esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feasibility Study for a Nonlinear Guided Wave Mixing Technique
## 2 Theory
### Phase Matching Mode Theory  
The phase matching mode technique is normally used for measuring guided wave nonlinearity in a plate. The most important phenomenon of nonlinearity is the second harmonic wave. Generally, its signal has double the frequency of the primary wave. In a bulk wave, the primary wave velocity is the same as the second harmonic wave velocity. Therefore, the two components arrive at the same time, so measuring the bulk wave nonlinearity becomes simple. However, in a guided wave, the two components have different velocities because of the dispersion characteristic. This makes studying nonlinear guided waves difficult. Because the arrival times of the two components are different, signal reception at the same position is almost impossible. However, fortunately there are a few mode cases where the primary and second harmonic wave velocities are equal. This combined mode and frequency is called the phase matching mode [35]. Based on the phase matching mode, nonlinearity measurement could be conducted in a plate or pipe using guided waves.  
Figure 3: Guided wave mixing concept with mixing signal in a plate.  
Equations (16) and (17) shows the symmetric mode equation and double frequency harmonic symmetric mode equation, respectively. Equation (17) represents the second harmonic component. \(k\) and \(h\) mean the wave number and half of plate thickness respectively. In this study, only the symmetric mode is considered for the phase matching mode experiment. Here, \(p\) and \(q\) are given by \(p^{2}=(\omega/c_{l})^{2}-k^{2}\) and \(q^{2}=(\omega/c_{s})^{2}-k^{2}\). Figure 4a shows the phase velocity dispersion curves, while Figure 4b shows the group velocity dispersion curve based on Equations (16) and (17) for aluminum (Al 1050):  
\[\frac{\tan(qh)}{\tan(ph)}=-\frac{4k^{2}pq}{\left(k^{2}-q^{2}\right)^{2}} \tag{16}\]  
\[\frac{\tan(2 qh)}{\tan(2 ph)}=-\frac{4k^{2}pq}{\left(k^{2}-q^{2}\right)^{2}} \tag{17}\]  
One example of phase matching mode is the component with 3.4 MHz frequency and S1 mode, and the second harmonic wave with 6.8 MHz frequency and S2 mode. Both frequency components are the intersection point (black circle) of each dashed line in Figure 4a,b. These two components have the same phase velocity and group velocity. The phase matching mode concept was studied by [PERSON] et al. [35]. Experimental application of the phase matching mode was studied by [PERSON] et al. [36].
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 2 Materials and Methods
### Data and Experiment Details
#### 2.5.3 Vegetation Index Data  
In GF-2, the UGS area has lower reflectance in the visible and higher reflectance in the near-infrared. This study combines GF-2 multi-temporal remote sensing images to introduce urban green space vegetation characteristics. The vegetation characteristics were selected as NDVI [30], which is an index widely used for plant growth assessment, as shown in Figure 6, and its calculation formula is as follows:  
\[NDVI=\frac{NIR-R}{NIR+R} \tag{1}\]  
where \(NIR\) is the value of the near-infrared band, and \(R\) is the value of the red band.  
Figure 5: Spatial attention submodule.  
Figure 6: Example of NDVI image.
