esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 2 Related Work
### Localization-Free Routing Protocols
Similarly, energy optimization of underwater sensor network is explored recently through depth-based routing [35], clustering scheme [36], machine learning technique [37], and reinforcement learning [38]. A comparative study on the applications, deployment, and routing techniques in UWSNs is also carried out in [39]. The most closely related work with our study is WDFAD-DBR [19] which uses the two-hop methodology and considers depth as well as residual energy of the nodes, PFN of the nodes, and locality of the node for the next forwarder (master node) selection. Such parameters significantly contribute to addressing the problem of void and energy holes in the network. Additionally, Data packet reception is limited to only master nodes, which contribute to energy efficiency and data packet collision avoidance. In addition to these features of WDFAD-DBR, our proposed protocol also uses adaptive transmission power adjustment based on the locality of the next forwarder to further reduce the energy consumption. Other features and working of the protocol are explained in the later sections. All the UWSN routing protocols, discussed in this section, are listed in Table 1 along with their advantages and disadvantages.
## 4 Application  
The MP-SAWS can detect subtle variations in various physical quantities, including temperature and pressure. After integrating magnetostrictive materials, the MP-SAWS can also detect precise changes in magnetic fields. By utilizing finite element simulation software, incorporating diverse physical fields, and employing parameter scanning, the sensing characteristics of the MP-SAWS for temperature, pressure, and magnetic fields can be obtained. These characteristics can be applied to specific magnetocardiography (MCG) scenarios, and the measurement results can be displayed through characteristic frequency curves.
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
## 3 SR Method for CLLC Resonant Converters Based on State-Trajectory Models
### SR Strategy Based on State Trajectories  
In this paper, we propose a synchronous rectification method based on the gate signal delay duty cycle and the switching duty cycle calculated above.  
Due to the difference between the synchronous rectification strategies for the over-resonant state and the under-resonant state, it is necessary to determine whether the system is in an over-resonant or under-resonant state based on the normalized switching frequency.  
The duty cycle of the synchronous rectifier signal is selected according to the determined resonant state, as defined by Equation (5) or Equation (32).  
Therefore, the synchronous rectification signal only requires sampling the input voltage, output voltage, and output current, eliminating the need for additional sampling circuits. This enables the dynamic real-time synchronization of the circuit state. The flowchart of the proposed synchronous rectification scheme for the CLLC resonant converter is illustrated in Figure 9. The CLLC resonant converter employs closed-loop control based on the voltage difference, and the control block diagram is presented in Figure 10.
## 3 System Design  
We proposed an Identifier and Locator Decoupled Multicast approach (ILDM) based on ICN to solve the problems faced in IP multicast. To avoid overload and traffic concentration of the core, we proposed a dynamic core management and selection mechanism. To make full use of low-load links and alleviate traffic aggregation, we also designed a path state-aware multicast tree node (MTN) selection mechanism to select the MTN with the best path state for each receiver to join. Firstly, we introduced the details of our multicast system design from four aspects: system overview, dynamic core management and selection, member event handling, and Path State-Aware Multicast Tree Node Selection Mechanism. Then, we also gave a comparison of ILDM and PIM-SM to illustrate how ILDM solved the problems faced in IP multicast.
# Electrical Property Measurement on Antistatic Coating During Aging Process
## 2 Materials and Methods
### Material Type for Measurement  
The aim of the experimental part of our work was to test experimentally prepared paints based on solvent-type silicone acrylate binder containing CaTiO\({}_{3}\) (calcium titanate) and TiO\({}_{2}\) (titanium dioxide) pigments coated with a layer of CPs based on PANI and PPY salt by chemical oxidative polymerization in terms of their electrical conductivity. The electrical properties of the formed protective coatings were compared with a protective coating without active pigments, which contained only an uncoated neutral limestone filler CaCO\({}_{3}\) (calcium carbonate).  
We focused on how the antistatic paints were able to conduct the generated charge to earth potential. As mentioned above, tested antistatic materials/paints differ in their chemical composition and this influences the particular surface conductivity of these paints.  
