esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Numerical Approach
#### 2.2.1 Neutral Phase Modeling  
The initial DSMC simulation is based on the mathematical framework as proposed by [24] for modeling rarefied flow cases, for which the Kudsen number is  
Figure 5: Example of electric current change during radial probe movement through the ion beam. The blue curve denotes the SSG current, the purple curve is the first (plasma) grid current, and the black curve represents the second (accel) grid current.  
\[\text{Kn}=\frac{\lambda}{\text{L}}=\frac{1}{\sigma n\text{L}}\gg 0.1 \tag{3}\]  
throughout the entire domain. In Equation (3), \(\lambda\) denotes the mean-free path, L is the characteristic length, and \(\sigma\) is the collision diameter of particle-particle collisions, which is defined as  
\[\sigma=\pi d^{2}\,, \tag{4}\]  
where \(d\) is the center-center distance between two particles. Since xenon is a monatomic gas and the flow regime becomes highly rarefied in the exhaust plume, the widely used variable hard sphere (VHS) model proposed by [16] is utilized. A viscosity coefficient of \(\omega=0.85\), reference diameter \(d_{\text{ref}}=574\,\text{pm}\), temperature exponent \(T_{\text{ref}}=273\,\text{K}\) and velocity \(g_{\text{r},\text{ref}}=210\,\text{m}\,\text{s}^{-1}\) are used for Xe-Xe collisions [24].  
A schematic of the domain boundary conditions is shown in Figure 6, with the corresponding numerical boundary conditions for the DSMC simulation outlined in Table 1. Symmetry conditions are utilized in the \(xz-\)plane to reduce the computational cost. The total simulation time is \(1\,\text{ms}\) to ensure sufficient time-steps for obtaining the averaged final distribution. A constant time-step of \(1\times 10^{-7}\,\text{s}\) is employed to assure adequate temporal resolution of the particles' mean collision time. The particle weighting factor is adapted iteratively to ensure that at least five particles are present in each cell when the simulation has reached quasi-steady-state. We devise a fully structured hexahedral mesh consisting of approximately \(1\,\text{million}\) cells, producing a total of \(150\,\text{million}\) super-particles located in the simulation domain in quasi-steady-state.  
Figure 6: Schematic representation of the computational domain and applied boundary conditions utilized in the PIC-DSMC simulations for analyzing the self-neutralization of the ion beam. The geometry of the vacuum chamber, including the placement of parts of the RIT discharge chamber, grid and exhaust parts (marked as _TTruster_), the location and orientation of the probes (metal wires attached to dielectric casings) within the ion beam path, and the configuration of the chamber walls. Boundary conditions are depicted to illustrate the interaction points for ions and electrons with the objects encountered during the experiment, such as inlet and outlet conditions, thruster and probe boundaries, and wall interactions. The schematic specifies the conditions under which particles are introduced, reflected or absorbed, providing a visual guide to the simulation setup supporting the study’s findings on SEE and beam neutralization dynamics.
## 3 Result
### VNVMC Classification
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**DB Set**} & \multirow{3}{*}{**Measurement**} & \multicolumn{6}{c}{**Conventional Model**} & \multicolumn{3}{c}{**Proposed Model**} \\ \cline{3-10}  & & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**ANN**} & \multicolumn{3}{c}{**DNN**} & \multicolumn{3}{c}{**LSTM**} \\ \cline{3-10}  & & & **V** & **NV** & **V** & **NV** & **V** & **NV** & **V** & **NV** \\ \hline \multirow{4}{*}{Set1} & \multirow{2}{*}{Ref} & V & 62.04\% & 37.96\% & 77.67\% & 22.33\% & 81.95\% & 18.05\% & 89.97\% & 10.03\% \\ \cline{2-10}  & & NV & 33.25\% & 66.75\% & 22.82\% & 77.18\% & 18.82\% & 81.18\% & 9.98\% & 90.02\% \\ \cline{2-10}  & & AUC & 0.71 & 0.87 & 0.89 & & 0.97 \\ \cline{2-10}  & & TA & 65.47\% & 77.31\% & 81.39\% & 90.01\% \\ \hline \multirow{4}{*}{Set2} & \multirow{2}{*}{Ref} & V & 72.66\% & 27.34\% & 75.64\% & 24.36\% & 74.39\% & 25.61\% & 82.80\% & 17.20\% \\ \cline{2-10}  & & NV & 30.11\% & 69.89\% & 28.92\% & 71.08\% & 25.47\% & 74.53\% & 17.17\% & 82.83\% \\ \cline{2-10}  & & AUC & 0.79 & 0.82 & 0.83 & & 0.91 \\ \cline{2-10}  & & TA & 70.63\% & 72.29\% & 74.49\% & 82.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results of the proposed model and conventional model for all frequencies of the ES. Abbreviations; voluntary (V), non-voluntary (NV), reference (Ref), total accuracy (TA). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Freq**} & \multicolumn{6}{c}{**Set1**} & \multicolumn{6}{c}{**Set2**} \\ \cline{2-10}  & **SVM** & **ANN** & **DNN** & **LSTM** & **SVM** & **ANN** & **DNN** & **LSTM** \\ \hline