## 2 Theoretical Model
### Distribution of the Temperature of a Jet
#### 2.4.2 Shock-Induced Temperature Rise  
In the jet-formation process, a shock wave with a pressure of up to 40 GPa can be formed inside the liner material at the instant when the blast wave formed after the detonation of the explosive acts on the liner [26]. The compressive action of the powerful shock wave leads to an increase in the internal temperature of the material. The rise in the material's temperature due to the shock wave, \(\Delta T_{s}\), can be calculated using the following equation [16]:  
\[\Delta T_{s}=T_{0}exp\bigg{[}\frac{\gamma_{0}}{v_{0}}(v_{0}-v_{1})\bigg{]}+ \frac{P(v_{0}-v_{1})}{2C_{V}}+\frac{exp\big{[}\frac{\gamma_{0}v_{1}}{v_{0}} \big{]}}{2C_{V}}\int_{v_{0}}^{v_{1}}Pexp\bigg{(}\frac{\gamma_{0}v_{1}}{v_{0}} \bigg{)}\bigg{[}2-\frac{\gamma_{0}}{v_{0}}(v_{0}-v_{1})\bigg{]}d\upsilon, \tag{13}\]where \(P\) is the peak pressure of the shock wave, \(v_{0}\) is the initial specific volume of the material, and \(v_{1}\) is the post-wave specific volume of the material, which can be calculated using the following equation:  
\[v_{1}=\frac{C_{0}^{2}}{2 PS^{2}}\left[\sqrt{1+\frac{4 PSv_{0}}{C_{0}^{2}}}+\frac{2 S(S-1)v_{0}P}{C_{0}^{2}}-1\right], \tag{14}\]  
where \(C_{0}\) and \(S\) are the relationship constants of the shock-wave velocity \(U_{s}\) and the particle velocity \(U_{p}\), respectively. The shock wave is immediately followed by a rarefaction wave. The action of the rarefaction wave leads to a decrease in the temperature of the material. The residual temperature of the material after the action of the rarefaction wave, \(\Delta T_{2}\), is calculated as follows [22]:  
\[\Delta T_{2}=\Delta T_{s}exp\left[\frac{\gamma_{0}}{v_{0}}(v_{s}-v_{0})\right], \tag{15}\]  
where \(v_{s}\) is the specific volume of the shocked material. Therefore, the rise in the material's temperature at any given time (\(t\leq t_{b}\)) during the jet-formation process can be expressed as follows:  
\[\Delta T=\Delta T_{1}+\Delta T_{2}. \tag{16}\]  
Substituting Equations (5)-(8) and (16) into the constitutive model provides the distribution of the temperature of the SCJ at any given time.
# A LeViT-EfficientNet-Based Feature Fusion Alzheimer's Disease Diagnosis
## 2 Materials and Methods
### LeViT-Based Feature Extraction  
LeViT offers a powerful platform to handle a wide range of medical image processing tasks, including classification, object detection, and segmentation [40]. It demands fewer parameters compared to traditional CNN models. The self-attention mechanism can learn interpretable representations of the MRI images. The global context modeling technique captures holistic information associated with the MRI images. The patch extractor transforms the image shape from 224 \(\times\) 224 \(\times\) 3 into 250 \(\times\) 14 \(\times\) 14. A shrinking attention block is used to reduce the size of the activation maps. These features have motivated the author to employ LeViT to extract AD patterns from the MRI images. However, LeViT faces challenges in capturing fine-grained local details, affecting the ability to locate the smaller objects. To overcome this limitation and improve the performance of LeViT-based feature extraction, the author integrated spatial transformer networks (STNs) [45] with LeViT architecture. Initially, an STN is built to perform spatial transformation on the MRI images and extract features based on the region of interest. A feature extraction model is constructed using the LeViT backbone. The extracted features are passed through the LeViT in order to capture high-level representations of the spatially transformed features. Figure 3 highlights the enhanced LeViT model for the feature extraction.  
Figure 2: The recommended EfficientNet B7-Based Feature Extraction.  
A fully connected layer with the Softmax function is used to classify the features based on the severity. Equations (1) and (2) show the computational forms of \(STN\) and _LeViT_ models.  
\[F=STN(C,I) \tag{1}\]  
where \(F\) is the image feature, \(STN\)() is the spatial transformer network function, C is the input channel, and \(I\) is the image.  
\[F=LeViT(C,Cl,F) \tag{2}\]  
where \(F\) is the image feature, \(C\) is the input channel, \(Cl\) is the AD classes, and \(LeViT\)() is the function for implementing the \(LeViT\) model.  
