esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parameter Optimization of the Semi-Active Energy Regenerative Suspension with an EHA
### Optimization Objectives and Constraints  
In order to improve the energy regenerative performance under the requirement for meeting a certain damping performance, the genetic algorithm is used to optimize the parameters of the EHA, which can reduce the risk of being trapped in a locally optimal solution and make the optimization results more accurate [21; 22].  
The parameter optimization of the semi-active energy regenerative suspension with an EHA is a nonlinear optimization problem of a single target and multiple variables. Taking energy regenerative power as the optimization goal, taking a certain damping performance as the constraint condition,  
Figure 8: Influence of the back electromotive force constant of the generator on evaluation indicators: **(a)** Influence of the back electromotive force constant of the generator on the damping performance; **(b)** Influence of the back electromotive force constant of the generator on energy regenerative performance.  
and taking the effective area of the hydraulic cylinder piston \(A\), the displacement of the hydraulic motor \(q\), and the back electromotive force constant of the generator \(k_{e}\) as optimization variables, the genetic algorithm optimization toolbox from Matlab is used to optimize the parameters.  
The objective function is the RMS value \(\sigma_{Prog}\) of the energy regenerative power of the semi-active energy regenerative suspension with an EHA, and \(\sigma_{Prog}\) is given by:  
\[\sigma_{Prog}=\sqrt{\frac{\sum\limits_{i=1}^{N}{P_{reg_{i}}}^{2}}{N}}. \tag{29}\]  
In the optimization toolbox, the objective function is required to be minimized. In this paper, however, the RMS value of the energy regenerative power is required to be maximized, so the method to minimize the negative value of the objective function is feasible.  
The constraint condition in this paper is to meet a certain damping performance. According to automobile theory and other related literatures, the damping of vehicle suspension belongs to small damping, and the damping ratio \(\xi\) meets the condition \(0.2\leq\xi\leq 0.4\). Additionally, when the RMS value of the wheel dynamic load \(\sigma_{F_{d}}\) does not exceed \(1/3\) of the static load value, the probability of a wheel jumping off the ground is less than \(0.15\%\), which can ensure the comfort and safety of the suspension [23]. Meanwhile, the optimization toolbox requires that the constraint conditions are not positive. Thus, the constraint conditions meet:  
\[\left\{\begin{array}{l}0.2-\frac{c_{s}+c_{eq}(X)}{2\sqrt{k_{s} \cdot m_{s}}}\leq 0\\ \frac{c_{s}+c_{eq}(X)}{2\sqrt{k_{s}\cdot m_{s}}}-0.4\leq 0\\ \sigma_{F_{d}}(X)-\frac{1}{3}G\leq 0\end{array}\right. \tag{30}\]  
where \(X=\{x_{1},x_{2},x_{3}\}=\{A,q,k_{e}\}\) is the optimal parameter vector.
## 4 Circuit
### Final Circuit. Efficiencies  
The correct modeling of the parasitics in the test circuit serves as the basis for the design of the final circuit, whose schematic is presented in Figure 15. Thus, the best performance of the final circuit is situated in the 800-900 MHz band. This circuit is formed by a single stage of the HWCW multiplier (two capacitors are placed in parallel to reduce charge losses [23]) and two \(L\) matching sections. The first of them is expendable and must be eliminated when the antenna is physically connected to the circuit. It transforms the inner impedance of the generator (50 \(\Omega\)) into the impedance of the antenna (188 \(\Omega\)) by means of \(L_{m1}=7.5\) nH and \(C_{m1}=1.5\) pF. Otherwise, the circuit could not be measured with a 50 \(\Omega\) signal generator. The second \(L\) section is formed by \(C_{m2}=4.7\) pF, \(L_{m2_{1}}=8.2\) nH, and \(L_{m2_{2}}=4.3\) nH. It transforms the antenna impedance into the optimum source impedance \(Z_{s}\). This section is actually in charge of maximizing the power transfer between the antenna and the multiplier circuit in the frequency bands of 800 MHz and 900 MHz.  
Figure 15 shows the measured efficiency in the final circuit as a function of the input power. The 870 MHz input tone is provided by an Agilent E4438C vector signal generator. Simulating the effect of the sensor, a load of value \(Z_{L}=8\) k\(\Omega\) has been placed. If we consider the minimum activation power as the value where the efficiency drops to zero in Figure 15, the multiplier circuit starts to work  
Figure 14: Test circuit with the parasitic elements modeling (**a**), and a comparison between measurement and simulation (**b**). The measurement of the response in the final circuit appears marked in red.  
