esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 3 Design Considerations
### Design of Input Capacitance \(C_{o}\)  
As for output capacitor design, Equation (38) is used on condition that the maximum output voltage ripple \(\Delta v_{o,max}\) is equal to 0.1% of the output voltage \(V_{o}\), the value of \(R\) is 17.6 \(\Omega\), \(T_{s}\) is 10 us, \(D\) = 0.43, and \(V_{o}\) = 42 V. After some calculations, the value of \(C_{o}\) is larger than 244 uF. Eventually, a 470 uF capacitor is chosen, because the value of the electrolytic capacitor will be decreased if the switching frequency is increased.  
\[C_{o,min}=\frac{V_{o}DT_{s}}{\Delta v_{o,max}R} \tag{38}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scheme 24.** & **Specifications** \\ \hline Operation Mode & CCM \\ Input Voltage (\(V_{in}\)) & 24 V \\ Output Voltage (\(V_{o}\)) & 42 V \\ Ideal Duty Cede (\(D\)) & 0.43 \\ Switching Frequency (\(f_{s}\)) & 100 kHz \\ Output Rated Power (\(P_{o,attad}\))/Current (\(I_{o,attad}\)) & 100 W/2.38 A \\ Output Minimum Power (\(P_{o,min}\))/Current (\(I_{o,min}\)) & 10 W/0.238 A \\ \hline \hline \end{tabular}
\end{table}
Table 2: System specifications. CCM, continuous conduction mode.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Components** & **Specifications** \\ \hline Input Inductance (\(I_{in}\)) & 150 uH \\ Resonant Inductance (\(L_{r}\)) & 10 uH \\ Output Capacitance (\(C_{o}\)) & 470 uF \\ Clamp Capacitance (\(C_{c}\)) & 2.2 uF \\ Power Switches (\(S_{1}\) and \(S_{2}\)) & IRF540 \\ Output Diode (\(D_{o}\)) & STPS20120C \\ Field Programmable Gate Array (FPGA) & EP2C20F484C8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Component Specifications.
## 5 Applications  
### Description of Test Systems  
The considered test systems are an IEEE 69-bus distribution system [39] and 118-bus distribution system [40]. The total active and reactive load demands of the two systems are 3.802 + j2.694 MVA and 22.709 + j17.04 MVA, respectively. The total power losses for the two distribution systems in the initial case are 225 kW and 1297.87 kW, respectively. The system configurations are shown in Figures 9 and 10. For testing the proposed reduction strategy in the distribution system with distributed RES, the test systems have been provided with RESs plants. The integrated plants in the IEEE 69-bus and 118-bus distribution systems are listed in Table 1, respectively. The PV modules type considered in this paper is the SUNSET Poly-Crystalline model [PTSP250P-6-60], which contains 60 PV cells. The parameters of this module were estimated experimentally in [41]. The PV module parameters are listed in Table 2. Also, the considered WT parameters are listed in Table 3.
## 3 Results and Discussions
### Analysis of Indoor Air Temperature
#### 3.1.1 ANOVA of Indoor Air Temperature  
ANOVA is used to study the relationships between the four factors and the average indoor air temperature. The ANOVA Table 5 is calculated using the numerical simulation results of Table 3 and the Formulas (1)-(7) in Section 2.2. As shown in Table 5, the \(R^{2}\) is 0.998, which means that these four factors can explain 99.8% of the variation of the average indoor air temperature. ANOVA is also known as the \"_F-test_\"; in statistics, the significance level used for ANOVA is usually 0.05 or 0.01. The degree of freedom of the factors is 4, and that of the error is 8. Looking up the \(F\) distribution table, when the significance level is 0.05, the \(F\) value is 3.84; when the significance level is 0.01, the \(F\) value is 7.01. If the variance ratio of a factor is greater than \(F_{0.01}\) (7.01), the probability of that factor affecting the test index is 99%, and the significance level of this factor is considered to be highly significant. If the variance ratio of a factor is between \(F_{0.05}\) (3.84) and \(F_{0.01}\) (7.01), the probability of this factor affecting the test index will decrease to 95%, and then the significance level of this factor is considered to be significant. If the variance ratio is less than \(F_{0.05}\) (3.84), the effect of this factor is not significant. As shown in Table 5, the \(F\) values of the four influencing factors are all much greater than 7.01. Therefore, the four influencing factors have a highly significant influence on indoor air temperature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Item** & **Value** \\ \hline Ideal Load System & Constant heating set point & 18 °C \\  & Constant cooling set point & 26 °C \\  & Operating time & 8:00–9:00 a.m. and 5:00–9:00 p.m. \\ \hline Other heat dissipation in the room & Equipment & A TV (15 W/m\({}^{2}\)) \\  & & A light (7 W/m\({}^{2}\)) \\ \cline{2-3}  & People & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Settings in the numerical simulation [39; 42].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sum of Deviation** & **Degree of** & **Mean Square** & **Variance Ratio (\(F\))** & **Significance** \\  & **Squares (\(SS\))** & **Freedom (\(df\))** & **(\(MS\))** & **Variance Ratio (\(F\))** & **Significance** \\ \hline Factor A & 8.460 & 4 & 2.115 & 895.397 & Highly Significant \\ Factor B & 2.501 & 4 & 0.625 & 264.667 & Highly Significant \\ Factor C & 0.527 & 4 & 0.132 & 55.766 & Highly Significant \\ Factor D & 0.298 & 4 & 0.075 & 31.575 & Highly Significant \\ Error & 0.019 & 8 & 0.002 & - & - \\  & & \(R^{2}\) = 0.998 & & & \\  & & \(F_{0.05}(4,8)=3.84\); \(F_{0.01}(4,8)=7.01\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: ANOVA of indoor air temperature.
# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 3 Methods
### Pre-Processing
#### 3.2.4. Compute ECR and MAR  
In this study, the 68 facial landmark shape predictors shown in Figure 5 were employed to accurately identify the (x, y) positions of 68 facial points. Employing the Dlib library, face objects within video frames were detected, followed by the application of the shape predictor to accurately localize these landmarks. The shape predictor algorithm identifies and maps coordinates around crucial facial features extracted from the eyes, nose, mouth, and jawline. This methodological approach ensures detailed facial analysis and enables subsequent computational tasks. By integrating Dlib's shape predictor into our framework, we achieve robust and accurate facial feature localization essential for application in drowsiness detection.  
* Eye Closure Ratio--ECR is a metric calculated using the markings around the eyes. Each eye region has six (6) points. In Figure 5, the left eye markings are 37-42, and the right eye markings are 43-48. It is mathematically computed as follows: \[\text{ECR}=\frac{\text{Y}_{2}-\text{Y}_{1}}{2*(\text{X}_{2}-\text{X}_{1})}\] (1) where \(\text{Y}_{2}\) represents the markings of the upper eyelid, \(\text{Y}_{1}\) represents the markings of the lower eyelid, and \(\text{X}_{2}\) and \(\text{X}_{1}\) represent the markings of the corners of the eyes. According to [PERSON] et al. [25], ECR is a crucial parameter for detecting driver drowsiness. A threshold value is set, beyond which the driver receives a warning message of drowsiness. [PERSON] and [PERSON] [26] proposed 0.25 as the threshold mark for ECR, while [PERSON] et al. [27] suggested 0.15 as the threshold mark for ECR.
* Mouth Aperture Ratio--MAR is the ratio of the vertical distance between the upper (markings 50-54) and lower (markings 56-60) contours of the mouth to either twice [25; 29] or three times [30] the horizontal distance at the corners of the mouth (markings 43 and 46 or 37 and 40). MAR is calculated to assess yawning; similarly, the computation of the eye closure ratio (ECR) involves using the markings surrounding the mouth, and it is mathematically derived as follows: \[MAR=\frac{Z_{2}-Z_{1}}{2*(W_{2}-W_{1})}\;or\;\frac{Z_{2}-Z_{1}}{3*(W_{2}-W_{1})}\] (2) where \(\text{Z}_{1}\) represents the markings of the lower mouth contour, \(\text{Z}_{2}\) represents the markings of the upper mouth contour, and \(\text{W}_{1}\) and \(\text{W}_{2}\) represent the markings at the mouth ends. According to the paper by [PERSON] et al. [27], 0.1 is proposed as the threshold mark for MAR.  
Figure 5: The 68 points denoting facial landmarks [28].
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 1 Introduction
One of the main challenges of the IoV deployment in the smart cities is integration of all its components. Another challenge is to ensure reliable and real-time delivery of rapid emergency services and big scale of data collection between vehicular application and platform [5]. The rest of this article is structured as follows. In Section 2, we first present the related works. Section 3 presents the interactive model and architecture of IoV. Section 4 presents the system model and the proposed protocol aiming to enhance user experience and performance of traffic system. In Section 5 we simulate the presented protocol and evaluate its performance to prove its effectiveness. We compare the performance of various existing schemes with the proposed scheme. Finally, we discuss future scope related with the implementation of IoV. Finally, we discuss future scope related with the implementation of IoV in Section 6 and draw our conclusions.
