esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 3 Results
### WOA Experiment  
We chose a model that combines a dual-scale CNN network with BiLSTM for scene classification, which significantly enhances classification performance. However, the initial learning rate and regularization coefficients of the network model require optimization. To reduce the workload associated with manual parameter tuning and improve scene classification accuracy, we employ the WOA algorithm for automatic parameter adjustment. The WOA has been described in detail above. We utilize the Iterative Training Accuracy (ITA) to monitor the changes in accuracy throughout the training process, reflecting the optimization effects of WOA. The formula for ITA is as follows:  
\[\text{ITC}_{i}=1-(\text{Accuracy}_{\text{ train}})_{i} \tag{27}\]  
where i is the number of iterations, \(\text{Accuracy}_{\text{ train}}\) is the training accuracy, which according to the value of ITC can determine whether the WOA reaches the optimal solution. We select the CNN1 + CNN2 + BiLSTM network, which showed the best performance in the previous section, and further optimize it using the WOA algorithm. The WOA algorithm iteratively optimizes the parameters with an initial learning rate of 0.0116 and an L2 regularization parameter of 0.0012. The confusion matrix after WOA optimization is shown in Figure 15b. The right-most column of Table 6 presents the results of the WOA algorithm optimization for the CNN1 + CNN2 + BiLSTM network, which improves accuracy to 98.83% and increases the average F1 value to 0.988 compared to the previous results of the CNN1 + CNN2 + BiLSTM network. Additionally, the correct classification rates for all four types of scene classifications have increased. Therefore, the WOA algorithm not only  
Figure 15: Confusion matrix: (**a**) confusion matrix before WOA optimization. (**b**) confusion matrix after WOA optimisation.  
reduces the time required for manual parameter adjustment but also enhances the accuracy of scene classification.
## 4 Conclusions  
In this study, the values of optical band gaps and charge carrier lifetimes of Cu- and/or Zr-modified TiO\({}_{2}\) samples were determined by photothermal beam deflection spectrometry. According to the presented results, the lower light absorption range limit changed from 3.25 to 2.85 eV (381.5 to 435 nm) when adding 0.05% copper in the examined samples. In the case of Zr modification of TiO\({}_{2}\), the optical band gap remained unaffected within the range of determination uncertainty. Furthermore, modification of TiO\({}_{2}\) material changed its carriers' lifetimes. Introduction of 0.05% Cu decreased the charge carrier lifetime, but this decrease could be prevented to a large extent by additional modification with Zr (1%). It was also shown that loading of Cu and/or Zr ions increases both the material thermal diffusivity (by up to 6%) and conductivity (by up to 9%) as a result of changes in the band gap and structure of the material, which determines the heat transfer within the material and its exchange with the surroundings. Furthermore, the modification with Cu and Zr increased the material's surface area, enabling higher absorption of light at the surface of the photocatalyst as well as better pollutant adsorption and photodegradation. These results are in good agreement with the determined values of photocatalytic reaction rates which indicate that the highest photocatalytic performance is provided by TiO\({}_{2}\)/Cu (0.05%) and TiO\({}_{2}\)/Cu (0.05%) Zr (1%) materials.  
Conceptualization and review, M.A.; methodology, U.L.S., H.C. and D.K.; writing--original draft preparation, H.C. and D.K.; writing--review and editing, H.C., D.K. and M.F.;funding, H.C.; data curation, O.P. and Z.E.; supervision, M.F. and U.L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Slovenian Research Agency through research core funding program No. P1-0034 and P2-0393 and national project No. L7-1848.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We acknowledge the funding received from the SPIE-ICTP Anchor Research Program, funded generously by the International Society for Optics and Photonics (SPIE), as well as the ICTP for the support of the work through the TRIL Program.  
The authors declare no conflict of interest.
# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
###### Abstract  
As additive manufacturing (AM) processes become integrated with artificial intelligence systems, the time and cost of the fabrication process decrease. In this study, the raster angle, an important parameter in the manufacturing process, was examined using fused deposition modeling (FDM), an AM method. The optimal value of this parameter varies depending on the designed product geometry. By changing the raster angle, the distribution of stresses and strains within the printed object can be modified, potentially influencing the mechanical behavior of the object. Thus, the correct estimation of the raster angle is essential for obtaining parts with high mechanical properties. The focus of this study is to reduce the fabrication time and cost of products by intertwining machine learning (ML) systems with mechanical systems. Its novelty is that ML has never been applied for FDM raster angle estimation. The estimation and modeling of the raster angle were performed using five different ML algorithms. These algorithms include a support vector machine (SVM), Gaussian process regression (GPR), an artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). Data for training were generated using various shapes and geometries, then trained in the MATLAB software, and a prediction model between the input parameters and the raster angle was created. The predicted model was evaluated using five performance criteria. The RFR model predicts the raster angle in the FDM test data with R-squared (R\({}^{2}\)) = 0.92, an explained variance score (EVS) = 0.92, a mean absolute error (MAE) = 0.012, a root mean square error (RMSE) = 0.056, and a mean squared error (MSE) = 0.0032. These values are R\({}^{2}\) = 0.93, EVS = 0.93, MAE = 0.010, RMSE = 0.051, and MSE0.0025 for the training data. RFR is significantly superior to the other prediction algorithms. The proposed model predicts the optimum raster angle for any geometry.  
additive manufacturing; machine learning; FDM; raster angle; prediction Article
## 3 Experiment and Results
### Comparative Experiments  
The comparison of various models for the bus detection system using the YOLOv5 architecture provides valuable insights into the performance of these models. The evaluation criteria for these models included the inference time, the [EMAIL_ADDRESS], the number of parameters, and the GFLOPs. The Improved-YOLOv5 stood out from the other models, as it achieved the best tradeoff between these evaluation criteria. The inference time for the Improved-YOLOv5 was the lowest among all models, which means that it generated predictions quickly, making it well-suited for real-time applications. The [EMAIL_ADDRESS] score for the Improved-YOLOv5 was also impressive, indicating that it was highly accurate in detecting objects in an image. One of the most notable features of the Improved-YOLOv5 was its lightweight architecture. With only 717,020 parameters, the Improved-YOLOv5 required significantly less storage space than other models, making it more efficient for deployment in resource-limited environments. Moreover, with only 2.1 GFLOPs, the Improved-YOLOv5 required less computational power than the other models, reducing energy consumption and processing time. It is worth noting that YOLOv8n also achieved a high [EMAIL_ADDRESS] score, but it required more parameters and computational power than the Improved-YOLOv5. Similarly, the YOLOv5x achieved a high mAP score but at the cost of requiring significantly more parameters and computational power. Therefore, the Improved-YOLOv5 is the optimal choice for bus detection systems, as it achieves a better balance between accuracy, efficiency, and resource requirements. The comparison of the various models for the bus detection system using the YOLOv5 architecture highlights the superiority of the Improved-YOLOv5. Its low inference time, high accuracy, and lightweight architecture make it the ideal choice for real-time bus detection and tracking systems. The Improved-YOLOv5 represents a significant improvement over the previous models, offering the best performance and efficiency for this application. The performance comparison with various models can be found in Table 4 and Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **YOLOv5** & **Slim** & **Ghost** & **SimSPPF** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **Parameters** & **GFLOPs** \\ \hline
1 & ✓ & - & - & - & 0.921 & 0.899 & 0.923 & 1,763,224 & 4.1 \\
2 & ✓ & ✓ & - & - & 0.917 & 0.901 & 0.933 & 1,313,792 & 3.8 \\
3 & ✓ & - & ✓ & - & 0.92 & 0.902 & 0.923 & 940,628 & 2.3 \\
4 & ✓ & - & ✓ (Bone) & - & 0.931 & 0.883 & 0.935 & 1,286,412 & 2.9 \\
5 & ✓ & - & ✓ (Neck) & - & 0.922 & 0.906 & 0.925 & 1,409,024 & 3.4 \\
6 & ✓ & - & ✓ & ✓ & 0.93 & 0.89 & 0.935 & 941,012 & 2.3 \\
7 & ✓ & ✓ & ✓ & - & 0.91 & 0.896 & 0.927 & 716,636 & 2.1 \\
8\({}^{1}\) & ✓ & ✓ & ✓ & ✓ & 0.939 & 0.895 & 0.933 & 717,020 & 2.1 \\ \hline \hline \end{tabular}  
* In this study, the YOLOv5-Slim-Ghost-SimSPPF is referred to as the Improved-YOLOv5.  
