esa-sceva/satcom-chunk-collection huggingface dataset:
# Responses to the Impact of Drought on Carbon and Water Use Efficiency in Inner Mongolia
## 3 Results
### Effects of Drought on the CUE and WUE of Vegetation  
The correlation coefficients of CUE and DSI varied from \(-0.977\) to \(0.643\) (Figure 7a), with substantial geographical variability. In \(2.04\%\) of the research region, CUE was positively linked with the drought index (DSI), primarily in the Loop Plain, near the Yinshan Mountains, and in the Daxinganling area. This suggests that an increase in the drought index in these areas led to an increase in the CUE of vegetation, i.e., an increase in carbon storage. The negative correlation between the drought index (DSI) and the CUE was the  
Figure 5: Trends of CUE, WUE, NPP, GPP, and ET in Inner Mongolia from 2001 to 2020. (**a**) Temporal trends of CUE and WUE. (**b**) Temporal trends of NPP, GPP, and ET.  
Figure 6: Spatial distribution of the rates of change in CUE and WUE and their significance in Inner Mongolia from 2001 to 2020. (**a,b**) Rate of change in CUE and its significance. (**c,d**) Rate of change in WUE and its significance.  
strongest, and the area with the largest negative correlation, accounting for 97.96% of the watershed area, was primarily distributed near Ordos, Xilinguole, and Hulun Lake, where 50.1% and 21.7% of the areas had highly significant correlations (\(p<0.01\)) and significant correlations (\(p<0.05\)), respectively (Figure 7b).  
The correlations between WUE and DSI were negative and variable, from \(-0.973\) to \(0.953\) (Figure 7c), and their geographical distribution was more or less the inverse of that of CUE and DSI. The Mawusus Sands in western Inner Mongolia, Xilinguole in central China, and the region around Chifeng and Tongliao in the east were the primary locations with positive and negative correlations between DSI and WUE, accounting for 54.21% of the vegetation cover. In 45.79% of the research area, mostly in the Loop Plain and the Daxinganling region, there was a negative correlation between WUE and the drought index (DSI). Only 17.4% of points in the region were statistically significant (\(p<0.05\)), with only 6.2% of points showing a highly significant link (\(p<0.01\)) (Figure 7d).  
The correlation coefficients of CUE, WUE, and DSI for different vegetation types were compared, showing that except for GRA, CUE and WUE were negatively correlated with the DSI index. The correlation between CUE and DSI was \(-0.141\) in DNF and \(-0.585\) in GRA (Figure 8a). There was a 0.112 correlation between WUE and DSI in GRA, and a \(-0.212\) correlation between CUE and DSI in MF. The strongest correlation was seen between WUE and DSI in GRA (Figure 8b). Overall, the negative link between CUE and drought in Inner Mongolia was much stronger than that between CUE and WUE.  
Figure 7: Illustration of the spatial distribution of the correlation coefficients of CUE, WUE, and DSI in Inner Mongolia between the years 2001 and 2020. (**a,b**) Correlations between CUE and DSI and their significance; (**c,d**) correlations between WUE and DSI and their significance.
## 5 Results and Discussion
In Figure 12, the APE\({}_{\text{max}}\) of the MAGA-BP prediction model is only about 4%, while the APE of other prediction points is less than 2%, and the MAPE is 2.23%, which is also less than 3%. According to existing studies [29; 40; 51; 52; 53; 54], this prediction error is acceptable. Therefore, the MAGA-BP prediction model is reliable. Compared with the BP prediction model, only the sixth prediction point has a slightly higher deviation, and the deviations at other points are lower than those of the BP prediction model. Compared with the GA-BP model, only the deviations of the fourth and fifth points are slightly larger. This individual deviation phenomenon is normal, which is mentioned by [PERSON] et al. [30]. As a whole, the prediction model optimized by MAGA is more accurate.
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
## 2 Materials and Methods
### Evaluation of Image Processing Algorithm Effectiveness  
The human evaluator or quantitative evaluation methods can calculate the efficiency and effectiveness of image processing algorithms [17]. Some of the metrics approached and extensively used by researchers for evaluating the accuracy of image processing algorithms include dice similarity coefficient (\(DSC\)), Jaccard index, precision, and recall. In the present work, we use \(DSC\) and Jaccard index to assess the effectiveness of image thresholding with the implementation of the k-means clustering. We used \(DSC\) and Jaccard index for evaluating variation in flow area and vegetation growth. We calculate the efficiency of clustered segmented images to manually segmented images.  
