esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
###### Abstract
Unlike the conventional encryption method, where the ciphertext must be decrypted and re-encrypted to enable the receipt of a message, PRE does not require decryption. Therefore, it is a secure method for use in circumstances where data must be shared with third parties in an untrusted network [27]. PRE is a key solution to the problem of medical data leakage by outsourcing companies. In our proposed system, we set a proxy server between the data server and the end user and separated the proxy from the outsourcing company. Consequently, we were able to design a system that prevents the company from viewing EHRs in the system. In this study, we employed a PRE scheme to encrypt the symmetric key and achieve efficient access control. Basically, all EHRs are encrypted with symmetric keys, and each key is encrypted using the public key cryptography system as a common secure communication protocol such as SSL/TLS. In addition to this system, we adopt the blockchain to protect the original EHR and to provide access control for the case of data sharing to third parties. Furthermore, to prevent exposure of the EHR to the blockchain outsourcing company, we add the PRE [28] to the blockchain-based EHR management. In our system, the proxy server is separated from the blockchain scope, so the outsourcing company cannot interfere the PRE. The symmetric key is secured by the encryption process of the PRE and would be re-encrypted by the proxy when the legal user requests to receive the EHR. We first developed this blockchain-based model with PRE in [29], and in this work, we extend the proposed system. Detailed protocols and algorithms for managing healthcare data are presented herein. The main contributions of this study are as follows:We first propose a blockchain-based EHR management model. In each distributed ledger, the access information that reveals the user that can read the corresponding EHR and the hash value of the record are stored. Hence, only authorized users can download and retrieve the encrypted EHR;
2. In addition to the blockchain, we adopt a PRE scheme to enable secure and efficient EHR sharing. Moreover, the proxy is separated from the blockchain system, and the re-encryption key is generated and maintained by the system manager, not the outsourcing company. Hence, the company cannot open the encrypted EHRs;
3. We provide essential cryptography schemes to secure the EHRs, such as encryption/decryption and digital signature schemes. Hence, the confidentiality and integrity of the EHRs are guaranteed. The remainder of this paper is organized as follows: We first discuss the relevant studies in Section 2. In Section 3, we introduce the essential items for the proposed schemes, such as the blockchain and PRE. In Section 4, the blockchain-based EHR management scheme using PRE is described. In Section 5, the security improvements of the proposed scheme as well as the overall performance of the proposed scheme are discussed. Finally, the conclusions of this study and future studies are provided in Section 6.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 2 Model Overview
### Background and Related Work
Moreover, the throughput achieved by a subflow depends in subtle ways upon the throughputs of all other subflows belonging to the same MPTCP connection [4; 10; 15]. When packets are sent via diverse wireless paths, they can arrive at the receiver out of order or may be lost due to channel impairments. Lost packets must be retransmitted and out-of-order packets must be buffered for resequencing so that they can be delivered to the application in proper order. Both of these artifacts affect subflow throughput because of buffering delays and the need for retransmissions at either the data-link layer or the MPTCP level [15]. At a conceptual level, almost all coupled MPTCP congestion control designs focus on minimizing reordering delay and head-of-line (HOL) blocking. They provide both load balancing and congestion control by coupling the delays and losses across all subflows. Based on our understanding of the design space, we propose a new MPTCP algorithm \begin{table}
\begin{tabular}{c c c} \hline \hline
