esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Most researchers have focused on developing new methods for the future or improving basic implemented models to identify the optimal standalone feature set. Our research focused on four ideas. The first introduces a future communication model, while the others are improvements of old models or methods. (1) A new communication imagery model instead of a speech imager using a mental task: speech imagery is very difficult, and it is impossible to imagine the sounds of all characters in all languages. Our research introduces a new mental task model for all languages called lip-sync imagery. This model can be used for all characters in any language. This study was the first to use lip syncing for two sounds or letters. (2) New combinations of signals: Selecting an unsuitable frequency domain can result in inefficient feature extraction. Therefore, domain selection is important for processing. The combination of limited frequency ranges provides a preliminary method for creating a fragmented continuous frequency. For the first model, two intervals of 4 Hz were examined as filter banks and tested. The primary purpose was to identify the combination of filter banks with 4 Hz (scale of each filter bank) from the 4 Hz to 40 Hz frequency domain as new combinations of signals (8 Hz) to obtain good and efficient features by increasing distinctive patterns and decreasing similar patterns of brain activities. (3) A new bond graph classifier to supplement the SVM classifier: When linear SVM is used with very noisy data, the performance is decreased. However, we introduce a new linear bond graph classifier to supplement the linear SVM for noisy data. (4) A deep formula detection model: The data in the first layer are directly converted into formula coefficients (formula extraction model with Lagrange formula). The main goal is to reduce the noise in the subsequent layers for the coefficients of the formulas. From the second layer to the last layer, by using different functions, the effective formula coefficients (differentiation formulas between classes) are extracted so that the coefficients that have the most similarities between classes are removed in different layers. The effective coefficients of the formula are sent to the classifier of the new model to extract the roots of the members and identify the common roots of the class. The results range between 55% and 98%. The lowest result is 55% for the deep detection formula, and the highest result is 98% for the new combination of signals.  
A.N. conceived of the ideas. [PERSON] helped to complete the ideas. A.N. performed the numerical simulations. Z.F. validated the results. A.N. wrote the original draft. [PERSON] and A.N. edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61876147.  
Datasets are public datasets. Lip-Sync data are unavailable due to privacy.  
The authors declare no conflict of interest.
# Grayscale Iterative Star Spot Extraction Algorithm Based on Image Entropy
## 7 Experiment and Analysis
From Figure 10, it can be quantitatively observed that the number of correctly extracted star spots by the max-mean and max-median filtering algorithms is notably lower compared to the Top-Hat and the IEGI algorithms under the three practical conditions depicted in Figure 8. Among them, the IEGI algorithm extracted the highest number of correct star spots, with 93, 67, and 87, respectively. From Figure 11, it can be quantitatively observed that the Top-Hat algorithm exhibits a substantially higher number of false star spots compared to the other three detection algorithms. The number of false star spots in the other three algorithms is relatively small. It can be seen from Table 7 that only the results extracted by the IEGI algorithm indicate a high detection rate while maintaining a low error rate. From Figures 10 and 11, and Table 7, it can be concluded that although the max-mean filtering algorithm and the max-median filtering algorithm have a small number of false star spots in their extraction results, they extract fewer correct star spots. On the other hand, the Top-Hat algorithm, while extracting a number of correct star spots close to the IEGI algorithm, has a significant number of false star spots. Among the four \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Max-Mean**} & \multicolumn{2}{c}{**Max-Median**} & \multicolumn{2}{c}{**Top-Hat**} & \multicolumn{2}{c}{**IEGI**} \\ \cline{2-9}  & **Detection Rate** & **Error Rate** & **Detection Rate** & **Error Rate** & **Detection Rate** & **Error Rate** \\ \hline (a) & 7.78 & 2.22 & 11.11 & 5.56 & 46.11 & 141.11 & 51.67 & 10.56 \\ (b) & 10.26 & 1.71 & 13.68 & 6.84 & 48.72 & 53.85 & 57.27 & 3.42 \\ (c) & 13.33 & 0 & 14.00 & 2.67 & 39.33 & 33.33 & 58.00 & 6.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detection rate and error rate by different algorithms under different conditions. Figure 11: The number of false star spots detected by different algorithms under different conditions. algorithms, only the IEGI algorithm is able to achieve a large number of correctly extracted star spots while maintaining a small number of false star spots. This is consistent with the qualitative analysis results in Tables 5 and 6. In conclusion, for the three different practical conditions depicted in Figure 8, the IEGI algorithm extracts the largest number of correct star spots with only a small number of false star spots. Under various real-life stray light conditions, the algorithm proposed in this paper demonstrates superior star spot detection performance compared to the other three detection algorithms, making it highly valuable for engineering applications.
