esa-sceva/satcom-chunk-collection huggingface dataset:
# Accented Speech Recognition Based on End-to-End Domain Adversarial Training of Neural Networks
## 3 Accented Speech Recognition Based on End-to-End Domain Adversarial Training
### End-to-End Domain Adversarial Training Based on DANN for Accented Speech Recognition
#### 3.3.2 Domain Classifier  
The domain classifier plays a role in deciding whether the input data are the target (accented speech) or not. This binary classification problem does not require complex algorithms; hence, we used a simple deep neural network (DNN) architecture with four layers to construct the domain classifier, with the expectation that it would provide sufficient conditions for binary classification. The features learned from the feature extractor passed through four DNN layers, then the domain label \(d\) was outputted from the domain classifier. Afterward, the domain loss \(L_{d}\) was calculated by the difference between the predicted domain and original domain using the cross-entropy loss function. The gradient \(\frac{dL_{d}}{\partial\theta_{d}}\) was backpropagated into the downstream of the model, and the GRL reversed the gradient and multiplied it by a scalar \(\lambda\), delivering the reversed gradient \(-\lambda\frac{\theta L_{d}}{\partial\theta_{f}}\) to the feature extractor. Thus, the feature extractor could learn accent-invariant features in the next training steps.
# Hermite Quartic Splines for Smoothing and Sampling a Roughing Curvilinear Spiral Toolpath
## 2 Methodology for Smoothing Toolpaths
### Selecting Structure Curves  
To select the structure curves among isotherms and to build the toolpath, isotherms are first decomposed into isoHQ and then sampled.  
The analytical expression of patches, along with Equation (9), enables the computation of the curvilinear length of each patch (\(s_{patch}\)) and, consequently, the total length of an isoHQ. Using normalization (the normalized length is set to one), points can be sampled, equally spaced along an isoHQ:  
\[s_{patch}=\int_{0}^{1}\sqrt{\left(\frac{dx(u)}{du}\right)^{2}+\left(\frac{dy( u)}{du}\right)^{2}}du \tag{9}\]  
The selection of the structure curves is performed by evaluating the distance (\(D_{isoHQ}\)) between sampling points of 2 isoHQ (an external one \(isoHQ_{ext}\) and an internal one \(isoHQ_{int}\)  
Figure 5: General algorithm of isotherm decomposition in minimum \(C^{2}\) connected patches.  
and linking it with an acceptable distance (\(D_{\mathit{rte}}\)) equal to the radial tool engagement. If \(P_{j}\in isoHQ_{\mathit{ext}}\) and \(Q_{i}\in isoHQ_{\mathit{int}}\), Equation (10) is computed:  
\[D_{\mathit{isoHQ}}=MAX_{j}\{\,MIN_{i}[\,\sqrt{(\,P_{j}(x)-Q_{i}(x)\,)^{2}\,+\,( \,P_{j}(y)-Q_{i}(y)\,)^{2}}\,]\,\} \tag{10}\]  
* If \(D_{\mathit{isoHQ}}<D_{\mathit{rte}}\), the work is restarted with a more internal \(isoHQ_{\mathit{int}}\);
* If \(D_{\mathit{isoHQ}}>D_{\mathit{rte}}\), the penultimate evaluated \(isoHQ_{\mathit{int}}\) is chosen as the structure curve, and the loop is restarted.  
Thereby, the distance between two structure curves is under control and always under \(D_{\mathit{rte}}\), which guarantees machining without unmilled areas.
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 3 Vessel Data Preprocessing
### Data Preprocessing
#### 3.1.2 Outlier Detection and Cleaning  
For erroneous data, which refers to the cases where the sog, cog, or rot of trajectory points exceed normal thresholds, this study employs a rule-based method for identification, followed by linear interpolation to replace the erroneous values. This approach not only preserves the key information of the original data and eliminates the interference of outliers but also considers the spatiotemporal correlation between trajectory points through interpolation, thereby enhancing the accuracy and reliability of the data.
