esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity High-Throughput QC-LDPC Decoder for 5G New Radio Wireless Communication
## 2 5G New Radio LDPC Codes
### 5G New Radio QC-LDPC Characteristics
* **Step 1:*
* Consider the base graph BG1 or BG2 and select the value of \(k_{b}\) for the corresponding \(K\) and \(R\). * For BG1: \(k_{b}=22\). * For BG2: \(k_{b}=10\) if \(K>640\); \(k_{b}=9\) if \(560<K\leq 640\); \(k_{b}=8\) if \(192<K\leq 560\); and \(k_{b}=6\) elsewhere. * **Step 2:*
* Determine \(Z\) by choosing the minimum \(Z\) value in Table 2, such that \(k_{b}\times Z\geq K\). * **Step 3:*
* After the lifting size \(Z\) is determined, the corresponding shift coefficient matrix is then picked up from Table 1 (Set 1, Set 2,..., Set 8) according to \(Z\). * **Step 4:*
* Calculate the shifting coefficient value \(P_{i,j}\) by the modular \(Z\) operation, as defined in Equation (4). * **Step 5:*
* Substitute each entry in the final exponent matrix by the corresponding circulant permutation matrix or zero matrix of size \(Z\times Z\). The QC-LDPC code construction is accomplished, and a parity-check matrix \(H\) of size \(m_{b}Z\times n_{b}Z\) is achieved. In 5G QC-LDPC codes, a shortening and puncturing process is performed to achieve the desired information lengths and rate adaption. An illustration of the encoding process of 5G QC-LDPC codes is presented in Figure 2. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{Z}\)} & \multicolumn{8}{c}{_a_} \\ \cline{2-10}  & & 2 & 3 & 5 & 7 & 9 & 11 & 13 & 15 \\ \hline \multirow{5}{*}{\(j\)} & 0 & 2 & 3 & 5 & 7 & 9 & 11 & 13 & 15 \\ \cline{2-10}  & 1 & 4 & 6 & 10 & 14 & 18 & 22 & 26 & 30 \\ \cline{2-10}  & 2 & 8 & 12 & 20 & 28 & 36 & 44 & 52 & 60 \\ \cline{2-10}  & 3 & 16 & 24 & 40 & 56 & 72 & 88 & 104 & 120 \\ \cline{2-10}  & 4 & 32 & 48 & 80 & 112 & 144 & 176 & 208 & 240 \\ \cline{2-10}  & 5 & 64 & 96 & 160 & 224 & 288 & 352 & \\ \cline{2-10}  & 6 & 128 & 192 & 320 & & & & & \\ \cline{2-10}  & 7 & 256 & 384 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Lifting size \(Z\) supported by standard 5G QC-LDPC codes.
# Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage
## 3 Model Proposal and Solution Method
### Solution Method
\begin{array}{l}\mathbf{X_{0}^{(k+1)}}=\textit{shrink}_{p}(\mathbf{H}*\mathbf{F }^{(k+1)}-\mathbf{G}+\mathbf{\Lambda_{0}^{(k)}},\frac{1}{\beta_{0}})\\ \mathbf{X_{1}^{(k+1)}}=\textit{shrink}(\mathbf{K_{h}*\mathbf{F}^{(k+1)}}-\mathbf{V_{h}^{(k+1)} }+\mathbf{\Lambda_{1}^{(k)}},\frac{\mu_{0}}{\beta_{1}})\\ \mathbf{X_{2}^{(k+1)}}=\textit{shrink}(\mathbf{K_{v}*\mathbf{F}^{(k+1)}}-\mathbf{V_{v}^{(k+1)} }+\mathbf{\Lambda_{2}^{(k)}},\frac{\mu_{0}}{\beta_{1}})\\ \mathbf{X_{3}^{(k+1)}}=\textit{shrink}(\mathbf{K_{h}*\mathbf{V}_{h}^{(k+1)}}+\mathbf{\Lambda_ {3}^{(k)}},\frac{\mu_{1}}{\beta_{2}})\\ \mathbf{X_{4}^{(k+1)}}=\textit{shrink}(\mathbf{K_{v}*\mathbf{V}_{v}^{(k+1)}}+\mathbf{\Lambda_ {4}^{(k)}},\frac{\mu_{1}}{\beta_{2}})\\ \mathbf{X_{5}^{(k+1)}}=\textit{shrink}(\mathbf{K_{v}*\mathbf{V}_{h}^{(k+1)}}+\mathbf{K_{h}*\bm {V}_{v}^{(k+1)}}+\mathbf{\Lambda_{5}^{(k)}},\frac{\mu_{1}}{\beta_{2}})\end{array}  \right.. \tag{18}\]  
