esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Pose Estimation Experiments in Indoor and Outdoor Environments
#### 4.4.1 Datasets  
For indoor pose estimation, the ScanNet dataset was used for validation. This dataset consists of 1613 monocular images, each accompanied by corresponding pose and depth maps. It presents significant challenges for matching algorithms, including issues with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Overall** & **Illumination** & **Viewpoint** \\ \cline{2-4}  & & **Accuracy (\(\varepsilon\) \(<\) 1/3/5 Pixel)** & \\ \hline D2-Net [34] & 0.38/0.71/0.82 & 0.66/0.95/**0.98** & 0.12/0.49/0.67 \\ R2D2 [41] & 0.47/0.77/0.82 & 0.63/0.93/**0.98** & 0.32/0.64/0.70 \\ SP [32]+LG [38] & 0.39/**0.85**/**0.90** & 0.52/0.96/**0.98** & 0.28/**0.75**/**0.84** \\ Sparse-NCNet [42] & 0.36/0.65/0.76 & 0.62/0.92/0.97 & 0.13/0.40/0.58 \\ Patch2 Pix [43] & 0.50/0.79/0.87 & 0.71/0.95/**0.98** & 0.30/0.64/0.76 \\ LoFTR [36] & **0.55**/0.81/0.86 & 0.74/0.95/**0.98** & **0.38**/0.69/0.76 \\ MatchFormer [44] & **0.55**/0.81/0.87 & 0.75/0.95/**0.98** & 0.37/0.68/0.78 \\ OURS & **0.55**/0.83/0.88 & **0.76**/**0.96**/**0.98** & 0.33/0.69/0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the Homography Estimation Experiment. Bold values represent the best results among all methods.  
wide baseline matching and textureless regions. For outdoor pose estimation experiments, the MegaDepth dataset was employed. This dataset contains 196 outdoor scenes and over one million internet-sourced images, which include a wide range of viewpoint changes and repetitive textures. The images, taken from tourist photographs, exhibit significant variations in lighting and viewpoint, providing valuable experimental data for training robust pose estimation models.
# Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals
## 3 Experimental Setup and Linearization Results  
The EGMP model generates an envelope optimized for linearity, which reduces the nonlinear distortion at the PA output. However, as the nonlinear distortion grows with signal bandwidth, for certain signal bandwidth configurations, the baseband I-Q DPD (e.g., based on the GMP behavioral model) is still required to compensate for the distortion caused by the PA's nonlinear characteristic. As depicted in Figure 1, the proposed linearization strategy combines the shaping function, the envelope optimizer (based on the EGMP model), and an optional I-Q DPD. The following subsections provide a brief description of the experimental setup, as well as experimental results validating the proposed linearization strategy when considering a 60 MHz bandwidth new radio (NR) 5G signal.
## 5 Discussion
### Limitation and Future Works
#### 5.3.4. Integration of Biomechanical Considerations  
This study employed a simplified model that enables a parametric representation of essential movement characteristics. High-dimensional models require biomechanically consistent control for each joint and segment; however, their computational cost is substantial, making practical simulations challenging. The SLIP model allows for the expression of ground reaction force transmission and changes in the center-of-mass motion through stiffness parameters. Furthermore, the force control, introduced as a motion tactic, captures dynamic interactions beyond passive elements, such as the influence of upper body movement and arm swings on the center of mass. This approach enables a systematic examination of the biomechanical factors affecting running performance through abstract yet interpretable parameters. For example, in biomechanics, running economy is often evaluated based on factors such as reduced vertical oscillation and increased leg stiffness [30; 31; 32]. The proposed model parametrically represents these aspects while also allowing for the mathematical description of individual stiffness characteristics. Integrating a motion tactics model enables the quantification of key movement factors through comparisons with professional runners. Additionally, we acknowledge the importance of discussing the limitations and broader applications of this study. The concepts of \"motion strategy\" and \"motion tactics\" introduced here provide a structured framework for evaluating overall movement patterns while maintaining biomechanical coherence. This approach is valuable not only for research but also for practical applications in areas such as sports coaching. Clarifying the strategic aspects of movement supports systematic performance enhancement. Furthermore, a mathematical description of motor control characteristics provides a foundation for skill transmission, which has traditionally relied on empirical knowledge, fostering a more structured approach to movement optimization.