The \(DSC\) works on binary numbers, and its value ranges from 0% to 100% for no overlap to complete overlap between two data sets of image thresholding. The \(DSC\) is defined as:  
\[DSC=\frac{2|A\cap B|}{|A|+|B|} \tag{6}\]  
where \(|A|\) and \(|B|\) are the number of elements of set \(A\) and set \(B\), respectively.  
The Jaccard index (\(J\)) is the ratio of the intersection of two data sets to the union of the two sets. It also ranges from 0 to 100%, describing the same overlap conditions as \(DSC\). The Jaccard index (\(J\)) is given by:  
\[J(A,B)=\frac{|A\cap B|}{|A\cup B|} \tag{7}\]  
Figure 2: Flow chart of k-means clustering algorithm steps.
## 3 Results and Discussion
### Analysis of ANN
#### 3.2.2 Analysis of ANN and Created Correlations
However, in the same area, a very large amount of predictions with a low error are also existent, so the relative amount of large errors remains low. In summary, this concept for an enhanced ANN promises to deliver very good results even for the edges of available data.
## 2 Methods
### Mesh Sensitivity and Element Size  
A mesh sensitivity study was conducted for M2 and \(L_{\text{SVE}}=50\)\(\upmu\)m to evaluate the impact of varying element sizes (\(h=4,2\) and \(1\)\(\upmu\)m) on elastic and plastic responses. Figure 3 illustrates that the _Probability Density Functions_ (PDFs) for both bulk modulus, \(\kappa\), and average yield stress, \(Y\), remained consistent across all element sizes and converged as \(h\) is refined. This behavior indicates that the results were stable and not significantly influenced by element size. The finer mesh (\(h=1\)\(\upmu\)m) was chosen for the analysis to provide a high-resolution representation of the material.
# Functionalization Strategies and Fabrication of Solvent-Cast PLLA for Bioresorbable Stents
## 3 Results and Discussion
### PLLA Film Properties
**SC**: Solvent casting PLLA films; **TT**: Thermally treated SC films; **OP**: Oxygen plasma TT films; **SH**: Sodium hydroxide etched TT films; **ET**: Enzyme treated TT films. affect the PLLA chain length: Nor by generating polymerization, neither by degrading PLLA. The three functionalizing treatments could break the PLLA chains and, consequently, reduce the PLLA films M\({}_{\text{w}}\). No significant changes were observed after plasma activation (OP) and enzyme hydrolysis (ET) suggesting that these processes work at the outermost surface layer. On the contrary, SH showed a significant 10% reduction of the molecular weight compared to TT meaning the NaOH etching affects a deeper surface layer compared to the other treatments. A huge difference in crystallinity is observed between the REMEDY stent, 50.5 \(\pm\) 2.7%, and the processed films, results are below 26% (Table 2). The SC films presented a percentage of crystallinity four times lower than the PL65 pellet employed for its synthesis. This decrease of crystallinity due to the solvent casting process has already been reported [49] and is mainly associated with the fast chloroform evaporation not allowing polymer macromolecules to diffuse to the crystal growth front and overcome the energy barrier of deposition [50]. As expected, the thermal treatment helped increase crystallinity, which doubled in TT due to the chain reorganization [45; 51]. Moreover, the three activation treatments did not affect crystallinity. A tensile test was done on SC and TT to evaluate the effect of the thermal treatment on mechanical properties. TT films presented a five times higher Young's modulus (2.85 \(\pm\) 0.27 GPa) and two times higher UTS (62.2 \(\pm\) 5.7 MPa) compared to the SC films (E: 0.53 \(\pm\) 0.10 GPa and UTS: 28.9 \(\pm\) 2.6 MPa). The increased mechanical properties on TT films are related to their increased crystallinity [52] and the evaporation of solvent residues was produced by the thermal treatment. Indeed, solvent residues act as a plasticizer and dramatically reduce the tensile strength [49]. The Young's modulus and UTS of the TT films are in the upper range of the mechanical properties reported for the PLLA family [53]. This allows inferring that the obtained solvent-cast PLLA would probably respond properly to the mechanical stress occurring during the stent implantation, justifying the interest of obtaining 3D printed PLLA biodegradable stents by the solvent casting direct writing technique [32].
