esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 3 Results and Discussions
### Experimental Demonstrations
#### 3.2.1 Hybridization Demonstration  
A hybridization algorithm, similar to the one described in Section 2.3.4, was implemented in the experiment, coupling both the atom accelerometer and the electrostatic accelerometer. The EA was used to determine the CAI fringe index for each cycle. Furthermore for each cycle, the EA bias was evaluated thanks to the CAI. We can see in Figure 17 such an experimental implementation of this hybridization algorithm for a CAI interrogation time \(T=20\) ms, which illustrates, qualitatively, in a clear way, the gain that could be reached in a future space mission. These results show that the hybridized system takes the best part of each EA and atomic instrument, namely the short-term sensitivity of the EA and the long-term stability of the CAI. The hybridization algorithm shows a correction on the EA signal that begins to be effective from roughly 3 s of integration time. Note that the continuous feature of the EA allows exploring also the sensitivity region below 0.25 s, which is not the case for the CAI, which is limited by its measurement cycling frequency of 4 Hz.  
We would like to emphasize that these presented data do not correspond to what is really expected in terms of performance from an EA, a CAI, or a hybridized instrument in a future space mission (cf. Figure 6). The EA ground prototype was, for instance, designed specifically to sustain the vertical gravity acceleration. The performance of the EA along the vertical axis is therefore degraded due to the high voltage necessary for the proof-mass levitation.
## 2 Materials and Methods
### Materials Characterization  
The microstructure of the targets and morphology of the interfaces, together with their elemental composition, were investigated through scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDX), employing a FEI Quanta Inspect F electron microscope, operated at 30 kV; the specimens were coated with a thin layer of gold by DC magnetron sputtering in order to ensure surface conductivity. The phase composition and crystalline structure of the targets and films were evaluated with the help of X-ray diffraction (XRD), using a Shimadzu XRD 6000 diffractometer (targets) or a PANalytical Empyrean diffractometer (films), 20 ranging between 10 and 70\({}^{\circ}\).
## 3 Research Methodology
The probability of failure is estimated using Equation (2) based on the uncertainties of A and P. The substitution of the mean and standard deviation of P obtained using multicriterial weight analysis into Equation (6) leads to Equation (7) for the probability of failure prediction. Furthermore, from the perspective of the whole of the life cycle, the time-based performance curve can be showed as Figure 4. \(A(t)\) and \(P(t)\) would vary with time due to KPIs assessment and limited space, increasing need and gradually outdated technology etc. \(A(t)\) looks like a straight line owing to few abatements cost every year and its fluctuation cannot be reflected clearly. A reasonable hypothesis is made that at first, the gap between \(A(t)\) and \(P(t)\) is considered safe enough that the \((P(t)-\ A(t)<0)\ \leq l_{1}\), the service delivery performance is good. On the basis of the \(A(t)\) not varying significantly with time, the gap narrows over time due to reduction of the end-user's perceived service quality. When \(P(P(t)-\ A(t)<0)\) falls into the zone \([l_{1},l_{2}]\), the service delivery performance is acceptable. However, when the \((P(t)-\ A(t)<0)>l_{2}\), the service delivery performance is considered as poor. Reliability analysis is used to measure social PPPs service delivery performance through \(A(t)\) in terms of abatement cost ratio and \(P(t)\) in terms of end-user's satisfaction. In addition, it aims to consider project uncertainty and explore how the service delivered by the private sector could be improved to meet the end-user's expectation. The application of this approach is considered in the next section through its application to a case study of three Australian PPP schools.
