esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Dynamic Model for Free Vibration
\begin{split} A_{11}&=\lambda^{2}-\Omega^{2}+\frac{1-\mu}{2} \bigg{(}1+\frac{h^{2}}{12R^{2}}\bigg{)}n^{2},A_{12}=-in\lambda\frac{1+\mu}{2},\\ A_{13}&=in^{2}\lambda\frac{1-\mu}{2}\frac{h^{2}}{12R^{2}}-i\mu \lambda-i\lambda^{3}\frac{h^{2}}{12R^{2}},A_{21}=in\lambda\frac{1+\mu}{2},\\ A_{22}&=n^{2}-\Omega^{2}+\frac{1-\mu}{2}\bigg{(}1+\frac{h^{2}}{4R^{2}} \bigg{)}\lambda^{2},A_{23}=n+n\lambda^{2}\frac{h^{2}}{12R^{2}}\frac{3-\mu}{2},\\ A_{31}&=in^{2}\lambda\frac{h^{2}}{12R^{2}}\frac{1-\mu}{2}-i \lambda\mu-i\lambda^{3}\frac{h^{2}}{12R^{2}},A_{32}=-n-n\lambda^{2}\frac{h^{2} }{12R^{2}}\frac{3-\mu}{2},\\ A_{33}&=\Omega^{2}-\frac{h^{2}}{12R^{2}}\Big{(}n^{2}-1\Big{)}^{2}- \lambda^{2}\frac{h^{2}}{12R^{2}}\Big{(}2n^{2}+\lambda^{2}\Big{)}-1\end{split}  \tag{6}\]  
The non-trivial solutions of Equation (5) indicate the determinant of the characteristic matrix \([A_{3\times 3}]\) is zero. Figure 1: Coordinate system used for modelling. \[\det([A_{3\times 3}])=0 \tag{7}\]  
The expansion of Equation (7) offers a characteristic equation with dimensionless frequency \(\Omega\) and wavenumber \(\lambda\). The characteristic equation below can calculate the natural frequencies after selecting the dimensionless wavenumber based on the clamped-clamped boundary condition [17]. \[a_{6}\Omega^{6}+a_{4}\Omega^{4}+a_{2}\Omega^{2}+a_{0}=0 \tag{8}\]  
Symbols \(a_{i}\) (\(i=0,2,4,6\)) are the coefficients of Equation (8). The smallest of the three roots represents the flexural vibration, and the other two are in-plane vibrations [15]. Furthermore, the eighth-order algebraic equation for the dimensionless axial wavenumber \(\lambda\) can be obtained when dimensionless frequency \(\Omega\) is known. \[b_{8}\lambda^{8}+b_{6}\lambda^{6}+b_{4}\lambda^{4}+b_{2}\lambda^{2}+b_{0}=0 \tag{9}\]  
Symbols \(b_{j}\) (\(j=0,2,4,6,8\)) are the coefficients of Equation (9). The four pairs of roots for the polynomial function are of three types: pure real, pure imaginary, and conjugate complex. Moreover, the real, pure, imaginary, and complex roots correspond to propagating, near-field, and attenuating standing waves [17]. The basic properties of three vibrating waves explain the generation and propagation mechanism of the dynamic response of the shield tunnel lining.
# High Efficiency Continuous-Variable Quantum Key Distribution Based on ATSC 3.0 LDPC Codes
## 3 Proposed LDPC Codes for Information Reconciliation  
It can be easily seen from Equation (2) that for a given FER, a high reconciliation efficiency results in a good performance of final secret key rate. In order to further improve the performance of reconciliation efficiency, we first construct SC-LDPC codes based on ATSC LDPC codes introduced in Section 2.2, and then construct QC-LDPC codes by adopting the parity-check matrices of ATSC LDPC codes as the base matrices.  
Since the SNR of the quantum channel can be very low, we need LDPC codes of low code rates (i.e., \(R\leq 1/3\)) to be applied in the CVQKD system in order to show excellent error-correcting performance even at low SNR. As shown in Table 2, we constructed SC-LDPC codes and QC-LDPC codes based on ATSC LDPC codes at the code rates of \(2/15\) and \(5/15\), and applied these codes to the information reconciliation part of the CVQKD system.