## 4 Experimental Results and Analysis
### Error Analysis with Scatter Plots and Box Plots
This advantage can provide a more reliable reference for the decision-making of relevant departments in practical applications. For example, in terms of air quality control decisions, based on the accurate prediction of the model, pollution prevention and control measures can be arranged more reasonably. However, in some other months, the box plot of the LightGBM model shows that the dispersion of its predicted data is large, indicating that there is a certain degree of fluctuation in the prediction error, and the adaptability of the model can be further improved. Figure 10: Scatter Plots of PM\({}_{2.5}\) Predictions in 2023. The prediction performance of the Prophet model in most months is poor, and the boxes of the predicted data box plot are generally longer and the degree of dispersion is large. However, in months such as September to November, the lower quartile is close to that of the original data, with a deviation of about 7 \(\mathrm{\SIUnitSymbolMicro g/m^{3}}\), indicating that in these months, the model has a certain degree of accuracy in predicting the lower value part of the data. This may be because the change characteristics of PM\({}_{2.5}\) concentration in these months are more consistent with some patterns in the historical data learned by the model. However, the large error of the Prophet model in other months limits its application effect in the overall forecast of the whole year, and further optimization is needed to improve stability. Observing the data of each month throughout the year, in months with obvious seasonal characteristics, such as winter (December-February) or summer (June-August), the fluctuation trend of the box plot of the SARIMA model predicted data is more similar to the original box plot, and the deviation of key statistics (such as median, quartiles, etc.) is within 7 \(\mathrm{\SIUnitSymbolMicro g/m^{3}}\). This is due to the fact that the SARIMA model, as a classic time series model, can better capture the seasonal changes in the data. Therefore, the model has certain advantages in understanding and predicting the changes in PM\({}_{2.5}\) concentration caused by seasonal factors, and can provide valuable reference for the study and prediction of seasonal air quality changes. However, in some months, there are still some differences between its box plot and the original data box plot, indicating that the model still has certain limitations when dealing with some special situations or complex changes.
## 4 Conclusions  
In this research, the corrosion resistance and tribocorrosion resistance in [PERSON]'s physiological solution of TA3 implanted by Nb ions were systematically investigated. The observations of this study are as follows:  
The implantation led to pits on the surface caused by irradiation damage and cascade collision, but it also made the surface smoother. The XRD patterns showed that Nb ions mainly exist in the form of a solid solution. The nanoindentation tests showed that elastic modulus and abrasion decrease because of the irradiation effect during implantation.  
The potentiodynamic polarization curves revealed that the implanted samples indicated a higher corrosion potential and a lower current density, indicating that Nb ions can effectively improve the thermodynamic and kinetic stability of the passivation layer and promote the formation rate of passivation film.  
The tribocorrosion tests showed that the ion implanted samples show better thermodynamic stability and can still display better corrosion behavior after the wear behavior. The morphology of the wear scar showed that the Nb-implanted sample has a lower pitting tendency, but due to the surface's partial softening and brittle passivation film, the abrasive wear and oxidation wear degree of the sample increased.  
Conceptualization, P.Z., C.X. and H.H.; methodology, H.H., C.X. and F.L.; validation, P.Z., D.W. and H.H.; formal analysis, P.Z., D.W., H.H. and C.X.; investigation, H.H., C.X., F.L. and K.Y.; resources, P.Z. and D.W.; data curation, P.Z., C.X. and H.H.H.; writing--original draft preparation, P.Z., C.X. and H.H.; writing--review and editing, P.Z., C.X. and H.H.; visualization, C.X. and H.H.; supervision, P.Z.; project administration, P.Z.; All authors have read and agreed to the published version of the manuscript.  
This project was supported by the Natural Science Foundation for Excellent Young Scientists of Jiangsu Province, China (Grant No. BK20180068), the China Postdoctoral Science Foundation (Grant No. 2018M630555), Opening Project of Materials Preparation and Protection for Harsh Environment Key Laboratory of Ministry of Industry and Information Technology (Grant No. XCA20013-1).  
