esa-sceva/satcom-chunk-collection huggingface dataset:
# Broadband Flexible Microstrip Antenna Array with Conformal Load-Bearing Structure
## 5 Conclusions  
To implement the conformal load-bearing mechanical properties and broadband radiation characteristics, a novel structure for microstrip antenna design has been proposed in this paper. Firstly, our methods shed light on a CLA with integrated functions in terms of structure. Through the addition of honeycomb core layers and metallic reflection surfaces, the proposed antenna achieves an increase in bandwidth and a decrease in the SLL. With a load-bearing function and simple structure, the proposed antenna remains stable in EM performance. Initially, the antenna array is convenient to achieve and is extended by redesigning the antenna feeding network in the middle substrate layer. Meanwhile, providing an efficient and accurate solution for the design and simulation of CLA, an equivalent model of the honeycomb core layer is presented. The consistency of simulation and measurement results certified its accuracy. Since the consideration and simulation of the practical and challenging scattering problem, the reliability and reasonableness of the CLA design have been improved efficiently.  
**Author Contributions:** Conceptualization and methodology, C.T., Z.L. and K.Z.; software, C.T. and Z.L.; validation, C.T., Z.L. and H.Z.; resources, H.Z., M.W., C.F. and E.L.; writing--original draft preparation, C.T., Z.L., K.Z. and H.Z.; writing--review and editing, C.T. and H.Z.; project administration, H.Z., C.T. and E.L.; funding acquisition, H.Z., M.W., C.F. and E.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under grant 62071166, and grant 62071424, and in part by the Graduate Innovation Project Foundation of the Hebei Province under grant CXZZBS2021031, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
**11.67–13.76 and 14.33–14.83** & **20.68\%** & & & & \\
**This work** &
\begin{tabular}{c} **32.84 \(\times\) 36.65 \(\times\) 4.9** \\ (4 elements) \\ \end{tabular} & **11.08–14.1 (30\({}^{\circ}\) bending)** & **10.63–13.5 (60\({}^{\circ}\) bending)** & **10.63–13.5 (60\({}^{\circ}\) bending)** & **10.63–13.5 (20\({}^{\circ}\) bending)** & **10.63–13.2 (20\({}^{\circ}\) bending)** & **10.63–13.2 (20\({}^{\circ}\) bending)** & **10.63–13.2 (20\({}^{\circ}\) bending)** \\  & & & & & & \\  & & & & & & \\
**1.55–14.33 (90\({}^{\circ}\) bending)** & **123.12\%** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between proposed and similar conformal antenna array.
## 3 Performance Analysis of NOMA-Enabled HSTRNs
### Outage Probability
#### 3.2.2 AF Protocol  
The outage probability of \(D_{1}\) can be written as  
\[\mathcal{P}_{out,D_{1}}^{AF}=\Pr\left(\Gamma_{D_{1}\to x_{1}}^{AF}<\Psi_{1} \right). \tag{23}\]  
**Proposition 2**.: _The closed-form expression of outage probability of \(D_{1}\), in case of AF relaying, can be expressed as_  
\[\begin{split}\mathcal{P}_{out,D_{1}}^{AF}&=1-2\sum_{ i_{1}=0}^{m_{SR}-1}\cdots\sum_{i_{M}=0}^{m_{SR}-1}\Theta\left(M\right)\sum_{n_{1}=0}^ {N_{1}m_{1}-1}\sum_{q=0}^{\Delta-1}\left(\frac{\Delta-1}{q}\right)\frac{\left( \Lambda_{1}\phi\right)^{q}}{n_{1}!\left(\beta-\delta\right)^{\Delta}}\\ &\times\left(\frac{\kappa_{1}\left(\beta-\delta\right)}{\Lambda_{ 1}\phi\rho_{S}}\right)^{\frac{\Delta+q+n_{1}}{2}}e^{-\frac{\kappa_{1}\left( \beta-\delta\right)}{\rho_{S}}}K_{\Delta-q-n_{1}}\left(2\sqrt{\frac{\kappa_{1 }\left(\beta-\delta\right)}{\Lambda_{1}\phi\rho_{S}}}\right).\end{split} \tag{24}\]  
Proof.: Please refer to Appendix B for detailed proof of derivation.  
