esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
We effectively optimized the thickness of a pristine PEDOT: PSS electrode layer to achieve a high transmittance in the frequency range of 0.2-1.2 THz. We also measured the THz optical constants of two types of LC, namely, MDA-00-3461 and Mixture 1825. The results confirmed that Mixture 1825 has a much higher birefringence and absorption coefficient in comparison with MDA-00-3461. Furthermore, we demonstrated three types of TN-LC THz phase shifters using pristine PEDOT: PSS as electrode layers. The performances of these TN-LC phase shifters were evaluated in terms of the driving voltage, threshold voltage, and transmittance loss. The MDA-250 device had a much lower transmittance loss in the frequency range of 0.2-1.2 THz. On the other hand, the TN-LC phase shifter based on Mixture-250 was able to achieve a 90\({}^{\circ}\) phase shift at 1.2 THz with a V\({}_{\text{D}}\) of 7 V\({}_{\text{RMS}}\). The current TN-LC THz phase shifters exhibited a significantly lower threshold voltage and lower transmission loss with reasonable low driving voltage in comparison with previously reported results. A figure of merit (FOM) for LC phase shifters, taking into account phase shift achieved, loss and driving voltage required, was proposed. Based on this analysis, the combination of a pristine PEDOT: PSS electrode with high birefringence LC is suitable for THz phase shifter applications. The Mixture-250 device achieved an FOM of 6.65 degree-dB\({}^{-1}\).V\({}^{-1}\).  
**Author Contributions:** Conceptualization, A.K.S.; C.-L.P., and C.-S.Y.; software, A.K.S. and C.-S.Y.; investigation, A.K.S.; C.-L.P., and C.-S.Y.; data curation, A.K.S., C.-L.Y., and C.-S.Y.; writing--original draft preparation, A.K.S. and C.-S.Y.; writing--review and editing, C.-L.P. and O.W.; supervision, C.-L.P.; equipment and materials acquisition, H.-C.L., Y.-J.W., and Y.-H.L.  
**Funding:** This work was supported by Science Park R&D Endeavor Project with ACE Biomedical Solution Co., Ltd. (Project number 106A33), Zhubei City, Hsinchu County, Taiwan (R.O.C), and grants of the Ministry of Science and Technology (Project number MOST 106-2112-M-007 -022 -MY2 & MOST 104-2221-E-007 -093 -MY3), Taiwan.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Fine Fast Acquisition Scheme for a Communication and Navigation Fusion System
## 4 Derivation of Acquisition Time
Therefore, the block number \(\rm{N_{B}^{\prime}}\) and the acquisition time \(\rm{\overline{T}_{1}^{\prime}}\) taken for the following H1-SS can be expressed as  
Figure 8:9\[\rm{H_{Bd}(z)}=P_{Bd1}z^{t_{1}},h_{Bd}(z)=P_{Bd2}z^{t_{2}}\]0  
\[\rm{H_{Bd}(z)}=P_{Bd1}z^{t_{1}},h_{Bd}(z)=P_{Bd2}z^{t_{2}}\]1  
For the search space where all blocks are H0-blocks (H0-SS), the time taken with and without CPPI can be expressed as  
\[\rm{H_{Bd}(z)}=P_{Bd1}z^{t_{1}},h_{Bd}(z)=P_{Bd2}z^{t_{2}}\]2  
The positioning receiver tests every search spaces one by one until all available search spaces are tested. The positioning results can be given as soon as \(\mathrm{N}_{\mathrm{A}}\) (\(\mathrm{N}_{\mathrm{A}}\geq 4\)) PRN codes are successfully detected and tracking. MAT is defined as the time taken for successfully acquiring \(\mathrm{N}_{\mathrm{A}}\) PRN codes. Assuming there are \(\mathrm{N}_{\mathrm{S}}\) PRN codes after SSR method, the worst case is that all \(\mathrm{N}_{\mathrm{S}}\) search spaces are tested until \(\mathrm{N}_{\mathrm{A}}\) PRN codes are successfully detected and tracked, and the best case is that the first \(\mathrm{N}_{\mathrm{A}}\) search spaces are H1-SSs. Before the first H1-SS is detected, the time taken in every search space is \(\overline{\mathrm{T}}_{0}\). Once the first successfully acquisition shows up, the time taken in every search space becomes \(\overline{\mathrm{T}}_{\mathrm{r1}}\) or \(\overline{\mathrm{T}}_{\mathrm{r0}}\) because of the CPPI. Suppose that, among all \(\mathrm{N}_{\mathrm{S}}\) search spaces, the first H1-SS is the \(i\)-th search space and the \(\mathrm{N}_{\mathrm{A}}\)-th H1-SS is the \(j\)-th search space, the acquisition time corresponding to this case can be expressed as  
\[\rm{H_{Bd}(z)}=P_{Bd1}z^{t_{1}},h_{Bd}(z)=P_{Bd2}z^{t_{2}}\]3  
Each search space can be H1-SS with equal probability among all \(\mathrm{N}_{\mathrm{S}}\) search spaces. Based on the analysis above, the MAT can be expressed as  
\[\rm{H_{Bd}(z)}=P_{Bd1}z^{t_{1}},h_{Bd}(z)=P_{Bd2}z^{t_{2}}\]4  
Equation (39) indicates that factors including search space number \(\mathrm{N}_{\mathrm{S}}\), block number \(\mathrm{N}_{\mathrm{B}}\), dwell time \(\mathrm{t}_{1}\) and \(\mathrm{t}_{2}\), \(\mathrm{FAPT}\)\(\mathrm{t}_{\mathrm{p}}\) and detection performance have impact on MAT. SSR method introduced in Section 3.1 dramatically reduces the search space number and block number. DD-MAX/TC-CACFAR method introduced in Section 3.2 can effectively improve the detection performance to reduce the MAT. \[\rm{H_{Bd}(z)}=P_{Bd1}z^{t_{1}},h_{Bd}(z)=P_{Bd2}z^{t_{2}}\]5 (**a**) Transfer function between two H0-blocks; (**b**) Transfer function from H1-block to H0-block.
