dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation Programme and Settings A set of 30 simulations are performed to analyze the wind waves at the Garda Lake, with an initial water level taken equal to 0 m. The simulations are steady-state runs that consider a uniform wind field and a null wave spectrum at the upwind boundary, in order to evaluate only the wind-generated waves. The simulation programme includes 15 wind intensity V (2. 5-50 m/s) and 2 wind directions D W (Peler: 30 • N and Ora: 210 • N). The grids used for the simulations are structured and regular with a resolution of 100 m. This spatial resolution is chosen in order to properly describe the details of the Garda Lake based on the available bathymetric dataset. The grid for SWAN is formed by 138,444 elements and is North-oriented. Conversely, the grids for the STWAVE and CMS-Wave models, formed by 109,931 elements, are oriented parallel to the simulated wind direction, as suggested by the developers.
The comparisons were initially performed on an idealized case to focus on the effect of the differences among the wind input parameterizations. The differences in the investigated range of winds (10 m/s to 40 m/s) are significant. CMS-Wave gave inconsistent results especially for small fetches, due to an inherent threshold in the highest simulated frequency. SWAN predicted waves higher (up to 25% higher) than STWAVE. For SWAN, the ST6 Package was compared to the KOMEN one, and, as expected by the analysis of Christakos et al. [16] , lower values of Hs were predicted. An additional application on the Garda Lake (Italy) was carried out. This enclosed basin was selected as an example of a case where calibration is not possible due to the absence of combined information on wind and waves. The models were applied to two locations on the lake, where a recent project is underway, and differences in the wave height >20% were found, which are considered too large to be acceptable for design purposes.
This research was partially funded by the project " Development of a new Wave Energy Converter named Wave Attenuator ",  Bando Centro Giorgio Levi Cases  a tema vincolato, Prot. 8 del  20 January 2021 Fasc. 2021-III/13. 1 and by the Venice 2021 research grant funded by  Provveditorato for the Public Works of Veneto ,  Trentino Alto Adige , and  Friuli Venezia Giulia , provided through the concessionary of  State Consorzio Venezia Nuova and coordinated by CORILA (Consorzio per il coordinamento delle ricerche inerenti al sistema lagunare di Venezia ). authors declare no conflict of interest.
The characterizing macro pattern is encoded by an in-depth gradient that describes the relationship between the scale levels and their adaptive statistical prototype. Besides this handcrafted feature descriptor, a learning-based description is performed with the ResNet34 model for bark classification. Extensive and comprehensive experiments on challenging and large-scale bark datasets demonstrate the effectiveness of ms-SMBP to identify bark species and outperforming different multiscale LBP approaches. The tree trunk classification with ResNet34 shows interesting results on a very large-scale dataset.
In this study, we mainly try to answer the following fundamental concerns: (1) whether representing the texture information in the distribution level, to reflect the visual similarity of samples belonging to the same species, reduces the interclass similarity and the effect of imaging problems or not, and  (2)  what informative texture features should be used, and how should they be encoded to represent the characterizing macro pattern for a given bark structure, and finally, (3) whether a learning-based approach, like, convolutional neural networks is suitable for bark species classification using bark datasets with different sizes and species. Therefore, the main contributions of this work can be summarized as follows: • We introduce a new sampling scheme at high scale levels with different distance representations both at the spatial resolution level and at the neighboring level, to capture the characterizing bark macro pattern.
[45]  proposed the idea of residual blocks to build the ResNet model as follows: y = F (x, W i ) + x (2) where x and y are the input and the output of the considered layers. F (x, W i ) represents the residual mapping to be learned, and the element-wise addition F + x represents the shortcut connection  [45]. Fig. 4  shows the building block of ResNet. Using this elementary representation of residual block, the ResNet34 model consists of 34 layers. In their experiments, Carpentier et al. [47]  found that on the BarkNet dataset, the ResNet34 achieved the best accuracy when compared to the ResNet18 model. However, although, the CNN models have not been widely used for bark species classification, since no large bark dataset was collected before the BarkNet dataset, it would be interesting to compare the performance of different CNN models with different structures and depth and validate them on other bark datasets with varied sizes and bark species.
