esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Synchronous Control Strategy Based on Real-Time Impedance Detection
### Power Factor Angle of Antenna Feeder System  
The power factor \(\cos\varphi\) is the ratio of active power, P, to apparent power, S, in an AC circuit, commonly represented as \(\lambda\). \(\varphi\) is referred to as the power factor angle and can be obtained by subtracting the phase of port current from the phase of port voltage [25; 26; 27]. Let us assume:  
\[\begin{array}{c}\mathbf{\dot{U}}=\mathrm{U}\angle\varphi_{\mathrm{u}}\\ \mathbf{\dot{1}}=\mathrm{I}\angle\varphi_{\mathrm{i}}\\ \varphi=\varphi_{\mathrm{u}}-\varphi_{\mathrm{i}}\end{array} \tag{8}\]  
According to Equations (8)-(10), we can obtain the power factor angle, \(\varphi\), of the antenna feeder system, as shown in Section 3.1 and Figure 8.  
According to Section 3.1 and Figure 8, when the accuracy of \(L_{0}\) and \(L_{l}\) is n \(\times\) 10\({}^{-2}\)mH, the power factor angle, \(\varphi\), of the antenna feeder system is approximately between \(-40^{\circ}\) and \(+40^{\circ}\), which translates to a power factor of approximately \(0.766<\cos\varphi<1\). When the accuracy of \(L_{0}\) and \(L_{l}\) is n \(\times\) 10\({}^{-3}\)mH, the power factor angle, \(\varphi\), of the antenna feeder system is approximately between \(-4^{\circ}\) and \(4^{\circ}\), which translates to a power factor of approximately \(0.998<\cos\varphi<1\). It can be seen that the improvement of accuracy greatly reduces the power factor angle PHI of the antenna feeder system, and we can consider that the voltage signal and the current signal are approximately in phase. In general, \(\cos\varphi>0.8\) is required [27].  
Figure 7: The power factor angle, \(\varphi\), of the antenna feeder system when the carrier frequency of the MSK signal is \(f_{h}\) (**a**) The power factor angle, \(\varphi\), of the antenna feeder system when the accuracy of \(L_{0}\) reaches n \(\times\) 10\({}^{-2}\)mH; (**b**) The power factor angle, \(\varphi\), of the antenna feeder system when the accuracy of \(L_{0}\) reaches n \(\times\) 10\({}^{-3}\)mH.
## 5 Results and Discussion
### Compressive Strength Recovery  
Figure 11 shows that the compressive strength recovery performance of cement paste with the inclusion of capsules containing sodium silicate solution. Only the mix with the rough and weak line surface treatment capsules were tested, as the smooth surface generally had poor bonding strength. A noticeable compressive strength recovery of about 12.5% was observed on 100%OPC with the capsule surface treatment compared to those of 100%OPC without capsule at 28-days. No significant difference was observed on the compressive strength recovery performance of 100%OPC with regard to different capsule surface modifications. The increment of compressive strength recovery is due to the strength regain within the cement matrix as the sodium silicate reacts with the cementitious matrix and, consequently, forms a product to seal and heal the crack. Sodium silicate reactivity is controlled by the availability of calcium hydroxide produced by the cement reactivity, where the residual sodium silicate may transform to crystallize [56]. This could be associated with no significant effect on the strength recovery of 100%OPC cement paste with the capsule surface modification. On the other aspect, the high bonding strength of PS modified surface capsule ensured the capsule rupture, which was observed on the dispersion of healing agent solution. The strength regain obtained was due to the enhancement of hydration reactivity at early age, which was obtained by the chemical reactivity of the cementitious compounds with sodium silicate solution.  
The study also found that the modified PS capsule with sodium silicate increased the compressive strength recovery of 95%OPC + 5%SF cement paste by about 15% and 20% compared to those of the same blended mixture without capsules and 100%OPC (without capsule), respectively. As discussed earlier, the strength gain was due to the healing process associated with a combination of effects between continuous hydration of unreacted cementitious particles and the reactivity of sodium silicate with Ca(OH)\({}_{2}\) due to initial chemical reactivity between cementitious particles. This suggested that the presence of sodium silicate enhanced the strength development of cementitious composite at an early age. Additionally, the pozzolan physical characteristics enhanced the microstructure densification, where the finer particles filling the pores within the cement matrix. Thus, this result indicates that the modified PS capsule with sodium silicate method enhanced the autogenous self-healing of silica fume blended cementitious composite. Nevertheless, the compressive strength recovery increased with age regardless of capsule surface treatment or cement mixture.  
