dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, for a growing season that starts from a sowing date in September 2018 and an end with a harvesting date in July 2019, the RPG of 2019 was used as the ground sample because the year of harvest is taken into account in the RPG. Table  II  shows the total number of cultivated plots, the number of rapeseed plots, and the proportion of rapeseed plots in the total number of cultivated plots. 2) SAR images: The time-series of C-band (5. 405GHz) SAR images acquired by the Sentinel 1A (S1A) and Sentinel 1B (S1B) satellites were used. Both "ascending" (evening at 18:00 UT) and "descending" (morning at 06:00 UT) acquisitions were utilized in the VV and the VH polarizations. The pixel spacing of S1 images is 10 m x 10 m. The data are freely available from the European Space Agency's (ESA) website (https://scihub.
copernicus. eu/dhus/#/home). The revisit time of the S1 constellation is six days, meaning that one image is available for each orbit every six days. In this study a dataset of all available orbits over the study area was collected. All the acquired images regardless of the orbit number were stacked in chronological order. Table  II  shows the total number of S1 images acquired over the study site and the average number of images per month. Fig. 2  shows the temporal distribution and the overall coverage of the S1 orbits over the study site (each orbit has a unique acquisition time). The first acquisition in our study site is a "descending" image belonging to the orbit 81 (D81). The following image comes 24 hours later and is a descending acquisition as well from orbit 8 (D8). Then, the third acquisition comes 36 hours later as an ascending acquisition from orbit 30 (A30).
Six days after the first image, the cycle is repeated in the same way with a new image from D81 orbit. The hatched area in Fig. 2  represents the 3. 5 days period that separates every bunch of three images (D81, D8, A30) from the next bunch. The incidence angles vary on our study site from 23° to 38° for the orbit 81 and from 32° to 48° for the two orbits 8 and 30. The S1 images were calibrated using the S1 toolbox developed by ESA. Calibration was a two-step process; the first step was the radiometric calibration, which converted a digital number into a backscatter coefficient 𝜎° in linear units. The second step was the geometric correction, which was the process of ortho-rectifying the images using a 30 m digital elevation model from the Shuttle Radar Topography Mission (SRTM). For our dataset, which covers an area of 100 km x 100 km, the incidence angle varies by about 15°.
In order to reduce the effect of the incidence angle, a possible solution would have been to normalize its effect. However, the normalization function can be different from one crop to another but also different for the same crop between the different parts of the growth cycle (bare soil, well-developed vegetation, etc. ). Furthermore, it is not well known for each specific type of crop. When using a normalization function of the form cos(θ)  [36]  and a reference incidence angle of 35°, the variation in the radar signal due to the difference in the incidence angle is lower than 1 dB (our dataset acquired with incidence angles between 23° and 48°). As the form of the normalization function is not well known and the variation of radar signal due to the incidence angle is smaller than the increase of the signal due to vegetation growth (several dB), we did not normalize by the radar incidence angle, thus leaving the rapeseed mapping algorithms to overcome small fluctuations in the radar signal due to a variation in the incidence angle.
A. Temporal behavior of Sentinel-1 signals The temporal behavior of S1 over rapeseed fields was studied for the 2018, 2019 and 2020 growing seasons (the growing season starts in September and ends in July of the following year). For this study, an S1 dataset consisting of all three orbits (8, 30, 81) available over the study area was used. Since this work was conducted at plot scale, the average signal of all pixels in each plot was calculated to obtain a single representative value for each plot. The result of this process was two plot level time series (marked with a "p"), VVp and VHp.
A more detailed description of the selected algorithms is provided in Section D of methodology. The Adam optimizer was used to train all three DL algorithms  [25]. Parameters of Adam were fixed as: β1 = 0. 9, β2 = 0. 999, ε = 1e-07. The learning rate and weight decay were set respectively to 1e-5 and 1e-6. The batch size was set to 16 during training and a standard cross-entropy loss function was used. The input data were the S1 time series in two 1 https://github. com/cassiofragadantas/Colza_Classif channels at plot scale (VHp and VVp) covering three years (2018, 2019 and 2020). The classification process was performed with the aim of detecting the rapeseed fields, so two classes were used: rapeseed (positive class) and non-rapeseed (negative class). Non-rapeseed class consisted of other crops except rapeseed.