Thirdly, we add comprehensive experiments on two bark datasets, where we extend the original version from small-scale datasets, like Trunk12 and AFF, to large-scale datasets, like, Bark101 with more than 100 species and BarkNet with more than 23k bark samples. Finally, we experiment with bark classification using a deep convolutional neural network by training the ResNet34 model once from scratch on different bark datasets, and once using the transfer learning technique.
Experiments and results We perform a comparative evaluation of our descriptor on four different bark datasets, namely, Trunk12, AFF, Bark101, and BarkNet, and compare it with different multi-scale LBP-like approaches as well as statistical approaches. For the comparison methods, we use the codes shared by the original authors, otherwise, we implement the approach based on the original work. For a comprehensive comparison to our descriptor, all variants are implemented on their multi-scale representation using the optimal sampling and encoding settings as suggested in the corresponding work. All the comparison methods were reviewed in Section 2. The experimental setup is organized as follows: experiment n • 1: relates to the bark species identification and classification with our handcrafted texture descriptor on four different and challenging bark datasets.
We evaluate the following parameters: the statistical prototype representation, the macrostructure sampling scheme, and finally, the texture representation with different statistical descriptor combinations. Then, we provide a comparison with the state-of-the-art methods. Moreover, we evaluate the performance of our descriptor in differents color spaces, and provide comparison of the computational time of our descriptor and state-of-the-art descriptors on the Trunk12 and AFF datasets. experiment n • 2: in this experiment, we perform bark classification with the convolutional network ResNet34 in two ways. In the first one, the ResNet34 model is trained from scratch on each bark dataset. In the second one, we fine-tune the pre-trained ResNet34 model on each of the bark datasets. Details about the hyperparameters tuning and the quantitative results are presented for each classification approach.
Results on the Trunk12 dataset The Trunk12 2   [50]  dataset includes 12 distinct species from Slovenia. The bark samples were collected under controlled imaging conditions regarding scale and light. However, they show an important level of interspecies similarity. Classifier and performance measures Bark samples from the Trunk12 datasets were resized to a constant width of 1000 pixels, preserving the aspect ratio as performed in  [9, 11, 28]. We use the Nearest Neighbor classifier with the Leave One Out (1-NN-LOO) strategy as in  [12, 13, 15, 27]. For the (1-NN-LOO), we retain the rank one identification rate where one test bark image will be matched with the most similar image from all the remaining images of the dataset. This test image is attributed to the same class label as the most similar image returned.
The final identification rate is the average value of the rank one identification over all the samples of the dataset. Besides, we use 'Mean Average Precision' (MAP) which provides more general results over all the images of a dataset. The MAP value is estimated by the mean of the term APre(q) for each query q from a set of queries Qry. Formally: MAP = ∑ Qry q=1 APre(q) |Qry| ; APre(q) = ∑ g k=1 (Pre(k)rel(k)) relevant images for q ( 14 ) where g is the number of retrieved images, and Pre(k) is the precision at cut-off k in the list of retrieved images. rel(k) is a binary function that measures the relevance of retrieved image at rank k. Parameters evaluation Figs. 12 presents the performance of single and combined de- ) mostly perform better than the combination of all statistical descriptors based on the intrinsic global prototype, i.
Fig. 15  illustrates the retrieval responses of a bark sample from species 3 with the ms-SMBP m descriptors with the µ pattern prototype in Fig. 15 (a), and the µ scale prototype in Fig. 15(b). With  the local prototype, the first three responses do not belong to the same species as the request image. This might be explained by the fact that the µ pattern is an adaptive statistical prototype constructed from the distribution information for each specific macro pattern for a given bark structure. However, the macrostructure of bark species 3 from the Trunk12 dataset does not share a characterizing macro-pattern. Overall, the global identification score and the MAP value of the ms-SMBP with µ pattern remains significantly higher than the global score of ms-SMBP when the µ scale prototype is used, i.
