dataesr/scientific-paragraphs-categorization huggingface dataset:
Acoustic Emission (AE) technique The AE system consists of an eight channel AE Win system, a general-purpose interface bus (PCI-DISP4) and a PC for data storage analysis. Six piezoelectric transducers with a resonant frequency of 150 kHz have been used to convert the mechanical waves to electrical signals. Pull-out and tensile tests have been monitored by a three dimensional positioning. The sensors have been placed in 3D parallelepiped position on both free frontal surfaces of the sample with silicon grease as the coupling agent (Figure  4 ). The recorded AE amplitudes range from 0 to 100 dB. The detected signals have been amplified with a 40 dB gain differential amplifier. In order to overcome the background noise, the signal detection threshold has been set at a value of about 35 dB (value adjusted before every test) slightly above the measured background noise  [23].
This methodology employs the basic continuum mechanics theory of deformation to develop and implement a procedure for estimating surface displacements. A commercial software, Vic2D, has been used in this study to perform the image correlation. During the post treatment, every zone of interest of the image is divided into several finite subsets. The correlation consists of considering a subimage in the reference image and locating that subimage in the deformed image. The size of the subsets is taken as 29 x 29 pixels which is regularly spaced in the reference image (of size 2452 x 2056 pixels) in the form of a grid.
Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean science studies. Here, the performance of two generic chlorophyll-a algorithms (i. e. , a band ratio one, Ocean Colour X (OCx), and a semi-analytical one, Garver-Siegel Maritorena (GSM)) was assessed against two large in situ datasets of chlorophyll-a concentration collected between 1999 and 2016 in the Northeast Pacific (NEP) and Northwest Atlantic (NWA) for three ocean colour sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS).
noaa. gov/cw/index. html) provide daily chla data that are accessible to the public for fisheries and water quality applications. In Canada, Fisheries and Oceans have been relying on ocean colour for several decades to monitor the state of the marine ecosystem  [8, 9] , and in particular on chla concentration, and its derived product primary production, to infer ecosystem indicators that are at the foundation of ocean management. Chla concentration is derived from remote sensing reflectance (R rs ), which is the ratio of water-leaving radiance to downwelling irradiance corrected for sun geometry to allow for comparisons independent of locations, times and dates. Several approaches to infer chla from R rs have been developed and embedded by space agencies in their data processing software, including band ratio  [10]  and semi-analytical models  [11].
As its name suggests, band ratio algorithms exploit the ratio of wavebands in the blue and green to retrieve chla; the Ocean Colour X (OCx) (x stands for 2, 3, or 4 and indicates the number of bands that were used in the algorithm) suite of empirical algorithms have been developed by the National Aeronautics and Space Administration (NASA) using a global dataset of in situ measurements of chla concentration fitted to remote sensing reflectance (NOMAD, the NASA bio-Optical Marine Algorithm Dataset  [12] ) and a fourth-degree polynomial expression. On the other hand, semi-analytical algorithms (e. g. , Garver-Siegel Maritorena (GSM)) consist of optimizing bio-optical parameters (including chla concentration) in an approximate solution of the radiative transfer equation to match modelled reflectance to the reflectance measured by the satellite.
Note that the performance of algorithms that use remote sensing reflectance will be inherently dependent on the performance of the atmospheric correction procedure, which will not be addressed here. It has been shown that OCx  [15, 16]  and GSM  [17]  algorithms contain regional biases, such that even if its overall performance is satisfying, application to a given region results in systematic bias. Here, we assessed the performance of the OCx and GSM algorithms in Canadian waters (Northwest Atlantic (NWA) and Northeast Pacific (NEP)) using a dataset of in situ chla concentration that was collected by Fisheries and Oceans Canada as part of their monitoring of the marine ecosystem. Performance of these algorithms was evaluated for NASA's three sensors, namely the Sea-viewing Wide-field-of-View Sensor  (SeaWiFS, 1998 (SeaWiFS, -2010)) , the Moderate Resolution Imaging Spectrometer on the Aqua platform (MODIS-Aqua, 2002-current) and the Visible and Infrared Imaging Spectrometer on the NPP platform (VIIRS, 2012-current).
