esa-sceva/satcom-chunk-collection huggingface dataset:
# Bilateral Matching Decision Model and Calculation of High-Speed Railway Train Crew Members
## 1 Introduction
From an application perspective, the aforementioned studies extend the bilateral matching theory to various domains, including marriage, doctor-patient, admissions, and rescue scenarios, encompassing matching subjects such as individual-to-individual, individual-to-job, and individual-to-resource. Theoretically, these studies address the conversion of one-to-many bilateral matching issues into one-to-one scenarios by defining virtual subjects, thereby constructing multi-objective optimization models using linear programming techniques. These models are further simplified into single-objective formulations, or solved using intelligent multi-objective optimization algorithms, effectively resolving bilateral matching problems. The problem of crew matching in the field of high-speed railway crew planning research involves matching between individuals, where the process can be viewed as a decision-making process with the chief steward and stewards as the primary entities in bilateral matching. By introducing bilateral matching decision theory, theoretical support can be provided for efficient and stable crew matching. Accordingly, this paper applies bilateral matching decision theory to the problem of crew matching in high-speed railway trains. Based on the preference order theory, a one-to-many bilateral matching multi-objective optimization model is developed, considering the satisfaction of both matching entities. Optimization solvers and intelligent optimization algorithms are employed to conduct simulations and calculations for various problem scales under different scenarios. This not only enhances the field of high-speed railway crew planning research with significant application value but also expands the application scope of bilateral matching theory.
## 2 CO Device Implementation
### Pellet Fabrication and Sensitivity Test in CO Atmospheres  
The sensitivity tests consisted of measuring the changes in the NiSb\({}_{2}\)O\({}_{6}\) pellets' electrical resistance at different operating temperatures (100, 200, and 300 \({}^{\circ}\)C) and different concentrations of CO (0, 5, 50, 100, 200, and 300 ppm). The pellets of the material were elaborated using 0.3 g of the powder dried at 800 \({}^{\circ}\)C and compacting it by means of a hydraulic press (Simplex Ital Equip-25 Tons) applying 10 tons for 60 s. The dimensions of the pellets were of 0.5 mm in thickness and 12 mm in diameter. Prior to the gas detection measurements, two ohmic contacts of colloidal silver paint (Alfa Aesar, 99%) were placed on the surface of the pellets. Afterward, the NiSb\({}_{2}\)O\({}_{6}\) pellets were placed inside a measuring chamber (Chambers) with a vacuum capacity of 10\({}^{-3}\) torr. The partial pressure of the gases inside the  
Figure 1: Stages required for the CO detection device.  
chamber was controlled using a TM20 Leybold detector. The electrical resistance measurements were made using a Keithley 2001 multimeter. Finally, the response (sensitivity) was estimated according to the formula [20,22,24]: S = (G\({}_{\text{G}}-\)G\({}_{\text{O}}\))/G\({}_{\text{O}}\), where, G\({}_{\text{G}}\) Y G\({}_{\text{O}}\) are the pellets' conductances (1/electric resistance) in the test gas (CO) and air, respectively.
