esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
The connection diagram is shown in Figure 16. Corresponding pressure drop calibration is shown in Figure 17. The pressure drop was within the allowable pressure drop range, meeting the requirements of gas transmission. To highlight the advantages, we also evaluated a star-star pipeline network layout. Figure 18 shows the optimized star-star pipeline network configuration. A comparison of investments between different design schemes is presented in Figure 19. The original layout scheme required a total investment of CNY 5650.80 \(\times\) 10\({}^{4}\), while the optimized star-star layout incurred CNY 4938.28 \(\times\) 10\({}^{4}\), marking a 12.6% reduction in cost. The optimized star-tree layout scheme further reduced the total investment to CNY 4728.28 \(\times\) 10\({}^{4}\), which is 16.3% lower than that of the original scheme. These results demonstrate that both optimized pipeline network layouts achieve cost savings, with the star-tree configuration offering greater advantages. This is attributed to the shorter length of the second-level gathering pipelines between stations and the plant in the star-tree network, resulting in nearly 50% savings in pipeline investment costs and thereby reducing the overall network expenditure. Figure 12:0 Illustration of pipeline network connection simulation. Figure 12:1 Pipeline network layout pressure drop calibration results.
# Dentin Bond Strength of Calcium Silicate-Based Materials: A Systematic Review of In Vitro Studies
## 4 Discussion
### CSC Sealers  
CSC sealers including Ceraseal, EndoSequence BC, EndoSeal MTA, and BioRoot RCS, exhibited higher bond strengths compared to resin-based sealers like ADSEAL and RealSeal [11; 22]. This difference is likely attributed to intrafibrillar apatite deposition. Variances in the chemical composition of CSC sealers can influence their interaction with root dentin, significantly impacting their adhesion [22]. Calcium silicate-based cements possess hydrophilic properties and an alkaline pH, rendering them insoluble in tissue fluids and resistant to shrinkage during setting. Moreover, the moisture environment within the tooth plays a crucial role in the adhesion between CSC and root canal dentin. These sealers also demonstrate potential bioactive properties, forming calcium hydroxide and hydroxyapatite upon contact with water [11].  
Within the evaluated calcium silicate cements, TotalFill BC showcased the highest bond strength values when compared to BioRoot RCS, EndoCPM, Biodentine, and MTA [25; 39]. TotalFill is a recognized CSC known for its biological properties (biocompatibility, bioactivity, and antibacterial activity) and favorable physicochemical attributes [21]. In contrast to conventional filling cements, TotalFill BC's setting reaction is triggered by the moisture present in the dentinal tubules. This moisture prompts the sealer to form hydroxyapatite, ensuring optimal chemical adhesion between dentin and cement [21].  
On the other hand, MTA Fillapex presented the lowest adhesion strength values when compared to Apexit Plus, AH Plus, TotalFill, Biodentine, Endoseal MTA and SureSeal Root [29; 31; 33; 41]. MTA Fillapex is a cement based on the MTA formula. Its biggest difference is the resin matrix that it presents in its composition. It was created with the intention of combining the physicochemical properties of resins and the biological properties of MTA [48]. Its lower bond strength compared to other resin-based cements is because MTA does not bond to dentin, but the presence of the resins contained in MTA Fillapex increases the flow properties [53].  
It should be highlighted that the included studies did not assess the POBS of the tested sealers from the perspective of the differences in their composition, i.e., the proportion of calcium silicate, radiopacifier, fillers, etc. Variations in the materials' composition may lead to changes in their physical-chemical properties. This has been described previously regarding CSCs' biological properties, in the way that the inclusion of additives like resin may influence their cytocompatibility negatively [54]. Conclusions in this regard cannot be extracted from the evidence assessed in this systematic review.
