esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of the Extraction Conditions Using BBD  
#### 3.2.1 Extraction Model and Statistical Analysis  
Based on the results of the single-factor experiments, the extraction parameters were optimized using a BBD model. The results of 17 runs with 3 factors and 3 levels are shown in Table 2. The data was analyzed using Design-Expert V8.0.6.1 software (Stat-Ease Inc., Minneapolis, MN, USA).  
Figure 1: Effects of different extraction parameters: ultrasonication temperature (**A**), power (**B**), and time (**C**).  
The polysaccharide yield and test variables were obtained using the following quadratic polynomial equation:  
\[\mathrm{Y}=9.49+0.042\mathrm{A}+0.11\mathrm{B}+0.33\mathrm{C}+0.076\mathrm{AB}-0.039\mathrm{AC}+0.046\mathrm{BC}-1.09\mathrm{A}^{2}-0.64\mathrm{B}^{2}-0.54 \mathrm{C}^{2} \tag{2}\]  
where Y is the polysaccharides yield, A is the ultrasonication temperature, B is the ultrasonication power, and C is the ultrasonication time.  
The results of the variance (ANOVA) for the experiments are presented in Table 3. The determination coefficient (R\({}^{2}\) = 0.9830) and adjusted coefficient of determination (R\({}^{2}_{\mathrm{adj}}\) = 0.9612) illustrated that the regression model fits the data well and the model was highly significant in the range of the experimental variables. In addition, a low coefficient of variation (CV) of 1.90% indicated a high degree of precision and confirmed that the experimental values were reliable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Run** & **Ultrasonic** & **Ultrasonic Power** & **Ultrasonic Time** & **Extraction Yield** \\
**Temperature (\({}^{\circ}\)C)** & **(W)** & **(h)** & **(\%)** \\ \hline
1 & 70 & 200 & 3 & 7.729 \\
2 & 60 & 200 & 2.5 & 9.534 \\
3 & 50 & 200 & 3 & 8.653 \\
4 & 70 &  
The F-values and \(p\)-values were also used to evaluate the significance of the regression model. The independent variables (C), quadratic terms (A\({}^{2}\), B\({}^{2}\), and C\({}^{2}\)) and cross product coefficient (AC) significantly affected the polysaccharide yield (\(p<0.05\)). These results demonstrated that the ultrasonication time was the most important parameter influencing the extraction yield, followed by the ultrasonication power and temperature.
## 3 Design and Analysis for WPTDDM-FxLMS Algorithm
### Signal Extraction of Wavelet Packet Transformation  
In Section 3.1, the adaptive algorithm complexity in the MIMO system is significantly reduced, and the decentralized decoupling of the secondary path is also improved. However, the vibration source signal in underwater vehicles is very complicated, which has an influential impact on the control strategy; as shown in Equation (15), the input signal affects the convergence characteristics of the system to a certain extent. Theoretically, if the complicated input signal could be effectively decomposed into multiple narrowband signals, then the segmentation control may achieve a better control effect. Research has shown that [37] the wavelet packet transform can decompose the complicated input signals into multiple non-overlapping and narrower-frequency bands. This method can synchronously decompose the scale space and the wavelet space, which has a high resolution for the high-frequency section. Assuming that \(L^{2}(R)\approx V_{0}=U_{0}^{0}\) and an orthogonal scaling function is in space \(U_{0}^{0}\), there is a two-scale formula [38]:  
\[\varphi(t)=\sqrt{2}\sum\limits_{k=0}^{N-1}h_{k}\varphi(2t-k) \tag{19}\]  
\[\psi(t)=\sqrt{2}\sum\limits_{k=0}^{N-1}g_{k}\psi(2t-k) \tag{20}\]  
where \(g_{k}=\left(-1\right)^{k}h_{N-1-k}^{*}\) and \(\psi(t)\) is an orthogonal wavelet function. For a set of best orthogonal wavelet bases in \(L^{2}(R)\), the space \(U_{0}^{0}\) could be decomposed into  
\[U_{0}^{0}=\mathop{\cup}\limits_{j=1}^{J}\left(\mathop{\cup}\limits_{i=0}^{2^{ j}-1}U_{j+k_{j}}^{k_{j}+i}\right)\oplus U_{J}^{0} \tag{21}\]If the continuous time function is \(f(t)\in U_{0}^{0}\), the projection coefficient for \(f(t)\) on the orthogonal basis of \(U_{0}^{0}\) can be written as  
\[S_{k}^{0,0}=\left\langle f(t),\varphi(t-k)\right\rangle=\int_{R}f(t)\mu_{0}^{*}( t-k)d_{t} \tag{22}\]  
Similarly, the orthogonal basis projection coefficient for \(f(t)\) is \(\left\{S_{k}^{n,j}\right\}_{k\in Z}\) in each subspace \(U_{j}^{n}\), which is called the orthogonal wavelet packet transformation of \(f(t)\). Actually, the orthogonal wavelet packet transform will be expressed in a recursive form \(S_{k}^{n,j}\).  
