esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nonlinear Static Analysis for Bridges  
### General Considerations for Bridges  
Pushover can be used for the analysis of bridges, but some specifications are needed; some are provided by the Eurocode 8/2 [31] or are discussed in References [6; 33]. According to the scope of this work, dealing with the use of the pushover analysis, a relevant issue is the selection of the monitoring point (MP) for a bridge; the MP is the control joint used to plot the conventional capacity curve, and a wrong selection can compromise the analysis.  
For buildings, according to many years of experience, guidelines and codes, the monitoring point, generally suitable to any load pattern considered, is the center mass at the top floor; for a bridge, such a general prescription is not straightforward. Possible options could be (a) the position of the point where the maximum displacement of the superstructure is recorded [34]; (b) the deck center of mass; (c) the top of the pier nearest to the center of mass [3; 4; 5]; (d) the top of the pier corresponding to the maximum deck displacement [16]; or (e) a point of the deck determined according to the properties of the bridge [35; 36; 37; 38; 39]. According to the authors' experience, the monitoring point can be selected according to any of the procedures previously listed. In any case, the following remarks should be taken into account. The selection of a monitoring point corresponding, for a specific modal shape, to a null or quasi-null displacement value (e.g., in case of a twisting modal shape), or characterized by an opposite direction of displacement if compared with the global direction of the bridge deformation (the loading component of the loading pattern is inverted if compared to the resulting pushover direction), can lead to numerical problems or even null displacements (impossibility to plot the capacity curve and finalize the analysis), thus compromising the analysis process.  
Another relevant issue while performing pushover is the definition of the loading pattern. Indeed, many works dealing with NSA for bridges [33; 34] compared different methods, concluding that, for most of the cases, MPA was the most efficient procedure. The authors of this work agree upon this remark and decided to develop, starting from the use of the MPA already considered in a procedure previously proposed for buildings (the incremental modal pushover analysis (IMPA), [19]), the procedure for bridges detailed in the next section: the incremental modal pushover analysis for bridges (IMPA\(\beta\)).
# The Accuracy Assessment of Lithospheric Density Models
## 2 Numerical Model
### Geoid  
The geoid height \(N\) is defined by the following [74]:  
\[N(\Omega)=\frac{T(r_{g},\Omega)}{\gamma_{0}(\varphi)}, \tag{1}\]  
where the disturbing potential \(T\) (i.e., the difference between values of the actual and normal gravity potential \(W\) and \(U\), respectively; \(T=W-U\)) is stipulated at the geoid surface \((r_{g},\Omega)\). The normal gravity \(\gamma_{0}\) in Equation (1) is computed at the ellipsoid surface according to [PERSON]'s theory [75; 76] for the GR80 [77] parameters. The 3D position in Equation (1) and thereafter is defined in the spherical coordinate system \((r,\Omega)\), where \(r\) is the radius and \(\Omega=(\varphi,\lambda)\) denotes the spherical direction with the spherical latitude \(\varphi\) and longitude \(\lambda\). In the context of interpreting long-wavelength features in the geoidal geometry attributed to a sub-lithospheric mantle density structure, the disturbing potential \(T\) in Equation (1) can approximately (cf. [PERSON] [19; 78]; [PERSON] et al. [79]) be computed by using the following expression [74]:  
\[T(r_{g},\Omega)\cong\frac{\text{GM}}{\text{R}}\sum_{n=0}^{\overline{n}}\sum_{m =-n}^{n}\,\text{T}_{\text{n,m}}\,\text{Y}_{\text{n,m}}(\Omega), \tag{2}\]  
where \(\text{GM}=3986005\times 10^{8}\) m\({}^{3}\cdot\)s\({}^{-2}\) is the geocentric gravitational constant, \(\text{R}=6371\times 10^{3}\) m is the Earth's mean radius, \(\text{T}_{\text{n,m}}\) are the (fully-normalized) disturbing potential coefficients, \(\text{Y}_{\text{n,m}}\) are the (fully-normalized) surface spherical functions of degree \(n\) and order \(m\), and \(\overline{n}\) is the upper summation index of spherical harmonics.
# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 5 Conclusions  
The reliable delivery of safety messages in V2V communication requires the maintenance of channel congestion below a critical level. At the same time, an increased transmission rate is necessary for higher awareness. To address this challenge, we present an innovative RL framework that employs Q-learning to determine the most suitable transmission rate policy for BSMs. The aim of our proposed QBACC approach is to strike a balance between maintaining the channel congestion bit rate (CBR) below a specified threshold and maximizing awareness under different traffic conditions. To evaluate QBACC, we used two dynamic traffic models and compared them with existing approaches that employ constant transmission rates and another RL-based approach. The results showed that QBACC outperformed the other approaches by consistently maintaining the channel load at or near the specified level, without exceeding it, for both low and high traffic densities. It also achieved the best performance in terms of packet delivery and beacon error rates for high vehicle densities. For low vehicle density, the constant 5 Hz rate performed the best, but QBACC was a close second, with less than a 1% difference.  
We are currently working on enhancing QBACC by designing a comprehensive reward function that takes into account additional metrics, such as inter-packet delay and packet loss. We are also exploring the possibility of adjusting multiple parameters, such as  
Figure 14: Beacon error rate with 500 vehicles.  
Figure 13: Beacon error rate with 300 vehicles.  
transmission power and data rate, in addition to the transmission rate, to create a more robust Q-table.  
Paper design, X.L. and A.J.; methodology, X.L. and B.S.A.; writing--original draft editing, X.L.; writing--review and editing, A.J., B.S.A. and X.L.; simulation design, X.L.; simulation implementation, B.S.A.; data collection and visualization, X.L. and B.S.A.; data analysis, X.L., B.S.A. and A.J.; Supervision, A.J. funding acquisition, A.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSERC DG, grant no. RGPIN-2015-05641.  
Not applicable.  
The work of [PERSON] has been supported by a research grant from the Natural Sciences and Engineering Research Council of Canada (NSERC).  
The authors declare no conflict of interest.
## 4 Calculation Result
### Mechanism Analysis  
The soil-structure stiffness ratio [40] defining a rectangular frame structure is shown in Figure 24. In the finite element analysis software ABAQUS, the equivalent soil element and the underground structure are set to be fixed at the bottom, and the unit shear load, \(\tau\), is applied at the top and both sides of these elements. The deformations under the respective unit shear loads are then calculated separately. The deformation of the iso-substituted soil unit is recorded as \(\Delta_{soil}\), and the deformation of the structure is recorded as \(\Delta_{str}\). The soil-structure stiffness ratio, \(F\), is then solved according to Equation (7).  
\[F=\frac{\frac{1}{\Delta_{soil}}}{\frac{1}{\Delta_{str}}}=\frac{\Delta_{str}}{ \Delta_{soil}} \tag{7}\]  
Figure 25 shows the soil-structure stiffness ratios of the prototype structure and the seismic mitigation structure under five conditions of the extracted weak interlayer, with a shear wave velocity of 125 m/s and a thickness of the weak interlayer passing through the middle of the underground structure. These conditions are presented in the following order: 1 m, 2 m, 3 m, 4 m, and no weak interlayer passing through the homogeneous site. The soil-structure stiffness ratios of the prototype structure and the seismic mitigation structure for five conditions, with shear wave velocities of 150 m/s, 125 m/s, 100 m/s, 75 m/s, and no weak interlayer passing through the homogeneous site, are shown in Figure 26 for the extracted weak interlayer with a thickness of 3 m. The weak interlayer traversing the central part of the subsurface structure is also considered in the order of 150 m/s, 125 m/s, 100 m/s, 75 m/s, and no weak interlayer traversing the homogeneous site. From the calculation results, it can be observed that the soil-structure stiffness ratio of the seismic mitigation structure is greater than that of the prototype structure under all working conditions. This indicates that the horizontal displacement of the seismic mitigation structure is greater than that of the prototype structure due to the use of split columns, which leads to a reduction in the overall stiffness of the structure during seismic mitigation action.  