## 6 Implementation and Simulation
### Leach-KNN  
As was mentioned in Section 2 and shown in Figure 3, the implementation of the original LEACH contained 15 clusters. The network consists of nodes with varying energy consumption levels randomly sorted into 15 groups. Cluster heads, shown by black circles, connect all nodes to the central base station in the network. Although all nodes have identical connection and processing capabilities, their energy consumption varies.  
Some nodes, such as cluster heads, use more energy than others in the network. From Figure 3, some drawbacks can be noticed, such as three CHs with no connected nodes and more than two CHs being far away from the centre.  
The original LEACH and LEACH-KNN implementations were performed in MATLAB using the same parameters and initial conditions. After implantation, the simulation environment consists of 50 nodes randomly distributed in the 100 m \(\times\) 100 m field using the same parameters and initial conditions specified in Table 1. The network is heterogeneous, and the node sensors have different energy consumption levels. In the centre of the network is the base station (BS). Figure 8 shows the three-phase simulation using the LEACH-KNN  
Figure 8: Implementation of CH1–CH4 for LEACH-KNN. Phase A: Assigned coordination of the cluster heads—CH marked with a blue stars, Phase B: Nodes select CH—marked with blue circles around, Phase C: Prepare for next round.  
implementation as an example (for CH1, CH2, CH3, and CH4), which was performed using the parameters and initial conditions specified in Table 1.
###### Abstract  
This paper presents the synthesis of a 64 \(\times\) 64 thinned array, using the branch and bound technique with convex optimization and a low sidelobe level for satellite communication. The branch and bound technology decomposes optimization into several subproblems. The convex optimization transforms the optimized variable binary [0, 1] of the thinned array into a continuous variable, i.e., from 0 to 1. Based on the branch and bound technique with convex optimization, the fast and accurate convergence of the synthesis of large-scale thinned array was achieved, and 2400 elements were selected for a full 64 \(\times\) 64 array with a sidelobe level that satisfies the system requirements. The proposed thinned array with 2400 elements was simulated via full-wave 3D simulation. A prototype of the proposed thinned array was fabricated and measured. The simulated and measured results verified the effectiveness of the branch and bound technique with convex optimization.  
large-scale; thinned array; branch and bound technique; convex optimization; satellite communication 2025  
4 December 2024  
13 December 2024  
18 December 2024  
1
## 4 Security Threats
### Implementation
#### 4.4.7 Nepa, Cepa and LORA Parasite Attacks  
Recently, three new misbehavior scenarios have appeared in the literature, namely the Nepa (Network Endo-Parasite Attack), CEPA (Channel Esto-Parasite Attack) and LORA (Low-cost Ripple-effect Attack) attacks [62]. They pose a threat to the operation of modern mesh networks, but can also be executed against DSA schemes.  
In brief, in the Nepa and CEPA parasite attacks, an attacker may illegally occupy the spectrum portions considered most valuable in a DSA network in order to increase his profit, degrade the received QoS and the satisfaction levels of his competitors, or even lower the reputation of special transmission opportunities [49].  
In the case of LORA, an attacker may on purpose falsely announce to his competitors, or even to the spectrum sharing mechanism, that he will occupy a special spectrum portion, even if he has no right in doing so. As a consequence, any potential user will either avoid competing for them, or hasten to switch to a new one to preserve normal operation.
# A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading
## 4 Experiment and Results
### Experimental Results and Analysis
#### 4.2.3 Module Ablation Experiments  
In order to verify the effectiveness of CFEN and ASPP of MaMNet, the ablation experiments are conducted in this section, the loss weights are set to the optimal loss weights i.e., (\(\alpha\), \(\beta\)) = (0.5,1.0).  