\[S_{l}^{2n,(j+1)}=\sum_{k\in Z}h_{k-2l}^{*}S_{k}^{n,j} \tag{23}\]  
\[S_{l}^{2n+1,(j+1)}=\sum_{k\in Z}g_{k-2l}^{*}S_{k}^{n,j} \tag{24}\]  
where \(h_{k}\) and \(g_{k}\) can be regarded as low-pass filtering and high-pass filtering coefficients. The band number of the wavelet packet decomposition depends on its decomposition scale \(M\), and \(U_{m}^{n}\) represents the \((n+1)\)-th frequency band on the \(m\)-th layer, \(n=0,1,2...2^{m}-1\), where \(J=2^{m}\) and \(m\) is the wavelet decomposition layer number. Assuming that \(f_{s}\) is the sampling frequency of the input signal, it can be detected for the frequency band (\(0\)-\(f_{s}/2\)) by the Nyquist principle, and the bandwidth of each frequency band in the m-th layer is \(fs/2^{m+1}\).
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Bear  
BEAR proposed in [43] aims to prolong network lifetime. This works in 3 phases: (1) Initialization phase: in this phase, nodes share their location and information related to their levels of residual energy; (2) Tree building phase: In this phase, the location information obtained in the initialization phase is used to choose a) neighbor nodes, and b) facilitating and successor sensor nodes, depending on a cost function value. To balance the energy consumption between facilitator and successor nodes, the proposed protocol chooses those nodes whose levels of residual energy are higher, compared to the network's average residual energy level. (3) Selection of a relay and transmission of data packet to that node. BEAR improves overall load distribution by dividing the network into sectors, and achieving intra-sector and inter-sector energy balancing. However, it has certain drawbacks, such as the 4-way handshake in the neighbor-finding phase, which increases overhead. Moreover if a hello message does not receive a response, the message is retransmitted with increased transmission range, which increases contention and communication overhead even further.
## 1 Introduction  
In recent years, the global demand for data traffic from mobile networks and higher and faster data rates and very low latency have increased due to the high demand for various applications and the ability to connect thousands of devices simultaneously. In this fact, the fifth generation (5G) of the cellular communication system, i.e., New Radio (NR), offers these features and requirements through specific and different scenarios, such as ultra-high mobility, ultra-high connection, and ultra-high traffic [1; 2; 3; 4; 5], compared to legacy networks such as LTE [6]. Generally, radio network planning (RNP) is a critical topic and is considered to be the primary step in implementing a wireless network such as a 5G network. The services and applications provided by 5G NR networks are grouped into three use case scenarios, namely enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (uRLLC), and massive machine-type communication (mMTC), and all these use cases are applied to the Internet of Things (IoT) [7; 8; 9]. The level of importance of each capability in the different use cases is shown in Figure 1.  
Figure 1: Key capabilities in different use cases and scenarios [7].  
The frequency bands allocated to the 5G NR network are classified into three main categories according to Table 1.  