Next, the outage probability of \(D_{2}\) can be expressed as  
\[\mathcal{P}_{out,D_{2}}^{AF}=\Pr\left(\Gamma_{D_{2}\to x_{2}}^{AF}<\Psi_{2} \right). \tag{25}\]  
Then, \(\mathcal{P}_{out,D_{2}}^{AF}\) is rewritten by  
\[\mathcal{P}_{out,D_{2}}^{AF}=1-\Pr\left(\bar{\gamma}_{SR}>\frac{\kappa_{2} \left(\|\mathbf{h}_{2}\|^{2}\phi+1\right)}{\phi\|\mathbf{h}_{2}\|^{2}}\right). \tag{26}\]  
Following the similar steps indicated in Appendix B, the closed-form expression of outage probability of \(D_{2}\), in case of AF relaying, can be formulated by  
\[\begin{split}\mathcal{P}_{out,D_{2}}^{AF}&=1-2\sum_{ i_{1}=0}^{m_{SR}-1}\cdots\sum_{i_{M}=0}^{m_{SR}-1}\Theta\left(M\right)\sum_{n_{2}=0}^ {N_{2}m_{2}-1}\sum_{q=0}^{\Delta-1}\left(\frac{\Delta-1}{q}\right)\frac{\left( \Lambda_{2}\phi\right)^{q}}{n_{1}!\left(\beta-\delta\right)^{\Delta}}\\ &\times\left(\frac{\theta_{2}\left(\beta-\delta\right)}{\Lambda_ {2}\phi\rho_{S}}\right)^{\frac{\Delta+q+n_{2}}{2}}e^{-\frac{\theta_{2}\left( \beta-\delta\right)}{\rho_{S}}}K_{\Delta-q-n_{2}}\left(2\sqrt{\frac{\theta_{2} \left(\beta-\delta\right)}{\Lambda_{2}\phi\rho_{S}}}\right).\end{split} \tag{27}\]
## 2 Proposed Approach
### Cubic Spline Extrapolation Technique  
In Figure 4, as voltage collapse occurs at bus \(i\), the impedance-matching criterion holds; i.e., \(\left|Z_{i}^{\mathrm{L}}\right|=\left|Z_{i}^{\mathrm{th}}\right|\). Thus, the estimated maximum loading point at which voltage collapses can be made by equating an approximate function, that extrapolates the trajectory of \(\left|Z_{i}^{\mathrm{L}}\right|\), to \(\left|Z_{i}^{\mathrm{th}}\right|\). A cubic spline function is selected as the approximate function in this study. The fundamental idea behind constructing a spline function is to divide the interval into a collection of subintervals and construct a different approximating low-order polynomial in each subinterval. In cubic splines, third-order polynomials are used in the subintervals between each pair of data points. Thus, the curves obtained from each subinterval are smooth.  
For example, given \(m\) data points, there are \(m-1\) subintervals, and the equation of the polynomial in the \(j\)th subinterval is expressed as  
\[f_{j}\left(z\right)=a_{j}+b_{j}\left(z-z_{j}\right)+c_{j}\left(z-z_{j}\right) ^{2}+d_{j}\left(z-z_{j}\right)^{3} \tag{10}\]  
for each \(j=1,\,2,\,\cdots,\,m-1\). Overall, there are \(m-1\) equations. Since each third-order polynomial has four coefficients, a total of \(4\left(m-1\right)=4m-4\) unknowns have to be determined. All of the coefficients \(a_{j}\), \(b_{j}\), \(c_{j}\), and \(d_{j}\) can be found by applying the method proposed in [29]. In this work, the variable \(x\) in Equation (10) is replaced with the calculated \(\left|Z_{i}^{\mathrm{L}}\right|\), and \(m\) is set to three.  
The cubic spline extrapolation function is constructed by the coordinates of the given data points \(\left(\left|Z_{i}^{\mathrm{L}}\right|,\,\lambda\right)\), where \(\lambda\) denotes the load increase parameter. The function is then used for calculating the extrapolated value of \(\left|Z_{i}^{\mathrm{L}}\right|=\left|Z_{i}^{\mathrm{th}}\right|\). Since \(m\) is set to three, the inputs to the function are three sets of data points \(\left(\left|Z_{i}^{\mathrm{L}}\right|,\,\lambda\right)\) and the point of \(\left|Z_{i}^{\mathrm{th}}\right|\). The output from the function is the estimated maximum load point \(\lambda^{\mathrm{es}}\).