## Appendix A Analysis of Polarizabilities in Resonance Condition  
By taking the trace of Equations (6)-(8), and by considering Equation (4), one straightforwardly obtains the following:  
\[\text{Tr}\left(\mathbf{\alpha}^{\alpha\alpha}(-\omega;\omega)\right) =\frac{1}{\hbar}\sum_{k}\frac{\tilde{D}_{0k}}{\omega_{k}-\omega-i \Gamma/2}+\frac{1}{\hbar}\sum_{k}\frac{\tilde{D}_{k0}}{\omega_{k}+\omega+i \Gamma/2}, \tag{11}\] \[\text{Tr}\left(\mathbf{\alpha}^{\alpha m}(-\omega;\omega)\right) =\frac{1}{\hbar}\sum_{k}\frac{\tilde{R}_{0k}}{\omega_{k}-\omega-i \Gamma/2}+\frac{1}{\hbar}\sum_{k}\frac{\tilde{R}_{k0}}{\omega_{k}+\omega+i \Gamma/2},\] \[\text{Tr}\left(\mathbf{\alpha}^{mm}(-\omega;\omega)\right) =\frac{1}{\hbar}\sum_{k}\frac{\tilde{G}_{0k}}{\omega_{k}-\omega-i \Gamma/2}+\frac{1}{\hbar}\sum_{k}\frac{\tilde{G}_{k0}}{\omega_{k}+\omega+i \Gamma/2},\]  
where \(\tilde{R}_{k0}=\text{Tr}\left(\mathbf{\mu}_{k0}\otimes\mathbf{m}_{0k}\right)=\text{Tr} \left(\mathbf{\mu}_{0k}^{*}\otimes\mathbf{m}_{k0}^{*}\right)=\text{Tr}\left(\mathbf{\mu}_{ 0k}\otimes\mathbf{m}_{k0}\right)^{*}=\tilde{R}_{0k}^{*}\). We notice that these identities hold because the dipole operators are Hermitian operators, which implies \(\mathbf{\mu}_{k0}=\mathbf{\mu}_{0k}^{*}\), and \(\mathbf{m}_{0k}=\mathbf{m}_{k0}^{*}\) (similar identities hold for \(\tilde{D}_{k0}\) and \(\tilde{G}_{k0}\) as well). This implies that the resonance and off-resonance terms of Equation (11) are related to each other by complex conjugation, as expected [43]:  
\[\text{Tr}\left(\mathbf{\alpha}^{\alpha\epsilon}(-\omega;\omega)\right) =\frac{1}{\hbar}\sum_{k}\frac{\tilde{D}_{0k}}{(\omega_{k}-i\Gamma /2)-\omega}+\frac{1}{\hbar}\sum_{k}\frac{\tilde{D}_{0k}^{*}}{(\omega_{k}-i \Gamma/2)^{*}+\omega}, \tag{12}\] \[\text{Tr}\left(\mathbf{\alpha}^{\epsilon m}(-\omega;\omega)\right) =\frac{1}{\hbar}\sum_{k}\frac{\tilde{R}_{0k}}{(\omega_{k}-i\Gamma /2)-\omega}+\frac{1}{\hbar}\sum_{k}\frac{\tilde{R}_{0k}^{*}}{(\omega_{k}-i \Gamma/2)^{*}+\omega},\] \[\text{Tr}\left(\mathbf{\alpha}^{mm}(-\omega;\omega)\right) =\frac{1}{\hbar}\sum_{k}\frac{\tilde{G}_{0k}}{(\omega_{k}-i\Gamma /2)-\omega}+\frac{1}{\hbar}\sum_{k}\frac{\tilde{G}_{0k}^{*}}{(\omega_{k}-i \Gamma/2)^{*}+\omega}.\]  
In resonance condition (\(\omega\approx\omega_{k}\)), the quantity \(\Delta\omega_{k}\equiv\omega-\omega_{k}\) is small.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## Abbreviations  
(In alphabetical order): A/D (analog to digital), AOTF (acoustic-optic tunable filter), BAT (brown adipose tissue), CBV (cerebral blood volume), CFD (constant fraction discriminator), CPU (central processing unit), CSF (cerebro-spinal fluid), CT (computed tomography), CW (continuous wave), DE (diffusion equation), DOM (discrete ordinate method), DOT (diffuse optical tomography), DPF (differential pathlength factor), FD (frequency-domain), FEM (finite element method), FS (fluorescence diffuse optical spectroscopy), FT (fluorescence diffuse optical tomography), GPST (generalized pulse spectrum technique), GPU (graphic processing unit), ICCD (intensified charge coupled device), ICG (indocyanine green), IRF (instrumental response function), MC (Monte Carlo), MOBIIR (model-based iterative image reconstruction), MPPC (multi-pixel photon counter), MRI (magnetic resonance imaging), NIRS (near-infrared spectroscopy), NN (neural network), PET (positron emission tomography), pMC (perturbation Monte Carlo), PMT (photomultiplier tube), POCD (postoperative cognitive dysfunction), RTE (radiative transfer equation), SC (supercontinuum), SD (source-detector), SiPM (silicon photomultiplier), SPAD (single-photon avalanche diode), TAC (time-to-amplitude converter), TCSPC (time-correlated single photon counting), TD (time-domain), TDC (time-to-digital converter), TE (telegraph equation), TOF (time-of-flight), TR (time-resolved), 2D (two-dimensional), 3D (three-dimensional).
