esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The present work being implemented in this study will focus on the three preprocessing filters: notch filter, high-pass filter, and common average reference (CAR) montage without downsampling. The neglect of downsampling was considered, as it would be necessary if we needed to reduce data size for computational efficiency; however, the adopted dataset had a sampling frequency that is sufficient for capturing relevant EEG bands. Feature extraction used statistical features, wavelet analysis, fractal dimensions, Hirth parameters, higher-order spectra, and a combination of all the previously mentionedfeatures. Related classifier techniques from the ER-EEG pipeline [7] included GSVM (an SVM classifier with rbf kernel) and CART (regression tree). An SVM classifier with a polynomial kernel and the LinearSVM classifier were introduced to the MI-EEG pipeline [14] specifically only using CSP features without Riemannian features. Lastly, this latest work utilized all four classes from the BCI Competition IV-2a dataset (left and right hands, feet, and tongue-related classes), highlighting that there were no additional artifact removal methods used other than the current preprocessing filtering mentioned above. This study introduces features from the field of emotion recognition (ER), which are specifically designed to capture complex, non-linear brain patterns, which can address some challenges in the motor imagery field, like the low signal-to-noise ratio (SNR) and difficulty in distinguishing between similar MI tasks (i.e., left-hand vs. right-hand movements). The cross-application of both domains, ER-EEG and MI-EEG, is the novel approach that this study offers. The lack of research in applying emotion recognition (ER) feature extraction methods to motor imagery (MI) tasks justifies the need for our current research.
# A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
## 5 Conclusions  
The classification of different groups of QoS is done using machine learning (ML). The input predictors were studied to determine the data robustness. In terms of BER, SNR, and maximum number of users, the suggested system's performance is analyzed analytically. Hypothesis testing is used to prove the ML input data robustness. The test showed 0.98, 0.97, and 0.98 for Q-factor, SNR, and BER, respectively. The multicollinearity of the data is studied using Pearson correlation and VIF as they are revealed for the dataset. The Q-factor has 4.8 VIF while the values for SNR and BER are 3.88 and 1.44. These results prove that each component is important for ML classification, as there is no existing multicollinearity between any components. Then, ML is used to classify whether the code weights are even or odd for both cases SPD and DD (QoS cases for video and audio). Hence, the ML is formulating a classification problem to classify four classes. ML evaluation shows promising results, as the DT and the RF classifiers achieved 94% and 99% accuracy, respectively. The ROC curves and AUC scores of each case are revealed, showing that the RF outperforms the DT classification performance. The study of other features, such as polarization and eye opening that significantly affects ML, is still under research.  
Conceptualization, S.A.A.E.-M. and A.M.; methodology, S.A.A.E.-M. and A.M.; software, S.A.A.E.-M. and A.M.; validation, S.A.A.E.-M., A.C., H.Y.A. and M.Z.; formal analysis, A.M., A.C., H.Y.A. and A.N.K.; investigation, S.A.A.E.-M., A.M. and H.Y.A.; resources, S.A.A.E.-M. and A.M.; writing--original draft preparation, S.A.A.E.-M. and A.M.; writing--review and editing, A.C. and H.Y.A.; visualization, A.C. and H.Y.A.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# A Comprehensive Review on Clay Soil Stabilization Using Rice Husk Ash and Lime Sludge
## 6 Conclusions  
This comprehensive review aimed to assess the efficacy of clay soil stabilization using green materials, including RHA and LS, to determine the optimum quantity of RHA and LS. Adopting alternative green materials for clay stabilization offers a win-win solution, benefiting both the environment and the construction industry. The construction sector plays a crucial role in promoting sustainability by reducing carbon emissions, preserving natural resources, and decreasing reliance on chemical stabilizers. Based on the analyzed studies, an increase in the content of RHA and LS led to an increase in the OMC of clay soil and a corresponding reduction in its MDD. Adding RHA up to 10% improved the UCS and CBR values, after which a decline was observed. Similarly, using LS content of up to 8% for structural purposes and up to 4% for roadway and pavement purposes enhanced the UCS and CBR values. Moreover, the combined use of RHA and LS resulted in a significant increase in UCS and CBR values. For optimal stabilization, a combination of 6% RHA and 7% LS is recommended for structural purposes, while 10% RHA and 4% LS are ideal for roadway and pavement applications. Finally, future studies can focus on conducting a comprehensive review of the mechanical characteristics of various soil types treated with RHA and LS, including an in-depth analysis of stabilization effectiveness, variations in strength parameters, and the long-term durability of treated soils under diverse environmental conditions. Such research can provide valuable insights into optimizing application methods, understanding soil-behavior interactions, and expanding the practical applications of RHA and LS in sustainable geotechnical engineering.  