## 3 Analysis of Parameter for Designing Pickup Coil in PCB
### External Magnetic Field Interference  
Various external current traces can exist in the vertical and horizontal directions around the pickup coil in the PCB, as shown in Figure 8. As the pickup coil A is located inside the PCB as shown in Figure 12, in case the current flows through the external trace located at the distance of \(x\) and height \(y\) from the pickup coil, the external magnetic flux can pass through the closed-loop area of the coil. Moreover, the magnetic flux is given by Equation (21), and it is added to the amount of original magnetic flux flowing into the magnetic flux incident area of the pickup coil. The total magnetic flux (22) \(\phi_{tot}\) is obtained by summing the magnetic flux \(\phi_{1}\) due to the original current and the magnetic flux \(\phi_{ext}\) due to the external current.  
\[\phi_{ext}=\frac{\mu_{o}\cdot b\cdot I}{4\pi}\ln\frac{4x^{2}+\{2(y+a)\}^{2}}{4 x^{2}+4y^{2}} \tag{21}\]  
\[\phi_{tot}=\phi_{1}+\phi_{ext1}+\phi_{ext2} \tag{22}\]  
Accordingly, the voltage at the output stage of the coil is as shown in the following equation, and it affects the mutual inductance and measurement accuracy of the coil.  
\[\begin{split} v_{tot}=v_{1}+v_{est1}+v_{est2}+\\ N\frac{\mathrm{d}\phi_{tot}}{\mathrm{d}t}=M\frac{\mathrm{d}_{tot }}{\mathrm{d}t}\end{split} \tag{23}\]  
where \(v_{1}\) is the induced voltage due to the original current, and \(v_{ext}\) is the induced voltage due to the external current.  
Therefore, it is necessary to consider the influence of the position of the external conductor in situations in which accurate switch current measurement in case of designing the pickup coil in PCB is required.
## 4 Simulation of Parameter Identification for Envelope Current Signal
### Analysis of Envelope Current Signal of an Electric Locomotive  
The following is a detailed analysis of an electric locomotive running current signal. The sampling frequency is \(f_{\mathrm{s}}\) = 5000 Hz, and the total number of data points is \(N\) = 5000. The parameters of the DE algorithm are selected as for the previous example.  
First of all, the reconstructed signal and absolute error are obtained by using Hanning WIFFT and the envelope parameter estimation method, as shown in Figure 6.  
According to Figure 6, it is obvious that the actual measured current signal has the characteristic of an oblique envelope. Additionally, the larger prediction errors of envelope parameters result in significant difference between the reconstructed signal and the original signal. Furthermore, the RMSE is 0.1834 and the maximum absolute error is about 0.3966.  
Then, the improved method based on WIFFT and DE is used in the analysis. The reconstructed signal and absolute error are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \(a^{*}\) **(\%)** & \(b^{*}\) **(\%)** & **RMSE** & \(max\)**[x(n) \(-x_{c}\)(n)]** \\ \hline
40 dB & 0.6669 & 0.0576 & 0.7762 & 2.5009 \\
50 dB & 0.1529 & 0.0284 & 0.2557 & 1.0617 \\
60 dB & 0.0568 & 0.0117 & 0.1082 & 0.3385 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter identification and error of the noisy oblique envelope signals.  
Figure 6: Analysis results of envelope current signal of an electric locomotive when employing the WIFFT and envelope parameter estimation method: (**a**) The comparison of original signal and the reconstructed signal; (**b**) Absolute error of original signal and reconstructed signal.  
Figure 7 demonstrates that the envelope parameters, harmonic amplitude and phase parameters are more accurate after the optimization of DE algorithm. In addition, the reconstructed signal is closer to the original signal, which means the precision of approximation is higher. Moreover, the RMSE is 0.0516 and the maximum absolute error is about 0.2116.
## 4 Evaluation Study
### Results
In an attempt to compare the results derived from our evaluation study with the results derived from other studies conducted in the cybersecurity domain reporting CTI processes, regarding each metric, we observe that:  
* **Silhouette coefficient**: our result (\(s=0.792\)) is similar to or better than the coefficient scores reported regarding the clustering of the [PERSON] and [PERSON] families (\(s=0.725\)) and [PERSON] family (\(s=0.786\)) used in CTI processes [47], clusters containing malicious URLs (\(s=0.383\)) [48], and TTP profile extraction and group clustering in IoT (0.610) [49]. * **Calinski-Harabasz score**: our result (\(s=2350\)) is in between the scores reported in other studies regarding TTP profile extraction and group clustering in IoT (\(s=3416\)) [49] and uncovering cybertimes in social media through natural language processing techniques (\(s=1088\)) [50]. Figure 5: Heatmaps produced for the evaluation metrics of each experiment run. (**a**) Silhouette coefficient. (**b**) Calinski–Harabasz score. (**c**) Davies–Bouldin index. * **Davies-Bouldin index**: our result (\(DB=0.348\)) is similar or lower to the scores reported regarding TTP profile extraction and group clustering in IoT (\(DB=0.737\)) [49] and the enhancement of data quality in real-time CTI (\(DB=0.561\)) [51]. * **Performance timing**: the maximum execution time (in seconds) for the clustering was \(t=209\). The DBSCAN performance timings vary widely in the literature (e.g., \(t=11\)[52], \(t=83\)[53], and \(t=2460\)[54]). However, this metric highly depends on the computational resources, the data size, and the complexity of the machine-learning problem. Since CAVeCTIR contributes to the post-incident forensics investigation, the reported timing is acceptable.
