esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Kalman Filter Design for Real-Time Estimation of Electric Power Characteristic Of Fuel Cells
### Simplified Model and Kalman Filter Design
Variations of the (average) stack temperature \(\vartheta_{\text{FC},k}\), the anode and cathode gas inlet temperatures \(\vartheta_{\text{AG},k}\) and \(\vartheta_{\text{CG},k}\), and the hydrogen mass flow \(\dot{m}_{\text{H}_{2},\text{in},k}\) are currently treated implicitly in a model-free manner by the time-independent process noise covariance \(\mathbf{C}_{\text{v},k}\). To include these further dependencies directly in the identification model, aiming at a recursive stochastic identification, Equation (12) can be replaced straightforwardly by using the matrix Kronecker product \(\otimes\) in terms of_  
\[P_{\text{EL},k}\approx\left(\mathbf{I}_{k}^{[m_{1}-1]}\otimes\hat{\mathbf{m}} _{\text{H}_{2},\text{in},k}^{[m_{2}-1]}\otimes\vartheta_{\text{AG},k}^{[m_{3} -1]}\otimes\vartheta_{\text{CG},k}^{[m_{4}-1]}\otimes\vartheta_{\text{FC},k}^{ [m_{5}-1]}\right)\cdot\tilde{\mathbf{x}}_{k} \tag{26}\]  
_with the augmented state vector \(\tilde{\mathbf{x}}_{k}\in\mathbb{R}^{\prod_{i=1}^{5}m_{i}}\). To account for the aforementioned physical dependencies, the vectors_  
\[\mathbf{I}_{k}^{[m_{1}-1]}=\left[ \begin{matrix}0&I_{k}^{1}&I_{k}^{2}&\dots&I_{ k}^{m_{1}-1}\end{matrix} \right]\, \tag{27}\]  
\[\hat{\mathbf{m}}_{\text{H}_{2},\text{in},k}^{[m_{2}-1]}=\left[ \begin{matrix} \dot{m}_{\text{H}_{2},\text{in},k}^{0}&\dot{m}_{\text{H}_{2},\text{in},k}^{1} &\dot{m}_{\text{H}_{2},\text{in},k}^{2}&\dots&\dot{m}_{\text{H}_{2},\text{in}, k}^{m_{2}-1}\end{matrix} \right]\, \tag{28}\]  
\[\vartheta_{\text{AG},k}^{[m_{3}-1]}=\left[ \begin{matrix}\theta_{\text{AG},k}^ {0}&\theta_{\text{AG},k}^{1}&\theta_{\text{CG},k}^{2}&\dots&\theta_{\text{AG},k}^{m_{3}-1}\end{matrix} \right]\, \tag{29}\]  
\[\vartheta_{\text{CG},k}^{[m_{4}-1]}=\left[ \begin{matrix}\theta_{\text{CG},k}^ {0}&\theta_{\text{CG},k}^{1}&\theta_{\text{CG},k}^{2}&\dots&\theta_{\text{CG },k}^{m_{4}-1}\end{matrix} \right]\, \tag{30}\]  
_and_  
\[\vartheta_{\text{FC},k}^{[m_{5}-1]}=\left[ \begin{matrix}\theta_{\text{FC},k}^ {0}&\theta_{\text{FC},k}^{1}&\theta_{\text{FC},k}^{2}&\dots&\theta_{\text{ FC},k}^{m_{5}-1}\end{matrix} \right] \tag{31}\]  
_of monomials of \(I_{k}\), \(\dot{m}_{\text{H}_{2},\text{in},k}\), \(\vartheta_{\text{AG},k}\), \(\vartheta_{\text{CG},k}\), and \(\vartheta_{\text{FC},k}\) need to be included in the extended model in Equation (26) to which the Kalman filter design can be generalized easily._  
**Remark 2**: _To achieve a suitable convergence of the vectors \(\mathbf{x}_{k}\) (respectively, \(\tilde{\mathbf{x}}_{k}\)) towards a reasonable approximation of the actual current-power dependency, measured data need to be gathered so that the dependency between the variables to be estimated and the electric fuel cell power is sufficiently excited in a persistent form. A corresponding strategy for the fundamental model in Equation (12) is presented in Section 3._
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 7 Contributions to the Use of USVs for Disaster Applications
### Proposed Control, Computer Vision, and Planning Applications  
For the N-Boat II, the idea is to design and develop a control system capable of controlling the sailboat displacement with enough generality, in such a way that it can be implemented in other sailboats [249]. Another goal is to be able to navigate using low-cost sensors such as a compass, GPS, and windsock. We have tested three types of controllers using fuzzy, Proportional-Integral-Derivative (PID), and an empirically defined simple proportional control. We have developed a mathematical model for the system architecture and control paradigms. The main advantage of having such a model established is that we could create and validate a simulation environment for the sailboat, accelerating the design of the currently used control laws, avoiding unnecessary field trials with the sailboat and reducing logistics costs. The PID controller is currently implemented in the N-Boat II prototype and working robustly.  
