esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Work
### Siamese Network Framework  
The Siamese network framework of our tracking algorithm is shown in Figure 2. It consists of two asymmetric branches: the target branch and the search branch. In the search branch, we design and apply a small convolutional backbone feature extraction network for feature extraction. In the target branch, we design and apply a backbone feature extraction network that uses a combination of small convolutional kernels and jump-layer connections for feature extraction. First, we will design a small convolutional kernel backbone feature extraction network and apply it to the search branch. Given the wide range of applications of convolutional neural networks in computer vision [28; 29], the newly designed backbone feature extraction network consists of 14 convolutional layers, 3 maximum pooling layers, 13 batch normalization layers, and 4 activation layers. We define convolutional layers 1, 2, and 3 plus 3 normalization layers plus 1 maximum pooling layer as conv1; convolutional layers 4, 5, and 6 plus 3 normalization layers plus 1 activation layer plus 1 maximum pooling layer as conv2; convolutional layers 7, 8, and 9 plus 3 normalization layers plus 1 activation layer plus 1 maximum pooling layer as conv3; convolutional layers 10, 11, 12, 13 plus 3 normalization layers plus 1 activation layer is defined as conv4; convolution layer 15 is defined as conv5; the specific information of the backbone feature extraction network is shown in Table 1. Compared with AlexNet, the newly designed backbone feature extraction network uses a \(1\times 1\) small convolution kernel to downsize the network channel several times, which not only reduces the number of parameters of the network, but also ensures the computational speed while increasing the depth of the network, so that the network can further obtain the deep features of the target. In addition, using a \(1\times 1\) small convolutional kernel can increase the nonlinearity, mix the cross-channel information, and improve the generalization ability of the network. In the table above, CONV*-BN represents the normalization layer after the convolutional layer, CONV*-BN-ReLu represents the activation layer after the normalization layer after the convolutional layer, and Maxpool represents the maximum pooling layer. Secondly, on the basis of the small convolutional kernel backbone feature extraction network, we add the hopping layer connection feature fusion and apply it to the target branch. After the object template feature extraction branch conv3, bilinear interpolation is used for downsampling, and the intermediate features after downsampling are fused with the deep features after conv5, and the fused features can take into account the semantic discriminative ability of the deep features and the spatial structural details of the shallow features, so as to express the target more comprehensively, and the calculation formula is as follows:  
\[f=conv5(conv4(conv3(conv2(conv1(z)))))+\lambda\alpha(conv3(conv2(conv1(z)))), \tag{1}\]
## 2 Problem Statement
### Reinforcement Learning for Mobile Robot Path Planning
For instance, if the robot is predicted to intersect with an obstacle at time \(t+1\), the state transition probability can penalize such trajectories by assigning lower likelihoods: \[\mathcal{P}(s^{\prime}|s,a)\propto\exp\left(-\frac{\text{dist}(s^{\prime}, \mathcal{O}_{t})^{2}}{2\sigma_{\text{obs}}^{2}}\right),\] (18) where \(\text{dist}(s^{\prime},\mathcal{O}_{t})\) is the distance between the predicted state and the obstacle's trajectory at time \(t\), and \(\sigma_{\text{obs}}\) controls the sensitivity to obstacle proximity. In real-world scenarios, the robot's sensors introduce noise, leading to uncertainty in the observed states. The state transition probability density can be modified to include the perception model:  
\[\mathcal{P}(s^{\prime}|s,a)=\int\mathcal{P}(s^{\prime}|s^{\prime})\mathcal{P} (s^{\prime}|s,a)\,ds^{\prime}, \tag{19}\]  
where \(\mathcal{P}(s^{\prime}|s^{\prime})\) is the sensor noise model, typically modeled as Gaussian:  
\[\mathcal{P}(s^{\prime}|s^{\prime})=\mathcal{N}(s^{\prime};s^{\prime},\Sigma_ {\text{sensor}}). \tag{20}\]  
The _reward function_ is designed to encourage goal-reaching behavior, penalize collisions, and promote efficient navigation. For the differential drive mobile robot model presented above, such a reward function can be expressed as follows:  
\[r(s,a)= \begin{cases}+R_{g},&\text{if }\|[x\,y]^{\top}-[x_{g}\,y_{g}]^{\top} \|\leq\epsilon,\\ -R_{c},&\text{if }(x,y)\in\mathcal{O},\\ -\alpha\|[x\,y]^{\top}-[x_{g}\,y_{g}]^{\top}\|-\beta\|a\|^{2},&\text{otherwise}, \end{cases}  \tag{21}\]  
where\([x\;y]^{\top}\) is the robot's current position in the state \(s=[x\;y\;\theta\;v\;\omega]^{\top}\),
* \([x_{g}\;y_{g}]^{\top}\) is the goal position in the global frame,
* \(\epsilon\) is a small threshold to determine if the robot has reached the goal (e.g., within a radius of \(\epsilon\) around the goal position),
* \(R_{g}\) is a positive scalar reward assigned when the robot reaches the goal,
* \(R_{c}\) is a scalar penalty assigned if the robot's position lies within an obstacle region \(\mathcal{O}\),
* \(\alpha\) is a weighting factor penalizing the Euclidean distance between the robot's current position and the goal,
* \(\beta\) is a weighting factor penalizing the energy expenditure represented by the squared magnitude of the action vector \(a=[u_{\varphi}\;u_{\omega}]^{\top}\).
