esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 4 Discussion
### Image Categories
#### 4.3.3 Texture Analysis  
To further investigate the significant variation in model performance across different wall types, a texture and feature analysis was conducted using Gray-Level Co-occurrence Matrix (GLCM) metrics.  
Crack images on plain walls typically exhibit smooth and uniform background textures, resulting in high contrast between cracks and the surrounding surface. This facilitates precise crack identification. GLCM analysis supports this observation, with a low contrast value of 0.41 and high homogeneity of 0.85 (Figure 24), indicating minimal texture variation and a consistent grayscale distribution--conditions favorable for accurate segmentation.  
In contrast, crack images on mottled walls present irregular textures, stains, and spot patterns that may resemble cracks, increasing the risk of misclassification. Corresponding GLCM values indicate a high contrast of 2.79, low energy of 0.03, and moderate homogeneity of 0.58 (Figure 24), reflecting high background complexity and considerable grayscale variation, both of which challenge the segmentation capabilities of models such as SegNet.  
For brick walls, while the background texture is relatively consistent, geometric interference from brick patterns and mortar joints complicates the analysis. GLCM results show a moderate contrast of 0.86, the highest correlation (0.88), and a lower homogeneity of 0.66 compared to plain walls (Figure 24). These metrics indicate a structurally repetitive grayscale pattern. However, such repetition can lead to confusion between cracks and structural edges, causing the model to misclassify mortar lines as cracks or to fragment continuous cracks.  
These findings highlight that segmentation models trained on crack images from a single wall type may struggle to generalize across other surface types, due to inherent differences in texture characteristics.
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 4 CNN Model for Arabic Short Vowels Classification
### Hyperparameters Tuning
```
Input aDataset = Audio dataset labels = Labels of classes train_files, val_files, test_files = split(aDataset(80,10,10)) Output Accuracy = Model accuracy Loss = Model learning loss y_pred = Predicted labels Algorithm Begin waveform_ds = Map waveform and labels from aDataset spectrogram_ds = Map spectrogram and labels from waveform_ds Function preprocess_dataset(files) output_ds = Map waveform_ds from files_ds of files output_ds = Map spectrogram_ds of output_ds Return output_ds Endfunction train_ds = preprocess_dataset from train_files val_ds = preprocess_dataset from val_files test_ds = preprocess_dataset from test_files input_shape = Shape of spectrogram in spectrogram_ds norm_layer = Normalization in preprocessing model = Sequential(input_shape, Resizing(32, 32), norm_layer, layers) trainNetwork train_ds, val_ds, callbacks = [lr_scheduler,callback_Early_stopping, model_checkpoint_callback, tensorboard_callback] load weights of best_model model train accuracy = Evaluate(train_ds) model val accuracy = Evaluate(val_ds) model test_accuracy = Evaluate(test_ds) Loss_graph metrics['loss'], metrics['val_loss'] Accuracy_graph metrics['Accuracy'], metrics['val_Accuracy'] End
```  
**Algorithm 1** Steps of classification of Arabic short vowels on a fully optimized and fine-tuned neural network  
Figure 3 shows the resulting architecture after fine-tuning and applying hyperparameter tuning to the baseline architecture. As can be seen, hyperparameters have been added in the form of batch normalization and dropout layers in addition to the optimizer selection and learning weight. FC1 and FC2 represent the two fully connected layers for classification followed by the SoftMax activation function that assigns a probability to each of the output classes.
# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 6 Conclusions  
This research proposed stability-aware geographic routing in EH-WSNs, which considered not only residual energy and harvested energy, but also estimated \(PRR\) of wireless link and location information to select routing paths from a source node to a sink node.  
The simulation results showed that the SAGREH outperformed the EHWA in relation to \(PDR\), average hop count, and average energy consumption. Specifically, the SAGREH provided a higher \(PDR\) and a lower average hop count and energy consumption than the EHWA did, meaning that the proposed method provided better performance in a more realistic environment.  
Future research will investigate stability-aware energy harvesting for reliable high-speed data transmission in multi-rate and realistic physical layers.  
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01059186) and by the Ministry of Education (MOE) and National Research Foundation of Korea (NRF) through the Human Resource Training Project for Regional Innovation (no. 2014H1C1A1066943).  
The main contributions of [PERSON] were to create the main ideas and execute performance evaluation by extensive simulation while [PERSON] and [PERSON] worked as the advisor of [PERSON] to discuss, create, and advise the main ideas and performance evaluations together.  
The authors declare no conflict of interest.