The following conclusions can be drawn from Table 5:  
1. When the CFEN module is added to the branch for DME grading, DME _Ac_ improved by 0.029 and DR _Ac_ improved by 0.010; when the CFEN module is added to the subnet for DR grading, _Joint Ac_ improved by 0.009 and DR _Ac_ improved by 0.019. The above results demonstrated that the CFEN module explored more correlation features for DME grading and DR grading.
2. Due to the addition of the ASPP module, _Joint Ac_, DME _Ac_, and DR _Ac_ are improved by 0.020, 0.019, and 0.047 respectively in the joint grading course. From the experimental data, it can be seen that ASPP can better extract global lesion features and improve the grading performance.
3. Due to the CFEN and ASPP module, the highest values of _Joint Ac_, DR _Ac_, and DME _Ac_ are obtained. These results demonstrate that MaMNet can effectively extract the self-features, correlated features between DME and DR, and the global features of DR to enhance the grading performance.
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 4 Interference Cancellation  
As shown in Figure 3, MUI can be present in the system whenever two or more users are under the same beams. However, other users who are far away are free from interference. Since the RIS does not has the capacity to cancel the interference produced in the first hop, a zero-forcing (ZF) technique is adequate for this precoding stage [31; 32]. Therefore, the complete transmission block is precoded by using ZF. Additionally, for that user under MUI, a block diagonalization (BD) precoding technique is first used [33]. The BD technique is implemented to design the phase and the amplitude of the reflected signals in the RIS. Note that the ZF technique cancels the channel. However, CSI in the second hop is used to transmit the spatial modulated signals, assuring that the complete information can be retrieved by the users at the destination. For the implementation of the system, two strategies are used. Strategy I is used when some users are under MUI, as shown in Figure 3. Strategy II can be used when all users are under MUI or all users can see the entire RIS.
## 4 Multimodal Fusion for Infrastructure Inspection
#### 3.3.3 Ensemble Models and Attention Mechanisms  
To further enhance the performance of AI models in GPR interpretation, ensemble methods have emerged as a powerful strategy. These techniques combine the outputs of multiple models to leverage their complementary strengths. For example, stacking architectures that integrate CNNs with gradient boosting classifiers can significantly improve classification accuracy and generalization. CNNs are adept at learning spatial patterns and extracting hierarchical features from radargrams, while gradient boosting methods such as XGBoost are effective at handling structured data and correcting errors made by earlier models in the ensemble. By combining these distinct capabilities, the overall system becomes more robust to noise and variability in GPR data [57, 58].  
In addition to ensembling, attention mechanisms have been increasingly adopted to further refine model focus. These mechanisms dynamically weight the importance of different regions in the input data, allowing the model to concentrate on critical features such as hyperbola peaks, object boundaries, or subtle reflection patterns. This selective focus improves interpretability and enables the more accurate detection of features, even under challenging conditions. Moreover, both ensemble strategies and attention mechanisms are particularly valuable in scenarios with a class imbalance or limited labeled data--common challenges in GPR applications. Together, they help mitigate overfitting, enhance feature discrimination, and ultimately deliver more reliable interpretation results.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 5 Experimental Results