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
## 3 Proposed Three-Coil PT-WPT System
### Online Load Resistance Identification and System Status Detection  
For the three-coil PT-WPT system to operate in the exact state, ensuring identical coupling coefficients between adjacent coils is imperative. The equivalent load-resistance data and real-time information on the load \(R_{L}\) and the distance between coupling coils must be acquired.  
By combining (3) and (13) with \(U_{in}=I_{T}R_{T}\), the estimated load resistance can be derived as follows:  
\[R_{L-est}=\frac{\pi^{2}}{8}\left[\frac{L_{R}}{L_{T}}\bigg{(}\frac{U_{in}}{I_{ T}}-r_{T}\bigg{)}-r_{R}\right] \tag{14}\]  
In [3], a system operating mode analysis was performed by detecting the working frequency and comparing the relationship between the operating frequency and the intrinsic resonant frequency. However, in the operating broken state of the three-coil PT-WPT system, a bifurcation frequency exists, making it challenging to judge the operating mode through a frequency comparison. To discern whether the system is functioning within the PT-exact state, it is proposed to calculate the inverter output voltage and current RMS using reflected impedance analysis.  
Figure 4: Negative resistance control circuit and inverter output waveform.  
Figure 3: Inverter control circuit diagram implementing negative resistance.  
The equivalent transmitting-side circuit of the system is shown in Figure 5, and the input impedance \(Z_{in}\) of the system is expressed by (15):  
\[Z_{in}=r_{T}+j\omega L_{T}-j\frac{1}{\omega C_{T}}+\frac{\omega^{2}M_{T1}^{2}}{j \omega L_{1}-j\frac{1}{\omega C_{1}}+\frac{\omega^{2}M_{R1}^{2}}{j\omega L_{R}- j\frac{1}{\omega C_{R}}+R_{Leq}+r_{R}}} \tag{15}\]  
Upon substituting the expression for operating frequency from (4) to (15),  
\[Z_{in}=r_{T}+r_{R}+R_{Leq} \tag{16}\]  
In the PT-exact and resonant states, the system exhibits a consistent input impedance. This input impedance consistently complies with (16) when the system functions in the resonant state. Therefore, the system's input impedance can be computed as a reference value by transmitting a PWM signal with a fixed frequency before the system commences operation. This input impedance calculation was conducted during self-oscillation control, issuing a misalignment warning when the system was in the broken region. The flow of the system's working state detection is illustrated in Figure 6.  
Figure 5: Equivalent circuit diagram with the reflected impedance on the transmitting side.  
Figure 6: Flowchart of system working status detection.
# Field Emission from Carbon Nanostructures
## 2 Field Emission from Carbon Nanostructures
### Field Enhancement Factor, \(\beta\)  
The field enhancement factor represents a key figure for characterizing the field emitter performance. Typical experimental procedures to get an estimation of \(\beta\) rely on the analysis of the current-voltage characteristics (\(I\)-\(V\)) by means of the F-N theory. From Equation (1), a linear dependence is expected for the so-called F-N plot, in which \(\ln(I/V^{2})\) is reported as a function of \(1/V\), with the macroscopic electric field far from the nanotube given by the applied voltage \(V\) divided by the anode-cathode distance, \(E=V/d\). Accordingly, \(\beta\) can be obtained from the slope \(m\) of the linear dependence, being \(m=b\cdot\phi^{3/2}\cdot d/\beta\).  
