esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Parameter Values and Reference Simulations  
The optimized parameter values are listed in Table 2. Compared with the continuous measurements of water flow at the network outlet, the performance of the urban 2D/1D modeling can be assessed. The simulation results are illustrated in Figure 6.  
According to the simulation results in Figure 6, the performance of the water flow simulation with the DTM + LiDAR topographic data and detailed land-use information is satisfying. The NSE values for all of the six studied rainfall events are greater than 0.8, which indicates a quite satisfactory modeling performance for the investigated urban catchment. This encouraging result confirms that the 2D-surface and 1D-sewer TRENOE model is a promising modeling approach for urban stormwater quantitative simulations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**2D-Surface Modeling**} \\ \hline  & \
\begin{tabular}{c} Impervious surface \\ (road, sidewalk, etc.) \\ \end{tabular} &
\begin{tabular}{c} Previous surfaces \\ (grass, trees, etc.) \\ \end{tabular} \\ \hline Initial loss (mm) & 0.5 & 3 \\ Suction head at the wetting front (m) & 0.01 & 0.05 \\ Residual soil moisture content & 0.1 & 0.4 \\ Manning’s N values for surface rundoffs & 0.015 & 0.2 \\ Saturated hydraulic conductivity (m/s) & 1.0 \(\times\) 10\({}^{-8}\) & 1.0 \(\times\) 10\({}^{-5}\) \\ Initial loss (mm) & 0.5 & 3 \\ \hline \multicolumn{3}{c}{1D sever and roof modeling} \\ \hline Manning’s N for fewer networks & 0.014 \\ Manning’s N for virtual sub-basins & 0.012 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values for the 2D-surface and 1D-sewer modeling.  
Figure 6: Water flow simulations using the urban 2D/1D TRENOE model with the DTM + LiDAR topography and detailed land-use data. The simulated discharges at the network outlet (solid blue lines) are compared with the measured data (red circles). Rainfall is plotted on the upper part. For events **(a)** 8 October 2014; **(b)** 12 October 2014; **(c)** 3 November 2014; **(d)** 15 November 2014; **(e)** 25 November 2014; **(f)** 12 December 2014.
# Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
## 2 Materials and Methods
### Proposed Algorithm
#### 3.2.2 Leave-One-Out Cross-Validation  
To verify the generalization capability of the proposed algorithm, a LOOCV (Leave-One-Out Cross-Validation) using the forgery methods present in the collected dataset is conducted. It is examined whether newly introduced forgery data is effectively detected when the model is trained without a specific forgery method. In this experimental analysis, one forgery method is excluded from the training process, the model is trained on the remaining dataset, and the excluded method is used as the test set.  
For this evaluation, print attack and replay attack, which are commonly applied forgery techniques in the collected datasets, are used for testing. The results of this experiment are presented in Tables 5 and 6. As observed in the results, strong generalization capability is demonstrated by the proposed approach, effectively detecting forged data generated using previously unseen methods.
### Contributions and Structure of Study  
The contributions of this study are six-fold, as follows:  
* Provide a Systematic Literature Review (SLR) for the state-of-the-art approaches concerning ML, DL, and combined ML-DL approaches to detect attacks in RPL-based 6 LoWPAN.
* Introduce theoretical and practical steps for conducting SLR studies that pave the way for other researchers to conduct their SLRs in any field of academic research.
* Provide a taxonomy on contemporary research directions in RPL-based 6 LoWPAN.
* Demonstrate demographic, statistical, and critical analysis on the existing studies with the implemented attacks and used tools.
* Clear description and analysis of the benchmark datasets created and used by existing studies in the RPL-based research field.
* Derive various security issues and challenges of previous studies and provide future research directions.  
The study is structured as follows. Section 2 summarizes relevant works and compares them to ours, while Section 3 presents the research questions and methods. Section 4 provides an overview of the methodology and stages of this study, identifies the databases, highlights the checking and refining criteria. In Section 5, we present the distribution of the final selected studies. Section 6 demonstrates the theoretical and practical steps for conducting the SLR study. In Section 7, we elucidate the result of the study according to the designed research questions in detail. Then, the existing challenges of the presentedstudies and some possible research directions are in Section 8. Finally, the conclusion and limitations of the study are presented in Section 9.