## 2 Fatigue Test Conditions and Results
### Fatigue Test Results  
Figure 2 shows the relationships between the maximum applied stress (\(\sigma_{max}\)) and the number of cycles to fracture (\(N_{t}\)) for the different stress ratios (5-\(N\) curves). When \(R=-1\), the \(S\)-\(N\) curve exhibited a step-wise (or duplex) shape. In addition, the gradient of the \(S\)-\(N\) curve changed twice (the changing point is hereafter referred to as the \"transition point\"). First, the fracture mode of the specimens was a surface fracture mode in the high stress regime, and the first transition point occurred at approximately \(5\times 10^{4}\) cycles. Then, a region in which surface fracture does not occur, which represents the fatigue limit of surface fracture, lasted until \(2\times 10^{7}\) cycles, which is regarded as the second transition point. After the second transition point, the fracture mode of the specimens changed into an internal fracture. Similarly, when \(R=-0.5\), the \(S\)-\(N\) curve of the specimens exhibited a step-wise shape. In addition, the first transition point at \(R=-0.5\) occurred at a similar cycle as \(R=-1\). However, the second transition point characterized by internal fracture appeared earlier than that of \(R=-1\). In contrast to \(R=-1\) and \(R=-0.5\), when \(R=0.1\), only one surface fracture was obtained. In addition, it was difficult to accurately determine the first transition point when \(R=0.1\). However, the second transition point at \(R=0.1\) appeared earlier than those of the other stress ratios, and the shape of the \(S\)-\(N\) curve was close to asymptotic under \(R=0.1\). The loading cycles between the first and second transition points reduced with an increase in stress ratios. The reason for evolution of the \(S\)-\(N\) shape of the \(\beta\) titanium alloy is not yet properly understood. However, these observations are consistent with the findings of [PERSON] et al., indicating that the trend in Figure 2 is a common fatigue characteristic of \(\beta\)-titanium alloys [15].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**A1** & **V** & **Fe** & **C** & **O** & **N** & **H** & **Ti** \\ \hline
4.15 & 21.17 & 0.15 & 0.013 & 0.14 & 0.012 & 0.0124 & Bal. \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the Ti-22V-4 Al (mass %).  
\begin{table}
\begin{tabular}{c c c c} \hline
**0.2\% Proof Stress** & **Tensile Strength** & **Elongation** & **Reduction of Area** \\
**[MPa]** & **[MPa]** & **[\%]** & **[\%]** \\ \hline
1154 & 1235 & 9.3 & 21.9 \\ \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of the applied Ti-22V-4 Al alloy.
## 2 Materials and Methods
### Experimental Section
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Width & \(w\) & 0.2 & m \\ Length & \(L\) & 0.2 & m \\ Height & \(H\) & 0.2 & m \\ Thickness of sorbent & \(\delta\) & \(3\times 10^{-4}\) & m \\ Channel height (air layer) & \(h\) & \(1.7\times 10^{-3}\) & m \\ Fin spacing & \(p\) & \(3.6\times 10^{-3}\) & m \\ Heat of adsorption & \(\Delta H\) & 2500 & kJ/kg \\ Density & \(\rho_{b}\) & 1500 & kg/m\({}^{3}\) \\ Specific heat capacity of the bed & \(c_{p,b}\) & 805 & kJ/kg\(\cdot\)K \\ Thermal conductivity of sorbent & \(k_{d}\) & 0.33 & W/m\(\cdot\)K \\ Porosity & \(\phi\) & 0.9 & - \\ Radius of the adsorbent particle & \(r\) & \(1.29\times 10^{-6}\) & m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of polymer honeycomb desiccant structure [9]. The Reynolds number (\(Re\)) was calculated by  
\[Re=\frac{\rho_{a}U_{a}D_{h}}{\mu} \tag{1}\]  
where \(\rho_{a}\), \(U_{a}\), \(D_{h}\), and \(\mu\) represent the density (kg/m\({}^{3}\)), velocity (m/s), hydraulic diameter (m), and dynamic viscosity of the air (kg/m.s), respectively. Figure 2 draws the psychrometric chart of the current experimental point. Points 1 and 2 denote the inlet and outlet conditions of the adsorption process, respectively. Desorption inlet and outlet states are represented by spots 3 and 4, respectively. Overall, the working principle of the desiccant dehumidification system was as follows:  