**MPTCP Algorithms** & **Increase on Path \(i\)** & **Decrease on Path \(i\)** \\ \hline
**LIA**[4] & For each acknowledgment, & For each packet loss, \\ \(\alpha=\frac{\max\left(W_{i}/\tau_{i}^{2}\right)}{\left(\sum\frac{W_{i}}{\tau_{i} }\right)^{2}}\sum_{i}W_{i}\) & \(\Delta(W_{i})=\min\left(\frac{1}{W_{i}},\frac{\alpha}{\sum_{i}W_{i}}\right)\) & \(W_{i}\leftarrow\frac{W_{i}}{2}\) as regular TCP \\ \hline
**OLIA**[9] & For each acknowledgment, & For each packet loss, \\ \(\alpha_{i}=\begin{cases}\frac{1/|Z|}{|C|},&i\in\mathcal{C}\\ \frac{1/|Z|}{|C|}&i\in\mathcal{X},|C|>0\\ 0&\text{otherwise}\end{cases}\) & \(\Delta(W_{i})=\left(\frac{\alpha_{i}}{W_{i}}+\frac{W_{i}/\tau_{i}^{2}}{\left( \sum\frac{W_{i}}{\tau_{i}}\right)^{2}}\right)\) & \(W_{i}\leftarrow\frac{W_{i}}{2}\) as regular TCP \\ \hline
**BALIA**[23] & For each acknowledgment, & For each packet loss, \\ \(\alpha_{i}=\frac{\max\left(\lambda_{i}\right)}{\lambda_{i}}\), \(\lambda_{i}=\frac{W_{i}}{\tau_{i}}\) & \(\lambda_{k}=\left(\sum_{k\in\mathcal{C}}\frac{W_{k}}{\tau_{k}}\right)^{2}\) & \(W_{i}\gets W_{i}-\frac{W_{i}}{2}\min\left(\alpha_{i},\frac{\lambda}{2}\right)\) \\  & \(\Delta(W_{i})=\frac{\lambda_{i}}{\tau_{i}(\sum\lambda_{i}^{2})}\frac{1+\alpha_ {i}+\alpha_{i}}{2}\) & \(\frac{\lambda_{i}+\alpha_{i}}{5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of MPTCP congestion control for LIA, OLIA, and BALIA. that generalizes existing algorithms and strikes a good balance among these properties. We implemented this algorithm in the Linux kernel, and we evaluated its performance experimentally by using our prototype.
## 2 Materials and Methods
### Interaction of Berberine Hydrochloride and the Recombinant FtsZ Protein  
The recombinant FtsZ protein (128 ug/mL) was assembled in an MES buffer (containing 50 mM Tris, pH 7.5, 5 mM MgCl\({}_{2}\), and 250 mM KCl), in the absence or presence of berberine hydrochloride according to the previous method [30]. GTP (0.25-8 mM) was added to the recombinant FtsZ solution and incubated for 30 min. Two methods were used to detect the effects of berberine hydrochloride (1.56, 6.25, and 25 ug/mL) on FtsZ aggregation: (i) two procedures: berberine hydrochloride and FtsZ protein were preincubated for 30 min and the mixtures were then incubated with GTP for another 30 min. The reaction solution consisted of 10 uL FtsZ (128 ug/mL), 1 uL GTP (20 mM), and 5 uL berberine; (ii) one procedure: berberine hydrochloride and FtsZ were incubated directly with GTP for 1 h. The effect of berberine on FtsZ polymerization was detected by 12% SDS-PAGE.  
Molecular docking (MD) was performed by AutoDock software to analyze the binding of berberine hydrochloride (a ligand) to _E. coli_ FtsZ (receptor) [30]. Briefly, the native ligand of FtsZ protein was removed, and its polar hydrogen atom and Kollman unified-atom partial charges were added. Active torsion groups and Gasteiger-Marsilli atomic charge of berberine hydrochloride were defined. Grid maps were calculated with a 70 \(\times\) 80 \(\times\) 80 point spacing of 0.375 A, centered at the point using Autogrid4. The docking was performed with a flexible ligand using Lamarckian Genetic Algorithm (LGA) with a translation step of 0.2 A, quaternion one of 5\({}^{\circ}\), and torsional one of 5\({}^{\circ}\). Other parameters were the default values.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 5 Outlook on Key Technologies for Lunar Constellation Navigation and Orbit Determination
### Systematic Design for Deeper Space  
The development of the lunar constellation should not only consider the needs of lunar exploration missions, but also take into account the relay communication and navigation and orbit determination requirements of other deep-space exploration missions, such as the exploration of Mars, asteroids and Jupiter. In order to achieve effective transmission of scientific data and provide reliable communication and navigation services, NASA has proposed a next-generation space Internet architecture, with the deep-space relay communication system as the main line to form an interplanetary network. The development of the lunar constellation system must also be comprehensively considered, coordinated, and utilized within the entire deep-space navigation system. At the same time, as a service guarantee system for subsequent exploration missions, the system must have a long service life, run for at least 10 years, and be able to provide services to more users, while also being easy to upgrade and maintain. The designers should strive to improve the system's cost-effectiveness ratio as much as possible.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Evaluation of Weighted Fusion Methodology
#### 3.3.6 Computational Efficiency Analysis  
The operational feasibility of our weighted fusion methodology depends not only on its accuracy improvements but also on its computational efficiency. We evaluated the computational overhead of applying the PCA-based weighting algorithm in an operational setting with intra-day rolling forecasts. After model training and provincial forecast generation, the complete weighting and fusion process consistently completed within 20 s, with five experimental runs yielding processing times of 14.89 s, 15.16 s, 16.04 s, 15.59 s, and 15.19 s, averaging 15.37 s.  
