dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study, LPUEs were solely derived from commercial data (noting (vessels over 15 m length) to limit the bias when comparing large (above 30 m) and small (below 10 m) vessels LPUEs. The fisheries data were largely dominated by the over 15 m length vessels compared to the 10-15 m length category for the considered gears, with 3-to 10-fold higher total effort in hours, 15-to 71-fold higher total landings, 4-to 58-fold higher maximum local LPUE (kg/ hr) and two to sixfold higher ocean coverage (see Table  S2 ). The   S4  and S5 ). F I G U R E 1 The main gears were considered separately, namely pelagic seine and midwater otter trawls for pelagic species and bottom otter and beam trawls for demersal species, to avoid effort comparison issues between gears (Figure  2 ). The quarterly data of fishing effort (expressed in hours), and landings (in tonnes), were aggregated into annual data.
| Scientific fisheries surveys The purposes of using scientific survey data are (i) to compare the estimate of potential fish production (OPFish) with another data set of higher precision but lower temporal and spatial coverage than commercial data, (ii) to explore the complementarity of the information included in these two fisheries data sets and (iii) to compare the exploitation levels in the North-East Atlantic and the Mediterranean Sea shelves as, for the latter, commercial data on a regular spatial grid were currently missing. (2003-2015, 9,415 hauls)  were used as the temporal coverage corresponded to the used satellite remote sensing data (MODIS-Aqua sensor since July 2002). We selected the CPUEs (kg. km -2 ) derived from the wing spread (net opening, see illustration in Figure  S4 ) since it was available for both surveys, and the door spread (distance between the two panels preceding the net) from DATRAS-BTS was shown to be less stable comparatively (see Figure  S6 ).
, 2019). To mitigate the uncertainty associated with water depth, we only retained bottom fisheries data from <500 m deep. A 200 m depth limit would have led to only accounting for on-the-shelf data in the high-resolution OT-DMF data, while shelf break data would have been included in the coarser resolution DCF data (selection using the central cell position). Even though satellite ocean colour does not detect the deep chlorophyll-a maximum, such oligotrophic environments are, however, generally considered to be substantially less productive than areas where chlorophyll-a is maximum at the surface, because of light limitation (exponential decrease of light with increasing depth). The OPFish, therefore, underestimates the natural fish production in oligotrophic environments (especially in the summer), where, in any case, fisheries data used in this study were relatively scarce (mostly in parts of the GSAs 5, The OPFish only uses biotic conditions (the chlorophyll-a gradient and content) and not the abiotic factors, thus strictly focuses on the feeding capacities of the global marine ecosystem.
A. Abbas et al. :  In-stream Escherichia coli modeling using high-temporal-resolution data monitoring can help prevent waterborne diseases from fecal pathogenic microorganisms, monitoring E. coli concentrations is time-consuming and expensive  (Cho et al. , 2016; Frolich et al. , 2017; Kim et al. , 2017). Furthermore, highfrequency datasets of E. coli concentration are scarce, and available long-term datasets are often inadequate to yield a continuous time series of fecal pathogenic microorganisms (van der  Leeuw, 2004). Modeling approaches can overcome this drawback in monitoring. Thus, they can be a means to determine the fate and transport of fecal pathogenic microorganisms at the catchment scale by simulating E. coli in environmental compartments, such as the soil surface and streams  (Ligaray et al.
Study site and data acquisition The study area is the 0. 6 km 2 Houay Pano headwater catchment, located 10 km south of the city of Luang Prabang, Lao PDR  (Boithias et al. , 2021a ) (Fig. 1 ). This catchment is representative of montane agroecosystems in Southeast Asia and is part of the long-term critical zone observatory network called multiscale TROPIcal CatchmentS (M-TROPICS), which is affiliated with the French research infrastructure OZCAR  (Gaillardet et al. , 2018). This site had undergone rapid land-use changes from 2011 to 2018 (Fig. S1a ). The characteristics of this area, including land-use information, are provided in the Supplement (Sect. S1). We collected weather, hydrological, E. coli concentration, and electrical conductivity data at 6 min time steps from 2011 to 2018. Rainfall, relative humidity, solar radiation, wind speed, and air temperature were measured with an automatic weather station Campbell Scientific BWS200, which was equipped with ARG100 (a 0.
coli dynamics during flood events using an automated sampler (ICRISAT) triggered by the water-level recorder to collect water after every 2 cm water-level change during flood rising and every 5 cm water-level change during flood recession. The total number of E. coli samples collected over the 2011-2018 period was 255. In addition, we collected the monthly numbers of poultry, swine, goats, and the number of people who visited the study area. These data were used to quantify the source of E. coli in this catchment (Rochelle-Newall et al. , 2016) (Fig. S1b ).