In Situ Samples DFO collects data on chla concentration using two methods, namely, Turner fluorescence (TF) and high-performance liquid chromatography (HPLC). While the most comprehensive dataset is the one obtained using the TF method (tens of thousands of data), here we used the dataset containing HPLC-derived chla concentration since it provides the most accurate estimates of chla and it is also the method recommended by space agencies for validation activities. A large dataset was compiled for the purpose of this study, consisting of the in situ samples gathered in the NWA and NEP which were analyzed in their respective DFO regional laboratories. In brief, water samples were rapidly filtered after collection through 25 mm GF/F glass fiber filters, immediately flash-frozen using liquid nitrogen, and stored in a -80 • C freezer until analysis in the laboratory.
The NWA samples were analyzed using a Beckman-Coulter Gold HPLC system (1998-2013) and more recently an Agilent 1200 (2013-2014) HPLC instrument following the protocol of Head and Horne  [19]  as modified by Stuart and Head  [20]. Similarly, the NEP samples were analyzed using a Waters Alliance System HPLC following the protocol of Zapata et al. [21]. The analysis was performed for 23 pigments for both locations, but only chla concentrations from both regions and fucoxanthin ( f ucox) concentrations from the NWA were used in this study. Note that chlorophyll-a concentration includes chlorophyll-a epimer and allomer pigments. The complete dataset consisted of 1857 samples from the NWA between 1999 and 2014, and 1231 samples from the NEP between 2006 and 2016, all collected within ten metres from the surface.
Both datasets showed a skewed normal distribution with a peak around 0. 43 (0. 42) mg m -3 in the NWA (NEP) Ocean and a similar range of variation of chla from 0. 03 to 29. 41 mg m -3 , but samples collected in the NEP had a mean concentration (2. 26 mg m -3 ) greater than in the NWA, where the mean concentration was 1. 68 mg m -3 (Figure  2a ). In the NWA, samples were collected mainly in spring (April to June) and fall (September to November) with a few exceptions of data collected in July and August, whereas the NEP dataset consisted of samples collected in winter (February to March), late spring-early summer (June to July) and late summer to early fall (August through October) (Figure  2b ). Both datasets captured the seasonal cycle of phytoplankton, which was consistent with the wide range of biomass concentrations.
Satellite Matchups The 2014 reprocessed datasets of SeaWiFS merged local area coverage (MLAC), MODIS-Aqua local area coverage (LAC), and VIIRS-Suomi NPP level-2 satellite scenes were downloaded from NASA's ocean colour L1/L2 browser (https://oceancolor. gsfc. nasa. gov/cgi/browse. pl?sen=am). For each image, a mask was applied to remove pixels with invalid data, defined as any pixel where one or more of the following criteria were met: 1. The pixel was marked by any of the following level 2 flags: atmospheric correction failure (ATMFAIL), deleted overlapping pixels (BOWTIEDEL), pixel overland (LAND), high sun glint (HIGLINT), pixel contains cloud or ice (CLDICE), radiance too high (HILT), high solar zenith (HISOLZEN), or high sensor zenith angle (HISATZEN). 2. The pixel contained a no-data (NA) value.
3. The pixel had more than one negative remote sensing reflectance (R rs ) value, implying the data might be flawed by the atmospheric correction procedure. A search was performed for potential matches in any satellite image taken within a day of the in situ sample, as exact sampling times were not always available in the database. In order to match a satellite value to an in situ measurement, the satellite image was projected using a Gnomonic projection centred on the in situ point, which stretched great circles to straight lines. In other words, the image is projected onto a plane tangent to the in situ point, from which Cartesian coordinates (x, y) can be extracted for each corresponding (longitude, latitude) pair. For the sake of speed and simplicity, the Pythagorean theorem was then used to compute the distance between the in situ point and each satellite pixel.
Though this method did not account for Earth's curvature, the computed distances at the maximum allowed radius around the in situ point (i. e. , 10 km) were found to be within ±23 m of the more accurate geodetic distance as calculated by the distGeo() function using R Statistical Software  [22]. A successful match was defined as the closest pixel, within a 10 km radius, that contained at least three valid pixels in a 3 × 3 pixel box extracted around the matching point. The distance between the in situ sample and the closest satellite pixel was recorded in the database. The median of the 3 × 3 pixel matrix was computed for each of the extracted remote sensing reflectance (R rs ) wavelengths, and the resulting dataset was considered as the total number of matches, mapped in Figure  1. From the initial dataset of 1857 and 1231 samples for the NWA and NEP respectively, only 416 (SeaWiFS), 530 (MODIS) and 176 (VIIRS) in situ samples were matched to valid satellite pixels in the NWA, and 45 (SeaWiFS), 487 (MODIS) and 342 (VIIRS) in the NEP (Table  1 ).
