esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridge Damping Extraction Method from Vehicle-Bridge Interaction System Using Double-Beam Model
###### Abstract  
When vehicles interact with a bridge, a vehicle-bridge interaction (VBI) system is created. The frequency and modal shape of VBI systems have been widely studied, but the damping of VBI systems has not been adequately investigated. In recent years, several incidents of abnormal bridge vibration due to changes in bridge damping have occurred and aroused widespread concern in society. Damping is an important evaluation index of structural dynamic performance. Knowing the damping ratio of a VBI system is useful for analyzing the damping changes while a bridge is in service. This paper presents a method to extract bridge damping values from a VBI system, which can serve as a guide for bridge damping evaluation. First, a double-beam theoretical model was used to simplify the VBI system for cases involving uniform traffic flow. The damping ratio equation for the simplified VBI system was obtained using the extended dynamic stiffness method (EDSM). A double-beam finite element model and a VBI finite element model were established. The damping ratios of the two models were separately calculated and then compared with the simplified VBI model results. The results verified the accuracy of the simplified method. This paper then explains that bridge damping values can be extracted by estimating the equivalent traffic flow parameters and using the damping formula for the simplified VBI system. The bridge damping ratios extracted using this method in an engineering case ranged from 0.75% to 0.78%, which is smaller than the range that was directly identified using monitoring data (0.83-1.19%). The results show that the method can effectively extract bridge damping ratios and improve damping ratio identification.  
vehicle-bridge interaction; double-beam system; extended dynamic stiffness method (EDSM); bridge damping Article
## 6 Real Data Analysis
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Parameter Estimates** & **BIC** \\ \hline Burr XII distribution & \(\hat{\alpha}\)=1.428458, \(\hat{\beta}\)=2.064648 & 193.0478 \\ Gamma distribution & \(\hat{\lambda}\)=1.172510, \(\hat{\alpha}\)=1.502313 & 200.3035 \\ Weibull distribution & \(\hat{\lambda}\)=1.047834, \(\hat{\omega}\)=0.796724 & 201.7417 \\ Lindley distribution & \(\hat{\eta}\)=1.747608 & 198.9121 \\ Log-logistic distribution & \(\hat{\lambda}\)=3.222481, \(\hat{\omega}\)=1.725157 & 196.4830 \\ Linear-exponential distribution & \(\hat{\lambda}\)=1.381229, \(\hat{\omega}\)=-0.113880 & 201.0009 \\ \hline \end{tabular}
\end{table} 
Table 6: MLEs of the candidate models and their BICs on the data set. Figure 7: Histogram of the remission time and its fitted Burr XII distribution. \[\Lambda=\frac{sup\limits_{\alpha=1.428458,\beta>0}L(\alpha,\beta;\underline{x})}{sup \limits_{\alpha>0,\beta>0}L(\alpha,\beta;\underline{x})}.\]  
Considering that \(-2\ln\Lambda\) approximately obeys a chi-square distribution with 1 degree of freedom, the \(p\)-value was calculated as \(0.9999952>0.05\), which indicates that under a significance level of 0.05, we cannot reject the null hypothesis. Therefore, we determined that the real data set obeys the Burr XII distribution with \(\alpha=1.428458\) and \(\beta\) unknown. Consequently, the classical and Bayesian estimates for selected points of the reliability function of the remission time data are presented in Table 7. For the Bayesian estimates, we adopted the non-informative quasi-density prior with \(m=0.5\), and the parameters in the GEL, QEL, and WQEL functions were considered as \(p=2,q=2\), and \(z=-1\), respectively. In Table 7, it is evident that the running time of the estimator under the WQEL function was slightly longer than that of the other estimators, but the difference was not significant. Considering that the accuracy of the estimator under the WQEL function was better than that of the other estimators, such a time cost is acceptable.