HSPF The HPSF model is a process-driven model that simulates processes at the catchment scale  (Bicknell et al. , 1997). It has been extensively used to model the fate and transport of E. coli in catchments  (Ahmadisharaf and Benham, 2020; Chin et al. , 2009)  and to develop total maximum daily loads of E. coli at various locations  (Mishra et al. , 2018; Yagow et al. , 1998). The original software was written in the FORTRAN programming language. The Hydrological Simulation Program Python (HSP2) was recently developed based on the Python programming language (van Rossum, 2007). HSP2 is a platform-independent software that extends the functionality of HSPF by allowing the use of dynamic variables and easier management of input and output files  (Heaphy et al. , 2015). The HSPF simulates the hydrological regimes by discretizing the catchment into pervious and impervious hydrological response units (HRUs).
Evapotranspiration, surface retention, surface infiltration, interflow, baseflow, and deep percolation are simulated at pervious HRUs, whereas surface retention and surface flow are simulated at impervious HRUs  (Bicknell et al. , 1997). The simulation of in-stream E. coli concentration in HSPF is based on a first-order kinetics approach, considering the decay rate  (Fonseca et al. , 2014). Detailed descriptions of hydrological and E. coli simulations can be found in  Bicknell et al. (1997). For this study, we converted the original FORTRAN code of the E. coli module of HSPF into the Python programming language. This allowed us to incorporate more efficient use of input data, such as the annual change in land use and the monthly bacterial source. In our study, HRUs were divided into four groups based on land use: "Forest", "Fallow", "Teak", and "Annual" crops.
To investigate the impact of land-use change on in-stream E. coli concentrations, we conducted E. coli simulations in two scenarios. In scenario 1, we used the land-use change and E. coli source information separately. In scenario 2, the number of input features was reduced by multiplying the E. coli source by land-use change. In this way, we calculated the E. coli source per area for each land use and used this as input instead of using land use and E. coli information as separate input features. LSTM is a special recurrent neural network designed to extract temporal features from sequence data  (Hochreiter and Schmidhuber, 1997 ). An LSTM cell is the basic building block of the LSTM (Fig. S2 ). It consists of three "gates" and two "states". The gates are "forget", "update", and "output", which decide what information to forget, allow in, and allow out from the LSTM "memory", respectively.
This prospective cell state (C <t> c ), along with the output from the "forget" and "update" gates, decides the current cell state (c <t> ) (Eq. 5). The current cell state and output gate control the output values from LSTM (h <t> ) forming the so-called hidden state (Eq. 6). The hyperbolic tangent (tanh) is another nonlinearity used in LSTM for the calculation of the cell state (Eq. 1) and the output state (Eq. 6). Equations (  1 )-(  6 ) are used to calculate the LSTM output, which is then compared with observed values to calculate the error. This study used the mean square error (MSE) as the error metric. We used the TensorFlow software v1. 15 for building the LSTM model  (Abadi et al. , 2016). We used an Intel ® Core™ i7-9700 processor with a graphics card of NVIDIA GeForce RTX 2080 with 12 GB of dedicated GPU memory, along with 64 GB of random-access memory for simulating surface, subsurface, and E.
Code availability. The code is available on reasonable request from the corresponding author. Data availability. The 6 min rainfall data (https://doi. org/10. 6096/MSEC. LAOS. 5, Silvera et al. , 2015a), water-level data (https://doi. org/10. 6096/MSEC. LAOS. 3, Silvera et al. , 2015b), land-use data (https://doi. org/10. 6096/MSEC. LAOS. 7, Silvera et al. , 2015c), weather station data (https://doi. org/10. 6096/MSEC. LAOS. 6, Silvera et al. , 2015d), E. coli data (https://doi. org/10. 23708/EWOYNK, Ribolzi et al. , 2021), soil map (https://doi. org/10. 23708/FFEDIR, Chanhphengxay et al. , 2021) and sub-catchment boundaries (https://doi. org/10. 23708/M8NJA0, Boithias et al. , 2021c) were collected from the M-TROPICS Critical Zone Observatory (https://mtropics. obs-mip. fr/, last access: 1 December 2021).