### RQ1: How Effective Is CIB? How Much Improvement Can It Obtain Compared with the Baselines?
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Our CIB**} & \multirow{2}{*}{**MAHAKIL**} & \multirow{2}{*}{**AdaC2 [45]**} & \multirow{2}{*}{**AdaBoost**} & \multirow{2}{*}{**SMOTE**} & \multirow{2}{*}{**RUS [35]**} & \multirow{2}{*}{**None**} \\  & & [44] & & [46] & & [40] & \\ \hline CM1 & 17.5\(\pm\)1.5 & 18.5\(\times\)1.3 \(\checkmark\) & **12.0\(\pm\)3.2** & **12.4\(\pm\)2.9** & **19.0\(\pm\)**1.0 & 18.0\(\pm\)1.9 & **12.9\(\pm\)3.6** \\ KC1 & 31.4\(\pm\)0.4 & 31.6\(\pm\)0.4 & **30.7\(\pm\)0.3** & **30.5\(\pm\)0.4** & 30.9\(\pm\)0.9** & **32.8\(\pm\)**0.8 & **28.0\(\pm\)1.0** \\ KC3 & **23.4\(\pm\)1.7** & **23.4\(\pm\)1.9 \(\checkmark\)** & **20.3\(\pm\)3.0** & **20.9\(\pm\)2.6** & **23.4\(\pm\)**1.6 \(\checkmark\)** & **23.4\(\pm\)**1.7 \(\checkmark\)** & **21.1\(\pm\)2.9** \\ MC1 & 6.5\(\pm\)0.9 & **5.2\(\pm\)1.2** & **7.7\(\pm\)**0.7 & **7.7\(\pm\)**0.7 \(\checkmark\)** & **5.1\(\pm\)1.2** & **4.4\(\pm\)**0.6** & **3.3\(\pm\)1.5** \\ MC2 & 35.6\(\pm\)2.2 & 35.6\(\times\)3.0 \(\checkmark\) & 35.5\(\pm\)2.3 & 36.1\(\pm\)1.5 \(\checkmark\) & 36.8\(\pm\)1.4 \(\checkmark\) & **36.9\(\pm\)**3.4 & 35.6\(\pm\)1.8 \(\checkmark\) \\ MW1 & 15.5\(\pm\)1.9 \(\checkmark\) & 15.1\(\times\)2.1 \(\checkmark\) & 14.0\(\pm\)2.5 & **12.9\(\pm\)2.9** & 15.3\(\pm\)2.3 \(\checkmark\) & **15.7\(\pm\)**2.1 & 13.7\(\pm\)3.5 \\ PC1 & **16.0\(\pm\)**0.7 & **15.4\(\pm\)0.8** & **15.7\(\pm\)**0.6** & 15.5\(\pm\)1.0 & 15.7\(\pm\)0.6 & **12.3\(\pm\)**1.8** & **14.3\(\pm\)1.6** \\ PC2 & 3.3\(\pm\)1.1 \(\checkmark\) & **3.9\(\pm\)**1.8 & **0.3\(\pm\)0.6** & **0.2\(\pm\)0.5** & 3.4\(\pm\)1.6 \(\checkmark\) & 3.0\(\pm\)1.1 & **0.2\(\pm\)0.5** \\ PC3 & **19.7\(\pm\)**0.5 & **19.1\(\pm\)**0.8 & **17.2\(\pm\)**0.9** & **17.0\(\pm\)**0.8** & 19.6\(\pm\)0.4 \(\checkmark\) & **17.0\(\pm\)1.4** & **16.5\(\pm\)**2.1 \\ PC4 & 20.2\(\pm\)0.6 & 21.0\(\pm\)0.5 & 21.6\(\pm\)0.3 \(\checkmark\) & **21.7\(\pm\)**0.2 & 20.5\(\pm\)0.4 & **15.0\(\pm\)1.5** & 21.3\(\pm\)0.4 \\ JM1 & 26.7\(\pm\)0.2 & 26.1\(\pm\)0.4 & 25.2\(\pm\)0.2 & 25.3\(\pm\)0.2 & 25.6\(\pm\)0.4 & **29.0\(\pm\)**0.1 & **22.2\(\pm\)**0.9** \\ \hline
**Average** & 19.6 & 19.5 & 18.2 & 18.2 & 19.6 & 18.9 & 17.2 \\ \hline
**Improvement of Average** & 0.4\% & 7.8\% & 7.8\% & 0.2\% & 4\% & 14.1\% \\ \hline
**Win/Tie/Lose** & 4/4/3 & 8/1/2 & 8/1/2 & 4/4/3 & 4/4/3 & 9/1/1 \\ \hline \hline \end{tabular} Note: (1) all numbers are in percentage (%) except the last two rows. (2) \(\checkmark\) denotes the corresponding method has no significant difference with the best method. (3) \(\checkmark\), and indicate our CIB method significantly outperforms the corresponding baseline with large, moderate, and small effect size, respectively.  
\end{table} 
Table 5: The comparison results of AGF for all methods in the form of mean \(\pm\) standard deviation. The best value is in boldface.