## 4 Optimal Scheduling Model
### Optimization Constraints  
For the multi-district RIES shown in Figure 1, the constraints can be categorized into CCHP constraints, SESS constraints and coupling constraints between the CCHP and SESS, as shown in Table 1.  
The constraints of the CCHP and SESS can be found in references [26; 31] and Section 2.2, respectively. At the same time, according to Table 1, the direct energy coupling constraints of the CCHP and SESS should be considered. That is to say, from the perspective of the coupling system and engineering practice, the cumulative value of the energy interaction of all users using the SESS for charging and discharging behavior at a certain scheduling moment should be equal to the energy input/output of the SESS itself at this moment, and the energy source of each charging and discharging behavior of the SESS should be the cumulative value of user charging/discharging behavior.  
\[\sum_{i=1}^{N}\Big{(}P_{i,t}^{SESS,dis}-P_{i,t}^{SESS,cha}\Big{)}=P_{t}^{SESS,vlea}-P_{t}^{SESS,abs}, \tag{14}\]  
For the CCHP established in this paper, as shown in Figure 2, it is also necessary to meet the waste heat balance constraints of the waste heat boiler, as shown below.  
\[H_{i,t}^{HE}/\eta_{he}+C_{i,t}^{AC}/\eta_{ac}=H_{i,t}^{GT}\gamma_{gt}\eta_{ whb}, \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Constraint Type** & **CCHP** & **SESS** & **Coupling Relationship** \\ \hline Equality & 1. Power bus energy balance constraints & 1. Energy continuity constraint & 1. CCHP-SESS energy \\ constraints & 2. Electric storage energy & & coupling constraint \\ relationship constraints & & & coupling constraint \\ \hline Inequality constraints & 1. Equipment operating power constraints & 1. Charging/discharging power constraints & / \\  & 2. Electric storage charging/discharging power constraints & 2. Capacity constraints & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimization constraints of multi-district RIES.
###### Abstract  
Introduction: The temporomandibular joint (TM) is affected in 30-45% of juvenile idiopathic arthritis (JIA) patients, with all JIA subtypes at risk for TMJ involvement. JIA patients with TMJ involvement may present with altered craniofacial morphology, including micrognathia, mandibular retrogendnia, a hyperdivergent mandibular plane angle, and skeletal anterior open bite. These features are also commonly present and associated with non-JIA pediatric patients with obstructive sleep apnea (OSA). Materials and Methods: The study was comprised of a group of 32 JIA patients and a group of 32 healthy control subjects. CBCT images were taken for all patients and were imported into Dolphin Imaging software. The Dolphin Imaging was used to measure the upper airway volumes and the most constricted cross-sectional areas of each patient. Cephalometric images were rendered from the CBCT data for each patient, and the following cephalometric values were identified: SNA angle, SNB angle, ANB angle, anterior facial height (AFH), posterior facial height (PFH), mandibular plane angle (SN-MP), FMA (FH-MP), overjet (OJ), and overbite (OB). Airway volumes, the most constricted cross-sectional area values, and cephalometric values were compared between the JIA and control groups. Results: For airway values, statistically significant differences were seen in the nasopharynx airway volume (\(p=0.004\)), total upper airway volume (\(p=0.013\)), and the most constricted cross-sectional area (\(p=0.026\)). The oropharynx airway volume was not statistically significant (\(p=0.051\)). For cephalometric values, only the posterior facial height showed a statistically significant difference (\(p=0.024\)). Conclusions: There was a significant difference in airway dimensions in the JIA patients as compared to the control patients. In addition, the posterior facial dimensions seem to be affected in JIA patients. The ODDs ratio analysis further corroborated the findings that were significant.  