## 2 Materials and Methods
### Models and Verification  
#### 2.5.1 Model Construction  
The model construction process is outlined in the following four steps.  
Step 1: Pipe network drawing. Relevant data for the irrigation pipeline network are collected, including parameters such as pipe material, diameter, length, and valve locations. The actual pipeline network is simplified into a CAD format, with key nodes identified, including locations, lengths, intersection points, and outlet positions of the main pipelines.  
Step 2: Model import. The CAD file of the pipeline network is imported into EPANET. The basic information of the pipeline network is modified and refined based on actual data, and the model is discretized. Due to the large scale and length of the pipeline network, the node spacing along the pipeline is set to 50 m to ensure model accuracy, particularly with respect to the elevation data of the pipeline network.  
Step 3: Input parameters. The necessary parameters for the pipeline network, such as geographical coordinates, elevations, and topology structure, are input. Roughness coefficients for the pipelines are set, and the water source is configured, along with the status of each inlet and outlet. The actual water supply flow required by the branch pipes is used as the initial data to populate the calculation model.  
Step 4: Model solving. Hydraulic calculations are performed using EPANET to determine parameters such as pressure head and flow rate at various nodes in the pipeline network, employing the Hazen-Williams (H-W) head loss formula for hydraulic modeling. Once the calculations are completed, the model construction is finalized [34].
# Special Issue \"Fuzzy Decision Making and Soft Computing Applications\"
###### Abstract
In the first [15], to solve the Cauchy problem, three fuzzy numerical methods, based on the combination of fuzzy transform with one-step, two-step, and three-step numerical methods, are introduced. The error analysis of the new fuzzy methods is discussed, showing more accurate results compared with other existing methods. The other two papers by the same authors [16; 17] report parts I and II of the same voluminous work, where new approximation methods for solving systems of ordinary differential equations (SODEs) using fuzzy transform are introduced and discussed. In particular, different modified numerical schemes and new representations of basic functions are proposed, the error analysis of the new approximation methods and the properties of the uniform fuzzy partition are examined, and numerical examples showing improved accuracy are presented. A further work [18] assesses how three shaking procedures affect the performance of a metaheuristic General Variable Neighbourhood Search algorithm. The different schemes were applied on benchmark instances of the Traveling Salesman Problem to examine the potential advantage of any of the three metaheuristic schemes, showing similarities and differences among different methods. 3. **Decision-making applications employing fuzzy logic and soft computing. ** Contributions in this field show a variety of possible applications. In the context of the characterization of basmati rice product value using an image-based grading process, the authors in [19] propose a model for quality grade testing and identification, using a novel digital-image-processing- and knowledge-based ANFIS. This approach provides capabilities to simulate the behaviour of an expert in the characterization of rice grains using their physical properties, and compared to other machine learning techniques, its results are promising in terms of classification accuracy and efficiency. In the field of electron beam (EB) measurements, the author of [20] presents a novel method of restoring the EB measurements that are degraded by linear motion blur. The author's approach is based on a fuzzy inference system and a Wiener inverse filter, providing autonomy, reliability, flexibility, and real-time execution, in restoring highly degraded signals without requiring exact knowledge of EB probe size, and a demonstration is given by comparing ground truth signals with restorations. Finally, in [21], the motion control of mobile robots in a cluttered environment with obstacles is considered. In particular, to control the motion of a mobile robot using an eye gaze coordinate as inputs to the system, the paper presents an intelligent vision-based gaze guided robot control, utilizing an overhead camera, an eye-tracking device, a differential drive mobile robot, vision, and an interval-type-2 fuzzy inference tool. Experiments and simulation results indicate that the system can successfully perform operator intention, modulating speed and direction accordingly. This research received no external funding.