## 3 Materials and Method
### Interpreting DCCAC Results for Battery SOH Evaluation  
In the context of battery SOH evaluation, DCCAC allows for the comparison of voltage signals from a battery under test with a reference battery in good condition. The main hypothesis is that batteries with deteriorated SOH exhibit a decay in correlation with healthy batteries across multiple scales. By analyzing this behavior, it becomes possible to determine whether a battery is in good SOH condition or approaching failure.  
When interpreting the DCCAC results, a battery with good SOH should exhibit a significant and increasing DCCAC value as the time scale increases, inside the zone B,green color, presented in Figure 2. This indicates that the voltage signals maintain strong cross-correlation, which is expected for batteries operating under similar conditions. In contrast, batteries with degraded SOH show weaker correlation, with DCCAC values that remain low or statistically non-significant across different time scales, partially inside the zone A, grey color, presented in Figure 2. This pattern suggests a disruption in the expected voltage behavior during engine ignition.  
Another key aspect of the analysis is the presence of results falling within the non-significant cross-correlation area. Batteries with severely deteriorated SOH tend to have most of their computed DCCAC values within this non-significant range, inside the zone A, grey color, presented in Figure 2. This means that their voltage signals do not exhibit a meaningful correlation with a healthy reference battery, reinforcing the conclusion that the battery is no longer performing optimally. The ability to distinguish between significant and non-significant cross-correlation is crucial for establishing a clear threshold for battery failure detection.
## 4 Communication Frame
### Wireless Communication  
Wireless communication can be solved in different ways, e.g., each source of information, like battery status, GPS position, firmware version, etc., can have its own polling command. This results in short data frames in both directions: BS \(\leftrightarrow\) MU. Unfortunately, it has one major disadvantage: it creates many wireless data frames and channel occupancies. Note that each payload is encapsulated with a preamble and error detection bits.  
The final implementations of the communication protocol [18] have been tested in a realistic operating environment. The results showed that the project constraints were met. The maximum measured query rate (i.e., the next query after receiving the previous response, 100 Mbps Ethernet) was **81 queries per second**. This translates to a minimum total roundtrip time (PC \(\rightarrow\) BS \(\rightarrow\) MU \(\rightarrow\) BS \(\rightarrow\) PC) of **12.3 ms**. The roundtrip time on the \"hardware level\", i.e., BS \(\rightarrow\) MU \(\rightarrow\) BS, is **8.5 ms**. Compared to the expected 4.4 ms for wireless communication, 4.1 ms (48\(\%\)) for the microcontroller units' processing time is used (Table 11).  
A 100 Mbps Ethernet network delay had the least impact on total roundtrip delay: \(\sim\)0.5-1 ms (4.1-8.1\(\%\)). It can be noted that high-level software (PC) has introduced \(\sim\)2.8-3.3 ms delay--over 20\(\%\) of the total value.
## 5 Experimental Results and Discussion  
This section first introduces experimental configuration for watermarking model training and prototype video data authentication framework implementation. Then, we present a comprehensive evaluation of the performance of the FIAE-GAN watermarking model. We also evaluate latency incurred by different operation stages in the video data authentication process and analyze their financial cost. Finally, we discuss the security features of the proposed solutions that can prevent diverse attacks on video data service platforms under distributed network environments.