Figure 24: Calculate model of the soil-structure stiffness ratio.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 3 Queueing Models
### ON/OFF Model  
In Figure 1b, we introduce the intermittent (ON/OFF) queue, so as to capture the mobile peer's connections and disconnections. Messages arrive in the system following a Poisson process with rate \(\lambda>0\), and are placed in a queue waiting to be \"served\" with rate \(\mu>0\) (also exponentially distributed).  
With regard to the server, we assumed that its operation followed an on-off procedure. More specifically, the server remains for an exponentially distributed time, with parameter \(\theta_{\text{ON}}\), in the ON state. While in that state, the server provides service to messages. Let \(T_{\text{ON}}\;=\;1/\theta_{\text{ON}}\) be the time during which the server is ON. Upon the expiration of this time, the server enters the OFF state for an exponentially distributed period with rate \(\theta_{\text{OFF}}\). While in that state, the server stops providing service to messages. Let \(T_{\text{OFF}}\;=\;1/\theta_{\text{OFF}}\) be the time during which the server is OFF. Similar to the M/M/1 queue, an ON/OFF queue \(q_{\text{on/off}}\) can be defined via the tuple:  
\[q_{\text{on/off}}=(\lambda,\lambda^{\text{on/off}},\mu,T_{\text{ON}},T_{\text {OFF}}). \tag{3}\]  
where \(\lambda\) is the messages' arrival rate, \(\lambda^{\text{on/off}}\) is the messages' output rate, and \(\mu\) is the service rate for the messages' processing during \(T_{\text{ON}}\). Output process \(\lambda^{\text{on/off}}\) is intermittent since, during \(T_{\text{OFF}}\) intervals, no messages depart from the queue. Regarding the case where \(T_{\text{ON}}\) expires while a message is still under service, we assumed that the server stops the processing of the message and continues in the next \(T_{\text{ON}}\) period.  
We denote by \(\Delta^{\text{on/off}}\) the mean response time for the \(q_{\text{on/off}}\) queue, i.e., the time that a message spends in the system.  
In [16; 17; 19], we provided the following formula for the estimation of \(\Delta^{\text{on/off}}\):  
\[\Delta^{\text{on/off}}\;=\;\frac{E(n)_{\text{on/off}}}{\lambda}. \tag{4}\]  
where \(E(n)_{\text{on/off}}\) refers to the average number of messages that exist in the system (both in the server and the queue) [17].
## 4 Simulation Results
### Performance Indexes and Evaluation Criteria  
A performance test and a test of changes in motion dynamics were used to verify the proposed control scheme. The following quantities were applied to estimate the performance:  
1. The mean integrated absolute error, i.e., \(MIA=\frac{1}{l_{f}-l_{0}}\int_{l_{0}}^{l_{f}}|\chi_{e}(t)|dt\), where \(\chi_{e}=\Delta p_{x},\Delta p_{y},\Delta\zeta_{1},\Delta\zeta_{2}\);
2. The mean integrated absolute control, i.e., \(MIAC=\frac{1}{l_{f}-l_{0}}\int_{l_{0}}^{l_{f}}|P(t)|dt\), where \(P=T\cos\psi,T,\sin\psi\);
3. The root mean square of the tracking error, i.e., \(RMS=\sqrt{\frac{1}{l_{f}-l_{0}}\int_{l_{0}}^{l_{f}}\left\|e(t)\right\|^{2}dt}\), with \(\left\|e(t)\right\|=\sqrt{\Delta p_{x}^{2}+\Delta p_{y}^{2}}\) (\(\Delta p_{x}\), \(\Delta p_{y}\) mean the position error in the reference frame). The estimation of the effects of the dynamics is carried out by means of the following quantities [42; 43]: 1. The kinetic energy for the total vehicle and corresponding to each quasi-velocity, i.e., \(K_{T}=\frac{1}{2}v^{T}Mv=\frac{1}{2}\zeta^{T}N\zeta=\frac{1}{2}\sum_{i=1}^{3 }N_{i}\zeta_{i}^{2}=\sum_{i=1}^{3}K_{i}\).