For Lutra boats, a vision-based system using an RGBD camera for real-time obstacle detection is under development. An obstacle avoidance system is essential because, in its current autonomy level, the USV features a waypoint navigation strategy. The problem is that the autopilot system navigates in a straight line between waypoints, assuming no obstacles in the path. In a disaster scenario such as flooding, we cannot assume this is true because there are usually moving debris, carried by the strong water flow. As CNN are state of the art for object detection and recognition [252; 253; 254], our system uses an NVIDIA* Jetson board for CNN processing and obstacle detection. Once an obstacle is detected, it can be classified either as debris, survivors, or other vessels. One mandatory requirement is that the obstacle avoidance procedure must follow the COLREGS and maintain a safe distance from eventual survivors--to avoid injuring them. The integration of obstacle detection, avoidance, and COLREGS-based path planning is currently under development.
# A Modality-Enhanced Multi-Channel Attention Network for Multi-Modal Dialogue Summarization
## 4 Experiment
### Ablation Study  
We conducted three ablation experiments on MEMA to ascertain the contribution of each modality to the summarization performance. The results, as detailed in Table 2, clearly demonstrate the benefits of a trimodal approach. The complete MEMA model, incorporating audio, visual, and textual modalities, achieved the highest scores across all metrics, with a ROUGE-1 of 43.55, ROUGE-2 of 30.58, ROUGE-L of 41.11, BLEU-1 of 28.37, BLEU-2 of 20.98, BLEU-3 of 15.20, and BLEU-4 of 11.95. When the audio modality was removed, there was a noticeable drop in performance, with the ROUGE-1 reduced to 42.56, ROUGE-2 to 25.71, and BLEU-4 to 9.44. A similar decline was observed when the visual modality was excluded, highlighting its importance. The most significant reduction occurred when audio and visual modalities were removed, underscoring the synergy between the modalities in enhancing summarization quality. These findings confirm that the integrated audio-visual information significantly enhances the textual output, validating MEMA's design of leveraging cross-modality features. This result emphasizes that, while single modalities contribute to performance, their combined effect is crucial for the achievement of optimal results in multi-modal dialogue summarization.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** \\ \hline MEMA & **43.55** & **30.58** & **41.11** & **28.37** & **20.98** & **15.20** & **11.95** \\ w/o audial modality & 42.56 & 25.71 & 38.92 & 26.72 & 19.03 & 14.17 & 9.44 \\ w/o visual modality & 42.77 & 26.72 & 37.54 & 26.12 & 19.31 & 13.89 & 8.22 \\ w/o both & 41.82 & 24.99 & 36.01 & 25.71 & 18.89 & 13.46 & 7.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study of MEMA.
## 4 Discussion  
The study findings offer valuable insights into the effectiveness and influence of a HRS on patients with chronic diseases. While the weekly administration of the TeNDER system surveys yielded positive results, there was no evidence of improvement over the course of the pilot.  
The majority of patients rated the HRS as user-friendly and easy to use, with positive responses mostly observed for high values. Patients also reported that the system's functionalities were integrated and consistent and did not require further technical support.  
They were generally satisfied, with a majority finding the service helpful in their daily life and willing to recommend it to others. However, there is still some feedback to be considered, especially regarding the personalization of recommendations and the possibility of including deeper and more complex ones.  
Finally, the QoL survey showed that the HRS had a positive impact on the patients' QoL, with no patient reporting that their QoL had worsened due to the system. The majority of patients reported either maintained or improved QoL.