We painted coatings based on single-component paint that are soluble in xylene onto laboratory samples. Xylene is a solvent used for methyl-silicone resin in which white titanium pigment is added. This pigment is titanium dioxide TiO\({}_{2}\), which is the most commonly used compound of titanium. Typically, it has the highest covering capability among all other pigments. The filler of these paints is Omyacarb 2 VA. It is a carbonate in the form of white marble powder. Our coatings contain zinc; therefore, they have the paint form, as the weight of the zinc is considerably high. The paints have the same consistency as one-component paints.  
We used the paints on the following samples:  
* BISIL (Borosilicate Glass);
* CaTiO\({}_{3}\);
* CaTiO\({}_{3}\)/PANI;
* CaTiO\({}_{3}\)/PPY;
* TiO\({}_{2}\)/PANI;
* TiO\({}_{2}\)/PPY;
* PANI.  
The paints were applied to two different materials:  
* A dielectric surface--ordinary sheet glass with 0.25 m \(\times\) 0.10 m dimensions and a thickness of 3 mm;
* A conductive surface--steel plate S 235 JRG2 with 0.10 m \(\times\) 0.10 m dimensions and a thickness of 2 mm.  
For all of the paints, 4 samples were prepared: 2 on the dielectric surface and 2 on the conductive surface. The coatings that we used in our experiments are primarily designed for metallic constructions, so we focused on the volume resistance of that material with the coating. All the samples with coating were prepared by the same method and the same process, thus, the thickness of the coating on all samples used in our experiments was equal to 0.18 mm, and it had no effect during comparison validation of the samples.  
However, when using a dielectric material surface for a coating application, a few questions have to be discussed. The coating should be carefully grounded to make it work effectively in a particular application. Furthermore, if the conductivity of the coating is too high, there is a higher risk of spark discharge caused by the electrostatic charge accumulated on a dielectric pad. The appropriate coating material selection should also take into account the material type of the surface on which the coating is applied.
## 3 Experiment and Results
### Pupil Segmentation and Extraction in Images  
We used the SURF (Speeded Up Robust Features) [40] algorithm to detect key points in the images, extract and filter the coordinates and response values of the key points, and then sort the key points by their x-coordinates. The key points were divided into left and right groups, and the weighted center of each group was calculated based on the response values. The final result was the central region of the left and right pupils (which closely matches the position of the pupil reflection point). A 128 \(\times\) 128 pixel area centered on this point was cropped as the image data. To avoid data leakage from the training set to the test set, only the left eye portion of all images was cropped.
## 3 Results  
### Performance Comparison by Models  
All model performances exceeded 0.8, except for TSS values of 0.783 in the MLP model of _A. gracilipes_, suggesting that the developed model is reliable for predicting the potential distribution of the two ant species (Table 2). In particular, the AUC exceeded 0.89 for all models in both species with high accuracy and sensitivity, which indicated an accurate assessment of the actual occurrence.  
In terms of model type, the MaxEnt model exhibited the highest performance, whereas the MLP model showed the lowest value for both species, possibly due to low specificity. When comparing the performance metrics, the AUC showed a relatively higher value than the TSS values, regardless of model and species. The MLP model showed the largest difference between the AUC and TSS, suggesting its relatively high dependency on the sampling size compared to others. All the models showed pAUC larger than 1, consistent with other metrics showing a good model performance. In this metric, the MLP showed the lowest value, while the MaxEnt and RF showed similarly high values. When considering the number of occurrence data that differed by species in the same model, the AUC of the MaxEnt model for _A. gracilipes_ was larger than that for _S. invica_, whereas MLP exhibited a  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & \multicolumn{3}{c}{_S. invica_} & \multicolumn{3}{c}{_A. gracilipes_} \\ \hline
**Model** & **MaxEnt** & **RF** & **MLP** & **MaxEnt** & **RF** & **MLP** \\ \hline AUC & 0.949 & 0.939 & 0.911 & 0.967 & 0.940 & 0.894 \\ pAUC & 1.960 & 1.970 & 1.930 & 1.930 & 1.930 & 1.800 \\ TSS & 0.923 & 0.879 & 0.815 & 0.906 & 0.882 & 0.783 \\ Accuracy & 0.951 & 0.941 & 0.907 & 0.930 & 0.940 & 0.890 \\ Sensitivity & 0.973 & 0.941 & 0.906 & 0.977 & 0.910 & 0.944 \\ Specificity & 0.949 & 0.938 & 0.909 & 0.929 & 0.972 & 0.839 \\ \hline \hline \end{tabular}  
* RF: Random Forest, and MLP: multi-layer perceptron.  