2. The mean kinetic energy relating to each quasi-velocity and the total kinetic energy: \(K_{m}=\text{mean}\left(K\right)=\sum_{i=1}^{3}\text{mean}\left(K_{i}\right)\).
3. The deformation of each velocity resulting from dynamic coupling, i.e., \(\Delta\zeta_{i}=\zeta_{i}-\
u_{i}\), where \(i=1,2,3\).
# Research on Three-Phase Wireless Power Transfer System
## 1 Introduction
Oak Ridge National Laboratory proposed a three-phase inductance wireless power transfer system with bipolar phase winding, which uses a rotating magnetic field to achieve smoother power transmission characteristics in time than a single-phase system [15]. [PERSON] proposed a multi-transmitting coil and multi-receiving coil structure MCI WPT system that focuses on solving displacement problems [16]. The National ZTE University proposed a vehicle-mounted charging system that uses a two-layer overlapping winding of three-phase to three-phase square coils [17]. The single-phase structure in the above literature can withstand far less power than the three-phase to three-phase coupling structure. The coupling coefficient relationship between multi-coil and asymmetric structures is more complex. Therefore, this paper will further study the symmetrical three-phase to three-phase coupling structure to optimize and improve the performance of the wireless power transfer system. This paper proposes a three-phase coupling structure that is more suitable for high-power applications. The PWM wave generated by DSP is added to the control end of the MOSFET tube of the three-phase full-bridge inverter to control the output frequency and phase of the inverter. The energy is transmitted to the receiving coil, and the load on the receiving side is transferred through the transmitting coil. The structure proposed in this paper greatly reduces the volume of the system and is more suitable for high-power applications. Firstly, the mutual inductance model of the three-phase system is introduced, and the relationship between the model parameters is derived. Then, different coil structures are compared, and the parameters of the system are adjusted by COMSOL Multiphysics 5.6 to determine the optimal coupling mechanism. Finally, an experimental prototype with an output power of 200 W and a working frequency of 47.5 kHz is established to verify the feasibility and effectiveness of the topology.
# Research on Tracking Technique Based on BPSK-CSK Signals
## 1 Introduction
Literature [29] proposed an efficient demodulation algorithm based on chunked FFT, which combines parity chunked baseband data from the traditional time domain operation to the frequency domain operation, and effectively reduces the complexity of correlation value calculation. Literature [30] proposed a frequency domain demodulation algorithm for CSK signals based on partial-output FFTs, which reduces the computational complexity of demodulation by optimising the butterfly structure of conventional FFTs and computing nodes to eliminate irrelevant values. In this paper, we propose to transmit BPSK signals and CSK signals using time-division multiplexing. Subsequently, we detail the BPSK-CSK signal model, propose a BPSK-CSK signal tracking method based on time-division multiplexing, compare and analyse the BER and tracking error with other tracking methods, and complete the validation of the method to compare, analyse, and reduce the computational complexity. The BPSK-CSK signal tracking method proposed in this paper combines the high information transmission rate of CSK-modulated signals and the low BER of BPSK signals, and the results provide a solid foundation for high-precision GNSS services.
## 4 Topology Optimization of Valve Motion Components Based on Thermal-Fluid-Mechanical Coupling
### Check Valve Reverse Flow State Thermal-Fluid-Mechanical Coupling Simulation Conditions
#### 4.2.2 Mesh Division  
The check valve reverse flow state model in Figure 11 is reasonably simplified and the flow channels are extracted. The model mesh division and mesh independence check were carried out to obtain the thermal-fluid-mechanical coupling model mesh, as shown in Figure 12. This contains two parts, fluid domain mesh and solid mesh, and is used for fluent and static structural calculations, respectively.  
