esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Fourier Transform Infrared Spectrometry (FTIR)  
Infrared spectroscopy is used to identify the bonds present in organic molecules, allowing for the determination of functional groups based on their characteristic vibrations at specific wavelengths [48]. It is important to highlight that obtaining the FTIR spectrum of all the compounds present in the fiber was not possible due to constraints in research funding.  
Figure 5 shows the results of FTIR obtained for two samples of the achira fiber, showing absorption peaks at 3329.14 cm\({}^{-1}\) and 3321.42 cm\({}^{-1}\), indicating axial deformations of the hydroxyl (OH) bond due to the presence of cellulose. The peaks at 2920.23 cm\({}^{-1}\) and 2924.09 cm\({}^{-1}\) are observed due to the stretching of the SP3 C-H bonds from cellulose vibration [47]. The peak value at 1616 cm\({}^{-1}\) indicates the stretching of aromatic C=C with a strong conjugated C-C bond, attributed to the lignin content in the fiber [49]. The peak in the band at 1319 cm\({}^{-1}\) is attributed to a strong acyl C-O with a superimposed C-H stretching of the phenols and esters. The bands in the natural fibers may vary around \(\pm\)16 cm\({}^{-1}\) from their position [47].  
Lignin is one of the three main components that constitute the plant cell wall, along with hemicellulose and cellulose, which serve as its raw material. The presence of this natural adhesive (biopolymer) in different areas of the plant's natural fiber weakens the  
Figure 5: Fourier Transform Infrared Spectrometry (FTIR) for Sample 1 and Sample 2.  
mechanical properties of the composite materials, as it hinders optimal bonding between the matrix and the fiber [50].
# Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
## 3 Database
### Model Validation and Evaluation  
To ensure the accuracy and predictive effectiveness of the established model, the validation and evaluation process of model performance is essential. This study compared and screened the predictive performance of base learners and utilized the stacking strategy to establish a prediction model. Figure 6 illustrates the overall analysis and modelling process of the prediction model. To assess the effectiveness of the model, the mean square error (MSE), mean absolute error (MAE), determination coefficient (\(R^{2}\)), and root mean squared error (RMSE) were utilized as the evaluation metrics. When the value of \(R^{2}\) is closer to 1, and the values of MSE, MAE, and RMSE are closer to 0, it indicates a better predictive performance of the model. The evaluation indicators are calculated using the following formulas [26; 46; 47; 48]:  
\[\text{MSE}=\frac{1}{\text{n}}\sum_{i=1}^{\text{n}}{\left(\hat{y}_{\text{i}}- \text{y}_{\text{i}}\right)^{2}} \tag{4}\]  
\[\text{MAE}=\frac{1}{\text{n}}\sum_{i=1}^{\text{n}}{\left|\hat{y}_{\text{i}}- \text{y}_{\text{i}}\right|} \tag{5}\]  
\[\text{RMSE}=\sqrt{\frac{1}{\text{n}}\sum_{i=1}^{\text{n}}{\left(\hat{y}_{ \text{i}}-\text{y}_{\text{i}}\right)^{2}}} \tag{6}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{\text{n}}{\left(\hat{y}_{\text{i}}-\text{y}_ {\text{i}}\right)^{2}}}{\sum_{i=1}^{\text{n}}{\left(\hat{y}_{\text{i}}-\text{ y}_{\text{i}}\right)^{2}}} \tag{7}\]  
where \(n\) is the number of input-output data in the algorithm, \(\hat{y}\) is the predicted value of the model, \(y\) is the true value of the model, and \(\overline{y}\) is the average value of the model predictions.  
Figure 5: Heat map of input parameter correlation distribution.
## 3 Results and Discussion
### Influence of Wetlands on Water Quality
#### 3.5.5 Total Solids Assessment  
For TS, there was a trend of increasing values from upstream to downstream in the Pantanal area between the years 2005 and 2014. However, it is observed that from the year 2015 onwards, there has been a progressive reduction in the concentration of TS in the Pantanal wetland area (Figure 14).  