## 3 Results and Discussion
### Weight Coefficient Analysis for the Mixed Loss Function  
In Section 2.5, we described how we combined the advantages of the dice loss and focal loss and weighted the sum of the two loss functions as a mixed loss function to address the challenges posed by the imbalanced classes and difficult samples. Given the different principles of the two loss functions, this section discusses the impact of the mixed loss function on the segmentation performance of models with different weighting coefficients. To this end, we set the values of (\(\lambda_{1}\): \(\lambda_{2}\)) in Equation (7) to (1:0), (0.9:0.1), (0.8:0.2), (0.7:0.3), (0.6:0.4), (0.5:0.5), (0.4:0.6), (0.3:0.7), (0.2:0.8), (0.1:0.9), and (0:1), respectively. Figure 8 shows the loss curves of the validation set during ISTD-DisNet training with the different weighting coefficients, where Figure 8a is the initial phase of training (0-15 epochs) and Figure 8 is the unfrozen phase of training (50-150 epochs). It can be seen that, for the pavement distress two-class segmentation task with a small percentage of target pixels, the  
Figure 7: Heatmap visualization with the ISTD-TE dataset. The red box is the focus area.  
loss curve fluctuates the most when only the dice loss function is used, i.e., the gradient changes drastically and the training is unstable. When using only the focal loss function, the oscillations are not obvious, but the convergence is slow. When (\(\lambda_{1}\):\(\lambda_{2}\)) takes the value of (0.7:0.3), the loss curve fluctuation is the smallest, the loss value decreases the fastest, and the value of the convergence is the lowest, which indicates that the training process is more stable at this time and that training time can be saved.  
Figure 9 demonstrates the variation in the \(F1\) score and \(mIoU\) of ISTD-DisNet on the test set when different weighting coefficients are assigned to the two types of loss functions. It can be seen that, compared with the use of the focal loss alone, the use of the dice loss alone can better improve the model's distress segmentation performance. When the (\(\lambda_{1}\):\(\lambda_{2}\)) ratio of the mixed loss function is (0.7:0.3), the model's segmentation performance is the best, indicating that, at this time, the mixed loss function is able to take into account the distribution of the number of samples, but at the same time, it is also able to better optimize the classification accuracy. Taken together, the combination of the focal loss and dice loss is a more effective way to solve the imbalance of positive and negative samples in pavement distress segmentation. For similar tasks, the weight coefficients can be selected and adjusted according to the characteristics of the dataset and the needs of the model, which can achieve better classification results.
# Experimental Study on Cavity Formation and Ground Subsidence Behavior Based on Ground Conditions
## 2 Research Materials and Methods
### Research Method
#### 2.4.2 Experimental Conditions  
The model ground was simulated under various conditions to analyze the ground subsidence mechanism with respect to the groundwater level, relative density, and fines  
Figure 5: Illustrations of (**a**) initial cavity formation and (**b**) ground subsidence.  
Figure 6: Research flowchart.  
content. To analyze this mechanism with respect to the degree of ground compaction, the model ground was simulated by adjusting the relative density (Dr) of the standard soil to 60% and 75%. These relative densities simulate a loose state reflecting typical field conditions and a dense state (approximately 75%) representing relatively well-compacted backfill, respectively. Experiments were also performed at groundwater levels of 10, 20, and 30 cm. The relative density, which is an indicator of the density of the ground, is used to quantify the relative compactness of the ground [22]. It is calculated using Equation (1), which utilizes the void ratio in the natural state (\(e\)), minimum void ratio (\(e_{min}\)), and maximum void ratio (\(e_{max}\)) [23].  
\[\mathrm{D_{r}(\%)=\frac{e_{\mathrm{max}}-e}{e_{\mathrm{max}}-e_{\mathrm{min}} }\times 100} \tag{1}\]  
The change in the groundwater level refers to the height toward the ground surface from the upper part of the sewer pipe (bottom of the soil box), and the ground subsidence tendency with respect to the degree of saturation of the ground was examined. The water content during compaction has no significant impact on ground subsidence [24; 25]. Thus, the water content was set to 5%, which is sufficient for model ground simulations [26; 27].  