Figure 15: Measured efficiency as a function of the input power in the final circuit (at 870 MHz).  
from \(-15\) dBm onwards. For the harvested power levels, close to \(0\) dBm, the rectifying efficiency is \(30\%\). A drop in the efficiency of approximately a \(7\%\) is also observed when the input power is reduced by half (\(-3\) dBm) from \(5\) dBm downwards. On the other hand, the efficiency saturates from \(10\) dBm onwards. A drop in the efficiency can even be observed above \(15\) dBm, which is caused by the diodes. They are specifically designed to operate within a small signal region, so their performance is degraded above this input power.
## 3 Results
### Functional Connectivity  
To showcase the efficacy of the functional connectivity (FC) comparison, a series of simulations are carried out. The CPU data are analogous, i.e., an externally acquired EEG scan representing four distinct types of brain states. The goal is to hypothetically create a \"virtual brain twin\" of the CPU data by obtaining a GPU parametrization that alignswith the observed CPU data. The externally obtained FCs are compared to the online computed FC from the simulation of the TVB-AdEx model on the GPU in order to find the best matching sets of parameters reproducing the model's behaviour. Parameter sets with values \(S_{0}(g=0.0,b_{e}=0.0)\), \(S_{1}(g=0.3,b_{e}=24)\), \(S_{2}(g=0.4,b_{e}=72)\), \(S_{3}(g=0.4,b_{e}=120)\) are considered. For a full description on how to make use of this comparison, see Section 2. The reported fitness represents the Pearson correlation between the external (CPU) and the FC computed online with a GPU, i.e., Equation (6):  
\[\rho_{xy}=\frac{\text{Cov}(x,y)}{\sigma_{x}\sigma_{y}}\,. \tag{6}\]  
The results of the four FC comparisons conducted on data from simulations of the model for different states are presented in Figure 1. In total, 16,384 parameter combinations are considered. The results provide a visual representation of the distribution of the top 10 distinct solutions across the parameter space for the different parameter sets. In order to enhance the visualization, a solution sphere has been constructed, with the radius of each dimension represented by the respective range values. The results indicate an overall parameter match, achieving approximately 70% similarity with the reference FC.  
It is essential to note that FC comparisons lack a unique solution, due to the stochastic nature of the AdEx model as Brownian noise mimics neural fluctuations and activates the model. The GPU, in this context, showcases its ability to discover multiple characterizations for the model that closely align with the FC. In studies involving FC comparisons, a intersession correlations are deemed high when exceeding 70% [32]. The obtained results affirm that the GPU can successfully replicate such outcomes, producing highly correlated FC results for externally acquired data-sets.  
Figure 1: Visual representation of the best 10 solutions; fitting GPU TVB-AdEx to CPU TVB-AdEx reference FC matrices for four different parameter sets indicates good mapping between stochastic models for analyses of brain dynamics with no unique solution. The value of \(El_{e}\) and \(El_{i}\) is \(-64\) mV for the reference FC. The 3D spheres depict the solution space of the GPU TVB-AdEx and the center red dot represents the input parameters of the reference CPU TVB-AdEx FC.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 1 Introduction
Firstly, we establish a communication model of intra-platoon communication in a single platoon scenario. The intra-vehicle communication process is divided into two part: back-off process of channel competition for transmitting control messages and queuing process of packets in buffers. The back-off process of broadcasting is modeled using a 1-D Markov chain and the queuing process is established exploiting M/G/1/K queuing theory. 2. Secondly, we consider an unsaturated and imperfect condition in our analytical model. The theoretical solution of packet delay and packet dropping rate are derived considering different factors together, including the imperfect wireless channels, various platoon size, distribution of packet generation rate. 3. Thirdly, results have been provided in a single platoon scenario under a wide range of settings. The accuracy of the analytical model is evaluated. The impacts of bit error rate, packet generation rate, and platoon size on the intra-vehicle communication performance are investigated. The effect of intra-platoon communication performance on the stability of platoon is discussed. The manuscript is organized as follows: Section 2 overviews the related research on performance analysis of vehicle communication in VANETs. In Section 3, the concept of platoon including platoon structure model, vehicle control model and communication model are introduced. In Section 4, we present our performance analysis approach in detail. The experiments, relevant results, and analysis are shown and discussed in the following Section 5. Section 6 summarizes the conclusions and future work.