[PERSON] et al. [55] found that the restoration of natural wetland areas in the urban basin of the Tualatin River, Oregon, United States, was effective in reducing TS concentrations between the years 2001 and 2016. On the other hand, in the study conducted in the wetlands of the Duck Creek basin, Alaska, United States, no significant difference was found for TS at the inlet and outlet of the wetlands [56]. Plain wetlands, like those in this study, slow down the speed of water and retain it for longer in the floodplain, resulting in downstream solids reduction through sedimentation [55]. TS still play an important role in the cycle and transport of aquatic nutrients. Free phosphorus is generally associated with suspended sediments in the water column, bottom sediments, or aquatic microbiota in organic form, so that water flows along with TS favor the transport of phosphorus in the aquatic environment [56]. Understanding the influence of fluctuations in solid transport in Pantanal wetlands is highly relevant, as transported sediments can be a limiting factor to primary biological productivity downstream, reducing the fertility of wetland areas and affecting their support capacity. The loss of flood pulse fertilization services has been documented in several natural systems worldwide, mainly associated with dam constructions that regulate the natural flow of sediments downstream [57].
## 4 Results
### Features Extraction Results Using CNN-1  
This section will discuss the findings of feature extraction using the Convolutional Neural Network (CNN-1). We found that the accuracy achieved by CNN-1 for individual classes ALL, AML, and MM are 77.27%, 98.91% and 92.22%, respectively. The discrete statistics of CNN-1 upon ALL, AML and MM, are cited below in Table 6.  
Overall accuracy achieved by CNN-1 on ALL, AML and MM was 89.63%. The overall statistics of CNN-1 are given below in Table 7.  
The confusion matrix of CNN-1 is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Class** & **Accuracy\%** & **Sensitivity\%** & **Specificity\%** & **Precision\%** & **False Positive\%** & **F1-Score\%** \\ \hline ALL & 77.273 & 77.273 & 96.154 & 90.667 & 3.8462 & 83.436 \\ \hline AML & 98.913 & 98.913 & 98.315 & 96.809 & 1.6854 & 97.849 \\ \hline MM & 92.222 & 92.222 & 90 & 82.178 & 10 & 86.911 \\ \hline \end{tabular}
\end{table}
Table 6: Statistics of CNN-1 Model of Individual Classes.  
\begin{table}
\begin{tabular}{c c c} \hline
**Types** & **Training** & **Testing** & **No. of Samples** \\ \hline Acute lymphoblastic leukemia & 205 & 88 & 293 \\ \hline Acute myeloid leukemia & 215 & 92 & 307 \\ \hline Multiple Myeloma & 211 & 90 & 301 \\ \hline \end{tabular}
\end{table}
Table 5: Samples used for classification.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis of obtained data was performed with SPSS. Three-way analysis of variance (ANOVA) and Tukey tests were performed for the analysis of fermentation times, post-acidification, TA, cell growth, VOCs, acids, carbohydrates, and rheology data. Sensory data were analyzed with two-way ANOVA. Statistical evaluation of the results for the intensity perception included three-way MANOVA (multivariate analysis of variance) with Wilks test to identify the overall sample differences and ANOVA to find for which attribute there were significant differences, both considering the factors product, judge and replicate as well as their two-way interactions. The Least Significant Difference (LSD) test was used to detect significant differences among the product samples when the attributes had a significant product effect. Rolling correlations were performed with rheological experimental measurements and sensory perception data. Correlation coefficients between 1 and 0.6 and between \(-1\) and \(-0.6\) were accepted as significant. Principal Component Analysis (PCA) was performed to correlate VOCs and flavor attributes for each of the bases and all culture combinations. For all statistical analyses, except for the rolling correlations, significant differences were assumed at 95% confidence intervals (\(\bar{p}<0.05\)).