## 4 Experimental Design
### Baseline  
We compare our proposed LinearTGAN with the bicubic interpolation method [42] and four SOTA SR methods, including CARN [41], BSRGAN [43], Real-ESRGAN [24], and TransENet [44]. CARN enhances hierarchical feature learning through cascading residual connections and group convolutions, enabling efficient SR with reduced computational complexity. BSRGAN introduces a more realistic degradation model that effectively simulates complex real-world image degradations through a series of meaningful operations, including blur, downsampling, noise addition, JPEG compression, and their combinations with appropriate random parameters. This realistic degradation modeling allows BSRGAN to handle diverse and complex real-world scenarios, producing more natural-looking reconstructions. Real-ESRGAN extends ESRGAN by incorporating a high-order degradation model, allowing it to handle complex real-world degradations. TransENet, a transformer-based SR model, utilizes self-attention mechanisms to model global contextual relationships, improving high-frequency texture reconstruction and structural coherence in HR images.  
Other SR models, such as SAN [45], HAN [46], and IGNN [47], were excluded from this comparison due to their reliance on computationally intensive attention mechanisms, increased processing demands, and limited accessibility compared to the selected approaches. This study systematically evaluates LinearTGAN in terms of reconstruction accuracy, perceptual fidelity, and computational efficiency, benchmarking its performance against established SR models. By providing a rigorous comparative analysis, this evaluation offers valuable insights into the strengths and limitations of LinearTGAN relative to existing SR techniques.
# Fault Sensing Using Fractal Dimension and Wavelet
## 5 The Proposed Fusion Sensing Method  
From the above studies, it was found that the IFBD recognition algorithm could recognize the single-phase fault, double-phase faults, and the three-phase fault easily. Additionally, it had a slight advantage in classifying the detailed single-phase faults of \(A\), \(B\), and \(C\). In contrast, the DMWCSP and DMWCTP algorithms could recognize the detailed single-phase faults of \(A\), \(B\), and \(C\). However, they had relative difficultly in recognizing the detailed double-phase faults of \(AB\), \(AC\), and \(BC\).  
The flow chart of the FS method is shown in Figure 5. The FS method is described below.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Fault** & \(\mathbf{I_{0}}\) & \(\mathbf{I_{a}}\) & \(\mathbf{I_{B}}\) \\ \hline Grounded fault of \(A\) & 0.2997 & 0.2997 & 0.2997 \\ Grounded fault of \(B\) & 4.5535 & 4.5535 & 0.0003 \\ Grounded fault of \(C\) & 4.8586 & 0.0001 & 4.8586 \\ Phase-between fault of \(A\) and \(B\) & 0.0000 & 4.6010 & 2.3005 \\ Grounded faults of \(A\) and \(B\) & 4.5173 & 4.6010 & 1.8867 \\ Phase-between fault of \(A\) and \(C\) & 0.0000 & 2.7855 & 5.5721 \\ Grounded faults of \(A\) and \(C\) & 4.2387 & 2.0056 & 5.5721 \\ Phase-between fault of \(B\) and \(C\) & 0.0001 & 5.0865 & 5.0865 \\ Grounded faults of \(B\) and \(C\) & 0.2786 & 4.9471 & 5.2260 \\ Grounded fault of \(A\), \(B\), and \(C\) & 0.0000 & 4.6010 & 5.5721 \\ \hline \end{tabular} \begin{tabular}{c} ITPC \\ \end{tabular} \begin{tabular}{c} EFC \\ \end{tabular} \begin{tabular}{c} CKTM \\ \end{tabular} \begin{tabular}{c} CDWC \\ \end{tabular} \begin{tabular}{c} EMWC \\ \end{tabular} \begin{tabular}{c} EMC \\ \end{tabular} \begin{tabular}{c} ITPC: Input the three-phase current \\ \end{tabular} \begin{tabular}{c} EFC: Extract the fault component \\ \end{tabular} \\ \hline \end{tabular} \
\begin{tabular}{c} CKTM: Calculate K-transform modulus \\ \end{tabular}
\begin{tabular}{c} CDWC: Calculate the discrete wavelet coefficients to the 2 ed level of the detailed part \\ EMWC: Extract the maximum wavelet coefficient of the 2 ed level of the detailed part \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 4: Maximum wavelet coefficients \(I_{0}\), \(I_{a}\), and \(I_{\beta}\) of the different faults.  