# LDPC-Coded CAP with Spatial Diversity for UVLC Systems over Generalized-Gamma Fading Channel
## 4 Results and Discussion
Figure 5: BERs of LDPC-coded CAP with spatial diversity UVLC systems over turbulent channels.
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 7 ANN Results with Simulated Degradation on _I-V_ Curves
### Comparison with Other ANN Solutions  
In Table 11, the main characteristics of the proposed ANN architecture are compared with other ANN solutions proposed in recent years--this is briefly commented on in the introduction section. The table includes only the methods suitable for the _online_ diagnosis and faults detection of PV sources. The comparison is made in terms of the _ANN architecture, inputs_ required during online operation, _data for the training phase_ (usually performed in the offline mode), and _PV granularity_, which means the level of applicability of the method (panel-, string- or array-levels). The different types of _detected faults_ and the level of _complexity_, which could have a significant impact on the embedded system implementation, are also included in the comparison. It is worth noting that the solution described in [13] uses two independent ANNs, similar to the approach developed in this manuscript, but the ANNs architecture and the type of detected faults are completely different. The other methods are mainly devoted to string or array diagnosis; thus, they are not suitable for detecting degradation in a single PV panel. The comparison also put into evidence that the selection of the appropriate method strongly depends on the size of the PV source and on what types of faults must be detected. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Degradation Factor** & \multicolumn{4}{c}{**Estimated Param. Variation**} \\ \hline  & \(\mathbf{\Delta I_{ph}}\)\% & \(\mathbf{\Delta I_{sat}}\)\% & \(\mathbf{\Delta\eta\%}\) & \(\mathbf{\Delta R_{s}}\)\% & \(\mathbf{\Delta R_{sh}}\)\% \\ \hline \multirow{2}{*}{\(I_{ph}^{deg}=0.96I_{ph}\) (\(-4\%\))} & \(\mathbf{-3.73}\) & \(-0.9\) & \(0.09\) & \(10.5\) & \(-22.3\) \\  & (1.52) & (8.8) & (0.68) & (13.7) & (20.4) \\ \hline \multirow{2}{*}{\(I_{sat}^{deg}=2.5I_{sat}\) (+150\%)} & \(0.57\) & \(\mathbf{103.0}\) & \(-2.96\) & \(14.0\) & \(0.7\) \\  & (1.39) & (25.2) & (0.63) & (16.0) & (3.8) \\ \hline \multirow{2}{*}{\(\eta^{deg}=0.97\eta\) (\(-3\%\))} & \(-0.29\) & \(2.8\) & \(-\mathbf{3.30}\) & \(4.8\) & \(0.1\) \\  & (1.74) & (8.0) & (0.77) & (10.6) & (5.3) \\ \hline \multirow{2}{*}{\(R_{s}^{deg}=1.7R_{s}\) (+70\%)} & \(0.64\) & \(10.1\) & \(-0.12\) & \(\mathbf{61.1}\) & \(-3.0\) \\  & (1.18) & (8.3) & (0.58) & (13.3) & (7.4) \\ \hline \multirow{2}{*}{\(R_{sh}^{deg}=0.2R_{sh}\) (\(-80\%\))} & \(0.06\) & \(5.9\) & \(0.30\) & \(10.4\) & \(-\mathbf{64.9}\) \\  & (0.85) & (8.0) & (0.54) & (15.7) & (13.7) \\ \hline \multirow{2}{*}{without deg.} & \(-0.87\) & \(-3.3\) & \(-0.76\) & \(-2.2\) & \(2.3\) \\  & (1.62) & (6.2) & (0.44) & (15.4) & (11.1) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average of SDM parameters variation (in %) estimated with ANN.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Advances on the MAC Layer
#### 4.2.2 Downlink Transmission  
The entire procedure for downlink transmission is depicted in Figure 9. The access point knows the data to be sent toward all stations and can schedule the RUs to the receiving stations for the next TXOP. Then, it transmits a multi-user RTS frame and the stations respond with a CTS to the access point through their scheduled RUs. Afterward, the access point simultaneously transmits the entire data on the scheduled RUs to the receiving stations. The transmission possibly covers multiple packets. When the transmission is over, the stations acknowledge the correct reception of the data with a single Block Acknowledgment (BA).
## 3 Results
### Effect of Organic Acids on V. destructor Fall off Honeybees  
Honeybees carrying mites received topical treatment on the back and were gathered in a micro-colony of five bees. Seventy-two hours post exposition, the proportion of fallen mites from the control group was 4.74% [CI95: 0.58-18.30], which was expected as mites do not fall naturally from their hosts. They were represented with a dashed line and gray area for CI95 (Figure 5).  