3.2.3 Solving Sub-Problems Containing Dual Variables \(\mathbf{\Lambda_{i}(i=0,1,2,\cdots,5)}\)  
For the sub-problems containing dual variables \(\mathbf{\Lambda_{i}(i=0,1,2,\cdots,5)}\), the objective functions are expressed as  
\[\left\{ \begin{array}{l}J_{\mathbf{\Lambda_{0}}}=\underset{\mathbf{\Lambda_{0}}}{max} \beta_{0}\bigg{\langle}\mathbf{\Lambda_{0}},(\mathbf{H}*\mathbf{F}^{(k+1)}-\mathbf{G})-\mathbf{X_{0 }^{(k+1)}}\bigg{\rangle}\\ J_{\mathbf{\Lambda_{1}}}=\underset{\mathbf{\Lambda_{1}}}{max}\beta_{1}\bigg{\langle} \mathbf{\Lambda_{1}},(\mathbf{K_{h}*\mathbf{F}^{(k+1)}}-\mathbf{V_{h}^{(k+1)}})-\mathbf{X_{1}^{(k+1 )}}\bigg{\rangle}\\ J_{\mathbf{\Lambda_{2}}}=\underset{\mathbf{\Lambda_{2}}}{max}\beta_{1}\bigg{\langle} \mathbf{\Lambda_{2}},(\mathbf{K_{v}*\mathbf{F}^{(k+1)}}-\mathbf{V_{v}^{(k+1)}})-\mathbf{X_{2}^{(k+1 )}}\bigg{\rangle}\\ J_{\mathbf{\Lambda_{3}}}=\underset{\mathbf{\Lambda_{3}}}{max}\beta_{2}\bigg{\langle} \mathbf{\Lambda_{3}},(\mathbf{K_{h}*\mathbf{V}_{h}^{(k+1)}})-\mathbf{X_{3}^{(k+1)}}\bigg{\rangle} \\ J_{\mathbf{\Lambda_{4}}}=\underset{\mathbf{\Lambda_{4}}}{max}\beta_{2}\bigg{\langle} \mathbf{\Lambda_{4}},(\mathbf{K_{v}*\mathbf{V}_{v}^{(k+1)}})-\mathbf{X_{4}^{(k+1)}}\bigg{\rangle} \\ J_{\mathbf{\Lambda_{5}}}=\underset{\mathbf{\Lambda_{5}}}{max}\beta_{2}\bigg{\langle} \mathbf{\Lambda_{5}},(\mathbf{K_{v}*\mathbf{V}_{h}^{(k+1)}}+K_{h}*\mathbf{V}_{v}^{(k+1)})-\mathbf{X _{5}^{(k+1)}}\bigg{\rangle}\end{array}
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
## 3 The Localization Method Using Bearing-Only Measurements
### Run the RPEKF Based on Bearing-Only Measurements of Passing Ships
#### 3.2.2 Measurement Update Step  
We next present the measurement update step in the EKF. Suppose that the AUV measures the bearing of sound of \(N_{k}\) ships at sample-index \(k\). Let \(\{n_{k,1},n_{k,2},\ldots,n_{k,N_{k}}\}\) denote the index of the measured ship at sample-index \(k\). Since there are \(N\) ships in total, we have  
\[\{n_{k,1},n_{k,2},\ldots,n_{k,N_{k}}\}\subset\{1,2,\ldots,N\}. \tag{37}\]  
Using Equation (29), the measurement associated to the \(n_{k,s}\)-th ship (\(s\in\{1,2,\ldots,N_{k}\}\)) is  
\[\mathbf{z}_{k}^{n_{k,s}}=b_{k}^{n_{k,s}}=h^{n_{k,s}}(\mathbf{X}_{k})+\mathbf{w }_{k}^{m}. \tag{38}\]  
In each EKF track, we perform the EKF measurement update associated to each ship in \(\{n_{k,1},n_{k,2},\ldots,n_{k,N_{k}}\}\). The EKF measurement update indicates that we apply the EKF Equations (6) and (7) sequentially.  