Each classification was performed with 70% of the ground samples as training and 30% as test data, split randomly in 5 independent realizations. The classification was carried out in two main parts including rapeseed fields mapping using the same year for training and testing, and evaluating the transferability of the models obtained by each algorithm in each year onto other years. In each part, related factors that might affect the accuracy of the mapping were tested. 1) Rapeseed fields mapping using the same year for training and testing: Rapeseed fields mapping using the same year for training and for testing was conducted using all four algorithms. To train the algorithms and test the models all ground samples mentioned in Table  II  (large annual sample dataset) were employed. 2) Rapeseed fields mapping using smaller training sample size: The effect of the training sample size on the performance of the four algorithms in the detection of rapeseed fields was evaluated using the same year for training and for testing and four different sample sizes including 100, 300, 500 and 1000 samples.
The input time series were VV and VH time series at plot scale (VHp and VVp). The training samples were drawn by keeping the same ratio between positive and negative classes as found in the large annual sample dataset (imbalanced training data). For example, in the large dataset of the year 2018, the percentage of rapeseed and non-rapeseed samples was about 3% and 97%, respectively. Therefore, with a training sample size of 1000, the number of rapeseeds fields in the training samples was around 30 and the number of non-rapeseed fields was around 970. 3) Transferability of the models using different years for training and testing: The transferability of models was investigated by training the four algorithms using samples from one year and deploying the models on the other years. The large annual sample dataset shown in Table  II  was used as the base sample dataset.
The phenological shift in the growth stage of rapeseed was simulated in dataset of 2018, considered as the training year. The trained algorithm with the shifted dataset of 2018 was used to classify the dataset of 2020, considered as the test year. Two periods of phenological shift were simulated by removing days from the beginning of the training time series (the whole S1 time series was thus shifted by 15 or 30 days). In order to obtain the same size for the training and test data sets, the days were removed from the last part of the test time series, which did not include the main phenological cycle of rapeseed. More precisely, after removing the days from the beginning of the training dataset, the same size for training and testing was achieved using the overlapping part of both time series.
C) Algorithms 1) Random Forest: Random Forest (RF) algorithm consists of several decision trees, where the results of trees are merged to create the final results  [28] ,  [44]. It is a meta-estimator that adapts multiple decision tree classifiers to multiple parts of the dataset, and applies the average of all parts to improve the accuracy of results and control over-fitting  [45]. Although RF is not sensitive to the selection of hyperparameters  [37] , the accuracy of the results of the RF approach is related to the number of trees. The number of trees in this study was set as 100. Other parameters were set as default. 2) Multi-layer Perceptron: Multilayer Perceptron (MLP) algorithm is the DL model that has the simplest and most traditional architecture, where neurons in each layer are connected to all neurons in the neighboring layers  [35] ,  [46].
The advantage of this model is that it outperforms most state-of-the-art models with less preprocessing. The efficiency of this algorithm in analyzing complex multivariate time series classification, such as action recognition, and the possibility to deploy this algorithm on constrained systems was shown by Karim et al. [39]. The default model hyperparameters proposed by Karim et al. [39]  were used in our experiments. 4) InceptionTime: The InceptionTime algorithm was introduced by Fawaz et al. [40]  for multivariate time series analysis. It is a collection of five deep learning models, each consisting of two residual blocks which, in turn, contain three socalled Inception modules. The Inception modules apply multiple one-dimensional convolutional filters of different lengths to the input data, which allows the network to extract features from the time series at different scales.