The authors declare no conflict of interest.
## 3 Experimental Results and Analysis
### Dynamic Elastic Modulus Variation  
The dynamic elastic modulus of the temperature-water-coupled sandstone specimen with axial pressure is shown in Figure 9.  
Figure 8: Dynamic peak strain and change with axial pressure.  
Figure 9 shows that the elastic modulus of the sandstone sample decreases with increasing axial pressure. The dynamic the elastic modulus of the temperature-water-coupled sandstone specimen is 27.93 GPa when no axial pressure is applied, 26.44 GPa when the axial pressure is 0.5 MPa, and 7.23 GPa when the axial pressure is 4 MPa, with a decrease of 19.21 GPa. The decrease for this phenomenon occurs because of the action of axial pressure on the sandstone specimen, causing the internal structure of the specimen to be damaged, which decreases the dynamic compressive strength and increases the dynamic strain, leading to a decrease in the elastic modulus of the sample. The modulus of elasticity decreases. As the axial pressure increases, the internal cracks in the sandstone continue to nucleate and expand, accompanied by the formation of new cracks, making the specimen more susceptible to deformation and damage, resulting in a sharp decrease in the modulus of elasticity. The dynamic modulus of elasticity of the temperature-water-coupled sandstone specimen shows a decreasing trend with increasing axial pressure. The fitted functional relationship is shown in Equation (3), with a correlation coefficient of 0.9641, and the negative correlation of the quadratic function is obvious.  
\[E(P)=29.611-2.814P-0.675P^{2}\ \ (R^{2}=0.9641) \tag{3}\]  
where \(E(P)\) represents the dynamic elastic modulus of the sandstone sample under the coupling effect of temperature and water.
## 5 Geometric Implications for Optimizing PD Arrays for Received Power  
Equation (16) illustrates the dependency of received power on AOI \(\varphi\), attenuation coefficient \(c\), and relative light intensity at location of receiver \(\tau_{\alpha}\). However, the level of influence of each feature in mobile UOWC may be more dynamic based on the environmental properties. While PD arrays have often been used to improve the signal-to-noise ratio (SNR), the geometric implications for PD placement are unclear. Here, we simulate the effect of PD placement under different directional, rotational, and attenuation parameters as applicable to mobile UOWC.
# Error Probability of a Coherent \(M\)-ary PSK FSO System Influenced by Phase Noise
## 3 Error Rate Analysis
### Symbol Error Probability  
Based on FSM, the average SEP of the coherent \(M\)-ary PSK FSO system with ideal coherent detection (the phase noise is absent) is  
\[P_{s}^{\varphi=0}=1-\int\limits_{-\pi/M}^{\pi/M}\!\!f_{\bar{\psi}}(\psi)\, \mathrm{d}\psi=1-\frac{1}{M}-\sum_{n=1}^{\infty}\frac{2b_{n}}{n}\sin\Bigl{(} \frac{n\pi}{M}\Bigr{)}. \tag{20}\]  
In the case of the coherent FSO system in the presence of the phase noise, a conditional SEP is found as  
\[P_{s}(\varphi)=1-\int\limits_{\varphi-\pi/M}^{\varphi+\pi/M}\!\!f_{\bar{\psi} }(\psi)\mathrm{d}\psi=1-\frac{1}{M}-\sum_{n=1}^{\infty}\frac{2b_{n}}{n}\sin \Bigl{(}\frac{n\pi}{M}\Bigr{)}\cos(n\varphi), \tag{21}\]  
After averaging (21) over the PDF of the phase noise given in (7), the SEP of the coherent PSK FSO system is derived as  
\[P_{s}=\int\limits_{-\pi}^{\pi}\!\!P_{s}(\varphi)f_{\varphi}(\varphi)\mathrm{d }\varphi=1-\frac{1}{M}-\sum_{n=1}^{\infty}\frac{2\pi b_{n}c_{n}}{n}\sin\Bigl{(} \frac{n\pi}{M}\Bigr{)}, \tag{22}\]  
where the Fourier coefficients \(b_{n}\) and \(c_{n}\) are previously defined in (18) and (8), respectively. The expression for the Fourier coefficients in (18) was derived for the first time in this paper. This expression allows us to evaluate the numerical value of SEP for any values of the channel parameters after application in (22).  