\end{table}
Table 2: Model performance metrics for both ant species.  
higher AUC in the _S. invicta_ model than in _A. gracilipes_. Thus, AUC may be affected by the type of model rather than the sample size, whereas the MPL model had a relatively high dependency on the sample size. Both MaxEnt and MLP models exhibited higher TSS in the _S. invicta_ model than in the _A. gracilipes_, suggesting that TSS was more sensitive to sample size compared with AUC and pAUC. Random forest exhibited consistent values for AUC and TSS for each species; thus, the difference between AUC and TSS was almost constant. A similar consistency was also shown in the pAUC and accuracy, indicating relatively higher robustness for the sample size of the RF model compared with other models.  
Overall, MaxEnt showed the best performance for both species, whereas RF resulted in consistent results even with a small sample size. In addition, larger datasets for MLP model training may lead to improved model performance, but at present, lower performance compared to the other models was demonstrated.
# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 2 Related Work
### Transfer Learning  
The main idea of transfer learning is to transfer labeled data or knowledge structures from related domains to accomplish or improve the learning of the target domain or task. One of the main assumptions in traditional machine learning algorithms is that training and test data must be in the same feature space and have the same distribution. Transfer learning relaxes the basic assumption that training and test data may be in different feature spaces or follow other data distribution [20]. Specifically for the target detection task of this paper, the task of the source domain is defined as the target detection based on the sizeable visible dataset ImageNet, and the task of the target domain is defined as the target detection based on the small infrared dataset FLIR. Since both infrared imaging and visible imaging are similar, they collect target information for imaging through optical systems. The migration learning approach can be used to initialize the parameters of the infrared image target detection model with the pre-trained model on the visible image dataset. Eventually, the model can be fine-tuned and trained using the infrared image dataset.
## 4 Experiment  
### Metrics  
To assess the quality of the EIT image-reconstruction method proposed in this study, we compared the EIT effect of this algorithm with those of four widely used EIT image-reconstruction algorithms. The evaluations of these algorithms are based on three parameters: the peak signal-to-noise ratio (\(PSNR\)), structural similarity index measure (\(SSIM\)), and learned perceptual image patch similarity (\(LPIPS\)).
## 5 Results and Discussion
### PSD Performance  
Figure 8 depicts a comparison of the PSD performances for the MB-Hadamard precoding under various band numbers \(L\). As shown in the figure, the rectangular PSD characteristics of the DMT are maintained, which indicates that the MB-Hadamard precoding neither affects the signal PSD nor damages the spectral efficiency. Furthermore, the sideband spectrum is reduced, which is advantageous for hybrid signal transmissions in short range POF systems.  
Figure 6: Complementary cumulative distribution function (CCDF) performance comparison.
## 2 Methodologies
### Collision Avoidance Algorithm
Here, the \(E\) frame is not defined during take-off and landing because the CA algorithm is not activated during these phases. Then, the relative angle \(\rho_{h}\) defined in the horizontal plane is shown in Figure 3, and \(\rho_{v}\) is defined in the vertical plane, as displayed in Figure 3. Note that \(\rho_{h}\) and \(\rho_{v}\) provide the relative location of the obstacle from the quadcopter with respect to the horizontal and vertical planes, respectively. Based on this information, the rotation matrices to determine the avoidance direction are obtained by the criteria listed in Table 1.  
