esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mechanism Analysis for Active Control
### On the resonant frequency of the ribbed plate B
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{Cavity modes}} & \multicolumn{1}{c}{\(1\)} & \multicolumn{1}{c}{\(2\)} & \multicolumn{1}{c}{\(3\)} & \multicolumn{1}{c}{\(4\)} & \multicolumn{1}{c}{\(5\)} & \multicolumn{1}{c}{\(6\)} & \multicolumn{1}{c}{\(7\)} & \multicolumn{1}{c}{\(8\)} & \multicolumn{1}{c}{\(9\)} & \multicolumn{1}{c}{\(10\)} \\ \cline{3-11} \multicolumn{1}{c}{} & \multicolumn{1}{c}{(0,0)} & (1,0,0) & (0,1,0) & (1,1,0) & (2,0,0) & (2,1,0) & (0,2,0) & (3,0,0) & (1,2,0) & (3,1,0) \\ \hline \multicolumn{11}{c}{} & \multicolumn{1}{c}{**Case 4**} \\ \multirow{3}{*}{(1.2)} & \multicolumn{1}{c}{Before control} & \(0.01\times 10^{-6}\) & \(0.07\times 10^{-7}\) & \(0.21\times 10^{-6}\) & \(0.02\times 10^{-6}\) & \(0.09\times 10^{-7}\) & & \\  & \([-13\%]^{-1}\) & \([-13\%]^{-1}\) & \([-70\%]^{-1}\) & \([-13\%]^{-1}\) & \([4\%]^{-1}\) & & & \\ \cline{2-11}  & \multirow{2}{*}{After control} & \(0.15\times 10^{-7}\) & \(0.14\times 10^{-7}\) & \(0.36\times 10^{-7}\) & \(0.45\times 10^{-7}\) & \(0.15\times 10^{-7}\) & & & \\  & & \([-140\%]^{-1}\) & \([-13\%]^{-1}\) & \([38\%]^{-1}\) & \([-143\%]^{-1}\) & & & & \\ \hline \multirow{3}{*}{(3.1)} & \multicolumn{1}{c}{Before control} & \(0.03\times 10^{-7}\) & \(0.01\times 10^{-7}\) & \(0.44\times 10^{-7}\) & \(0.05\times 10^{-7}\) & \(0.02\times 10^{-7}\) & & \\  & \([58\%]^{-1}\) & \([22\%]^{-1}\) & \([10\%]^{-1}\) & \([40\%]^{-1}\) & \([-12\%]^{-1}\) & & & \\ \cline{2-11}  & \multirow{2}{*}{After control} & \(0.42\times 10^{-8}\) & \(0.22\times 10^{-8}\) & \(0.75\times 10^{-8}\) & \(0.91\times 10^{-8}\) & \(0.38\times 10^{-8}\) & & & \\  & & \([55\%]^{-1}\) & \([16\%]^{-1}\) & \([-143\%]^{-1}\) & \([35\%]^{-1}\) & \([-126\%]^{-1}\) & & & \\ \hline \multirow{3}{*}{(2.2)} & \multicolumn{1}{c}{Before control} & \(0.03\times 10^{-7}\) & \(0.02\times 10^{-7}\) & \(0.61\times 10^{-7}\) & \(0.07\times 10^{-7}\) & \(0.02\times 10^{-7}\) & & \\  & \([-13\%]^{-1}\) & \([-12\%]^{-1}\) & \([-10\%]^{-1}\) & \([-13\%]^{-1}\) & \([4\%]^{-1}\) & & & \\ \cline{2-11}  & \multirow{2}{*}{After control} & \(0.04\times 10^{-7}\) & \(0.04\times 10^{-7}\) & \(0.10\times 10^{-7}\) & \(0.13\times 10^{-7}\) & \(0.04\times 10^{-7}\) & & & \\  & \([-142\%]^{-1}\) & \([-130\%]^{-1}\) & \([38\%]^{-1}\) & \([-140\%]^{-1}\) & \([41\%]^{-1}\)
## 5 Discussion
Here, comfort is set as a default as long as the controller trigger is not pressed, whereas any discomfort can be expressed through the magnitude of how much the controller trigger is pressed. With this method, participants are required to press the controller trigger to mark a situation and to specify the rating for the driving behavior at the same time. This could lead to the following problems. If participants take more time to think about the situation and their rating, the time mark moves away from the respective situation. If participants press immediately to mark the corresponding situation, but want to adjust their rating, it complicates the interpretation of the data or makes it even impossible in extreme cases. Considering these aspects could also explain the binary usage of the controller trigger in [36]. Here, it was observed that the value range of the hand controller was not fully utilized. The participants either did not press at all or pressed the trigger to the full extent. The hand controller may, therefore, have been used to mark existing discomfort rather than to express its magnitude. On the basis of the three aspects mentioned above and the experiences in [36], a marker was set when participants begin to give their verbal evaluation, marking the evaluated situation in the data, but giving unlimited time for evaluation and reasoning. A preliminary test study had already shown that participants also needed up to a minute to evaluate a situation and occasionally corrected them while talking about their reasoning. The verbal feedback also makes it easier to collect different evaluation criteria for the same situation, as in this case comfort and safety. Due to the free choice and weighting of the evaluation criteria, participants were enabled to evaluate not only discomfort but also comfort. The utilization of the entire scale shows that participants were able to not only distinguish between comfort and discomfort in a binary way, but also perceive and evaluate gradations of both characteristics. However, most of the ratings are above zero, which might be explained by the cautious driving style of both the automated system and the safety driver, who generally avoided safety-critical driving behavior. In