Several models have been proposed to study the dependence of \(\beta\) on nanotube geometrical properties, i.e., length \(l\) and radius \(r\). The intrinsic field-enhancement factor of a single CNT in the  
Figure 3: Carbon allotropes: Graphite (3D), Graphene (2D), CNT (1D), Fullerene (0D) and Diamond (3D).  
planar diode configuration can be written [74] as \(\beta=1.2\cdot\left(2.5+\frac{l}{r}\right)^{0.9}\). Typical \(\beta\) values in the range of \(3\times 10^{4}\) to \(5\times 10^{4}\) can be obtained in individual CNTs. Vice versa, when dealing with CNT films typical values are in the range of \(1\times 10^{3}\) to \(3\times 10^{3}\)[75]. A calculation of the field enhancement factor in the vicinity of a CNT apex has been also reported by [PERSON] et al. [76], studying the dependence of \(\beta\) on the lateral separation between CNTs, anode-substrate separation d, and on the geometrical parameters (height \(h\) and radius \(r\)) of the CNTs. They showed that emitted current, calculated in the framework of F-N theory, is reduced if the nanotubes are separated from each other less than twice their height. It has also been shown that when the inter-tube distance (\(s\)) becomes \(s<h\), the field enhancement factor decreases rapidly for reducing \(s\). The current emission is optimized when inter-tube distance is comparable to \(h\)[77].  
Moreover, the anode-cathode distance is relevant as well if it is shorter than \(3h\). In particular they predicted that the turn-on field, which is the minimum field required to initiate FE, decreases for decreasing cathode-anode distance; however, for distance larger than \(3h\), the turn-on field results are constant and independent from the geometry of the electrodes. [PERSON] et al. [78] used a simple model of a floated sphere inside a standard parallel plate configuration to take into account the anode-cathode separation, and showed that \(\beta=\left(h/r+3.5\right)+A\left(h/d\right)^{3}\), \(A\) being a constant. This implies that turn-on field or voltage can be reduced by decreasing the anode-cathode distance.
## 4 Numerical Illustration
### Computational Aspects  
All computing was made using the programming language R. The PLN regression model with varying dispersion was estimated using the EM algorithm, which was presented in Section 3. In addition, the ML estimates of the parameters of the NBI regression model with varying dispersion and the ZIP regression model were obtained using the generalized additive models for the location, scale, and shape (GAMLSS) package in R [14].  
Note that a rather strict criterion was used, and it took the algorithm quite a large number of iterations to converge. In particular, the stopping criterion was set as \(tol=10^{-12}\). Note also that the M-step involves two Newton-Raphson iterations, and hence it is important to identify the choice of meaningful initial values for the vectors \(\mathbf{\beta}_{1}\) and \(\mathbf{\beta}_{2}\), as this can increase increase the computational time requirements for the EM algorithm and make it more difficult to locate the global maximum. We obtained good initial values for \(\mathbf{\beta}_{1}\) by fitting the simple Poisson regression. Additionally, we obtained good initial values for \(\mathbf{\beta}_{2}\) by (i) calculating \(Var(k_{i})\) for the eight different risk classes which can be formed using all available risk factors and the observations \(i=1,\dots,n\) and (ii) calculating \(E(k_{i})\) for the eight different risk classes and using the log-link function for \(\phi_{i}\) (see Equation (6)), so we solve Equation (4) with respect to \(\mathbf{\beta}_{2}\). However, we also checked with many other starting values for \(\mathbf{\beta}_{2}\) in order to ensure that the global maximum had been obtained. For all cases, the EM algorithm converged to a similar solution. The standard errors were computed by using the standard approach of [15].  