# Verifying Mutual Exclusion Algorithms with Non-Atomic Registers
## 3 Uppaal Modeling with Atomic Registers
### Checking Algorithm's Correctness
#### 3.2.5 Liveness-2  
A process in NCS does not forbid other processes to enter their CS if the following query is satisfied:  
E<> exists(i:pid)Process(i).NCS && exists(j:pid)Process(j).CS  
From the model checking of the Uppaal model in Figure 1 it emerged that the solution is correct from all the mutual exclusion properties. Properties were verified both when NCS is normal (an arbitrary time can elapse before the next competition of the process) and in the case the NCS location is urgent (a process immediately re-enters after complete access to its CS). The linear overtaking \(ov=N-1\) was confirmed. In the case NCS is urgent, and \(N=5\), the suprema query terminates after 792 s with a memory usage of 12.6 GB, on a Win11 Pro desktop platform, Dell XPS 8940, Intel i7-10700 (8 physical cores), [EMAIL_ADDRESS] GHz, and 32 GB RAM, using version 5 of Uppaal 64 bit.  
The Algorithm 2 was refined in [25] by using only three output bits per process (_a w s: a_ active _w_a_ waiting shutting). The waiting room has only the input door. An active process wants to enter the waiting room. While waiting, further processes signaling they would also enter are allowed to gain the waiting room. Otherwise, the door is shut. Processes exit the waiting room according to their IDs. This algorithm too, with the correction noted in [31], was modeled by our proposed Uppaal-based method with atomic memory. Model checking revealed that for \(N>2\), the model has no deadlock and admits a linear overtaking, but mutual exclusion can be violated. That is, multiple processes can enter their CS simultaneously.  
The Uppaal model shown in Figure 2 was derived from the algorithm in [25] by tailoring it to \(N=2\) processes and by modifying the condition for abandoning the busy-waiting in P9. The current process is denoted by \(i\) and its partner by \(j=3-i\). The informal algorithm behind Figure 2 can be easily deduced from the model.  
The model in Figure 2 is a fully correct mutual exclusion solution (see also Figure 3). It will be used later in this paper as the arbitration unit in a tournament tree organization [11; 14; 26; 27] for \(N\geq 2\) processes, under both atomic and non-atomic registers.  
Figure 2: The Szymanski algorithm in [25] for Process(\(i\)), adapted for \(N=2\) processes.
# A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector
## 4 Use Cases
### V2X Research  
The proposed platform has been also used to extend the experiments presented in [21]. In this paper, a V2X communications architecture is proposed for off-road autonomous vehicles; however, all experiments are carried out in golf cart-like vehicles, with a limited maximum speed. For this reason, several additional experiments have been carried out using the proposed platform in order to further validate the V2X architecture in an actual car moving at medium and high speeds.  
As described in Section 3.9, the vehicle has Internet connection capabilities through a 4G router. This connection is used to connect to a VPN, as introduced in [21], which is only accessible to specific permitted agents (other vehicles, infrastructure or pedestrians). This VPN grants an extra security layer to perform the communication, regardless of the distance between the agents, which makes it a viable option for communicating at high speeds.  
In order to validate the proposed V2X architecture in this system, two new scenarios have been created. In each scenario, the presented vehicle is moving while communicating with a static golf cart. The information shared between each vehicle consists of position, orientation and vehicle status, and it is transmitted at 20 Hz. The new experimental scenarios are detailed below:  
* **Highway:** The vehicle is moving on a highway at an average speed of 100 Km/h;
* **Urban:** The vehicle is moving in urban and peri-urban areas at an average speed of 45 Km/h.  
Following the original metrics used in [21], Figure 14 shows the round-trip time (RTT) of the network packets that are exchanged between vehicles in each scenario.  
