule-based combination of the NASA Team  (Cavalieri et al. , 1984)  and NASA Bootstrap  (Comiso, 1986)  ice concentration datasets in the same 25  × 25  grid, covering the years from 1978 to 2021, with daily, grid-based uncertainty estimates. The observed SIC budget (Fig. B1 ) shows that the Southern Ocean sea ice is generally transported to the ice edge at lower latitudes by advection and melts there, with divergence yields open water and thus promotes freezing of ice  (Holland and Kwok, 2012; Uotila et al. , 2014). It is important to note that the calculated SIC budget observations were considered as "true values" in our study, despite the uncertainties and biases in the ice drift observations, such as the overall overestimation of 5% compared to the buoy measured velocities  (Kimura et al.
Similar to the seasonal cycles of SIE and SIA, the CTRL run's SIV remains close to the ensemble mean. However, the differences between SIVs simulated based on different parameter sets are much greater than for SIEs (Fig. 2c ), for instance in winter, the maximum values of SIVs in the ensemble members are more than twice as large as the minimum values. Additionally, there are some model runs whose SIV cycles are detached from other members, which is most evident in winter. For the ensemble mean sea ice thickness, thicker sea ice of up to two meters is maintained year-round in the western Weddell Sea (Fig. B3a, c ), which appears to be higher than the previous observation-based dataset of 1. 2 to 1. 5 meters  (Haumann et al. , 2016 , in their Extended Data Figure  2 ). However, the lack of observations from the same period as this study precludes a direct comparison.
Acknowledgements. The authors acknowledge  CSC -IT Center for Science, Finland , for HPC computational resources. Financial support. PU was supported by the  Academy of Finland  (Project  322432 ), and the  European Union 's  Horizon 2020 research and innovation framework programme  under Grant agreement no. 101003590  ( PolarRES  project). XQL was supported by the  National Natural Science Foundation of China  (Grant No. 42076011  and Grant No. U1806214 ) and YFN was supported by  Scholarship from China Scholarship Council  ( CSC. 202006330054 ).
Code and data availability. The model code for NEMO4. 0-SI 3 is available from the NEMO website (https://www. nemoocean. eu/, last access: 1 March 2022). The parameter sets, configuration files and scripts for running NEMO4. 0-SI 3 are archived on https://doi. org/10. 5281/zenodo. 6780342 (Nie, 2022). The atmospheric forcing was provided by the DRAKKAR consortium through the following link: https://ige-meom-opendap. univ-grenoblealpes. fr/thredds/catalog/meomopendap/extract/FORCING_ATMOSPHERIQUE/DFS5. 2/ALL/catalog. html (last access: 22 February 2022). The NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4 (Meier et al. , 2021) data can be downloaded from National Snow & Data Center (https://nsidc. org/) by registering for an EarthData account. The KIMURA ice drift data are available from the authors on request.
6. Acceptability. The user population must be willing to submit their biometric trait to the system. 7. Circumvention. The ease with which it is possible to mimic a trait of an individual and therefore fool the biometric system. Biometric systems generally present a series of elements that allow you to acquire the signals that have been sensed, some of the most important elements are the following. 1. Sensor module. Capture the biometric information of the individual in the form of images, audio, video, or some other signal. 2. Feature vector extraction module. The biometric information is processed to extract discriminant features that represent the captured trait. 3. Database module. In this module, the biometric information is stored after the stage known as enrollment, this information is processed and with it a pattern or model is created.
III. RELATED WORKS The investigations of biometric systems present interesting proposals for the acquisition and treatment of the information obtained, among which is the work of  [9]  is a biometric authentication identification system is proposed using the palm, finger and iris impression using a Mapping of Minutiae (Minutiae), likewise the Discrete Wavelet Transformation (DWT) algorithm is used for the encryption and concealment of information and is tested in an e-business system. On the other hand  [10]  proposes a biometric identification system for the use of mobile devices together with a user authentication process, which serves to adapt to the different conditions of biometric identification. Likewise,  [11]  presents an electronic access control system through the application of a single key locking mechanism using a quick Response Code (QR) and using a symmetric key cryptography algorithm and a Reed-Solomon code.