In what follows, we evaluate different representations of the macrostructure in terms of the radial distance at the spatial resolution level, and the angular distance at the neighboring level, as a function of the macrostructure size L. Table  1  lists the performance of the combined descriptor ms-SMBP on the Trunk12 dataset under different configurations. Starting by the influence of the locality parameter R 1 , it can be seen that for a given spatial resolution distribution, the performance increases the further the macrostructure location is from the center pixel, i. e. , the value of R 1 increases, meaning that distant pixels allow more informative features to be encoded than local ones, and that the center pixel is not necessarily well characterized by its very local sampling points. We believe that this is mostly related to the bark image's resolution.
Regarding the neighborhood scheme, the AllP consistently and significantly performs better than the OneP sampling schemes. For a large macrostructure, the intensity distribution information is more reliable when a large number of sampling points is used, i. e. , AllP, and that the OneP sampling with directional information is insufficient to reflect the intensity distribution from large macrostructures. In the following experiments, the configuration {L = 9, R 1 = 3, D s = 2, AllP} will be used to encode the bark texture images from the Trunk12 dataset. So far, the representation of the bark texture by the combination of all statistical descriptors was shown to perform significantly better than a texture representation with a single statistical descriptor. However, it would be interesting to examine the identification performance of different descriptor combinations.
Fig. 16  shows the identification scores and MAP values for all combinations based on two statistical descriptors ms-SMBP m , and Table  2  presents the performance of the top three combinations of three and four statistical descriptors on the Trunk12 dataset. Interesting scores were provided by the ms-SMBP MinMaxMeanMed descriptor, which approximates the ms-SMBP's scores with a reduced final dimension. However, when all five descriptors are combined, ms-SMBP, enhanced performance over all other combinations is achieved. This shows that different statistical representations of the intensity distribution of the bark structure provide complementary information. Comparative results Table  3  lists the performance obtained by the proposed statistical descriptor combinations and those obtained by the state-of-theart approaches on the Trunk12 dataset.
Results on the AFF dataset The AFF dataset 3   [8]  contains bark samples from eleven of the most common species in Austria were pictured with a variable number for each species. AFF's images show high interspecies similarity and high intra-species variability. Besides, imaging issues like scale and light changes, natural problems like lichen and scars are present (see Fig. 1(a) ). Like the Trunk12 images, bark samples from the AFF dataset were resized to a constant width of 1000 pixels, preserving the aspect ratio as performed in  [9, 11, 28]. On the AFF dataset, the a Score for descriptors are listed from  [15]  for gray-scale texture representation. 1-NN-LOO classifier is used, and performance are expressed in terms of average score and MAP values as detailed in 4. 1. 1. Parameters evaluation Following the parameters tuning process, we present in Table  4  identification rates of the combined ms-SMBP with different macrostructure configurations.
For the locality parameter R 1 , we found that large values achieve better identification rates, meaning that distant pixels are more informative than local ones to describe texture structure from bark images with large resolutions. For the radial distance between consecutive levels and the neighboring points scheme, the highest score was found with D s = 2 and AllP respectively. Therefore, the optimal configuration to represent the bark surface of images from the AFF dataset is {L = 9, R 1 = 5, D s = 2, AllP}.
Comparative results Table  5  presents the identification rates achieved by our statistical descriptors and those achieved by different state-of-the-art descriptors. From Table  5  we can report the following remarks: The proposed ms-SMBP descriptor achieved the highest score on the AFF dataset with 81. 38% outperforming all methods under consideration. Moreover, we found that descriptors with a combination of four, three, and two statistical representations significantly surpass all multi-scale and statistical LBP variants. For MAP results, the ms-SMBP achieves comparative results. Further improvement over the dimensionality of the final ms-SMBP can be found when the configuration {L = 3, R 1 = 5, D s = 2, AllP} is used (see Table  4 ). The ms-SMBP descriptor achieve an identification score of 74.