Figure 13: Experimenting with different human–machine interfaces (HMIs).  
As expected, the number of outliers increases when moving at higher speeds. Due to the use of 4G, the handover between access points may also influence this high number of outliers. Nevertheless, the average RTT values, presented in Table 1, are below 100 ms, which is adequate for non critical messages.  
In addition to the average RTT values, Table 1 also shows the interquartile range (IQR) of the data and the maximum RTT values obtained for each scenario. The high maximum values, along with the rest of the outliers, are mainly due to lost packets that must be re-transmitted, thus producing a higher RTT value. This problem does not affect continuous, non-critical flows of information, such as localization or images, because those streams do not require re-transmission when a packet is lost. Instead, the next (and newest) message is sent.
# Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior
## 1 Introduction
However, the edge smoothing technique of local filtering suffers from halo artifacts, so the improved algorithms [16; 17; 18; 19; 20; 21] were proposed for this problem. (2) If the image scene contains a large area of the sky or a bright white object, the dark channel prior will invalid, resulting in severe distortion of the restored image. To improve this phenomenon, [PERSON] et al. [22] estimated the transmission map of the sky and non-sky areas and then combined it with the refined transmission map to remove the haze. Although the visibility of the sky area can be improved, this approach generally reduces the recovery performance among adjacent boundaries. [PERSON] et al. [23] proposed a large sky region detection algorithm based on SVM classification, which uses two different strategies to obtain more accurate atmospheric light according to its detection results. Finally, the multiscale open dark channel model is used to adaptively calculate the dark channel for dehazing. [PERSON] et al. [24] proposed saliency prior to hazy imaging, which can distinguish white objects from dense haze by saliency detection. On the basis of the saliency prior, both an accurate airlight and a correct transmission map can be obtained from images containing large white objects, and finally, these images can be restored successfully. In addition, some authors have proposed tolerance mechanisms to correct the transmission of false estimates for such bright regions. The tolerance mechanism mainly refers to using this as a threshold to judge whether there is a bright area in the haze image and then performing segmentation processing on the sky and other areas of the image to achieve accurate correction of the transmittance image [25]. However, the tolerance values in their algorithms are generally the best or empirical values obtained by trial and error. In this paper, an adaptive tolerance mechanism is studied to adaptively determine the tolerance value according to the characteristics of the image to achieve the best dehazing effect. The simulation results showed that the improved algorithm greatly improves the dehazing performance. Not only is the restored image clear and natural, but the useful information in the image is not lost. The remainder of this paper is organized as follows. Section 2 first introduces the dark channel prior dehazing algorithm and its defects. Then, in Section 2.4, we describe the tolerance mechanism algorithm to solve the defect of the dark channel prior and its deficiency. In Section 3, we describe the details of the improved algorithm in this paper. In Section 4, we present and analyze the experimental results. Finally, we summarize this paper in Section 5.
# Accumulative Roll Bonding--A Review
## 4 Properties
### Damping  
Having both high damping capacity and high strength is important for structural materials that are subjected to dynamic loading conditions; nonetheless, an increase in damping is usual accompanied by a decrease in strength. It is evident that the SPD produces high strength metals through producing UFG microstructures, furthermore, severely deformed materials typically contain innumerable lattice defects giving rise to internal friction or material damping. Having a high density of grain boundaries formed by SPD is another reason for UFGed materials to have high damping properties.  
[PERSON] et al. [119] investigated the damping capacity of ARBed UFG Al samples where a torsional pendulum apparatus was used to measure the internal friction values versus the strain amplitudes. Both ARBed pure Al sheets exhibited high damping capacity as well as high strength attributing to the vibration of the dislocations whose ends are tightly bonded with grain boundaries of the ultrafine grains and hence could attenuate vibrational energies. [PERSON] et al. [120] explored the damping behavior of ARBed Al/SiC\({}_{\text{p}}\) multilayer composites and extracted the first-mode damping capacity as a function of amount of reinforcing particles(Figure 17). Increasing the amount of reinforcement led to increased damping capacity and that the obtained values of the reinforced samples were greater than monolithic Al6061 alloy specimens due to internal friction caused by SiC and its accompanying modification effects on the matrix microstructure. Based on the microstructural studies, a lot of holes, detachments, weak imperfect bonding were observed at the interface between the reinforcements and the host metal matrix, all contributing to damping capacity enhancement [121]. It was also found that increasing ARB cycles, the number of layers, thickness reduction ratios and temperatures leads to better damping capacity [120].  