# _microorganisms_
###### Abstract  
Aim: To develop a reproducible biofilm model consisting of _Enterococcus faecalis_ (_E. faecalis_) and _Perlhyomonas gingivalis_ (_P. gingivalis_) and to evaluate the interaction between the two bacterial species. Methodology: _E. faecalis_ and _P. gingivalis_ were grown in mono-culture, sequential, and co-culture models for 96 h in a 96-well polystyrene microtiter plate under both aerobic and anaerobic conditions separately. The viability of the two bacterial species in the biofilms was quantified by polymerase chain reaction (qPCR). Biofilm thickness and protein contents were measured using confocal laser scanning microscopy (CLSM). Two-way analysis of variance (ANOVA) was performed to analyze cell viability and biofilm thickness among different culture models cultivated under either aerobic or anaerobic conditions. The level of significance was set at \(p\) < 0.05. Results: Different culture models tested did not show any significant difference between the viable cell counts of both _E. faecalis_ and _P. gingivalis_ cultivated under aerobic and anaerobic conditions (_p_ > 0.05). Biofilm was significantly thicker (_p_ < 0.05) in the co-culture models compared to the mono-culture and sequential models. Protein contents in the biofilms were more pronounced when both bacterial species were co-cultured under aerobic conditions. Conclusions: _E. faecalis_ appeared to shield _P. gingivalis_ and support its continued growth in oxic (aerobic) conditions. The co-culture model of _E. faecalis_ and _P. gingivalis_ produced a significantly thicker biofilm irrespective of the presence or absence of oxygen, while increased protein contents were only observed in the presence of oxygen.  
aerobic conditions; biofilm model; confocal laser scanning microscopy; cell viability +
Footnote †: journal: _Microorganisms_
# Comparison Between Titanium and Thermally Activated Prostheses in Stages Surgery
## 3 Results
### Comparison of Hearing Results Between the Two Protheses  
Air Conduction (AC)  
The model, shown in Figures 4 and 5, revealed a significant improvement in AC from preoperative to postoperative measurements in both groups (MatriX and NiTiBOND) (\(\beta\) = 19.96, \(p\) < 0.001). However, there was no significant difference between the two prosthesis types (MatriX vs. NiTiBOND) in terms of AC improvement (\(\beta\) = \(-\)0.71, \(p\) = 0.751). Additionally, the interaction between time and prosthesis type was not significant (\(\beta\) = 1.48, \(p\) = 0.475), indicating that the degree of postoperative improvement did not differ significantly between the two prosthesis groups.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **MatrixX (n = 65)** & **NiTiBOND (n = 90)** & **Total (n = 155)** & \(p\)**-Value** \\ \hline \multirow{4}{*}{Gender} & Age & 51.0 (39.0–59.0) & 49.5 (41.2–59.5) & 50.0 (40.5–59.0) & 0.865 \\  & female & 42 (42.9\%) & 56 (57.1\%) & 98 (63.2\%) & 0.760 \\  & male & 23 (40.4\%) & 34 (59.6\%) & 57 (36.8\%) & 0.760 \\  & Side (left) & 35 (40.7\%) & 51 (59.3\%) & 86 (55.5\%) & 0.727 \\  & CN VII Dehiscence & 5 (7.7\%) & 0 (0\%) & 5 (3.2\%) & \\ \hline \end{tabular}
\end{table}
Table 1: Demographic data for patients in both groups.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & \multicolumn{2}{c}{**Preoperative**} & \multicolumn{3}{c}{**Postoperative**} \\ \hline  & **MatriX (n = 65)** & **NiTiBOND** & **Total (n = 155)** & \(p\)**-Value** & **MatrixX (n = 65)** & **NiTiBOND** & **Total (n = 155)** & \(p\)**-Value** \\ \hline Median AC & 52.5 (42.5–62.5) & 51.2 (45.0–62.2) & 51.2 (43.8–62.5) & 0.725 & 31.2 (25.0–37.5) & 29.4 (23.8–37.5) & 30.0 (23.8–37.5) & 0.755 \\ Median BC at & 21.2 (17.5–30.0) & 20.0 (15.0–28.8) & 21.2 (16.2–30.0) & 0.571 & 18.8 (13.8–26.2) & 17.5 (15.0–24.7) & 17.5 (13.8–25.0) & 0.899 \\  & BC at 4 kHz & 35.0 (20.0–50.0) & 25.0 (20.0–40.0) & 30.0 (20.0–40.0) & 0.111 & 30.0 (20.0–50.0) & 30.0 (20.0–40.0) & 30.0 (20.0–42.5) & 0.324 \\ Median ABG & 29.1 \(\pm\) 9.0 & 30.6 \(\pm\) 8.3 & 29.9 \(\pm\) 8.6 & 0.297 & 11.2 (7.5–13.8) & 10.0 (7.5–13.8) & 0.810 \\ \hline \end{tabular}
\end{table}
Table 2: Hearing outcomes pre- and post-operation between the groups.  