This shows that the ms-SMBP encoding is efficient and discriminative. For the species 11, which records the lowest identification rate among the AFF species, we believe that it is because most samples from species 11 are partially covered with lichen as shown in Fig. 19 , which alters the texture information. However, with the MRELBP descriptor, median patches around each neighboring point can reduce the noise effect. Nevertheless, overall, the performance of the proposed ms-SMBP and its combinations on the challenging AFF dataset allow us to attest to the effectiveness of bark texture description using statistical representation. In what follows, we exam these performance on large-scale datasets.
Results on the Bark101 dataset The Bark101 4   [15]  is a sub-collection of bark samples extracted from PlantCLEF 2017 dataset,  5  part of the ImageCLEF 6  challenge. Bark images were manually segmented by  [15] , so the resulting images include only the bark surface. Bark classification of Bark101 samples is challenging due to the considerable number of classes, i. e. , 101, and the small and unbalanced number of image samples for each class, in addition to the intra-class variation problem. Following the evaluation protocol in  [15] , half of the bark images from each species are used for training and the remaining half for testing. The bark classification is performed by the following classifiers on the normalized histogram (nH): • The Nearest Neighbor classifier (1-NN) using the L1 distance metric.
Parameters evaluation In this section, we evaluate the performance of the proposed descriptor on the Bark101 dataset. This dataset presents an interesting study case for the generalization of our descriptor for the bark species classification task using more than one hundred bark species. Table  6  lists the classification scores of bark species from Bark101 using the 1-NN classifier. From Table  6 , similar observations are made on the Bark101 dataset regarding the optimal configuration of the macrostructure. For the R 1 parameter, large values are correlated with large classification scores when the AllP neighboring scheme is used. The following configuration allows to achieve the best classification rate for the combined ms-SMBP on the Bark101 dataset: (L = 9, R 1 = 5, D s = 2, AllP) with 33.
66%. Comparative results Table  7  presents the comparative results on the Bark101 dataset with different state-of-the-art methods. From Table  7  it is clear    that our descriptor achieves the best classification scores on the Bark101 dataset both with the 1-NN classifiers and the SVM rbf classifiers, which slightly outperforms the non-parametric 1-NN, i. e. , 33. 66% and 37. 29% respectively. However, although the obtained scores significantly outperform the LS-LCoLBP  [15]  descriptor. Bark classification from the Bark101 dataset is particularly challenging. One reason for that could be the imbalanced bark classes coupled with considerable intra-species variability.
Results on the BarkNet dataset BarkNet 7 [47] is a collection of more than 23k bark images from the region near Quebec City in Canada. Authors in  [47]  7 https://storage. googleapis. com/barknet-1/ a Score for descriptors are listed from  [15]  for gray-scale texture representation. collected bark samples from different trees and distinct locations, and a forestry expert annotated the samples with the corresponding true species. A fair amount of variation on illumination and scale was includes during the collection stage to approximate real-life application. For the classification setup, we followed  [47]  by using 5-fold cross-validation on the trees, where 80% forms the training set and the remaining 20% forms the testing set. For the bark images from BarkNet, we follow the same resizing steps as in  [47] , i.
Comparative results Once the optimal configuration is found, we present the comparative results to multi-scale LBP variants in Table  9. The proposed ms-SMBP and its combinations outperform all the methods under consideration both with the 1-NN and the SVM lin classifiers. However, although our proposed descriptor achieved the highest scores on the BarkNet dataset, these results show that half of the tested bark samples were not correctly classified. We believe that this limitation is caused by the considerable number of bark samples belonging to species that share the same plant family, thus a large interspecies similarity. Nevertheless. these results and those on the Bark101 dataset confirm our findings on the Trunk12 and AFF datasets and demonstrate that our approach is efficient for bark classification on large-scale datasets.