1. Firstly, the desiccant material was heated by the hot air, which was provided by the air supply unit for the desorption phase. This process allowed the water vapor molecules, which were trapped in the desiccant material, to evaporate. Therefore, the desiccant material was regenerated and recovered the ability of moisture adsorption. 2. When the desiccant material reached the equilibrium, the process was switched into the adsorption process by using the air regulator. The desiccant material adsorbed the water vapor molecules. At the same time, in the interface of the desiccant material, the heat of adsorption was released. 3. Lastly, the process was repeated for three up to four cycles to obtain adequate data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Process**} & \multicolumn{5}{c}{**Parameters**} \\ \cline{2-6}  & \(T_{in}\) **(K)** & \(X_{in}\) **(g/kg)** & \(RH_{in}\) **(\%)** & \(U_{a}\) **(m/s)** & \(Re\) \\ \hline \multirow{4}{*}{Desorption} & 308 & 12.2 & 35.23 & 0.16 & 447.6 \\  & 318 & 11.5 & 20.19 & 0.16 & 526.6 \\ \cline{1-1}  & 328 & 13.7 & 15.5 & 0.16 & 593.7 \\ \cline{1-1}  & 338 & 13.8 & 9.10 & 0.15 & 701.9 \\ \cline{1-1}  & 345 & 13.5 & 6.63 & 0.15 & 712.7 \\ \hline Adsorption & 293 & 9.2 & 60.25 & 0.16 & 304.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental conditions of the desiccant dehumidification system. \(T_{in}\): Temperature inlet, \(X_{in}\): Humidity ratio inlet, \(RH_{in}\): Relative humidity inlet, \(U_{a}\): air velocity, \(Re\): Reynolds number. Figure 2: Psychometric chart of the current experimental point. Tdes: desorption temperatures.
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 2 Materials and Methods
### Image Segmentation and Classification  
The percentage of plants per sampling area were determined by manual counting followed by a segmented image of the crop and grass weeds in each georeferenced point, obtained manually at 0.80 m height from the soil by an RGB sensor in a Xiaomi Redmi 8 (Xiaomi Co, Beijing, China) smartphone camera with 12 MP. Figure 2 shows the overall workflow of the image segmentation procedure.  
Image segmentation was performed using QGIS software version 3.28.4 [22]. From each image, Red (R), Green (G), and Blue (B) bands were extracted to determine Excess Green Index (ExG) (Equation (7)), and then the GRASS GIS module r.neighbors was used to calculate maps using the spatial neighborhood [23].  
\[\text{ExG Index}=2\times\text{G}-\text{R}-\text{B}, \tag{7}\]  
GRASS r.neighbors operation sum makes each cell category value a function of the category values assigned to the cells around it, and stores new cell values in an output raster map layer. For spatial neighborhood operations, the diameter of the circular area was set to 9 pixels. In two steps, the value of this index was determined for the classification  
Figure 1: Delimitation of the areas under study and sampling points in Plots I, II, and III.  
of the image into 3 zones: (i) soil, (ii) crop, and (iii) weeds. First, soil was distinguished from the vegetation by the application of a filter of 0 or 1 for ExG Index values below and above 50, respectively; secondly, weeds and crops were distinguished from values below and above 120, respectively (Figure 3).
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 6 Results
### Functional Prototype of FIDO2 CAP
#### 6.2.2 Integration of WAWA in the OpenNDS Captive Portal  
The FIDO2 CAP prototype includes the integration of the WAWA server with the OpenNDS captive-portal network authentication system. This integration adapts the WAWA server to communicate with the OpenNDS Enforcement Device and API Server as defined by the FIDO2 CAP protocol. The integration allows the authorisation of the User Equipment traffic after the FIDO2 authentication with the WAWA server. The following are some specific results achieved during the development of the FIDO2 CAP prototype.  
Firstly, with this integration, our prototype benefits from the compatibility of OpenNDS. For instance, the integrated solution triggers the Captive Portal Detection (CPD) technology embedded in the most used operating systems, showing the captive-portal page once connected to the network.  
Other relevant results are that the same WAWA server can be configured to work with different OpenWRT routers (or Access Points) to control access in different networks. This way, the resulting prototype provides a central point of authentication compatible with a multi-gateway scenario. The same user can be authenticated in different networks, creating different sessions that can be viewed from the administration panel, while all gateway requests are managed independently.  