This processing time is well within the operational requirements for grid dispatch centers, which typically operate on 5-15 min decision cycles. The minimal computational overhead ensures that our methodology can be deployed in real-time operational environments without creating bottlenecks in the decision-making process.
# An Elastic Interface Model for the Delamination of Bending-Extension Coupled Laminates
## 4 Delamination Crack Analysis  
### Energy Release Rate  
Under I/II mixed-mode fracture conditions, the _energy release rate_ can be written as \(\mathcal{G}=\mathcal{G}_{\text{I}}+\mathcal{G}_{\text{II}}\), where \(\mathcal{G}_{\text{I}}\) and \(\mathcal{G}_{\text{II}}\) are the contributions related to Fracture Modes I and II, respectively. In LEFM, the energy release rate identifies the path-independent \(\mathcal{J}\)-integral introduced by [PERSON] [57]. Choosing an integration path encircling the interface between sublaminates, as detailed in Appendix C, we obtain:  
\[\mathcal{J}=\frac{1}{2}k_{z}\Delta w^{2}(0)+\frac{1}{2}k_{x}\Delta u^{2}(0) \tag{42}\]  
or, by recalling Equation (5),  
\[\mathcal{J}=\frac{\sigma_{0}^{2}}{2k_{z}}+\frac{\tau_{0}^{2}}{2k_{x}}, \tag{43}\]  
where \(\sigma_{0}=\sigma(0)\) and \(\tau_{0}=\tau(0)\) are the peak values of the interfacial stresses at the delamination front. The two addends in Equation (43) correspond to the Mode I and Mode II contributions to the energy release rate, respectively [41]. Since compressive normal stresses at the crack tip (\(\sigma_{0}<0\)) do not promote crack opening, the modal contributions to the energy release rate can be written as  
\[\mathcal{G}_{\text{I}}=\mathcal{H}(\sigma_{0})\frac{\sigma_{0}^{2}}{2k_{z}} \quad\text{and}\quad\mathcal{G}_{\text{II}}=\frac{\tau_{0}^{2}}{2k_{x}}, \tag{44}\]  
where \(\mathcal{H}(\cdot)\) denotes the Heaviside step function. By substituting Equation (18) into (44), we obtain the following expressions for the balanced case:  
\[\mathcal{G}_{\text{I}}=\frac{\mathcal{H}(\sigma_{0})}{2k_{z}B^{2}}\left(\sum \limits_{i=1}^{4}f_{i}\right)^{2}\quad\text{and}\quad\mathcal{G}_{\text{II}}= \frac{1}{2k_{x}B^{2}}\left(f_{5}+f_{6}\right)^{2}. \tag{45}\]  
Likewise, by substituting Equation (32) into (44), we obtain the following expressions for the unbalanced case:  
\[\mathcal{G}_{\text{I}}=\frac{\mathcal{H}(\sigma_{0})}{2k_{z}B^{2}}\left(\sum \limits_{i=1}^{6}g_{i}\right)^{2}\quad\text{and}\quad\mathcal{G}_{\text{II}}= \frac{1}{2k_{x}B^{2}}\left(k_{x}\beta_{0}\sum\limits_{i=1}^{6}\frac{g_{i}}{u_ {i}\left(\mu_{i}^{2}-\alpha_{3}\right)}\right)^{2}. \tag{46}\]  
Lastly, to characterize the ratio between the modal contributions to the energy release rate, we introduce the _mode-mixity angle_[11],  
\[\psi=\text{sign}\left(\tau_{0}\right)\arctan\sqrt{\frac{\mathcal{G}_{\text{ II}}}{\mathcal{G}_{\text{I}}}}, \tag{47}\]  
ranging from \(0^{\circ}\) (pure Mode I) to \(90^{\circ}\) (pure Mode II).