After fusing the features, the author normalized the features using feature-wise normalization to prevent numerical instability. Finally, a fully connected layer with the Softmax function was used to generate the outcome. The outcomes were stored as a vector.
# Evaluation of Snowboarding Helmets in Mitigation of the Biomechanical Responses of Head Surrogate
## 4 Discussion
They mounted a fresh porcine scalp on the Hybrid III headform. They found an insignificant difference in the peak rotational acceleration between the conventional and anti-rotational helmets. This insignificant difference was attributed to the addition of a porcine scalp on the outermost layer of the Hybrid III headform. In this work, compared to the conventional helmet, considerable reductions in equivalent stress (by 50-60%) and MPS (by 50-60%) were seen with anti-rotational helmets (Figure 8). This finding is consistent with the finding of [PERSON] et al. [8]. They have reported a ~60% reduction in brain strain with anti-rotational helmets. In summary, these results highlight the critical design-specific factors of helmets in mitigating biomechanical responses. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Sr** & **Study** & **Impact Velocity (fm/s)** & **Testing Methods** & **Helment Type** & \multicolumn{4}{c}{**Peak Biomechanical Responses**} \\ \cline{6-9}
**No.** & & & & **Linear Acceleration** & \multicolumn{2}{c}{**Angular Velocity**} & \multicolumn{2}{c}{**Angular Acceleration**} \\ \hline  & & & & **Peak** & **Pulse** & **Pulse** & **Pulse** & **Pulse** \\  & & & & **Magitude (g)** & **Duration (ms)** & **(rad/s)** & **Duration (ms)** & **(rad/s)** & **Duration (ms)** \\ \hline \multirow{2}{*}{1.} & [PERSON] et al. [23] & \multirow{2}{*}{4.1} & \multirow{2}{*}{Impact of headform on snow surface} & No helmet & 82 & - & - & - & - \\ \cline{4-8}  & & & Conventional & - & - & - & 7291 & 9 \\ \cline{4-8}  & & & MIPS & - & - & - & 4056 & 12 \\ \hline \multirow{2}{*}{2.} & [PERSON] et al. [24] & \multirow{2}{*}{5.3} & Impact of headform on snow surface} & No helmet & 120 & 6 & - & - & - \\ \cline{4-8}  & & & Conventional & 65 & - & 20 & - & 4100 & - \\ \hline \multirow{3}{*}{3.} & [PERSON] et al. [22] & \multirow{2}{*}{4.8} & Impact of headform on rigid awil} & MIPS & 60 & - & 8 & - & 2000 & - \\ \cline{4-8}  & & & WaveCell & 60 & - & 9 & - & 1800 & - \\ \hline \multirow{3}{*}{4.} & \multirow{3}{*}{This work} & \multirow{3}{*}{4.6 \(\pm\) 0.2} & \multirow{3}{*}{Impact of head surrogate using linear impactor} & No helmet & \(93\pm 3\) & 7 & \(25\pm 2\) & 70 & \(10562\pm 619\) & 8 \\ \cline{4-8}  & & & Conventional & \(38\pm 3\) & 8 & \(22\pm 2\) & 70 & \(9504\pm 823\) & 8 \\ \cline{4-8}  & & & & MIPS & \(30\pm 2\) & 8 & \(9\pm 1\) & 90 & \(3846\pm 775\) & 11 \\ \cline{4-8}  & & & & & & & & \\ \cline{4-8}  & & & & & & & & \\ \hline WaveCell & \(20\pm 2\) & 10 & \(11\pm 1\) & 90 & \(2900\pm 545\) & 12 \\ \cline{4-8}  & & & & Silica pad & \(24\pm 3\) & 9 & \(13\pm 2\) & 80 & \(4100\pm 550\) & 11 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of head kinematics (peak values and pulse durations) obtained in this work against snowboarding studies in the literature.