[PERSON] et al. [121] studied the damping capacity of ARBed Al/LLZNO\({}_{\text{p}}\) composites at different temperatures where the damping capacity improved when the ARB cycles increased reaching its highest value after 10 ARB cycles at 320 K, being 4.5 times higher than that of monolithic samples. They attributed the increase in damping capacity to the uniform distribution of LLZNO particles and better bonding strength between the particles and the matrix material.
# Variability of Interpolation Errors and Mutual Enhancement of Different Interpolation Methods
## 2 Materials and Methods
### Research Methodology
#### 2.2.5 Global Weighted Fusion  
Different datasets exhibit varying degrees of accuracy, which impact their contribution to the fusion process, thereby affecting the results. The optimal fusion ratio can be determined by applying appropriate weights, leading to the best results. Image fusionweights are derived from the original data errors, and the data are fused accordingly [47]. The formula for this process is as follows:  
\[w=\frac{\sigma_{2}^{2}-p\sigma_{1}\sigma_{2}}{\sigma_{1}^{2}+\sigma_{2}^{2}-2p \sigma_{1}\sigma_{2}} \tag{6}\]  
\[y_{2}=wf_{1}+(1-w)f_{2} \tag{7}\]  
where \(w\) is the fusion weight (ranging from 0 to 1), \(\sigma_{1},\sigma_{2}\) are the standard deviations of the fused images, and \(p\) is the correlation coefficient between the errors of the two images. \(f_{1},~{}f_{2}\) represents the original data before fusion.
## 4 Image Processing Methods of the Mode Shape
### Image Gradient and Binarization  
After obtaining the energy distribution area and the main vibration direction of a mode, it is necessary to estimate the maximum vibration area of this mode in order to determine the installation location of the transducer when exciting this mode. To deal with this issue, the binarization method of the image processing is used by setting a maximum pixel threshold. The pixel value is set to 1 if it is greater than the threshold and 0 if it is less than the threshold. The pixel value of the non-rail area is set to 0 in order to eliminate disturbances. The outline of the rail is plotted by the edge extraction method of the image gradient. The gradient edge extraction and binarization image of mode 3 are shown in Figure 15.  
After binarization, it can be seen that the maximum vibration point of mode 3 is in the rail web center. Therefore, in order to excite mode 3, a transducer is installed in the center of the rail web and excited in the \(y\) direction.  
In the same way, mode 7 is binarized. As shown in Figure 16, the maximum vibration point of mode 7 is located on both sides of the rail head in the \(z\) direction.
## 3 Simulation Results
### Design of the Intermediate Cavity  
The intermediate cavity has been designed with the initial dimensions from the analytical formulas provided in Section II. The dimensions of the cavity have been optimized using electromagnetic simulation tools (eigenmode solver) to achieve the cavity parameters (the operating frequency and mode of operation matching the specification) for the desired performance of the klystron. A schematic diagram showing the geometrical parameters of the cavity used for optimization is shown in Figure 2.  
Figure 3 shows the cut-plane view of the electric field distribution of the operating mode inside the proposed intermediate cavity while Figure 4 shows the cut-plane view of the magnetic field pattern of the same. The frequency of the operating mode is found to be 28.53 GHz from the CST Microwave studio simulations, when taking the gap as 0.27 mm and the drift tube radius as 0.18 mm. Moreover, the shunt impedance of the operating mode is obtained to be 35.07 M\(\Omega\), Q = 720 and the R over Q is 48.7 according to the microwave studio calculations.  
Figure 3: Electric field distribution of the 2\(\pi\) mode (cut-plane view) for the intermediate cavities with CST.  
Figure 2: RF cavity considered for simulation: ladder-type multibeam EIK intermediate cavity.  
To validate the simulation results, the cavity has been simulated using ANSOFT HFSS. All the parameters, e.g., the resonant frequency, Q, and electric and magnetic field distribution of the operating mode of the proposed RF cavity, have been obtained from the HFSS simulation.  