The fines content was adjusted by adding completely dried marine clay to the standard sand. Based on this, the ground subsidence pattern with respect to the particle size characteristics of the ground was assessed. The standard sand and clay were mixed by calculating their weight for a relative density of 60%, and the ground subsidence pattern with respect to the groundwater level was analyzed by setting the fines content to 5%, 7.5%, 10%, 12.5%, 15%, and 20%. The experiment was performed under 24 cases of experimental conditions, as listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Case** & **Relative Density (Dr)** & **Fines Content (\%)** & **Groundwater Level (cm)** & **Water Content (\%)** \\ \hline
1 & 75\% & & 10 \\
2 & (1.533 t/m3) & 0 & 20 \\
3 & & 30 \\
4 & & 10 \\
5 & & 0 & 20 \\
6 & & 30 \\
7 & & 10 \\
8 & & 5 & 20 \\
9 & & 30 \\
10 & & 10 \\
11 & & 7.5 & 20 \\
12 & & 30 \\
13 & 60\% & 10 & 20 \\
14 & (1.498 t/m3) & 10 & 20 \\
15 & & 30 \\
16 & & 10 \\
17 & & 12.5 & 20 \\
18 & & & 30 \\
19 & & & 10 \\
20 & & 15 & 20 \\
21 & & & 30 \\
22 & & & 10 \\
23 & & 20 & 20 \\
24 & & & 30 \\ \hline \end{tabular}
\end{table}
Table 3: Experimental conditions.
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 3 Single Damage Location Detection
### Detection Result  
The training set and the valid set are inputted into the proposed convolutional neural network. The training accuracy and the valid accuracy increase with the increase of the number of iteration steps, and finally reach a stable state. Training accuracy and valid accuracy are shown in Figure 6. When the number of iteration epoch is 100, the training accuracy is 1.0 and the valid accuracy is 0.9953. The detection accuracy has met the requirements of engineering.  
A detection model can be obtained by training the dataset. Test set is used to test the detection performance of the model, and the test result is shown in Table 3. The number of samples in the test set is 396. The recognition accuracy of \"c0\" is 100%, which is the highest. The recognition accuracy of \"c6\" is the lowest, at 92.2%. The average recognition accuracy of the four categories is 96.7%. The result showed that this method can identify single damage location accurately.  
In order to display the predicted results of the test set more intuitively, a visualization tool called \"t-SNE\" was employed to visualize the raw data and the output data of the convolutional neural network. \"t-SNE\" is a nonlinear dimensionality reduction technique, it is particularly well suited for the visualization of high-dimensional datasets. Thus, the raw data and the output data of CNN are displayed in two dimensions, as shown in Figure 7. The two-dimensional display of raw data is terrible. There is no distinction between the categories. However, the two-dimensional display of the output data of CNN is perfect. There is a clear distinction between the categories. The results showed that the proposed convolutional neural network has a powerful ability to extract features automatically.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Count**} & \multicolumn{8}{c}{**Predicted Damage Location**} \\ \cline{2-9}  & **c0** & **c1** & **c6** & **c11** & **Total** & **Accuracy** \\ \hline \multirow{4}{*}{Actual damage location} & c0 & 88 & 0 & 0 & 0 & 88 & 100\% \\ \cline{2-9}  & c1 & 4 & 107 & 1 & 0 & 112 & 95.5\% \\ \cline{2-9}  & c6 & 7 & 0 & 95 & 1 & 103 & 92.2\% \\ \cline{2-9}  & c11 & 0 & 0 & 1 & 92 & 93 & 98.9\% \\ \cline{2-9}  & Total & 99 & 107 & 97 & 93 & 396 & Overall: 96.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The test results.  
Figure 6: Training accuracy and valid accuracy.  
Figure 7: The two-dimensional display of data. (**a**) raw data (**b**) output data of CNN.
# Toward Efficient Mobile Electric Vehicle Charging under Heterogeneous Battery Switching Technology
## 3 System Model
### Heterogeneous Battery Switching Process  
The process of heterogeneous BS is presented in Figure 2 step-by-step and elaborated below.  