Those numerical results related to the effect of imperfect compensation of phase error on error probability presented in [28; 29] can be checked by using Fourier coefficients given by (19) in combination with formula for SEP (22).  
If symbols are mapped into bits by Gray rule, then the bit error rate (BER) can be simply approximated as BER \(\approx\) SEP / \(\log_{2}M\) ([35], p. 271).  
The proof of convergence of the series in (22) is presented in the following subsection, where estimation of the truncation error in summation (22) is also given.
## 3 Results and Discussion
### Receptor Binding Pocket Analysis  
Potential ligand binding pockets of ER-\(\alpha\), PPAR-\(\alpha\), and EGFR were predicted by the POCASA web server. Predicted binding pockets are shown in Figure 1. For ER\(\alpha\), the pocket ranked number 1 by POCASA had a volume of 233 A and a VD of 2355. Pockets 2 and 3 had volumes of 87 A and 74 A and VD of 420 and 180, respectively. The large diversity of pocket volumes and VD for ER indicate that this receptor can bind to a variety of ligands; thus it was predicted to potentially interact favorably with the designed conjugates and peptides. Studies have shown that when several naturally occurring flavonoids were docked to ER, many interacted with Glu353, Arg394, Thr347, and Asp351 residues [52].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Peptide Sequence** & **Mutation Position** & **SVM Score** & **Hydrobobicity** & **Hydrobatibility** & **Hydrobilicity** & **pI** \\ \hline YIHWYGYTPQN & None & 0.88 & \(-0.14\) & \(-1.77\) & \(-1.08\) & 7.08 \\ \hline FHWYGYTPQN & 1 & 1.00 & \(-0.08\) & \(-1.36\) & \(-1.10\) & 7.09 \\ \hline YIWYGYTPQN & 2 & 0.77 & \(-0.03\) & \(-1.00\) & \(-1.21\) & 5.87 \\ \hline YIHWYGYTHQN & 8 & 0.82 & \(-0.17\) & \(-1.93\) & \(-1.13\) & 7.25 \\ \hline YIHWYGYTPQD & 10 & 0.85 & \(-0.15\) & \(-1.77\) & \(-0.80\) & 5.09 \\ \hline
**Peptide Sequence** & **Secondary Structure** & \multicolumn{2}{c}{**Solvent Accessibility/Residue**} & \multicolumn{2}{c}{**C Score**} \\  & & **in the Sequence \({}^{*}\)** & \multicolumn{2}{c}{**C Score**} \\ \hline YIHWYGYTPQN & CCC CCC CCC CCC & 7-4-3-4-4-3-4-4-7-8 & \(-0.58\) \\ \hline FHWYGYTPQN & CCC CCC CCC CCC & 6-4-3-4-4-3-4-4-7-8 & \(-0.56\) \\ \hline YIWYGYTPQN & CCC CCC CCC CCC CCC & 7-4-3-4-4-3-4-4-7-8 & \(-0.68\) \\ \hline YIHWYGYTPQD & CCC CCC CCC CCC CCC & 7-4-3-4-4-3-4-4-7-8 & \(-0.46\) \\ \hline \end{tabular} \({}^{*}\) Numbers given are for each residue in the sequence, in the range of 0 to 9, with 9 having the highest solvent exposure and 0 having lowest solvent exposure.  
\end{table}
Table 3: Properties of Designed Anticancer Peptides, (**a**) Peptide AntiCP Results, (**b**) I-TASSER Results.  
For PPAR-\(\alpha\), the pocket number 1 had a volume of 165 A and a VD of 1447. Pockets 2 and 3 had volumes of 196 A and 76 A and VD of 1406 and 204, respectively. In previous studies, molecular docking of ligands with PPAR-\(\alpha\) showed binding occurred in mostly hydrophobic pockets and interacted with Tyr314, Tyr464, Ser280, and His440 [53]. The POCASA results for EGFR showed that pocket number 180 was ranked number 1 and had a volume of 428 A and a volume density of 1954.