Results on distinct color spaces So far, we presented the performance of the handcrafted features for bark classification. These features were extracted from the gray-scale images. However, although the color information for the bark is not elementary information to distinguish between varied species, but was used as complementary information, few works have proposed to report the performance of their representation on distinct color spaces. In this experiment, we evaluated three distinct color spaces including, RGB, HSV, and YCbCr. The ms-SMBP texture features are extracted from each color channel and then concatenated to form the final color descriptor. Table  10  summarizes the classification results on bark datasets for distinct color spaces. From Table  10  it can be seen that when the color information is considered from the YCbCr color space, a significant improvement of classification scores is achieved when compared to their gray-scale representation.
For instance, on the Trunk12 and AFF datasets, when the ms-SMBP descriptor is used, which combines all statistical representations, the following improvements are reported: +4. 58% and +4. 82% respectively, and when the combination ms-SMBP MinMaxMean is used, we found an improvement of +14% and +6. 7% respectively on the classification scores. Moreover, these results outperform the one reported by the color representations of  [14]  and  [15]. On large datasets, like Bark101, the best result on the YCbCr color space is achieved when the SVM rbf classifier is used, i. e. , 48. 10%. Moreover, these results provide an improvement over the gray-scale representation by more than 10% and over the method of  [15]  by +17%. On the BarkNet dataset, consistent behavior of the ms-SMBP can be seen when we compare its classification score with the one from gray-scale representation.
Computational time In this section, we evaluate the features extraction time and the dimensionality of the ms-SMBP descriptor, its different combinations, and the state-of-the-art descriptors on the Trunk12 and AFF datasets. We run computations on a 2. 3 CPU machine with 32gb memory using the available MATLAB codes of the state-of-the-art approaches with the original parameters setting and multi-scale representation. The feature extraction time is the average time of overall samples of the bark dataset. It is expressed in seconds. Bark samples are of size 1334 × 1000 for the Trunk12 images and (478 -1812) × 1000 for the AFF images. Texture features are extracted from the gray-scale representation. Recall that the dimensionality of the proposed descriptor is giving by 2 L × |M|, where L is the number of scales of the macrostructure, and |M| is the cardinality of the statistical set used to calculate the underlying descriptor, for instance, |M| = 5 for the ms-SMBP descriptor since it combines all statistical descriptors.
The descriptor that achieved competitive identification scores on the Trunk12 and AFF datasets is the MRELBP  [38] , as listed in Tables  3  and 5  respectively. For our ms-SMBP descriptors, we report the feature extraction time using the optimal configuration of each dataset, namely, {L = 9, R 1 = 3, D s = 2, AllP} on the Trunk12 dataset, and {L = 9, R 1 = 5, D s = 2, AllP} on the AFF dataset. However, it can be seen from Tables  3  and 5  that comparable to higher scores of the ms-SMBP when compared to the MRELBP's score can be achieved with small number of scales. Specifically, on the Trunk12 dataset, ms-SMBP returns a comparable score to MRELBP (75. 06%) when a macrostructure of four scales is used with the configuration {L = 5, R 1 = 4, D s = 3, AllP}. On the AFF dataset, more interesting score can be achieved by ms-SMBP when the configuration {L = 3, R 1 = 5, D s = 2, AllP} is used with 73.
43% against 74. 45% for the MRELBP descriptor. Therefore, we consider adding the computation time and dimensionality of these descriptors to this evaluation. It can be seen from Table  11  that the computation time of the ms-SRBP descriptor and its combinations is moderate when the optimal configuration is used on the Trunk12 and AFF datasets. However, the feature extraction time and the dimensionality can be further significantly reduced when the sm-SMBP descriptors encode small sized macrostructures, like the ms-SMBP * MinMaxMeanVar with L = 5 scales that achieved the same identification score as the MRLBP, i. e. , 75. 06%, and comparable feature extraction time, but with a very compact representation of the final feature descriptor, 128 against 800 for the MRELBP, that is more than 6× compact without any loss of accuracy.