esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 1 Introduction  
Typical strategies are loosing the abilities to fulfill the growing demands of enormous wireless data rate and the heterogeneous density in wireless cellular networks for the 5 th generation (5G) and beyond [1]. Heterogeneous networks (HetNets) have been considered as one of the most promising technologies for engaging better mobile coverage and higher cellular network capacity [2], which are developed by the Third Generation Partnership Project Long Term Evolution--Advanced (3 GPP LTE-A) [3]. Transmitting nodes characterized by their graded coverage abilities within the HetNets are categorized into multiple layers accordingly to reinforce the service quality of mobile communications in a heterogeneous way [4].  
As a new cellular paradigm, the deployment of small cells underlaid by a traditional macro cell is proved to be more efficient for communication due to the reduced transmission distance. However, the deployment of HetNets is challenging in several ways. For one thing, the coverage disparities of the transmit nodes in HetNets [5] would cause uneven traffic loads across different layers, which makes the users within the network more sensitive to the cell association policies. For another thing, massive small cell deployment would complicate the interference environment either among or across different layers, which would jeopardize the improvement of network spectral efficiency.  
In order to cope with the unbalanced traffic load as well as the cross layer interferences, the problem of joint user scheduling and cell association must be further considered for the HetNets. To this end, this paper focuses on the network utility optimization of a two layer HetNet with multiple carrier bands fully reused across different network layers from a cell association and user scheduling angle.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Propagation Models for Simulation
#### 3.15.4 Rician Fading Model  
Rician fading is a stochastic process for radio signal propagation anomalies induced by partial message suppression. Multi-path interference occurs when a signal reaches the recipient through two separate pathways, at most one of which is altering (prolongation or decreasing) [51]. Whenever one of the pathways, usually a line-of-sight signal, is considerably stronger than most others, Rician fading occurs. A significant dominating feature is found in Rician fading. The in-phase and quadrature-phase components of the received signal are simultaneously Gaussian random variables, comparable to Rayleigh fading. Owing to a determinate powerful waveform, the average rate for (at minimum) a single part is non-zero in Rician fading. A Rician distribution describes the amplitude gain at the reception. With parameter K, the Rician amplitude r can be expressed as  
\[r=\sqrt{(\sigma x_{1}+A)^{2}+\sigma x_{2}^{2}} \tag{11}\]  
where \(x_{1}\) and \(x_{1}\) are quadrature values, while \(A^{2}/2\sigma^{2}\cong K,\ \ \sigma x_{i}\) is a random variable along with the variance \(\sigma^{2}\). The mean-squared value of the Rician distribution is expressed by  
\[A^{2}+2\sigma^{2}=2\sigma^{2}(K+1) \tag{12}\]  
and the normalized power envelope is derived from the previous two equations as  
\[\frac{r^{2}}{P}=\frac{1}{2(K+1)}\Big{[}(x_{1}+\sqrt{2K})^{2}+x_{2}^{2}\Big{]} \tag{13}\]\(P\) is the power in the dominating path as expressed by a large-scale model. The outcome of a sizable propagation model may be modulated with this power envelope. The power anticipated by the large-scale scenario is assumed to be the mean-squared value of the envelope. This power includes the dominant-path and multi-path power in the Rician model. In some transmission schemes, representing simply the value in the dominant channel will be much more suitable for large-scale power computation. The above equation is divided by \(A^{2}=2\sigma^{2}K\). The normalized power envelope then becomes  
\[\frac{r^{2}}{P}=\frac{1}{2(K)}\left[(x_{1}+\sqrt{2K})^{2}+x_{2}^{2}\right] \tag{14}\]  
where \(P\) is the power in the dominating route as represented by such a large-scale model.
# Mechanical Properties of Cement Concrete with Waste Rubber Powder
## 3 Results and Discussion  
### Optimal Design Based on the Mix Design of the RSM  
The experimental and predicted responses of the 17 runs of experiments on 7 d and 28 d flexural strength are provided in Table 5. The flexural strength was correlated with the water-cement ratio, sand ratio, and 10-mesh waste rubber powder content. The obtained quadratic regression models are given in Formulas (4) and (5).  
\[\begin{array}{c}7\text{ d flexural strength}=-34.13+17.55A+2.13B+0.34C-0.38 AB-0.14 AC\\ -6.00\times 10^{-3}BC-11.9A^{2}-0.03B^{2}-6.49\times 10^{-3}C^{2}\end{array} \tag{4}\]  
Figure 10: Anti-freezing test of waste rubber powder cement concrete.  