# Dynamic Phase Comparison Planar Direction-Finding Algorithm on Satellite Radio Receiver
## 3 Simulation Results and Analysis
### Comparison of the Dynamic Phase Comparison and the Amplitude Comparison Algorithms  
From Section 2.4, it is evident that both the dynamic phase comparison algorithm and the amplitude comparison algorithm face different angular ambiguity issues, with the former offering a larger DF range. Moreover, simulation results from the previous subsection indicate that the dynamic phase comparison algorithm exhibits lower DF accuracy near azimuth angles of \(0^{\circ}\), \(90^{\circ}\), and \(180^{\circ}\). Therefore, it might be beneficial to consider integrating the traditional amplitude comparison algorithm to analyze DF results at different angles with those of the dynamic phase comparison algorithm. This integration could improve DF accuracy while expanding the DF range.  
Taking the example of a satellite receiver equipped with a dual orthogonal dipole antennas and two-channel receiver, this section conducts a simulation of the two algorithms during satellite motion. Based on the simulation from the previous subsection, the antenna on the satellite receiver is changed to a dual orthogonal dipole antenna. As shown in Figure 7, it is assumed that the two antennas are aligned with the \(X\)-axis and \(Y\)-axis directions in the coordinate system, respectively. The satellite moves in the positive direction of the \(X\)-axis during the short-term DF process.  
The simulation parameters are set with an SNR of \(-10\,\mathrm{dB}\) and a receiver sampling rate of \(6.4\,\mathrm{MHz}\), and each sampling involves 10 cycles of the signal. Figure 8 shows the one simulation result of the two direction-finding algorithms, and Figure 9 shows the average absolute error curves from one thousand simulations under three different SNR conditions. The amplitude comparison algorithm only calculates errors below \(90^{\circ}\).  
The simulation results indicate that when the azimuth angle is within the range of approximately \(30^{\circ}\) to \(80^{\circ}\), the dynamic phase comparison algorithm generally exhibits relatively high accuracy; meanwhile, the amplitude comparison algorithm shows relatively high accuracy when the azimuth angle is close to \(0^{\circ}\) and \(90^{\circ}\).  
Hence, by integrating these two DF algorithms, it is possible to further improve DF accuracy with the higher DF range.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 3 Results
### XGBoost Prediction Results  
This study utilized the XGBoost model to predict landslide occurrence probabilities and compared the obtained probabilities with specific rainfall characteristics, including rainfall duration, total rainfall, and rainfall intensity, to visualize their distribution patterns. Rainfall duration was selected as the horizontal axis, while total rainfall and rainfall intensity were plotted on the vertical axis. A two-dimensional mapping approach was used to illustrate the relationships among these variables. The color gradient in the figures represents the probability of landslide occurrence, with deep red indicating high-probability areas and lighter shades representing lower-probability areas.  
As shown in Figure 7a, the landslide probability increased as total rainfall rose. Notably, when total rainfall ranged between 10 mm and 100 mm, the probability exhibited a sharp upward trend, surging from approximately 0.2 to nearly 0.8. In cases of high total rainfall (\(\geq\)100 mm), the landslide probability approached its maximum value.  
As shown in Figure 7b, when the rainfall duration was between 1 and 10 days, and rainfall intensity fell within the range of 10 mm/day to 100 mm/day, the landslide probability changed significantly, mostly rising from about 0.2 to its maximum. Moreover, when the rainfall duration exceeded 10 days, even with moderate rainfall intensity (5 mm/day to 10 mm/day), the probability of landslide occurrence remained high, typically ranging between 0.5 and 0.8.
###### Abstract  
The inherent mineralological alignment in stratified rock formations engenders pronounced mechanical anisotropy, presenting persistent challenges across geological, geotechnical, and petroleum engineering disciplines. While substantial progress has been made in modeling transversely isotropic media, current methodologies exhibit limitations in reconciling theoretical predictions with complex failure mechanisms. This investigation examines the anisotropic response of diverse lithologies through triaxial testing across bedding orientations (0-90\({}^{\circ}\)) and confinement levels (0-60 MPa), revealing a pressure-dependent attenuation of directional strength variations. Experimental evidence identifies three dominant failure modes: cross-bedding shear fracturing, bedding-parallel sliding, and hybrid mechanisms combining both, with transition thresholds governed by confinement intensity and bedding angle. Analytical comparisons demonstrate that conventional single weakness plane models produce characteristic shoulder-shaped strength curves with overpredictions, particularly in hybrid failure regimes. Conversely, the modified patchy weakness plane formulation achieves superior predictive accuracy through parametric representation of anisotropy gradation, effectively capturing strength transitions between end-member failure modes. The Pariseau criterion, though marginally less precise in absolute terms, provides critical insights into directional strength contrasts through its explicit differentiation of vertical versus parallel bedding responses. These findings advance the fundamental understanding of anisotropic rock behavior while establishing practical frameworks for optimizing stability assessments in bedded formations, particularly in high-confinement environments characteristic of deep reservoirs and engineered underground structures.  