Conceptualization, H.R.M., E.P., N.C. and T.M.; methodology, H.R.M., E.P., N.C. and T.M.; formal analysis, H.R.M., E.P. and T.M.; investigation, H.R.M., E.P. and T.M.; resources, E.P., N.C. and T.M.; writing--original draft, H.R.M.; writing--review and editing, H.R.M., E.P., N.C. and T.M.; supervision, E.P., N.C. and T.M.; project administration, E.P., N.C. and T.M.; funding acquisition, E.P., N.C. and T.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the project \"NGS--New Generation Storage\", with reference C644936001-00000045, financed by the Recovery and Resilience Plan and the Next Generation EU European Funds.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not available.  
**Acknowledgments:** The authors would like to thank the Recovery and Resilience Plan.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Proposed Scheme
### Analysis of Unmanned Store Events  
When an event is triggered, we will receive three images and the location of the merchandise cabinet. The three images are: the store's top view image, the product image when the event is triggered as shown in Figure 1a, and the product image after the event is triggered, as shown in Figure 1b. Through this information, we can judge which customer took which item in the store.  
We use the images taken before and after, put them into the YOLO v4 model, and the model will respectively calculate the categories and quantities of these two images; the difference in quantity between the two images will be the quantity purchased (or put back) into the merchandise cabinet by the customer. From the top view image obtained directly above the store, the identity of the customer can be judged. First, we use the position of the trigger commodity cabinet in the image to find the closest customer frame as the event trigger through the shortest Euclidean distance or we use the OpenPose method, as shown in Figure 6a,b, respectively. After obtaining the image of the trigger, it is compared to the images of all customers in the store one by one by using the Lightweight PRIED network to find out which customer triggered the event, and the purchased goods are placed into the shopping cart list corresponding to the customer found, as shown in Figure 6c.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **YOLO v4** & **Openpose** & \begin{tabular}{c} **PREID** \\ **Network** \\ \end{tabular} & \
\begin{tabular}{c} **Siamese** \\ **Network** \\ \end{tabular} &
\begin{tabular}{c} **Marker** \\ **Detection** \\ \end{tabular} \\ \hline Speed (ms) & 45 & 77 & 100 & 120 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The execution time of each model.  
Figure 6: (**a**) Locating the event trigger by Euclidean distance; (**b**) locating the event trigger by OpenPose; (**c**) find the customer ID in the store.
# Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System
## 3 Conventional Channel Estimation Methods
### Threshold Channel Estimation  
The LS channel estimation method is widely used in OFDM systems due to its simple implementation. However, the disadvantage of the LS method is that it ignores the effect of AWGN. However, when the  
Figure 2: The STBC model of the MIMO-OFDM system.  
signals transmit from the channel, it will be affected by the AWGN inevitably. The AWGN will affect the accuracy of the estimated channel impulse response (CIR). At the same time, the BER of the system will increase due to the AWGN in the estimated CIR. Hence, AWGN is a key factor and it is critical to reduce the AWGN in the estimated CIR. Many noise suppression channel estimation methods have been studied to reduce the noise and improve the accuracy of channel estimation [10]. The threshold channel estimation method can filter out the AWGN, which can be shown in Figure 3.  