## 4 Experiment Analysis
### The Influence of Own Parameters on the Experimental Results  
In this subsection, to verify the effect of its own parameters on the clustering effect, we conducted experiments with the Wine dataset as an example, and the experimental results of the control parameter \(\psi\) and parameter \(t\) are shown in Figure 8. When \(\psi=\text{len(wine)}/5\), as the number of isolation trees increases, the number of wrong points becomes less and less. When the number of isolation trees is the same, as \(\psi\) increases, the experimental results are better. Therefore, the experiments show that when the number of \(\psi\) and \(t\) is within the maximum limit, the clustering effect is better.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Algorithm** & **ACC** & **NMI** & **ARI** & **Algorithm** & **ACC** & **NMI** & **ARI** \\ \hline Ionosphere & & & & Waveform & & & \\ \hline IKDC & **1.000** & **1.000** & **1.000** & IKDC & **0.980** & **0.984** & **0.955** \\ DPC & **1.000** & **1.000** & **1.000** & DPC & 0.968 & 0.958 & 0.936 \\ DBSCAN & **1.000** & **1.000** & **1.000** & DBSCAN & 0.524 & 0.152 & 0.135 \\ KNN-DPC & **1.000** & **1.000** & **1.000** & KNN-DPC & 0.635 & 0.218 & 0.223 \\ SNN-DPC & 0.998 & 0.899 & 0.950 & SNN-DPC & 0.598 & 0.326 & 0.311 \\ FCM & 0.850 & 0.442 & 0.488 & FCM & 0.628 & 0.374 & 0.353 \\ \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline  & & Waveform & & & \\  & & & (noise) & & & \\ \hline IKDC & 0.952 & 0.873 & 0.852 & IKDC & **0.983** & **0.979** & 0.959 \\ DPC & 0.882 & 0.710 & 0.627 & DPC & 0.969 & 0.958 & 0.936 \\ DBSCAN & 0.910 & 0.753 & 0.741 & DBSCAN & 0.968 & 0.957 & 0.935 \\ KNN-DPC & 0.904 & 0.743 & 0.727 & KNN-DPC & 0.970 & 0.960 & 0.940 \\ SNN-DPC & **0.966** & **0.878** & **0.899** & SNN-DPC & 0.974 & 0.963 & **0.974** \\ FCM & 0.949 & 0.850 & 0.834 & FCM & 0.891 & 0.862 & 0.936 \\ \hline \end{tabular}
\end{table}
Table 6: ACC, NMI, and ARI of six algorithms on different real datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Algorithm** & **ACC** & **NMI** & **ARI** & **Algorithm** & **ACC** & **NMI** & **ARI** \\ \hline Libras & & & & Spectrometer & & \\ \hline IKDC & **0.503** & 0.543 & **0.401** & IKDC & **0.979** & **0.975** & **0.975** \\ DPC & 0.420 & 0.514 & 0.345 & DPC & 0.968 & 0.958 & 0.936 \\ DBSCAN & 0.458 & 0.626 & 0.309 & DBSCAN & 0.968 & 0.957 & 0.935 \\ KNN-DPC & 0.497 & 0.634 & 0.361 & KNN-DPC & 0.970 & 0.960 & 0.940 \\ SNN-DPC & 0.494 & **0.661** & 0.393 & SNN-DPC & 0.974 & 0.963 & 0.974 \\ FCM & 0.183 & 0.216 & 0.069 & FCM & 0.891 & 0.862 & 0.936 \\ \hline WDBC & & & & Seeds & & \\ \hline IKDC & 0.917 & 0.720 & **0.751** & IKDC & **0.934** & **0.763** & **0.830** \\ DPC & 0.830 & 0.373 & 0.373 & DPC & 0.910 & 0.716 & 0.742 \\ DBSCAN & 0.631 & 0.530 & 0.530 & DBSCAN & 0.624 & 0.423 & 0.487 \\ KNN-DPC & 0.870 & **0.766** & 0.436 & KNN-DPC & 0.914 & 0.734 & 0.766 \\ SNN-DPC & 0.874 & 0.535 & 0.650 & SNN-DPC & 0.924 & 0.754 & 0.789 \\ FCM & **0.928** & 0.609 & 0.631 & FCM & 0.900 & 0.691 & 0.727 \\ \hline \end{tabular}
\end{table}
Table 7: ACC, NMI, and ARI of different clustering algorithms on different real datasets.