Finally, thanks to the integration with OpenNDS, the solution is compatible with a real deployment scenario that we used to validate the system (see Section 6.3). OpenNDS can be installed on OpenWRT router firmware, which can form part of a final production environment easy to deliver with multiple hardware routers. These devices can run the OpenNDS captive-portal software, configured to be used with the integrated developed WAWA server. This way, when a client connects to the network via OpenWRT, it gets redirected to the WebAuth authentication server, who manages the request accordingly. After the client authentication, the authentication server waits for the periodic requests and confirms the client authorisation, redirecting them to the original HTTP request.
# Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method
## 3 Boltzmann Machine Learning Based on Spatial Monte Carlo Integration Method
### Boltzmann Machine Learning Based on First-Order SMCI Method  
Applying the 1-SMCI method to Boltzmann machine learning is achieved by approximating the intractable expectations, \(\mathrm{E}_{\mathrm{BM}}[x_{i}x_{j}\mid\mathbf{w}]\), by the 1-SMCI method in Equation (22) with \(k=1\). Although Equation (22) requires sample points \(\mathcal{S}\) drawn from \(P_{\mathrm{BM}}(\mathbf{x}\mid\mathbf{w})\), as discussed in the previous section, we can avoid the sampling by using dataset \(\mathcal{D}\) instead of \(\mathcal{S}\)[22]. We approximate \(\mathrm{E}_{\mathrm{BM}}[x_{i}x_{j}\mid\mathbf{w}]\) by  
\[m_{ij}^{\mathrm{ISMCI}}(\mathbf{w};\mathcal{D}):=\mathrm{E}_{1}[\mathbf{x}_{\{i,j\}} \mid\mathbf{w},\mathcal{D}]=\frac{1}{N}\sum_{\mu=1}^{N}\sum_{x_{i}x_{j}\in \mathcal{X}}x_{i}x_{j}P_{\mathrm{BM}}(x_{i},x_{j}\mid\mathbf{x}_{N_{1}(\{ij\})}^{ (\mu)},\mathbf{w}). \tag{23}\]  
Since  
\[P_{\mathrm{BM}}(x_{i},x_{j}\mid\mathbf{x}_{N_{1}(\{i,j\})}^{(\mu)},\mathbf{w}) \propto\exp\Big{\{}(U_{i}(\mathbf{x}_{\mathrm{d}(i)}^{(\mu)},\mathbf{w})-w _{j}\mathbf{x}_{j}^{(\mu)})x_{i}+(U_{j}(\mathbf{x}_{\mathrm{d}(i)}^{(\mu)},\mathbf{w})-w_{ j}\mathbf{x}_{i}^{(\mu)})x_{j}+w_{j}x_{i}x_{j}\Big{\}}, \tag{24}\]  
the order of the computational complexity of \(e_{ij}^{\mathrm{ISMCI}}(\mathbf{w};\mathcal{D})\) is the same as that of \(m_{ij}^{\mathrm{MFLE}}(\mathbf{w};\mathcal{D})\) with respect to \(n\). For example, when \(\mathcal{X}=\{-1,+1\}\), Equation (23) becomes  
\[m_{ij}^{\mathrm{ISMCI}}(\mathbf{w};\mathcal{D}) =\frac{1}{N}\sum_{\mu=1}^{N}\tanh\Big{[}\tanh^{-1}\Big{\{}\tanh \big{(}U_{i}(\mathbf{x}_{\mathrm{d}(i)}^{(\mu)},\mathbf{w})-w_{j}\mathbf{x}_{j}^{(\mu)} \big{)}\tanh\big{(}U_{j}(\mathbf{x}_{\mathrm{d}(i)}^{(\mu)},\mathbf{w})-w_{j}\mathbf{x}_{i}^ {(\mu)}\big{)}\Big{\}}+w_{j}\Big{]}, \tag{25}\]  
where \(\tanh^{-1}(x)\) is the inverse function of \(\tanh(x)\).  