## 4 Adaptive Robust Model Free Control
### Design of Control Based on ANFSMC
\begin{split} u&=u_{eq}+u_{cor}+u_{NN}\\ &=-\bar{\mathbf{M}}\frac{1}{\gamma_{2}}\frac{n}{m}((e^{[2-m/n]} )+\gamma_{1}a|e|^{a-1}\dot{e}^{[2-m/n]})+\hat{\mathbf{F}}(t)\\ &-\mathfrak{o}_{\mathcal{C}k}+\bar{\mathbf{M}}\dot{\mathbf{ \lambda}}_{d}-\bar{\mathbf{M}}(\eta+\hat{Z})sign(s)-\hat{w}^{T}\xi(x)\end{split}  \tag{21}\]  
Then,  
\[ \begin{split}\dot{V}_{2}&=s(-\gamma_{2}\frac{m}{n}|e| ^{\frac{m}{n}-1}\bar{\mathbf{M}}\hat{w}\xi(x)-\gamma_{2}\frac{m}{n}|e|^{\frac {m}{n}-1}(\eta+Z)sign(s))\\ &+\bar{\mathbf{M}}s\gamma_{2}\frac{m}{n}|e|^{\frac{m}{n}-1}\widetilde {w}\xi(x)\\ &=-s(\gamma_{2}\frac{m}{n}|e|^{\frac{m}{n}-1}(\eta+Z)sign(s))\\ &=-\gamma_{2}\frac{m}{n}|e|^{\frac{m}{n}-1}(\eta+Z)|s|\end{split}  \tag{22}\]  
It can be seen that \(\dot{V}_{2}\leq 0\); therefore, the convergence and stability of the system is confirmed through the Lyapunov criterion. This completes the proof. **Remark 1**. : _For the nonlinear system in Equation (10), when the sliding variable in Equation (12) is convergent to zero, the system tracking error rapidly approaches zero within limited time. The finite time \(t_{p}\), which is defined as the time from \(e(t_{0})\
eq 0\) to \(e(t_{0}+t_{p})=0\), is determined by [41]:_  
\[t_{p}=\frac{\frac{m}{n}|e(t_{0})|^{1-\frac{n}{n}}}{\gamma_{1}(\frac{m}{n}-1)} \cdot H\bigg{(}\frac{m}{n}\frac{\frac{m}{n}-1}{(a-1)\frac{m}{n}};1+\frac{\frac {m}{n}-1}{(a-1)\frac{m}{n}};-\gamma_{1}|e(t_{0})|^{a-1}\bigg{)} \tag{23}\]_where \(H(\cdot)\) represents a Gauss hypergeometric function._  
**Remark 2**. : _The TDE error is bounded by a constant \(Z\) that is defined as \(\|\hat{F}(t)-F(t)\|_{\infty}\leq Z\) when \(\bar{\text{M}}\) satisfies the following equation:_  
\[\|I-M_{k}^{-1}(q)\bar{M}\|<1 \tag{24}\]  
_The proof details are described in [42]. In practical applications, the value of the gain \(\bar{M}\) can be adjusted from a smaller positive one to a larger one until the system starts oscillating. Therefore, it is not difficult to tune \(\bar{M}\) to meet the criteria in Equation (24)._  
To further reduce the chattering effects, the symbolic function sign(s) is changed to the saturation function sat(s) [40]. The correction signal \(u_{cor}\) of Equation (17) becomes  
\[ \begin{split} u_{cor}&=-\bar{\text{M}}(\eta+\widehat {Z})sat(s,\sigma)\\ &=-\bar{\text{M}}(\eta+\widehat{Z})\left\{\begin{array}{ll}-1& \text{if }s<-\sigma\\ \frac{s}{\sigma}&\text{if }|s\mid\leq\sigma\\ 1&\text{if }s>\sigma\end{array}\right.\end{split}  \tag{25}\]  
where \(\sigma>0\) is the boundary layer thickness.