# Selection of Grid Road Networks Based on Raster Data
## 3 Method for Multiscale Representation of High-Density Transportation Networks
### Elimination of Road Intersection Pixels  
Due to the local non-uniformity of road superpixel segmentation, redundant pixels at road intersections can be formed. For example, Figure 4a shows the selected road superpixels after road superpixel matching and selection, and we can see that many projecting pixel blocks exist at the road intersections, such as superpixel \(I\) in the partially enlarged area in Figure 4b. To remove the redundant pixels at road intersections and ensure geometric accuracy, we can perform the following steps in detail:  
Figure 3: Road superpixel matching and selection. (**a**) Road superpixels at two adjacent levels. (**b**) Main direction generated by the Hough line detection method. (**c**) Marking of road intersection superpixels. (**d**) Matching of road intersection superpixels. (**e**) Superpixel graph construction and shortest-path calculation. (**f**) Final road selection result.  
Step 1: Calculate the intersecting lines between the selected road superpixels and original raster road networks. In Figure 4a,b, the red pixels are the results of calculating the intersecting lines;  
Step 2: Endpoint detection of intersecting lines. When the eight neighbors of road pixel \(E\) contain only one road pixel, road pixel \(E\) can be determined as an endpoint pixel. In Figure 4b, superpixel \(I\) contains three intersecting lines marked with green and six endpoint pixels, namely, \(E_{1}\), \(E_{2}\), \(E_{3}\), \(E_{4}\), \(E_{5}\), and \(E_{6}\), marked with black;  
Step 3: Label the intersecting lines that have no adjacent superpixels, such as the intersecting line \(E_{5}E_{6}\), finding and connecting the nearest endpoint pixels of the endpoint pixels of the labeled intersecting lines. For example, the nearest endpoint pixels of endpoint pixels \(E_{5}\) and \(E_{6}\) are \(E_{2}\) and \(E_{4}\), respectively; in this case, the endpoint pixels \(E_{2}\) and \(E_{4}\) should be connected;  
Step 4: Remove all the pixels located in the closed area of the newly connected line segment and the labeled line segment in the road intersection superpixel. For example, in Figure 4b, the pixels located in the closed area of the new connected line segment \(E_{2}E_{4}\) and labeled line segment \(E_{5}E_{6}\) should be removed. The remaining pixels constitute the final selection results of the raster road networks. Figure 4c shows the results of eliminating redundant pixels at intersections of the road network in Figure 4a, and Figure 4c is the final selection result of the road networks in Figure 3a.
## 4 Verification and Accuracy Analysis of Single-Frequency Occultation Processing
### relTEC Correctness Verification
The analysis of the relTEC change rate form above three angles--direct plot the relTEC change rate with the altitude of 0-120 km (Figure 1), y = x consistency comparison (Figure 2), Spearman correlation coefficient histogram (Figure 3)--shows that in the occultation observations of BDS B1I signals, the relTEC calculated by the combination of single-frequency pseudorange carrier phase shows good consistency with the relTEC calculated by the dual-frequency carrier phase, and the result of the single-frequency occultation processing is ideal.