In the formulation of the proposed algorithm, there are several free parameters that need to be determined by a user. In particular, \(\alpha\) and \(\gamma\), which are the newly introduced parameters in this paper, are critical because they highly affect the performance of the proposed approach. Here, \(\alpha\) plays a role in determining the portion of each avoidance direction. For example, when \(\alpha=1\), the quadcopter only performs a horizontal avoidance maneuver, and when \(\alpha=0\), it only flies on a vertical plane. When the quadcopter's operation altitude is less than the height of buildings (i.e., obstacles to the quadcopter) in urban areas, the horizontal avoidance maneuver is sufficient. However, when the quadcopter is operating near building tops, a combination of the horizontal and vertical maneuvers is preferable since the horizontal maneuver only may require a longer trajectory than the combined maneuvers. Therefore, \(\alpha\) needs to be properly determined between \(0\) and \(1\). In addition to this, \(\gamma\) directly affects the direction of \(\mathbf{f}_{r,e}\), as shown in Figure 2. When \(\gamma=0\) deg, the proposed algorithm is performed as the same with the conventional approach. When \(\gamma\) is greater than \(90\) deg, \(\mathbf{f}_{r,e}\) (i.e., the repelling direction) is in the direction of the obstacle. This means that \(\mathbf{f}_{r,e}\) does not play a role in repelling the quadcopter anymore. Hence, \(\gamma\) is needed to be selected between \(0\) and \(90\) deg. These two parameters will be analyzed in the next section. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Relative Angle** & **Rotation Matrix** & **Remark** \\ \hline Horizontal plane & \(\rho_{h}\geq 0\) & \(R_{h}=R_{3}\) & \(R_{3}=\begin{bmatrix}\cos\gamma&-\sin\gamma&0\\ \sin\gamma&\cos\gamma&0\\ 0&0&1\end{bmatrix}\) \\ Vertical plane & \(\rho_{v}\geq 0\) & \(R_{v}=R_{2}\) & \(R_{2}=\begin{bmatrix}\cos\gamma&0&\sin\gamma\\ 0&1&0\\ -\sin\gamma&0&\cos\gamma\end{bmatrix}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Criteria for rotation matrices to determine the avoidance direction. Figure 3: Definitions of (**a**) \(E\) frame and relative angles between the quadcopter and the nearest obstacle in (**b**) horizontal and (**c**) vertical planes.
# Architectural Design for Digital Twin Networks
## 6 Tackling Challenges in DTN Implementation
### Data Modeling  
Data modeling is naturally tied to the challenge of scalability and essentially comprises the hybrid approaches displayed in Figure 7. As highlighted in the scalability discussion, combinations of methods must be employed when the network becomes too complex for simulation, mathematical representation, or even ML-based solutions alone.  
One particularly important and interesting aspect of data definition for the DTN is its ability to track data historically. While this historical tracking is not mandatory, it is both convenient and highly beneficial, especially in this case. It allows for the creation of additional environments where new data modeling techniques can be tested and validated against the same datasets as existing models. This capability significantly accelerates the development of increasingly complex data models while ensuring they are reliably validated. Furthermore, it enhances the testing and research phases, particularly in developing improved hybrid approaches.
# A Brief Survey of Fixed-Parameter Parallelism
## 7 Parallel Algorithms Using FPT Methods
### Iterative Compression  
Iterative compression is inherently serial, as elements of the input are added one at a time. Therefore, escaping the \(n\) factor in the time complexity of a parallel algorithm would be impossible in classical iterative compression algorithms regardless of the number of processors used.  
A workaround to escape the \(n\) factor is to apply some approximation scheme before the actual search for a solution begins. Assume there exists some \(\rho\)-factor approximation algorithm [38] (for any constant \(\rho\)) for a problem which we require a solution of size at most \(k\) for (maximization problems also apply similarly). Applying this algorithm will produce a solution of size \(t\), knowing that \(t\leq\rho*OPT\) (where \(OPT\) is the minimum possible solution size). If \(t>\rho*k\), then the instance is a No-instance since \(OPT>k\) in this case.  
After this simple trick, we can start with a (feasible) solution of size at most \(\rho*k\), and our objective is to reduce it to a solution of size at most \(k\).  