The small number of matchups compared to the initial dataset attests to the difficulty of compiling archives of satellite data that match in situ samples for validation purposes and emphasizes the need for recurrent measurements such as monitoring programs and moored automatic sensors. Finally, chla concentration was derived using the OCx algorithm (Section 2. 4. 1) and pixels with a coefficient of variation (computed as the ratio of the standard deviation to the mean of all pixels available in the 3 × 3 matrix) exceeding 0. 5 were removed from the matchup dataset.
The comparison between band ratio models is then simplified since the slope, intercept, and consequently, the bias are all equal, and only the RMSLE and r 2 need to be evaluated. The in situ data used for the optimization of the original OCx algorithm is the global NASA Bio-Optical Marine Algorithm Dataset (NOMAD, version 2). Each sensor has a different set of wavelengths in the blue and green part of the spectrum (Table  1 ), such that the band ratio algorithm parameters are optimized for each sensor and are referred to as OC3M for MODIS, OC4 for SeaWiFS, and OC3V for VIIRS.
e. , b bp (443)) and the power-law-like decrease with increasing wavelength by the exponent Y. The coefficient a ph , given in Table  A10  of Appendix A, was determined using in situ measurements of chla and phytoplankton absorption at the wavelengths of interest, and computing the mean value of absorption divided by chla across all available records within the AZMP database that spans from 1998 to 2014. The a w coefficients were derived from tabulated values with a 2. 5 nm resolution from 400 to 700 nm  [29] , and when necessary, interpolated to the corresponding wavelengths of each sensor. Similarly, the b bw coefficients were derived from a 10 nm resolution table of values generated by Smith and Baker  [30]. The spectral slope of yellow substances absorption S and the power-law decrease of particulate backscattering Y were optimized for use in the original algorithm to 0.
02061 and 1. 03373 respectively using a large simulated dataset  [31]. The updated GSM models in this study tuned S and Y using the dataset collected from the NWA and NEP (see Section 2. 4. 4 for details). R rs,satellite (0 -) was retrieved by taking the median R rs,satellite (0 + ) of the 3 × 3 matrix at each wavelength for a given match, then converting it from surface to underwater reflectance using Lee et al. [32] : R rs,satellite (0 -) = R rs (0 + ) 0. 52 + 1. 7R rs (0 + ). ( 8 ) Non-linear least squares regression, using the Gauss-Newton algorithm of the nls() function  [24]  to minimize the difference between modelled and satellite-derived R rs , was implemented with a maximum of 30 iterations to derive the remaining three unknowns in Equation (3): chla, the absorption coefficient of coloured detrital and dissolved material at the reference wavelength (a dg (443)), and the backscattering coefficient of particulate matter at the reference wavelength (b bp (443)).
Regional Tuning of GSM Similarly to the optimization of the OCx algorithm, R rs (λ) for a given satellite matchup was computed as the median of the corresponding 3 × 3 pixel matrix centered on the matchup pixel. Three modifications to the original GSM algorithm were tested. First, a new optimized exponent, P, was added to the chla term to correct for systematic underestimation at high chla and overestimation at low chla (see Sections 3. 2 and 3. 3) that were potentially caused by the change of a ph with increasing chla (i. e. , packaging effect  [33] ). The expression of the total absorption became: a(λ) = a w (λ) + chla P × a * ph (λ) + a dg (443) × exp(-S(λ -443)). (10) Second, the g parameters (see Equation (  3 ) and Table  A8 ) were optimized and spectral dependency was included as well as a new term g 3 (Equation (  11 )) using the synthetic dataset from the IOCCG working group on remote sensing of inherent optical properties  [31].
