dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of the Gap-Filling Procedure Even if better results are obtained for clear sky conditions, the correlation between estimated and measured surface irradiance is quite low, except in Qomolangma station. The inaccuracy observed in the estimation of the incident radiation may be partially explained by the fact that some of the data used in the computation of the atmospheric transmissivity are scarcely available over the Plateau (Table  5 ). Then, the missing data need to be replaced by less accurate estimates, like weekly or monthly average values (Figure  2 ), knowing that the quality of the output is directly linked to the quality of the input data. From Table  5 , it appears clearly that the most problematic atmospheric variable is the AOD. However, due to the high altitude of the Tibetan Plateau, the atmosphere is thin and the AOD is very small over that area  [61].
At-Sensor Validation To deepen the validation of the solar radiation budget time series, a comparison with satellite data at the same spatial resolution is performed. This validation method allows to overcome the footprint issue faced when comparing satellite estimates to ground measurements over heterogeneous landscape, as illustrated later on (Section 4). The main objective is to verify if the retrieval of the atmospheric transmissivity for the estimation of surface irradiance is accurate. To do so, the surface irradiance is estimated following the proposed method. Next, the surface reflectance provided by MODIS products is used to compute the surface reflected radiance. The latter is then converted to TOA reflected radiance using atmospheric transmissivity values computed with the same approach but for the view zenith angle instead of the sun zenith angle.
Finally, the estimated TOA reflected radiance is compared to the TOA reflected radiance measured by MODIS. The atmospheric transmissivity and the surface irradiance being estimated broadband, a prerequisite to this comparison is the conversion of the spectral reflectance and radiance data provided by MODIS to broadband estimates. The validation is performed over the study area subset (135 × 135 km, over strong topography in the South part of the Plateau) and over the whole year 2010, considering only clear sky days. The results of the comparison are presented in Figure  10.
Finally, the estimated TOA reflected radiance is compared to the TOA reflected radiance measured by MODIS. The atmospheric transmissivity and the surface irradiance being estimated broadband, a prerequisite to this comparison is the conversion of the spectral reflectance and radiance data provided by MODIS to broadband estimates. The validation is performed over the study area subset (135 ˆ135 km, over strong topography in the South part of the Plateau) and over the whole year 2010, considering only clear sky days. The results of the comparison are presented in Figure  10. This comparison highlights a constant shift between the estimated and the measured TOA reflected radiance. This is confirmed by the equation of the regression which shows a slope of 1. 517 and an intercept very close to 0.
There are two potential reasons for this shift. First, the assumptions made to convert the measured spectral radiance to broadband radiance could lead to an underestimation of the broadband radiance. As the land cover is relatively stable over the test area, this underestimation could generate a constant bias. Second, the atmospheric transmissivity computed by MODIS to retrieve the surface reflectance is lower than the one estimated with the approach proposed in this paper. In this study, the atmospheric transmissivity for clear days is around 0. 9 which is confirmed by the data measured at the ground. It is also possible that those two potential errors add to each other. However, this comparison shows that the way the atmospheric transmissivity is modelled in the proposed method is providing suitable results for clear sky days and confirms that the problem mostly come from cloudy conditions.
Discussion The validation showed better solar radiative flux retrieval for clear sky and especially for instantaneous irradiance. The quality and the availability of the MODIS data products used in the method have been quoted as a potential source of error. The temporal difference between the time used in the computation, which is the exact MODIS overpass time, and the 30 min average ground observations used for the validation was also mentioned, especially linked to the rapid evolution of the cloud cover over the Plateau. Considering this point, the distinction between clear sky and cloudy sky for the validation based on the MODIS cloud fraction information was also analyzed. A small test was run to identify the cloudy conditions based on the difference between the ground measurements and the top of atmosphere irradiance.