## 4 Results and Discussion
### Results and Discussions of Kano-IPA Analysis
#### 4.4.2 The IPA Plot  
Based on the TF-IDF algorithm, we obtained the weight of each sub-attribute with respect to the six attributes concerning four hotel star ratings. Then the importance of the six attributes concerning four hotel star ratings was calculated respectively by Equation (15), as shown in Table 11. On the whole, the importance of value, service and sleep quality varies across the hotel star ratings, while other attributes' importance fluctuates slightly and are considered as very important for all hotels. This finding is consistent with previous research that revealed that customers of high-star hotels are more likely to value some intangible attributes (i.e., service and sleep quality) [40]. Specifically, with the increase in hotel stars, the importance of value decreases, while the importance of service and sleep quality increases. In other words, customers who select high-star hotels pay more attention to service and sleep quality, and consider value as less important. On the contrary, customers who choose low-star hotels highly emphasize value, but consider service and sleep quality as less important. The importance of value shows a significant downward trend with the improvement in hotel stars, coinciding with [PERSON]'s [108] research.  
With the obtained importance and performance of each attribute concerning four hotel star ratings, the IPA plots can be constructed, as shown in Figure 7. Location and cleanliness are located in Q I in hotels of all stars, which indicates that location and cleanliness should be well remained for their high importance and performance. Value, service and sleep quality are located in Q III in hotels of all stars, with low importance and performance, so they are of low priority for improvement. In contrast, room is located in Q IV in hotels of three stars, two stars and below, urgent to be improved, while it is located in Q I in four and five star hotels, indicating it is the hotels' strength.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hotel Star Rating**} & \multicolumn{6}{c}{**Attribute**} \\ \cline{2-7}  & **Value** & **Location** & **Service** & **Room** & **Cleanliness** & **Sleep Quality** \\ \hline Two-star and below & 0.1354 & 0.2412 & 0.0990 & 0.2566 & 0.2574 & 0.0104 \\ Three-star & 0.1328 & 0.2446 & 0.0962 & 0.2337 & 0.2690 & 0.0238 \\ Four-star & 0.0692 & 0.2267 & 0.1353 & 0.2508 & 0.2978 & 0.0202 \\ Five-star & 0.0315 & 0.2626 & 0.1545 & 0.2315 & 0.2812 & 0.0387 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The importance of the six attributes concerning four hotel star ratings.
## 4 Simulation Results and Analysis
### Step Simulation with Different Prediction Horizons  
To determine the best prediction horizon of the explicit MPC controller, a step simulation experiment is carried out. The \(N_{h}\) command changes 4%, \(N_{l}\) command changes 1.5%, and \(SPL\) command changes \(-\)4.5%. The simulation results are shown in Figure 6. It is noticed that the prediction horizon \(N_{p}\) and control horizon \(N_{c}\) are always equal in this paper. The range of the prediction horizon \(N_{p}\) is from 1 to 5.  
Figure 6: _Cont._  
From Figure 6a, the control effect of \(N_{h}\) is similar at different prediction horizons. The settling times are all less than 4 s. When the prediction horizon \(N_{P}\) is 5, the step response has the smallest overshoot. However, all the settling processes of \(N_{l}\) have different degrees of oscillation in Figure 6b. When the prediction horizon \(N_{P}\) is 5, the settling time is 2.43 s, while the others are more than 4 s.  
As the explicit MPC controller is a three-variable controller, there are strong interactions between different control loops. The control effect of \(SPL\), especially when \(N_{P}\) is 1, is terrible. The step response cannot follow the command. From Figure 6c, it can be seen that the control effect gradually becomes better with the increasing of the prediction horizon.  
To show the control effect more intuitively, the root mean square errors (RMSE) between the step responses and commands are calculated by  
\[RMSE=\sqrt{\frac{\sum_{i=0}^{N}\left(r_{ef,p}-y_{p}\right)^{2}}{N}} \tag{37}\]  
where \(N\) is the number of sampling time points, \(r_{ef,p}\) is the command value, and \(y_{p}\) is the step response.  
Table 1 gives the comparison of the control error at different prediction horizons. The prediction horizon 4 has the smallest RMSE of \(N_{h}\), and the prediction horizon 5 has the smallest RMSE of \(N_{l}\) and \(SPL\). The difference of \(N_{h}\) between the prediction horizon 4 and 5 is only 0.063%, which can be neglected. There is no doubt that the prediction horizon 5 is the best choice. When the prediction horizon \(N_{P}\) is more than 5, the computation burden increases significantly, and the computing accuracy improves a little. So, the prediction  
Figure 6: Step responses at different prediction horizons. (**a**) Variation of speed of high pressure rotor. (**b**) Variation of speed of low pressure rotor. (**c**) Variation of total sound pressure level of jet noise.  
horizon \(N_{P}\) is chosen as 5 in this paper. The regions of the polyhedral partition at the prediction horizon 5 are shown in Figure 7.