## 4 Experiments and Results
### Implementation Details  
The model is optimized using the focal loss function [41], set with a weight of 1.0 and a \(\gamma\) value of 6, which serves as our primary objective to address class imbalance and focus on hard-to-classify examples. Similar to the methods employed in GLNet [20], we integrate two auxiliary losses and apply a regularization coefficient \(\lambda\) set to 0.15 to further refine the model's performance. As shown in Figure 4, the loss curves demonstrate a good fit, with both training and validation losses consistently decreasing and starting to plateau around the 20 th epoch. This stabilization indicates that the model has effectively learned the underlying patterns in the data without overfitting, as evidenced by the convergence of the two curves to a similar minimum loss value.  
Figure 4: The training loss curve of GLSNet on the DeepGlobe dataset.  
To effectively assess and optimize the graphics processing unit (GPU) utilization for our model, we have employed the \"gpustat\" command-line utility. This tool provides us with detailed insights into GPU usage, which is crucial for enhancing our model's computational efficiency. All training and testing are conducted on a single NVIDIA 1080 Ti GPU card. This approach not only eliminates the need for gradient computation across multiple devices but also guarantees the replicability of our results. Moreover, to balance the GPU load and ensure stable training dynamics, we have established a batch size of six for all training iterations. Our experiments are executed within the PyTorch framework [42], chosen for its flexibility and powerful dynamic computational capabilities. For optimization, we have selected the Adam optimizer [43], renowned for its robust performance in handling sparse gradients.  
Heeding research that demonstrates the benefits of assigning distinct learning rates to local and global branches for improved training outcomes, we have integrated this strategy into our model, GLNet [20]. Specifically, we have set the global branch learning rate to \(\beta_{1}=1\times 10^{-4}\) and the local branch learning rate to \(\beta_{2}=2\times 10^{-5}\). This differential rate allows each branch to learn at a pace that is well-matched to the nature of the data it processes.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation
### Indoor Environment with Multiple High Frequency Acoustic Waves  
Our system is built on the assumption that a person in a library or a hospital can leverage their smartphone to locate the loudspeakers in the environment. However, in daily life, these speakers are used for other purposes most of the time, i.e., broadcasting music or announcements. Thus, we need to detect the robustness of our system when the the same acoustic source transmits multiple frequency acoustic signals.  
We put only one speaker in the underground parking lot environment and then let the speaker broadcast a high frequency acoustic signal along with or without the common music. We also set \(L=5\) m, 10 m, 20 m, 30 m, \(\beta=0^{\circ},\phi=45^{\circ}\) and \(\theta=10^{\circ}\). In Figure 12a, we can detect that when \(L\leq 20\) m, the VUCA system is stable for the acoustic source transmitting both single and multiple acoustic signals. When \(L>20\) m, the results have been affected.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Position of Blocker** & **1 m** & **3 m** & **5 m** & **7 m** & **9 m** \\ \hline
**Standard Deviation** & 4.3\({}^{\circ}\) & 3.7\({}^{\circ}\) & 3.6\({}^{\circ}\) & 3.8\({}^{\circ}\) & 4.1\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Standard deviation of non-line of sight circumstances.  
Figure 12: (**a**) the same acoustic source with single and multiple waves; and (**b**) the CDF of the angular error in a noisy environment.  
The reason for the above-mentioned results is that when the speaker sends multiple signals, the signal strength of each component becomes weaker. The solution to this problem is that we can use the acoustic source with higher power. Fortunately, in the indoor application scenario such as a shopping mall or a hospital, there are high power speakers in a passageway or a conference room in most cases.  
We also have another assumption that the different loudspeakers transmit various high frequency acoustic signals to distinguish different locations in indoor environments. If the user knows a specific frequency of his/her target acoustic signal, our system can guide the user to his/her destination. Thus, we also need to verify the direction finding ability of the VUCA system when the multiple acoustic sources transmit different high frequency acoustic signals in the environment.  
Three loudspeakers are planted in the underground parking lot. These speakers broadcast acoustic signals with different frequencies of 19,500 Hz, 20,500 Hz and 21,500 Hz. In addition, the distances from all three loudspeakers to the user are the same. We treat one of the speakers as target speakers and let the relevant position of the user and the target speaker be \(L=12\) m, \(\beta=0^{\circ},\phi=45^{\circ}\) and \(\theta=0^{\circ}\). We evaluate the mean angular error of direction finding under single source, double source and triple source circumstances. From Table 3, we learn that the VUCA system functions well on the above assumption, and it is very crucial in daily use.