Figure 4: Pre- and postoperative AC across frequencies in MatriX group. The vertical axis represents the hearing level in decibels (dB).
# Sparse Parabolic Radon Transform with Nonconvex Mixed Regularization for Multiple Attenuation
## 2 Methodology
### High-Resolution Sparse Parabolic Radon Transform  
The regularization method of using L-norm as a penalty term can make the result sparse and obtain a high-resolution parabolic Radon transform with sparse constraints. Common regularization methods include \(L_{0}\) regularization, \(L_{1}\) regularization, \(L_{2}\) regularization, \(L_{q}\) regularization and the unit sphere geometry diagram of L-norm, shown in Figure 2. Since there are no corners in the constraint region of \(L_{0}\) regularization, and it is difficult to have zero solutions, this method has sparsity. The constraint region of \(L_{2}\) regularization also has no corners and it can avoid the overfitting of the model, but its solutions do not have the sparsity property. The constraint region of \(L_{1}\) regularization is a square. The \(L_{1}\) regularization is convex optimization problem, and it is sparse. It can be solved using the iterative soft threshold algorithm (ISTA) [38] and the fast iterative shrinkage-thresholding algorithm (FISTA) [39]. However, some numerical experiments [21] showed that \(L_{q}\) (\(0<q<1\)) regularization has much better signal recovery capability than \(L_{1}\) regularization minimization. As shown in Figure 2, the solution of \(L_{q}\) (\(0<q<1\)) regularization is more likely to be at a corner, which proves its sparsity.  
The multiple suppression based on the parabolic Radon transform problem can be formulated as following minimization problem of \(L_{q}\) regularization:  
\[\min_{m}\{\frac{1}{2}\|\mathbf{d}-\mathbf{A}\mathbf{m}\|_{2}^{2}+\alpha\|\mathbf{m}\|_{q}^{q}\} \tag{11}\]  
where \(\|\mathbf{m}\|_{q}\)=\(\left(\sum_{i=1}^{N}\left|m_{i}\right|^{q}\right)^{1/q}\) and \(\alpha>0\) is the regularization parameter.  
\(L_{q}\) (\(0<q<1\)) norm constrains whole seismic wavefield data to improve the inversion accuracy. However, in order not to cause signal damage and noise residue, sparse constraints of primary and multiple reflections are considered, respectively, according to the differences between them to achieve a better multiple suppression.
# A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm
## 2 Simulation Analysis of Artificial Neural Network (ANN) Modeling  
### Selection of ANN  
We used the EMCM model to first construct theoretical training data, which was then used to complete the learning and training of the ANN. According to the results of the simulation analysis, the types and technical parameters of the specific ANN algorithm will be achieved.  
MATLAB was employed to program and simulate the analysis. As Back Propagation (BP) ANN has a strong nonlinear mapping ability, flexible network structure, simple theoretical basis, and wide application range, it was used to first partition the training data. After data classification, the Radial Basis Function (RBF) ANN was used to fit the relationship between the magnetic induction intensity and the rotation angle in the space of the ball joint [7; 8; 9]. RBF ANN was chosen because it can approximate any nonlinear function with arbitrary accuracy and has the unique best approximation characteristic [10; 11; 12]. At the same time, its learning rules are simple and its convergence speed is fast.
# Survey of Blockchain-Based Applications for IoT
## 6 Data Integrity and Trust  
### Data Immutability and Tamper Resistance  
Each block in a blockchain contains the hash of the header of the previous block, thereby creating a connected chain. Suppose a block is tampered with, and its hash changes, triggering a cascading effect on the hashes of all subsequent blocks. This mechanism makes tampering with the blockchain readily detectable, as even a tiny modification disrupts the integrity of the entire chain, enabling the system to reject the altered blocks. This characteristic is fundamental to blockchain security, with immutability reinforced by conditions such as maintaining the longest chain and verifying multiple blocks before validating a transaction [3].  