addition to the aspects of the data collection methodology, the presence of a safety driver must also be taken into account. Although the participants were instructed to evaluate each situation as if they are in the driver's seat and alone in the test vehicle, it cannot be ruled out that the presence of a safety driver has nevertheless contributed to a subconscious increase in the feeling of security. Furthermore, it must be taken into account that the seating position of the test person on the passenger side can also have an impact on the evaluation in some situations. Examples include the lateral positioning within the lane and lateral distances to surrounding vehicles or static environmental objects. For the majority of rated situations, however, the seating position is considered as neglectable.
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 2 Related Work
### Routing Protocols Based on Hierarchy
The proposed protocol LEACH-KANG consisted of the Kangaroo Method-based adaptive routing protocol and LEACH protocol, which utilized the optimal path from CHs to BS to transmit data. The proposed protocol extended the network life-cycle and saved energy. The paper [33] analyzed the LEACH-based protocols and compared their performances in detail. The comparison results were involved in the network life-cycle, energy consumption, CH selection and network type. Finally, the authors summarized some characteristics of LEACH-based protocols. [PERSON] et al. [34] proposed a novel LEACH protocol, i.e., CH-LEACH (Clustering Hierarchy LEACH) to reduce energy consumption. The architecture and principle of the protocol were introduced. Then, the simulation results were given based on energy efficiency and network life-cycle. The authors in [35] focused on security issues of LEACH protocols to study attack-defense techniques. After the related LEACH protocols were discussed, some possible attacks were analyzed, such as the hello flood attack, Sybil attack and selective forwarding attack. Finally, the pros and cons of these protocols were listed.
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 1 Introduction
examined the issue of rapid resolution of DD integer cycle ambiguities on short-baseline schemes by simulating an Iridium-like LEO constellation to augment GPS [20]. The result shows that higher ambiguity success rate and better ambiguity precision can be obtained through a hybrid MEO-LEO constellation scheme. Besides, more detailed simulation experiments and analyses have also been conducted to evaluate the performance of precise point positioning (PPP) with LEO constellation augmented GNSS [21; 22; 23]. Although there are many studies on the augmentation of LEO satellites, few experiments have been conducted on comprehensively investigating the medium- and long-range RTK performance. In this paper, we focus on single-baseline RTK results using four kinds of LEO constellations in combination with GPS, all of which are based on the simulated pseudorange and carrier phase observations at ground tracking stations. The models considered for RTK in this work are divided into two types: short and medium-to-long baselines, which have been further introduced in Section 2, together with the simulation method of ground-based data. In Section 3, different constellation configurations, simulation situations, and data processing strategies are introduced, respectively, aiming to prepare for RTK positioning. After that the performances of different LEO constellations augmented GPS RTK are analyzed in Section 4 comprehensively. In Section 5, we will make an initial assessment of combined GPS/LEO RTK for different length baseline at mid-latitudes. Finally, the conclusions are summarized in Section 6.
# Multimodal Natural Disaster Scene Recognition with Integrated Large Model and Mamba
## 4 Experiments
### Experimental Analysis
#### 4.4.3 Classification Report  
To thoroughly analyze the issues encountered during the model's classification process, we randomly selected a single run for evaluation. The performance was assessed using four metrics: precision, recall, F1-score, and support. The results are presented in Table 5.  
The analysis reveals that the model performs exceptionally well across all 11 categories in terms of precision, with values consistently above 0.93. Notably, the precision for hail, lightning, and hurricanes approaches 1, while the performance in the snow, frost, and earthquake categories is slightly lower, at 0.937, 0.938, and 0.944, respectively. In terms of recall, hail, wildfire, lightning, and hurricanes achieve near-perfect scores, while the recall for the smog category is only 0.876, with the remaining categories all exceeding 0.95.  