* Step 1: The GC checks each BSS in the network periodically and aggregates update information of each BSS. Aggregated information includes the ongoing charging session, number of depleted batteries in the switching station, battery stock availability in the switching station, and available battery types.
* Step 2: The mobile EV checks their SOC continuously. Once the SOC is below the minimum threshold value, the mobile EV informs the GC to require BS service along with its battery type.
* Step 3: Once a battery switch demand is requested to GC, the GC compiles the list of BSS in the network and ranks all of them according to waiting time and switching queue of vehicles. After this job is over, aggregated information regarding the required battery type of EV sends the report back to \(R_{V}\) and refers to the best choice in the network.
* Step 4: The EV confirms the recommendation and sends the reply to GC for reservation.  
Figure 1: Generalized detailed overview of heterogeneous BSS process.
# An Approach for Anomaly Detection in Network Communications Using _k-Path_ Analysis
## 4 Three-State Markovian Approach
### Modeling, Estimation, and Hypothesis Testing
)\) is asymptotically distributed according to a \(\chi^{2}\) distribution with degrees of freedom equal to the number of free parameters. However, this only applies under certain conditions (see [PERSON] and [PERSON], 2001, p. 516 [26]). Let \(\Lambda(e,\cdot)\) denote the distribution of \(\lambda(e,\cdot)\). Let \(\Lambda(e,\cdot)\) be defined as follows:  
\[\Lambda(e,\cdot)=B(e,\cdot)\times G(e,\cdot)\ \ \text{where}\ \ B(e,\cdot) \sim\text{Bernoulli}(p_{e})\]  
and  
\[G(e,\cdot)\sim\text{Gamma}(\tilde{\chi}_{e},\eta)\]  
where \(p_{e}\) and \(\tilde{\chi}_{e}\) are specific parameters exclusively for \(\Lambda(e,\cdot)\), and \(\eta\) is specific exclusively for \(\Lambda(\gamma,\cdot)=\sum_{e\in\gamma}\Lambda(e,\cdot)\). Given that \(\lambda(e,\cdot)\) could be zero, \(\Lambda(e,\cdot)\) must have a point mass at zero, captured by \(B(e,\cdot)\). To model the positive part of the distribution of \(\Lambda(e,\cdot)\), the Gamma distribution is appealing as it equals a \(\chi^{2}\) distribution with degrees of freedom \(\
u_{e}\) when \(\tilde{\chi}_{e}=\frac{\
u_{e}}{2}\) and \(\eta=2\)The asymptotic distribution of \(\lambda(e,\cdot)\) is then the sum of independent random variables distributed according to a \(\chi^{2}\) distribution with zero inflation. Let \(\lambda_{r}(e)=\lambda(e,I_{r})\) be the statistic of the generalized likelihood ratio test on \(e\) for a sequence of observations \(\mathbf{s}(e,I_{r})\); through maximum likelihood estimation, for a sample of size \(m\), \((\lambda_{1}(e),\lambda_{2}(e),\cdots,\lambda_{m}(e))\), we estimate the parameters \(p_{e}\) and \(\xi_{e}\). The likelihood of the sample is given by:  
\[\mathcal{L}(p_{e},\xi_{e},\eta|\lambda_{1}(e),\lambda_{2}(e),\cdots,\lambda_{ m}(e))=\prod_{q=1}^{m}(1-p_{e})^{\mathbb{I}(\lambda_{q}(e)=0)}\times\left(\frac{ \lambda_{q}(e)^{\xi_{e}-1}\exp^{-\frac{\lambda_{q}(e)}{\eta}}}{\Gamma(\xi_{e} )\eta^{\xi_{e}}}\right)^{\mathbb{I}(\lambda_{q}(e)>0)}.\]  
The log-likelihood is given by:  
\[ \begin{split}\ell(p_{e},\xi_{e},\eta|\lambda_{1}(e),\lambda_{2} (e),\cdots,&\lambda_{m}(e))=\sum_{q=1}^{m}\mathbb{I}(\lambda_{q} (e)=0)\text{log}(1-p_{e})+\mathbb{I}(\lambda_{q}(e)>0)\\ &\times\left((\xi_{e}-1)\text{log}(\lambda_{q}(e))-\frac{\lambda_ {q}(e)}{\eta}-\xi_{e}\text{log}(\eta)-\text{log}(\Gamma(\xi_{e}))\right)\end{split}  \tag{1}\]
* **Estimation of \(p_{e}\)**: To achieve this, we advocate for the use of maximum likelihood estimation.