10 Hz & 0.96 & 0.99 & 0.97 & 0.99 & 0.93 & 0.92 & 0.93 & 0.99 \\ \hline
15 Hz & 0.96 & 0.97 & 0.96 & 0.99 & 0.96 & 0.94 & 0.97 & 0.99 \\
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 4 Results
### Higher Elevations Field Trial  
Figure 13 shows the cumulative distribution of \(RSRP\) at different elevations. By increasing the height, at first, the \(RSRP\) improved. According to (1)-(3), the main reason for this behavior is that by increasing height, the probability of \(LOS\) communications increases, resulting in lower pathloss. In addition, according to (5) and (6), when the drone altitude increases above the breakpoint pathloss is similar to the free space propagation. However, as the drone goes to higher altitudes (in this case 170 m), there is a significant loss in the \(RSRP\) signal level. By increasing altitude, the distance between the drone and eNodeB increases and results in larger pathloss and reduces the eNodeB antenna gain and receive power. In addition, since the eNodeB antennas are down tilted by increasing the height the probability of serving drones by mainlobes decreases. Therefore, at higher altitudes, drones will be served by the sidelobes of antennas, which results in lower antenna gain than the mainlobes of eNodeB antennas serving UEs at lower altitudes. However, the \(RSRP\)  
Figure 11: \(RSRP\) distribution at ground level.  
values are concentrated in \(-65\) dBm and \(-85\) dBm for different heights, which shows that coverage for drones at the measured altitudes is satisfactory.  
Another key measurement parameter is \(RSRQ\), which indicates the received signal quality level in a network and the effect of interference from adjacent eNodeBs. The \(RSRQ\) can be calculated as,  
\[RSRQ=\frac{N\times RSRP}{RSSI}. \tag{10}\]  
Figure 14 presents the \(RSRQ\) at different heights. As results show, by increasing the elevation the \(RSRQ\) level was reduced considerably. While by increasing the height the probability of LoS increases and a higher level of the desired signal can be received, but on the other hand, the interference level from adjacent cells also will be increased. It should  
Figure 12: Data rates by different mobile network operator: (**a**) Umobile, (**b**) Celcom, (**c**) Maxis, and (**d**) Digi.  
Figure 13: Reference signal received power (\(RSRP\)) distribution at different altitudes.  
be noted that aerial UEs can see a larger number of adjacent eNodeBs than ground UEs. Therefore, aerial UEs receive higher interference energy from the neighboring cells.  
By comparing the results, it can be revealed that in higher altitudes, e.g., 170m, the \(RSRQ\) level decreases significantly. This is due to the 'umbrella effect', in which eNodeB antennas are down tilted to optimize terrestrial coverage. Besides, in such altitudes, the drone is in range of sidelobes of neighbor cells and experiences severe interference from the downlink transmissions of those cells. Therefore, in terrestrial LTE networks, drones are expected to experience more severe interference at higher altitudes than altitudes less than eNodeB height.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 2 Relevant Work
### Rule-Based Deployment Method  
The rule-based deployment methods primarily select actions according to predefined behavioral rules of the agents during deployment. These methods do not involve data-driven training or optimization of strategies during the deployment process. In rule-based methods, the decision-making mechanism typically extracts relevant features from the initial state and quantitatively evaluates the global impact of deploying in a certain area. Subsequent deployment plans are then selected based on predefined rules. [PERSON] et al. [11] proposed a two-stage branch-and-bound algorithm to solve the single-allocation p-hub location problem. Additionally, [PERSON] et al. [12] applied the branch-and-bound algorithm to address both uncapacitated single-allocation and multi-allocation p-hub center problems.  