The later showed that the days identified as cloudy according to this method were significantly different that the ones identified as cloudy based on the MODIS cloud fraction. This discrepancy could lead to the lower correlation observed at BJ and Namco for incident fluxes estimation under clear sky. Another explanation for the low correlation between the estimated and the measured fluxes is linked to the spatial footprint of the two compared datasets. Those errors add up to the error due to the inaccuracy of the computation. First, the use of ground measurements at higher temporal resolution would improve the validation. Second, it is necessary to assess the impact of the spatial footprint when validating a variable estimated from remote sensing with ground measurement. As shown in Figure  11 , the stations are located within pixels comprising several land cover types.
Conclusions The method proposed in this paper allows to model the solar radiation budget instantaneously and daily, at the square kilometer level over the entire Tibetan Plateau using MODIS data products. Even if the overall accuracy is equivalent to already existing products  [21, 63] , this method provides solar radiation estimates at the square kilometer level on a daily basis whereas the other net radiation datasets covering that area provide the same estimates but at a coarser spatial or temporal resolution. Furthermore, it uses the local solar incident angle in the computation of irradiance while the terrain geometry is often neglected. Using MODIS data products offers the advantage of freely available data, which are widely used and continuously improved. Those data are at a suitable spatial and temporal resolution for this study and 10 years of archived data for long-term analyses are available.
This study highlighted the relevance of those products to model the atmospheric transmissivity for clear sky despite an important lack of AOD data over the Tibetan Plateau. The eight-day albedo product is not sufficient to properly estimate the reflected fluxes due to its low temporal resolution and the fact that its retrieval is probably affected by the topography of the underlying terrain. The use of the new daily MODIS albedo product may solve the temporal issue while some topographic correction should also be considered. The method was tested and used to produce a three-year time series, from 2008 to 2010. As for other large scale solar radiation budget products, the validation remains an issue and shows some discrepancies with the ground data. To overcome this problem, a sensor comparison was also performed to evaluate the proposed method.
The main improvement brought by this method is then the higher resolution of the produced data and the integration of topography, even if the latter was still not sufficient to compensate for the very rough terrain of the Tibetan Plateau and should be improved to integrate the impact of surrounding terrain at pixel or at sub-pixel level. Some work should also be done to provide a better conversion from instantaneous to daily considering the cloud cover and water vapor variation over the day. To conclude, this simple method is fully operational and based on freely available data, it offers the possibility to anyone to produce a reliable solar radiation budget dataset, keeping in mind the described limitations. The improvement of the existing data products, in parallel with the development of more efficient topographic correction and cloud integration, the method could be further improved.
Acknowledgments: This work has been jointly supported by: the  EU-FP7  project  CEOP-AEGIS  (Grant nr. 212921 ), the  NRSCC-ESA Dragon program - Hydrology  Project  ID 10680 , the  SAFEA Long-term Projects of the 1000 Talent Plan for High-Level Foreign Experts  ( WQ20141100224 ) and the  National Key Basic Research Program of China  (Grant no. 2015CB953702 ).
Sample preparation and image acquisition CRC data were collected from the Anatomical pathology (Anapath) Department at the CHU Nancy-Brabois Hospital. Tissue samples were obtained from sequential resections of colons from 30 patients with colorectal cancer. Sections of 5 μm thickness were extracted and stained with the widely used haematoxylin and eosin (H&E) stains, to reduce image processing requirements. Images were captured at low magnification (× 40) using an optical microscope mounted with a charge coupled device (CCD) camera. A Liquid Crystal Tunable Filter (LCTF) was placed in the optical path between the light source and the CCD camera, providing a bandwidth accuracy of 5 nm and a controllable wavelength through the visible spectrum from 500 to 650 nm  [24]. This technique, known as hyperspectral or multispectral imaging  [25] , can capture images of tissue samples at different spectral frequencies.
In this study, 16 multispectral bands were acquired in the wavelength range of 500-650 nm, with 9. 375 nm steps between successive bands. The data of 30 CRC patients (10 BH patients, 9 IN patients and 11 Ca patients) were obtained for analysis, giving a total of 160 BH images, 144 IN images, and 176 Ca images. Data are available in S1 Zip (https://figshare. com/s/5e9c65848bb0aa1f4032 or DOI: 10. 6084/m9. figshare. 2076220). The identification of each PT type from multispectral images was done by a senior histopathologist, confirming the diagnosis. Finally, images were denoised using an average filter and rescaled to a resolution of 512 × 512 pixels.