## 4 Methodology
### Vision-Based Localization Information Acquisition
#### 4.2.2 Step Length as a Visual Translation Component Constraint  
Within each gait interval, the magnitude of the translation component \(\Delta\mathbf{T}_{\text{vis}}\) in the visual pose transformation matrix should approximate the estimated step length \(l_{s}\), that is, \(||\Delta\mathbf{T}_{\text{vis}}||^{2}\approx l_{\text{s}}\). If the error between them exceeds a threshold (e.g., \(|||\Delta\mathbf{T}_{\text{vis}}||^{2}-l_{\text{s}}|>0.2\) m), the correction of the pose transformation matrix is triggered. The correction follows the formula:  
\[\mathbf{T}_{\text{c}}=\mathbf{T}_{\text{vis}}\cdot\text{Scale}(\frac{l_{\text{s}}}{|| \Delta\mathbf{T}_{\text{vis}}||^{2}}) \tag{14}\]  
where \(\text{Scale}(\cdot)\) denotes the scaling function. This method effectively corrects visual drift caused by feature mismatches. For instance, if the actual step length is \(l_{\text{s}}=0.4\) m, but the visual estimate is 0.7 m, the pose transformation is scaled down to approximately 0.4 m after incorporating the step length constraint, thereby correcting the drift.
# Recent Trends of Authentication Methods in Extended Reality: A Survey
## 4 Authentication Methods in XR
### Inference-Based (Biometric) Authentication
#### 4.2.1 Physical Biometrics
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _Concept_ & **Ref.** & **Year** & **Scheme Name** & **Classifier** & **Features** & **Equip.** & **Sample** & **AN\% (PER)** \\ \hline \multirow{9}{*}{**[98]**} & \multirow{9}{*}{2020} & DenseNet-201-Iris & & & & (6.35) \\ \cline{3-7}  & & DenseNetBC-100-Iris & & & & (7.25) \\  & & DCF-SHD-Iris & & & & (31.13) \\  & & LG-SHD-Iris & & & & (31.78) \\ \cline{3-7}  & & CSBCA-Iris & & & & (34.34) \\  & & DCT-HD-Iris & & & & (36.44) \\  & & LG-HDIris & & & & (34.80) \\  & & DenseNet-201-Peri & & & & (5.86) \\ \cline{3-7}  & & DenseNetBC-100-Peri & & & & (12.33) \\  & & BSF-Peri & & & & (34.77) \\  & & LBP-Peri & & & & (35.58) \\  & & TreeLBP-Peri & & & & (31.27) \\  & & HOG-Peri & & & & (28.51) \\ \cline{3-7}  & & DeepIFrisNet-Iris & & & & (7.42) \\ \cline{3-7}  & & MobileNetV3-Iris & & & & (16.18) \\ \cline{3-7}  & & MobileNetV3-Peri & & & & (9.40) \\ \cline{3-7}  & & ResNet-Peri & & & & (12.79) \\ \cline{3-7}  & & Fusion & & & & (6.38) \\ \cline{3-7}  & & DeepIFrisNet & & & & (12.48) \\  & & DenseNet & & & & (5.80) \\ \cline{3-7}  & & MobileNet & & & & (13.32) \\ \cline{3-7}  & & ResNet & & & & (7.74) \\  & & DCT-SHD & & & & (31.66) \\  & & LG-SHD & & & & (31.32) \\ \cline{3-7}  & & CSBCA & & & & (34.58) \\  & & DCT-HD & & & & (34.97) \\  & & LG-HD & & & & (33.47) \\ \cline{3-7}  & & VGG19 & & & & & (9.18) \\ \cline{3-7}  & & VGG19-SS & & & & (7.06) \\ \cline{3-7}  & & MobileNetV3-SS & & & & (10.44) \\ \cline{3-7}  & & MobileNetV3-SS & & & & (9.84) \\ \cline{3-7}  & & BSIF & & & & (23.80) \\ \cline{3-7}  & & BSIF-SS & & & & (22.25) \\ \cline{3-7}  & & Tree-LBP & & & & (27.38) \\ \cline{3-7}  & & Tree-LBP-SS & & & & (24.97) \\ \cline{3-7}  & & Eye-MM & & & & 92.75 \\ \cline{3-7}  & & Eye-MM80 & & & 32 features & & 90.68 \\ \cline{3-7}  & & & L1 & & & & (6.83) \\ \cline{3-7}  & & & L\#1 & & & & 1000 \\ \cline{3-7}  & & MobileNetV3-SS & & & & & (10.4) \\ \cline{3-7}  & & MobileNetV3-SS & & & & & (9.84) \\ \cline{3-7}  & & BSIF & & & & (23.80) \\ \cline{3-7}  & & BSIF-SS & & & & & (22.25) \\ \cline{3-7}  & & Tree-LBP-SS & & & & & (24.97) \\ \cline{3-7}  & & Eye-MM & & & & & 92.75 \\ \cline{3-7}  & & Eye-MM80 & & & 32 features & & & 90.68 \\ \cline{3-7}  & & & L1 & & & & \\ \cline{3-7}  & & LBP & & & & & (6.83) \\ \cline{3-7}  & & SIFT & & & & & 1000 \\ \cline{3-7}  & & CASIA Thousand & & & & & 1000 \\ \cline{3-7}  & & Bath00 & & & & 800 & 99.50 \\ \cline{3-7}  & & & UBIRIS-v2 & & & & 261 & 98.92 \\ \cline{3-7}  & & Iris-Focus * & GK & Iris patterns & & & 79 (CRR) \\ \cline{3-7}  & & Iris-Defocus * & LG & Glint & & & 77 (CRR) \\ \cline{3-7}  & & IPN-Defocus * & HD & & & & 7 (CRR) \\ \cline{3-7}  & & EyeVEIL-Focus & GK & Iris patterns & & & & 91 (CRR) \\ \cline{3-7}  & & & LG & Glint & & & & \\ \cline{3-7}  & & EyeVEIL-Defocus & HD & & & & & 0 (CRR) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of physiological biometric mechanisms and schemes in XR.
## 3 Methodology
### Data Preprocessing, Feature Selection, and Feature Scaling
#### 3.2.2 Standardization with StandardScaler  
After feature selection, StandardScaler was applied to normalize the selected features. StandardScaler transforms each feature to have zero mean and unit variance, ensuring that features are on the same scale, which is an essential step for models like ANN and SVM, which are sensitive to input magnitudes.  
**StandardScaler mathematical formula:**  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Description** & **Example Attack Types in the Dataset** \\ \hline DoS/DDoS &
\begin{tabular}{c} Overwhelms resources to make a system or \\ network unavailable to legitimate users. \\ \end{tabular} & _neptune_, _smurf_, _back_, _teardrop_, _pod_, _land_ \\ \hline Probe & Attempts to gather information about the network & _portsweep_, _ipsweep_, _nmap_, _satan_ \\ \hline User to Root (U2R) & Attempts to gain root (admin) access from a & _buffer_overflow_, _loadmodule_, _perl_, _rootkit_ \\ \hline Remote to Local (R2L) & Tries to gain unauthorized access to a local system & _guess_passwod_, _ftp_write_, _inap_, _plf_, _warezclient_, _warezmaster_, _multihop_, _spy_ \\ \hline \end{tabular}
\end{table}
Table 2: Attack categories in NSL-KDD.  
For each feature x\({}_{j}\), standardization is performed as follows:  
\[\mathrm{z}_{j}\ =\ \frac{xj-uj}{\sigma j}\]  
where  
\(\mathrm{u}_{j}\) = mean of feature x\({}_{j}\);  
\(\sigma_{j}\) = standard deviation of feature x\({}_{j}\);  
\(\mathrm{z}_{j}\) = standardized value.  
Standardizing features improves model convergence and prevents any single feature from disproportionately influencing the model due to a larger numeric range.