## 3 Results and Discussion  
### Crystal Structure  
Figure 1 illustrates the structure of monolayer and multilayer Mo\({}_{2}\)C-materials with different functional groups. These materials have a structure consisting of Mo atoms located in the outer layers, sandwiching the layers of C and two layers of Mo atoms halogenated by atomic layers -F, -OH, and -O on each side, thus creating a similar surface chemistry. In comparison, all MXenes with surface functional groups are more stable than bare MXenes [11].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **MXenes** & **Gap (eV)** & \begin{tabular}{c} **a = b** \\ **(Å)** \\ \end{tabular} & \begin{tabular}{c} **c** \\ **(Å)** \\ \end{tabular} & \begin{tabular}{c} **c** \\ **(Å)** \\ \end{tabular} & \
\begin{tabular}{c} **d Mo-C** \\ **(Å)** \\ \end{tabular} &
\begin{tabular}{c} **d Mo-(E, O, H)** \\ **(Å)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Monolayer} & Mo\({}_{2}\)CF\({}_{2}\) & 0.278 & 3.2786 & 35.7215 & 2.1148 & 2.3029 \\ \cline{2-7}  & Mo\({}_{2}\)C(OH)\({}_{2}\) & 0.117 & 3.3012 & 36.2148 & 2.1264 & 2.9868 \\ \cline{2-7}  & Mo\({}_{2}\)CO\({}_{2}\) & - & 2.8836 & 30.0007 & 2.1608 & 2.0678 \\ \hline \multirow{4}{*}{Bilayer} & Mo\({}_{2}\)CF\({}_{2}\) & 0.258 & 3.2793 & 35.7128 & 2.1151 & 2.3032 \\ \cline{2-7}  & Mo\({}_{2}\)C(OH)\({}_{2}\) & 0.012 & 3.3013 & 36.2149 & 2.1264 & 2.9868 \\ \cline{2-7}  & Mo\({}_{2}\)CO\({}_{2}\) & - & 2.8837 & 30.0008 & 2.1609 & 2.0678 \\ \hline \multirow{4}{*}{Trilayer} & Mo\({}_{2}\)CF\({}_{2}\) & 0.249 & 3.2798 & 35.7058 & 2.1154 & 2.3036 \\ \cline{2-7}  & Mo\({}_{2}\)C(OH)\({}_{2}\) & - & 3.3013 & 36.2149 & 2.1264 & 2.9868 \\ \cline{1-1} \cline{2-7}  & Mo\({}_{2}\)CO\({}_{2}\) & - & 2.8837 & 30.0008 & 2.1609 & 2.0678 \\ \hline \end{tabular}
\end{table}
Table 1: Band gap energy, lattice constants, and bond lengths of Mo\({}_{2}\)CF\({}_{2}\), Mo\({}_{2}\)C(OH)\({}_{2}\), and Mo\({}_{2}\)CO\({}_{2}\).  
The lattice constants and bond lengths of Mo\({}_{2}\)CF\({}_{2}\), Mo\({}_{2}\)C(OH)\({}_{2}\), and Mo\({}_{2}\)CO\({}_{2}\) are summarized in Table 1. It should be noted that these parameters were nearly the same for each structure because the number of layers increased while the lattice parameter along the c axis remained unchanged. The results of structural optimization for Mo\({}_{2}\)CF\({}_{2}\) are similar to the previous report [32], with a difference of only less than 1 because the same simulation package and pseudopotential were used. Experimental results on the band gap energy, lattice constant, and bond length of different Mo\({}_{2}\)C-MXenes are still lacking.
## 4 Parametric Study
### Inclination Angle of the Main Cables  
In this section, the behavior of the suspended SFT was analyzed, according to the inclination angle of the main cables. The inclination angles were set to 12 degrees, 25 degrees, and 35 degrees, as shown in Figure 14, to analyze the effect of the arrangement method of the main cable on the behavior of the SFT. The common variables of the analytical model include a wave condition of 6 m (8 s), a BWR of 1.2, a tube external diameter of 23 m, and an installation depth of 70 m.  
Figure 15 shows the motion, acceleration, reaction force, section force, and section moment of the SFT, according to the inclination angle of the main cable. Figure 15a displays the absolute maximum motion of the SFT, according to the inclination angle, which exhibits little change of motion with the variables. However, it can be seen that the sway motion of Type 2 increased when it was placed at 35 degrees. The sway value affects the change in the acceleration, which was approximately doubled, as shown in Figure 15b.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Type 1**} & \multicolumn{2}{c}{**Type 2**} \\ \cline{2-5}  & **Min (MN)** & **Max (MN)** & **Min (MN)** & **Max (MN)** \\ \hline BWR 1.1 & 17.1 & 33.7 & –2.82 & 12.6 \\ \hline BWR 1.2 & 62.5 & 92.5 & 0.289 & 8.26 \\ \hline BWR 1.3 & 114.0 & 148.0 & 3.57 & 9.55 \\ \hline \end{tabular}
\end{table}
Table 2: Maximum and minimum axial section forces of Type 1 and Type 2.  