Figure 10: Bonding strength characteristic of capsules with variations of mortar mix combinations (\({}^{\Psi}\) [PERSON] et al. (2015)).
## 4 Experiment and Results
### Machine-Learning Algorithm  
Naive Bayes and SVM are validated with 10-fold cross-validation. K-fold cross-validation is a re-sampling method used to increase the statistical reliability of the classifier performance measurement when the amount of data is insufficient. The random forest has a cross-validation method in it so that  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reserved Words** & **Malware** & **Benign** & **Benign with JavaScript** \\ \hline var & 7.72 & 0.36 & 12.76 \\ if & 6.51 & 7.50 & 19.59 \\ this & 0.91 & 9.25 & 24.90 \\ function & 1.56 & 0.16 & 5.03 \\ return & 1.28 & 4.39 & 2.96 \\ for & 1.31 & 30.74 & 21.35 \\ null & 1.96 & 10.26 & 36.55 \\ false & 0.49 & 1.00 & 5.70 \\ new & 0.45 & 1.18 & 2.40 \\ true & 0.61 & 1.32 & 7.37 \\ while & 0.41 & 0.27 & 0.44 \\ try & 0.05 & 0.05 & 0.77 \\ catch & 0.05 & 0.04 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average count of each reserved word in JavaScript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Readable Strings** & **Malware** & **Benign** & **Benign with JavaScript** \\ \hline app & 2.40 & 0.46 & 7.19 \\ frc & 1.16 & 0 & 0 \\ plugIns & 0.95 & 0 & 0 \\ buf & 0.90 & 0 & 0.01 \\ sum & 0.90 & 0.32 & 0.38 \\ arr & 0.82 & 0.02 & 0.14 \\ num & 0.69 & 0.25 & 0.24 \\ doc & 0.69 & 0.26 & 0.21 \\ nPage & 0.52 & 0 & 0 \\ getAnnots & 0.52 & 0 & 0 \\ subject & 0.38 & 2.49 & 2.01 \\ syncAnnotScan & 0.38 & 0 & 0 \\ proc & 0.17 & 0.13 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The average count of top readable words in JavaScript.  
it can form an ensemble tree with different subsamples and tests the ensemble tree model with the remainder. Thus, there is no need to perform cross-validation separately.  
We first built two models. One is built using a structure feature set and the other is built using structure and content feature set. Precision, recall, and F-measure were checked for each class. We then tested the proposed model against the base samples (benign samples containing multiple JavaScript) and the adversarial samples (variants of base samples after injecting malicious codes).  
In Tables 6 and 8, 11,097 malicious PDFs and 9000 benign PDFs obtained from Contagio malware dump were used for training and testing. In Tables 7 and 9, 115 benign PDF documents included JavaScript and 101 malicious PDF documents injected malicious code were used for the test.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 7 Conclusions  
In this paper, we investigated the vehicle location privacy-preserving problem in IoV and proposed a location privacy-preservation method based on dummy locations under road restriction. In the proposed RR-DLS method, the effective distance is introduced  
Figure 8: Anonymous entropy of different dummy location selections algorithms.  
Figure 9: Effective distance of two different location selection algorithms. (**a**) mean; (**b**) variance.  
to represent the characteristics of location distribution in order to improve the validity of dummy locations. A dummy location selection algorithm under road restriction was addressed according to anonymous entropy and effective distance. Security analysis results show that the proposed RR-DLS method can resist collusion attack and inference attack effectively. Performance analysis and simulation results show that the proposed RR-DLS method can effectively protect the vehicle location privacy and ensure the accuracy of LBS service. Furthermore, the proposed RR-DLS method increases the computation overhead at the vehicle user and communication overhead at RSU and the LBS server.  
In the future, we will study the problem of vehicle trajectory privacy preservation in continuous LBS scenario.  