## 4 Experimental Setup and Results
### Neuron Model Comparison  
Experiments shown in Figures 18 and 19 compare different neuron models (LIF, Lapicque, Synaptic, Alpha). These results underscore the adaptability of SNNtrainer3D in  
Figure 19: Performance Comparison regarding Accuracy and Loss for each of the four types of neurons after training different SNN architectures with the same number of neurons on the MNIST dataset. More exactly, for 50 neurons in one hidden layer.  
Figure 18: Performance Comparison regarding Accuracy and Loss for each of the four types of neurons after training different SNN architectures with the same number of neurons on the MNIST dataset. More exactly, for 30 neurons in one hidden layer.  
supporting diverse neuron types and the importance of selecting appropriate models for specific tasks.  
As can be seen in Figures 18-21, it's evident that increasing the number of neurons improves both the training and test accuracy, which is a consistent trend across all neuron types.  
Synaptic neurons perform exceptionally well in smaller networks, leading to accuracy and minimizing loss, indicating their learning and generalization efficiency with limited computational resources. Conversely, Alpha neurons demonstrate significant performance gains as the network grows, particularly in early iterations. This suggests their suitability for larger SNN architectures, where they excel in learning speed and outcome.  
When looking at the most extensive network with 25 neurons, LIF neurons show a strong start in accuracy, hinting that they might be better for scenarios where a more gradual learning approach is beneficial. Interestingly, the training and test loss graphs reveal that Synaptic neurons maintain a consistently lower loss across various network sizes, reinforcing their robustness in learning and generalizing. However, with a network of 20 neurons, Alpha neurons exhibit a lower test loss, aligning with their high test accuracy and suggesting an optimal balance for mid-sized networks. Lapicque neurons, in contrast, tend to lag behind the other models in terms of performance metrics across all network sizes. This might imply that they are less efficient under the conditions tested, perhaps due to inherent limitations in their model or less optimal parameter settings for this specific task. In conclusion, the results from Figures 16 and 17 show that the choice between neuron models for SNNs should be influenced by the size of the network and the specific requirements of the task at hand. Synaptic neurons are highly effective for smaller networks, while Alpha and LIF neurons become more advantageous as the network size increases.
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 3 Evaluation of Weld Seam Detection  
The performance of the model is verified using four main object detection metrics. Specifically, the _Precision_, _Recall_, _mAP_, and _F1 Score_ were adopted for evaluation in this  
Figure 7: Process flow diagram of Scaled Yolov4-based weld seam detection and tracing.  
study [45], and the equations are shown in the following Equations (18)-(22). The higher the value of _Precision_, _mAP_ and _F1 Score_, the better the detection result of the weld seam shapes.  
\[Precision=\frac{TP}{TP+FP}*100\% \tag{18}\]  
\[Recall=\frac{TP}{TP+FN}*100\% \tag{19}\]  
\[mAP=\frac{\sum_{c=1}^{C}Average\ Precision(c)}{C} \tag{20}\]  
\[F1\ Score=\frac{2-Precision-Recall}{Precision+Recall} \tag{21}\]  
\[IoU=\frac{Area\ of\ overlap}{Area\ of\ union} \tag{22}\]  
where \(C\) = number of classes;  
\(TP\) = true positive; detected bounding box overlaps with ground truth boxes;  
\(FP\) = false positive; detected bounding boxes away from ground truth boxes;  
\(TN\) = true negative; does not predict bounding boxes in unwanted region;  
\(FN\) = false negative; fail to predict bounding boxes with ground truth boxes.  
Further inference speed and memory utilisation for different YOLO algorithms were analysed and compared with the detection performance.