The advantage of rule-based methods lies in their ability to find the globally optimal solution for the current scenario, making them effective when the candidate site space is relatively small. However, as the deployment space increases, the computational complexity grows significantly, making it nearly impossible to find an optimal solution within a reasonable time frame. Moreover, due to the limited adaptability of predefined rules, this approach is unable to provide reasonable site selection solutions in complex and dynamically changing environments.
## 2 Platform Scheduling Model
### Heterogeneous Signal Processing Platform  
The heterogeneous signal processing platform is a common platform consisting of modular, standardized, and universal hardware units connected by bus or switch. Various signal processing functions are implemented by loading reusable, portable, scalable, and easily upgradable standardized software modules on the platform. Heterogeneous signal processing platforms adopt common high-performance hardware platforms in hardware architecture, such as ATCA, CPCI, VPX, etc. A large number of different types of processors in the platform, including FPGA, DSP, GPU, GPP, etc. In the software architecture, we build a standardized and regulated hierarchical new software architecture. The difficulty of the cross-platform operation and portability of components in heterogeneous platforms is solved by shielding the differences between the underlying layers through system abstraction.  
The software architecture of a heterogeneous signal processing platform is mainly divided into a hardware platform layer, an operating system layer, a driver abstraction layer, a core framework layer, a management service layer, and an application layer. The operating system layer and the driver abstraction layer belong to the operation support service layer, which can shield the underlying processing unit from hardware differencesin the operating system, communication, memory, and file operation, and provide a unified and standardized interface for the upper layer. The core framework layer is based on the container technology (including FPGA container, process container, thread container, etc.) to shield differences in processor task scheduling and implement application component-based scheduling services. The management service layer includes system operation management environment and visual development management to improve the visibility and portability of application development. The application layer includes the component library and the application console, which are responsible for completing the signal processing function. The software architecture of the heterogeneous signal processing platform is shown in Figure 1.
## 3 Results and Discussion  
Broiler farms' electric loads vary by the hour according to the weather's effect on the building environment. The diurnal fluctuation is more prominent in the warm season from May to September (Figure 6) but less significant in winter months. There are also seasonal electric load fluctuations, with overall higher load in the warm season than in the cold season (Figure 7). A maximum hourly power load of 35 kW occurred in July on this four-house broiler farm, while a minimum hourly power demand of 4.6 kW happened in January (Figure 7). The high electric power demand during the afternoon hours of summer months was attributed to the large number of exhaust fans running to cool houses. These fans have electric motors varying from 375 to 1125 W nominal nameplate power that each run up to 2500 h annually to remove heat from and provide cooling to broilers [27]. Overall, the four-house broiler farm consumed approximately 127,000 kWh of electricity annually.  
Figure 6: Sample diurnal pattern of hourly electric power load of the broiler farm in January and July.
## 3 Case Study  
### Chuong Duong Bridge Introduction  
Chuong Duong Bridge (Figure 2) was one of the first large bridges to be built in Hanoi, Vietnam. The bridge was built in the period 1983 to 1985. Chuong Duong bridge connected the Hoan Kiem district to the Long Bien district, with a total length of 1230 m (from the abutment on the Hoan Kiem side to the abutment on the Long Bien side). The main bridge is a steel truss bridge, comprising 11 spans with the following measurements: 88 m + 92 m + 89.94 m + 89.28 m \(\times\) 7 + 84.88 m. Of these, the three spans in the Hoan Kiem district (88 m + 92 m + 89.94 m) are continuous structures, while the other spans are simple steel trusses.  
The entire width of the bridge is 20.06 m (Figure 3). The bridge carries a four-lane road: two lanes in the middle for cars and buses, with the other two reserved for motorcycles. The bridge is designed with a load of H-30 on the main lane and H-6 on both sides of the cantilever. In the first days of being put into operation, this bridge had a traffic turnover of 7000-8000 vehicles per day. However, the number of vehicles crossing the bridge has since increased dramatically, now reaching tens of thousands of vehicles per day.  
The main truss rods have an H-shaped cross-section, whose height depends on their position (see Table 4 for the details). At the truss nodes, a 32 mm thick panel is used to connect the truss ends.  
Figure 3: Detailed cross section of Chuong Duong bridge.  
Figure 2: Chuong Duong bridge.