## 3 Modelling and Simulation
### Mode #4 Non-Harmonic Supply and Nonlinear Load  
#### 3.4.1. Symmetrical Network, Non-Symmetric Load  
The load asymmetry has been provided by step change of the resistor \(R_{a}=18.4\ \Omega\) to \(9.2\ \Omega\) in phase \(a\) at the time of \(0.1\ \mathrm{s}\) (after settling).  
Figure 25: Apparent—(a), active—(b), blind—(c), and distortion (d) power components during transient states for 3-, 5-, and 2-phase systems under inverter-supplied linear RL load.  
Figure 26: Network voltages (**a**), load, and neutral currents (**b**).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Load** & \(R_{a}\) **[f]** & \(L\) **[mH]** & \(Z\) **[\(\Omega\)]** & \(\tau_{a}\) **[ms]** & \(\varphi_{a}\) **[\({}^{\circ}\)el.]** & \(\cos\varphi_{a}\) **[\(-\)]** & \(\sin\varphi_{a}\) **[\(-\)]** \\ \hline before & 18.4 & 43.93 & 23 & 2.39 & 36.76 & 0.8 & 0.6 \\ after & 9.2 & 43.93 & 16.59 & 4.78 & 56.33 & 0.5545 & 0.8322 \\ \hline  & & & & & & & \\ \end{tabular}
\begin{tabular}{c c c c c c} \hline
**Time** & \(U_{a,rms}\)**[V]** & \(U_{\beta,rms}\)**[V]** & \(U_{0,rms}\)**[V]** & \(I_{a,rms}\)**[A]** & \(I_{\beta,rms}\)**[A]** & \(I_{0,rms}\)**[A]** \\ \hline t = 0.1 s & 229.7 & 229.8 & 0 & 9.971 & 9.998 & 0 \\ t = 0.2 s & 229.7 & 229.7 & 0 & 12.42 & 9.991 & 1.846 \\ \hline \end{tabular} \(R_{a}\) changed from 18.4 \(\Omega\) to 9.2 \(\Omega\) at 0.2 s.  
\end{table}
Table 37: Load parameters for steady states before and after load change.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Time** & \(S_{av}\) **[VA]** & \(P_{av}\) **[W]** & \(Q_{av}\) **[Var]** & \(D_{av}\) **[VAd]** & \(PF\) **[\(-\)]** & \(P_{0 av}\) **[W]** & \(THD_{i}\) **[\%]** \\ \hline t = 0.1 s & 6888 & 5510 & 4134 & 0 & 0.8 & 0 & 0.0 \\ t = 0.2 s & 7727 & 5442 & 5404 & 957 & 0.7039 & 0 & 12.39 \\ \hline \end{tabular}
\end{table}
Table 38: Simulation results before and after the change (at steady states).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Time** & \(U_{a,rms}\)**[V]** & \(U_{\beta,rms}\)**[V]** & \(U_{0,rms}\)**[V]** & \(I_{a,rms}\)**[A]** & \(I_{\beta,rms}\)**[A]** & \(I_{0,rms}\)**[A]** \\ \hline t = 0.1 s & 229.7 & 229.8 & 0 & 9.971 & 9.998 & 0 \\ t = 0.2 s & 229.7 & 2 In this case, the network neutral is connected to the load neutral. Therefore, current \(I_{N}\) flows through the neutral wire despite the voltage zero-sequence being zero. It is interesting that active power in steady state after the transient is a little bit smaller than before, Figure 27b, although load resistance in phase \"a\" was changed to one-half. However, the phase current \(i_{a}\) and current of neutral \(I_{N}\) are in antiphase (shifted by 180\({}^{\circ}\) el.), Figure 27b. So, the power loss (Joule loss) in neutral wire acts negatively on the load active power. During the transient state, waveforms feature oscillating characters, and power component \(Po\) is zero due to zero-sequence component \(Uo\) equal to zero.
## 4 Results
### Inlet and Outlet Area on the Cavity
The changes in CIDZ and EIDZ as \(A/V^{2/3}\) increases are compared from the initial moment to \(t_{s1}\) and from moment \(t_{Crest}\) to \(t_{s2}\), with the time interval from \(t_{Crest}\) to \(t_{s2}\) recorded as \(\Delta t_{s2}\), as shown in Figure 10.  