anisotropic strength criteria; triaxial experiments; layered rock; Pariseau's model; fitting method +
Footnote †: journal: Applied Sciences
# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 5 Discussion  
### Effect of NearPseudo on Different Multi-Classification Algorithms  
As shown in Table 5, the impacts of NearPseudo on different multi-classification algorithms vary. To reveal the mechanism, Pearson correlation analysis was used to analyze the relationship between two variables: (1) mean change in overall accuracy from baseline unbalanced dataset to balanced dataset after processing in the sub-training set  
Figure 3: Impact of two hyperparameters on overall accuracy (OA) in three sub-training sets. (**a–c**) are representative of the change of OA as the number of the nearest neighbor samples (\(k\)) increase in sub-training set **T1**, **T2**, and **T3**, respectively. (**d–f**) are representative of the change of OA as the number of samples (\(q\)) increase in sub-training set **T1**, **T2**, and **T3**, respectively. All data are mean \(\pm\) standard error from five measurements, and the dashed line shows a linear correlation between OA and each hyperparameters.  
**T2** and (2) variance of F1-score for each class before processing. Notably, lower values for variance correlated with better capacity of most machine-learning algorithms to handle imbalanced datasets, and vice versa. For example, the F1-score of minority class (NO. 12) by using classifier LR was 8.0%, which is high variance (variance = 24.7%), whereas the F1 score of minority class (NO. 12) by using classifier RF was 63.2%, which is low variance (variance = 13.64%) (Figure 4 and Table 5). Compared with RF, LR obtained prejudicial and biased classification results in minority, which increased the difference in F1 scores between each class. Consequently, the variance of the F1 score for each class in the LR classifier was larger.  
As shown in Figure 4, the values of mean change increased as the variance of F1 score for each class without balance. That is, the effect of NearPseudo on accuracy is related to the capacity of most machine-learning algorithms to handle imbalanced datasets. The more negative the impact of the imbalance data on performance of algorithms, the more likely it is that NearPseudo improves the classification accuracy. Conversely, when the algorithm is less affected by imbalanced datasets, there is no additional balanced method required. For the LR classifier, the goal is to find the parameters with optimal values when the minimum value of loss function is obtained. Each class in LR requires sufficient samples to fit the fixed number of parameters. If lacking samples in a minority class, the classifier will perform poorly in this class.
## 3 Results and Discussion of Computational Experiments
### Contour-Propagation Algorithm
From here we see that the lowest Dice value is obtained in the CTV structure in Scenario 5, with the B-spline Dice equal to 0.8312, while the demon Dice is equal to 0.9227. Figure 8: The diagram shows how each of the parameters intervene in order to obtain the optimal transformation in the _matRad-contourPropagation_ algorithm. Figure 9: Comparison between registrations with demons and B-spline for the 6 breast CT structures, using the Dice coefficient. A qualitative evaluation of the contour was previously performed in a similar study in which the propagated contours were rated as clinically acceptable, requiring minor editing. Although different deformable image registration algorithms were used, the contours of the structures were manually done by the professional medical oncologist, which allows Equation (10) to work and measure the similarity and quality of the structures' registration. Figure 10 allows for a visual comparison between the structures contoured by a professional medical oncologist (Figure 10a-d at the top) and the ones estimated by the demon algorithm (Figure 10e-h at the bottom). The images are organized in such a way that the contours corresponding to Scenario 2 (in both modalities) are observed on the left. As we move to the right side, we see the results until Scenario 5. Observe that the differences are very small.