Conceptualization, X.X. and H.C.; methodology, X.X. and H.C.; software, X.X.; validation, X.X. and L.X.; formal analysis, X.X. and H.C.; investigation, X.X.; resources, L.X.; data curation, X.X.; writing--original draft preparation, X.X. and H.C.; writing--review and editing, H.C.; supervision, H.C.; project administration, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Fund of National Natural Science Foundation of China under Grant 61671410, and the Science and Technology Department of Zhejiang Province under Grant 2018R52046 and Grant LGG18F010005.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Total Residual Oxidant in Direct OCI\({}^{-}\)-Injection and Electrolysis  
#### 3.1.1 Direct OCI\({}^{-}\)-Injection  
Effects of the initial OCI\({}^{-}\) concentration on TRO in the direct injection of OCI\({}^{-}\) to seawater were investigated. The direct OCI\({}^{-}\)-injection experiments were carried with WS and ES by varying OCI\({}^{-}\) concentrations (0.5, 0.7, 1, 10, 50, and 150 mg/L as Cl\({}_{2}\)) and their results are presented in Figure 2. Irrespective of the initial OCI\({}^{-}\) concentrations, the TRO concentration dropped rapidly in the beginning and was stabilized within 1 min for both ES and WS (Figure 2a,b). Chlorine demand of both WS and ES increased with the increase of the OCI\({}^{-}\) dose. While the chlorine demand of WS increased by 26-fold, that of ES increased by only 12-fold, as the OCI\({}^{-}\) dose increased from 0.5 to 150 mg/L. When the OCI\({}^{-}\) dose of \(\geq\)10 mg/L was applied, WS showed a significantly higher chlorine demand compared to that of ES (Figure 2c). These phenomena might be explained by the characteristics of the seawater used in this study, such as organics or nitrogen contents [22]. In other words, once chlorine is added to seawater, reactions between chlorine and chlorine-demanding substances such as Br\({}^{-}\), NH\({}_{4}\)\({}^{+}\), NO\({}_{2}\)\({}^{-}\), Fe\({}^{2+}\), and residual organics occur rapidly [14; 23; 24]. Nonetheless, the chlorine demand became obvious when the dose was high. In the case where 150 mg/L OCI\({}^{-}\) was injected to WS, the chlorine demand reached 13.0 mg/L (Figure 2c). On the other hand, the chlorine demand reached 0.2 mg/L when 0.5 mg/L OCI\({}^{-}\) was injected. Similar results have been reported by others, e.g., [PERSON] et al. [25], [PERSON] et al. [26], and [PERSON] et al. [27].
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 6 Discussion
In summary, while significant progress has been made in understanding and improving positioning systems for UUVs, there remains a need for more up-to-date and detailed research, particularly concerning maintenance costs, practical applications, and accuracy. Future work should focus on bridging these gaps to enhance the effectiveness and cost-efficiency of positioning systems in underwater environments.
## 3 Results and Discussion
### CCN Properties and Their Relationship to Air Mass Origins
The aerosols of Continental origin are more likely to be affected by these aging processes because an abundant supply of gaseous and particulate substances from these sources is likely. The larger the size of the aerosol, the easier it is to become activated CCN. From the above results, it can be concluded that the particle size distribution is the most important factor determining \(N_{\text{CCN}}\) in the free tropospheric airmass at the MFRS during summer.