When sharing data via a blockchain, only the hash digest of large datasets is typically stored in the chain to prevent excessive growth. The data reside off-chain in shared databases like the InterPlanetary File System (IPFS). The IPFS is a decentralized protocol for storing and sharing files. It uses hashing to create unique identifiers called Content Identifiers (CIDs) based on the content of a file. CIDs ensure data integrity and immutability, allowing efficient file retrieval across distributed nodes without relying on centralized servers, thereby enhancing resilience and scalability. This on-chain hash serves as a fingerprint, ensuring that any alterations to off-chain data are immediately detectable.  
Figure 8 below shows the general steps of the IPFS process.  
1. Node-A uploads the file to the IPFS, which is split into chunks, and a unique CID is generated based on the hash value;
2. IPFS stores a file distributed across the IPFS network for decentralized storage. It sends CID to Node-A.
3. Node-A records the CID in the blockchain, which is saved in a blockchain transaction/smart contract to ensure its immutability and traceability;
4. Node B retrieves the CID from the blockchain. Node-B queries the blockchain to obtain the CID associated with the file.
5. Node-B requests a file from the IPFS and uses the CID to fetch the file from the IPFS network.  
8. Node-B verifies and downloads the file, and its integrity is checked against the CID before it is reassembled and used.  
To achieve end-to-end data integrity, additional measures are often required to secure the entire data pipeline beyond the blockchain network, thereby addressing potential vulnerabilities at each stage [45].
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 4 Proposed Method
### Location Estimation  
The estimation process of the nodes location is computed according to [16]:  
\[e(x)= \frac{\sum_{i=1}^{N}s_{i}(x)\ w_{i}(p)}{\sum_{i=1}^{N}w_{i}(p)} \tag{1}\] \[e(y)= \frac{\sum_{i=1}^{N}s_{i}(y)\ w_{i}(p)}{\sum_{i=1}^{N}w_{i}(p)} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Id** & **Intersection** & **Intersection** & **Complement** & **Complement** & **Remove?** \\  & **Set** & **Elements** & **Set** & **Elements** & \\ \hline
0 & [1; 7; 8; 9] & 4 & [2; 3; 5] & 3 & No \\
1 & [0, 6, 7; 8; 9] & 5 & [3; 5] & 2 & Yes: Low complement element than average \\
6 & [1, 8; 9] & 3 & [3; 4; 5] & 3 & Yes: Low intersection element than average \\
8 & [0, 1, 6; 7] & 4 & [2, 3; 4, 5] & 4 & No \\ Total (Average) & 16 (4) & & 12 (3) & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average Filtering Table.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Id** & **Intersection** & **Intersection** & **Complement** & **Complement** & **Distance to** & **Remove?** \\  & **Set** & **Duplicate** & **Set** & **Duplicate** & **Blind (m)** & \\ \hline
0 & [1, 7; 8; 9] & No & [2; 3; 5] & No & 21 & No \\
1 & [0, 6; 7; 8; 9] & No & [3; 5] & Yes (with node 7) & 9 & No \\
6 & [1, 8; 9] & No & [3; 4, 5] & No & 20 & No \\
7 & [0, 1, 8; 9] & No & [3; 5] & Yes (with node 1) & 10 & Yes \\
8 & [0, 1, 6; 7] & Yes (with node 9) & [2; 3; 4, 5] & No & 12 & No \\
9 & [0, 1, 6; 7] & Yes (with node 8) & [2, 4, 5] & No & 15 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 5: Duplication Cleaning Table.  
where, \(e(x)\) and \(e(y)\) represent the estimated coordinates for node \(p(x\),\(y)\). \(S\) denotes the sample points of the current time, N is the current total number of the samples for node \(p\) and \(w\) represents the weight for sample \(s\) at the current time. When there is no new sample produced, the current estimation point is the same as the previous estimation point.  
The resampling process is performed by the normal nodes to remove samples with lower weight due to a fixed sample number and duplication of these samples in the new sample set. This process includes inserting the current samples set into the new sample set containing the probability that corresponds to its weight. The sample with a smaller weight will have a lower chance to be selected and vice versa. The location estimation of a blind node is highly affected by the closeness value, sample number and weight of its neighbor nodes as explained in the coming subsection.