## 3 Review of Existing Channel Models for Underground Mines
### Single-Mode Waveguide Model  
Tunnel structure in underground mines can guide EM waves throughout their path and this type of EM wave propagation has been modeled as a waveguide [23]. In the single-mode model, the distributed field of EM waves is examined by adding propagated modes on the excitation plane with the help of Maxwell equations. The single-mode waveguide model becomes the base for modeling in underground tunnels, especially for evaluating path loss in lower frequencies of the ultra-high frequency (UHF) band [7]. However, this model is considered valid only for the dominant mode and for evaluating the propagation loss in the far-field region [14]. Several efforts have been made for the correction of weak areas by modifications, but no model is valid for path loss in the near-field region. Moreover, this model fails to predict accurate results on higher frequencies and does not cover the surface roughness of tunnel walls [24].
## 3 Analysis Method of iNterference on DME
### Calculation for P(D)  
As mentioned above, reply pulses consist of measuring and identification pulses, moreover, identification pulses has priority to transmit. What we are concerned with is whether measuring pulses is interfered, and identification pulses can be thought to be inter-system interference. Interference probability can be calculated as [10], since the characteristic of identification pulses is the same as that of measuring pulses, the probability of measuring pulses not interfered by identification pulses \(P\left(D\right)\) is given as follows:  
\[P\left(D\right)=1-\lambda_{id}\cdot(\tau_{m\_r}+\tau_{m\_r})=1-\frac{5}{40} \times 1350\times 2\tau_{m\_r}=1-337.5\cdot\tau_{m\_r} \tag{14}\]  
where \(\tau_{m\_r}\) is the reply pulse pair duration defined as \(\tau_{m}\) shown in Figure 1, its value can be seen from Table 1.
## 2 Background and the Related Work
### SDN with Multi-Controllers
Although this method effectively detects both inter-domain and intra-domain attacks, it lacks an efficient mitigation module. The mitigation procedure they propose only reduces the intensity of the attack by dropping malicious flows and provides no communication mechanism for attack source blocking. This approach necessitates manual intervention for the permanent elimination of ongoing attacks. [PERSON] et al. [27] present a Secure Flow Management (SFM) model for detecting and mitigating UDP flooding attacks in multi-controller SDNs. The SFM monitors traffic statistics and applies threshold-based policies to dynamically block malicious flows. However, this study is limited to scenarios with tree-like topologies, allowing only one connection per controller, which can cause unnecessary strain on controller's resources, potentially leading to performance bottlenecks. Additionally, none of these researchers focus on the optimal number and placement of controllers; exploiting optimal controller placement would have resulted in better performance in terms of congestion, scalability, and speed (faster detection and mitigation). Thus, the majority of the existing literature dealing with threats in SDN accounts only for single-controller scenarios, and those operating in multi-controller environments do not present optimized and general solutions. Additionally, these models are deployed in an arbitrary network topology with an arbitrary placement of controllers, which usually delivers unrealistic or biased performance metrics such as speed, throughput, and bandwidth. Existing work has mostly implemented an attack detection action by the controller;this approach keeps the controller busy and occupied. For ML training, datasets from the Canadian Institute of Cybersecurity (CIC) have been widely used, which come with high processing and packet capture overhead. Keeping in mind all these shortcomings, our proposed model first obtains two real-life network topologies containing multiple controllers. Then, it performs an optimal placement of controllers. The attack detection process is handled by the central IDS which is independent of the controller, saving precious controller resources for other tasks. Additionally, this work incorporates the use of a primary dataset that produces a more authentic representation of the behavior of the SDN network without packet capture and a lower computational overhead.