\end{table}
Table 3: Ablation experiments.
## 3 Results
### Specific Amino Acids Affect X. citri Persister Cell Resuscitation  
As a strategy to survive adverse conditions, the entry into persistence must be accompanied by a complimentary, sensory-based mechanism that will allow the cell to resume growth after stress, a method that has been established for _E. coli_[18]. Therefore, we tested whether different amino acids could also differentially influence _X. citri_ resuscitation. For this purpose, persister cells were induced by tetracycline (followed by a ciprofloxacin treatment to kill non-persisters) and plated on an M9 solid medium supplemented with one of the following amino acid groups: #1 (Ala, Arg, Cys, Phe, Ser), #2 (Gly, His, Thr, Val, Tyr), #3 (Asn, Ile, Lys, Pro, Trp) or #4 (Asp, Glu, Gln, Leu, Met) [42]. We observed that group #4 sustained faster and better growth of persisters since more and larger colonies were present in these plates, followed by group #3 of amino acids (Figure 5A).  
The next step was the screening of individual amino acids of group #4 to identify which one was responsible for inducing _X. citri_ resuscitation. We decided to also evaluate the amino acids of group #3, which was the second-best group to resu  
Figure 4: N-acetylcysteine (NAC) prevents metal-induced persister formation. Tetracycline-induced rates of persister cells (“Tet” black bar) are not affected by the addition of NAC during the pretreatment (“NAC + Tet”, black bar). Copper and zinc-induced rates of persistence (respectively “Cu”, dark gray bar; and “Zn” light gray bar) are affected by concomitant addition of the antioxidant NAC (“Cu + NAC”, dark gray bar; and “Zn + NAC” light gray bar). Results are shown as the mean of biological replicates and the symbols ** (\(p<0.01\)) and *** (\(p<0.001\)) indicate significant differences using Student’s \(t\)-test. NS indicates the absence of significative difference.  
Curiously, when individually screening the 10 different amino acids of groups #3 and #4, we found that the best amino acid to wake _X. citri_ persister cells, i.e., the one that led to larger colonies, was proline, which was among group #3, and not #4 (Figure 5B). Among group #4, the best amino acids were Gln and Asp (Figure 5B), but proline from group #3 was even better. In addition, among all these individual amino acids screened, isoleucine plates (of group #3 of amino acids) were the only ones that did not have any _X. citri_ colonies. We, therefore, hypothesized that maybe, isoleucine could have partially inhibited the waking of _X. citri_ persisters in group #3 (Figure 5A), masking proline activity. In order to test this hypothesis, persister cells were obtained as described before, and the bacterial suspension was plated on different combinations of proline and isoleucine. Indeed, plates containing only proline yielded larger colonies (Figure 5C) than plates containing proline and isoleucine combined, which had much smaller colonies. Again, plates containing only isoleucine had no colonies.  
Figure 5: _Cont._
# Machine Learning Approach for Arabic Handwritten Recognition
### Experimental Results  
The last stage of developing a handwriting recognition system is testing the system. This process used scaled images as input to the Arabic handwritten text recognition system. Two different datasets of Arabic handwritten text, that is, the KHATT and AHTID/MW, were used to cover all forms of Arabic text. Therefore, characters and words with different forms and widths were used in our experiments.  
The scaled images were passed through the ResNet model, followed by the BiLSTM-CTC layers and the language model post-processing stage. Table 5 shows the results of our system using the KHATT and AHTID/MW datasets with different BiLSTM layers.  