These three metrics are unitless as a result of the log transformation, and the latter two metrics have an ideal value of one, stemming from the reverse log transformation that converts them from linear to multiplicative space. Third, the intercept, slope, and coefficient of determination (r 2 ) were computed for the linear regression of log-transformed satellite-derived chla on log-transformed in situ chla using the Standard Major Axis method of Type 2 regression in the lmodel2() function  [34]. Finally, a metric referred to as the "win ratio" based on Seegers et. al. [35] , was also computed. The full dataset of in situ/satellite matchups for a region/sensor was pared down to a new total value, T valid , which only included matchups with valid satellite-derived chla for all algorithms. This facilitated comparisons between the errors of individual matchups for each algorithm.
For a single in situ/satellite pair, the algorithm that generated the lowest magnitude of error was the "winner". Each model's total number of "wins" in the set, wins model , was then divided by T valid : win ratio model = wins model T valid. ( 15 ) For example, if a dataset contains 500 matchups for which every algorithm generates a valid satellite chla concentration, and one of the algorithms (e. g. , POLY1) produces the lowest error out of all algorithms for 70 of the 500 matchups, then the win ratio of POLY1 would be 70/500 = 0. 14.
2 and 3. 3). Regarding the coefficients for high degree polynomials (i. e. , >= 2), there were no distinct patterns and the coefficients of the regional algorithms tend to be different and often opposite in sign from the original coefficients. The removal of the 443 nm band in the new band ratio algorithms may explain the differences between the original and regional coefficients of the algorithms. The numerator of the band ratio in the OCx algorithm varies between two or three possible wavebands depending on the sensor, where one of the options is the 443 nm band. Our analysis revealed that this band produced more outliers in the polynomial relationship between log-transformed in situ chla and the log-transformed band ratio than other numerator wavebands, which is particularly obvious in the NWA MODIS dataset (Figure  3 ).
When the algorithms were applied to the identical subset of retrievals, GSM_ORIG showed the highest win ratio for MODIS and VIIRS, while narrowly losing to OC4 in the SeaWiFS dataset. The regional linear and fourth-degree polynomial fits (POLY1 and POLY4) provided very similar results for all sensors, which is consistent with the study of Laliberté et al. [15]  in the Gulf of Saint-Lawrence (Canada).
e. , GSM_GC and GSM_GS), which exhibited low RMSLE of 0. 16 and 0. 17 for GSM_GC and GSM_GS, respectively. As in the NWA, regional tuning generally improved the mean error, with the exception of the OCx and band ratio models in the MODIS dataset, where the magnitude of mean error increased, attesting to the good performance of the ocean colour model currently in use for this region and sensor. The semi-analytical algorithms (GSM-type) exhibited higher win ratios in general across all sensors, with GSM_ORIG showing the highest win ratio for MODIS and VIIRS (0. 28 and 0. 25 respectively), while GSM_GS showed the highest win ratio for SeaWiFS. Ultimately, the simple linear band ratio model (POLY1) obtained the highest score across the three sensors but tied with POLY4 and the regional GSM models in the SeaWiFS dataset.
It is worth noting that other variations of the band ratio models (POLY2 and POLY3) also performed well, with the third-degree polynomial (POLY3) outperforming POLY1 in the SeaWiFS set, and POLY2 obtaining a higher score than other polynomials in the NWA dataset (Table  A1 ). Similarly, the MODIS NEP dataset contained an exception to the pattern of high-scoring GSM_GC and GSM_GS, where GSM_GCGS outperformed the two. The correlation analysis described in Section 2. 6 revealed that the magnitude of uncertainty in the retrieved chla (|E chla |) was strongly positively correlated to chla concentration for all sensors, regions, and algorithms, with r 2 varying from 0. 40 (VIIRS, GSM_GC, NWA) to 0. 97 (GSM_ORIG, NWA, all sensors). GSM_GC presented an exception in the NEP SeaWiFS dataset with a weaker correlation between |E chla | and chla that can be explained by the very low number of matchups (Tables  5  and A2 ).
Same as Figure  6 , for the NEP. A t-test and ANOVA also revealed that in the NWA, the average µ error across algorithms in the spring (-0. 51, -0. 60, and -0. 60 mg m -3 for MODIS, SeaWiFS, and VIIRS respectively) were generally significantly different (opposite in sign and higher in magnitude) than in the fall (0. 20, 0. 19, and 0. 032) (see Table  A3 ), but there were no significant differences in the mean error between seasons in the NEP (Table  A4 ). We did not find any correlation between the number of matchups and the magnitude of the uncertainties for a given season/sensor/region except when only a very low number of matchups was available (e. g. , MODIS in spring 2005 in the NWA, 2 matchups, Figure  6 ) and one bad matchup could negatively weight the entire matchup dataset. In the NEP (Figure  7 ), there was a large number of outliers spread evenly across spring and fall seasons, unlike the NWA, where spring blooms generated the highest spread of error.