7%). Moreover, smaller differences between the min and max of F1 are obtained with InceptionTime (0. 3%), which shows the stability of the classification results across the years using this algorithm. In contrast, the highest differences between the min, and the max of F1 are obtained with the LSTM-FCN (2%), showing higher variation in the accuracy across years. Concerning the precision and recall, the highest precision is achieved by RF (97. 4%), but it also showed the lowest recall (92. 7%). Meanwhile, the smallest difference between recall and precision is achieved by InceptionTime and MLP. 2) Rapeseed fields mapping using smaller training sample size: The effect of four training sample sizes including 100, 300, 500 and 1000 on the accuracy of the rapeseed map is shown in TABLE Ⅴ. In this case, the classification was performed using small training samples in order to mime the ratio between the rapeseed and non-rapeseed classes as found in the large annual sample dataset (imbalanced training data).
For example, in the large dataset of the year 2018, the percentage of rapeseed and non-rapeseed classes was 3% and 97%, respectively, so in the case of classification of this year with the training sample size equal to 1000, the number of rapeseeds on training samples was 30 and non-rapeseed was 970. In TABLE Ⅴ the average accuracy metrics of the three years 2018, 2019 and 2020 are presented (e. g. , Mean-100 samples is the average of each metric over three years of classification using 100 ground samples). TABLE Ⅴ shows that the Inception Time and RF provide higher accuracy metrics than the other algorithms. Both RF and Inception Time yielded F1 values greater than 90% when using a sample size greater than 300. In the case of 100 samples, these two algorithms respectively showed F1 values of 67.
3) Transferability of the models using different years for training and testing: The accuracy of rapeseed fields mapping using different years for training and testing was evaluated. This investigation is of some interest in the absence of ground sampling over the same territory for certain years. Thus, the possibility of creating the rapeseed map using the ground samples of the other years was tested. The transferability of the models was carried out using large sample sizes since results of section B-2 of results showed that LSTM-FCN and MLP did not give accurate results with smaller sample sizes. To evaluate the ability of the AI algorithms to map the rapeseed fields for each year using the ground sample of other years, six combinations of train and test data were created (train on one > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < year and apply on the remaining two years).
The results obtained with 15-days and 30-days shift in the rapeseed growth cycle between training and test datasets showed that using InceptionTime and LSTM-FCN, it remains possible to classify the rapeseed fields with an F1 value higher than 84. 7% when there is a shift of up to 30 days between the training and test years (F1 value higher than 85. 6% for a shift of 15 days). However, the F1 value of InceptionTime was 5% higher than LSTM-FCN. On the other hand, RF and MLP had poor accuracy (F1 value less than 35% and 5% respectively for a shift of 15 and 30 days) and thus they cannot be used in the case of temporal transfer when there is shift in growth cycle of rapeseed between the training and test year. These findings provide functional advantages that facilitate the procurement of information about rapeseed which is a strategic crop in the global trade and plays an important role in global food security.
However, when using only 100 samples, InceptionTime did not perform better than F1 value of 59. 3. The accurate classification performed by InceptionTime using sample sizes greater than 300 showed the lower effect of sample size on the performance of which confirms the stability of the results of this algorithm compared to the other two ANNs. Although DL is known as a method that requires a large training dataset, Fawas et al. [40]  mentioned that providing the accurate results using the small sample size by InceptionTime as another advantage of assembling the several inception networks in this algorithm. In the case of a sample size of 100, RF gave an F1 value 7% higher than InceptionTime. RF as a machine learning model, provides F1 values between 90% and 92% using the sample sizes larger than 300 samples (300, 500, and 1000), which shows that when the number of samples increases, no great improvement occurs in the accuracy of classification by RF.
On the contrary, the performance of LSTM-FCN and MLP decreased dramatically using the sample size smaller than 1000, therefore, these two > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < algorithms are not recommended for rapeseed classification in the case of small sized samples. In summary, taking into account the effect of the phenological cycle shift, the number of S1 images, and the size of training samples on the ability of our four algorithms to provide a solution to the constraints concerning the ground samples, TABLE Ⅸ illustrates the recommended algorithms for each use case. It is possible to use all algorithms in the case of the temporal transfer when there is no shift in the training and test data. InceptionTime can be used in all tested cases, although its performance is not the highest for the case of 100 samples, and temporal transfer with a smaller number of S1 images.