## 3 Results and Discussion
### Surface Potential and Current
#### 3.1.2 Current Verification  
Taking [4] as a reference, the proposed model is implemented with real device effect models in Verilog-A and compared with simulations of a 22-nm industrial design kit, which adopts the BSIM-IMG HSPICE built-in model. The accuracy is verified for an n-type short-channel device with \(L=20\) nm, \(W=80\) nm, \(T_{OX1}=1.33\) nm, \(T_{OX2}=20\) nm and \(T_{Si}=6\) nm. Figure 5a shows \(I_{DS}\) versus \(V_{gs}\) using the proposed surface potential (SP) approximation combining a computationally efficient charge density model (CDM). Figure 5b shows \(I_{DS}\) versus \(V_{ds}\) using the proposed surface potential approximation, combining the proposed accurate charge density model.
## 3 Non-Thermal Technologies in Food Processing
### Ultrasounds
#### 3.1.4 Application in Dairy Products  
In the dairy products industry, the use of ultrasounds can be useful for microbial inactivation, fat reduction, product homogenization, and improvement of the organoleptic properties and nutritional value. Moreover, it is a time-saving process, since it reduces the time required for cheese ripening and fermentation [78, 106, 107, 108]. This treatment has gained much consideration in the last few years in particular for fermented products, since it helps reduce the processing time and enhance the probiotics viability for products with low lactose content. For the latter, US helps to reduce undesirable taste and probiotics or \(\beta\)-galactosidase additions [106].  
The longer the US treatment, the better the stabilization of the emulsion and the droplet size reduction [85; 107]. The droplets size has been seen to be reduced both for skimmed milk and goat milk, and an increased homogenization has been observed for cream. During the US process, milk deterioration and lipid oxidation can be avoided by reducing the treatment time and temperature. In yogurt, high-intensity US can be used to make a better homogenization and emulsification by reducing the milk fat globule size, and it can also reduce the fermentation time by improving lactose hydrolysis, enhance gel strength and firmness by increasing the coagulation properties of whey proteins, and stimulate probiotic bacteria. In ice cream manufacturing, during the freezing process, US reduces the ice crystal size, freezing time, and block crust on the freezing surface [108; 109; 110].  
Still considering dairy products, US (>400 kHz) can be applied as a method to fractionate liquids, which arrange themselves in layers. For milk, ultrasonic frequencies of 0.4 MHz and 1.6 MHz are used for 5 min at 35 \({}^{\circ}\)C [85].
# Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
## 2 Materials and Methods
### Model Training  
The model was created using TensorFlow Object Detection API \"Application Programming Interface\", an open-source platform with a preceding software library that was developed by Google Brain team. It allows the use and customization of many saved networks termed as pre-trained models. They were previously trained on a large general dataset, which meant that a pre-trained model could be taken advantage of to effectively work as a learned generic model, which aids in reducing the amount of data needed to train a model from scratch [19].  
Several pre-trained models were experimented with. These models are: ssd_mobilenet_v2, RetinaNet, and EfficientDet-D0, which were all pre-trained on coco_2018 dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Contact Area Count (\%) in the Whole Dataset** & **Contact Area Count (\%) in the Training Set** \\ \hline Overlap within enamel & 1124 (57.6) & 555 (52.8) \\ Overlap within restoration & 352 (18.0) & 226 (21.5) \\ Overlap at DEJ & 162 (8.3) & 83 (7.9) \\ Overlap within dentin & 109 (5.6) & 64 (6.1) \\ Overlap within restoration & 204 (10.5) & 123 (11.7) \\ \hline Total & 1951 (100) & 1051 (100) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contact areas label distribution.  
Among the three different object detection model architectures, EfficientDet achieved the best performance, as it required the lowest number of training epochs with similar accuracy. Accordingly, the EfficientDet-D0 model was chosen for this study, which is a light and small version of EfficientDet (Figure 1) [20].  