There are various techniques to approximate tensors by taking advantage of the HOSVD, those that will be implemented in this paper are the so called Truncated HOSVD (T-HOSVD) and Sequentially Truncated HOSVD (ST-HOSVD). Even if only for some special cases HOSVD provides optimal results ([VNVM14, BDHR17, DOT18]), it is possible to present an estimate of the tensor approximation errors. Indeed the core of the present paper will be application of T-HOSVD and ST-HOSVD techniques, their modern versions and the error made by using them. Indeed in the last section we will apply some possible HOSVD implementations to tensors in which we stored RED and NIR bands. Next from the compressed tensors we get NDVI rasters and over them we compute biodiversity indexes. So we will be able to compare our biodiversity estimations from compressed data with those realised over original data.
Results In this section we will first describe the data used: RED, NIR and NDVI bands of different territories, provided by NASA,  [Did18b, Did18a]. The first step in our analysis will be computing the biodiversity index over the NASA NDVI imageries. Then we will generate a new NDVI from RED and NIR using Definition 2. 4, since we do not know how NASA generates NDVI from the other two bands. Over these "relative" NDVI we will compute the biodiversity indexes. Next we will generate 3-order tensors with just RED and NIR bands. Then we will approximate these tensors with T-HOSVD and ST-HOSVD. Lastly from the approximated tensors we will extract RED and NIR imageries to get "approximated" NDVI. Over these last NDVI we compute the biodiversity indexes. In conclusion we will measure the error made in estimating biodiversity when we use approximated NDVI instead of NASA NDVI or relative NDVI.
4. 1. Data. From MOD13C2v006 and MOD13A3v006, both sensed by MODIS, cf. [CJ] , and available at  [Did18b, Did18a] , we select the RED, NIR rasters and the NASA computed NDVI. MOD13C2v006 is a product characterized by 13 layers, each of which stores an imagery of the Earth with different properties. Each raster is a matrix of 3600 rows and 7200 columns. The side of each pixel corresponds to 5600 m, which is the spatial resolution. The imageries are monthly: they are obtained from the daily data through NASA's algorithms. The chosen data from January 2010 until December 2018 are in hdf format. MOD13A3v006 is a similar product with a higher spatial resolution. While each image from the previous dataset covered the entire Earth's surface, each one from this second dataset covers just 1200 × 1200 m 2.
We select the 20 components, called granules, to compose an Europe's map. We download in GEOTiff the granules from RED, NIR bands and from the NDVI computed by the NASA, from June 2011 until December 2018. Also in this case they are obtained by the NASA scientists from daily data. The final dimension of most of the rasters is 4800 rows and 6000 columns. We do not talk about every used test elements from MOD13A3v006 dataset, since those of December 2016 and of December 2017 do not include all the 20 granules. Moreover we remark that rasters of December 2012 and December 2015 have the correct dimension, but they have respectively one and two missing areas. Lastly NASA stores the data in 16-bit signed integers. The next step is creating 3-order tensors. Taking advantage of the GDAL dependencies for python, we simply convert rasters into matrix, removing the metadata useless for our aims.
Then we store NDVI, RED and NIR into 3-order arrays. Definition 4. 1. Let T E be the set of all the tensor A ∈ R n 1 ⊗ R n 2 ⊗ R 3 with n 1 = 4800 and n 2 = 6000 such that • A •,•,1 is the NDVI raster, • A •,•,2 is the RED raster band, • A •,•,3 is the NIR raster band of Europe dataset for every month and for every year. The cardinality of T E is n E = 91. Similarly let T W be the set of all the tensor A ∈ R m 1 ⊗ R m 2 ⊗ R 3 with m 1 = 3600 and m 2 = 7200 such that • A •,•,1 is the NDVI raster, • A •,•,2 is the RED raster band, • A •,•,3 is the NIR raster band of Earth dataset for every month and for every year. The cardinality of T W is n W = 108. Remark 4. 2. Henceforth we will denote with R ⊗E the tensor space R n 1 ⊗ R n 2 ⊗ R 3 , where E = (n 1 , n 2 , 3), n 1 = 4800 and n 2 = 6000 and with R ⊗W the tensor space R m 1 ⊗ R m 2 ⊗ R 3 , where W = (m 1 , m 2 , 3), m 1 = 3600 and m 2 = 7200.