## 6 Results
### Comparison with Other Models
#### 6.3.1 Comparison with Methods Using DL on Different Types of Images  
Several methods were reviewed in the related work section and different DL methods were applied on different types of breast cancer images. Section (A) of Table 4 shows their performance results. As shown in the table, the achieved accuracy of the different methods was above 90%. They used different types of images: histopathology, mammography, and MRI. The best achieved accuracy of 94.31% in [13] was after using a U-Net CNN on mammography breast images. On the other hand, our CNN approaches to thermal images achieved accuracy rates of 99.34%, 99.49%, and 99.32% for SF, HD, and SL AMs, respectively (see Section (D) of Table 4). In addition, the AMs with the CNN achieved higher scores than [16] in sensitivity and specificity metrics, with 9% and 6%, respectively. In the measure of precision, all proposed models, including the CNN without AMs, achieved results higher than [12]. The CNNs with the three AMs achieved a competitive result with an AUC ROC of 0.999, while the study of mammograms achieved 0.9931 [15], where our result was higher. This proved that the use of a CNN with an AM on thermal images greatly enhanced the detection accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Approaches**} & \multicolumn{2}{c}{**Imaging**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**(Recall)**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**AUC**} \\  & & & & & & & & & \\ \hline \multicolumn{10}{c}{**A-**} \\ \hline
[12] & CNN-LSTM & Histopathology & 91\% & - & - & 96\% & - & \\ \hline
[13] & U-Net CNN & Mammography & **94.31**\% & - & - & - & - & \\ \hline
[14] & CNN & Histopathology & 91.4\% & - & - & - & - & \\ \hline
[15] & CNN & Mammography & 93.5\% & - & - & - & - & 0.9931 \\ \hline
[16] & CNN-RNN & Mammography & 90.59\% & 89.88\% & 92.42\% & - & - & \\ \hline \multicolumn{10}{c}{**B-**} \\ \hline
[19] & CNNs-Bayes & \multirow{4}{*}{Thermal} & \multirow{4}{*}{98.95\%} & \multirow{4}{*}{-} & \multirow{4}{*}{-} & \multirow{4}{*}{-} & \multirow{4}{*}{-} & \multirow{4}{*}{-} \\  & optimization & & & & & & \\ algorithm & & & & & & & \\ \hline
[20] & DCNNs & Thermal & 95.8\% & 76.3\% & 99.40\% & - & - & \\ \hline
[24] & multi-input & \multirow{4}{*}{Thermal} & \multirow{4}{*}{97\%} & \multirow{4}{*}{100\%} & \multirow{4}{*}{83\%} & \multirow{4}{*}{-} & \multirow{4}{*}{-} & \multirow{4}{*}{0.99} \\  & CNN & & & & & & & \\ \hline \multicolumn{10}{c}{**Static (color) = 98\%**} \\ \multicolumn{10}{c}{Static (grayscale) = 95\%**} \\ \multicolumn{10}{c}{CNNs} & Thermal & \multirow{4}{*}{Dynamic (color) = 95\%} \\ \multicolumn{10}{c}{Dynamic (grayscale) = 92\%**} \\ \multicolumn{10}{c}{**Dynamic (grayscale) = 92\%**} \\ \multicolumn{10}{c}{**C- Approaches using DL with AM**} \\ \multicolumn{10}{c}{} \\ \hline
## 3 System Models
### Latency Models of a Client  
There are four steps in each global iteration for FL, and so the latency of a global iteration equals the sum of the latency among these four steps. The local model training latency in Step (2) and local model uploading latency in Step (3) are different among the clients, depending on their computing and communications capacities. In addition, the global model broadcast latency in Step (1) and model aggression latency in Step (4) are the same for all the clients and are normally negligible as compared to local model training and uploading latency. Thus, we define the latency of client \(i\) in a global iteration as follows.  
\[t_{i}=t_{i}^{comp}+t_{i}^{upload}, \tag{3}\]  
where \(t_{i}^{comp}\) is the computing latency of client \(i\) in training its local model over its local data samples in Step (2), and \(t_{i}^{upload}\) is the uploading latency of client \(i\) in uploading its local model to the FL server in Step (3).
# Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code
## 5 Results and Discussion
### Convergence and SER Performance
The improvement is obtained by optimizing the error-correction properties of the fixed-length codewords and the irregular channel code without decreasing the transmission efficiency. For the quantitated source symbols, the reconstruction SNR (RSNR) is also illustrated in Figure 8. It can be seen that, in the region of \(E_{b}/N_{0}\geq 4\) dB, our proposed scheme with L = 6 also has the best RSNR performance compared to the other schemes. These results can be attributed to its best SER performance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Scheme** & **Convergence Threshold** \\ \hline SSCC & 5.2 dB \\ LBC-RSC & 4.8 dB \\ VLC-IrRNSC & 4.6 dB \\ LBC-IrRNSC & 4.6 dB \\ Proposed \(L~{}=~{}6\) & 4.2 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Convergence performance of different source channel coding schemes. Figure 7: The SER performance of different systems over the AWGN channel.