# Nonlinear Dynamics of an Internally Resonant Base-Isolated Beam under Turbulent Wind Flow
## 3 Perturbation Analysis
\begin{split}\dot{A}_{1}&=\Big{(}d_{10}+\bar{U} \hat{d}_{10}\Big{)}A_{1}+\bigg{(}\frac{d_{2}}{\bar{U}}+\hat{d}_{2}+i\tilde{d}_ {2}\bigg{)}A_{1}^{2}\bar{A}_{1}+\hat{u}\frac{d_{1}}{\bar{U}}A_{1}^{3}e^{-it \epsilon_{\epsilon}}+\hat{u}d_{11}A_{2}e^{it(\varphi_{1}-\sigma_{\epsilon})} \\ &+\hat{u}d_{12}\bar{A}_{1}e^{it\epsilon_{\epsilon}}+\hat{u}\frac {d_{3}}{\bar{U}}A_{1}A_{2}\bar{A}_{1}e^{it(\varphi_{1}-\sigma_{\epsilon})}+ \hat{u}\frac{d_{4}}{\bar{U}}A_{1}\bar{A}_{1}^{2}e^{it\epsilon_{\epsilon}}\\ &+\bigg{(}\frac{d_{5}}{\bar{U}}+\hat{d}_{5}+i\tilde{d}_{5}\bigg{)} A_{2}\bar{A}_{1}^{2}e^{it\epsilon_{\epsilon}}+\hat{u}\frac{d_{6}}{\bar{U}}A_{1}^{2} \bar{A}_{2}e^{it(\varphi_{\epsilon}-\sigma_{i})}\\ &+\bigg{(}\frac{d_{7}}{\bar{U}}+\hat{d}_{7}+i\tilde{d}_{7}\bigg{)} A_{1}A_{2}\bar{A}_{2}+\hat{u}\frac{d_{8}}{\bar{U}}A_{2}^{2}\bar{A}_{2}e^{it( \varphi_{1}-\sigma_{\epsilon})}\\ &+\hat{u}\frac{d_{9}}{\bar{U}}A_{2}\bar{A}_{1}\bar{A}_{2}e^{it \epsilon_{\epsilon}}\\ \dot{A}_{2}&=\Big{(}d_{22}+\bar{U}\hat{d}_{22}\Big{)} A_{2}+\hat{u}d_{21}A_{1}e^{it(\sigma_{\epsilon}-\sigma_{i})}+\bigg{(}\frac{d_{ 13}}{\bar{U}}+\hat{d}_{13}+i\tilde{d}_{13}\bigg{)}A_{1}^{3}e^{-it\sigma_{i}}\\ &+\hat{u}\frac{d_{14}}{\bar{U}}A_{1}^{2}A_{2}e^{-it\sigma_{ \epsilon}}+\hat{u}\frac{d_{15}}{\bar{U}}A_{1}^{2}\bar{A}_{1}e^{it(\sigma_{ \epsilon}-\sigma_{i})}+\bigg{(}\frac{d_{16}}{\bar{U}}+\hat{d}_{16}+i\tilde{d}_{ 16}\bigg{)}A_{1}A_{2}\bar{A}_{1}\\ &+\hat{u}\frac{d_{17}}{\bar{U}}A_{2}^{2}\bar{A}_{1}e^{it(\sigma_ {\epsilon}-\sigma_{\epsilon})}+\hat{u}\frac{d_{18}}{\bar{U}}A_{2}\bar{A}_{1}^{ 2}e^{it\sigma_{\epsilon}}\\ &+\hat{u}\frac{d_{19}}{\bar{U}}A_{1}A_{2}\bar{A}_{2}e^{it(\sigma_ {\epsilon}-\sigma_{i})}+\bigg{(}\frac{d_{20}}{\bar{U}}+\hat{d}_{20}+i\tilde{d}_ {20}\bigg{)}A_{2}^{2}\bar{A}_{2}\end{split}  \tag{18}\]  
where the coefficients are given in Appendix B. It is worth noting that the structural geometric and inertial nonlinear terms give rise to the coefficients indicated as \(\tilde{d}_{j}\), \(j=2,5,7,13,16,20\), which contribute as imaginary parts of the relevant terms. The real form of the AME is obtained using the polar transformation, namely \(A_{1}(t)=\frac{a_{1}(t)}{2}e^{i\theta_{1}(t)}\) and \(A_{2}(t)=\frac{a_{2}(t)}{2}e^{i\theta_{2}(t)}\), to be substituted in Equation (18). Then, separating real and imaginary parts and defining the phase differences \(\psi_{1}(t)=2\theta_{1}(t)-\sigma_{\epsilon}t\) and \(\psi_{2}(t)=\vartheta_{2}(t)-3\vartheta_{1}(t)+\sigma_{i}t\), the real AME become:\[
# Abstract Text Summarization with a Convolutional Seq2 seq Model
## 2 Related Work
### Seq2 seq Model  
Seq2 seq modeling has been the synonym of encoder-decoder structures based on RNN [31]. Firstly, the encoder RNN handles an input sequence \(\mathbf{x}=x_{1},...,x_{m}\), \(m\) denotes the number of elements and then obtains the corresponding hidden state \(\mathbf{z}=(z_{1},...,z_{m})\). The decoder RNN uses \(\mathbf{z}\) as an input and generates the output \(\mathbf{t}=(t_{1},..,t_{n})\) one by one from left to right. When generating the output \(t_{i+1}\), decoder will produce a new hidden state \(h_{i+1}\) via the previous state \(h_{i}\), along with a representation vector \(g_{i}\) of the previous target language word \(y_{i}\), and a conditional input \(c_{i}\) based on the decoder input \(\mathbf{z}\). Based on the above generic formulation, many seq2 seq models try to have novelties on RNN type or conditional input.  