We suppose that this is linked with the water surface greater in World rasters than in Europe's ones. Similarly we compute the error A j -B j F for every A j ∈ I i and B j ∈ Ĩi for every j ∈ {1,. , n i } for every i ∈ {E, W }. With the same notation already introduced, we present some statistics for Rényi index. min v = min{v 1 ,. , v n } and max v = max{v 1 ,. , v n }. Dataset E[e] E[.
Dataset E In this case the mean error per pixel for both the dataset is smaller than the previous mean. One possible explication could be that Rényi index takes into account only frequencies while Rao index considers both values and their frequencies. However in this case variance in World error is smaller than in Europe, while in the Rao case there is the opposite situation. Moreover we observe that the minimum and the maximum approximation error in the World dataset for both the indexes is realised by the same element, i. e. April 2018 and May 2014 respectively. This consideration holds also for Europe dataset: the minimum error comes from April 2018 and the maximum from December 2012. 4. 3. Approximated NDVI. Before applying the approximation tensors codes, described in the Appendix, Sections 6.
Then T R,h = g R,h (T h ) for every h ∈ {E, W }. Similarly let g N,h : R ⊗h → R ⊗h be the function that associates the tensor B such that B •,•,1 = B •,•,3 = A •,•,3 and B •,•,2 = A •,•,2 , to each tensor A ∈ T h for every h ∈ {E, W }. Then T N,h = g N,h (T h ) for every h ∈ {E, W }, To compute T-HOSVD and ST-HOSVD, we fix five multilinear target ranks. Definition 4. 9. Let R = {10, 50, 100, 500, 1000} be a set with the given order fixed, then the target multilinear rank we choose are r j = (i j , i j , 2) for every i j ∈ R for every j ∈ {1,. , 5}. Let T k,h,j be the set of T-HOSVD approximation at multilinear rank r j of tensors from the set T k,h for every h ∈ {E, W }, for every k ∈ {N, R} and for every j ∈ {1,. , 5}. Similarly let S k,h,j be the set of ST-HOSVD approximation. Before presenting results related to indexes computation, we have some data about storage memory use.
Since it depends on the core tensor and on the projectors dimensions, which are equal for T-HOSVD and ST-HOSVD, we report only a table for each dataset. For each tensor A ∈ T k,W the ratio between the memory used for storing the core tensor and the projectors over the memory used for storing A is the same, for every k ∈ {N, R}. For tensors of T k,E it holds the same, except for those elements composed by a lower number of granules, for every k ∈ {N, R}. Since they are 2 over 91, we neglect them and in the table we present the ratios of memory usage for each rank approximation. We call these ratios absolute compression ratios, because they have as denominator the memory used to store two time RED band and once NIR band, or vice-versa. In the following table they are present as Abs. R, when RED band raster is repeated and as Abs.
Rank Definition 4. 10. Let N k,h,T,j = f h (T k,h,R,j ) and let N k,h,S,j = f h (S k,h,R,j ) every j ∈ {1,. , 5} and for every h ∈ {E, W }. We call elements of N k,h,T,j ∪ N k,h,S,j approximated NDVIs. Definition 4. 11. Let I R,h,k,j be the set of Rényi index computed over elements of N R,h,t,j for every h ∈ {E, W }, for every t ∈ {T, S} and for every j ∈ {1,. , 5}, and call them i j -approximated estimates for every i j ∈ R and for every j ∈ {1,. , 5}. Remark 4. 12. Notice that we have 4 indexes for approximated estimates set: 1 st index: indicates the repeated matrix in the starting tensor R for RED and N for NIR; 2 nd index: the belonging dataset, E for Europe and W for Earth; 3 rd index: the approximation algorithm, T for T-HOSVD and S for ST-HOSVD; 4 th index: is associated with the target multilinear rank.
The last step is computing the error with respect to the original estimates, i. e. A k -C k,j for every A k ∈ I h and for every C k,j ∈ I R,h,T,j ∪ I R,h,S,j , for every k ∈ {1,. , n h }, for every j ∈ {1,. , 5} and for every h ∈ {E, W }. Moreover we compute the error with respect to relative estimates, i. e. B k -C k,j for every B k ∈ Ĩh and for every C k,j ∈ I R,h,T,j ∪ I R,h,S,j , for every k ∈ {1,. , n h }, for every j ∈ {1,. , 5} and for every h ∈ {E, W }. Now we present some statistics for the errors per pixel with respect to original estimates, stored in vector epO and relative estimates, in vector epR for Europe dataset, for both the decomposition techniques for each target multilinear rank. In the following table we report as rank only the significant component of the multilinear rank, for simplicity.