The recognition rates are improved in both datasets when using three BiLSTM layers. The WER is reduced by 4.29% for the KHATT dataset and by 5.37% for the AHTID/MW dataset. Therefore, the proposed model performs better, and the results are improved when using 3 layers of the BiLSTM network.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Batch size & 24 \\ Epoch & 300 \\ Learning rate & 1.0 \\ Optimizer & Adadelta \\ EarlyStopping & Validation loss \\ Dropout rate & 0.2 \\ \hline \end{tabular}
\end{table}
Table 4: Parameters for the training process.  
Additional experiments were performed on the AHTID/MW dataset to test our proposed system performance. As seen below, the best performance is obtained by using 3-BiLSTM layers, which results in a 17.42% WER and 6.6% CER. Figures 11 and 12 show the relation between the CER and WER with the epoch number, respectively. As shown in Figures 11 and 12, the CER and WER decrease as the epoch number increases during the training process until it reaches epoch number 300. The results of our proposed system on the KHATT and AHTID/MW datasets confirm the robustness of our system.  
To validate our system's performance, we compared our results with the most recent works on Arabic handwriting recognition systems. Table 6 shows the results of recent works obtained from the test set of the KHATT and AHTID/MW datasets. The experimental results showed that our system had an impressive recognition accuracy with a WER of 27.31% and a CER of 13.2% on the test set of the KHATT corpus. ResNet is a resilient CNN architecture designed specifically for extracting information from textual images. The  
Figure 11: CER versus epoch number.  
Figure 12: WER versus epoch number.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Model** & **CER\%** & **WER\%** \\ \hline \multirow{2}{*}{KHATT} & 2-BiLSTM Layers & 15.8 & 31.6 \\  & 3-BiLSTM Layers & 13.2 & 27.31 \\ \hline \multirow{2}{*}{AHTID/MW} & 2-BiLSTM Layers & 7.4 & 22.79 \\  & 3-BiLSTM Layers & 6.6 & 17.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of our system for the two datasets.  
fundamental concept underlying ResNet models is employing residual blocks to enhance the precision of the models. Residual blocks rely on the idea of an \"identity shortcut connection\" that allows for the skipping or bypassing of one or more levels.
## 2 Materials and Methods
### Enhanced Object Detection
#### 2.3.1 Noise Suppression  
A factor affecting object detection is noise interference. In particular, when detecting small objects, noise blurs the boundaries, degrading detection performance. This is because the boundary information of small objects is relatively weak compared to that of large objects, and the stronger the noise, the lower the detection accuracy [25; 26]. To address this issue, a bilateral filter is applied to effectively suppress noise while preserving object boundaries.  
Commonly used noise removal filters tend to lose not only the noise but also object boundary information. However, bilateral filters perform filtering by simultaneously considering spatial distance and pixel intensity differences. This allows the object boundaries to be preserved while other regions are blurred, effectively removing noise. A bilateral filter is applied to an image as follows:  
\[\mathrm{I^{bil}}=\frac{1}{\mathrm{K_{n}}}\mathrm{\sum_{k,l}}\mathrm{exp} \left(-\frac{\left(\mathrm{x_{c}}-\mathrm{k}\right)^{2}+\left(\mathrm{y_{c}}- \mathrm{l}\right)^{2}}{2\sigma_{\mathrm{s}}^{2}}\right)\mathrm{exp}\left(- \frac{\left(\mathrm{I^{roi}}\left(\mathrm{x_{c}},\mathrm{y_{c}}\right)-\mathrm{ I^{roi}}\left(\mathrm{k},\mathrm{l}\right)\right)^{2}}{2\sigma_{\mathrm{r}}^{2}} \right). \tag{5}\]  
Here, \(\mathrm{k_{n}}\) denotes the normalization constant, which prevents distortion while maintaining the brightness and color of the image after filtering. \(\left(\mathrm{x_{c}},\mathrm{y_{c}}\right)\) denotes the pixel coordinates of the center of the kernel created in the \(\mathrm{I^{roi}}\) image, and \(\left(\mathrm{k},\mathrm{l}\right)\) denotes the coordinates of other pixels within the kernel. \(\sigma_{\mathrm{s}}^{2}\) and \(\sigma_{\mathrm{r}}^{2}\) denote the Gaussian kernel variances for spatial distance and pixel intensity differences, respectively. The bilateral filter combines these spatial distance and color differences to remove noise around the object while preserving the boundaries.  