Next, Figure 15c indicates the absolute maximum reaction force on the foundation of the SFT model, according to the inclination of the main cable. In the case of Type 1, the reaction force decreased slightly as the angle increased. Conversely, for Type 2, the reaction force increased with the inclination angle. This is related to the lateral motion, and the reaction force increased with the rise of the sway motion, as shown in Figure 15a.  
Next, Figure 15d,e display the maximum section force and section moment of the SFT model, according to the inclination angle. They were slightly elevated but showed almost similar values. It can be seen that 12 degrees, which is the smallest variable, can be sufficient to control the motion.  
Figure 14: Inclination angle of the main cables.  
Figure 15: _Cont._
# Influence of Surface Tilt Angle on a Chromatic Confocal Probe with a Femtosecond Laser
## 1 Introduction
Furthermore, in view of the scanning confocal probe's principle of detecting the \(z\)-position of the object to be measured, the focus of most of these studies has been on the effect of the confocal response on peak detection. Here, our confocal probe is not a scanning confocal probe but a chromatic confocal probe. Furthermore, the TL method decodes the \(z\)-position of the measurement object not from the peak wavelength of the confocal response curve but from the wavelength of the sidelobe minima of the confocal response curve. In this study, theoretical and experimental investigations on the influence of the object tilt angle on the TL method are demonstrated. At first, a general numerical model for the chromatic confocal probe is constructed, and theoretical equations for numerical calculations are discussed. Based on the theoretical equation, numerical calculations are then performed and compared with the results of scanning confocal probes in previous studies to discuss the effect of the object tilt angle on the TL method. Finally, experiments are conducted to verify the theoretical calculations and to discuss the effect of the object tilt angle on the TL method. Here, our previous studies have mainly focused on proposing a \(z\)-position detection method [22; 26], extending the \(z\)-position measurement range [23; 27], and optimizing the optical system [24; 25]. On the other hand, this report differs from our previous studies in that it aims to report the results of an investigation into the effect of the tilt angle of the measurement object on the proposed TL method. Theoretical Equations for the Tracking Local Minimum Method of Mode-Locked Femtosecond Laser Dual-Fiber Differential Detector-Type Chromatic Confocal Probe
# Plant Diseases Identification through a Discount Momentum Optimizer in Deep Learning
## 4 Results
### Convolutional Neural Networks  
In the experiment, the DM method is applied to a variety of models. ResNet and DenseNet are typical convolutional neural networks' architectures, which are efficient and widely-used in literature. We consider testing the task of plant diseases classification with 50-layer ResNet and the 121-layer DenseNet. We select DM as the baseline algorithm and include comparisons with SGD, SGDM, and NAG non-adaptive learning rate methods. For DM, SGD, SGDM, and NAG, the first 30 epochs use learning rate \(\alpha=1.0\), the next 30 epochs use \(\alpha=0.1\), and the final 30 epochs use \(\alpha=0.01\). For SGDM and NAG, the momentum parameter \(\beta\) is directly applied to default value 0.9.  
**Plantvillage-ResNet50** ResNet50 has 50 layer deep CNNs with skip connections for image classification. We test our algorithm with the ResNet50 model on the plantvillage dataset. We compare the performance of DM, SGD, SGDM, and NAG. The results are shown in Figure 3, from which we can see that the DM algorithm is significantly better than SGDM and NAG. We notice that the training speed and generalization performance of DM are relatively superior SGD at the initial 30 epochs. In the later, DM and SGD share competitive results, while DM is generally slightly better.  
**Plantvillage-DenseNet121** DenseNet121 is a 121-layer deep CNNs with dense connections. Results of this experiment are reported in Figure 3. As is expected, the overall performance of each algorithm on ResNet50 is similar to that on DenseNet121. We can see that the DM method performs better than the non-adaptive ones in training. In addition, compared with non-adaptive learning rate methods, it converges as fast as SGD and achieves a bit higher accuracy on the plant diseases identification task.  
Figure 2: Hyper-parameters \(\mu\) and \(\lambda\) sensitivity experiments with DenseNet121 on the plantvillage dataset.