The proportion of fallen mites from the bees was significantly higher for citric acid at 580 mg/mL than the control group with 63.3% (CI95: 43.8-71.6) (Figure 5, Binomial GLM \(p\)-value = 0.0003, see Tables 3 and A2). Caution must be taken with citric acid at 25 mg/mL. Indeed, it was significantly higher than the control group. However, their CI95s were overlapping. No difference was detected between 25 and 150 mg/mL for citric acid or any concentrations of formic and tartaric acids in the control group. The proportion of fallen mites from the bees was significantly higher, with 53.3% (CI95: 34.3-71.6) for lactic acid at 150 mg/mL and 90% (CI95: 73.4-97.8) at 600 mg/mL, seventy-two hours post-treatment (Figure 5, Binomial GLM \(p\)-value for 150 mg/mL = 0.0005, see Tables 3 and A2).  
The highest number of fallen mites occurred during the first twenty-four hours of lactic acid. The highest concentration of lactic acid was toxic for bees, inducing high mortality after forty-eight hours (Figure 6; Kaplan-Meier: \(p\)-value = 0.022), as well as tartaric acid 600 mg/mL (Kaplan-Meier: \(p\)-value = 0.036) and citric acid 25 mg/mL (Kaplan-Meier: \(p\)-value < 0.0001). Therefore, none of the tested concentrations were considered toxic for bees except 600 mg/mL of lactic and tartaric acids as well as 25 mg/mL of citric acid.  
Figure 5: Effects of citric, formic, lactic, and tartaric acid concentrations on _V. destructor_ female removal from honeybees cumulated over seventy-two-hours post-treatment. Dashed line represents the control group. The grey area around dashed line represents the confidence interval of 95%. Color bars represent confidence interval of 95%; *** stands for \(p\) < 0.001, and * for \(p\) < 0.05 compared to the control (see Table 3 for detailed statistics).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Acids** & **Conditions** & **72 h Post-Treatment \(p\)-Value** \\  & & **Adjusted** \\ \hline Lactic & Control vs. 25 mg/mL & 0.5829 \\ Lactic & Control vs. 150 mg/mL & **<0.0001** \\ Lactic & Control vs. 600 mg/mL & **<0.0001** \\ Tartaric & Control vs. 25 mg/mL & 1 \\ Tartaric & Control vs. 150 mg/mL & 0.2657 \\ Tartaric & Control vs. 600 mg/mL & 0.4515 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 4 Conclusions
The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results. \(\rho_{\text{cem}}\), \(\rho_{\text{water}}\) and \(\rho_{\text{pol}}\) (quantitative values are given in Table 1, the cement paste-related volume fraction of the polymer reads as [9]:  
\[f_{\text{pol}}^{cp}=\frac{V_{\text{pol}}}{V_{\text{cem}}+V_{\text{water}}+V_{ \text{pol}}}=\frac{\frac{p}{\rho_{\text{pol}}}}{\frac{c}{\rho_{\text{cem}}}+ \frac{w}{\rho_{\text{water}}}+\frac{p}{\rho_{\text{pol}}}}=\frac{\left(p/c \right)\frac{\rho_{\text{water}}}{\rho_{\text{pol}}}}{\frac{\rho_{\text{water} }}{\rho_{\text{cem}}}+\left(w/c\right)+\left(p/c\right)\frac{\rho_{\text{water }}}{\rho_{\text{pol}}}}. \tag{1}\]  
The cement paste-related volume fractions of the other material phases (cement, capillary pore, and hydrate gel) follow from [PERSON] hydration model [41; 51] and the consideration of the additional polymers as:  
\[ \begin{split} f_{\text{cem}}^{cp}&=\frac{20(1-\xi) }{20+63(w/c)}\left(1-f_{\text{pol}}^{cp}\right)\geq 0\,,\\ f_{\text{por}}^{cp}&=\frac{63\left[(w/c)-0.367\xi \right]}{20+63(w/c)}\left(1-f_{\text{pol}}^{cp}\right)\geq 0\,,\\ f_{\text{hyd}}^{cp}&=\frac{43.15\xi}{20+63(w/c)} \left(1-f_{\text{pol}}^{cp}\right).\end{split}  \tag{2}\]  
See Figure 1 for an illustration. The phase volume fractions at the scale of the hydrate foam, in turn, follow as [29]:  
\[f_{j}^{hf}=\frac{f_{j}^{cp}}{1-f_{\text{cem}}^{cp}},\quad j\in\{\text{hyd, por, pol}\}\,. \tag{3}\]  
The hydration model of [PERSON] is further extended for polymer-modified cement pastes that exhibit entrapped air porosity \(f_{air}^{cp}\). Then, the calculation of the \"cement paste-related\" volume fractions of the cement and the hydrate foam matrix is extended to include entrapped air \(f_{air}^{cp}\) additionally:  
\[f_{\text{hf}}^{cp}+f_{\text{cem}}^{cp}+f_{air}^{cp}=1. \tag{4}\]  
The volume fractions of the cement and the hydrate foam at the cement paste scale are derived as:  