## 4 Results
### Discussion  
This research presents a robust DR image classifier that achieves state-of-the-art performance by employing a lightweight image classifier and knowledge distillation concept. The teacher model employed in this system is a fusion model composed of ResNet152V2 and Swin Transformer. Although the teacher model has a relatively large size, the model is only leveraged to train the student model. The Xception classifier is used as the student model that achieves accuracies of 86.23% and 90.41% with only cross-entropy loss on the APTOS and IDRiD datasets, respectively. The model's performance improves by an average of 2.07% by combining distillation loss. The performance further improves by 11.05% with the integration of modified CBAM on the APTOS dataset and by 1.38% on the IDRiD dataset. The student model achieves an overall accuracy of 99.04%, which outperforms the teacher model by 0.13% on the APTOS dataset. Nonetheless, on the IDRiD dataset, the student model fails to outperform the teacher model, although the student model achieves a noteworthy performance. Three major reasons can be attributed to the exceptional performance of the student model. Firstly, the integration of the modified CBAM block plays a crucial role in achieving high accuracy. Since the pre-trained classifiers are trained on ImageNet, all the extracted features are not fully relevant to the classification process. Employing an attention module helps boost performance. Secondly, the temperature hyperparameter employed in the training process of the student model is set to 10, which is a higher value. This allows the student model to learn from the distillation loss while primarily focusing on the cross-entropy loss. Finally, the extensive ablation study with a series of preprocessing techniques helped the model achieve a noteworthy performance.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Architecture** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline DenseNet201 [64] & 0.86 & - & - & 0.7251 \\ ResNet50 [31] & 0.82 & - & - & - \\ Texture attention, spatial attention [26] & 0.851 & - & 0.903 & 0.984 \\ DenseNet121 [65] & 0.9730 & - & - & - \\ Squeeze excitation-based dense network [65] & 0.86 & 0.77 & 0.7 & 0.73 \\ VGG16 and capsule network [66] & 0.99 & - & - & - \\ Semi-supervised auto-encoder graph network [67] & 0.944 & - & 0.84 & - \\ Improved ResNet-50 [68] & 0.9825 & 0.9986 & 0.9584 & 0.99856 \\ DenseNet-121 [69] & 0.9836 & 0.98 & 0.98 & 0.98 \\ Parallel CNN [10] & 0.9727 & 0.96 & 0.95 & 0.95 \\ Proposed & 0.9904 & 0.9906 & 0.9904 & 0.9903 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison with existing literature on the APTOS dataset.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Architecture** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline ResNet [70] & 0.9029 & 0.8875 & 0.9689 & - \\ Cross-disease attention [71] & 0.926 & - & - & - \\ KNN [72] & 0.94 & - & - & - \\ GNN [73] & 0.773 & - & - & - \\ Proposed & 0.9417 & 0.9429 & 0.9417 & 0.9415 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison with existing literature on the IDRiD dataset.
# A Novel Analytical Approach for Spacecraft Fly-Around Formation Design with a Low-Thrust Maneuver
## 2 Analytical Guidance Law Design
### Fly-Around Formation Analysis
\[e_{\min}=\left\|\frac{1}{\underline{r}v^{2}}\mathbf{v}\times\left(\underline{r} \times\mathbf{v}\right)-\frac{\underline{r}}{\underline{r}}\right\| \tag{17}\]  
By analyzing Equation (17), when condition \(\underline{r}\bot\mathbf{v}\) is satisfied, the formula \(e_{\min}=0\) is established, and the fly-around trajectory is a spatial circle centered on point \(q\). When the minimum eccentricity requirement is met, the corresponding fly-around period is as follows:  
\[T_{e_{\min}}=2\pi\frac{\underline{r}}{v} \tag{18}\]  
It can be concluded that when the artificial gravitational coefficient satisfies the constraint \(\underline{r}v^{2}/2<\mu_{s}<\underline{r}v^{2}\), with the increase of \(\mu_{s}\), the eccentricity and period of the fly-around trajectory decreases. When constraint \(\mu_{s}>\underline{r}v^{2}\) is satisfied, with the increase of \(\mu_{s}\), the eccentricity increases, and the period also decreases.
# Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR
## 3 Low Frequency Expansion  
The basic idea of the convolution model is as follows: when a seismic wavelet passes through the underground reflection interface, it will produce a reflection wave, which can be superimposed to obtain a seismic record. Figure 1 shows the synthetic seismogram convoluted by seismic wavelet (30 Hz) and seismic reflection coefficients. It can be seen that the seismic reflection coefficients are sparse in time domain, and its spectrum is full bandwidth, while the low-frequency information of synthetic seismic record is missing, which is caused by the filtering effect of seismic wavelet. Due to the time-domain sparsity and the frequency-domain full bandwidth of seismic reflection coefficients, [PERSON] et al. [22] and [PERSON] et al. [5] successively proposed the problem of sparse inversion with structural L1 norm constraints. In the frequency domain, the full-bandwidth spectrum is reconstructed through a limited bandwidth, thereby recovering the full-bandwidth energy to a certain extent.  
Figure 1: Schematic diagram of synthetic seismogram. (**a**) Convolution, (**b**) frequency spectrum of seismic reflection coefficient, (**c**) frequency spectrum of seismic wavelet.
# An LLC Converter with Capacitive Insulation
## 2 Proposed Circuit Configuration  
Figure 3a shows the circuit structure of a typical LLC converter. The plastic film capacitor \(C_{r}\) works as a resonant capacitor, which is usually connected between the primary winding of the transformer and the primary side ground.  