We have \(N_{k}\) measurements at sample-index \(k\). Thus, each EKF track keeps running the Equations (6) and (7) \(N_{k}\) times iteratively, while using measurements in Equation (38). In other words, each EKF track uses \(\mathbf{z}_{k}^{n_{k,k}}\) as the first measurement at sample-index \(k\). The EKF track applies the Equations (6) and (7), while using \(\mathbf{z}_{k}^{n_{k,1}}\) as the measurement \(\mathbf{z}_{k}\) in Equation (5). Next, the track uses \(\mathbf{z}_{k}^{n_{k,2}}\) as the second measurement at sample-index \(k\). The EKF track applies the Equations (6) and (7), while using \(\mathbf{z}_{k}^{n_{k,2}}\) as the measurement \(\mathbf{z}_{k}\) in Equation (5). Iterate this until the track uses \(\mathbf{z}_{k}^{n_{k,N_{k}}}\) as the last measurement at sample-index \(k\). The EKF track applies the Equations (6) and (7), while using \(\mathbf{z}_{k}^{n_{k,N_{k}}}\) as the measurement \(\mathbf{z}_{k}\) in Equation (5).  
In this way, each EKF track can process the sound of \(N_{k}\) ships at sample-index \(k\). Once the measurement update is done using \(N_{k}\) measurements at sample-index \(k\), each EKF track updates both the state vector and its error covariance using Equation (8).
## 11 Discussion
### How the Proposed Framework Addresses the Challenges  
Section 4 outlines the challenges faced by developers and organizations in adopting a risk management framework and standards for assuring security and privacy of WBAN applications.  
* Lack of trained staff, responsibilities, budget, and management support--this framework consists of a list of assets, threats, vulnerabilities, and controls with implementation details which are specific to WBAN applications. The implementation of this framework requires minimal security expertise and will help to reduce development time, and thereby development cost.
* The existing standards are too complex and complicated to implement--this framework provides detailed guidance on how to conduct each step of the risk management process. This guidance should greatly assist developers with limited experience in implementing a risk management process.
* Limited knowledge about healthcare regulatory requirements and standards--the framework is based on recommendations and best practice guidelines provided by regulations such as HIPPA and GDPR, and by standards such ISO/IEC 80001-2-2, TIR 57, NIST 800-53 and ISO 27002.
* Understanding the data flow around the system and what assets need to be protected--the framework provide guidance on conducting security risk assessment at both the requirements analysis and the system architecture phases. This guidance will help the organization understand how data flows around the system and to identify the assets that need protection.
* Comprehensive understanding of the architecture for WBAN security and privacy--the framework outlines the possible assets, threats and vulnerabilities, and providesguidelines on how to conduct the architecture review. Additionally, the framework identifies the security requirements that need to be considered during the development of the architecture of a WBAN application. This will help organizations to obtain a comprehensive understanding of WBAN architecture.
* Identifying appropriate security controls with respective implementation details--the framework provides appropriate security controls, along with their implementation details, for a WBAN application. The implementation details will assist a developer to implement the security controls.
* Due to a vast number of security controls, the challenge is prioritizing these controls in addition to planning releases without compromising security and privacy--the security risk score which is identified during the security risk evaluation and treatment stage can be used to prioritize the risk and respective security risk control.
* Security mechanisms for sensor device nodes--the framework suggests using very lightweight encryption and decryption processes. This framework recommends use of the AES symmetric cryptographic algorithm and the Diffie-Hellman process for key exchanges between mobile applications and sensor devices.
## 2 Related Work  
### Map Representation  
Map representations for warehousing logistics can provide a visualization of facility structures, pathways, obstacles, and storage locations and therefore facilitate enhanced spatial comprehension and path planning optimization. Mainstream methods include topological maps [7], which utilize node-edge structures to represent spatial connectivity and accessibility relationships, feature-based maps [8] that construct spatial representations via sensor that capture significant environmental characteristics, and symbolic maps [9] that employ abstract symbols to represent spatial information and structures. These map representations find primary application in advanced task planning and decision support systems but lack detection abilities for obstacles.  
Other methods, e.g., vector maps [10], continuous space maps [11], three-dimensional maps [12], depth maps [13], etc., project detected geometric information and location specifications into coordinate systems and obtain accurate topological representations via sophisticated calculations. Although they display significant advantages in rapid obstacle detection, extra computation complexities should be considered, and advanced detection equipment is required. Therefore, utilization of these methods is limited.  