Figure 4: Flow chart of the calculation of the maximum wavelet coefficients.
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM)  
SEM analysis was performed for three samples to examine the microstructure of the TPU and the effect of the addition of antimony trioxide and organo-clay on the dispersion of fillers inside the polymer matrix. A continuous matrix of polymer (TPU-1) is seen in Figure 3a. Figure 3b,c shows the morphology of the composite with different magnifications in which the antimony trioxide was uniformly dispersed in the polymer matrix (TPU -3). The dispersion of the filler (white particles) can be seen from the SEM images. In Figure 3d,e, uniform dispersion of the organo-clay along with antimony trioxide at different magnifications can be observed in the TPU-6 samples. This uniform dispersion is the key reason behind the improvement of the mechanical properties of the composites. These results show that there was a synergistic effect between organo-clay and antimony trioxide, which resulted in the improvement of the flame retardancy and mechanical strength of the TPU composites.
## 2 Composition, Feature and Utilization of Slag
### Utilization of slag
#### 2.3.3 Applications in Sinter Raw Material, CO\({}_{2}\) Capture and Flue Gas Desulfurization  
Slag with more than 50% CaO content can be used as sintering flux and partially replace commercial lime. The addition of slag can improve sinter's quality, reduce fuel consumption produced by the exothermic of Fe and FeO oxidation reaction and reduce the cost of sinter [37]. In America and Germany, the amounts of slag used as sinter raw materials were more than 56% and 24%, respectively [38]. In China, Baoshan Iron and Steel Group (Bao Steel) has been recycling slag used for sintering since 1996. Currently, slag that can be recycled every year has stabilized at 150,000 tons [39].  
There is a lot of CaO in slag. Consequently, under mild temperatures and CO\({}_{2}\) pressure conditions, it is possible to store CO\({}_{2}\) in the form of carbonate through the use of slag slurry [40]. slag is high in CaO, especially free CaO, which can be used for desulphurization. [PERSON] et al. [41] found that desulfurizer that came from the mixing of slag, fly ash and plaster together in definite proportions had the effect of desulphurization. Through theoretical research, [PERSON] et al. [42] testified that it was feasible to condense flue gas by using slag to desulphurize. [PERSON] et al. [43] researched the wet flue gas desulfurization with waste slag and powder slag and found that through reasonable design and proper operation, the rate of wet desulfurization with slag could reach more than 60%. However, this technique is still in the laboratory research phase currently.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 5 Performance Evaluation
### Discussion  
A comparative simulation analysis of the proposed schemes with respect to the existing scheme AHH-VBF is discussed in this section. The discussion is based on the comparison of the aforementioned performance metrics.
# Optimal Control of a Virtual Power Plant by Maximizing Conditional Value-at-Risk
## 2 Problem Formulation
### CVAR Model  
Let \(f(x,y)\) be a loss function with a set of decision variables \(x\) and random variables \(y\). \(\rho(y)\) is a probability density function for VAR with \(\beta\) confidence level and can be represented in Equation (1). By using the model in [28; 29], we have  
\[\text{VAR}_{\beta}(x)=\min\{\alpha\in R;\int_{f(x,y)\leq\alpha}\rho(y)dy\geq\beta\} \tag{1}\]  
CVAR, for a confidence level \(\beta\), is defined as  
\[\varphi_{\beta}(x)=E[f(x,y)|f(x,y)\geq\text{VAR}_{\beta}(x)]=\frac{1}{1-\beta }\int_{f(x,y)\geq\text{VAR}(x)}f(x,y)\rho(y)dy \tag{2}\]  
CVAR of the cost, calculated in NT$, can be approximated by  
\[F_{\beta}(x,\text{VAR})=\text{VAR}+\frac{1}{1-\beta}\int_{y\in R}{[f(x,y)- \text{VAR}]\rho(y)dy} \tag{3}\]  
Figure 1: The VPP’s architecture.  