Figure 4 compares the results before and after the application of the bilateral filter. Figure 4a shows the segmented ROI image. Figure 4b shows the result after applying the Laplacian filter to the original segmented image to examine the noise effect that occurs during image magnification, where the object boundaries are emphasized but noise is also amplified. In contrast, Figure 4c shows the result after applying the bilateral filter to suppress noise, followed by the Laplacian filter. Here, the object boundaries are preserved, and background noise is significantly reduced.  
Figure 4: Comparison before and after the application of bilateral filter: (**a**) original segmented image; (**b**) image after applying the Laplacian filter; (**c**) image after applying bilateral filter followed by the Laplacian filter.
# Advanced Micro/Nanocapsules for Self-Healing Coatings
## 4 Classification of Self-Healing Coatings Based on the Polymeric Matrix
### Thermoset Polymers
#### 4.1.3 Polyimide-Based Self-Healing Coatings  
Polyimides are a category of high-performance polymers that demonstrate thermal chemical resistance, mechanical strength, and stability. Nowadays, there is a remarkable interest in synthesizing polyimide-based self-healing coatings because of the demand for long-lasting and resistant materials that can be used in various applications, such as protective coatings, electronics, and aerospace.  
[PERSON] et al. developed a new type of colorless polyimides with a self-healing function and resolved the trade-off between functionality and durability. These coatings have the ability of self-healing, rapidly restoring any damage caused by external stress [48]. The team of [PERSON] et al. fabricated two-dimensional lamellar polymer composite coatings based on polyimide/cardanol together with benzoxazine copper. The obtained coatings exhibited excellent anti-corrosion properties and enhanced mechanical performances [49]. [PERSON] et al. synthesized durable high-performance materials using polyimides, revealing enhanced corrosion resistance properties, an anticorrosion effect against high temperatures, and long-term robustness [50]. [PERSON] and his team investigated combinations of polyimide-based nanomaterials together with polyhedral oligomeric silsequioxane in order to produce coatings that exhibit improved stability in mechanical properties. The self-healing mechanism was assigned to the creation of a silica thin layer [51].
## Appendix A Example for the Small Influence of the Temperature on the Strain Measurements  
This section assumes that the reader is familiar with the results presented in this paper. As discussed in Sections 2 and 5.2 correlates the measured signal in Stage II to a temperature-value instead of a strain-value since no mechanical load is acting upon the fiber while the epoxy is still liquid. For the proprietary OBR software performing the analysis of the backscattered amplitude, this does not matter since a linear relationship holds up between these two interpretations as long as it is made sure that not both loads (temperature and strain) are applied at the same time:  
\[\varepsilon_{\text{apparent}}=8.09\cdot T-168. \tag{1}\]  
where \(T\) is the temperature in degrees Celsius, or the change in the backscatter signal interpreted as temperature and \(\varepsilon_{\text{apparent}}\) stands for the same signal interpreted as strain.  
During later stages, mechanical strain is transferred upon the fiber and the above assumption is no longer valid. However, if independent temperature measurements are carried out, this relationship can be used to subtract the influence of the temperature from the determined strain values. In Figure 11, this is done exemplary for the 10 cm sample.  
Unfortunately, not even the many sensors used, e.g., for the 60 cm sample (cf. Figure 8), have a good enough spatial resolution of independent temperature measurements to do this for each measurement point along the fiber. However, if just the core of the sample is considered, the authors think it is justified to use the average temperature (cf. Figure 7) for the calculation of the apparent strain due to temperature effects which has to be subtracted from the overall measured signal (black dots in Figure 11). As proclaimed, Figure 11 shows that the influence of the temperature upon the actual strain is small on the strain values itself and on the CTE's calculated from those. The authors admit that this is largely due to the rather high strain values that were observed in the here presented experiments. In experiments that exhibit significantly lower shrinkage at higher temperature, this compensation may be necessary to quantify the actual strain.