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 4 Performance Analysis
### AN Assisted Transmission
\begin{split} Z_{6}&=\Pr\left\{\max_{E\in\Phi_{3}^{ 3}}\left\{\frac{\left(P_{S}M_{S}|h_{E}|^{2}L\left(r_{E_{B}}\right)\right)}{P_{ A}M_{s}|h_{E}|^{2}L\left(r_{E_{B}}\right)+\sigma_{E}^{2}}\right\}<x\right\}\\ &\triangleq U\left(x-\frac{p_{x}}{P_{A}}\right)+U\left(\frac{p_{x}}{P _{A}}-x\right)\exp\left(-\theta_{S}\lambda_{E}\int_{0}^{\infty}\Pr\left(|h_{E}| ^{2}>\frac{x\sigma_{E}^{2}}{L\left(r_{E_{B}}\right)\left(P_{S}M_{S}-P_{A}M_{S}x \right)}\right)\left(1-e^{-\beta r_{E_{B}}}\right)r_{E_{B}}dr_{E_{B}}\right)\\ &\triangleq U\left(x-\frac{p_{x}}{P_{A}}\right)+U\left(\frac{p_{x}}{P _{A}}-x\right)\exp\left(-\theta_{S}\lambda_{E}\left(\left(\frac{\left(N_{x}-1 \right)!}{\Gamma\left(N_{N}\right)}\right)\sum\limits_{m=0}^{\infty}\frac{ \mathrm{N}-1}{n!\mathrm{m}^{\prime}}\times\frac{\left(\frac{m_{x}+2}{n!\mathrm{ m}^{\prime}}\right)}{\alpha_{N}w^{\frac{m_{x}+2}{n}}}\right.\right.\\ &\quad\quad\left.-\left(\frac{\mathrm{Y}\left(2\beta\rho\right)}{ \beta^{2}}-\frac{w^{N_{N}}}{\Gamma\left(N_{N}\right)}\sum\limits_{n=0}^{ \infty}w\frac{\mathrm{Y}\left(\alpha_{N}\left(N_{N}+n\right)+2,\beta\rho \right)}{\beta^{2}\left(N_{x}\left(N_{N}+n\right)+2}\right)\right)\right).\end{split}  \tag{32}\]Substituting Equations (28) and (34) into Equation (4), the secrecy throughput is derived as  
\[ \begin{split}\eta_{b}&=\left(1-p_{cob}\right)\left(1-p_ {sob}\right)R_{S}\\ &=\left(1-\sum_{i\in\{L,N\}}\left(\frac{\Upsilon\left(N_{i},\,\frac {\left(2^{R_{S}}-1\right)\sigma_{D}^{2}}{P_{S}M_{S}L(r_{D})}N_{i}\right)}{ \Gamma\left(N_{i}\right)}\right)P_{i}\left(r_{D}\right)\right)\\ &\times\exp\left(-\theta_{S}\lambda_{E}\left(F+E_{N}-E_{L}\right) \right)R_{S}.\end{split}  \tag{35}\]  
**Remark 3**: _From Equation (28), it is explicitly shown that the COP is a decreasing function about transmitting power. Adding more transmitting power could help the improvement of reliability performance. In the case of \(E\in\Phi_{3}\), the eavesdroppers are located in the sector secrecy guard zone, and the transmitter allocates a portion of the power to transmit the AN to confuse the eavesdroppers. From Equation (34), we see that the SOP has a close relationship with central angle \(\theta_{S}\), eavesdropper density \(\lambda_{E}\) and AN power. Additionally, Equation (35) shows that, in addition to central angle \(\theta_{S}\) and eavesdropper density \(\lambda_{E}\), the transmit power allocation factor \(\mu\) is of vital importance._  
In addition, the eavesdroppers may be in the case of \(E\in\Phi_{1}\) and \(E\in\Phi_{2}\), the derivation process and results are similar to those in Section 4.1.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 3 Analysis of Equalization Methods
### DNN and RBF-NN
This input matrix was the input of the radial basis function, and the Gaussian kernel function was adopted as the activation function in the hidden layer considering its approximation to the limited bandwidth effect in the time domain, as analyzed in Section 2. The output layer was a linear combination of outputs from the hidden layer. The training process of this RBF-NN utilized the original PRBS-9 signal as the training target, and then adjusted the weights in the hidden layer and the output layer. The final output of the RBF-NN was [32]:  
\[\mathrm{y}(\mathrm{n})=f(\sum_{i=0}^{L}\omega_{i}\phi_{i}(\mathrm{x}_{i})+ \mathrm{b}), \tag{12}\]  
where \(\mathrm{b}\) is the extra bias item, and \(\phi(\mathrm{x})\) is the Gaussian kernel function with the expression:  
\[\phi(\mathrm{x})=\mathrm{e}^{\frac{\|\mathrm{x}-\mathrm{y}^{\prime}\|^{2}}{2 \sigma^{2}}}. \tag{13}\]  
The RBF-NN had several advantages compared with a traditional DNN for its unique structure and training process, as summarized in Table 2. The first advantage was that the activation function of the RBF-NN was more suitable to approximate the bandwidth limitation and nonlinear effects. Due to the flexible choice of various activation functions,  
Figure 9: Structure of RBF-NN. the RBF-NN could compensate for channel models with nonlinear characteristics, especially when using the Gaussian kernel function as its radial basis function. The second advantage was that the RBF-NN was a local approximation neural network due to the impact of one neuron on another one being related to their Euclidean distance, and it only required changing part of the weights during the training process instead of changing all the weights for each iteration. Traditional neural networks, such as DNNs and CNNs, utilize the latter method for the training process and consume many more resources and take a longer time. The third advantage was that the RBF-NN owned a simpler neural network structure since it only contained three layers compared with other NNs with several hidden layers. This simple structure also contributed to decreasing the training time.