# Fingerprint Based Codebook for RIS Passive Beamforming Training
## 2 System Model  
The RIS-aided wireless communication system model is presented in Figure 1. The TX contains \(M\)-element antennas, while the RX has a single antenna. Meanwhile, the RIS, which is used to reflect the TX incident signal to the RX, consists of \(N\) passive elements. In our study, we used RIS to overcome direct link blockage and extend network coverage. Consequently, the received signal at the RX, \(y_{r}\), will contain the signal of the LOS TX-RX link and the reflected TX-RIS-RX link. This \(y_{r}\) can be presented as:  
\[y_{r}=(\mathbf{G_{2}\theta G_{1}+H_{o}})\mathbf{V}x_{t}+n, \tag{1}\]  
where \(\mathbf{H_{o}}\in\mathbb{C}^{1\times M}\) is the channel matrix between the TX and RX, \(\mathbf{G_{1}}\in\mathbb{C}^{N\times M}\) is the channel from the TX to the RIS, while \(\mathbf{G_{2}}\in\mathbb{C}^{1\times N}\) is the channel from the RIS to the RX. Moreover, the RIS diagonal phase shift matrix is expressed by \(\mathbf{\theta}=diag\{\mathbf{\theta}\}\), where \(\mathbf{\theta}=[\theta_{1},\theta_{2},\dots,\theta_{N}]^{T}\) is the reflecting passive beamforming vector at the RIS and \(\theta_{n}=\gamma e^{\phi_{n}}\). Here, the \(\phi_{n}\in[0,2\pi)\) is the value of the RIS element \(n\) phase shift, and \(\gamma\in[0,1]\) is the amplitude of the reflection coefficient. Moreover, \(\mathbf{V}\in\mathbb{C}^{M\times 1}\) denotes the active TX beamforming vector, \(x_{t}\) is the transmitted symbol by the TX, and \(n\sim\mathcal{CN}\big{(}0,\sigma^{2}\big{)}\) refers to the received additive white Gaussian noise at the receiver, where \(\sigma^{2}\) is the noise power. For the efficient performance of the RIS aided beyond the 5G network, the system should design the beamforming vectors for both the TX and RIS, i.e., \(\mathbf{V}\) and \(\mathbf{\theta}\), respectively. As an advantage of CB-based solutions, the process of A-PBF can be separated to reduce system complexity [11]. Moreover, because of the fixed TX and RIS positions, the active beamforming vector can be designed once using any convenient BF method in the literature [16]. Hence, in this work, we focus on obtaining the PBF vector \(\mathbf{\theta}\), which is still under study by researchers in the field.
## 2 Related Techniques
### Strategies for Surrogate Model
#### 2.2.3 Radial Basis Function  
Radial basis function (RBF) [14; 29; 30; 31] is classified within the ambit of local approximation neural networks. With modifications to a limited set of weights, it can proficiently approximate any non-linear function. This model adeptly addresses patterns that are intricate to discern in systems, underpinning its foundation on the presumption that data integrity persists notwithstanding noise perturbations. As a multivariate discrete data interpolation technique, the RBF model interpolates across intricate design spaces via the weighted summation of rudimentary functions. These elementary functions are designated as kernel functions. Intrinsically, these kernel functions manifest as radially symmetric functions centered on the sample points. Given a set encompassing n sample points, the RBF model's formulation is presented subsequently.  
\[\hat{\mathrm{f}}(\mathrm{x})=\hat{\mathrm{w}}\varphi=\sum_{i=1}^{n}\mathrm{w }_{i}\varphi(||\mathrm{x}-\mathrm{c}_{i}||) \tag{6}\]  
RBF distinguishes itself as a non-parametric modeling approach, seamlessly fitting an extensive array of functions, spanning from discrete to pronouncedly non-linear variants. A pivotal merit lies in their conservative computational expenditure during model construction, paired with a stalwart convergence propensity. Such attributes have catalyzed the widespread incorporation of RBF models across diverse applications.