This analysis examines the effect of changes in \(A/V_{Vessel}\)\({}^{\frac{2}{3}}\) during the depressurization process on CIDZ and EIDZ in conjunction with Equation (4). An increase in \(A/V_{Vessel}\)\({}^{\frac{2}{3}}\) results in a corresponding increase in \(q\), which directly leads to an increase in the volume of CIDZ. Simultaneously, it reduces the absolute time required to reach \(\overline{m}_{s}\), bringing EIDZ closer to the inlet and outlet of the cavity, thereby enhancing its driving effect on the gas within the cavity. Based on the previously analyzed mechanism of the influence of increasing \(A/V_{Vessel}\)\({}^{\frac{2}{3}}\) on the inertial forces within the cavity, a further examination of its impact on the axial loads within the cavity is conducted. Figure 11 compares the variations in the relative axial force \(\overline{F_{A}}\) at the A end face and the relative axial force \(\overline{F_{B}}\) at the B end face, along with the difference in relative axial forces \(\Delta\overline{F}\) as \(A/V_{Vessel}\)\({}^{\frac{2}{3}}\) increases from 0.011 to 0.272. This analysis primarily focuses on the first oscillation period. A comparison is made between the minimum value during the decrease in the relative axial force and the maximum value during the increase, with specific values presented in Table 7. It can be observed that as \(A/V_{Vessel}\)\({}^{\frac{2}{3}}\) increases, both \(\overline{F_{A}}\) and \(\overline{F_{B}}\) begin to oscillate, with their amplitudes and periods continuously increasing. Additionally, the phase difference between the two oscillations gradually increases, resulting in oscillations in \(\Delta\overline{F}\) with a significantly larger amplitude. Figure 10: _Cont._  
During the fast transient response of the flow path under the sudden change boundary, an increase in \(A/V_{Vessel}\)\({}^{\frac{2}{3}}\) directly amplifies the effects of inertial forces within the cavity. This not only induces oscillations in the axial load responses at both ends of the cavity but also results in a lack of synchronization in the oscillation phases; the larger the value of \(A/V_{Vessel}\)\({}^{\frac{2}{3}}\), the greater the phase difference. The aforementioned research indicates that, compared to current studies that do not consider the effects of inertial forces within the cavity, accounting for these forces increases the complexity of the axial load evolution process during the fast transient response under the sudden change boundary. Moreover, this complexity escalates with the increasing influence of inertial forces. Figure 11: _Cont._  
Figure 10: Comparison of flow field \(Ma\) variation in different \(A/V_{Vessel}\)\({}^{\frac{2}{3}}\) single cavity flow path (a) discharge process; (b) charge process.
## 5 Challenges and Future Research Directions
### Big Data and UDN Integration  
The integration of big data analytics with UDNs brings both advantages and challenges. While big data-driven optimization can improve resource allocation and energy efficiency, the large number of IoT devices generating extensive datasets leads to increased computational complexity and power consumption. Future research should focus on AI-powered data processing techniques that enable real-time network resource management and adaptation to traffic fluctuations [54].
# The Combined-Unified Hybrid Censored Samples from Pareto Distribution: Estimation and Properties
## Appendix C Proof of Theorem 3  
Proof.: Consider the relation  
\[\int\cdots\int A\alpha^{r}y_{1}^{-\alpha}y^{j}dx=E(Y^{j})=\frac{j!}{\alpha^{ j}},\]  
then  
\[\int\cdots\int Ay_{1}^{-\alpha}y^{j}dx=j!\alpha^{-(r+j)}.\] (A14)  
Integrating both sides of (A14) \((j+1)\) times over \(\alpha\), we get  
\[\int\cdots\int Ay_{1}^{-\alpha}\frac{y^{j}}{(\log y_{1})^{j}}dx=\frac{j!\alpha ^{-(r-1)}}{(r-1)r(r+1)\ldots(r+j-1))},\]then  
\[\int\cdots\int A\alpha^{r}y_{1}^{-\alpha}\frac{y^{j}}{(\log y_{1})^{j+1 }}dx = E\left(\frac{Y^{j}}{(\log y_{1})^{j+1}}\right)=\frac{j!(r-2)!\alpha}{(r+j-1 )!} \tag{101}\] \[= \alpha B(j+1,r-1),\]  
where \(B(j+1,r-1)\) is the beta function defined as \(B(j+1,r-1)=\int_{0}^{1}{t^{j}(1-t)^{r-2}dt}\).  