We can now list statistics about the Earth dataset. Similarly in vector epO there are the errors per pixel with respect to original Rényi estimates, while in epR with respect to relative estimates for each target multilinear rank. We remark that even in this case on average ST-HOSVD technique leads to lower original and relative errors than T-HOSVD, for the last two and for the last one target multilinear rank respectively. Moreover we observe that relative error mean is slightly lower than original one, as we expected. Minimum and maximum of both errors decrease for increasing multilinear rank components. Certainly the most stunning value is the variance of relative error per pixel at the last approximation. For both T-HOSVD and ST-HOSVD it is lower than 10 -4. We include the images associated to Rényi index in the five approximations for the Europe worst case and the Earth best case, both from ST-HOSVD approximation technique.
In the next example we will consider the element of Earth dataset, which realises the minimum error. Example 4. 15. Firstly we show in Figure  3  the Rényi estimate over NASA NDVI of October 2017. As we said in the Example 4. 14 Rényi index provides quite high biodiversity values. Indeed also in this case there are many Earth areas with a biodiversity value close to 4. 5. Then we present the same index over self-made and approximated NDVI. Even if the small printing dimensions reduce the detail precision, our eyes do not perceive at first glance much difference between the last three index approximations and the index of Figure  3. However with a closer observation, for example, we notice that islands of the Pacific ocean disappear in first approximation and they partially reappear in the last two images.
Besides in the best case we have both original and relative error per pixel near to 11%, which is appreciable, as we will see. Lastly we remark an interesting and unexpected twist. If in the previous analysis T-HOSVD performed better when multilinear rank components were small with respect to ST-HOSVD, in the present case T-HOSVD provides better result than the other algorithm when the first two multilinear rank components are greater that 500. Notice also that in this case the relative error is on average greater than the original one. This phenomenon is at least in part linked to the two incomplete elements, December 2012 and 2015. Indeed for these elements the relative error is slightly less than twice the original error. Now we present some statistics for the original and relative errors of Rao estimates for Earth dataset.
Moreover as in the previous case the variance decreases when the first two multilinear rank components grow. Before moving to the analysis of error related to Rao index over N N,h,p,j for every h ∈ {E, W }, for every p ∈ {T, S} and for every j ∈ {1,. , 5}, we show the best case for Rao index for Europe and the worst case for Earth dataset in the following examples. In both the case we use as approximation technique T-HOSVD. Example 4. 20. Looking at Rao index computed over Europe NDVI of December 2014, in Figure  7 , we immediately notice that biodiversity is much lower than in the Rényi case, 4. 14. Besides we underline that even in this case Rao index computed over self-made NDVIs is enough close to its computation over NASA NDVI. Lastly we highlight that in this particular NDVI element the north Europe area is entirely set to missing values, which are shown with white colour.
Therefore we can assume that the error is the minimum since this peculiarity. Next we have in Figure  8A  the same index computed over self-made NDVI and the approximated Rao estimated at different multilinear rank. It is significant that it is hard to distinguish the Rao index computed over NASA NDVI from the ones computed over the forth and the fifth approximated NDVI. Next we analyse the worst case of Rao index for the Earth dataset. Next we show in Figure  10  the Rao index computed over self-made and approximated NDVIs. It is clear observing the approximated Rao pictures and index of 9 that the high error is linked with the overestimation of biodiversity in the North pole area. Indeed it is a remarkable phenomenon in the first four approximations. In Figure  10F  it tends to decrease, but it is still appreciable.
e. B k -C k,j for every B k ∈ Rh and for every C k,j ∈ R N,h,T,j ∪ R N,h,S,j , for every k ∈ {1,. , n h }, for every j ∈ {1,. , 5} and for every h ∈ {E, W }. In conclusion we report some statistics about the original and the relative errors per pixel, stored respectively in vector epO and epR for the Europe dataset. Comparing Table  8  with Table  10 , we notice that the average original and relative error per pixel are greater in the second case. In other words, starting from a tensor with repeated NIR band is not convenient for computing Rao index over Europe elements. Besides we underline that the minimum relative and original error are realised when the starting tensor has RED band repeated. We can not affirm nothing about the maximum original and relative error, since for i j ∈ {10, 100} it is lower when the repeated band is the NIR, while for i j ∈ {50, 500, 1000} when it is repeated RED band.