## 5 Simulation Results and Performance Analysis
### Simulation Environment Parameters  
In this paper, simulation experiments are carried out in the MATLAB 2018a software environment. Using a random walk model in an opportunistic network, the nodes' corresponding geographic locations at different times are combined to represent the node's movement trajectory, and each node in the network can be used as a source node, relay node, and destination node [23]. A distance threshold is used to define whether data exchange occurs when two nodes are in contact, and, to ensure the comparability of the experiments, the same packet caching and deletion strategies are used and the packet generation rate is adjusted based on the network load. In addition, it is assumed that all nodes are not selfish or malicious and that they can freely collaborate with each other to exchange information. The main parameter settings of the simulation scenario are shown in Table 2.  
In the simulation experiments, packet delivery rate, average transmission delay, and routing overhead are used to evaluate the network performance of different routing protocols, which are defined in detail as follows:  
Figure 3: CNN–EAA algorithm framework.  
* Packet Delivery Ratio: the ratio between the total number of packets delivered to the destination and the total number of packets sent from the source node, reflecting the impact of routing on network performance;
* Average Transmission Delay: the average value of the time taken by a packet to reach the destination node from the source node, which is affected by the frequency of encounters between nodes, packet storage waiting time, etc., and reflects the timeliness of the routing policy in delivering messages [24];
* Routing Overhead: Routing overhead is usually related to the number of packet forwards and network bandwidth consumption, and is used to measure the resource usage efficiency and scalability of the routing policy.  
The packet delivery rate, routing overhead, and average delay calculations are mainly dependent on the contact between nodes, the exchange of packets, and the store and forward process.
## 3 SAR Payload Design  
In relation to the SAR payload design of the S-STEP with an innovative shape, two important system aspects--parameter and architecture design processes--are described herein. Specifically, the SAR payload subsystem design process involves generating a series of system specifications. This is preceded by the mission design process that broadens the user requirements and translates to the requirements of the system element and subsystem.  
The key mission parameters for the SAR user requirements of the S-STEP microsatellite include wide coverage, frequent revisits, lower Earth orbit, multimode swaths, fine resolution, small incidence angle, lightweight, small volume, and low power consumption. For the SAR system design, the fundamental performance parameters associated with the image quality are the system impulse response, spatial resolution, peak and integrated sidelobe ratio, range and azimuth ambiguity ratio, NESZ, and data rate of the SAR payload. We selected the military-grade or automotive-grade components for each unit of the SAR payload that were compatible with the space-grade parts as a low-cost approach under the NSP.
# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 4 Results
### Experimental Evaluation of Trained DNN Models
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. of Hidden Layers**} & \multirow{2}{*}{\
\begin{tabular}{c} **No. of Hidden Layer Nodes** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Statistics of** \\ **MAPE** \\ \end{tabular} } & \multicolumn{4}{c}{**Round of 5-Fold Cross Validation**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-10}  & & & **1** & **2** & **3** & **4** & **5** & **Mean** \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{30} & Mean & 6.63 & 7.23 & 6.90 & 7.10 & 7.13 & 7.00 \\  & & STD & 0.56 & 0.70 & 0.44 & 0.56 & 0.68 & 0.59 \\ \cline{2-10}  & & & Mean & 6.05 & 6.11 & 6.25 & 6.07 & 6.22 & 6.14 \\  & & STD & 0.42 & 0.42 & 0.41 & 0.24 & 0.25 & 0.35 \\ \cline{2-10}  & & & Mean & 5.53 & 5.55 & 6.04 & 5.41 & 5.74 & 5.65 \\  & & STD & 0.39 & 0.23 & 0.82 & 0.29 & 0.19 & 0.38 \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{40} & \multirow{4}{*}{30} & Mean & 6.53 & 6.32 & 6.61 & 6.50 & 6.29 & 6.45 \\  & & STD & 0.48 & 0.41 & 0.42 & 0.17 & 0.12 & 0.32 \\ \cline{2-10}  & & Mean & 5.76 & 5.80 & 5.66 & 5.55 & 5.86 & 5.73 \\  & & STD & 0.31 & 0.16 & 0.24 & 0.31 & 0.21 & 0.25 \\ \cline{2-10}  & & & Mean & 5.18 & 5.42 & 5.28 & 5.15 & 5.25 & 5.26 \\  & & STD & 0.26 & 0.30 & 0.26 & 0.25 & 0.15 & 0.24 \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{30} & Mean & 6.13 & 6.32 & 6.63 & 6.38 & 6.50 & 6.39 \\  & & STD & 0.13 & 0.60 & 0.53 & 0.47 & 0.58 & 0.46 \\ \cline{1-1} \cline{2-10}  & & Mean & 5.60 & 5.60 & 5.43 & 5.66 & 5.92 & 5.64 \\ \cline{1-1}  & & STD & 0.42 & 0.40 & 0.28 & 0.66 & 0.39 & 0.43 \\ \cline{1-1} \cline{2-10}  & & Mean & 5.31 & 5.25 & 5.34 & 5.07 & 5.10 & 5.22 \\ \cline{1-1}  & & STD & 0.36 & 0.44 & 0.41 & 0.30 & 0.27 & 0.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical analysis results of MAPE of DNN models for predicting afternoon ore production according to the number of rounds of 5-fold cross validation. Figure 10: Results of 5 × 5-fold cross validation for predicting ore production during afternoon. In this study, the optimum DNN model was chosen as the one corresponding to the lowest mean and standard deviation of MAPE value for the validation data (i.e., four hidden layers and 50 hidden-layer nodes) for accurate prediction of afternoon ore production.