VI. CONCLUSION This paper presents an assessment of artificial intelligence algorithms for mapping rapeseed fields using S1 time series data. The accuracy of rapeseed mapping was assessed for four algorithms considering (1) the effect of the sample size for training, (2) the ability to deploy a model trained on one year on other years (3) the effect of the number of available S1 images in the time series and finally (4) the effect of a possible phenological shift between studied years. In the case where large databases (nearly 100,000 samples) used for training, this paper demonstrated that RF, InceptionTime, LSTM-FCN and MLP were capable of accurately mapping the rapeseed fields using only S1 SAR time series using the same year for training and for testing (F1 score between 95. 0% for RF and 95.
Indeed, adjusting these parameters based on the data could improve the model's performance and ensure optimal results. In addition, to tackle more challenging transfer scenarios between training and test datasets, an interesting avenue for future work would be to apply more robust training techniques (e. g. , with data augmentation) and account for domain changes beyond time lags. The findings of this paper are not only useful for remote sensing researchers, but they also facilitate the detection of rapeseed fields for other stakeholders such as policy makers and insurance companies, even with the constraints of collecting ground samples for each year.
Using a shift in the phenological cycle between training and test years: TABLE Ⅷ shows the results of the accuracy assessment of the transferability of the models with the simulation of the 15-day and 30-day shifts in the phenological cycle of rapeseed in 2018 as training data without any modification in the 2020 dataset as test. InceptionTime and LSTM-FCN provided the best accuracy among other algorithms with nearly similar accuracy metrics with and without the time shift. With a 15-days shift in the phenological cycle of rapeseed, the InceptionTime algorithm gave rapeseed fields map with F1 value equal to 92. 6% (the F1 value of 94. 3% for the same combination of years without shift). In the case of classification using > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < Metric Algorithm Without shift 15 days shift 30days shift Metric Algorithm One orbit (2018/2019, Three orbits (2018/2019, F1% - RF 95.
2% for the same combination of years without shift). On the other hand, lower performance was obtained for RF and MLP achieving F1 values of 34. 6% and 29. 1% respectively for 15 days' shift. For a 30 days' shift, RF and MLP had F1 values lower than TABLE Ⅶ EFFECT OF S1 ACQUISITION DENSITY ON THE ACCURACY OF RAPESEED FIELDS MAPPING. TRAINING WAS PERFORMED USING THE 2018 DATASET AND TESTING WAS PERFORMED ON THE 2019 AND 2020 DATASETS. THE TWO CASES USED ARE DATA CORRESPONDING TO ORBIT 8 (ONE ORBIT) AND DATA 5%. In the case of 15 days shift the low value of recall (below 20%) caused the poor performance of both RF and MLP, whereas in the case of 30 days' shift, both recall (close to 0%) and precision (55. 8% and 18. 9% for RF and MLP respectively) were low for these two algorithms. Thus, in the case of a phenological shift CORRESPONDING TO ORBITS 8, 30 AND 81 (THREE between the training year and the test year, RF and ORBITS).
THE RESULTS CORRESPOND TO THE AVERAGE MLP were not able to produce accurate rapeseed fields PERFORMANCE OBTAINED OVER 2019 AND 2020. map. However, LSTM-FCN and InceptionTime were capable of maintaining good accuracy in the presence of a phenological shift between the training and mapping years. TABELⅧ RESULTS OBTAINED BY APPLYING A TIME SHIFT OF 15 AND 30 DAYS ON THE TRAINING DATASET OF 2018, WITHOUT MODIFYING THE TEST DATASET OF 2020. WITHOUT SHIFT = THE CURRENT PHENOLOGICAL CYCLE OF RAPESEED IN THE DATASETS USED FOR TRAINING AND TESTING.