Figure 9: Schematic diagram of chloride ion penetration test device (1—direct current stabilized voltage power supply; 2—test plume; 3—copper electrode; 4—concrete sample; 5—3.0%NaCl solution; 6—0.3%mol/L NaOH solution; 7—direct current digital voltmeter; 8—specimen washer).  
28 d flexural strength \(=-28.46+13.25A+1.88B+0.32C-0.3 AB-0.07 AC\)\(-6.25\times 10^{-3}BC-10.4A^{2}-0.03B^{2}-6.74\times 10^{-3}C^{2}\) where A = water-cement ratio; B = sand ratio; and C = 10-mesh waste rubber powder content.  
Diagnostic plots were employed to assess the adequacy of the chosen model [33]. The model volatility was illustrated through residuals, indicating the variance between the predicted and calculated values. The association among the model residuals, calculated values, and predicted values is illustrated in Figures 11 and 12.  
Figures 11 and 12 illustrate the residuals of each experimental scheme in the model, showing consistently small deviations. Both the experimental and predicted results followed a similar pattern, displaying a predominantly linear distribution. This uniformity indicates the model's high accuracy and its suitability for predicting and optimizing flexural strength at 7 and 28 days. Utilizing the constraints outlined in Formula (6), the optimal mix proportion of waste rubber powder cement concrete was derived from the model. Subsequently, 10 sets of optimized schemes were chosen and are detailed in Table 6.  
\[\left\{\begin{array}{l}0.35\leq\text{Water-cement ratio}\leq 0.45\\ 32\%\leq\text{sand ratio}\leq 36\%\\ 5\%\leq 10-\text{mesh waste rubber powder content}\leq 15\%\\ 4.47\leq\text{7d flexural strength}\leq 5.6\\ 5.01\leq 28\text{d flexural strength}\leq 6.18\end{array}\right. \tag{6}\]  
Analysis of the optimization design results in Table 6 revealed that with a constant water-cement ratio and a sand ratio within the range of 33% to 36%, the waste rubber powder content should not surpass 10%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Water-Cement**} & \multirow{2}{*}{**Sand Ratio (\%)**} & **10-Mesh Waste** & **7 d Flexural** & **28 d Flexural** \\  & & & **Rubber Powder** & **Strength** & **Strength** \\  & & & **Content (\%)** & **(MPa)** & **(MPa)** \\ \hline
1 & 0.35 & 34 & 5 & 5.6 & 6.18 \\
2 & 0.35 & 33 & 8 & 5.57 & 6.14 \\
3 & 0.36 & 33 & 7 & 5.56 & 6.12 \\
4 & 0.
## 5 Pairwise Clock Synchronization under Unknown Random Delays
### Composite Particle Filtering  
The idea of composite particle filtering is to model the estimation problem in the form of a state-space representation and to exploit an optimal Bayesian approach for state estimation [9]. In terms of clock offset estimation, the system model in Equation (4) is rewritten in compact form matrix representation:  
\[\underbrace{\left[\begin{array}{c}U_{i}\\ V_{i}\\ \end{array}\right]}_{\triangleq\mathbf{y}_{i}}=\underbrace{\left[\begin{array} []{cc}1&1\\ 1&-1\\ \end{array}\right]}_{\triangleq\mathbf{B}}\underbrace{\left[\begin{array}{c}d \\ \theta\\ \end{array}\right]}_{\triangleq\mathbf{x}_{i}}+\underbrace{\left[\begin{array} []{c}X_{i}\\ Y_{i}\\ \end{array}\right]}_{\triangleq\mathbf{\omega}_{i}} \tag{36}\]where \(\mathbf{\omega}_{i}\) can assume any distribution. Since the value of clock offset is time varying, it is assumed to obey a Gauss-Markov dynamic model:  
\[\mathbf{x}_{i}=\mathbf{x}_{i-1}+\mathbf{\
u}_{i-1} \tag{37}\]  
where the noise term \(\mathbf{\
u}_{i-1}\) is modeled as a Gaussian random variable with zero mean and covariance matrix \(\mathbf{Q}_{k-1}=E[\mathbf{\
u}_{i-1}\mathbf{\
u}_{i-1}^{T}]\). The aim is to obtain the minimum mean square error (MSEE) estimator of state \(\mathbf{x}_{i}\), which is expressed as the conditional mean state estimator:  
\[\hat{\mathbf{x}_{i}}=E[\mathbf{x}_{i}|\mathbf{y}_{1:i}] \tag{38}\]  
where \(y_{1:i}=[y_{1},\cdots,y_{i}]^{T}\) stands for the observations up to time \(i\).  