Residual AWGN existing at the estimated LS channel coefficients in the time domain could be reduced by reasonable selection of a threshold value. The threshold channel estimation results \(\hat{\mathbf{h}}_{\text{thre},i}(k)\) after noise suppression are expressed as [23]:  
\[\hat{\mathbf{h}}_{\text{thre},i}(k)=\left\{\begin{array}{ll}\hat{\mathbf{h}}_{\text{ LS},i}(k),&\left|\hat{\mathbf{h}}_{\text{LS},i}(k)\right|\geq\lambda\\ 0,&\left|\hat{\mathbf{h}}_{\text{LS},i}(k)\right|<\lambda\end{array}\right.,0\leq k <N, \tag{6}\]  
where \(\lambda\) is the threshold. Its value is relevant to the noise variance \(\hat{\sigma}_{\text{n}}^{2}\). It will change with the change of the dynamic channel because the noise is difficult to separate from the channel. The CFR \(\hat{\mathbf{H}}_{\text{thre},i}(k)\) can be obtained by the FFT operation of \(\hat{\mathbf{h}}_{\text{thre},i}(k)\).
###### Abstract  
Biogas plants for sewage and organic waste treatment are rapidly expanding. While these facilities provide valuable benefits, such as renewable energy production and the promotion of circular economy practices, they also emit airborne particles of biological origin, which may pose potential health risks. This study aims to evaluate, by in vitro assay, the cytotoxic and genotoxic potential of PM\({}_{10}\) sub-fractions (0.49-10 \(\upmu\)m and \(<\)0.49 \(\upmu\)m) generated in eight different plants, also assessing the endotoxin component using the Limulus Amebocyte Lysate (LAL) assay. Human embryonic lung fibroblasts (HELF) were exposed to organic extracts of particulate matter (PM). Cytotoxic effects (XTT assay) were analyzed, along with the modulation of gene expression involved in DNA repair (ERCC1, XRCC1, XPA, and XPF) and IL-8 production as a marker of inflammatory response. PM\({}_{10}\) and endotoxin concentrations varied significantly among the plants (ANOVA, \(p<\) 0.01), with PM\({}_{10}\) levels ranging from 14 to 18,000 \(\upmu\)g/m\({}^{3}\) and endotoxin content from 1 to 138 EU/m\({}^{3}\). Exposure significantly increased ERCC1 and IL-8 expression by 25% and 53%, respectively (paired \(t\)-test, \(p<\) 0.01). IL-8 expression correlated with endotoxin exposure (Spearman's rho = 0.35; \(p<\) 0.01). A deeper understanding of the biological component of airborne PM\({}_{10}\) can enhance risk assessments for occupational and nearby resident communities' safety.  
IL-8; DNA repair; genotoxicity; endotoxin; PM\({}_{10}\); oxidative stress; waste treatment Article
## 3 Discussion on Results
### Results on Global Costs of Measures and Primary Energy Consumption
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**\% Reduction in Energy Consumption**} & \multicolumn{2}{c}{**Amortisation of Investment (Years)**} \\ \hline \multirow{2}{*}{**Type of School Building**} & **Elements** & \multirow{2}{*}{**L1**} & \multirow{2}{*}{**L2**} & \multirow{2}{*}{**L3**} & \multirow{2}{*}{**L1**} & \multirow{2}{*}{**L2**} & \multirow{2}{*}{**L3**} \\  & **Improved** & & & & & & \\ \hline \multirow{4}{*}{Type A} & R & 24.5\% & 24.5\% & 25.3\% & 4 & 5 & 5 \\  & FR & 28.1\% & 28.5\% & 30.2\% & 5 & 6 & 6 \\  & WR & 30.8\% & 31.0\% & 32.2\% & 15 & 15 & 15 \\  & WFR & 34.8\% & 35.9\% & 38.1\% & 14 & 14 & 14 \\ \hline \multirow{4}{*}{Type B} & F & 15.8\% & 17.1\% & 18.6\% & 5 & 4 & 5 \\  & FR & 16.6\% & 17.8\% & 19.2\% & 7 & 7 & 7 \\  & WF & 23.1\% & 27.1\% & 29.8\% & \textgreater{}30 & \textgreater{}30 & 29 \\  & WFR & 24.6\% & 2A) and N3 F (Types B and C) would be an average saving of 50,752.68 euros per year, 37,391.75 euros and 295,590.91 euros, respectively. In addition, annual emission reductions would be 79,687.2 KgCO2, 56,640.1 KgCO2 and 583,828 KgCO2, respectively.
# Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children
## 4 Results  
This research evaluated the performance of a ANN-based classification algorithm to detect FASD in children using the result of psychometric, DTI, and saccade tests and comparing the accuracy of the model with the SVMR developed in Reference [15]. The cross-validation process showed that the psychometric data model outperformed the rest. After this process, we saved the best model weight for each of the different data, and then we estimated the accuracy with all the data available. Table 4 shows the the accuracy level for each of the data type and its comparison to results obtained by [PERSON]'s model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**K-Fold Cross-Validation**} \\ \cline{2-13}  & **Method** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Avg** \\ \hline \multirow{4}{*}{**Fold Cross-Validation**} & Accuracy (\%) & 76.92 & 84.61 & 84.61 & 84.62 & 61.53 & 76.92 & 69.23 & 69.23 & 69.23 & 75 & 75.19\% \\  & Loss & 0.56 & 0.42 & 0.45 & 0.50 & 1.29 & 0.47 & 0.76 & 0.61 & 0.58 & 0.36 & 0.60 \\  & MAE & 0.23 & 0.15 & 0.15 & 0.15 & 0.38 & 0.23 & 0.31 & 0.31 & 0.31 & 0.25 & 0.24 \\ \hline \multirow{4}{*}{**Fold Cross-Validation**} & Accuracy (\%) & 88.89 & 55.56 & 77.78 & 70.59 & 64.71 & 70.59 & 47.06 & 70.59 & 70.47 & 52.94 & 61.51\% \\  & Loss & 0.40 & 0.86 & 0.46 & 0.72 & 0.69 & 0.70 & 0.73 & 0.51 & 0.64 & 0.70 & 0.64 \\  & MAE & 0.11 & 0.44 & 0.22 & 0.29 & 0.35 & 0.29 & 0.53 & 0.29 & 0.24 & 0.47 & 0.32 \\ \hline \multirow{4}{*}{**Fold Cross-Validation**} & Accuracy (\%) & 47.37 & 57.89 & 63.16 & 63.16 & 63.68 & 68.42 & 72.22 & 72.22 & 66.67 & 72.22 & 65.70\% \\  & Loss & 0.70 & 1.30 & 0.66 & 1.59 & 0.63 & 0.60 & 0.58 & 0.59 & 0.66 & 0.60 & 0.79 \\  & MAE & 0.53 & 0.42 & 0.37 & 0.37 & 0.26 & 0.32 & 0.28 & 0.28 & 0.33 & 0.28 & 0.34 \\ \hline \multirow{4}{*}{**Fold Cross-Validation**} & Accuracy (\%) & 68.75 & 56.25 & 68.75 & 81.25 & 66.67 & 73.33 & 60.00 & 66.67 & 26.67 & 53.33 & 62.16\% \\  & Loss & 0.78 & 0.62 & 0.53 & 0.61 & 0.77 & 0.63 & 0.66 & 0.65 & 0.89 & 0.69 & 0.68 \\ \cline{1-1}  & MAE & 0.31 & 0.44 & 0.31 & 0.19 & 0.33 & 0.27 & 0.4 & 0.33 & 0.73 & 0.47 & 0.38 \\ \hline \multirow{4}{*}{**Fold Cross-Validation**} & Accuracy (\%) & 56.25 & 53.33 & 66.67 & 80.00 & 60.00 & & & & & & 63.25\% \\ \cline{1-1}  & Loss & 0.80 & 0.67 & 0.55 & 0.82 & 0.77 & & & & & & 0.72 \\ \cline{1-1}  & MAE & 0.44 & 0.47 & 0.33 & 0.2 & 0.4 & & & & & & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: K-fold cross-validation of models.  
In the next subsection, the results of each (Psycometric, Antisaccad, Prosaccade, and DTI) of the tests are shown and discussed.