## 5 Simulation Experiments
### Overall Energy Consumed (OEC)  
The most important aspect of SD-EAR is that it completely eliminates broadcasting during route discovery. This saves a huge number of messages. Also, route selection strategy in SD-EAR is extremely energy-efficient. The eligibility of a route depends upon the pairwise distance between consecutive nodes, the number of alternatives, the residual energy, the rate of energy depletion, etc. A sleeping strategy is proposed too, that examines the need of sleep of a node after receiving a sleep request. A sleep is granted by the SDN controller if the zonal topology has a provision of its alternative, so that the alive communication session does not face any link breakage. The provision of forceful sleep instruction from the SDN controller to different nodes is possible in SD-EAR. Link breakages are repaired proactively. All of these things greatly reduce the overall energy consumption, or OEC, in SD-EAR compared with its competitors. This is seen in Figures 12 and 17. However, as the number of nodes increases, the OEC also increases for all of the protocols, but still, the growth is least steep for SD-EAR.
## 3 Results and Analysis
### Results and Analysis of Modified Bitumen Mixture Test
#### 3.3.5 Fatigue Test  
The test results are shown in Table 11.  
As shown in Table 11, the K values of the Sasobit-modified, XT-W3-modified, and Evotherm-modified bitumen mixtures increased, and the \(n\) values decreased compared with the K and \(n\) values of the base bitumen mixtures. The K values of the Sasobit-modified, XT-W3-modified, and Evotherm-modified bitumen mixtures increased by 47.74%, 11.97%, and 25.92%, respectively, indicating that incorporating modifiers can effectively improve the fatigue resistance of bitumen mixtures. However, the K values of all three modified bitumen mixtures were lower than those of the SBS-modified bitumen mixtures, whereas the \(n\) values were higher than those of the SBS-modified bitumen mixtures. Therefore, the fatigue resistance of the SBS-modified bitumen mixture was optimal.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Bitumen Mixture** & **R\({}_{\textbf{B}}\)/MPa** & \(\epsilon_{\textbf{B}}\)/\(\mu\epsilon\) & **S\({}_{\textbf{B}}\)/MPa** \\ \hline Virgin Bitumen & 11.85 & 3062.9 & 3868.88 \\
3\% Sasobit & 13.11 & 2989.66 & 4385.11 \\
5\% XT-W3 & 12.42 & 3114.54 & 3987.75 \\
0.6\% Evotherm & 10.84 & 3189.17 & 3399.00 \\ SBS & 13.48 & 3364.49 & 4006.55 \\ \hline \end{tabular}
\end{table}
Table 10: Results of the low-temperature small beam bending test.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 4 Results and Discussion
### Evaluation Metrics and Methods  
In this section, we detail the metrics and methods employed for evaluating the performance of the neural network. We selected the mean of pulse rate error (ME), standard deviation of pulse rate error (STDE), mean absolute error (MAE, see Equation (9)), and RMSE (Equation (10)), along with Bland-Altman plots to quantify the level of agreement between the estimated and ground-truth pulse rate values.  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|PR_{i}-\widehat{PR}_{i}\right| \tag{9}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(PR_{i}-\widehat{PR}_{i}\right)} \tag{10}\]  
Here, the pulse rate estimated from the image sequence is denoted \(\widehat{PR}\) (Equation (8)), and the ground-truth pulse rate is denoted as \(PR\). Pulse rate values estimated with the 3D CNN network were also compared with other state-of-the-art methods:  
* pixel averaging in the green channel (GREEN), from [PERSON] et al. [39];
* independent component analysis (ICA), from the work of [PERSON] et al. [50];
* chrominance method (CHROM), from [PERSON] et al. [49];
* plane orthogonal to skin tone (POS) method, from [PERSON] et al. [49].  
These four methods were implanted using iPhys, an open toolbox released by [PERSON] and [PERSON] [83]. The red, green, and blue signals were interpolated with a shape-preserving cubic function to 30 Hz before launching the methods. After computing their respective PPG signals, the four benchmark methods share a common procedure: We processed the signal with a 3 rd order Butterworth filter with cutoff frequencies set to [0.667, 4] Hz, which correspond to [40, 240] bpm. The signal was then interpolated with a cubic spline function at a frequency of 256 Hz to refine peaks. Beat-to-beat pulse rate values were finally computed from the interbeat intervals.