SLAM (Simultaneous Localization and Mapping) [14] technology enables real-time positioning and environmental mapping without external sensing infrastructure. But, high computational costs and susceptibility to error accumulation in large-scale or featureless environments should be considered, and hence its utilization is restricted in HDS systems.  
Besides these drawbacks, the existing map representations also struggle to accurately depict the hierarchical traffic constraints between main tracks and sub-tracks in the structured environment of an HDS systems. Traditional raster maps usually treat storage space as uniform grids, failing to account for the global connectivity of main tracks and the unidirectional dependency of sub-tracks. This limitation results in increased redundant node searches and frequent route conflicts during path planning. To overcome these limitations, we introduce the mesh-tree grid map topological representation to effectively distinguish track levels and enforce optimal path planning for HDS systems.
## 3 Establishment of Bone Density Prediction Models and Analysis of Parameter Effects
### Parameter Sensitivity Analysis of Bone Density Prediction Models  
The Sobol method is a variance-based sensitivity analysis method that enables a sensitivity analysis of strongly non-linear, nonmonotonic models [26] and relies on the estimation of the following values:  
\[S=\frac{V_{x}[E(y|x)]}{V(y)}, \tag{5}\]  
In this equation, \(y\) denotes the output variable,\(x\) denotes the input variable, \(E(y|x)\) represents the expected value of \(x\) conditional on \(y\) taking a constant value, and \(V(y)\) is the total output variance of the output \(y\).  
Setting the model as \(y=f(x),(x=x_{1},x_{2},\ldots,x_{k})\), \(x_{i}\) obeys a uniform distribution, and \(f^{2}(x)\) is integrable in the domain of definition; it can be decomposed as  
\[f(x_{1},\ldots,x_{k})=f_{0}+\sum_{i=1}^{k}f_{i}(x_{i})+\sum_{1\leq i<j\leq k} f_{ij}(x_{i},x_{j})+\ldots+f_{1,2,\ldots,k}(x_{1},x_{2},\ldots,x_{k}), \tag{6}\]  
Figure 8: Effect of process parameters’ interaction on cancellous bone density. (**a**) Response plot of filling density versus material flow rate interaction; (**b**) response plot of interaction between filling density and layer thickness; (**c**) response plot of layer thickness versus material flow interaction.  
Normalizing the above equation yields the following equation:  
\[\sum_{i=1}^{k}S_{i}+\sum_{1\leq i<j\leq k}S_{ij}+\ldots+S_{1,2,\ldots,k}=1, \tag{13}\]  
The global sensitivity of each input parameter is the sensitivity of all included parameters \(x_{i}\). The global sensitivity definition index can be expressed by the following equation:  
\[S_{Tl}=S_{i}+\sum_{l\
eq j}S_{ij}+\cdots, \tag{14}\]  
The global sensitivities of the individual printing parameters have been computed, and the outcomes of these sensitivity analyses are summarized in Table 8. To provide a more intuitive visualization of the relative impact of various printing parameters on the printed density, the first-order and total-order sensitivities of the prediction model are depicted in bar charts, as illustrated in Figure 9.  
As shown in Figure 9, the disparity between the global sensitivity and the first-order sensitivity of the predictive model for bone density across distinct bone layers is negligible, suggesting a minimal role of parameter interactions. The quantitative outcomes indicate that filling density exerts the predominant influence, followed by the material flow rate, while the impact of layer thickness appears to be inconsequential.