The main contribution of this paper is that, until now, 5G NR network planning with a C-band and millimeter wave (mm-wave) band was yet to be performed in Ecuador for the metropolitan and business areas in the north zone in Quito city [10; 11; 12; 13]. Our goal is to show the resumed planning, designing, and evaluation of a 5G NR network in the dense urban and urban area of Quito city in Ecuador by using a C-band/mm-wave band applying a densification process using macro/micro cells in the network planning [14]. For this purpose, we use the Link budget and propagation model calculations to determine the path loss values, propagation values, and cell radius in order to calculate the number of sites required in the target zones. All these activities are applied by considering the coverage and capacity planning, dense urban/urban areas, and frequency of operation. The data calculated is tested using a planning tool called Atoll ver. 3.4.0, and our results are reflected in the simulation results from the synchronization signal--RSRP (SS-RSRP), synchronization signal to noise ratio (SS-SNR), and downlink/uplink throughput data rate plots for each scenario.  
This paper is organized as follows: Section 2 defines the architecture of the 5G network, methodology, planning concepts, and employed system parameters. Section 2 also introduces 5G network planning into zones of Quito city; here, the results of the link budget and each parameter for the simulations are also presented. Section 3 presents the simulation results to verify the effectiveness of the suggested planning in Quito city, and Section 4 shows the conclusions.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 5 Experiment Results and Analysis
### Ultrasonic Guided Wave Test Waveform
#### 5.2.2 The Uncertainty of Waveforms at the Same Position  
During the signal acquisition process of non-destructive testing experiments, the signal sensors continuously sample at a fixed sampling frequency. In this study, sequences with a length of 200 sampling points were used as the original samples. The dataset consisted of 127 groups of ultrasonic guided waveforms collected under different corrosion levels. As shown in Table 4, during the training process, the labeled dataset of 762 groups of data was randomly shuffled, and it was divided into a training set of 610 groups and a test set of 152 groups in an 8:2 ratio. During the actual detection process, multiple factors can affect the test results, such as sensor position, angle, coupling condition, environmental noise, etc. These factors introduce various distortions and interferences, increasing the complexity of the waveform. Figure 14 shows the guided wave signals collected at the same position, and it can be observed that even when the sensor remains stationary, there are significant differences in the reflected signals. This makes it more difficult to identify the characteristics of the waveforms.  
Figure 14: Corrosion ultrasonic guided wave signal collected at the same position after 72 h of corrosion.  
Figure 13: Guided wave signals of anchor bolts with different degrees of corrosion.  
The randomness of the CNN network during the training process can lead to different recognition results that continuously change. In order to obtain a more objective and accurate recognition rate, it is common to increase the sample size and take the average of multiple experimental results to reduce the influence of objective conditions on the experimental results.  
Under the prediction results of the CNN model, we can observe from Figure 15a that the loss function converges to the order of \(10^{-3}\). The classification recognition results of anchor bolt corrosion can be represented by the prediction accuracy and the final confusion matrix. The MS-CNN effectively extracts features from guided wave signals, achieving a prediction accuracy of 99.4% with only one waveform misclassified.
## 4 Finite Element Analysis
### Interface Contact Modeling  
According to the experimental analysis, the damage on the contact surface happens in three stages, i.e., linear elastic behavior, initial damage behavior, and damage development behavior, which correspond to the elastic stage, damage initiation criterion, and damage evolution, respectively, in the ABAQUS cohesive behavior model. Therefore, the cohesive behavior model was used to simulate the bonding of the interface between the steel tube and the concrete, as well as the process of damage evolution. ABAQUS software provides two cohesive force models, namely, the cohesive element model, and the surface-based cohesive behavior model. When the surface-based cohesive behavior model is set up with cohesive contact and friction contact at the same time, the friction contact remains in a dormant state before the surface-based cohesive behavior is destroyed, and the friction contact will be triggered only after it is destroyed, which is consistent with the actual experiments, so the surface-based cohesive behavior model was used to simulate steel tube-concrete interface contact behavior.  