By using the 1-SMCI learning method, the true gradient, \(\Delta_{ij}^{\mathrm{MLE}}(\mathbf{w};\mathcal{D})\), is thus approximated as  
\[\Delta_{ij}^{\mathrm{ISMCI}}(\mathbf{w};\mathcal{D}):=\frac{1}{N}\sum_{\mu=1}^{N} \mathbf{x}_{i}^{(\mu)}\mathbf{x}_{j}^{(\mu)}-m_{ij}^{\mathrm{ISMCI}}(\mathbf{w};\mathcal{D}), \tag{26}\]  
and therefore, the optimal \(\mathbf{w}\) in this approximation is the solution to the simultaneous equations:  
\[\frac{1}{N}\sum_{\mu=1}^{N}\mathbf{x}_{i}^{(\mu)}\mathbf{x}_{j}^{(\mu)}=m_{ij}^{\mathrm{ ISMCI}}(\mathbf{w};\mathcal{D}). \tag{27}\]The order of the total computational complexity of the gradients in Equation (23) is \(O(N|E|)\), which is the same as that of the MPLE. The solution to Equation (27) is obtained by a gradient ascent method with the gradients in Equation (26).
## 3 Acquisition of Spectral Polarization Information
By varying the polarization state of the input light source and utilizing the detected polarization state of the reflected light, the value of the pBRDF matrix element of the sample can be determined when the source is \(0^{\circ}\) polarized, at which time the incident Stokes is \(\left(1\;1\;0\;0\right)^{\text{T}}\). In the simplified case of not considering the circular polarization component and satisfying the coplanar condition [41], both \(S_{1}\) and \(S_{2}\) in the Stokes vector are zero, the reflected light Stokes vector and degree of polarization (DOLP) can be reduced to Equations (7) and (8); the derivation of the detailed formulas can be found in the reference [42]. The intensity value, Stokes value, and pBRDF matrix element value of the sample at each wavelength were chosen as characteristic data for cluster analysis in this paper [43]. \[ \begin{pmatrix}S_{0}^{\text{r}}\\ S_{1}^{\text{r}}\\ S_{2}^{\text{r}}\end{pmatrix} = \begin{pmatrix}f_{00}&f_{01}&0\\ f_{10}&f_{11}&0\\ 0&0&f_{22}\end{pmatrix}\begin{pmatrix}1\\ 1\\ 0\end{pmatrix} = \begin{pmatrix}f_{00}+f_{01}\\ f_{10}+f_{11}\\ 0\end{pmatrix}  \tag{7}\]  
\[\text{DOLP}_{0^{\circ}}=\frac{\sqrt{\left(S_{1}^{\text{r}}\right)^{2}+\left( S_{2}^{\text{r}}\right)^{2}}}{S_{0}^{\text{r}}}=\frac{f_{10}+f_{11}}{f_{00}+f_{01}} \tag{8}\]
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 3 Materials and Methods
### Methods
#### 3.2.2 BiLSTM Model  
LSTM solves the long-term dependency problem of RNN and performs well in processing time series with a long time span. However, the transmission of the unit state in LSTM is unidirectional from front to back [24], so the LSTM model can only learn the meteorological features of past moments and cannot learn the meteorological features of future moments. BiLSTM means bidirectional LSTM, which means that the signal propagates backward and forward in time [25]. The BiLSTM can learn the features of future meteorological information while using past meteorological information and apply recursion and feedback to it. Its prediction results are more accurate than unidirectional LSTM [26].  
The LSTM network unit comprises a cell, an input gate, an output gate, and a forget gate. The state unit is employed to coordinate the operation of the whole network. The calculation formula of the LSTM network unit implemented by gating is as follows:  
\[f_{t}=\sigma\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\Big{)} \tag{2}\]  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{3}\]  
\[\widetilde{C}_{t}=\tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{4}\]  
\[C_{t}=C_{t-1}\cdot f_{t}+\widetilde{C}_{t}\cdot i_{t} \tag{5}\]  
\[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}) \tag{6}\]  
\[\mathbf{h}_{t}=o_{t}\cdot\tanh(\mathbf{c}_{t}) \tag{7}\]  
Here, \(f_{t}\) is the forget gate. \(i_{t}\) is the input gate and \(o_{t}\) is the output gate. \(C_{t}\) is the internal state at time t. \(W\), \(U\), and \(b\) are the model parameters.  