In Equation (3), CVAR can be expressed as a discrete function as in Equation (4).  
\[F_{\beta}(x,\text{VAR})=\text{VAR}+\frac{1}{m(1-\beta)}\sum_{k=1}^{m}\left[f(x,y^ {k})-\text{VAR}\right] \tag{4}\]  
\(F_{\beta}(x,\text{VAR})\) is an estimated value of CVAR cost. \(y^{k}\) are the k samples of variable \(y\).  
VAR is an estimate of loss for a given probability of occurrence. The given probability is called the confidence level, which represents the degree of the VAR. The VAR value is a threshold. We know the probability of future losses exceeding this threshold, but do not know how bad the situation is when the loss exceeds this threshold.  
CVAR cost refers to the condition where loss exceeds the given VAR value. CVAR takes into account the characteristics of tail events, satisfaction of positive homogeneity, transmission immutability, and subadditivity. It can overcome several disadvantages of the VAR measure and become more accurate in risk assessment. The difference between VAR and CVAR is shown in Figure 2. VAR relates to the probability of loss excess and CVAR is the expectation of loss excess. CVAR is generally preferable, is more widely used, and is used in this paper.
# Aircraft Mode S Transponder Fingerprinting for Intrusion Detection
## 4 Performance Evaluation with Real Data
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & \(P_{D}=0.7\) & \(P_{D}=0.8\) \\ \hline \hline
**Scenario 1** & & \\ \hline KNN+KS \(M=500\), \(K=30\) & \(<\)0.0028 & 0.0032 \\ KNN+KS \(M=500\), \(K=60\) & \(<\)0.0022 & 0.0030 \\ KNN+KS \(M=500\), \(K=90\) & 0.0021 & 0.0035 \\ \hline KNN+KS \(M=1000\), \(K=30\) & \(<\)0.0011 & 0.0016 \\ KNN+KS \(M=1000\), \(K=60\) & 0.0008 & 0.00167 \\ KNN+KS \(M=1000\), \(K=90\) & 0.0007 & 0.0020 \\ \hline KNN+KS \(M=\infty\), \(K=30\) & \(<\)0.00051 & 0.0014 \\ KNN+KS \(M=\infty\), \(K=60\) & 0.0004 & 0.0014 \\ KNN+KS \(M=\infty\), \(K=90\) & 0.0003 & 0.0014 \\ \hline Phase and Frequency \(M=1000\) & 0.0167 & 0.04224 \\ \hline
**Scenario 2** & & \\ \hline KNN+KS \(M=1000\), \(K=60\) & 0.0008 & 0.00217 \\ \hline Phase and Frequency \(M=1000\) & 0.1144 & \(>\)0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result Comparison. Figure 8: Comparison of the proposed method performance with the performance obtained by the method proposed in [31]. In general, the performance in terms of probability of false alarm are increased by at least an order of magnitude with respect to the method proposed in [31], namely, Phase and Frequency. Moreover, for the more-demanding second scenario, the performance is close to that obtained in the first one, with a negligible performance reduction. On the contrary, the results obtained with the method proposed in [31] suffer a big degradation.
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 3 Proposed Method
### Proposed Clustering Algorithm
```
Read: k // Specify the number k of clusters to assign Randomly initialize k centroids repeat  expectation // Assign each UE to its closest centroid maximization // Compute the new centroid of each cluster untilThe centroid positions do not change
```  
**Algorithm 1**UE k-means algorithm. As shown in Algorithm 1, specifying the number of clusters is very important; using the elbow method, we can find the optimal number for the clustering algorithm. Optimization of the Number of Clusters with the Elbow Method  
The elbow technique compares the percentage of the number of clusters that will form an elbow at a given moment to the appropriate number for the clustering algorithm. This technique uses visual analysis to evaluate the consistency of the optimal number of clusters [34]. As presented in Figure 6, the elbow approach plots within-cluster sum squares (WCSS ) values on the Y-axis and the number of clusters on the X-axis to create the graphic using the WCSS concept. Therefore, we calculated the value of WCSS for various k values between 1 and 10, as shown in Equation (1). \[WCSS=\sum_{ \begin{subarray}{c}C_{k}\\ C_{k}\end{subarray} }^{C_{k}}(\sum_{ \begin{subarray}{c}d_{m}\\ d_{i}mC_{i}\end{subarray} }^{d_{m}}distance(d_{i},C_{k})^{2}), \tag{1}\]  
where \(C\) is the cluster centroids and \(d\) is the UE in each cluster. Figure 6: Elbow Method Graph.