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 3 Verification and Analysis
### Practical Data Based Verification
#### 3.2.2 Flight Route Design  
Assisted with remote-control, the UAV moved according to a pre-set flight route and collected data at the same time. The initial position of the experiment was at 100.464452\({}^{\circ}\) E, 41.021293\({}^{\circ}\) N, with an altitude at 980 m, and the yaw angle was 350\({}^{\circ}\) (the defined range of yaw angle is 0-360\({}^{\circ}\); the true north is defined as 0\({}^{\circ}\); the clockwise direction is positive). The whole process included,  
1. Take off at an acceleration of 40 m/s\({}^{2}\) with a 12\({}^{\circ}\) pitch angle, until the UAV reaches an altitude of 1500 m;
2. Turn right at an roll angle of 30\({}^{\circ}\) until the yaw angle reaches 75\({}^{\circ}\);
3. Move forward at an acceleration of 5 m/s\({}^{2}\), in the meantime, climb to an altitude of 3500 m with a 12\({}^{\circ}\) pitch angle, then execute a 450 s-long level flight;
4. Turn right at an roll angle of 30\({}^{\circ}\) until the yaw angle reaches 90\({}^{\circ}\);
5. Accelerate to 200 m/s, climb to an altitude of 6400 m with a 10\({}^{\circ}\) pitch angle, then execute a 450 s-long level flight;
6. Turn right at an roll angle of 30\({}^{\circ}\) until the yaw angle reaches 270\({}^{\circ}\), then execute a 350 s-long level flight;
7. Turn right at an roll angle of 35\({}^{\circ}\) until the yaw angle reaches 90\({}^{\circ}\), then execute a 300 s-long level flight;
8. Turn right at an roll angle of 35\({}^{\circ}\) until the yaw angle reaches 270\({}^{\circ}\);
9. Descend to an altitude of 1500 m with a -10\({}^{\circ}\) pitch angle;
10. Return to the initial position.
## 6 Experimental Results
### g.729  
We also study the behavior of the log likelihood ratio for the ITU-T standardized codec G.729 at 8 Kbps. Even though G.729 is not widely used at this point, we examine its performance because of its historical importance as the forerunner of today's best codecs, and also so that we can compare AMR-NB at 7.95 Kbps to G.729 at 8 Kbps, both of which fall into the category of code-excited linear prediction (CELP) approaches [23].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Bit Rate** & **\% Frames with** & **\% Frames with** & **\% Frames with** & **PESQ-MOS** & **SNR (dB)** \\
**(Kbps)** & **\(D\) \(<\) 24** & **24 \(<\) D \(<\) 72** & **\(D\) \(>\) 72** & & \\ \hline
40 & 98.46 & 1.54 & 0 & 4.35 & 29.03 \\
32 & 83.08 & 16.92 & 0 & 4.155 & 23.67 \\
24 & 44.3 & 46.5 & 9.2 & 3.643 & 17.86 \\
16 & 9.2 & 46.15 & 44.62 & 2.858 & 13.50 \\ \hline \end{tabular}
\end{table}
Table 2: \(D\) and PESQ values for G.726 “A lathe is a big tool”.  
Tables 3 and 4 show the percentage of frames in the various \(D\) ranges along with the PESQ-MOS value for the two sentences \"We were away a year ago\" and \"A lathe is a big tool\". SNR is not included, since it is not a meaningful performance indicator of CELP codecs.  
As expected, the PESQ-MOS values are near 4.0 in both cases and in neither table does any fraction of \(D\) exceed the perceptually significant threshold. Strikingly, however, in both tables, more than 50% of the frames have a \(D\) value above the statistically significant threshold. To think about this further, we note the bit allocation for the G.729 codec is, for each 20 ms interval, 36 bits for the linear prediction model, 28 bits for the pitch delay, 28 bits for the codebook gains, and 68 bits for the fixed codebook excitation. One fact that stands out is that the parameters for the linear prediction coefficients are allocated 36 bits when 24 bits/20 ms frame is considered adequate [25]. In other words, the spectral match should be quite good for most of the frames, and yet, based on \(D\), more than 50% of the frames imply a less than high quality spectral match over the two sentences.  
Further analysis of this observation is possible in conjunction with the AMR-NB results.
## 2 Materials and Methods
### Supervised Data Creation  
Object Detection  
We used MATLAB for all the steps related to object detection, classifying the coronary arteries into three categories--right coronary artery (RCA), left anterior descending artery (LCA-LAD), and left circumflex artery (LCA-CX). When annotating vessels in each slice, we aimed to align the bounding box center with the arterial center while maintaining a fixed size of 64 \(\times\) 64 pixels whenever possible. If an arterial segment extended horizontally beyond the bounding box edges, we expanded the bounding box accordingly. For discontinuous arteries or those with significant branching within a single slice, we assigned multiple bounding boxes to ensure accuracy. When multiple arteries appeared in a single bounding box, classification was performed according to the following guidelines: If all arteries were fully enclosed, we selected the highest-intensity artery for central alignment. If a lower-intensity vessel intersected the bounding box boundary, it was assigned a separate bounding box centered on its lumen.  