Considering that the desired descriptor dimensionality aims to expedite the classification phase as well as reduce the noise and redundant feature sizes, we apply dynamic time wrapping (DTW) to determine the optimal alignment between the sub-levels of hierarchical MSHs. Unlike recent works that rely directly on individual joint locations, our study is related to explicit skeleton model approaches. In particular, this study capitalizes on a new feature space that has not been previously considered. Therefore, all the human skeleton joints are represented as a collection of measured features in static pose and joint motion nature. An action classification is performed using the extreme learning machine (ELM) classifier and SH-based skeleton representation. The proposed method is evaluated based on recent skeletonbased 3D action datasets.
In certain action recognition methods, the features are developed in complex models to form the representation of the motion sequences. 3D joint positions are commonly extracted as features through four mechanisms. First, raw 3D data are recognized directly without any further processing  (Raptis et al. , 2008; Shimada and Taniguchi, 2008; Wang and Lee, 2009). Second, these data are further processed to address certain challenges  (Barnachon et al. , 2013; Wang et al. , 2012; Zhao et al. , 2013). Third, the distances between each joint can be used as a distance-based feature vector for each frame  (Antnio et al. , 2012). Fourth, the features for the selected joints can be simply calculated with reference to the relative distance between joints  (Wang et al. , 2012). In  (Hussein et al. , 2013) , the human body skeleton was interpreted by directly constructing 3D skeleton joint locations as covariance descriptors, and the temporal evolutions of the action dynamic were modeled using a temporal hierarchy of covariance descriptors.
The proposed representation explicitly models the 3D geometric relationships among various body parts using rotations and translations. Given that the Lie group was a curved manifold, the researchers mapped all action curves from the Lie group to its Lie algebra, and the temporal evolutions were modeled with DTW. Our proposed method is di↵erent in many aspects over the previously published methods. To more explicitly examine these issue, we now include the comparison in terms of number of joints used, choice of classifier, computation complexity and dataset variability. In term of number of joints, several techniques such as [  (Yang and Tian, 2012; Hussein et al. , 2013; Raviteja et al. ; Evangelidis et al. , 2014; Ohn Bar and Trivedi, 2013; Wang and Lee, 2009) ] adopted all skeleton joints for feature extraction.
Whereas, our method adopted only nine joints. For the classification task, we used ELM which provides a very high recognition accuracy with very fast training time compared to many other classification methods such as : • (SVM) used  (Hussein et al. , 2013) , • (NBNN) used in  (Yang and Tian, 2012)  and  (Seidenari et al. , 2013). • (AdaBoost ) used in  (Bloom et al. , 2012) , • (HHM) used in  (Xia et al. , 2012). The calculation of our features are computationally light than many previous works which try to classify a large 3D point, such in  (Raptis et al. , 2011) ,  (Raviteja et al. ) ,  (Ohn Bar and Trivedi, 2013) ,  (Zhu et al. , 2013) , and  (Li et al. , 2010). Moreover, in our method, we perform the evaluation on a variety of datasets compared to many approaches (  (Xia et al. , 2012) ,  (Yang and Tian, 2012) , and  (Seidenari et al.
Experimental Setup To evaluate the e↵ectiveness of the proposed method, we perform action recognition on the proposed feature representation and recently published datasets (i. e. , MSR-Action 3D  (Li et al. , 2010) ,G3D  (Bloom et al. , 2012) , Florence 3D Action  (Seidenari et al. , 2013) , and UTKinect-Action  (Xia et al. , 2012) ). These datasets are used as benchmarks in the experiment. The action complexity of these datasets varies from simple to complex sequences. In addition to depth data, skeleton data are also provided by these datasets using a Kinect sensor http://msdn. microsoft. com/en-us/library/ hh855352. aspx as required. In all experiments, an ELM classifier is used with the proposed representation. For each dataset, the state-of-the-art skeleton-based methods are extensively compared with the proposed approach.