###### Abstract  
Social networks have become integral to daily life, allowing users to connect and share information. The efficient analysis of social networks benefits fields such as epidemiology, information dissemination, marketing, and sentiment analysis. However, the direct publishing of social networks is vulnerable to privacy attacks such as typical 1-neighborhood attacks. This attack can infer the sensitive information of private users using users' relationships and identities. To defend against these attacks, the \(k\)-anonymity scheme is a widely used method for protecting user privacy by ensuring that each user is indistinguishable from at least \(k-1\) other users. However, this approach requires extensive modifications that compromise the utility of the anonymized graph. In addition, it applies uniform privacy protection, ignoring users' different privacy preferences. To address the above challenges, this paper proposes an anonymity scheme called TC\(\alpha\)-PIA (**T**ree **C**lustering and **a**-**P**artial **I**somorphism **A**nonymization). Specifically, TC\(\alpha\)-PIA first constructs a similarity tree to capture subgraph feature information at different levels using a novel clustering method. Then, it extracts the different privacy requirements of each user based on the node cluster. Using the privacy requirements, it employs an \(\alpha\)-partial isomorphism-based graph structure anonymization method to achieve personalized privacy requirements for each user. Extensive experiments on four public datasets show that TC\(\alpha\)-PIA outperforms other alternatives in balancing graph privacy and utility.  
social networks; 1-neighborhood attack; isomorphism; privacy preservation +
Footnote †: journal: Medical Informatics
## 4 POD with Ambiguity Resolution
### Results
#### 4.2.3 Baseline Validation  
The GRACE-FO mission offers the possibility of validating the computed orbit, in particular, the along-track component, with the ultra-precise KBR measurements [33; 68]. Figure 10 illustrates the STDs of KBR residuals (named KBR STD hereafter) calculated using FA, DD AR, and SD AR orbits. The effect of antenna PCV correction on PBD is also illustrated. Figure 10a presents the KBR STDs of the SD AR orbit with PCV corrections (blue dots), the SD AR orbit without PCV corrections (green dots), and FA orbit with PCV corrections (red dots). PCV corrections used to calculate FA orbit are generated from the SD AR solution. Figure 10b is the result of the DD AR solution.  
Both SD and the DD AR lead to a significant reduction in KBR residuals. Compared with the FA result, the SD AR orbit improves the KBR STD from 5.9 to 3.1 mm, and the DD AR orbit improves the STD to 1.6 mm. With PCV corrections, the KBR STD of the SD AR solution decreases from 3.1 to 1.8 mm, and the STD of the DD AR orbit decreases from 1.6 to 0.9 mm. The baseline precision of the SD AR solution gains similar performance, as presented by [PERSON] et al. [33]. The DD AR orbit achieves a sub-millimeter level as reported  
Figure 10: Daily STDs of KBR residuals of (**a**) FA orbit with PCV corrections (red dots), SD AR orbit without PCV corrections (green dots), and SD AR orbit with PCV corrections (blue dots); (**b**) FA orbit with PCV corrections (red dots), DD AR orbit without PCV corrections (green dots), and DD AR orbit with PCV corrections (blue dots).  
Figure 9: RMSs of SLR residuals derived from FA (red dots), DD AR (green dots), and SD AR (blue dots) solutions. The upper panel is for the GRCC satellite and the lower panel is for the GRCD satellite.  
in previous research [6; 19; 69]. Comparisons with JPL's orbit and SLR data show that the SD AR solution possesses better performance, while in the KBR validation, the residual STD of the DD AR orbit is smaller. In other words, the SD AR orbit has better absolute orbit accuracy, while the DD AR orbit gets better relative performance. This is explained by two facts: (a) phase bias products for SD AR are not free of errors and these errors may degrade the KBR validation [35]; (b) both JPL's orbit and the SD AR orbit in this investigation adopt the single receiver ambiguity resolution method and a better consistency can be inferred.