\[ \begin{split} f_{\text{cem}}^{cp}&=\frac{20(1-\xi )}{20+63(w/c)}\left(1-f_{\text{pol}}^{cp}\right)(1-f_{air}^{cp}),\\ f_{\text{hf}}^{cp}&=1-f_{air}^{cp}-f_{\text{cem}}^ {cp}.\end{split}  \tag{5}\]The amount of entrapped air is estimated based on the macroscopic bulk densities of the cement pastes, which are determined after demolding, at material ages of 20 h. For the plain reference paste, the bulk density is equal to \(1.99\,\mathrm{g}\,\mathrm{cm}^{-3}\). The cement paste P1 exhibits a bulk density of \(1.88\,\mathrm{g}\,\mathrm{cm}^{-3}\). The reduction refers to the added polymer whose density is smaller compared to the cementitious components. The density of paste P2 is even more reduced and amounts to \(1.79\,\mathrm{g}\,\mathrm{cm}^{-3}\), which is related to the higher fraction of entrapped air. Supposedly, paste P1 does not contain entrapped air. Then, comparing the bulk densities of the two polymer-modified cement pastes yields the volume fraction of entrapped air porosity in the cement paste P2, \(f_{\mathrm{air}}^{cp}=4.8\%\).
## 3 Results and Discussion
### Comparison of the Numerical and Optical Results
#### 3.5.2 Comparison of the Average Strain Values in Zone 2  
For this comparison, the middle-and side-weakened plates were used. In this case, Zone 2 was evaluated, see in Figure 6. These zones are 10 mm long and are positioned in the middle of the length of the smaller cross-sections. The results of each geometry were organized into tables, which can be seen in Table 8.  
The results show that in the case of higher strain values, only the multilinear material model was able to provide accurate results. As an example, the strain images of the side-weakened plate can be seen in Figure 14.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Average Strain (\%)** & **Relative Errors** \\ \hline Optical measurement & 0.745 & - \\ The linear elastic material model. with standard Young’s modulus & 0.721 & \(-3.22\%\) \\ The linear elastic material model with Young’s modulus until limit stress & 1.728 & 131.96\% \\ Multilinear & 0.766 & 2.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average strain values of the simple plate and the relative errors based on the optical measurement.  
Figure 13: The four strain images of the simple plate.
# Untargeted Profiling and Differentiation of Volatiles in Varieties of Meat Using GC Orbitrap MS
## 2 Reagents and Materials  
### Chemicals  
Analytical standards of benzene, toluene, styrene, p-cymene, o-xylene, dl-limonene, 1-pentanol, 1-hexanol, 1-octen-3-ol, 1-decanol, \(\alpha\)-terpineol, benzenemethanol, benzeneethanol, 3-methylphenol, methyl octanoate, ethyl acetate, methyl butanoate, vinyl hexanoate, octyl formate, 2-pentylfuran, propanoic acid, butanoic acid, hexanoic acid, heptanoic acid, decanoic acid, 2-ethylhexanoic acid, carbon disulfide, 2-acetyl-2-thiazoline, benzothiazole, dimethyl sulfone, 3-methylthioproponal, 2-butanone, acetoin, nervlactone, 2,3-pentanedione, 2-nonanone, 3-undecanone, 6-methyl-5-hepten-2-one, 1-octen-3-one, 6-methyl-2-heptanone, \(\gamma\)-hexalactone, \(\gamma\)-octalactone, \(\gamma\)-nonanolactone, butylolactone, 1,8-cineole, 2-phenoxyethanol, 2-methoxy-4-vinylphenol, (2E,4E)-nonadienal, (2E,4E)-decadienal, hexadecanal, 2-octenal, (2E,4E)-heptdiagnenal, (2E)-nonenal, (2E)-heptenal, hexanal, nonanal, decanal, benzaldehyde, benzeneacetaldehyde, (2E)-undecanal, (2E)-decenal, dodecanal, acetaldehyde, furfural were purchased from Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Meanwhile, heptanal, octanal, 2-heptanone, 2-octanone, 3-octanone, dimethyl sulfide, pentanal, 2-methyl-3-heptanone, and n-alkanes (C7-C40) were supplied by Sigma-Aldrich (Shanghai, China). 2-Pentylfuran, 2-heptylfuran, 2-hexylfuran, and 2-butylfuran were of gas chromatography grade and obtained from Alfa Aesar (Shanghai, China). High-performance liquid chromatography (HPLC)-grade methanol was purchasedfrom Merck (Darmstadt, Germany). Anhydrous ether, n-hexane, chloroform, and ammonium acetate were analytical-grade and purchased from Sinopharm Chemical Reagent Co., Ltd. (Beijing, China).