The optimal state estimator consists of two steps. In the first step, the posterior distribution \(p(\mathbf{x}_{i-1}|\mathbf{y}_{1:i-1})\) is updated and supposed known, then the predictive distribution is updated as follows:  
\[p(\mathbf{x}_{i}|\mathbf{y}_{1:i-1})=\int p(\mathbf{x}_{i}|\mathbf{x}_{i-1})p (\mathbf{x}_{i-1}|\mathbf{y}_{1:i-1})d\mathbf{x}_{i-1} \tag{39}\]  
where the transition PDF is obtained from Equation (37). In the second step, the posterior distribution at time \(i\) is evaluated:  
\[p(\mathbf{x}_{i}|\mathbf{y}_{1:i})=C_{i}\cdot p(\mathbf{x}_{i}|\mathbf{y}_{1: i-1})p(\mathbf{y}_{i}|\mathbf{x}_{i}) \tag{40}\]  
where \(p(\mathbf{y}_{i}|\mathbf{x}_{i})\) represents the likelihood function obtained from Equation (36), and:  
\[C_{i}=\left(\int p(\mathbf{x}_{i}|\mathbf{y}_{1:i})p(\mathbf{y}_{i}|\mathbf{x }_{i})d\mathbf{x}_{i}\right)^{-1}\]  
is a normalization constant.  
In the composite particle filtering algorithm adopted in [38], the posterior and predictive distributions are recursively modeled by finite Gaussian mixtures, and the components of the Gaussian mixtures are updated using the Kalman filter in conjunction with particle filters. Computer simulation results illustrate that the performance of the composite particle filter algorithm is superior in the presence of delays with unknown distribution (such as Gamma, Weibull, log-normal or arbitrary mixtures) relative to the performance exhibited by the MLEs derived specifically under the assumption of Gaussian or exponential delays.
# Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network
## 4 Case Studies Using the Proposed System
### Feature Visualization Analysis
Based on the two-dimensional visualization comparison results of Figures 7 and 8, it can be concluded that the MSCNN-SK network proposed in this paper can effectively extract the deep fault features of analog circuits. The extracted features have a high degree of discrimination and concentrated distribution, which is beneficial to further fault classification.
# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 3 Model of VEN with Privacy Preservation
### Mobility Model  
In a VEN, the high mobility of the ICVs may lead to changes in communication connection and affect the task offloading progress. When the ICV is out of the coverage of the RSU, the connection between the ICV and the MEC server for task offloading through V2I communication is disconnected. \(t_{ij}^{link}\) is described as the duration of the V2I transmission link between ICV-\(i,\,i\in V\) and RSU-\(j,j\in R\), given by  
\[t_{ij}^{link}=\frac{\left(\tau\big{|}\frac{p_{i}}{\tau}\big{|}-p_{i}\right) \cdot V}{L\lambda}. \tag{1}\]  
When the distance between two ICVs exceeds their maximum communication range, the V2V communication will be disconnected. The duration between ICV-\(i\) and VEC-\(k\) staying connected is represented as \(t_{ik}^{link}\), which can be calculated as [33]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(\mathcal{R}/R\) & Set/number of RCVs \\ \(\mathcal{V}/V\) & Set/number of ICVs \\ \(ij\) & The ICV index \(i\in V\)/ the RSU index \(j\in\mathcal{R}\) \\ \(\lambda\) & ICVs’ arrival rate \\ \(p_{i}/s_{i}\) & ICV’s position/speed \\ \(C_{i}/D_{i}^{in}/D_{i}^{out}/t_{i}^{max}/Q_{i}\) & Vehicular task \(T_{i}^{\prime}\)s required computational resource/input data size/output data size/maximum completion deadline/priority \\ \(t_{ij}^{link}/t_{ik}^{link}\) & Link duration of V2I communication/V2V communication \\ \(r_{ij}^{lLL/DL}\), \(r_{ik/lki}\) & Transmission rate of V2I communication/V2V communication \\ \(f_{ICV}/f_{VEC}/f_{j}^{MEC}/F\) & Computation resource of the ICV/VEC/allocated to MEC-\(j\)/MEC server \\ \(N_{i(t)}/N_{(t)}\) & ICV-\(i\) pseudonym changing decision/number of ICVs changing the pseudonym at time \(t\) \\ \(P_{max}/P_{loss}/P_{i}(t)\) & ICV’s maximum privacy/privacy loss/actual privacy at time slot \(t\) \\ \(U_{i}^{ICV},U_{i}^{VEC},\ U_{i}^{MEC}\) & Weighted cost function of ICV-\(i\) under different offloading strategies \\ \(\mathcal{X}/\mathcal{A}/\mathcal{N}\) & Offloading strategy/computational resource allocation/pseudonym changing decision sets of ICVs \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of main notations.  