## 5 Non-Uniform Discrete Fourier Transforms
The _non-uniform discrete Fourier transform_ (NDFT) of \(\hat{\kappa}\coloneqq[\hat{\kappa}(m)]_{m\in\{-M,\ldots,M-1\}^{d}}\) at the nodes \(\Omega^{k}\subset\mathbb{R}^{d}\) is defined by  
\[\left[F_{k}\hat{\kappa}\right]_{i_{\ell}}\coloneqq\sum_{m\in\{-M,\ldots,M-1\}^{ d}}\hat{\kappa}(m)\,\exp\left(-\mathrm{i}\frac{\pi}{\tau}m^{\intercal}x_{i_{\ell}} ^{k}\right),\quad i_{\ell}\in[n_{\ell}], \tag{17}\]  
and the adjoint NDFT of \(\alpha\in\mathbb{R}^{n_{\ell}}\) on the set \(\Omega^{\ell}\) is given by  
\[[F_{\ell}^{*}\alpha]_{m}\coloneqq\sum_{i_{\ell}=1}^{n_{\ell}}\alpha_{i_{\ell} }\,\exp\left(-\mathrm{i}\frac{\pi}{\tau}m^{\intercal}x_{i_{\ell}}^{\ell} \right),\quad m\in\{-M,\ldots,M-1\}^{d}, \tag{18}\]  
cf. [43] (Section 7). Therefore, the approximation (16) can be written as  
\[\beta=\mathbf{\mathcal{K}}^{(k,\ell)}\alpha\approx\mathbf{F}_{k}(\hat{\kappa}\odot F _{\ell}^{*}\alpha). \tag{19}\]  
The procedure is summarized in Algorithm 4. ```
1:Input: Vector \(\alpha\in\mathbb{R}^{n_{\ell}}\), kernel function \(\kappa\) in (12), parameters \(\epsilon_{\mathrm{B}}>0\), \(M\in\mathbb{N}\)
2:precomputation
3: Compute the regularized kernel \(\kappa_{\mathrm{R}}\) by (13)
4: Compute the periodized kernel \(\tilde{\kappa}\) by (14)
5: Compute the discrete Fourier coefficients \(\hat{\kappa}(m)\), \(m\in\{-M,\ldots,M-1\}^{d}\), by an FFT, see (15)
6:end
7: Compute the adjoint NDFT \(\mathbf{F}_{\ell}\alpha\), see (18)
8: Compute the pointwise product \(\hat{\beta}\coloneqq\hat{\kappa}\odot F_{\ell}\alpha\)
9: Compute the NDFT \(\beta\coloneqq F_{k}\hat{\beta}\), see (17)
10:return\(\beta\approx\mathbf{\mathcal{K}}^{(k,\ell)}\alpha\)
```  
**Algorithm 4** NFFT-based fast summation  
There are fast algorithms, known as _non-uniform fast Fourier transform_ (NFFT), allowing for the computation of an NDFT (17) and its adjoint (18) in \(\mathcal{O}(M^{d}\log M+N)\) steps up to arbitrary numeric precision; see e.g., [44; 45] and [43] (Section 7), where \(N=\left\lVert\mathbf{n}\right\rVert_{\infty}\). Note that the direct implementation of (16) requires \(\mathcal{O}(M^{d}N)\) operations. We call the Sinkhorn algorithm where the matrix-vector multiplication is performed via (19) the _NFFT-Sinkhorn algorithm_. If we fix the Fourier expansion degree \(M\), which is possible because of the smoothness of \(\kappa\), then we end up at a numerical complexity of \(\mathcal{O}(KN)\) for each iteration step of the NFFT-Sinkhorn algorithm for trees. In the case of a circle (Algorithm 3), we can apply the fast summation column by column for the matrix-matrix product with \(\mathbf{\mathcal{K}}^{(k,k+1)}\), yielding a complexity of \(\mathcal{O}(KN^{2})\) for each iteration step.
# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 2 Materials and Methods
### Jump Attention Module (JAM)
The formula for the channel attention mechanism is given in Equation (3). \[ \begin{array}{rl}M_{c}(H)&=\sigma(Conv(MLP(AvgPool(H))+MLP(MaxPool(H)))\\ &=\sigma(Conv(W_{1}(W_{0}(H_{avg}^{c}))+W_{1}(W_{0}(H_{max}^{c}))))\end{array}  \tag{3}\]  
According to Equation (4), the number of feature vector channels is reduced by half after passing the last convolution layer. Input high-dimensional features of size \(2C\times H\times W\) for the high-level features, generating \(C\times 1\times 1\) attention weights with the same number of channels as the low-dimensional features. Finally, after passing the low-level channels  
Figure 4: Jump attention module. Figure 5: Channel attention. through a 3 \(\times\) 3 convolutional layer, the attention-processed feature map is obtained by multiplying it with the attention weights, calculated as in Equation (4). \[M_{c}(L)=\sigma(Conv(W_{1}(W_{0}(H_{arg}^{c}))+W_{1}(W_{0}(H_{max}^{c}))))\times Conv _{3\times 3}(L) \tag{4}\]  
At this point, the size of the generated jump feature map is \(C\times 2H\times 2W\), which is the same size as the high-dimensional features after one upsampling, and the low-dimensional features and high-dimensional features after channel attention processing are stitched together to obtain the feature map obtained from this round of upsampling and the feature map used in the next round of upsampling, as in Equation (5). \[H_{next}=Upsample(H)+M_{c}(L) \tag{5}\]  
In summary, the output feature map of the attention module in this paper has exactly the same size and number of channels as the low-level feature map, and there are not too many weights, so it can be easily added to the existing network and can effectively extract the picture ground channel and spatial attention information, which can well save hardware resources.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 3 Method, Part 1: Specular Reflection Point Calculation