The proposed circuit is shown in Figure 3b. The single resonant capacitor is divided into two Y1 capacitors, named \(C_{r1}\) and \(C_{r2}\), so that safety regulations can be met. In the proposed LLC converter, the transformer plays a role in the functions of the resonant tank, voltage gain design, and common mode touch current blocker. The transformer is necessary for the reasons above. However, the volume and power density of the transformer are still improved via the reduction in transformer insulation materials.  
Since the proposed method can keep the original transformer and minimize the insulation material in the transformer, the touch current is solved by isolation and the size is reduced. The circuits in Figure 3a,b have the same equivalent circuit shown in Figure 3c.  
Compared with the method in [24], the mathematical theory of the proposed method is the same as the conventional LLC converter, so it can be driven by existing LLC control ICs and can be easily implemented for industrial applications.
## 3 Results
### Determination of Optimum Binder Content  
To determine the OBC, three graphs were produced--stability, bulk-specific gravity, and air void--each of which is plotted against the percentage of binder content. Marshall stability is an important parameter, as it measures the strength of the mixture and its ability to withstand traffic loads. The bulk-specific gravity measures the density and compaction of a mixture, while the VIM quantifies the amount of void space within the mixture. In accordance with the recommendations of the Asphalt Institute, the OBCs were selected in a way to satisfy the following requirements [61; 64]:  
1. Maximum Marshall stability;
2. Maximum bulk-specific gravity;
3. Median air void (4% for SMA).  
Table 5 shows the average optimum binder content for each CBA mixture. The results indicate that the optimum binder content increased with an increase in the CBA content, except for mixtures containing 100% CBA replacement, in which the optimum binder content decreased slightly.  
The effect of the CBA on the optimum binder content was evaluated, and the statistical significance of the differences between all mixtures was tested using a one-way analysis of variance (ANOVA). The analysis of variance (ANOVA) results are shown in Table 6. The analysis of variance (ANOVA) results indicate that the difference in the optimum binder content with varying CBA contents is not statistically significant, as the \(p\)-value (0.065) exceeds the alpha level of 0.05. This indicates that the differences in the OBC across mixtures with varying CBA percentages were insufficient to produce significant changes in the performance of the mixtures. Minor variations in the OBC did not substantially impact the overall properties of the asphalt mixture, allowing the use of a constant OBC value across all mixtures. In this study, the optimum binder content of the control mixture, which was 5.9%, was utilized in all performance tests.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mixture** & \begin{tabular}{c} **Maximum Stability** \\ **(\%)** \\ \end{tabular} & **4\% of Air Void, VIM** & \
\begin{tabular}{c} **Maximum Bulk-Specific** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Optimum Binder** \\ **(\%)** \\ \end{tabular} \\ \hline CBA-10 & 4.95 & 5.95 & 6.79 & 5.90 \\ CBA-20 & 5.89 & 5.47 & 6.86 & 6.07 \\ CBA-30 & 5.35 & 5.36 & 7.52 & 6.08 \\ CBA-50 & 5.07 & 6.25 & 6.92 & 6.08 \\ CBA-70 & 5.48 & 6.12 & 6.80 & 6.13 \\ CBA-100 & 5.95 & 5.81 & 6.55 & 6.10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimum binder content for different CBA contents.  
Figure 7: Voids in mineral aggregate (VMA) vs. CBA mixtures.
## 6 On-Site Data Monitoring
### Analysis of Data Monitoring Results
#### 6.2.2 Monitoring of Surrounding Rock Convergence in Return Air Roadway  
As indicated by the monitoring data, refer to Figure 15 for details, the differentiated support method resulted in minimal roof and floor convergence when the working face was 60 m ahead. Then, as the working face advanced toward the roadway, it continued to rise until the working face was 40 m past the roadway, at which point the roof-to-floor convergence rate remained relatively high. The convergence of the surrounding rock initially increased and then stabilized at a distance of 50 m behind the working face. The maximum convergence between the roof and floor was approximately 486 mm, with the roof subsiding by 192 mm, and the floor heave was about 294 mm [41], which was reduced by about 30%. Typically, the deformation of the roadway's surrounding rock remained under 10% of its size, maintaining good integrity, as illustrated in Figure 17.