# C3 VFC: A Method for Tracing and Quantification of Microglia in 3D Temporal Images
## 4 Results
### Measuring Tracing Consistency  
The temporal tracing result using our method is attached as a video in the supplement. The video demonstrates the consistent tracing result over time and fluidity of motion. Consistent temporal tracing is imperative for accurate motility and morphology analysis. Consistency in this context means the reconstruction results and accuracy are agreeable over time. For example, correspondence between branches would be necessary to find the change in length or the velocity of a branch path over time. Figure 8 depicts snapshots of the 3D traced image over a few time points.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **C3 VFC** & **APP2** & **FMST** & **MST** \\ \hline \(ESA_{12}\) & **3.85 \(\pm\) 2.67** & 6.18 \(\pm\) 7.80 & **4.60 \(\pm\) 0.889** & 4.92 \(\pm\) 1.08 \\ \(ESA_{21}\) & **7.77 \(\pm\) 5.95** & 15.4 \(\pm\) 13.7 & 29.6 \(\pm\) 27.23 & 33.4 \(\pm\) 28.5 \\ ABESA & **5.81 \(\pm\) 3.64** & 10.8 \(\pm\) 7.71 & 17.1 \(\pm\) 13.6 & 19.2 \(\pm\) 14.2 \\ DSA & **7.93 \(\pm\) 4.07** & 13.1 \(\pm\) 8.76 & 18.0 \(\pm\) 14.2 & 20.3 \(\pm\) 14.7 \\ PDS & **0.614 \(\pm\) 0.124** & 0.779 \(\pm\) 0.114 & 0.890 \(\pm\) 0.028 & 0.899 \(\pm\) 0.0197 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance evaluation of results. The five measures computes the distance of the baseline manually traced results to the results from the respective methods. A lower value signifies better performance for all distance measures. The values in bold signify the best scores for each measure.  
We measure consistency over consecutive time frames by calculating the average of the standard deviation of the measures between each time frame. The consistency results are shown in Figure 9. The five measures describe the differences between the reconstruction results and the baseline result. Therefore, the standard deviation between the measures over time frames show how similar the results are over time. The results from C3 VFC, FMST, and MST show a low average standard deviation which constitute to better consistency. FMST and MST results are consistently over segmented which could be a desired attribute.
# Coupled Elastic-Plastic Damage Modeling of Rock Based on Irreversible Thermodynamics
## 2 Basic Framework of the Elastoplastic Damage Coupled Model
### Elastic Damage Model  
The damage criterion is a function of the damage-related thermodynamic force \(Y\). According to statistical damage theory and laboratory data, it has been found that damage evolution follows an exponential function [68; 97; 98; 99]. Therefore, the damage function can be expressed as:  
\[F^{\omega}=\omega_{c}(1-\exp(B_{\omega}Y))-\omega \tag{45}\]  
where \(\omega_{c}\) is the asymptotic damage value during the residual deformation stage; \(B_{\omega}\) controls the damage evolution rate.  
In the case of elastic damage without plastic flow, one can set \(F^{\omega}=G^{\omega}\), and by the consistency condition:  
\[d\lambda^{\omega}=\frac{\partial F^{\omega}}{\partial Y^{\varepsilon}}\epsilon ^{\varepsilon}:D_{0}:d\varepsilon/H^{\omega} \tag{46}\]  
\[H^{\omega}=\frac{\partial F^{\omega}}{\partial\omega}\frac{\partial G^{\omega }}{\partial Y^{\varepsilon}} \tag{47}\]  
Figure 2: Loading direction and bedding plane angle \(\theta\).  
The incremental form of the stress-strain relationship is:  
\[\left\{\begin{array}{l}d\sigma=D^{\epsilon\omega}:d\epsilon\\ D^{\epsilon\omega}=D-\frac{\partial\zeta^{\omega}}{\partial Y^{\epsilon}}\frac{ \partial Y^{\epsilon\omega}}{\partial Y^{\epsilon}}(D_{0}:\epsilon^{\epsilon })\otimes(\epsilon^{\epsilon}:D_{0})/H^{\omega}\end{array}\right. \tag{48}\]  
where \(D^{\epsilon\omega}\) is the fourth-order tensor of elastic damage stiffness.  