Since the actual damage condition of the interface between the steel tube and concrete in this study was of the slip-open type, and there was no tension or tearing, the maximum stress criterion was used for judgment, as shown in Equation (6). When the value of the following equation reaches 1, the criterion will be effective, and then the contact point will be ready to enter the damage evolution stage.  
\[Max\Big{\{}\frac{\sigma_{S}}{\sigma_{h}^{2}},\quad\frac{\sigma_{S}}{\sigma_{ g}^{2}},\quad\frac{\sigma_{S}}{\sigma_{t}^{2}}\Big{\}} \tag{6}\]  
Based on the process analysis introduced in Section 3.2, it can be seen that the bond-slip process of steel tube-concrete columns is divided into a rising section and a residual section. Based on the shapes and eigenvalues of the experimental P-S curves, the \"two-stage\" approach was adopted to address the constitutive relationship of the round steel tube-concrete interface with different structural measures for internal welding [39, 40].  
\[\tau=\left\{\begin{array}{ll}G_{s}S&(S\leq S_{s})\\ \tau_{s}&(S>S_{s})\end{array}\right. \tag{7}\]  
Figure 7: Finite element model diagram.  
where \(G_{s}\) is the bond stiffness, \(G_{s}=\tau_{s}/S_{s}\).  
Surface-based cohesive behavior models were used for the interactions between the core concrete and the steel tube, and between the rebar ring and the steel tube, while hard contact was used for the interactions between the rebar ring and the concrete, and between the two types of core concrete.
## 3 Test Results and Analysis
### Ultimate Pile Tip Resistance  
The tip resistance value of each pile can be estimated through the axial force value measured by the strain gauge at the bottom to investigate how various grouting conditions affect the ultimate pile tip resistance. The findings are presented in Figure 14, which shows that pile N00's ultimate tip resistance is 0.21 kN, accounting for 25.9% of the pile top load. The authors also found that the tip resistance of the pile side grouting group ranged from 0.54 kN to 0.87 kN, which indicates that grout diffuse to the pile bottom from the pile side outlet, but the effect on the pile tip resistance is limited. In contrast, the tip resistance of pile tip grouting and combined grouting group had a positive correlation with grouting volume, which increased from 0.53 kN and 1.39 kN to 2.43 kN and 2.65 kN, respectively, as grouting volume increased from 1 L to 3 L. The actual grouting efficiency was found to be better in the combined grouting group, where the grouting volume at the pile tip only accounted for half of the total grouting volume.  
Figure 14: Ultimate pile tip resistance and the ratio of ultimate pile tip resistance to load.  
Figure 13: Distribution of ultimate pile lateral friction resistance with different grouting pressures. (**a**) Pile tip grouting group; (**b**) pile side grouting group.  
The transfer efficiency of a load can be represented by the ratio of pile tip resistance to the ultimate load. The bearing ratio of the pile side grouting group was kept at a low level (10.4-15.7%) due to the large lateral friction resistance, while the bearing ratio of the combined grouting group was stable between 31.5% and 33.9%. The authors also found that pile lateral friction resistance and tip resistance increased synchronously with the increase in grouting volume. Regarding pile tip grouting, the bearing ratio initially increased to 48.6% and then decreased to 42.4% with the change in grouting volume, indicating that the return of grout had a greater effect on lateral friction resistance when the grouting volume was large. The insufficient tip resistance of T16 was attributed to factors such as soil compactness at the pile bottom, grouting details, and grout diffusion.
# Digital Twin Conceptual Model within the Context of Internet of Things
## 4 Communication Network Layer
It is a leap from 4G technology as it will provide a bandwidth of 1 GHz and data rates up to 10 Gbps. Furthermore, 5G networks will have a latency of less than 1 ms and a scalable capacity of 50,000 bits/s/Hz/km\({}^{2}\). The 5G networks will have the ability to support nearly 65,000 connections at any given time. These features will enable applications that are time sensitive, such as real-time communications, with great precision and accuracy. Since 5G technology will operate on the radio frequencies of 28 GHz and 60 GHz for increased bandwidth, it is utilizing the millimeter wave (mm wave) technology. However, millimeter waves are highly susceptible to interference and cannot travel long distances without significant attenuation. Hence, base stations for 5G must be placed no more than 200 m or 300 m from each other, but these base stations will be much smaller than traditional base stations of earlier generation communication technologies [18]. Table 1 shows some of the important parameters of the abovementioned communication networks.