Figure 5a shows the electric field distribution of the cavity, and Figure 5b shows the magnetic field distribution of the proposed cavity. The frequency of the mode is found to be 28.52 GHz, and the quality factor is also found to be 708 from the HFSS simulations. The final optimized geometrical design parameters of the intermediate cavity for the Ka-band are given in Table 2, and the simulated results are shown in Table 3.  
Figure 4: Magnetic field distribution of the 2\(\pi\) mode (cut-plane view) for the intermediate cavities with CST.  
Figure 5: Electric field and magnetic field distribution of the intermediate cavities with HFSS. (a) E-field distribution; (b) M-field distribution.
# Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires
## 5 Comparative Analysis
### Energy Consumption in EERP  
In EERP, the energy spent by an ordinary node is represented by \(\text{E}_{\text{ON}\_\text{EERP}}\) and is calculated as shown in Equation (10).  
\[\text{E}_{\text{ON}\_\text{EERP}}=\text{E}_{\text{SE}\_\text{INF}}+\text{E}_{ \text{RE}\_\text{BS-SEL}}+\text{E}_{\text{SE}\_\text{JON}}+\text{E}_{\text{ RE}\_\text{BS-FRM}}+\text{E}_{\text{RE}\_\text{BS-SCH}}+\text{E}_{\text{RE}\_ \text{CH}\text{SCH}}+\text{E}_{\text{SE}\_\text{DATA}} \tag{10}\]  
Here, \(\text{E}_{\text{SE}\_\text{INF}}\) is the energy consumed in communicating the information message to the BS. \(\text{E}_{\text{RE}\_\text{BS-SEL}}\) is energy spent in receiving the CH selection message from BS. \(\text{E}_{\text{SE}\_\text{JON}}\) is the amount of energy spent in sending a JOIN_REQ message to its CH. \(\text{E}_{\text{RE}\_\text{BS-FFM}}\) represents the energy consumed in receiving frame information from BS. \(\text{E}_{\text{RE}\_\text{BS-SCH}}\) denotes the energy spent in receiving the inter-cluster communication schedule from BS. \(\text{E}_{\text{RE}\_\text{CH-SCH}}\) denotes the energy spent in receiving the intra-cluster communication schedule from CH. \(\text{E}_{\text{SE}\_\text{DATA}}\) denotes the energy spent in sending data to its CH. The energy consumed by the CH is denoted by \(\text{E}_{\text{CH}\_\text{EERP}}\) and is calculated by using Equation (11).  
\[\text{E}_{\text{CH}\_\text{EERP}}=\text{E}_{\text{SE}\_\text{INF}}+\text{E}_{ \text{RE}\_\text{BS-SEL}}+\text{E}_{\text{RE}\_\text{JOIN}}+\text{E}_{\text{ RE}\_\text{BS-FFM}}+\text{E}_{\text{RE}\_\text{BS-SCH}}+\text{E}_{\text{BR}\_\text{CH} \text{SCH}}+\text{E}_{\text{SE}\_\text{DATA}} \tag{11}\]  
Here, \(\text{E}_{\text{BR}\_\text{CH}\text{-SCH}}\) is the energy consumed by the CH in broadcasting the schedule in its cluster. \(\text{E}_{\text{SE}\_\text{DATA}}\) is the energy spent in sending the data to its upstream CH or BS. \(\text{E}_{\text{EERP}}\) denotes the total energy spent in the cluster in an EERP frame and is calculated by using Equation (12). Here, \(\text{E}_{\text{AON}\_\text{EERP}}\) denotes the energy spent by all the ordinary nodes in the cluster.  
\[\text{E}_{\text{EERP}}=\text{E}_{\text{AON}\_\text{EERP}}+\text{E}_{\text{CH} \_\text{EERP}} \tag{12}\]  
The proposed model saves more energy compared to LEACH, ES-MAC, and EE-MAC. EERP considers three parameters to do its job. The TE is compared with the RE of a node to decide if it can be selected as the new CH. In the case of a CH, it is decided whether the CH can continue to function as the CH of the cluster. The DE ensures that only the nodes within the proximity of an event report the event. The VP ensures that idle listening in CHs is reduced.