2006 ). SIMPER analysis based on the Bray-Curtis dissimilarity distance was run to determine the species that contribute most in differentiating the two groups tested by ANOSIM. These analyses were performed with the free PAST 4. 03 software. Finally, in order to visualize the pattern of species composition over both practices we used Non-metric multi-dimensional scaling (NMDS) on data table including the 9 and 5 most abundant (>20 ind. ) species (for earthworms and Collembola, respectively) and the 56 and 37 plots with at least one individual (for earthworms and Collembola, respectively). We used the vegan R-package with the function 'meta-MDS' for species ordination by NMDS and the function 'env t' to calculate correlations between ordination axes and environmental variables (cover crop characteristics and age of CA practice), where signi cance is based on 999 random permutations of the data table  (Oksanen et al.
Overall mite prevalence was calculated for each host species and each colony/ area using the statistical package Quantitative Parasitology 3. 0 50. Whittaker's beta diversity, which describes the   S1. change in community composition at different scales, was calculated for each host species and each colony/area using PAST (version 2. 17c)  51. To evaluate the completeness of mite sampling, species accumulation curves were generated for each procellariiform species, pooling data from the different colonies/areas. Species accumulation curves represent the observed mite species number (S obs ) and estimate the mite species number which would be collected as the number of samples approaches the population size. These analyses were conducted with PERMANOVA + for PRIMER v6  52. The influence of geography on the composition and structure of feather mite assemblages was tested at different spatial scales: large spatial scale (among the five regions mentioned above) and small spatial scale (among colonies within an area, e.
We used metaMDS function of the vegan v2. 5-4 R package to perform a non-metric multidimensional scaling (NMDS) to obtain an ordination of mite infracommunities for different host species and at different spatial scales (colony, area and region)  53. This analysis was assuming Jaccard dissimilarities that were obtained with the vegdist function (binary = T). To visualize the ordinations, we used the phyloseq, ggplot2, and wesanderson R packages  [54] [55] [56]. To assess the effects of host species and spatial scale on the composition and structure of mite communities, we conducted a permutational multivariate analysis of variance (PERMANOVA) with either host species, genus, or spatial scale as a fixed factor. For the factors host species and genus, we also performed an additional analysis in which we included the date of origin of each genus (estimated divergence time for that genus) to better account for the phylogenetic relatedness of the taxa.
For example, when analyzing the effect of the host genus, we used the approximate date of origin of each genus as a fixed factor rather than genus identity. In addition, when analyzing the effect of the host species in the case of Cape Verde Islands, in PERMANOVA models, the date of origin of each genus was included as the first factor followed by host species identity. The date of origin of each genus (MYA) was obtained from TimeTree using the evolutionary timeline tool  57. PER-MANOVAs were then performed using the adonis2 function from vegan on Jaccard distance matrices with 999 iterations. When a significant interaction between factors was detected, separate PERMANOVAs for each factor were performed, including post-hoc pairwise contrasts, (R package pairwiseAdonis v0. 0. 1), with a Bonferroni correction for multiple tests  58.
Funding This work was supported by the  APIF postgraduate project  from the  University of Barcelona , by a Grant of the  Romanian Ministry of Research, Innovation and Digitization, CNCS/CCCDI-UEFISCDI , project number  PN-III-P1-1. 1-PD-2019-0611 ,within  PNCDI III  and by an  Institutional Performance Project for Excellence Financing in RDI , contract no. 2PFE/2021  for L. M. S. Financial support was also provided by  REN2002-01164/GLO ,  CGL2006-01315/BOS ,  CGL2009-11278/BOS  and  CGL2013-42585-P  from the  Spanish Government, Fondos FEDER  and  BIOCON04/099  from  Fundación Banco Bilbao Vizcaya Argentaria.
PADI-web collects data from the Google News aggregator in 16 languages. Both tools further implement classification and information extraction algorithms to filter and extract the relevant outbreak information in a structured format from the free text, such as the place, date, and host of a described outbreak. Finally, HealthMap provides users with a world map interface to visualise the reports and information sources that report outbreaks. PADI-web provides users with a list of information sources and news content that reports outbreaks. Previous evaluations of the EBS tools in use today, including HealthMap and PADI-web, focused mainly on the assessment of their extrinsic performance, such as timeliness, positive predictive value, or sensitivity (Se) in detecting outbreaks from the sources they monitor, compared to official disease outbreaks  [6, 7].