In this study, a fine-tuning approach was used for pre-trained model customization. This is done by unfreezing then training the last layers of the frozen model base, together with the newly added classifier layers. This allows us to modify the representations needed in the model for feature detection and make them more relevant for the specified task [19]. Training of the model to identify errors was done using the training set within an open-source product from Google Research, called Google Colab, using GPU \"Graphics Processing Unit\" runtime. The initial learning rate was chosen as 0.001 with further exponential decay. There were 7000 training steps, with a batch size of 16 [21].
## 5 Simulink Results and Discussion
### Analysis of FLOS Simulation Results  
When designing the desired trajectory, the continuous parameterized reference trajectory is discretized into multiple navigation points. This method is also used in [34,35].  
Results of traditional LOS, ALOS proposed in [37], and ELOS proposed in [34] are used for comparative analysis. ELOS:  
\[\left\{\begin{array}{l}\psi_{d}=\alpha_{k}(\omega)+\arctan(-\frac{\psi_{e}}{ \Delta}-\hat{\beta})\\ \hat{\beta}=\frac{\hat{g}}{U\cos(\psi_{d}-\alpha_{k})}\\ \hat{p}=-kp-k^{2}y_{e}-k\big{[}U\sin(\psi_{d}-\alpha_{k})-\dot{a}_{k}x_{e} \big{]}\\ \hat{g}=p+ky_{e}\\ \end{array}\right. \tag{37}\]  
ALOS:  
\[\left\{\begin{array}{l}\psi_{d}=\gamma_{p}+\tan^{-1}\left(-\frac{1}{\Delta} y_{e}-\hat{\beta}\right)\\ \hat{\delta}=\gamma\frac{U\Delta}{\sqrt{\Delta^{2}+\left(y_{e}+\Delta\beta \right)^{2}}}ky_{e},\gamma>0\\ \end{array}\right. \tag{38}\]  
In the matlab/simlink simulation, we choose a fixed step = 0.001 s. The parameters of the guided law are as follows: the forward-looking distance \(\Delta\) = 2 m, the radius of the navigation point range \(R\) = 0.01 m, the gain of ALOS \(\gamma\) = 0.3 in Formula (38), and the gain of ELOS \(k\) =1.5 in Formula (37). The parameters of FLOS choice are \(k_{1}\) = 0.5, \(k_{y}\) = 0.5, \(l_{1}\) = 2, and \(l_{2}\) = 5.
# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 2 System Model  
In this paper, we consider an end-to-end (E2E) NS system where public and non-public network slices coexist. In terms of public network slicing, since most of the researchers primarily focus on eMBB and URLLC slices, we take them into account. Thus, a total of three types of slices are considered: eMBB slices, URLLC slices, and NPN slices. The system architecture is shown in Figure 1.  
The system consists of two parts: the radio access network (RAN) and the core network (CN). These two parts are connected via the backhaul links. The RAN is mainly responsible for allocating wireless spectrum resource blocks (RBs) to users in the slice. The CN mainly maps the VNFs to general servers in the physical network.
## 4 Handover and Mobility Process in 5G HetNet
### 5G HetNet HO Issues  
The deployment of UDSC and the massive number of connected UEs in 5G lead to an ultra-dense network (UDN), causing several HO management issues. These issues include high HOR, frequent and UHO, HOPP, RLF, signaling overhead, and ICI. These problems can degrade UEs' HO performance and reduce seamless connectivity in 5G HetNet. Figure 8 illustrates the causes and consequences of HO issues in 5G HetNet.  
Figure 8: HO issues in 5G HetNet.
# Wavefront Aberration Measurement Deflectometry for Imaging Lens Tests
## 2 Principles
Thus, by obtaining the coordinates of \(F\) and \(S_{2}\) in the experiment, the angular aberration \(\beta\) can be measured according to Equation (2). Then, the wavefront aberration of the imaging lens can be obtained by integrating the angular aberration \(\beta_{x}\) and \(\beta_{y}\).
# Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering
## 5 Results
### Overall Results  
The experiment was conducted based on two Thai QA datasets: Thai Wiki QA and iApp Wiki QA. We compared the results in three aspects: quality of the synthesized question-answer pairs, training strategies, and baseline models used in this experiment--WangchanBERTa and mT5-Base. Furthermore, we also evaluated the results in exact match (EM), word-level F1 (W-F1) and Syllable-level F1 (S-F1). The results are summarized in Table 4 for Thai Wiki QA and Table 5 for iApp Wiki QA. The training set statistics, including the generated dataset, are illustrated in Table 6.  
In the result description, we created the combination name for readily referring to the tested model. The combination name consists of three parts: the QA models, training strategies, and augmented data used. The QA models have two possible types: WangchanBERTa (WBT) and mT5-Base (mT5). Training strategies have two possible strategies: Sequence (SEQ) and Merge (MRG). Lastly, the generated data used in the experiments have two types: Filtered (FLT) and ALL. All three parts connect together with the dash symbol (-). For example, mT5-SEQ-FLT refers to using the mT5 model fine-tuned with filtered generated data and the Sequence strategy.  
We compared the results with the baseline models which are the question answering models that were trained with real human-labeled data only. In most cases, using generated data could improve the performance of every metric. In the Thai Wiki QA dataset, using the mT5-SEQ-FLT provided the best performance combination that beat the result of the baseline of mT5-Base by 6.28%, 5.14%, and 4.94% for EM, word-level F1, and syllable-level F1, respectively. In the iApp Wiki QA dataset, the best performance combination used the mT5-SEQ-ALL, which beat the baseline result of mT5-Base by 28.69%, 18.51%, and 19.44% EM, word-level F1, and syllable-level F1, respectively. Using all generated data with iApp Wiki QA could slightly improve performance compared to using the filtered generated data because the human-labeled training set of iApp Wiki QA has a smaller number of samples, compared to those of Thai Wiki QA, and it needs more data to fine-tune. However, the results between using the filtered generated pairs and all the generated pairs are not much different. We can conclude that using mT5-SEQ-FLT is the best combination that outperforms both datasets.
# Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems
## 5 Experiments
### Experiments on the NSL-KDD Dataset
#### 5.1.1 NSL-KDD Binary  
Full Features  
The first binary classification experiment considered the training of the model with all the features extracted during the data preparation phase (Section 4.2). Since the NSL-KDD subsets used for training and testing (\(train_{f}\) and \(test_{f}\)) had a total of 123 columns each, the neural network's input layer has 123 nodes.  
The first step is to train the neural network on this version of the training subset and assess the results achieved. We used the Keras library to build the model and fine-tune the hyperparameters, as mentioned in Section 4.3. We used Early Stopping (ES) to prevent overfitting the network. One problem which can lead to overfitting is using too many epochs to train the network. Hence, ES allows the user to set many training epochs, but it stops the process once the model performance reaches the best possible result and before it drops. The confusion matrix for this model can be seen in Table 6. The confusion matrix makes it easier to see which classes are easily confused by the model, and from this matrix it can be seen that the number of False Negatives (FN) is 3296, which is very high. This means that the model wrongly classified 3296 attack records as normal traffic. The number of false positives is equal to 662, which means that the model wrongly classified 662 records as attacks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} \\ \cline{3-4}  & & **Normal** & **Attack** \\ \hline \multirow{3}{*}{\(test_{f}\)} & Normal & 9049 & 662 \\  & Attack & 3296 & 9537 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for NSL-KDD “full features” binary classification experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware** & **Specification** \\ \hline GPU & NVIDIA GeForce GTX 1050 \\ \hline Memory & 16 GB system memory \\ \hline Storage & 256 GB SSD \\ \hline GPU Memory & 4 GB GPU memory \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hardware specifications of the computer used for training.