## 2 Materials and Methods
### Steps for Conducting LCCA  
The developed LCCA program was used to evaluate different alternatives to traditional HFST applications, incorporating selected performance indicators derived from the BPN, DFT, and MPD results. Three performance prediction models were employed to convert frictional measurement data into predicted skid number (SN) values, as outlined in the Performance Prediction Models section. These predicted SN values were then compared with the recommended or adopted terminal SN following the rehabilitation matrix proposed in Table 3. This matrix is based on frictional limits and binder stability because some binders including both emulsions failed before they reached the terminal polishing cycles, which in the TWPD test used for this study were set at 140 K cycles. To find out if the binder could withstand the terminal polishing cycles, the stability criterion was first assessed. After the binder satisfied this requirement, the frictional limit was taken into account. By contrasting the suggested and predicted terminal SN values, this limit was established. To facilitate this, a series of automated Microsoft Excel spreadsheets was developed, allowing users to calculate the net present value (NPV) for various HFST strategies by accounting for current and future costs (including maintenance) using inflation and interest rates. Figure 4 illustrates the LCCA calculation framework with major input data categorized into project-specific and material-specific details.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**What if?** & **Action** \\ \hline \(\text{N}_{\text{polishing\,cycle}}\leq\text{N}_{\text{terminal\, polishing\,cycle}}\) (140 K) & Remove the old HFST and add a new one. \\ \hline Predicted terminal SN \(\geq\) recommended terminal SN & Do nothing. \\ \hline Predicted terminal SN \textless{} recommended terminal SN & Replace the old HFST application and add a new one. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rehabilitation matrix.  
Figure 4: LCCA calculation framework.
# A Simple and Stable Atmospheric Pressure Electrodeless Water Vapor Microwave Plasma Torch
## 2 Experimental Setup  
Figure 1 shows a schematic view of the water vapor microwave plasma system. It consists of the 2.45 GHz microwave generator, waveguide components, water container, ceramic tube, and wick. The microwave power supply provides a maximum power of 1000 W and is connected to a magnetron (LG, M246, Seoul, South Korea) to generate microwaves. The water-cooling system is a circulating water tank to prevent the microwave generator from overheating. The waveguide cavity is composed of a rectangular waveguide (WR340) and a dual-port tapered waveguide. The quartz tube is vertically inserted into the opening of the upper and lower surfaces of the dual-port conical waveguide. It is directly connected with the water container to guide and generate a plasma torch. The quartz tube has an inner diameter of 24 mm, an outer diameter of 28 mm, and a length of 600 mm. The ceramic tube is placed inside the quartz tube, its lower end is fixed at the bottom of the water container, and its upper end is in the center of the dual-port tapered waveguide. The main component of ceramics is silicon dioxide, which is produced from pressed powder material. The dielectric permittivity of the ceramic tube is 3.86, and its melting temperature is about 1200 \({}^{\circ}\)C. The wick is placed inside the ceramic tube, partially exposing the nozzle. The characteristics of water vapor microwave plasma are detected by the infrared thermometer (Guide infrared, PS400, Wuhan, China) with a high-temperature lens (GD-20), a high-speed camera (Phantom, V1612, Dallas, TX, USA) matching with a microscopic zoom lens (Navitar 12-X), a high-definition camera (Sony, A7M3, Tokyo, Japan), and optical emission spectroscopy (Ocean Optics, HR4000 CG-UV-NIR spectrometer, Orlando, FL, USA).  
The dual-port tapered waveguide in the experiment can concentrate the microwave energy at its center, as shown in Figure 2. However, the microwave energy in the experiment is not enough to directly ionize water vapor to produce plasma. Therefore, we will ignite the wick in the ceramic tube and then excite the high-temperature flame through the microwave. Flames already contain a high concentration of plasma species, so they can easily produce plasma by focusing relatively low-power microwaves into a high-temperature flame. When the plasma generated by the flame is excited, it will have an extremely high temperature, which can heat the ceramic tube, evaporate the water at the bottom of the ceramic tube and fill the whole quartz tube. Subsequently, water vapor becomes the ion source of the latest plasma, forming a water vapor plasma torch. The inner diameter of the ceramic tube in the experiment is 0.8 cm and the outer diameter is 1 cm; the inner diameter of the quartz tube is 2.4 cm and the outer diameter is 3 cm; the main components of the wick are cotton yarn.