Only a few data were available for SeaWiFS in the NEP, which were concentrated mainly in 2009 and 2010, and therefore the correlation and ANOVA results for this region/sensor dataset were less reliable. All variations of GSM also returned fewer valid retrievals than the band ratio algorithms, mainly due to negative remote sensing reflectances at either end of the spectrum, which prevented the model from generating a valid fit (Table  6 ). Table  6. Missing retrievals by GSM_GS algorithm for each region/sensor combination, subdivided by the cause of lost data, with the percentage of lost retrievals given for each cause. R rs (41X) is the remote sensing reflectance value at 412 nm for MODIS and SeaWiFS, and 410 nm for VIIRS. R rs (6XX) varies from 645 to 671 nm depending on sensor. Note that there were no instances of negative model chla, unlike a dg (443) and b bp (443).
Uncertainties Related to Geographic Considerations The correlation analysis of |E chla | on latitude, bathymetry and distance from in situ to satellite measurements was carried out to investigate if these variables could explain some of the biases and discrepancies observed between the in situ and satellite-derived chla (Table  7 ). There were no correlations observed between |E chla | and latitude, which is an interesting result given the range of latitudes in the NWA dataset from approximately 38 • to 61 • N. Possible exceptions exist in the NWA VIIRS and NEP SeaWiFS datasets, but as mentioned in the previous section, the SeaWiFS dataset had very few data, and the high latitude data in the VIIRS dataset were all retrieved during the spring bloom (see Figure  1a, b ), when chla concentration would typically be higher and thus |E chla | would likely also be higher as the two were collinear (see Section 3.
4. 1). There were low negative correlations between |E chla | and open ocean bathymetry in the NWA MODIS dataset, and larger negative correlations in the NEP, which again can be explained by the collinearity of chla concentration and |E chla | and the fact that in the open ocean subset, bathymetry was also negatively correlated with chla concentration (r = -0. 47). As for seasonal bias, the t-test on the coastal and offshore datasets in the NWA revealed that the mean uncertainties for both datasets were statistically different in the regional band ratio models for MODIS and SeaWiFS, with µ error ranging from 0. 14 to 0. 42 mg m -3 for the coastal regions and -0. 31 to -0. 20 in open ocean, indicating that this family of algorithms systematically underestimated chla offshore and overestimates in shallower water, while the difference was less significant with the GSM-type algorithms (see Table  A5 ).
The pattern reversed in the NEP, where the regional band ratio underestimated coastal chla (-0. 90 to -0. 77 mg m -3 ) and overestimated chla in the open ocean (0. 076 to 0. 27), again ignoring the SeaWiFS dataset due to insufficient data. Distance between a satellite matchup and the in situ measurement was up to 10 km to maximize the number of matchups, though over half the matchups in each region/sensor subset were within 2 km of the measurement. Regression of |E chla | on the distance did not reveal a relationship (|r| < 0. 1) for any datasets except NWA VIIRS GSM_ORIG, and the small NEP SeaWiFS dataset, suggesting that in open waters, where spatial variations in chla are small (mesoscale) compared to the coastal environment, the stringent criteria of pixel to in situ collocation can be relaxed to increase the number of matchups.
Discussion Satellite-derived chla concentration remains the most used product to infer global scale information on the status of the marine ecosystem, and non-specialists represent an important fraction of ocean colour data users. Here we have assessed the performance of two algorithms currently implemented in NASA's SeaDAS software (https://seadas. gsfc. nasa. gov) in Canadian waters (Atlantic and Pacific Oceans) to inform on biases associated with these algorithms in those regions. In addition, we carried out modifications of these original models by optimizing their parameterization and formulation to correct for regional bias in the NWA and NEP. Both OCx and GSM exhibited a negative overall bias particularly in the NWA, where low chla were overestimated and high concentrations were underestimated, which resulted in a linear fit of satellite-derived on in situ chla with a slope lower than one.