Similarly, integrating both sides of (100) \((j+2)\) times over \(\alpha\), we have  
\[E\left(\frac{y^{j}}{(\log y_{1})^{j+2}}\right)=B(j+2,r-2)\frac{\alpha^{2}}{j+1}. \tag{102}\]  
Now to find the mean and variance of \(\bar{\alpha}\), we get  
\[E\left(\frac{1}{\log y_{2}}\right)=E\left(\frac{1}{\log y_{1}-Y}\right)=E\left[ \frac{1}{\log y_{1}}\left(1-\frac{Y}{\log y_{1}}\right)^{-1}\right]=\alpha \sum_{i=0}^{\infty}B(j+1,r-1), \tag{103}\]  
using the the following relation \(\sum_{k=0}^{\infty}B(x,y+k)=B(x-1,y)\), see [PERSON] and [PERSON] [32], we have  
\[E\left(\frac{1}{\log y_{2}}\right)=\alpha B(r-1,1)=\frac{\alpha}{r-2}, \tag{104}\]  
and  
\[E\left(\frac{1}{\log y_{2}}\right)^{2}=\sum_{j=0}^{\infty}(j+1)E\left(\frac{Y ^{j}}{(\log y_{1})^{j+1}}\right)=\alpha^{2}\sum_{j=0}^{\infty}B(j+2,r+2)=\frac {\alpha^{2}}{(r-2)(r-3)}. \tag{105}\]  
Then  
\[E(\bar{\alpha}) = E\left(\frac{r}{\log y_{2}}\right)=\frac{r\alpha}{r-2},\] \[Var(\bar{\alpha}) = \frac{r^{2}\alpha^{2}}{(r-3)(r-2)^{2}},\] \[MSE(\bar{\alpha}) = \frac{(r^{2}+2r-6)\alpha^{2}}{(r-3)(r-2)^{2}}.\]  
For the unbiased estimator of \(\alpha\), we consider  
\[\hat{\alpha}=\frac{r-2}{r}\bar{\alpha}=\frac{r-2}{\log y_{2}},\]  
then  
\[Var(\hat{\alpha})=\frac{\alpha^{2}}{r-3}<V(\bar{\alpha})\text{ and }RE(\bar{ \alpha})=\frac{r-3}{r}\]
## 3 Considering the ALS Workflow and Its Impact on the Efficacy of Data Products
### Planning and Data Collection
#### 3.1.3 Discrete Return _versus_ Full Waveform Digitizing Systems
When comparing data obtained separately by discrete and FWD systems it is important to account for system configuration in order to ensure that they are acquiring under similar operational conditions. It is also worth mentioning that both discrete and FWD systems are trending toward employing narrower pulse widths and faster detector electronics which increase the range resolution and narrow the low vegetation performance gap between them [44].
# Exploiting Outage and Error Probability
## 4 Simulation Results and Discussions
### Network Performance Parameters--Discussion
#### 4.2.4 Packet Drop  
Figure 11 compares E-ACE, ACE with CARQ and EEDBR in terms of packet drop. It is actually the difference between total number of packets sent to sink and the total number of packets successfully received at sink. In simulations, packet is considered dropped when BER at destination is greater than the threshold after double or triple retransmissions. Packet is also considered as dropped when there is no neighbour to cooperate. As each node sends single packet per time slot and there is no data aggregation at relay nodes, therefore, maximum number of sent packets to sink are equal to total number of nodes.  
In ACE, packet drop rate is negligible and close to E-ACE. Whereas, EEDBR has packet drop rate close to 70%. Figure 11 shows performance improvement of E-ACE as compared to EEDBR and CARQ. EEDBR relies on single link for data transmission and no cooperative mechanism is involved. Therefore, it has maximum packet drop count. EEDBR shows increase in packet drop rate in the later course of simulations due to availability of fewer alive nodes for data transportation to sink. CARQ shows more packet drop despite of using retransmission mechanism using two nodes because of non incorporation of data combining technique at the destination. Also, the relay selection mechanism in CARQ is dependant on distance only, therefore, energy balancing is not achieved and cooperative node may nodes die out who was responsible for retransmission.