A similar approach was applied to the left coronary system. The left main trunk (LMT) was categorized under the LCA-LAD label unless it was clearly continuous with the LCA-CX segment, in which case it was labeled as LCA-CX. When the LCA-LAD and LCA-CX were distinctly separate but fit within a single bounding box, additional bounding boxes were created to prevent the merging of distinct vessels in one annotation. Variants such as the high lateral branch, arising between the LAD and LCX, were included in the LCA-LAD category if they could not be distinctly separated in a given 2D slice. This overall annotation definition is illustrated in Figure 1.  
The YOLOX [19] model was trained in MATLAB with an initial learning rate of \(5.0\times 10^{-4}\) and a piecewise schedule reducing by 0.99 each epoch. The momentum was set to 0.9, and an L2 regularization factor of \(5.0\times 10^{-4}\) was applied. We used a mini-batch size of 192, with the maximum number of epochs set to 5, and performed gradient clipping via the L2-norm method, with a threshold value of 30. No separate validation set was specified, and training loss was monitored as the objective metric. Training proceeded in a multi-GPU environment, utilizing moving averages for batch normalization statistics. These hyperparameter settings were not extensively optimized but were selected to ensure stable training under our hardware constraints.
# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
## 5 Discussion
### Limitations and Future Outlook  
Although the proposed method outperformed the existing DL-based OTFS signal detection scheme, there are still limitations to this type of method. The neural network is used as a data-driven based method, which heavily relies on labeled data and empirical parameters, and it is sensitive to data. The generalization ability should be further studied. Furthermore, data-driven deep learning takes neural networks as a black box. The interpretability of this type of model is not as clear as in traditional signal detection methods.  
We can take a look at the future outlook of the DL-based receiver of OTFS. First, for a data-driven DL receiver, the joint channel estimation and signal detection can be considered for the whole system's optimization, and then, for a model-driven DL receiver, the expert knowledge can be integrated with deep learning. There are some previous studies considering expert knowledge and deep learning for OFDM systems. For OTFS, researchers have started trying to integrate DL into submodules or train some key parameters by DL. The integration of DL and expert knowledge also seems attractive for future research. Finally, a more powerful structure of deep neural networks could be designed according to the system's features and objectives.
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 4 Experimental Results
### Evaluation Metrics
#### 4.3.8 AUC-ROC  
The AUC-ROC is a crucial measurement to evaluate the performance of generated classification models. A ROC plot represents the trade-off between true positives and false positives, which actually indicates the correlation between specificity and recall. Furthermore, AUC specifies the amount of separability power of the classifier. The AUC range is from 0 to 1. Therefore, the higher the AUC means the model has better performance at recognizing the minority and majority classes.
# Pose Estimation Utilizing a Gated Recurrent Unit Network for Visual Localization
## 4 Conclusions  
In this paper, a pose estimation method utilizing a GRU network is proposed. The GRU network predicts yaw angles based on sequential yaw angle data. The predicted yaw angle is used in the proposed method for yaw angle correction at the cornering section where rotational error and translation error tend to increase. The GRU network is trained by the sequences of yaw angle data at the cornering section obtained from the classical VO and an accurate sensor. During testing of the GRU network, the next yaw angle is predicted by the GRU network taking the yaw angles estimated by the classical VO as the input. In this way, more accurate yaw angle is obtained by fusing the yaw angles estimatedby the classical VO and the GRU network with NCC as the weighing factor of the fusion mechanism. With the estimated yaw angle, the rotation matrix is reconstructed for subsequent use.  
Simulation results with 11 sequences of the KITTI dataset show that with the classical VO employing the proposed method, the performances in terms of translation error and rotation error are improved significantly. In addition, the proposed method requires less computational effort for learning when compared with the learning-based VO using images as the input data. Since the proposed method is combined with the classical VO without any change in the original pipeline, the proposed method can be applied to various VO methods. The proposed method can be applied not only to feature-based VO methods, as for the simulations conducted in this paper, but also to direct and learning-based VO methods. The proposed method suggests that the prediction based on the GRU network can improve the performance in unstable areas such as cornering section.  
Conceptualization, S.K.; data curation, S.K. and I.K.; formal analysis, S.K.; investigation, S.K.; methodology, S.K.; project administration, S.K.; resources, S.K.; software, S.K. and I.K.; supervision, S.K. and D.H.; validation, S.K.; visualization, S.K.; writing--original draft, S.K.; writing--review and editing, S.K., I.K., L.F.V., and D.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20 CTAP-C153040-02).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.