If not considering adding the attention mechanism, models may merely utilize the final encoder state \(z_{m}\) by setting the conditional input \(c_{i}\) equal to \(z_{m}\) for each \(i\)[32] or initializing the input to the first decoder as \(z_{m}\)[31]. Those models [5; 33] with attention mechanism calculated \(c_{i}\) as a sum of \((z_{1},...,z_{m})\) with different weights at each time step. These weights of the sum are the so-called attention scores, which enable the decoder to focus on the different parts of the input sequence when generating the outputs. To compute attention scores, it needs to compare each encoder's output state \(z_{j}\) with a combination of the last prediction \(y_{i}\) and previous decoder state \(h_{i}\). In the end, the results will be normalized to a distribution over the input sequence.  
Many seq2 seq model choose LSTM [34] and GRU [32] as RNN models. Both networks extend Elman RNNs with a gating mechanism which enables the memorization of information of previous time steps so as to process long-term dependencies. Recently, bi-directional encoders are proposed to capture both future and past contexts [5]. In practice, models with many layers are often equipped with shortcut or residual connection [10].
# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 4 Experiments and Results
### Ablation Experiment  
To verify the contribution of the Multi-Scale Convolutional Attention (MSCA) module, we conducted ablation experiments on the FV-USM and SDUMLA-HMT datasets, evaluating the accuracy variations when the model is used with and without MSCA. As shown in Table 5, the incorporation of multi-scale convolution and attention mechanisms enhances the model's ability to capture fine-grained features, thereby improving recognition accuracy. The use of the MSCA module resulted in a 0.61% increase in accuracy on the FV-USM dataset and a 0.79% increase on the SDUMLA-HMT dataset.
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
The following four thresholding methods namely probabilistic thresholding method [12; 13], Type-I and Type-II errors method [14; 15; 16; 17], ROC curve method [9; 13] and control chart method via Box-Cox transformation [18] are the most common thresholding methods being deployed in the MTS which will also be discussed in further details in the next section. The aim of this study is to compare the effectiveness of these four common thresholding methods in MTS methodology. To the best of the authors' knowledge, no comparison works have been conducted to evaluate the effectiveness of these four common thresholding methods in MTS. The reports found in the literature were mainly focused on demonstrating the usage of the threshold methods based on unique case studies of the researchers. It is therefore the motivation of this paper to compare the classification performance of these four common thresholding methods in the MTS across several datasets. The paper is presented as follows, a theoretical overview of the fundamental concept of MD and MTS is explained in Section 2. A brief discussion on the fundamental concepts of thresholding methods used in the MTS including the Quadratic Loss Function, Probabilistic Thresholding method, Type-I and Type-II Errors method, ROC curve method and Box-Cox Transformation method are discussed in Section 3. Sections 4 and 5 explain datasets used in the study which involved 20 datasets and the results and discussion of the comparison studies in evaluating the classification performances of the threshold methods. Section 6 concludes the key findings and contributions of this paper.