## 4 Performance Evaluation
### TRN Simulation Conditions  
In order to evaluate the performance of the proposed equation, TRN simulation was performed under the following conditions. CDGPS corrected position, GPS corrected velocity and attitude data were used as true data for TRN simulation. The simulation trajectory was the same as the 5 km flight test trajectory. The flight altitude and terrain height profile for the trajectory is shown in Figure 17. The average flight height was 5600 m which was about 5100 m higher than the maximum elevation of the terrain profile. The average altitude of the terrain profile was 223 m, and the standard deviation  
Figure 16: Horizontal position difference between closest points obtained using conventional method and proposed method: (**a**) horizontal graph; (**b**) histogram and cumulative probability at flight altitudes of 1 km; (**c**) 3 km; (**d**) 5 km.  
was 95 m. The IRA data obtained from the flight test were used for TRN simulations. The closest points calculated using the IRA output are marked in red (Figure 16a), and it seems that the IRA usually measured the points of the mountain peaks or ridges. For the TRN simulation, the Batch Processing algorithm introduced in Section 2 was used. The 13-state Extended Kalman Filter (EKF) based INS / TRN correction filter was applied to compensate for the velocity error of the INS. The velocity error estimated by the INS / TRN correction filter was used for TRN input compensation. Detailed conditions of the simulation are shown in Table 2. All of the other parameters were the same except for the formula for calculation of the relative position of the closest point.  
TRN simulations were performed for four cases to compare the performance difference according to \(\Delta p_{k}\), and the details of the simulation are shown in Table 3. The Monte Carlo (MC) simulation was performed 50 times for statistical error comparisons.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Object** & **Parameters** & **Value** \\ \hline Accelerometer & Bias (1\(\sigma\)) & 50 \(\mu\)g \\  & Random Walk (1\(\sigma\)) & 10 \(\mu\)g \\ Gyroscope & Bias (1\(\sigma\)) & 0.005 \({}^{\circ}\)/h \\  & Random Walk (1\(\sigma\)) & 0.005 \({}^{\circ}\)/\(\sqrt{h}\) \\ Initial Position Error & Latitude, Longitude, Height (1\(\sigma\)) & 30 m, 30 m, 15 m \\ Initial Velocity Error & E, N, U (1\(\sigma\)) & 0.1 m/s \\ Initial attitude Error & Roll, Pitch, Heading (1\(\sigma\)) & 0.05 mrad, 0.05 mrad, 1 mrad \\ Batch Processing TRN & Resolution of Ref. Matrix & 5 m \\  & Size of Ref. Matrix & 101 \(\times\) 101 \\  & Correlation Algorithm & MAD \\  & Position Fix Period & 40 s \\ DEM & Resolution & 0.3 arcsec, (\(\approx\) 10 m) \\ IRA & Output Frequency & 50 Hz \\ INS / TRN CF & Execution cycle & 50 Hz \\  & Measurement noise (R) & (10 m)\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation condition.  
Figure 17: Simulation trajectory: (**a**) horizontal flight trajectory; (**b**) flight altitude and terrain height profile.
## 2 Materials and Methods
### Cytotoxicity Evaluation and Morphological Observation  
An MTT assay was used to investigate the cell-killing effect of the synthesized magnetic NPs as described before [29]. Briefly, 5 \(\times\) 10\({}^{3}\) cells/well were seeded in 96-well microplates and exposed to increasing concentrations (0, 12.5, 25, 50, 100, 200, and 400 \(\upmu\)g/mL) of CoNiFe and CoNiZn NPs for 24 and 48 h. At the end of the exposure, the culture medium was carefully discarded, and 200 \(\upmu\)L of MTT solution (0.5 mg/mL) was added to each microwell and kept in an incubator for 3 h at 37 degC. Then, the supernatant was replaced with 200 \(\upmu\)L of DMSO to allow complete dissolving of formazan crystals. The plates were kept on a shaker for 15 min at 25 degC and analyzed using a SpectraMax multi-plate reader (Molecular Devices, Sunnyvale, CA, USA) at 570 nm. Results were expressed as percentages of viable cells calculated by dividing the optical density (OD) measured for treated cells by the OD measured for untreated cells \(\times\) 100. The half-maximal inhibitory concentration (IC\({}_{50}\)) was calculated using GraphPad Prism 7.0 Aa (San Diego, CA, USA) software.  