\[t_{ik}^{link}=\chi(|p_{i}-p_{k}|\leq T_{r})\frac{T_{r}-(p_{i}-p_{k})\text{sign}(v_ {i}-v_{k})}{|v_{i}-v_{k}|}, \tag{2}\]  
where \(T_{r}\) is the V2V transmission range under the fixed transmission power \(P\). \(\chi(\cdot)\) is the indicator function. If \(z\) is false, then \(\chi(\cdot)=0\), indicating ICV-\(i\) and VEC-\(k\) are disconnected. \(sign(\cdot)\) is the sign function, when \((p_{i}-p_{k})sign(v_{i}-v_{k})<0\) indicates that ICV-\(i\) and VEC-\(k\) are approaching; \((p_{i}-p_{k})sign(v_{i}-v_{k})>0\) indicates that ICV-\(i\) and VEC-\(k\) are moving apart.
# Life Cycle Assessment of Bridges Using Bayesian Networks and Fuzzy Mathematics
## 4 Case and Results  
### Case Description  
To better apply fuzzy mathematics to study the uncertain data of bridges, careful comparison and consideration have been made in the selection of bridge cases: bridges have similar structural types (cable-stayed bridges), similar lengths, the same construction schemes, and the same purpose (first-class highway bridge), and the designed lifetime is 100 years.  
Case 1: SQ is a canal bridge. The main bridge has a total length of 212 m, a width of 26.5 m, a beam height of 2.3 m. Figure 6 shows C cable-stayed bridge. The main tower is built using a creeping formwork. The side span beam and the girder #0 are cast in place with the bracket method, and girders #1-#16 are constructed with a hanging basket [56].  
Case 2: EH is in the Lao Dao Kou area of Shenyang, China. The main bridge is a cable-stayed bridge, with a span of 236 m, a width of 32 m, and a beam height of 3.16 m. The top plate of the beam is 26 cm thick, the bottom plate is 24 cm thick, as Figure 6 shows. The beam blocks #1-#13 (\(-\)#1- \(-\)#13, with #1-#16 on the left and \(-\)#1- \(-\)#16 on the right.) are built using the slide formwork method, and blocks #0 and #14-#16 (\(-\)#14-\(-\)#16) are built using the cast-in-place method [57].  
The contributions of the two cable-stayed bridges to environmental emissions in five phases are analyzed first, as shown in Figure 5. Data sources include design contracts and plans, data from the survey and design phase, construction drawings, geological survey reports, construction organization design, published research results, observed local transportation data, observation data obtained from environmental protection and meteorological departments, and the Ecoinvent and Bedec databases. The criteria for the use of research resources include rich experience in bridge engineering, scientific research theories, and comprehensive research and analysis capabilities.  
Figure 6: Schematic diagram of two bridges.  
According to the LCA analysis process, the energy consumption of the bridge occurs in five stages, mainly in the material manufacturing stage and the maintenance and operation stage.
# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 5 Simulation Analysis  
In order to verify the effectiveness and accuracy of the proposed model, a comprehensive evaluation was conducted on multiple datasets. To construct the test set, a series of data packets conforming to the 802.11p standard were transmitted using different channel models. In various environments, the Signal-to-Noise Ratio (SNR) range of the data packets was set from 15 dB to 40 dB, adjusted in steps of 0.25 dB. This process was repeated 30 times with different releases of the channel model for each environment, resulting in 15,000 test sequences.  