## 3 Comparison of Backbone-like and Mesh-like Clustering Strategies
### Modeling Parameters and Scenario Definition
#### 3.2.1 Wireless Node Model  
The OPNET Riverbed Modeler node models used are WLAN (Wireless Local Area Network) workstation, with 802.11p interface as the radio technology, and OLSR-based protocol (TFD-OLSR or CBL-OLSR) as the routing protocol above the UDP/IP stack. Each one of the routing protocols TFD-OLSR and CBL-OLSR have been implemented in a copy of the native OLSR code provided by Riverbed Modeler. An identical configuration of 802.11p interfaces and OLSR parameters has been adopted for TFD-OLSR and CBL-OLSR nodes. These parameters are reported into Table 1. According to OPNET modeler, WLAN parameters will result in a communication range of 500 m between any pair of vehicles in the road traffic.
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 4 ns-3 and mmWave Module
Our goal is utilizing both the RF links when the SINR received from both base stations is similar or the delta is small. Thus, we turn ON PD for a smaller delta threshold value and turn it OFF when delta is off the threshold limit. Duplicating packets with a higher delta will not be of benefit as much since one of the SINR values will be worse than the other, anything received on this worse link will be corrupt, and UE will always chose the packets received on the better SINR link.
# Laplacian Controllability and Observability of Multi-Agent Systems: Recent Advances in Tree Graphs
## Appendix A Spectral Methods for Controllability and Observability Analysis
The complete set of eigenvalues of the Laplacian of \(\mathcal{P}_{n}\) can be written as  
\[\lambda_{P_{n}}^{j}=2(1-\cos(\pi j/n)),\qquad j=0,\ldots,n-1 \tag{14}\]and associated eigenvectors  
\[(\mathbf{v}_{P_{n}}^{j})_{\mathbf{x}}=\cos\left(\frac{2\pi\kappa j}{n}\right) \qquad\kappa,j=0,\ldots,n-1. \tag{10}\]  
Considering the two class of matrices \(N_{p}\) and \(M_{q}\), their eigenvalues and eigenvectors are, respectively, [18]  
\[\left\{ \begin{aligned} \lambda_{N_{p}}&=2-2\cos\left[(2k-1) \frac{\pi}{2p+1}\right],& j=1,\ldots,p,\\ (v_{k})_{j}&=\sin\left[\frac{(p+j)(2k-1)\pi}{2p+1} \right],& k=1,\ldots,p,\end{aligned} \right. \tag{11}\]  
and  
\[\left\{ \begin{aligned} \lambda_{M_{q}}&=2-2\cos\left(k \frac{\pi}{q+1}\right),& j=1,\ldots,q,\\ (w_{k})_{j}&=\sin\left(\frac{jk\pi}{q+1}\right),& k=1,\ldots,q.\end{aligned} \right. \tag{12}\]  
For the sake of clarity of the rest of the paper, we remark that there are some relations among the eigenvalues and eigenvectors of \(L_{n}\), \(N_{p}\), and \(M_{q}\). **Proposition A2**. : _For any \(p\in\mathbb{N}\), \(q\in\mathbb{N}\), the following relations hold:_  
1. \(\Lambda_{M_{q}}=\Lambda_{L_{q+1}}-\{0\}\)_;_
2. \(\Lambda_{N_{p}}\subset\Lambda_{L_{2p+1}}\) _and, more precisely, considering the numbering of Equation_ (10)_,_ \(\lambda_{N_{p}}^{j}=\lambda_{P_{2p+1}}^{2j+1},j=1,\ldots,p\)_, namely eigenvalues of_ \(N_{p}\) _can be computed by selecting the odd values of index_ \(j\) _of_ \(L_{P_{2p+1}}\)_, from_ (10)_._  
Proof. : In regard to \(P1\), through standard determinant computation using the Laplace formula, it is possible to obtain  
\[\det(sI-L_{P_{n}})=s\cdot\det(sI-M_{n-1}),\]  
so that \(\lambda_{L_{n}}=\lambda_{M_{n-1}}\) plus the zero eigenvalue. As for \(P2\), by comparing (11) with (10), it is possible to prove by direct computation that all the eigenvalues of \(N_{p}\) are contained in the spectrum of \(L_{2p+1}\), and it is possible to obtain them by selecting from (10) the values corresponding to the odd values, from \(1\) to \(2p-1\), of (10).