## 5 Further Discussion  
In this section, we examine the alterations made to the ABH beam illustrated in Figure 1, which are detailed in Figure 15. The beam comprises three distinct segments: The initial segment extends across the intervals \([0,x_{b1}]\) and \([x_{b3},x_{b4}]\), with a consistent thickness of \(h_{b}\). The second segment, which spans \([x_{y1},x_{y2}]\), maintains a uniform thickness of \(h_{0}\). The third segment features an ABH profile, with its thickness varying according to a power law between the points \(x_{b1}\) and \(x_{y1}\), as well as between \(x_{y2}\) and \(x_{b3}\). The thickness variation of the beam can be expressed as follows:  
\[h(x)=\begin{cases}\epsilon(x-x_{y1})^{m}+h_{0},&x\in[x_{b1},x_{y1}]\\ h_{0},&x\in[x_{y1},x_{y2}]\\ \epsilon(x-x_{y2})^{m}+h_{0},&x\in[x_{y2},x_{b3}]\end{cases} \tag{32}\]  
where \(\epsilon\) and \(m\) represent the slope and the ABH order, respectively. The structure depicted in Figure 15 exhibits symmetry about both the line \(x=x_{b2}\) and the plane \(z=0\). Damping material is attached to both flanges of the beam, spanning the region \([x_{d1},x_{d3}]\), with a variable thickness denoted by \(h_{d}(x)\). The beam's ends are supported by rotational springs of stiffness \(K_{1}\) and translational springs of stiffness \(K_{2}\), allowing for a range of boundary conditions to be simulated by adjusting the values of \(K_{1}\) and \(K_{2}\). The parameters for the modified ABH beam are given in Table 2. The difference between Figures 1 and 15 is that the structure in Figure 15 includes an additional segment in \([x_{y1},x_{y2}]\), where the beam's thickness remains constant at \(h_{0}\). In this section, we focus on discussing the impact of adding a segment with constant thickness \(h_{0}\) on the system's ABH effect over the interval \([x_{y1},x_{y2}]\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Geometrical Parameters** & **Material Parameters** \\ \hline \(x_{b1}=0.1\) m & Beam \\ \(x_{b2}=0.22\) m & \(\rho_{b}=7800\) kg/m\({}^{3}\) \\ \(x_{b3}=0.34\) m & \(E_{b}=210\) Gpa \\ \(x_{b4}=0.44\) m & \(\eta_{b}=0.005\) \\ \(x_{f}=0.39\) m & Damping layer \\ \(\epsilon=0.5\) m\({}^{-1}\), \(m=2\) & \(\rho_{d}=950\) kg/m\({}^{3}\) \\ \(x_{y1}=0.2\) m, \(x_{y2}=0.24\) m & \(E_{d}=5\) Gpa \\ \(h_{0}=0.0002\), \(h_{b}=0.0052\) m & \(\eta_{d}=0.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the modified symmetric ABH structure  
Figure 15: The schematic diagram of a modified symmetric ABH beam.