Before formally evaluating the proposed CNN model, it was trained using the categorical cross-entropy loss function and the Adam optimization algorithm. Once the model is trained and its performance is verified to be stable, it is not necessary to retrain it every time it is used. However, should there be significant changes in environmental conditions or new types of environments are introduced, it may be necessary to retrain or adjust the model.  
In this study, the method of processing and inputting data into the Convolutional Neural Network (CNN) is a critical step. The CSI data, inherently complex-valued and containing channel gain and phase information between multiple antennas, must undergo specific preprocessing before being input into the CNN. To accommodate the CNN model, we map the real and imaginary parts of each complex number to two separate input channels. Specifically, the first channel handles the real part information, while the second channel processes the imaginary part information exclusively. After these processing steps, the data are input into the CNN as a one-dimensional (1D) array. Our CNN model features a one-dimensional convolutional layer (Conv1D) designed specifically to handle this format of data. Through this approach, the CNN can process the real and imaginary parts in parallel, effectively learning and extracting key features to identify different communication environments and conditions. This one-dimensional input method not only optimizes the learning efficiency of the model but also enhances processing speed and accuracy.
## 3 Simulation Results and Discussion
### DIBL Effect for Different Combinations of the Gate Oxide and Back Oxide Materials  
The DIBL effect is one of the SHE-like degradation effects which also depends on the properties of the gate oxide and BOX materials. For comparison with the SHE effect, the DIBL effect was simulated in a MOSFET with 2D MoS\({}_{2}\) as transistor channel with the four combinations of gate oxide and BOX materials mentioned above. The simulation results show that the DIBL effect for transistors with combinations 3 and 4 is more sensitive to gate length (Figure 7). In all ranges of transistor length, the DIBL effect is higher for transistors with combinations 3 and 4 than for transistors with combinations 1 and 2. This observation can be understood in the context of a simple model describing the capacitive coupling between the channel and the device electrodes, proposed in [12]. Within the context of a simple model, the modified expression for DIBL is expressed as follows [12]:  
\[DIBL=\frac{C_{D}}{C_{G}} \tag{1}\]  
where, \(C_{D}\) and \(C_{G}\) are drain-channel and gate-channel capacitances, respectively (Figure 8). Three components contribute to the drain-channel capacitance (\(C_{D}\)): the capacitive coupling through the channel layer (\(C_{D,ch}\)), the coupling through the BOX (\(C_{D,box}\)), and the coupling through the gate oxide (\(C_{D,ox}\)). Therefore, the drain-channel capacitance (\(C_{D}\)) is expressed as follows:  
\[C_{D}=C_{D,ch}+C_{Dbox}+C_{D,ox} \tag{2}\]  
The capacitance components \(C_{G}\), \(C_{D,ch}\), and \(C_{D,ox}\) depend on the dielectric constant of the channel \(\varepsilon_{MoS2}\), while \(C_{D,box}\) is unaffected by \(\varepsilon_{MoS_{2}}\) and depends on the dielectric constant of the BOX material. In our case, \(\varepsilon_{Al_{2}O_{3}}=9.3\), \(\varepsilon_{SiO_{2}}=3.9\) and \(\varepsilon_{Hf/O_{2}}=25\) and \(\varepsilon_{MoS_{2}}=3\). Therefore, since the dielectric constant of the channel \(\varepsilon_{MoS_{2}}\) is smaller, the gate capacitance \(C_{G}\) and the components \(C_{D,ch}\) and \(C_{D,ox}\) are reduced, and the DIBL effect is mainly defined by \(C_{D,box}\). Thus, for the considered transistors, the DIBL effect is defined by the dielectric constant of the BOX. In the range of combinations, the BOXs with the highest dielectric constant are in combinations 3 and 4, where oxide materials are HfO\({}_{2}\) with \(\varepsilon_{Hf/O_{2}}=25\), hence these combinations should show the highest DIBL effects. Therefore,  
Figure 7: DIBL effect dependence on the gate length for different combinations of gate oxide and back oxide materials.  
while the BOX material for the transistors with combinations 1 and 2 is SiO\({}_{2}\) with a lower dielectric constant (\(\epsilon_{SiO_{2}}=3.9\)), the DIBL effect for these transistors should be low. These suggestions are in perfect agreement with Figure 7. It can also be seen in Figure 7 that the gate oxide material does not have a considerable impact (or influence) on the DIBL effect in the considered range of variation of the gate length.