Finally, as was anticipated, in terms of CPU time, it took the NBI regression model with varying dispersion and the ZIP regression model less than one minute, and they both compared significantly more favorably to the PLN regression model with varying dispersion, which exceeded 30 min of CPU time. However, it should be taken into account that the PLN model has a density which does not exist in closed form, and that there were 14,143 observations in the sample of MTPL data that was examined in this article. For larger data sets with more features, the computing effort can be reduced if the E- and M-steps are executed in parallel across multiple threads to exploit the processing power of modern-day multicore machines.
# Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor
## 3 Results and Discussion
### Performance Evaluation
#### 3.3.3 Convergence Analysis  
Convergence is the arrival of a stable point at which the solution stops improving any further. However, if an algorithm converges in very early iterations at a poor suboptimal point, then it is called premature convergence. In this section, we compare these algorithms based on how well they can converge, how well they can avoid premature convergence, and how quickly they can converge. The convergence curves of all these algorithms against each dataset are plotted in Figure 4. First of all, if we discuss the convergence capability, then all algorithms converged in less than the first half of the iterations for all datasets. If we talk about premature convergence, then SA converged prematurely in most cases; however, SCA on DS1, HGSO on DS3, and GSA on DS5 also converged prematurely. Finally, if we analyze their convergence speed, then EO demonstrated very good convergence speed on DS4, DS5, and DS6. In addition to that, ASO also demonstrated good speed on DS1 and DS3, and SCA was better than all the other algorithms on DS2. If we rank these algorithms based on the convergence capabilities, then EO secured the first rank, SCA was the second best, and SCA managed the third-best position.  
The convergence speed of all algorithms is also measured in the average number of seconds. The average computational time along with standard deviations are presented in Figure 5. The results showed that SA took the least amount of time to converge on all datasets, which is obviously due to its premature convergence. Another reason may be that it is a single-solution algorithm. Similarly, GSA was the second-fastest algorithm on five out of six datasets. However, if we talk about the computational time taken by the best performers such as EO and ASO, then ASO took much less time than EO on all datasets except DS5.
## 2 Materials and Methods
### Bioactive Compounds and Antioxidant Capacity
#### 2.7.2 Phenolic Compounds Profile  
The analysis of phenolic compounds profile was performed by ultra-high performance liquid chromatography (UHPLC) using an Acquity chromatograph coupled with a TQD Acquity mass spectrometer (Micomass-Waters, Manchester, UK) with electrospray ionization (ESI) and using a C18 BEH Waters Acquity column (2.1 mm \(\times\) 50 mm \(\times\) 1.7 \(\upmu\)m particle size). A specific chromatographic method for the samples was developed according to [21], with modifications. Phase A was composed of acidic water (0.1% formic acid) and phase B of acetonitrile (HPLC grade). The gradient started at 5% A and 90% B; it increased to 50% B in 7.5 min, then to 100% B in 8.0 min, keeping these concentrations for 1.0 min and returning to the initial conditions in 9.1 min until 10 min. The conditions of the ESI analysis in negative mode were: capillary voltage of \(-\)3.00 kV, cone of \(-\)30.00 V, source temperature of 150 \({}^{\circ}\)C and desolvation temperature of 350 \({}^{\circ}\)C. In the tandem mass spectrometry analysis (MS-MS), used to examine the fragmentation of ions, a Collision-Induced Dissociation (CID) was performed at 20 V of collision energy. Initially, a full scan (\(m/z\) 100 to 1000) was carried out to check whether phenolics were present compared to a panel of standards. Finally, the analysis of the samples and the calibration curve of the standards in selected ion mode (SIM) were performed at \(m/z\) 191 (quinic acid), \(m/z\) 153 (protocatechuic acid), \(m/z\) 353 (chlorogenic acid), \(m/z\) 289 (catechin/epicatechin), \(m/z\) 163 (\(\rho\)-coumaric acid), \(m/z\) 609 (rutin), \(m/z\) 223 (synaptic acid), \(m/z\) 193 (ferulic acid) and \(m/z\) 447 (quercetin).