In a third experiment, the fingerprint biometric system shows the best Failure to Capture (FTC) performance compared to the facial and voice biometric system, therefore identified that a lower number of identification failures in the fingerprint biometric system. The IoT devices is highly used for the registration of biometric data, because allow a large number of actions to be carried out to process data, in  [31]  used a Raspberry Pi as a remote authentication and registration node. , the enrollment is successful and the captured data is sent to the cloud, The fingerprint and face capture sensors along with the Wi-Fi adapter are successfully connected to the Raspberry Pi, the captured data is sent to a biometric service Based on Microsoft Azure, the service extracts features as feature vectors are stored in a vector database.
The system that the authors propose allows control through the capture of biometric data, which is scalable. In  [32]  use a Raspberry Pi for user authentication through biometric data, the proposed work the collection of 6 samples of the same person footprint and 6 samples of fingerprints, in the data acquired performed the steps of Image Acquisition, Image Enhancement, Binarization, Thinning and Feature Extraction, the images that enter the are subjected to a preprocessing stage followed by the pattern matching stage, finally proceeding to a decision-making based on the match score if the given input is authenticated or not. The IoT system uses a version of Linux NOOBS and image recognition is done using OpenCV. The data recognition algorithm implemented for fingerprints is SIFT for feature extraction, a fold pattern orientation flow and fingerprint orientation normalization using the centroid rule.
In  [33]  developed a work for access to vehicles through biometric data, this work used a system implemented in a Raspberry Pi sensor to acquire fingerprints, the process consists of capturing the thumb to recognize if the registered fingerprint matches that of the base, all the car doors will automatically unlock, otherwise the car will not be unlocked. The system uses a Global System for Mobile Communication (GSM) module to communicate to the owner about access events or access attempts. In  [33]  propose the implementation of an Arduino Uno connected to a GSM module and an NFC reader and implement in Arduino Mini connected with a fingerprint sensor and an NFC card. The fingerprint detection algorithm is SmartFinger 3. 0. At work use authentication when a verification occurs, the system sends the user an SMS with the telephone number of the guest who wishes to enter to give access authorization, the NFC band allows to acquire fingerprints and will allow access when it coincides with registration on the device.
In  [34]  present an interesting work on the registration of biometric data with EGC analysis, firstly filter the ECG signal, by eliminating noise with linear or nonlinear methods, propose an adaptive wavelet decomposition (AWD). However, the AWD has a high demand on Signal Noise to Ratio (SNR) when reconstructing signals, furthermore, for this reason the authors add the Singular Vector Decomposition (SVD) to efficiently extract compressed features from the based on the reading of Electrocardiogram (ECG) signal and then recover a clean ECG signal from the noisy one. The authors highlight that the algorithms can be implemented in IoT devices using Python 2. 7, to filter out noise, allowing effective user authentication as well as data assurance. In  [35]  carry out an advanced study on the authentication and identification systems that are implemented in the current IoT technology, in which the Bayesian network algorithms, the radial based SVM in the kernel function and the dynamic time warping stand out.
The chip proposed is a manufactured in LP CMOS of 65 nanometers, the database used for the data obtained from the ECG is 741 people. The hardware was tested by ERR, and the 256-bit keys that were generated were tested by NIST randomness tests. In  [37]  proposed a DSA for a Biometric Authentication System (EBA) based on ECG, one of the contributions is that the authors managed to reduce the vulnerabilities of the EBA when time-based attacks were applied, reducing the latency of the system. The authors tested their system with attacks on a convolutional neural accelerator based on FPGA to recover the captured energy traces, being this work in verifying countermeasures to side channel attacks in EBA systems based on system times, without interfering in running system applications when authentication execution is not being enforced.