## 2 Materials and Methods
### Estimation of Rock Deformation Parameters  
At the end of last century, [PERSON] and [PERSON] et al. modified the geological strength index GSI, summarized the stored rock mechanics test data, and summarized the narrow Hoek-Brown strength criterion [29; 30]. After that, [PERSON] introduced rock mass disturbance coefficient D (the value range is 0-1 according to the disturbance degree) and proposed the generalized Hoek-Brown strength criterion through the comprehensive analysis of biaxial test data and the internal structure of rock mass.  
\[\sigma_{1}=\sigma_{3}+\sigma_{ci}\bigg{(}m_{b}\frac{\sigma_{3}}{\sigma_{ci}}+s \bigg{)}^{a} \tag{1}\]  
where \(\sigma_{1}\) is the maximum effective principal stress (MPa) at the time of rock failure; \(\sigma_{3}\) is the minimum effective principal stress (MPa) of rock failure. \(\sigma_{ci}\) is the uniaxial compressive strength of rock (MPa). \(m_{b}\), \(s\), and \(a\) are Hoek-Brown constants. \(m_{b}\) is the rock material constant, which can be obtained by experiment [31].  
The internal friction Angle and equivalent cohesion c of rock mass can be obtained by the formula.  
\[\varphi=sin^{-1}\left[\frac{6 am_{b}(s+m_{b}\sigma_{3n})^{a-1}}{2(1+a)(2+a)+6 am_{b}(s+m_{b}\sigma_{3n})^{a-1}}\right] \tag{2}\]  
\[c=\frac{\sigma_{c}[(1+2a)s+(1-a)m_{b}\sigma_{3n}](s+m_{b}\sigma_{3n})^{a-1}}{ \sqrt{1+\frac{6 am_{b}(s+m_{b}\sigma_{3n})^{a-1}}{(1+a)(2+a)}}} \tag{3}\]  
where \(\sigma_{3n}=\frac{\sigma_{3 nmax}}{\sigma_{ci}}\), \(\sigma_{3 max}\) is the upper limit of minimum principal stress of rock mass.  
The rock deformation modulus \(E_{t}\) is calculated using the following formula. Where \(E_{e}\) is the elastic modulus (GPa) of the intact rock in the laboratory.  
\[E_{t}=E_{e}\Bigg{[}0.02+\frac{1-\frac{D}{2}}{1+e^{\frac{6(a+15D-GSI}{11}}} \Bigg{]} \tag{4}\]  
where \(D\) is the disturbance coefficient and \(GSI\) is the geological intensity index.  
Figure 4: Test results of mechanical properties and permeability of sandstone. (**a**) Weakly consolidated sandstone. (**b**) Conventional sandstone [2].  
The elastic modulus \(E_{t}\) in the above formula can be used to calculate the parameters required in the numerical simulation, namely, the rock mass shear modulus \(G\) and the bulk modulus \(K\).  
\[G=\frac{\text{E}}{2(1+\mu)} \tag{5}\]  
\[K=\frac{\text{E}}{3(1-2\mu)} \tag{6}\]  
In this section, the Hoek-Brown criterion is used to estimate rock deformation parameters, which is intended to prepare for the establishment of the seepage numerical simulation model in the next chapter. The obtained rock mass mechanical parameters are shown in Table 2.
## 2 Experiment Settings
### Experimental Circuit and Acquisition System  
As the voltage required to initiate the electrical tree under DC voltage is excessively high, experiments often use AC voltage to induce the initial electrical tree, followed by the application of DC voltage. This experimental method was also adopted in this study. The characteristics of the initial electrical tree induced are presented below. To facilitate the description of the electrical tree's dimensions, its length and width are defined, as shown in Figure 5.  