## 3 Results
### Effect of Ultrasound-Only Treatment on NO Level  
Since, in the initial experiments, using blood caused adhesion of red blood cells to the channel wall coated with endothelial cells, we replaced blood with the complete medium containing phenol red [47]. The percentage increase in the NO level in comparison with the control group was 8.3% (\(p<0.01\), \(n=6\)), 17.2% (\(p<0.001\), \(n=6\)), 23.4% (\(p<0.001\), \(n=6\)), 10.8% (\(p<0.001\), \(n=6\)), and 2.5% for 0.1, 0.5, 1, 1.5, and 2 MPa, respectively, as shown in Figure 3. The NO level increased by 181.9% when ultrasound peak negative pressure levels increased from 0.1 to 1 MPa, and then the NO level was reduced by 89.3% when ultrasound peak negative pressure increased from 1 to 2 MPa. Figure 4 shows the PCD measured cavitation likelihood as a function of ultrasound peak negative pressure. A strong cavitation signal was detected when the ultrasound peak negative pressure was greater than 1.3 MPa, which indicates the threshold for inertial cavitation (Figure S3). This could explain the reduced NO level at high ultrasound peak negative pressure. However, the overall NO level was still higher than the control group within the selected parameter range, indicating that not all endothelial cells are dysfunctional at the selected ultrasound pressure. Impairment of endothelial-dependent relaxations results in reduced endothelium-derived NO synthesis, which is a characteristic of endothelial cell dysfunction [70].
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 4 Experimental Results and Discussions
### Training Faster R-CNN and Its Performance  
The dataset for training and test the Faster R-CNN was constructed by 81,810 still images, including 25,400 flame images and 25,410 smoke images collected from several data sources including YouTube video clips, the previous works [7; 27; 28; 29], and the Flickr-fire dataset. There were 31,000 non-fire images included in the dataset. The images were divided into 70% for training, 10% for validation, and 20% for test data. For training, the positive data were augmented by a horizontal flip of bounding a bounding box. Note that a fire should have the consistent shape after taking augmentation. Table 4 shows the training parameters for Faster R-CNN.  
The performance of the Faster R-CNN was measured by mean average precision (MAP) and is shown in Table 5, which is a better result than the previous paper due to the elaborate training method such as hard negative example mining. These include several false positive detections for clouds, chimney smoke, lighting lamp, steam, etc., which are almost undetectable without considering the temporal characteristics as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Method** \\ \hline Iteration & 250,000 \\ Step size & 70,000/150,000 \\ Weight decay & 0.0004 \\ Learning rate & 0.001 \\ Learning rate decay & 0.1 \\ Batch size & 1 \\ Pre-train weight & ResNet152(ImageNet) \\ RPN mini-batch size & 256 \\ OHEM sample size & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training Parameters of Faster R-CNN [26].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**mAP** & **Flame** & **Smoke** & **Non-fire** \\ \hline
88.76 & 88.92 & 88.75 & 88.61 \\ \hline \hline \end{tabular}
\end{table}
Table 5: mAP of Faster R-CNN [26].
## 5 Numerical Simulation and Experimental Verification
### Numerical Simulation Verification
In particular, the proposed soft-threshold segmentation method in this study achieves a more significant improvement in ICC and a reduction in IRE with a lesser loss in SSIM compared to the maximum entropy threshold segmentation method. The reduced-order Tikhonov algorithm can effectively enhance the SSIM and to some extent improve the ICC and reduce the IRE, although its improvement is not as significant as that achieved by using image segmentation algorithms. The proposed segmentation-reduced-order-Tikhonov algorithm (STS-RO-TK) can leverage the advantages of both algorithms, effectively maintaining a high SSIM while reducing IRE and improving ICC. From the perspective of alleviating the ill-posedness of the image reconstruction process, the fewer unknowns solved during the inverse problem-solving process, the less severe the ill-posedness. In this study, the images of the internal medium distribution within the reconstructed field were obtained at a resolution of 100 \(\times\) 100 pixels. Compared to the original reduced-order Tikhonov algorithm (RO-TK) regularization algorithm, which reduces 701 unknowns, the proposed segmentation-reduced-order-Tikhonov algorithm (STS-RO-TK) can reduce 2169 unknowns, thus improving the ill-posedness of the image reconstruction process. For further refinement of the reconstructed images, the segmentation-reduced-order-segmentation-Tikhonov regularization algorithm (STS-RO-STS-TK) can be applied after the segmentation-reduced-order-Tikhonov (STS-RO-TK) reconstruction to obtain images with even clearer boundaries.