3D airway analysis; 3D airway volumes; CBCT; orthodontics; oral and maxillofacial surgery patients. +
Footnote †: journal: Applied Sciences
## 2 Theoretical Prediction of Plastic Zone Range in High-Temperature Tunnels
### Analysis of Temperature Field in Gateway-like Structure Tunnel
#### 2.2.2 Prediction of Plastic Zone Range of Rock Mass under Thermo-Mechanical Coupling Effect  
The calculation of deep buried tunnels can be regarded as a plane strain problem. Given that the vertical load of the circular hydraulic tunnel is \(P_{0}\), symmetrical along the  
Figure 3: Relationship between strength curve and stress circle.  
transverse axis; the horizontal load is \(\lambda P_{0}\), symmetrical along the vertical axis; \(\lambda\) is the lateral pressure coefficient; and \(R_{1}\) is the radius of the circular tunnel, the stress solution of rock mass in the circular tunnel can be obtained as [24]:  
\[\left\{\begin{matrix}\sigma_{\gamma 3}=\frac{1}{2}(1+\lambda)P_{0}\Big{(}1- \frac{R_{1}{}^{2}}{r^{2}}\Big{)}-\frac{1}{2}(1-\lambda)P_{0}\Big{(}1-4\frac{R_ {1}{}^{2}}{r^{2}}+3\frac{R_{1}{}^{4}}{r^{4}}\Big{)}\cos 2\theta\\ \sigma_{\theta 3}=\frac{1}{2}(1+\lambda)P_{0}\Big{(}1+\frac{R_{1}{}^{2}}{r^{2}} \Big{)}+\frac{1}{2}(1-\lambda)P_{0}\Big{(}1+3\frac{R_{1}{}^{4}}{r^{4}}\Big{)} \cos 2\theta\\ \tau_{\pi\theta 3}=\frac{1}{2}(1-\lambda)P_{0}\Big{(}1+2\frac{R_{1}{}^{2}}{r^{2}} -3\frac{R_{1}{}^{4}}{r^{4}}\Big{)}\sin 2\theta\end{matrix}\right. \tag{18}\]  
If under a uniformly distributed load, the lateral pressure coefficient is \(\lambda=1\)[27]. By superposing the stresses of Equations (8) and (18), it can be obtained that  
\[\left\{\begin{matrix}\sigma_{r}=k\Bigg{(}\frac{1}{15}\Big{(}1-\frac{16A_{S}}{ r\pi^{2}}\Big{)}+\frac{\ln\frac{4\sqrt{\alpha_{S}}}{\sqrt{\pi}}}{\ln 4}\Bigg{)}+P_{0} \Big{(}1-\frac{A_{S}}{\pi r^{2}}\Big{)}\\ \sigma_{\theta}=k\Bigg{(}\frac{1}{15}\Big{(}1+\frac{16A_{S}}{\pi r^{2}}\Big{)} +\frac{\ln\frac{4\sqrt{\alpha_{S}}}{\sqrt{\pi}}-1}{\ln 4}\Bigg{)}+P_{0} \Big{(}1+\frac{A_{S}}{\pi r^{2}}\Big{)}\end{matrix}\right. \tag{19}\]  
The equations of large principal stress and small principal stress under the plane strain state in a polar coordinate system are [25]:  
\[\frac{\sigma_{1}}{\sigma_{3}}=\frac{\sigma_{r}+\sigma_{\theta}}{2}\pm\sqrt{ \left(\frac{\sigma_{r}-\sigma_{\theta}}{2}\right)^{2}+\tau_{\pi\theta}{}^{2}} \tag{20}\]  
By combining Equations (17), (19), and (20), the calculation equation of the plastic zone of the rock mass in the high-temperature gateway-like structure tunnel can be obtained as:  
\[\left\{k\Bigg{[}\frac{1}{15}\Big{(}1+\frac{16A_{S}}{\pi r^{2}} \Big{)}+\frac{\ln\frac{4\sqrt{\alpha_{S}}}{\sqrt{\pi}}-1}{\ln 4}\Bigg{]}+P_{0} \Big{(}1+\frac{A_{S}}{\pi r^{2}}\Big{)}\Bigg{\}}\bigg{(}\sqrt{1+\tan^{2}\varphi }-\tan\varphi\bigg{)}\right. \tag{21}\] \[\left.-\Bigg{\{}k\Bigg{[}\frac{1}{15}\Big{(}1-\frac{16A_{S}}{\pi r ^{2}}\Big{)}+\frac{\ln\frac{4\sqrt{\alpha_{S}}}{\sqrt{\pi}}}{\ln 4}\Bigg{]}+P_{0} \Big{(}1-\frac{A_{S}}{\pi r^{2}}\Big{)}\Bigg{\}}\bigg{(}\sqrt{1+\tan^{2}\varphi }+\tan\varphi\bigg{)}\right.\] \[\left.=\sigma_{c}\bigg{(}\sqrt{1+\tan^{2}\varphi}-\tan\varphi \bigg{)}\right]\]
# MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph
## 4 Experimental Results and Analysis
### Experimental Settings
#### 4.1.3 Experimental Details  
The proposed model is implemented in the PyTorch 2.5.1 library and trained on an NVIDIA RTX 3080 GPU with 100 GB memory (NVIDIA, Santa Clara, CA, USA). MTGNet uses the training set of the Argoverse 2.0 Motion Forecasting Dataset [24] for training and employs the Adam algorithm [47] as the optimizer with a weight decay of 0.01 to optimize the network parameters. The Transformer encoder in the model is configured with four attention heads and a 128-dimensional latent feature space to encode contextual dependencies. The model is trained for 100 epochs. The learning rate is adjusted step-by-step: starting at 1 \(\times\) 10\({}^{-3}\), it is reduced to 1 \(\times\) 10\({}^{-4}\) at the 10 th epoch, increased back to 1 \(\times\) 10\({}^{-3}\) at the 20 th epoch, and then reduced to 1 \(\times\) 10\({}^{-4}\) again at the 68 th epoch. Finally, the model generates 60 future prediction steps to achieve long-term trajectory prediction.  
Our pre-training epoch is set to 10 and is conducted on the Train dataset.  
The training progress over 10 epochs is illustrated in the Figure 5. As shown in Figure 5, both the pre-training loss and training loss exhibit a consistent downward trend, indicating that the model is effectively learning [48] and improving its performance. Initially, the pre-training loss is significantly higher than the training loss, reflecting the model's need for optimization at the early stages. As training progresses, the pre-training loss decreasessteadily, with the most notable reduction occurring in the first few epochs. Similarly, the training loss shows a gradual decline, reaching a much lower value by the final epoch. This consistent reduction in both pre-training and training losses demonstrates the model's ability to converge effectively [48], suggesting that the training process successfully minimizes the loss function and enhances the model's performance.
## 4 Mathematical Implementation of the Problem
### Objective Functions of the Participants in MO-TE  
The defined objective functions are based on maximizing H-MGs' and retailers' profits and minimizing the consumers costs and pollution. In more details, there are five objective functions as a multi-objective problem concerning total profit of Micro-grid (Equation (1)), profit of retailer (Equation (2)), the costs of H-MGs as Equation (3), the pollution function as Equation (4), and, finally, the costs of emitting the pollution as Equation (5), which is derived from consuming the thermal power.  
\[\begin{split} Max\sum_{\forall h}\sum_{\forall n}\sum_{\forall i }\sum_{\forall j}\left(U_{h,e,jn}+U_{h,e,n}^{ESd}+U_{h,ht,in}+U_{h,ht,n}^{TES _{d}}\right.\\ \left.-CO_{h,ht,in}-CO_{t,h,n}^{TES^{ch}}-CO_{h,e,n}^{ES^{ch}}- CO_{h,e,jn}\right)\times\Delta h\\ \left.Max\sum_{\forall h}\sum_{\forall n}\left(U_{h,e,n}^{Ret_{ t}}-CO_{h,e,n}^{Ret_{h}^{ch}}\right)\times\Delta h\\ Min\sum_{\forall h}\sum_{\forall n}\sum_{\forall k}\sum_{\forall c }\left(CO_{h,ht,n}^{k}+CO_{h,e,n}^{c}\right)\times\Delta h\\ \end{split} \tag{1}\]  
Figure 6: The schematic diagram of Market Clearing Price (MCP).  