## 3 Results & Discussion
### Analysis of Glass Envelope
(**b**) On the left, the full X-ray Photoelectron Spectroscopy (XPS) survey of each point scan with the three highest intensity peaks identified. Note that there is an arbitrary vertical shift applied to G1, G4, G5, and G8 to improve readability. On the right, a plot of peaks corresponding to binding energies from 400 to 0 eV. Labels for each relevant peak are included. Auger emissions, also reducing observed signal enhancement. Given that the amount of MgO is likely consistent throughout the glass, variations in MgO peak intensity likely arise from variations in the thickness of C-containing overlayers. While adventitious C is certainly present everywhere, it is likely distributed uniformly on the envelope interior, implying that it is the amount of deposited BaCO\({}_{3}\) that varies from region to region. Peak intensity data in Figure 3 for BaO/BaCO\({}_{3}\) and (Auger enhanced) MgO indicates that the Ba-containing layer is (i) thin, potentially less than 1 monolayer, at G1 and G2 (limiting Auger enhancement of the MgO signal); (ii) on the order of a monolayer (maximizing enhancement of MgO signal) at G3-G5; and (iii) thicker than a single monolayer--though less than 10 nm thick--at G7 (reducing, but not obstructing, Auger enhancement). Figure 4 is a comparison of XPS results for the glass envelope. For each peak labeled in Figure 3, the S/N ratio (included in the Appendix A), is plotted for each evaluated position. Contrasts in the S/N ratio observed from position-to-position indicate significantly dissimilar compositions.
# Forecasting the Number of Road Accidents in Polish Provinces Using Trend Models
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data/Model** & **Annual Data** & **Monthly Data** \\ \hline Exponential & y = 2216.7e\({}^{-0.029\kappa}\) & y = 3353.1e\({}^{-2E-05\kappa}\) \\ \cline{2-3}  & R\({}^{2}\) = 0.6897 & R\({}^{2}\) = 0.0613 \\ \hline Linear & y = \(-\)43.531x + 2119.9 & y = \(-\)0.0281x + 2606.2 \\ \cline{2-3}  & R\({}^{2}\) = 0.699 & R\({}^{2}\) = 0.0597 \\ \hline Logarithmic & y = \(-\)256.3 ln(x) + 2194.8 & y = \(-\)1178 ln(x) + 13969 \\ \cline{2-3}  & R\({}^{2}\) = 0.4206 & R\({}^{2}\) = 0.0602 \\ \hline Polynomial of 2 nd degree & y = \(-\)3.7986\(\kappa^{2}\) + 40.037\(\kappa\) + 1799.5 & y = 5E-06\(\kappa^{2}\) - 0.4703x + 11837 \\ \cline{2-3}  & R\({}^{2}\) = 0.8541 & R\({}^{2}\) = 0.0639 \\ \hline Polynomial of 3 rd degree & y = \(-\)0.0283\(\kappa^{3}\) - 2.8652\(\kappa^{2}\) + 31.631x + 1816.7 & y = \(-\)1E-09\(\kappa^{3}\) + 0.0002\(\kappa^{2}\) - 7.8062x + 113891 \\ \cline{2-3}  & R\({}^{2}\) = 0.8543 & R\({}^{2}\) = 0.0645 \\ \hline Polynomial of 4 th degree & y = \(-\)0.033\(\kappa^{4}\) + 1.4255\(\kappa^{3}\) - 23.761x\({}^{2}\) + 139.52x + 1673.4 & y = \(-\)1E-11x\({}^{4}\) + 2E-06\(\kappa^{3}\) - 0.1233\(\kappa^{2}\) + 3430.8x - 4E+07 \\ \cline{2-3}  & R\({}^{2}\) = 0.8631 & R\({}^{2}\) = 0.1418 \\ \hline Polynomial of 5 th degree & y = \(-\)0.0037\(\kappa^{5}\) + 0.1709\(\kappa^{4}\) - 2.6132\(\kappa^{3}\) + 10.833\(\kappa^{2}\) + & y = 7E-15\(\kappa^{5}\) - 1E-09\(\kappa^{4}\) + 0.0001\(\kappa^{3}\) - 5.2879\(\kappa^{2}\) + 111231x \\ \cline{2-3}  & 20.015x + 1789.5 & - 9E+08 \\ \cline{2-3}  & R\({}^{2}\) = 0.866 & R\({}^{2}\) = 0.1945 \\ \hline Polynomial of 6 th degree & y = 0.0013\(\kappa^{6}\) - 0.0879\(\kappa^{5}\) + 2.2988\(\kappa^{4}\) - 28.33\(\kappa^{3}\) + & y = 7E-18\(\kappa^{6}\) - 2E-12\(\kappa^{5}\) + 2E-07\(\kappa^{4}\) - 0.0095\(\kappa^{3}\) + 297.52\(\kappa^{2}\) \\ \cline{2-3}  & 161.71x\({}^{2}\) - 362.89x + 2083.8 & - 5E+06\(\kappa\) + 3E+10 \\ \hline \multicolumn{2}{c}{\(\mathrm{R}^{2}\) = 0.8748} & R\({}^{2}\) = 0.2818 \\ \cline{2-3}  & y = 2322.2x\({}^{-0.17}\) & y = 1E+07\(\kappa^{-0.862}\) \\ \cline{2-3}  & R\({}^{2}\) = 0.4065 & R\({}^{2}\) = 0.0618 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Trend models for the Warmin–Masurian province.