When only elastic deformation occurs, the damage-related thermodynamic force \(Y\) can be obtained from Equations (26) and (34). When plastic deformation occurs, it is necessary to consider the damage-related thermodynamic force \(Y^{P}\) caused by plastic deformation, using the plastic strain increment and the equivalent plastic strain increment to construct the plastic thermodynamic potential \(\phi^{p}\). Given the nonlinear relationship between stress and plastic strain, it is specifically defined as:  
\[\phi^{p}(\gamma^{p},\omega)=(1-\omega)\left(\int_{0}^{\epsilon_{m}^{p}}pde_{m }^{p}+\int_{0}^{\gamma^{p}}qd\gamma^{p}\right) \tag{49}\]  
where \(de_{m}^{p}\) is the plastic strain increment. The expression is:  
\[d\epsilon_{m}^{p}=tr(de^{p})/3 \tag{50}\]  
Combining Equations (26), (34) and (49), the expression for the damage-related thermodynamic force \(Y\) is:  
\[Y=-\frac{r}{2}\epsilon^{\epsilon}:D:\epsilon^{\epsilon}-(1-r)\left(\int_{0}^{ \epsilon_{m}^{p}}pde_{m}^{p}+\int_{0}^{\gamma^{p}}qd\gamma^{p}\right) \tag{51}\]  
From Equations (49) and (51), it is evident that the defined \(\phi^{p}\) and \(Y^{P}\) in this study depend on the plastic strain and stress state, and they can reflect the impact of changes in plastic strain and stress on the development of damage in rock materials.
## 3 Results and Discussion
### Influence of Projectile Material
#### 3.1.3 Damage Morphology
The damage area is relatively centralized with a rectangular perforative opening at the impact point. The area of the perforative opening is slightly larger than the cross-section of the CFRP projectile. The damage area on the impact surface surrounds the perforative opening and consists of slight damage and a certain degree of cracking and delamination. At the exit surface, the damage to the braided composite is much more serious and consists of massive fiber breakage, yarn pull-out, severe matrix cracking, and delamination in the bulged broom area. Figure 11: Damage to 2D triaxial braided composites impacted by titanium-alloy projectile. Figure 12: Local damage patterns of 2D triaxial braided composites impacted by titanium-alloy projectile. For perforation with various impact velocities, the morphology of the damage to the 2D triaxial braided composite target plate is of a similar pattern, although with a different extent of damage. A rectangular opening with an area of shear damage and local matrix cracking appears on the impact surface. In addition, a local bulge deformation area appears with fiber breakage and delamination, and the main failure modes in the damaged area are fiber tensile failure, yarn pull-out, matrix cracking, and delamination. From the analysis above, the target plate absorbs the least energy upon impact of the titanium-alloy blade projectile. The main reason for this result is that the titanium-alloy blade projectile is the hardest of the projectiles and has the smallest cross-sectional area for the impact surface. Therefore, when a titanium-alloy projectile impacts the composite target, the target plate is damaged in a shear pattern, which hinders the target plate in dissipating energy through a bending deformation. In addition, a shorter projectile-target interaction time means that the target plate has insufficient time to deform. As a result, the damage area of the target plate is also small.
# PID Control Assessment Using L-Moment Ratio Diagrams
## 1 Introduction
Only industrial verification of theoretical ideas tested by simulation makes it possible to definitively confirm their sense and practical applicability. Simulation studies make it possible to safely and quickly verify various ideas, before their plant tests, which bring ultimate confirmation. Such an arrangement of activities is presented in this paper. Simulation studies for classical benchmarks for PID controllers preceded industrial validation conducted at an ammonia plant at Grupa Azoty, Zaklady Azotowe \"Pulawy\" SA in Poland. The industrial validation used long-term data from the plant's operation collected after a project of comprehensive modernization of the installation control system, i.e., the design, tuning, and implementation of an advanced process control (APC) solution. The main contribution of this work is the introduction of an LMRD to control the performance assessment. This work consists of two elements. First, a simulation analysis was performed using common process PID control benchmarks. Next, an industrial validation was performed using an ammonia synthesis installation. Different from standard LMRD formulations, a new and appropriate scheme of LMRD is proposed and tested.The research compared the applied L-moments using well-known control performance measures: integral square error (ISE), integral absolute error (IAE), and basic KPIs of the control loop step response: overshoot (\(\kappa\)) and settling time (\(T_{\text{sel}}\)). Finally, the LMRDs were validated in a real application from the heat power generation industry. This paper starts with Section 2 describing the methods followed by the simulation analysis included in Section 3. Section 4 presents the industrial study results and Section 5 concludes the work and presents observed open issues for further research.