The number of hidden neurons of ELM is experimentally tuned for each dataset. To simplify the computation in each experiment, we set the waiting parameters as = 0. 5 and  = 0. 5. The frequency bands of MSHs are equal to 2. We consider the cross subject protocol for the test setting in all datasets. In particular, half of the subjects are used for training, and the other half for testing. We also divide the MSR-Action dataset into further subsets, AS1, AS2 and AS3 , similar to  (Li et al. , 2010). Each subset consists of eight actions, and action recognition is separately performed on each subset. In all experiments, we use nine joints from the body skeletal as the initial input joints to our proposed method, as shown in Fig 5. The features from these joints are initially used for the skeleton feature representation.
Comparison with Various Skeleton Features The performance of various skeleton representations is evaluated on all datasets, and the e ciency of the proposed MSHs method is compared with the various skeleton representations. Table  1  reports the accuracy of the proposed approach with the corresponding results of di↵erent representation methods based on the MSR-Action dataset. Our findings presented in this table are achieved using three levels of MSHs, while the window overlap in the second and third levels is preserved. Compared with other skeleton representations, the proposed method provides satisfactory results. In particular, the MSHs method improves the average accuracies over JL, PDJs, and MPV by 16. 07 %, 18. 62 %, and 9. 84 % , respectively. These observations clearly indicate the superiority of the proposed MSHs representation over existing skeleton representations.
The experiments on MSR dataset indicate that the MSHs-based method have better performance on subsets AS1, AS2, AS3 in modeling complex actions and di↵erentiating similar actions. Table  2  summarizes the recognition accuracies of various skeleton representations on the UTKinect-Action, Florence 3D Action, and G3D datasets. The results reveal that MSHs method significantly outperforms the other skeleton representations on these datasets. In using UTKinect dataset, the average accuracy of the proposed representation is 10. 5. % better than that of JL, 9. 92% better than that of PJDs, and 5. 42 % better than that of MPV. In the case of the Florence dataset, the average accuracy of the proposed representation is 9. 54 % better than the average accuracy of, 15. 8 % better than the average accuracy of PJDs, and 2.
43 % better than the average accuracy of MPV. respectively. In the case of the G3D dataset, the average accuracy of the MSHs-based skeleton representation is 13. 83 % better than the average accuracy of JL, 12. 47 % better than the the average accuracy of PJDs, and 10. 79 % better than the average accuracy of MPV. It is interesting to note that the results from MSHs clearly demonstrate the superiority of the MSHsbased method over various skeleton features on Florence, and G3D datasets.
Comparison with the state-of-the-art The same datasets are used to compare the performance of the proposed method with those of existing state-of-the-art methods. For each data set, the hidden neurons are reported separately. In all experiments, the results correspond to using three levels of hierarchical SHs, while preserving the overlap in the last two levels. Several recognition results on the MSR-Action 3D dataset are already available in the literature. Table  3  presents the recognition rate of the proposed approach along with those of the corresponding current methods. As indicated in this table, the proposed approach obtains the best results compared with those of most existing methods. In particular, our method provides good  Approaches Accuracy HO3DJ  Xia et al. (2012)  78. 97 EigenJoints  Yang and Tian (2012)  82.
(2013) , or incorporates geometry relation between local joints in order to models the rotation and translation invariants of the features as lie groups as in Raviteja et al. Some skeleton based methods like Yang and Tian (2012) use skeleton features based on pairwise di↵erences between joints. However, results obtained on MSR Action 3D dataset show that integrating the evolution of the whole skeleton during the sequence is more discriminative than taking into consideration the joints separately. In addition, the methods proposed in Yang and Tian (2012);  Xia et al. (2012) ; Ohn  Bar and Trivedi (2013)  have the lack of information about temporal nature of the action,making the recognition less e↵ective compared to our method. In this case, 780 hidden neurons are observed in ELM. For further evaluation, the proposed approach is applied to the skeleton sequences from UTKinect-Action, Florence, and G3D Action datasets.