This indicates that the model tends to classify hard-to-distinguish natural disaster scenarios as negative classes. In such cases, the model is more cautious when predicting the positive class (correct disaster categories), only making positive predictions when the sample closely aligns with the positive class features. As a result, the positive class predictions are highly reliable (high precision), but many true positive samples are missed, leading to lower recall. Additionally, due to the diverse forms within certain disaster categories, some forms may be underrepresented or lack distinct features, prompting the model to adopt a conservative approach to avoid false positives, which further reduces the recall for the positive class.  
Figure 3: Grad-CAM heatmaps for 11 natural disaster categories.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Hail & 1.000 & 1.000 & 1.000 & 127 \\ Snow & 0.937 & 0.978 & 0.957 & 137 \\ Earthquake & 0.944 & 0.986 & 0.965 & 69 \\ Rain & 0.982 & 0.956 & 0.969 & 113 \\ Flood & 0.976 & 0.976 & 0.976 & 84 \\ Wildfire & 0.989 & 1.000 & 0.994 & 88 \\ Hurricane & 0.990 & 0.990 & 0.990 & 98 \\ Lightning & 1.000 & 1.000 & 1.000 & 76 \\ Sandstorm & 0.988 & 0.964 & 0.976 & 83 \\ Haze & 0.987 & 0.876 & 0.929 & 89 \\ Frost & 0.938 & 0.989 & 0.963 & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification performance metrics by category.
## 4 Results and Discussion
### Evaluation of Defect Detecability  
The resolution for identifying the size of the defect is determined by the resolution of the IR camera, the distance to the inspection target, and the field of view (FOV). By converting the surface distance of the inspection target corresponding to one pixel (surface distance per unit pixel), the size of the defective area can be calculated through the number of pixels in the defective area. The size was estimated by calculating the ratio of the actual defect size and image resolution and can be expressed as Equation (10).  
\[Size=\mathrm{M}\times\frac{\mathrm{L}}{\mathrm{p}} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Defect Depth (mm)** & **Thinning Rate (\%)** & **Phase Angle (Radian)** & **Estimated Depth (mm)** & **Error (\%)** \\ \hline
2.4 & 40 & 0.5039 & 2.35 & 0.85 \\
3.0 & 50 & 0.7472 & 2.90 & 1.76 \\
3.6 & 60 & 1.0534 & 3.51 & 1.65 \\
4.2 & 70 & 1.6067 & 4.42 & \(-\)4.14 \\
4.8 & 80 & 1.9097 & 4.86 & \(-\)1.10 \\
5.4 & 90 & 2.2006 & 5.26 & 2.60 \\ \hline \end{tabular}
\end{table}
Table 4: Defect depth prediction at 0.01 Hz using phase angle and related error.  
Figure 11: Plot of phase difference and defect depth with the fitting curve of the third-order polynomial.  
where M is the number of pixels occupied by the defective area in image, P is the fraction number corresponding to the length of the measuring device, and L is the length of the actual sample through measurement.  
Table 5 shows the number of pixels in the image for defects with thinning rates of 40% to 90% at 0.01 Hz and the number of pixels calculated by Equation (10). A defect indicating 40 \(\times\) 40 mm in a real sample becomes 86 pixels and 64 pixels in a 640 \(\times\) 480 resolution image. Therefore, thinning defect can be estimated as 86 \(\times\) 64 = 5504 pixels in the binary image. As can be seen in Figure 10 and Table 5, the deeper the defect depth and the closer the defect is to the surface, negative errors change into positive errors. This is because the higher the thinning rate, the more external energy heating occurs on the surface, and the excessive heat conduction phenomenon becomes active due to the thickness difference between the sound area and the defective area. Conversely, as the thinning rate decreases, the diffusion of heat energy becomes slower. Therefore, less thermal contrast is generated, and defect detection is not perfect, resulting in negative errors. As a result of a defect such as B1 with a thinning rate of 40%, a negative error occurred because the calculated 1995 pixels in the image were 3509 pixels less than the estimated 5504 pixels in the calculation. The C1 defect with a thinning rate of 70% was calculated to be 5589 pixels in the image, differing only by 89 pixels from the theoretical calculation of 5504 pixels, and the actual defect size and ratio were similar. On the contrary, in the case of C3 defects with a thinning rate of 90%, the 6427 pixels calculated from the image were 923 pixels more than the 5504 pixels in the estimated value, so they appeared larger than the actual thinning and a positive error occurred.
# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
## 4 Analysis of Algorithm Results
### Objective Evaluation Indicators
#### 4.3.1 Structure Similarity Index Measure  
Structure similarity index measure (SSIM) is a metric used to compare the similarity of two images. The SSIM value is mainly based on three characteristics: structure, luminance, and contrast. Luminance is measured by the average gray value; contrast is measured by the gray standard deviation; and structure is measured by the correlation coefficient. The calculation method is as follows:  
\[\mu=\frac{1}{N}\sum_{i=1}^{N}x_{i} \tag{20}\]  
\[\sigma=(\frac{1}{N-1}\sum_{i=1}^{N}{(x_{i}-\mu)}^{2})^{1/2} \tag{21}\]  
where \(\mu\) is the average gray value, \(\sigma\) is the gray standard deviation, and \(C\) is the correlation coefficient.  
_SSIM_ is consistent with human visual characteristics in evaluating image quality. Its value falls in the range of \([0,1]\), where a higher value indicates a stronger similarity between the two images, reflecting higher image quality. Its calculation formula is as follows:  
\[SSIM(X,Y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{2}+\mu_ {y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{22}\]  
where \(X\) and \(Y\) denote the input image and output image, respectively; \(\sigma_{x}\) and \(\sigma_{y}\) are the standard deviations of image \(X\) and \(Y\), respectively; \(\mu_{x}\) and \(\mu_{y}\) are the grayscale averages of image \(X\) and image \(Y\), respectively; \(\sigma_{xy}\) is the covariance of the two images; and \(C_{1}\) and \(C_{2}\) are the correlation coefficients.  
Figure 3: Comparison of segmentation effect between improved Otsu algorithm and other image segmentation algorithms: (**a**) original image; (**b**) segmentation effect of traditional Otsu algorithm method; (**c**) segmentation effect of the K-means algorithm method; and (**d**) segmentation effect of improved Otsu algorithm.
## 4 Discussion
Therefore, in our experiments, confirming the expression of Sox9 in NTAPP-irradiated cells cultured for a longer period was necessary. It has been reported that the MSC markers CD44, CD73, and CD90, as well as the stem cell markers CD105 and CD146, are expressed in dental pulp cells. Of these, CD105 is highly expressed in deciduous teeth [31]. In the present study, we studied CD44, CD105, and CD146 and found that they are all expressed in non-treated cells; however, only the expression of CD105 was increased after NTAPP treatment. It is known that MSCs from various human tissues and organs are adherent to plastic, have a fibroblast morphology, and are positive for CD73, CD90, and CD105 [32; 33], but they vary in potency and self-renewal potential. Therefore, NTAPP treatment (3 times, 20 s each) is an effective tool that activates cell proliferation while maintaining and increasing MSC and stem cell markers in hDDPFs. The multi-gas plasma jet source can generate atmospheric plasma of various gas species, including argon, oxygen, helium, nitrogen, air, and carbon dioxide, at low gas temperatures (<57 \({}^{\circ}\)C) [34]. Nevertheless, a study has reported that the effect of plasma depends on the type of gas, and different gases change the active species produced, thus changing the effectiveness and speed of the treatment. [35] For example, NTAPP using nitrogen and carbon dioxide is effective for hemostasis [36]. Helium and argon have been used in many plasma studies, but we preferred the latter because of the lower temperature, according to the manufacturer's instructions. We found that NTAPP based on argon gas was effective for promoting proliferation in hDDPFs; however, further research is necessary to examine other gas species and elucidate the complex biological effects of NTAPP on human stem cells.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 3 Performance of Broadband Power Line Communication--Related Works
### Achievable Data Rates of Broadband Power Line Communication--Related Works
The throughput results for SNR = 20 dB and with 360 active subcarriers in the range from 1.8 to 28 MHz and a frequency spacing of 61 kHz are 18 Mbps for class 9 (little signal attenuation), 15 Mbps for class 5 (medium signal attenuation), 12 Mbps for class 5 (strong background noise \(-\)120 dBm/Hz), and 20 Mbps for class 1. The throughput results for SNR = 20 dB and with 917 active subcarriers for the windowed OFDM with carrier spacing of 24.414 kHz are 50 Mbps for class 9, 8 Mbps for class 5 and 0.3 Mbps for class 1. In a previous study [38], the achievable throughputs for class 1, 5, and 9 models for power line channel were introduced for IEEE P1901 standard, where 917 subcarriers are considered. The achievable theoretical throughput for class 1 was approximately 90 Mbps, resp. 135 Mbps for class 5 and 200 Mbps for class 9. There are several results of throughput measurements for MV, laboratory test bed LV measurement, and throughput simulation for LV. The simulation together with on-field measurement on LV voltage is missing. Therefore, this article focuses on throughput simulation for real topology together with on-field measurement for verification of simulation results.