After multiple iterations the specular point location will converge to within an acceptable accuracy and the iteration ends with a final best estimate of the specular point position. In summary, the iterative steps to estimate the surface specular point include,  
1. Determine an initial guess for the surface specular point location, \(\vec{S}_{1}\). This could be either (a) an estimate based on previous, propagated or modeled initial specular point positions or (b) using the estimate of the receiver position \(\vec{R}\), normalized to the Earth surface, calculated as, \[\vec{S}_{1}=\vec{S}_{i}=r_{i}\frac{\vec{R}}{\left|\vec{R}\right|}.\] (10)
2. Solve for the correction derivative using Equation (6). Path magnitudes are calculated using best estimates of receiver and transmitter positions from GPS navigation solution, propagated ahead slightly to account for the non-zero computational time with respect to the application of the values in real-time on the instrument. 3. Update estimated position of specular point from \(\vec{S}_{i}\) to \(\vec{S}_{i+1}\) using Equation (7). 4. Calculate estimated Earth surface using Equation (8). Adjust WGS84 ellipsoid height (\(h\)) to account for height of surface topography over land or ocean height variations as needed. 5. Project new corrected specular point estimate \(\vec{S}_{i+1}\) to the Earth's surface using Equation (9). 6. Start next iteration (step 2) with updated value for specular point \(\vec{S}_{i}\) calculated in Equation (9) until convergence criteria is met.
# Challenges Associated with Implementing 5G in Manufacturing
## 5 The Wireless Spectrum and 5G
A summary of the main wireless protocols and their attributes is in Table 5.  
Communications regulators globally are managing the allocation of spectrum for 5G usage; typically, the spectrum is being auctioned to telcos who are then offering a range of solutions to industry. In Europe, three major frequency bands have been made available for 5G use. These three bands are 700 MHz, 3.6 GHz and 26 GHz as shown in Table 5.  
When considering the heterogeneous approach to wireless networks it is important to understand that interaction of the wireless spectrum with cellular technologies will be a dominant driver for many sites. It should be noted that spectrum regulators in some countries have started the process of opening up shared spectrum for local use in specific bands, which will enable the deployment of private 5G networks and enable easier research and development deployments. For example, the regulator in the USA opened up the 3.5 GHz band as of January 2020 for commercial use known as the CBRS (Citizens Broadband Radio Service) band. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Frequency** & **Wavelength** & **Range** \\ \hline Wi-Fi (2.4G) & 2401 MHz-2483 MHz & 12 cm & 150 m \\ \hline Wi-Fi (5G) & 5150 MHz-5875 MHz & 5 cm & 120 m \\ \hline Bluetooth & 2400 MHz-2485 MHz & 12 cm & 10–100 m \\ \hline LoRA & 868 MHz & 35 cm & 10 km+ \\ \hline
3G & 900/2100 MHz & 33.3/14 cm & 100 m to \textgreater{}5 km \\ \hline
4G (LTE) & 800/1800 MHz & 37.5/17 cm & 100 m to \textgreater{}5 km \\ \hline
5G & 700 MHz/3.6 GHz/26 & 43 cm/8 cm/1 mm & 50 m to \textgreater{}5 km \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of wireless protocols.
###### Abstract  
This paper investigates system identification algorithms for modal identification of frame structures, such as a suspension bridge and an overhead transmission line-crossing frame, using ambient vibration measurements. The modal identification procedures include two novel blind source separation (BSS) algorithms, complexity pursuit method (CP) and generalized eigen decomposition method (GED), based on modern signal processing technology. Here, the frequency response function (FRF) method is introduced as an important reference to verify the effectiveness of the CP algorithm and GED algorithm. The effectiveness and accuracy of both types of algorithms are verified by numerical simulations and experiments on a suspension bridge. In addition, an engineering application of these two BSS methods is successfully implemented in an overhead transmission line-crossing frame. The results show that the two novel BSS learning rules (CP and GED) are capable of successfully identifying modal parameters of the civil structure under ambient excitation.  
Keywords:modal identification; blind source separation; complexity pursuit; generalized eigen decomposition; suspension bridges; overhead transmission line-crossing frame +
Footnote †: journal: Applied Sciences