## 4 Results
### Long-Duration Operation Test (LDOT)
The DL was run five times using different combinations of models and images between 0 and 1142 s. Six short peaks of 7 W unregulated power1 were recorded because the uplink command was received for DL execution purposes. The result of the LDOT showed that the algorithm took about 137 s to classify the images and consumed about 680 MB of RPi CM+ memory, referring to the 5-V line in Figure 9. At the end of this pass (1200 s above), both the RPi CM3+ and C-band transceivers were turned on for transferring the image data between the flash memories. Furthermore, the final classification results were appended to the image data for further analysis at the GS. Therefore, it verified the demonstration of running a DL-classification algorithm onboard KITSUNE simulated in orbit.
## 3 Results and Discussion
### Flexural Strength  
In Figure 11, the flexural strength of prisms with and without fibers is presented. The results indicate that the inclusion of PA fibers enhances the flexural strength of plain concrete, with the greatest increase observed at a fiber volume fraction of 0.5% (16%). The outcomes of this experiment are related to a previous study by [PERSON] et al. [79]. The results of this research indicate that incorporating 0.5% and 1.0% coir fibers in normal strength concrete (NSC) leads to improved performance in flexural strength. Furthermore, the inclusion of 0.5% coir fiber in high-strength concrete (HSC) results in improved flexural and tensile strengths. In both types of concrete, the addition of coir fibers leads to increased  
Figure 8: Compressive strength for cubes with powder glass and PA fibers (6 cubes/mix).  
ductivity and toughness. This enhancement is attributed to the high pozzolanic reactivity of cement, which promotes the formation of additional C-S-H gel [80]. The increased formation of C-S-H gel leads to a less dense hydration product around the fibers, which reduces the embrittlement of the composite and increases its ductility [81]. Additionally, the inclusion of PA fibers reinforces the matrix by acting as small reinforcing bars, which help to transfer stress and enhance the overall performance of the material. The PA fibers contribute to the enhancement of flexural strength in several ways, including increasing the toughness of the matrix, providing confinement to the concrete, and improving the bond strength between the fibers and the matrix, which results in more efficient load transfer and increased strength of the material [76, 82, 83]. In fact, when a composite material is subjected to stress, cracks may form in the matrix material that surrounds the fibers. These cracks can propagate and ultimately lead to the failure of the material. However, when natural fibers such as PA fibers are present, they can help to prevent these cracks from propagating by acting as bridges across the crack faces. These fibers are able to distribute the stress more evenly, which helps to reinforce the matrix and prevent further cracking. This bridging effect is particularly important in enhancing the tensile and flexural strengths of composite materials. The bridging effect also helps to improve the toughness and ductility of the material, making it more resistant to fracture and deformation [83].