## 4 Experimental Results and Analysis
### Influences of Wet Air Flow Rate at Dehumidifier Inlet on Experimental Results
#### 4.2.1 Influence of Inlet Wet Air Flow Rate of Dehumidifier on Outlet Air Temperature  
Figure 9 shows the variation curve of the air temperature \(T_{\text{out}}\) at the outlet of the dehumidifier under experimental conditions when the inlet hot and wet air temperature of the dehumidifier is \(T_{\text{in}}\) = 338.15 K and the flow rates are 2.5, 5.0, 7.5 and 10.0 m\({}^{3}\)/h, respectively. In the experiment, the hot and wet air continuously transfers heat to the paraffin through the heat pipes, resulting in the melting of the paraffin after the temperature rises. The temperature difference between the hot end and the cold end of the same heat pipe decreases with the increase in time; that is, its heat transfer driving force decreases, resulting in the rise of the temperature \(T_{\text{out}}\) of the air out of the dehumidifier until \(T_{\text{out}}\) tends to be stable. When the total amount of paraffin remains unchanged, the greater the \(V\), the greater the flow velocity of the hot and wet air, the faster the heat transfer and the shorter the time required for \(T_{\text{out}}\) to reach stability. The greater the \(V\), the higher the total enthalpy of the hot and wet air, and the greater the stable value of \(T_{\text{out}}\).  
Figure 8: Melting of paraffin on the upper surface of phase change heat storage for CASE4 at different \(T_{\text{in}}\).  
Figure 9: Variation of air temperatures at the outlet of dehumidifier with time at different inlet wet air flow rates.  
4.2.2 Effect of Inlet Wet Air Flow of Dehumidifier on Temperature Field of Phase Change Heat Storage  
Under the experimental conditions, when the air volume flow is =2.5, 5.0, 7.5 and 10.0 m\({}^{3}\)/h, respectively, the melting state of paraffin on the upper surface of the phase change heat storage at different times is shown in Figure 10. It can be seen from the figure that, when other conditions are the same, the longer the heat exchange time, the greater the melting amount of paraffin. The greater the \(V\), the faster the paraffin melts.  
When the experiment lasted for 60 min and the flow rate of the hot and wet air at the inlet of the dehumidifier was 10.0 m\({}^{3}\)/h, the paraffin in each stage in the phase change heat storage was basically melted. At 2.5, 5.0 and 7.5 m\({}^{3}\)/h, respectively, the melting amount of paraffin in each stage of the phase change heat storage was about 60%, 70% and 80%. That is, the greater the wet air flow at the inlet of the dehumidifier, the greater the flow velocity, the faster the heat transfer and the faster the paraffin melted.
## 4 Experimental Results and Analysis
### Performance Comparison with the Related Schemes  
The performance of the proposed scheme is compared with other important \((k,n)\)-XVC schemes in terms or pixel expansion and contrast. The state-of-the-art schemes [21; 22; 25] formulated the different constructions of the existing \((k,n)\)-XVC schemes. We compare the experimental \((3,5)\)-XVC scheme and \((4,5)\)-XVC scheme with three other schemes. The results are shown in Table 3. We can see that our scheme keeps the high unchanged contrast, and, at the same time, it allows the pixel expansion to change among a slight large interval. Only the scheme in [21] has perfect contrast, but it reveals the secret image on a fraction of the recovered image. The proposed scheme does not need any mathematical model to compute the basis matrices, it can be built on any existing \((k,n)\)-OVC scheme. However, the schemes in [22; 25] require complex computation by using linear coding or integer linear programming. Although the proposed scheme uses average contrast to represent the visual quality of the revealed secret image, it can recover each pixel of the secret image deterministically. It provides a good strategy to improve the image quality of the VC scheme.
# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
## 1 Introduction
(2) Aiming at the problem of the low resolution of the computed periodic map spectrum feature maps and small changes in the value of energy under different fault types, which leads to the poor effect of direct feature map recognition, the spatial channel attention mechanism was used to improve the GoogLeNet classification model to judge the importance of the information in the image and the position in the image to assign different weights to the various parts of the input feature maps and to provide a more effective feature map of the transformer fault types for the subsequent GoogLeNet method. (3) This study built an experimental platform for experimental transformer fault signal acquisition and applied the proposed method to the vibration signals to achieve transformer fault diagnosis and localisation. The rest of the paper is organised as follows: Section 2 describes the SCA-VMD data processing model and the computation process of periodic map spectrum feature maps. Section 3 describes the improved GoogLeNet fault diagnosis method based on the attention mechanism. Section 4 builds an experimental platform for transformer fault data acquisition and constructs sample data. The feasibility of the transformer fault diagnosis method based on SCA-VMD and improved GoogLeNet is verified. Section 5 discusses the contributions of this paper. Section 6 provides the conclusion of this report.
## 3 Results
### Results of In Vitro Testing of the Antioxidant Capacity
#### 3.1.5 Ferroion Chelation Method with Ferrosine  
The method of the chelation of ferroions with ferrosine is one of the methods used to determine antioxidant capacity. The antioxidant capacity was expressed as milligrams of EDTA equivalent per gram of sample. By this method, it was possible to test the antioxidant capacity of the hydrophilic samples only, while the antioxidant capacity of the lipophilic samples could not be determined. An EDTA calibration curve was constructed in the concentration range of 1-20 mg/mL.  
The absorbances for the hydrophilic samples were obtained (Table 17) and the milligrams of EDTA/gram of sample was calculated (Table 18).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **\% of Inhibition** & **\(\mu\)g AE/g of Sample** \\ \hline
1 & 55.89 & 19.73 \\
2 & 64.26 & 23.04 \\
3 & 6.08 & 0.07 \\
4 & 80.99 & 79.67 \\
5 & 73.38 & 26.66 \\
6 & 0.76 & 0.02 \\
7 & 33.46 & 10.84 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Results expressed as percentage of inhibition and in \(\mu\)g AE/g of sample (ABTS method).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SAMPLE** & **A1** & **A2** & **A3** & \(\begin{array}{c}-\\ X\end{array}\) & **SD** & **CV (\%)** \\ \hline BLANK & 0.526 & 0.525 & 0.527 & 0.526 & 0.0010 & 0.19 \\
1 & 0.232 & 0.231 & 0.232 & 0.232 & 0.0007 & 0.30 \\
2 & 0.188 & 0.190 & 0.188 & 0.188 & 0.0014 & 0.74 \\
3 & 0.493 & 0.493 & 0.494 & 0.493 & 0.0007 & 0.14 \\
4 & 0.099 & 0.101 & 0.100 & 0.100 & 0.0010 & 1.00 \\
5 & 0.141 & 0.139 & 0.139 & 0.140 & 0.0007 & 0.50 \\
6 & 0.522 & 0.521 & 0.522 & 0.522 & 0.0007 & 0.13 \\
7 & 0.350 & 0.351 & 0.350 & 0.350 & 0.0007 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Absorbances obtained for lipophilic extracts of cream samples (ABTS method).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SAMPLE** & **A1** & **A2** & **A3** & \(\begin{array}{c}-\\ X\end{array}\) & **SD** & **CV (\%)** \\ \hline BLANK & 0.526 & 0.525 & 0.527 & 0.526 & 0.0010 & 0.19 \\
1 & 0.232 & 0.231 & 0.232 & 0.232 & 0.0007 & 0.30 \\
2 & 0.188 & 0.190 & 0.188 & 0.188 & 0.0014 & 0.74 \\
3 & 0.493 & 0.493 & 0.494 & 0.493 & 0.0007 & 0.14 \\
4 & 0.099 & 0.101 & 0.100 & 0.100 & 0.0010 & 1.00 \\
5 & 0.141 & 0.139 & 0.139 & 0.140 & 0.0007 & 0.50 \\
6 & 0.522 & 0.521 & 0.522 & 0.522 & 0.0007 & 0.13 \\
7 & 0.350 & 0.351 & 0.350 & 0.350 & 0.0007 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Results expressed as percentage of inhibition and in \(\mu\)g TE/g of sample (ABTS method).  
The results obtained by this method show that the hydrophilic sample 7 had the highest value of antioxidant capacity. The results for the other samples were similar and much lower than those for sample 7.