## 2 Results
### Sorbitol Assay Effect of Essential Oils on the Cell Wall of C. albicans and C. glabata  
MIC values for C. _albicans_ and C. _glabrata_ were checked in the presence or absence of 0.8 M sorbitol as an osmotic protectant in the medium. In the case of lemon and geranium oils, the MIC values stayed unchanged what suggests lack of their influence on cell wall structure (Table 5). For basil oil, the MIC value in the presence of osmoprotectant was doubled for both tested species, indicating that cell wall may be a possible molecular target for this EO. In the presence of sorbitol, MIC values for thymme, clove, and, in particular, cinnamon oil were significantly decreased.  
Additionally, an assay comparing cinnamon oil and cinnamaldehyde MIC values in the absence or presence of osmoprotectant (sorbitol or glycerol) was carried out (Table 6). The assay was performed on two reference strains and two clinical isolates (_C. albicans_ 412 and _C. glabata_ 342, both isolated from Children's Memorial Health Institute's patients). MIC values for cinnamon oil and cinnamaldehyde  
Figure 2: Kinetic of _C. albicans_ ATCC 10231 and _C. glabata_ DSM 11226 growth under the influence of cinnamon oil in the concentrations equal to 0.016% (\(v/v\)) (MIC) or 0.031% (\(v/v\)) (MFC).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{_C. albicans_ ATCC 10231} & \multicolumn{4}{c}{_C. glabata_ DSM 11226} \\ \cline{2-7} EO & RPMI & RPMI + Sorbitol & RPMI + Ergosterol & RPMI & RPMI + Sorbitol & RPMI + Ergosterol \\ \hline thymme & 0.62 & 0.08 & 2.50 & 0.31 & 0.16 & 1.25 \\ lemon & 0.62 & 0.62 & 1.25 & 0.08 & 0.08 & 1.25 \\ geranium & 1.25 & 1.25 & 0.62 & 0.16 & 0.31 & 1.25 \\ cinnamon & 0.016 & \textless{}0.005 & 0.016 & 0.031 & 0.002 & 0.031 \\ basil & 0.31 & 0.62 & 0.31 & 0.31 & 0.31/0.62 & 0.16 \\ clove & 1.25 & 0.31 & \textgreater{}2.5 & 0.6 & 2.50 & \textgreater{}2.5 \\ AmB & 0.06 & & 0.5 & 0.06 & & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: MIC values of the tested oils in the presence and absence of sorbitol/ergosterol.  
were in the range 0.008-0.031% (\(v/v\)), while in the medium enriched with sorbitol these values decreased at least 4-fold, similar to the medium containing the added glycerol.
# A Power Processing Circuit for Indoor Wi-Fi Energy Harvesting for Ultra-Low Power Wireless Sensors
## The Proposed Power Processing Circuit for the Radio Frequency Energy Harvesting System (RFEHS)
### The Schematic Circuit  
The schematic circuit of the general block diagram is depicted in Figure 2. For the bridgeless rectification circuit, \(L_{1}\), \(L_{2}\), and \(C_{1}\) are the parameters of a matching \(LCL\) network. \(L_{3}\) is an additional boost component that functions together with \(L_{1}\), \(L_{2}\), and \(C_{1}\). \(D_{1}\) and \(D_{2}\) are diodes for rectification and additional to the two branches. The output filter capacitor of the bridgeless rectifier is \(C_{2}\). The DC voltage level of \(C_{2}\) is the input voltage of the push-pull circuit. The inductor \(L_{4}\) is the inductor used for the power processing circuit-boosting operation. As illustrated in the figure the two switches \(M_{1}\) and \(M_{4}\) are used to control a shunt connection to the common terminal. Both switches are turned to ON and OFF synchronously, and the timing depends on the value of \(R_{3}\) and \(C_{5}\). Diode \(D_{4}\) is turned ON and OFF with regards to the alternating push-pull signal. The push-pull circuit is an arrangement of \(L_{5}\), \(L_{6}\), \(M_{2}\), \(M_{3}\), \(C_{3}\), \(C_{4}\), \(R_{1}\), and \(R_{2}\). Diode \(D_{3}\) maintains the direction of the current flow to the filter capacitor \(C_{6}\). The power management unit and the energy storage components of the proposed system are represented as \(R_{PMU}\) and \(C_{STORE}\).  
Figure 1: The general block diagram of the proposed system.  
Figure 2: The schematic circuit of the proposed system.