## 4 Results
### Actuator Shape  
While the tip position is an important quantity to compare the accuracy of our model to others, the shape of the actuator is a qualitative measure that provides further insight  
Figure 8: Actuator 1 (**A**) and Actuator 2 (**B**) with load: tip position in \(x\)–\(y\) plane with extreme positions highlighted (top), coordinates of seven extreme positions (middle) and error at these positions (bottom).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \(E=180\,\mathrm{N}\) & \(E=200\,\mathrm{N}\) & \(E=220\,\mathrm{N}\) & \(E=E(F)\) \\ \hline \multirow{2}{*}{Actuator 1} & 0 N & 5.0 / 15.5 & 3.3 / 11.2 & 3.6 / 12.3 & 2.7 / 8.7 \\ \cline{2-6}  & 1 N & 3.8 / 15.4 & 2.9 / 12.4 & 2.8 / 11.9 & 2.1 / 8.7 \\ \hline \multirow{2}{*}{Actuator 2} & 0 N & 5.6 / 18.0 & 3.4 / 11.9 & 2.7 / 10.0 & 2.0 / 7.0 \\ \cline{2-6}  & 1 N & 4.0 / 16.6 & 3.1 / 13.0 & 2.6 / 11.4 & 2.3 / 9.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean error of the extreme positions (in percent) relative to actuator length and tip deflection, respectively, for varying extensional stiffness \(E\).  
into the model. Figure 9A shows the shape of Actuator 2 in Position 1, as well as the corresponding simulation results (centerline of the actuator solid and parallel offset curve dashed) for an extensional stiffness \(E=200\,\mathrm{N}\). While the shape of the actuator without load is well predicted by the simulations, the axial stretch of the actuator is predicted too large, which is in agreement with Figure 7B. With load applied, the deflection of the tip is larger in the simulation than in the experiment, in agreement with Figure 8B. Referring to the Discussion (Section 5), we consider the complex interrelation of pressure, forces and material stiffness to be the reason.  
In Figure 9B, the actuator is turned by \(90\,\mathrm{deg}\) such that the clamping is horizontal. In opposition to the vertical Position 1, the simulation predicts a larger deflection than in the experiment. This is caused by the shear modulus being assumed to be infinite in the simulation. The rings reinforcing the chambers of the fixed half of the actuator indicate that the cross-section is not perpendicular to the centerline of the actuator, which is an indication of shear.
# Surface Roughness Analysis in the Hard Milling of JIS SKD61 Alloy Steel
## 1 Introduction
The chatter vibrations of the cutting system are generated by a self-excitation phenomenon in the production of chip thickness [15]. In machining, the self-excited machine tool becomes unstable and chatter vibrations grow until the cutter jumps out of the cut [16]. In general, this is one of the common phenomena which negatively affects the machine tool, the cutting tool and the product quality. Therefore, the analysis of the dynamic parameters of the machine tool system is very important work which needs to be carried out to control the chatter vibration during machining operation. [PERSON] and [PERSON] [16] analyzed the structural dynamic of the machine tool and developed an analytical model for predicting the chatter vibration of the machine tool system. They noted that the chatter is a self-excited vibration which often occurs in the cutting system as machining with high depths of cut and cutting speed. Thus, they recommended machine tool users for proper selection of depth of cuts and spindle speeds in machining operations. Furthermore, by experimental study, [PERSON] _et al._[17] showed that the causes of poor surface finish are due to chatter vibrations of a machine tool during machining operation. From the above analysis, it can be seen that the optimal selection of cutting process parameters based on structural dynamic analysis of the machine tool can produce a good surface finish and high performance in machining process. Moreover, the several studies on the optimal manufacturing process have been performed to minimize cost, improve product quality, and enhance performance through the use of many diverse methods. Unfortunately, each methodology has a different level of efficiency in the computational process. No method has recently provided for the same degree of accuracy within all facets of the machining process. In order to fill in these gaps, in this study, an attempt has been made to optimize machining process parameters in the hard milling of hardened JIS SKD61 alloy steel with TiAlN coated carbide end mills based on both the Taguchi technique and RSM. The cutting parameters are selected based on the dynamic stability analysis of the machine tool. A series of experiments was designed by use of the Taguchi method. The machined surface profiles are measured by a Surftest SJ-400 (Mitutoyo Corporation, Kanagawa, Japan), and then analysis of variance is carried out by the Minitab 16 package software (Minitab, Inc., Philadelphia, PA, USA, 2010). The influences of cutting conditions such as cutting speed, feed rate, axial depth of cut, and material hardness on \(Ra\) are also considered. The second-order model is established to estimate \(Ra\) in the hard milling by the RSM. The single objective optimization is carried out to determine the best machining conditions for minimizing \(Ra\).