The performance of the proposed approach in this experiment is also compared with those of the corresponding methods. Table  4  compares our method with various state-of-the-art skeleton-based human action recognition approaches on the UTKinect dataset. The proposed approach gives comparable results. The average accuracy of the proposed representation is 5. 10% better than that given in  (Zhu et al. , 2013)  and 2. 08% better than that in  (Xia et al. , 2012). The number of hidden neurons in this experiment is 640. Table  5  reports the average recognition accuracies in the case of the Florence dataset. The results reveal that the accuracy of the proposed method is slightly higher than that citepd in  (Seidenari et al. , 2013). In particular, the performance of the proposed approach is superior over that of the state-of-the-art methods by 4.
13%. Our results in this table correspond to 500 hidden neurons for ELM. Fusion spatiotemporal  Zhu et al. (2013)  87. 90 HO3DJ  Xia et al. (2012)  90. 92 Space-time pose Rep. Devanne et al. (2013)  91. 5 SHs Al  Alwani and Chahir (2015)  91. 65 Proposed approach 93. 0 The performance of the proposed method is also assessed based on the G3D-Action dataset. Table  6  demonstrates the results, which indicate that our method evidently outperforms the existing skeletal joint-based state-of-the-art methods by achieving better accuracy by 0. 59%. In this experiment, 700 hidden neurons exist in the ELM.
Benefit of modified SHs Table  7  demonstrates that the addition of dynamic features expressed by the second-order term of the real SHs dramatically increases the recognition accuracy compared with the standard SHs (Al  Alwani and Chahir, 2015). The e ciency of using MSHs becomes evident when we compare them with the standard SH descriptors. In Table  4 , the recognition accuracies of MSHs are used and compared with those of the standard SHs given in (Al  Alwani and Chahir, 2015). The explicit estimation of angular speed and directions in terms of the second-order function presents a significant performance. For example, in the MSR-Action 3D dataset, the use of the quadratic term in MSHs improves the recognition accuracy by a substantial. 04% margin over the standard SHs. In the case of the UTKinect and G3D datasets, the MSHs add a significant improvement of 1.
35% and 0. 59% to their recognition accuracies respectively. Contrarily, in the Florence dataset, the recognition rate is decreased from 87. 5% for SHs to 86. 13% for MSHs. Our findings a rm that the angular speed component of the quadratic function in MSHs model is extremely important for action representation with curved displacement. Such a displacement cannot be fitted by the spatiotemporal features of the standard real SHs. To further asses the performance of the proposed approach, we also compare the performance of ELM with that of a support vector machine (SVM) classifier and report the obtained recognition accuracies on the same datasets. We simply use a linear SVM  (Chang and Lin, 2011)  to compare the recognition algorithms. The recognition accuracies corresponding to ELM and SVM are reported in Table  8.
Our finding indicates that ELM performs better than SVM based on the MSR dataset, achieving a recognition accuracy of 90. 98% (as opposed to the 86. 36 % attained by SVM). Nonetheless, both classifiers exhibit distinctive results in the case of the UTKinect dataset. The performance of SVM based on the Florence dataset is slightly lower than that of ELM; the recognition rate of the latter is 6. 07% higher than that of the former. For the G3D dataset, the result of ELM conforms to that of SVM.
For the action recognition, ELM was used. The performance of the proposed method was evaluated based on recent 3D skeletonbased datasets. We compared the proposed method with the existing state-of-the-art methods either by adopting pure skeleton data or by directly relying on depth data. The experimental results revealed that depending on the used datasets, the proposed method can obtain results similar to those of the extant methods or outperform them. The findings also indicated that MSHs are well suited for action representation with curved movement and angular direction changes. In summary, our newly proposed method is in line with the recently presented methods for 3D skeleton-based pose representation. The angular direction estimated from skeleton data and its derived SHs are relevant for action recognition and can be successfully used to capture temporal changes in action and obtain a high recognition rate.