### Theoretical Framework
#### 3.3.1 Node Influence Calculation
Finally, to normalize the propagation influence, the following normalization is applied:  
\[Inf_{e}^{\prime}(v)=\frac{Inf_{e}(v)-\min(Inf_{e}(v))}{\max(Inf_{e}(v))-\min( Inf_{e}(v))} \tag{15}\]  
As shown in Equation (14), the propagation influence (\(Inf_{e}(v)\)) of user \(v\) during the information dissemination process is primarily determined by the following three components: the user's PageRank (\(PR(v)\)), the local clustering coefficient (\(C_{v}\)), and the constraint coefficient (\(C_{s}(v)\)), which measures the structural hole. By considering the influence at the individual level, local characteristics, and global network scope, this approach offers a comprehensive evaluation of a user's role in information propagation. Equation (15) shows the normalization of the user's propagation influence \(Inf_{e}^{\prime}(v)\), providing a standardized measure of their impact in the dissemination process.
## 3 Results and Discussion
### Magnetic Field
#### 3.3.6 Stability  
The performance variability observed in ANN models, even when the network setup remains unchanged, is a common phenomenon and can be attributed to several factors. First and foremost, ANNs inherently depend on the initial weights assigned to their connections, which are typically initialized randomly. This initial condition can lead to different starting points for the learning process, resulting in divergent outcomes during training. Additionally, the data used for training play a crucial role. The dataset splitting for the training, validation, and testing process is performed randomly, which can result in different model performances. Stability analysis holds significant importance for ANN models, as it guarantees the robustness and dependability of their predictions. In this paper, each setup/configuration for the ANN model, regardless of being in each step of sensitivity analysis, was repeated 50 times to avoid any significant fluctuation. Then, the mean values of RMSE and R-squared were compared between models to assess their accuracy. As can be seen in Figures 9 and 10, the RMSE values are almost the same and fluctuations are not significant. Also, the SD of the data for electric field and magnetic field models was evaluated using Equation (1) and is only 4.88 \(\times\) 10\({}^{-4}\) and 4.81
## 2 Poseidon3D: Dataset of Anatomically Complex Dental Shapes  
A new public dataset for tooth segmentation in 3D surface scans is presented. It is named Poseidon's Teeth 3D (Poseidon3D). Poseidon3D contains 200 3D triangular meshes: 88 maxills and 122 mandibles.  
All data samples are real orthodontic cases scanned by various intra-oral scanners and by indirect scanners of wax impressions. The scanning process was handled by a professional clinician prior to the orthodontic procedure. Example cases are shown in Figure 1. All cases were anonymized, so it was not feasible to undertake a thorough analysis concerning ethnicity, gender, or age. However, the data come from multiple clinical sites across several continents. Shapes in Poseidon3D retain geometry information on vertex positions and topology, with an average face count of approximately 200,000, and do not carry any information on colors or textures.  
Individual tooth regions for each case were accurately labeled by a clinician and subsequently reviewed and refined to ensure the high quality of the labels. A single connected component always forms each region.  
Poseidon3D was collected by focusing on patient cases that noticeably deviated from healthy full dental arches. Cases containing teeth with significant orthodontic anomalies were accumulated, such as diastema (abundant space between upper incisors), abundant space between teeth in general, infraocclusion and retention (partially erupted teeth that do not reach the occlusal plane), mesial and distal inclination, multiple types of crowding of various severity, missing teeth, damaged teeth, teeth with visible roots, present third molars, and others.  
Compared to the only publicly available dataset called _3 DTeethSeg22_[18], the presented dataset is smaller in size (200 vs. 1800 mesh models). Still, it addresses significantly more abnormalities naturally present in orthodontic procedures. We anticipate that Poseidon3D will provide insights into the generalization capabilities of novel methods, particularly in their practical application in routine clinical practice, where abnormalities in dentition are prevalent. We randomly select five representative meshes from Poseidon3D and 3 DTeethSeg22 and provide the visual comparison in Figure 2. Twenty simple cases are included for additional evaluation of the generalization to complex cases.  
All data samples in STL format, along with per-face annotations of teeth masks, are available at [[https://tiborkubik.github.io/lmvseg-poseidon3d/](https://tiborkubik.github.io/lmvseg-poseidon3d/)]([https://tiborkubik.github.io/lmvseg-poseidon3d/](https://tiborkubik.github.io/lmvseg-poseidon3d/)) (accessed on 10 October 2024).  
Figure 1: Representative cases from Poseidon3D, a new public dataset of anatomically complex 3D dental scans introduced in this work. Focus on the anatomical variability across individual subjects. Colored teeth regions are the predicted segmentation masks by the proposed projection-based method. Zoom in to see the intricate anatomical details.