\[Min\sum_{\forall h}\sum_{\forall n}\sum_{\forall i}\left(E_{h,ni}^{Ret}+E_{h,ni}^{ CHP}+E_{h,ni}^{GB}\right)\times\Delta h \tag{4}\]  
\[Min\sum_{\forall h}\sum_{\forall n}\sum_{\forall i}\left(CO_{h,E,i}^{Ret}+CO_{h,E,j}^{CHP}+CO_{h,F,i}^{CHP}+CO_{h,E,i}^{GB}+CO_{h,F,i}^{GB}\right)\times\Delta h \tag{5}\]  
where \(U_{h,e}^{j,n},U_{h,ht}^{i,n}\), respectively, are the electrical and thermal utility/income caused by distributed energy resources j and i in H-MG n. Moreover, \(U_{h,e}^{ES_{d,i}},U_{h,ht}^{TES_{d,i}}\), respectively, are the income resulting from electrical and thermal discharges of ES and Thermal Energy Storage (TES) regarding H-MG n at time h. \(U_{t,e}^{Ret,d,i},CO_{t,e}^{Ret,i}\), respectively, are income/cost caused by selling/purchasing the electricity from/to retailer in H-MG n. In addition, \(CO_{h,h}^{k,n},CO_{h,e}^{c,n}\) are, respectively, electricity costs concerned to k and c consumer in H-MG n. In addition, \(E_{h,ni}\) indicates the pollution caused by retailers and CHP and GB. \(CO_{h,F},CO_{h,ni}\), respectively, represent the cost of fuel and pollution.
## 3 Dynamic CH Selection and Energy-Efficient Routing Design  
### Problem Statement  
The current challenges with WSNs are as follows: For WSNs to be energy efficient, an adequate selection of optimal solutions must be addressed in the network. Only when the appropriate technique is prioritized, both distance and energy, the network energy usages decrease. Both large- and small-scale WSN applications could benefit from the energy-efficient WSN. In WSN, rapid transmission of data from CH to BS uses more energy. It causes the network's hot spot problem, which results in packet loss. Because of the node's deployment in a hostile and unmanaged situation, the sensor node becomes inefficient and malfunctioning. Furthermore, while transferring data packets to their destination, the nodes' energy consumption is a major concern. As a result of the nodes' lack of energy, data transmission packets are dropped.  
Because the energy consumption within every node is primarily determined by the node's distance, energy utilization is openly proportional to the node's distance. Furthermore, packet loss is prevented in WSN by taking into account each node's energy. By establishing multi-hop routing among the network, the routing challenge can be overcome. The path from the source to the BS is created using the JILFA approach. The routing considers the nodes' residual energy, the distance between them, and the hop number in each cluster as optimal solutions. The packet loss across networks is decreased when these parameters are taken into account. As a result, an energy efficient WSN is modeled to perform well in two large and small-scale WSNs.
## 5 Deepfake-Driven Social Engineering Threats in Corporate Environments
### Deepfake Technology in Social Engineering  
Deepfake technology has become one of the major threats to social engineering, as it gives attackers the ability to create realistic but false digital content such as videos and audio clips. Through all of the interviews conducted, it was demonstrated that although there is an increased awareness of deepfakes in corporate environments, minimal efforts have been implemented to counteract them.  