Let us consider Vertex Cover as an example, and use the well known factor-two approximation algorithm, based on maximal matching, to obtain a solution of size at most \(2k\). For every vertex in the solution, it may either remain in the solution, or it leaves the solution with all its neighbors being inside the solution. It is possible to generate all \(O(2^{2k})\) choices, and, for each, check in \(FPP\)-time whether it results in a valid solution of smaller size. Observe that all vertices that left the solution will be removed from the graph while all their neighbors are marked in the solution and deleted as well. There will be no more than \(2k\) removals, and no more than \(k\) vertices can be in the solution, so all the described changes can be performed in \(FPP\)-time. Every potential solution can be verified in \(O(k)\) time using \(O(m)\) processors (where \(m\) is the number of edges in the graph) simply by checking whether at least one edge endpoint is among the \(k\) vertices.  
This demonstrates how iterative compression, an inherently serial approach, can be made \(FPP\) by making use of an initial approximate solution. The key is that, once an element is removed from the solution, some other set of elements is forced to be in the solution.
## 3 Methodology
### Identification Method of Lane Change Trajectory Data  
Vehicle operation trajectories are drawn according to the trajectory data set, and the entire lane-changing process of the vehicle is realized by marking the three crucial time nodes during the lane-changing process. The first time node, which belongs to the end of the lane change preparation stage and the start of the lane change execution stage, is the moment when the \(x\) coordinate of the vehicle changes greatly and is marked as A; the second time node, which is the end of the lane change execution stage and the start of the lane change adjustment stage, is the occurrence of the change in the ID of the lane where the driving vehicle is located and is denoted as B; the third time node, which is the end of the lane change adjustment stage, is the moment when the x coordinate of the driving vehicle tends to be stable and is denoted as C. Figure 2 is a schematic diagram of different stages and time nodes of the lane-changing procedure.  
The three time points shown in Figure 2 divide the lane-changing behavior of vehicles into three phases. It is important to master the data characteristics of different phases. Here, MATLAB software (MATLAB version: 9.8.0.1323502 (R2020a); Creator: MathWorks; Location: America) is used to extract and process the trajectory data in the following steps.  
Step 1: Extract all vehicle IDs that only change lanes once and store the trajectory data.  
Step 2: Take the lane ID as the unit, store the trajectory data of the same vehicle before and after the lane-changing behavior, and record the last vehicle information after the lane-changing behavior as point B. Point B is at the end of the execution stage and the start point of the adjustment stage.  
Step 3: Determine the end point of the lane change preparation stage, and record the end point as point A. The time period before A belongs to the lane change preparation stage, and the time period between A and B belongs to the lane change execution stage.  
Step 4: Determine the end points of the lane change adjustment stage, and record the end point as point C. The time period between B and C belongs to the lane change adjustment stage.  
After the time nodes at different stages of the vehicle lane-changing behavior are calibrated through the above identification steps, the corresponding lane-changing data can be extracted to provide a basic data set for research on lane-changing behavior.
## 3 Results and Discussion
### Stress Transfer Mechanism
#### 3.4.2 Intermediate Flange (\(\sim\)D\({}_{\text{b}}<L_{\text{f}}<L_{\text{s}}\))
After the superposition period is over, the delayed **F** wave enters the incident bar for the first time to form the first-transmitted \(\bf R_{n}\) (\(\bf R_{1}\)) wave. Indeed, there are also second and third \(\bf R_{n}\) waves transmitted (i.e., \(\bf R_{2}\) and \(\bf R_{3}\), respectively) for the intermediate flanges after the \(\bf R_{1}\) wave. The magnitudes of \(\bf R_{1}\), \(\bf R_{2}\), and \(\bf R_{3}\) waves were previously calculated when long flanges (\(L_{\rm f}\) = 30\(D_{\rm b}\)) were considered. The results are marked in Figure 4b (202.36, 67.45, and 22.48 MPa) and Figure 4c (121.42 and 24.28 MPa), respectively. The magnitude of the pseudo-one-step pulse was checked in the previous section using Equation (1). Its pulse duration period (\(\Delta t=2L_{\mathrm{s}}/C=0.116\) ms) is marked in Figure 4c. The calculated result (mark) is reasonably consistent with the FWHM value of the pseudo-one-step pulse.