Considering the influence of mesh quantity and mesh quality on the calculation results, the mesh is continuously refined to ensure a small numerical analysis error between adjacent mesh densities. According to the respective advantages of tetrahedral and hexahedral meshes, the flow channel model mesh is divided by a mixed tetrahedral/hexahedral mesh. At the same time, a reasonable grid height is chosen for the first layer of the boundary layer so that the grid nodes fall within the logarithmic law region to avoid the differences in numerical calculation results caused by the grid nodes falling on the viscous bottom layer. Considering the influence of boundary layers, five boundary layers are divided on the surface of the valve body and pipe. The grid height of the first layer is taken to be 0.8 mm, and the internal growth rate is 1.2.
# User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
## 7 Conclusions  
Multi-band solutions from RF to THz will play an important role in beyond 5G networks. However, optimized network planning, resource management and user association across multiple bands are needed to harness their full potential while mitigating the impact of higher propagation losses and interference at mmWave and THz frequencies.  
We presented a novel dynamic user association algorithm for optimizing load balancing in integrated RF/mmWave/THz cellular networks. The algorithm periodically evaluates channel conditions and blockage risks across multiple frequency bands to calculate association scores for each user-BS pair. By factoring in user priorities and network  
Figure 4: Coverage probability comparison.  
Figure 5: Throughput for the three bands by category.  
load metrics, it aims to reassociate users in a balanced way that optimizes overall resource utilization and QoE.  
The simulation results demonstrate the algorithm's ability to efficiently utilize network resources across diverse channel conditions that vary with frequency. It provides an effective way to harness the advantages of multiple spectrum bands while mitigating their intrinsic propagation trade-offs. The algorithm achieves more balanced load distribution compared to traditional max-SINR approaches, while avoiding bias towards lower frequency bands.  
The results also show that higher-frequency bands can offer advantages in terms of capacity and data rates, while lower-frequency bands provide better coverage. The choice of the optimal frequency band thus depends on specific application requirements, environment factors and performance trade-offs.  
Future work includes extending the model to capture beamforming and directional communication effects at higher frequencies. Experimental validation on a multi-band prototype system would provide further validation of the approach. Overall, dynamic user association algorithms present a promising solution for realizing the full potential of multi-spectrum 5G and beyond networks.  
Conceptualization, N.H. and X.F.; methodology, N.H.; software, N.H.; validation, N.H., X.F. and L.W.; formal analysis, N.H.; investigation, N.H., X.F. and I.W.; resources, N.H.; data curation, N.H.; writing--original draft preparation, N.H.; writing--review and editing, N.H., X.F., I.W. and A.A.; visualization, X.F.; supervision, X.F. and I.W.; project administration, X.F.; funding acquisition, I.W. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Toronto Metropolitan University Faculty of Science Dean's Research Fund, and the NSERC Discovery Grant (REF #: RGPIN-2022-03798) held by the 3 rd author.  
Data Availability Statement: Data are contained within the article.  
Authors would like to acknowledge the Dean of the Faculty of Science, TMU.  
The authors declare no conflicts of interest.
## 4 Results
### Lithofacies Associations
#### 4.1.5 FA5: Peat Swamp Deposits  
Description:  
FA5 primarily comprises fine-grained lithofacies, including gray thin-bedded argillaceous siltstone (Fr, Fm, Fl), gray-black medium-to-thick-bedded mudstone (M), gray-black thin-bedded carbonaceous mudstone (Mc), and coal (C), forming interbedded sequences of siltstone, mudstone, and coal seams (Figure 5L). Argillaceous siltstone typically exhibits ripple and horizontal bedding, while mudstone and carbonaceous mudstone predominantly display massive bedding, with occasional small-scale cross-bedding in siltstone or fine sandstone. Authigenic minerals, such as siderite, and plant fossils are abundant. Coal seams are well-developed, ranging from 1 to 10 m in thickness, with most classified as medium-thick seams (>1.5 m) and exhibiting good lateral continuity (Figure 6).  
Interpretation:  
FA5 is interpreted as swamp deposits, characterized by the widespread occurrence of coal seams and carbonaceous mudstone, indicative of a stable aquatic environment [64]. The presence of thick and multiple coal seams reflects prolonged periods of stable and humid climatic conditions with balanced rates of accommodation and peat accumulation [65]. The occasional occurrence of sand bodies suggests episodic deposition by coarse-grained traction currents.