# Symptom Burden and Dietary Changes Among Older Adults with Cancer: A Cross-Sectional Study
## 3 Results
### Dietary Change and Cancer Diet  
As part of the screening, we also interviewed three hundred patients using a nutrition module for diet changes. A total of 122/300 (40.67%) of the hospitalized patients stated that they were avoiding certain foods. Abstaining from alcohol was mentioned most frequently by 24/122 (19.67%) patients. This was closely followed by the avoidance of meat products and foods labeled as \"solid/hard\", each with 23/122 (18.85%) mentions. After being diagnosed with cancer, 49/300 (16.33%) patients preferred certain foods. Most frequently listed were liquid and pureed food 22/49 (44.90%) or soft food 10/49 (20.41%). Patients were also asked about the intake of dietary supplements. Overall, 106/300 (35.33%) used dietary supplements. A cancer diet was denied by the majority (278/299, 92.98%) of patients. No significant difference between age cohorts or gender was observed concerning diet changes. Patients of the \(\geq 85\) years age cohort used nutritional supplements more often than younger patients. However, the sample size (13 patients \(\geq 85\) years) was very small. Subgroup analysis did not reveal differences between cancer entities except for lung cancer. Compared to other patients, lung cancer patients are significantly more prone to avoiding certain foods (\(p=0.018\)). All subgroups are depicted in Table 3.  
Figure 1: Nutrition deficiency is not dependent on patient age; however, mobility is lower in patients \(\geq\)75 years. ns—non significant, ***—\(p\)-value \(<0.01\).
## 5 Multiobjective Optimization of Main Beam Structure Based on RSM
### Multiobjective Optimization Method Based on RSM  
Based on the simulation analysis data of the model, a structural multiobjective optimization method based on RSM [43,44] was proposed. First, we selected the design variables to be optimized, selected the sample points in the design space, and obtained the true response value of the objective function corresponding to the sample points through simulation experiments. Then, the response surface model was determined according to the response value, and the fitting response value of the target function corresponding to the sample point was obtained according to the response surface model. Finally, we verified whether the goodness of fit of the response surface model was reasonable by comparing the real response value with the fitted response value. If it was reasonable, we solved the objective function value iteratively and verified the optimization result, otherwise, we reselected the sample point and calculated again. the process is shown in Figure 24.  
Figure 23: Fatigue sensitivity characteristic curve of main beam.  
The above multiobjective optimization method based on SRM was applied to optimize and solve the model. The main steps were as follows:  
1. Determine the design variables to be optimized in the model and set their value ranges.
2. Select sample points in the design space and get the true response value of the target function corresponding to the sample points through simulation experiments.
3. Determine the response surface model according to the sample points and the real response value of the objective function.
4. Use the response surface model to calculate the fitting response value of the target function corresponding to the sample point.
5. Verify the goodness of fit of the response surface model by analyzing the true response value and the fitted response value. When the goodness of fit is reasonable, proceed to the next step; otherwise, reselect the sample points or increase the number of sample points, and establish the response surface model again.
6. Solve the optimal solution of the model objective function iteratively.
7. Verify the rationality of the optimized design.
# Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia
## 2 Materials and Methods
### Trends in Weather-Related Road Traffic Crashes  
In order to evaluate regional dynamics in weather-related road traffic crash frequency (severe injury, death), simple municipal-level linear trends were calculated in MS Excel [33]. Here, raw (unstandardized) data from 2006 to 2018 were considered. The total number of road traffic crashes with major/severe injury or death per municipality for each time window and weather condition (rain, snow, wind, fog) was calculated. The linear regression coefficient was then reclassified in three categories in the ArcGIS environment [34]. The first one comprised municipalities with an increasing number of road traffic crashes per weather condition, the second one municipalities with a decreasing number of these events, and the third category with either a constant rate or no road traffic crashes.