The morphology of MCF7 and HUVEC cells were monitored after exposure to 100 \(\upmu\)g/mL of CoNiFe and CoNiZn NPs for 48 h using an inverted phase-contrast microscope (IX70, Olympus, Tokyo, Japan).
## 4 Flexural Behavior
These formulas account for the tensile capacity  
Figure 11: Analyzed flexural behavior of ECC beams reinforced with GFRP bars: **(a,b)** effect of compressive strength of ECC on the ultimate flexural load and ultimate deflection; **(c,d)** effect of tensile strain of ECC on the ultimate flexural load and ultimate deflection; **(e,f)** effect of tensile strength of ECC on the ultimate flexural load and ultimate deflection; **(g,h)** effect of longitudinal reinforcement ratio on the ultimate flexural load and ultimate deflection [35]. provided by ECC materials. The proposed model demonstrated good agreement with the experimental findings. Despite the numerous advantages associated with FRP bars, their limited fire resistance remains a significant limitation for their utilization in the construction industry. Investigating the post-fire performance of FRP-reinforced FRCC members is crucial for assessing their serviceability and structural integrity. [PERSON] et al. [43] conducted both experimental and analytical investigations to evaluate the flexural behavior of FRC beams reinforced with GFRP bars following exposure to elevated temperatures of 20 \({}^{\circ}\)C, 250 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C. The findings indicated that the residual flexural performance of GFRP-FRC beams is significantly influenced by the temperature of exposure. It was determined that the deflection values predicted by the equations proposed by ACI 440.1R-06 [54] and ACI 440.1R-15 [33] closely aligned with the experimental results, whereas the equations proposed by CSA S806-12 [31] and [PERSON] et al. [55] showed very distinct values. The authors recommended further research that considers the manufacturing processes of the FRP bars, the specific exposure conditions, and the duration of exposure to heat, as well as the cooling procedures applied to the specimens. Table 2 provides a summary of the research and findings reported by various researchers regarding the flexural performance of FRCC reinforced with FRP bars. From this table, it can be concluded that the flexural performance of FRP in combination with FRCC is influenced by several factors, and efforts have been made to understand the structural response under various loading conditions and environmental factors, including the effects caused by differences in materials used for FRP and FRCC.
## Appendix A  
Prior to conducting the deformation detection experiment, continuous observations of the observation scene (where no deformation occurred) were performed 256 times to obtain the echo data, with intervals of 2.5 ms. After imaging processing, 256 SAR images of the target area were generated. The phase STD for each pixel in the imaging was calculated over the time series, and a statistical histogram was created, as illustrated in Figure A1.  
From the statistical results, it can be observed that the phase noise standard deviation of the majority of pixels within the observation scene is less than 0.3 rad. Phase noise can introduce measurement errors in deformation, which consistent with the relationship described in Equation (1) of the main text. Under the experimental design presented in this study, the system's deformation monitoring error is calculated to be approximately 1.43 mm. However, the primary objective of this experiment is to validate the feasibility of the theoretical framework; thus, a displacement increment of 2 mm was selected. On one hand, this increment exceeds the detection limit of 1.43 mm, ensuring that the deformation signal can be clearly detected without being obscured by phase noise. Additionally, the choice of an integer increment facilitates experimental design. On the other hand, the phase change induced by a 2 mm deformation is approximately 0.42 rad, which is significantly less than 2\(\pi\), thereby avoiding issues related to phase wrapping. In fact, both smaller and larger deformations can significantly affect deformation detection. When the deformation value is small, the deformation signal may be obscured by noise, rendering the corresponding deformation undetectable. Conversely, when the deformation value is excessively large, phase wrapping issues may arise, which can also impact the accuracy of deformation detection. Additionally, during data processing, the EPC principle was employed, allowing the separated transmission and reception mode to be treated as a single-site operation mode. Consequently, the altitude differences between the CBSs do not influence the accuracy of deformation monitoring.