The structure of the BiLSTM network is shown in Figure 5. The BiLSTM network is a bidirectional structure that makes the input flow in both directions to preserve future and  
Figure 4: Second-order GCN implementation process.  
past information. Therefore, BiLSTM-based models can better mine the association features of time series data.  
Suppose that \(\overset{\rightarrow}{h}_{t}\) is the hidden layer state of the forward LSTM network at time \(t\), and its calculation formula is as described in Equation (8).  
\[\overset{\rightarrow}{h}_{t}=\text{LSTM}\bigg{(}x_{t},\overset{\rightarrow}{h }_{t-1}\bigg{)} \tag{8}\]  
where \(x_{t}\) is the input at time \(t\) and \(\overset{\rightarrow}{h}_{t-1}\) is the hidden layer state of the forward LSTM network at time \(t-1\). \(h_{t}\) is the hidden layer state of the back LSTM network at time \(t\), and its calculation formula is as shown in Equation (9):  
\[\overset{\leftarrow}{h}_{t}=\text{LSTM}\bigg{(}x_{t},\overset{\leftarrow}{h }_{t-1}\bigg{)} \tag{9}\]  
where \(x_{t}\) is the input at time \(t\), and \(\overset{\leftarrow}{h}_{t-1}\) is the hidden layer state of the back LSTM network at time \(t-1\). The output of the BiLSTM network is the combination of two hidden layer states \(\overset{\rightarrow}{h}_{t}\) and \(\overset{\leftarrow}{h}_{t}\), to form the whole hidden state \(h_{t}\) of the network.
## 5 Simulation Results and Discussion
### Variation in UAV's Transmit Power (dBm) with Number of Vehicles (\(V\))
It is noted that if \(\varphi\) is too large, there is less emphasis on the UAV's power consumption and more vehicles are clustered together. This increases the UAV's power consumption and also increases delay. Thus, the value of the personalization parameter (\(\varphi\)) should be adjusted to optimize the training performance while satisfying the transmit power constraints of the UAV and overall delay. As we increased \(\varphi\), the model convergence time improved gradually. Figure 18 also implies that the attention mechanism maintains personalization  
Figure 16: Variation in normalized MSE losses with number of training episodes for PFL and PFL with attention mechanism scenarios. Here, number of vehicles is set to 50.  
through an adaptive approach. PFL also outperforms the traditional FL methods in terms of average delay for a varying number of vehicles and road length. Figure 19 illustrates the variation in normalized MSE losses with number of training episodes for varying the personalization parameter \(\varphi\). As illustrated in Figure 19, as the personalization parameter \(\varphi\) is gradually increased from 0.0000001 to 0.01 in multiples of 10, the MSE error losses tend to gradually decrease. For all the values of the personalization parameter \(\varphi\), the PFL model tends to converge after approximately 200 training episodes. However, for larger values of \(\varphi\) = 0.001 and \(\varphi\) = 0.01, the initial MSE losses are slightly lower compared to smaller values of \(\varphi\) = 0.0001 and \(\varphi\) = 0.00001. This implies a reduction in model convergence time and hence a reduction in the UAV's power consumption when computing global model hyper-parameters from local model hyper-parameters. Furthermore, it is noted that the local model hyper-parameters generated using the attention mechanism add approximately an additional 10% of attention weight parameters compared to the hyper-parameters of the PFL model. Although PFL uses a slightly smaller ratio of personalized parameters, it significantly outperforms FedAvg and FedSGD on latency and the UAV's power consumption metrics. Hence, PFL leads to a significant improvement in the models' ability to overcome data heterogeneity. The decrease in the MSE losses for PFL and PFL with attention mechanism indicates that the performance of PFL is improved with the attention mechanism. Figure 17: Variation in normalized MSE losses with number of training episodes for PFL and PFL with attention mechanism scenarios. Here, number of vehicles is set to 100. Figure 18: Variation in average packet delay (ms) in FedAvg, FedSGD, PFL, and PFL with attention mechanism scenarios for varying number of vehicles (\(V\)) for different road lengths (\(R_{L}\)).