During the interviews, we found that companies are aware of the potential harm of such impersonations; however, their existing measures are mostly related to general phishing and scam prevention. For instance, deepfake-based phishing often involves synthetic audio or video mimicking a manager's or an executive's voice instructing an employee to complete a financial transaction without verifying the standard protocols [12]. Company A and Company B clearly stated that they are providing training on phishingrecognition and scam avoidance. However, none have developed or implemented specific strategies or detection tools specifically designed to address deepfake-based impersonation.  
Deepfakes also pose threats to corporate stability and reputation in the transmission of fabricated statements or actions allegedly carried out by prominent individuals in an organization. For instance, a deepfake video could show the CEO engaging in unethical behavior, posing a risk of severe damage to the company's image [9]. We discussed this exact example with the companies.  
The potential for misuse of deepfakes is heightened by their scalability and accessibility. Increasingly, only minimal technical expertise is required to use intuitive deepfake tools. As a result, individuals with harmful intentions can execute highly persuasive social engineering tactics [12]. Deepfake technology applied in social engineering is one of the cases showing an increased need to raise awareness and implement sophisticated countermeasures to minimize its growing impact on corporate security and trust [1].
## 3 Results and Discussion
### Mechanical Properties
In [PERSON]'s definition, the anisotropy factor is determined from the \(C_{44}\) and \((C_{11}-C_{12})\). The elastic constant \(C_{44}\) represents the deformation resistance of a crystal against the shear stress applied across the (100) plane in the [010] direction. Therefore, [PERSON]'s definition is limited to cubic system. For a locally isotropy crystal, \(A=1\). As suggested by [PERSON] et al. [43], we calculate the universal anisotropy index \(A^{U}\) by:  
\[A^{U}=5\frac{G_{V}}{G_{R}}+\frac{B_{V}}{B_{R}}-6 \tag{9}\]  
Note that \(A^{U}=0\) is for a locally isotropy single crystal. The \(B\)/\(G\) ratio, also known as the [PERSON]'s ratio [44], describes the ductility or brittleness of the crystal. In the 1950s, [PERSON] analyzed his experimental data and found that a higher [PERSON]'s ratio indicated the ductility of the material. \(A\) cutoff value of 1.75 is generally recommended for the [PERSON]'s ratio. The Debye temperature is the key quantity in the quasiharmonic Debye model. It leads to an estimate of many physical properties, such as specific heat, elastic constants, thermal conductivity, and melting temperature. It also sets the scale for electron-phonon interaction in superconductors. We calculate the Debye temperature using the formula  
\[\Theta_{D}=\frac{h}{k_{B}}\sqrt[3]{\frac{3 nN_{A}\rho}{4\pi M}}v_{m} \tag{10}\]  
where \(\rho\) is the density, \(M\) the molecular mass, \(N_{A}\) Avogardo constant, and \(v_{m}\) the average sound velocity. The average sound velocity can be calculated from the [PERSON]'s equation  
\[v_{l}=\sqrt{\frac{3B+4G}{3\rho}},\;v_{t}=\sqrt{\frac{G}{\rho}} \tag{11}\]  
and  
\[v_{m}=\frac{1}{\sqrt[3]{\frac{1}{3}\left(\frac{1}{v_{l}^{3}}+\frac{2}{v_{l}^{ 2}}\right)}} \tag{12}\]  
We also calculate the melting temperature \(T_{m}\), using an empirical expression \(T_{m}(\mathrm{K})=354+4.50(2C_{11}+C_{33})/3\pm 300\) K. The calculated melting and Debye temperature, along with density and sound velocities \(v_{l}\), \(v_{t}\), and \(v_{m}\) are listed in \[B_{R}=\frac{(C_{11}+C_{12})C_{33}-2C_{13}^{2}}{C_{11}+C_{12}+2C_{33}-4C_{13}} \tag{4}\]0 We also calculated the phonon dispersion of the Pb\({}_{2}\)BiBrO\({}_{6}\) double perovskite. The results show that Pb\({}_{2}\)BiBrO\({}_{6}\) double perovskite is unstable.