The DC electrical tree experimental system is shown in Figure 6. The high-voltage circuit consists of a DC high-voltage source, a 0.1 \(\upmu\)F stabilizing capacitor, a 5 k\(\Omega\) protective resistor, a 1000 pF coupling capacitor, and a needle-plate sample. The DC high voltage source is a ZLT-\(\pm\)1200/50 (Yangzhou Xinyuan Electric Co., Ltd., Jiangsu, China). The sample is immersed in transformer oil, which is temperature-controlled using an oil bath heating system. The transformer oil also effectively prevents surface discharge and external discharge. The detection system consists of a partial discharge detection system and an electrical tree observation system. Partial discharge detection is performed using the pulse current method, which is a standard method for partial discharge measurements. The measurement frequency band is 10 kHz-1 MHz, and the measurement range is 0.1 pC\(\sim\)10,000 nC. The model of the oscilloscope used is Tektronix DPO MSO 4000 (Tektronix, Beaverton, OR, USA). The real-time observation of electrical treeing is conducted via a CCD system with an industrial camera. The model of the industrial camera used is MER2-160-227U3M/C (Daheng Imaging, Beijing, China). A light source is used to illuminate the sample, facilitating the observation of the electrical tree under the microscope. The industrial camera is set to an appropriate magnification to ensure the electrical tree is clearly visible. A real-time observation software installed on the computer connects with the camera, allowing the growth process of the electrical tree to be monitored and images to be saved at regular intervals.  
Figure 5: The electrical tree image initiated by AC electrical stress.  
Figure 6: Experiment circuit of DC electrical tree and PD.  
Appropriate experimental temperature and voltage settings are crucial to ensuring the successful progression of the electrical treeing experiment until breakdown occurs. After multiple trials, an experimental voltage of 25 kV and a temperature of 70 \({}^{\circ}\)C were selected. The experiment is carried out under constant temperature and voltage until the sample undergoes breakdown.
###### Abstract  
Wing-in-ground craft often encounter waves when flying over the sea surface, and the ground effect is more complicated than that of flat ground. Therefore, the aerodynamic characteristics of the NACA 4412 airfoil in proximity to wavy ground for a wide range of angles of attack is studied by solving the Reynolds Averaged Navier-Stokes equations. The validation of the numerical method is carried out by comparing it with the experimental data. The results show that the aerodynamic coefficients will fluctuate periodically when the airfoil moves over wavy ground at a small ride height. Except for the angle of attack of \(0^{\circ}\), the fluctuation trend of aerodynamic coefficients at other angles of attack is the same. The analysis of aerodynamic fluctuation amplitude found that the medium angle of attack should be selected as the design cruise angle of attack for wing-in-ground craft. The time-averaged aerodynamic coefficients in the case of wavy ground are almost the same as those of flat ground. Hence, wavy ground mainly causes a fluctuation in aerodynamic coefficients. Considering the difference between aerodynamic coefficients at the angle of attack of \(0^{\circ}\) and at other angles of attack, the flow field structure at an angle of attack of \(0^{\circ}\) and \(4^{\circ}\) is analyzed. The results reveal the aerodynamic characteristics of the airfoil moving over wavy ground, which gives a deeper understanding of the ground effect in the conditions of wavy surface/ground. This has a certain guiding significance for the design of wing-in-ground craft.  
WIG craft; airfoil; ground effect; wavy ground; wide range angles of attack; aerodynamic characteristic; flow field structure
## 3 Proposed Approach
### Feature Extraction
#### 3.2.3 Local Binary Patterns (LBP)  
The LBP technique proposed by [PERSON] et al. [86] is a robust and efficient classical type of textural descriptor for a wide range of applications [87]. Due to its computational simplicity, it is possible to analyse the images in real-time applications [88]. Also, by applying a non-overlap window, LBP considerably reduces the image matrix without losing the texture information [88]. In this method, the relationships between a pixel and its local neighbours are defined by a \"local pattern\". Consider \(P\) to indicate the circular neighbourhood pixel on a circle of radius \(R\). Let \(G_{c}\) represent the grey value of the centre pixel and \(Gp\) indicate the grey value of the neighbourhood pixels for \(p=0\), \(1\),..., \(p-1\). Based on the local structure around the centre pixel, circular points can be represented as 8-bit binary codes [87]. The local texture is defined by:  
\[\mathrm{T}=\mathrm{t}(G_{c},G_{0},\ldots,G_{p-1}) \tag{8}\]  
Figure 11: Different tissue types in the plaque area of IVUS images: (**a**) dense calcium, (**b**) necrotic core, and (**c**) fibro-fatty.  
The LBP of centre pixel is obtained by the following equation:  
\[LBP_{P,R}=\sum_{p=0}^{P-1}F\big{(}G_{p}-G_{c}\big{)}2^{p} \tag{9}\]  
where  
\[F(x)=\begin{cases}1,&x\leq 0\\ 0,&otherwise\end{cases} \tag{10}\]  
The \(LBP_{P,R}\) is calculated by subtracting the centre pixel from its neighbours [87].