esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Study on Surface Integrity of Milled Workpiece Surface
### Microscopic Topography of Milled Workpiece Surface
#### 3.2.2 Effect of Lubrication Parameters on the Surface Roughness of the Milled Workpiece  
By the effects of the lubrication parameters on the microscopic topography of the milled surface in the previous section, it can be seen that MQL does not always improve the surface quality. When the lubrication amount is low, MQL may deteriorate the surface quality of the workpiece. Figure 19 shows the trend of the surface roughness with the lubrication parameters. As can be seen from Figure 19, when the lubrication amount of MQL is 40 mL/h, the roughness of the milled surface is the largest. This is because the low lubrication amount does not effectively suppress the plastic deformation of the titanium alloy and makes the feeding tool mark more obvious. When the lubrication amount of MQL increases to 80 mL/h, the roughness of the milled surface decreases rapidly. At this time, a stable lubricant film is formed between the milling cutter and workpiece, which can reduce the scraping effect and inhibit the plastic deformation of the workpiece surface. When the lubrication amount increases to 120 mL/h, the roughness Sa and Sq change hardly. It indicates that when the lubrication amount reaches a certain value, continuing to increase the lubrication amount has a limited effect on the surface roughness of the workpiece.  
Figure 19: The trend of the roughness of the milled surface with the lubrication parameters.  
Effect of Milling Parameters and Lubrication Parameters on the Surface Hardening of Milled Workpiece  
During the milling process, plastic deformation occurs on the machined surface, causing twisting and deformation of the machined surface grains. The twisted deformation of the grains in turn can inhibit the plastic deformation of the material. The elongation and fibrillation of the grains can have a strengthening effect on the surface layer of the workpiece, forming a work-hardening layer. Surface hardening is the result of the comprehensive effect of a thermally coupled inhomogeneous stress field on the machined surface. The machining hardening phenomenon is widespread during the milling process. Milling parameters, lubrication methods, lubrication amount, and workpiece material can affect the hardening of the machined surface [31; 32; 33; 34].
# Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems
## 6 Conclusions  
This paper proposes linearly approximate signal detection scheme in the MIMO-OFDM system. At the first stage, the proposed detection scheme uses the partial ZF to increase the complexity linearly. The partial ZF divides the huge MIMO-OFDM system into \(N/2\,2\times 2\) MIMO-OFDM systems. After the partial ZF, the conventional DFE and QRD-\(M\) are used. The conventional DFE and QRD-\(M\) have high complexity in the huge MIMO-OFDM system. However, these nonlinear detection schemes are applied to \(2\times 2\) MIMO-OFDM systems, and it makes the whole complexity linear. The simulation results show that the proposed detection scheme has a higher error performance than the conventional ZF and slightly lower error performance than the conventional DFE. Also, the required number of complex multiplications for the proposed detection scheme is lower than the conventional ZF and the conventional DFE, according to the increased number of transmit antennas due to the partial ZF divides the huge MIMO-OFDM system into several small MIMO-OFDM systems. Therefore, the whole complexity for the proposed algorithm increases linearly, although the number of transmit antennas increases. The proposed algorithm can be widely used in the huge MIMO-OFDM systems that require very low complexity for practical implementations.
## 4 Numerical Simulation
### Correlation Coefficient (CC) Analysis  
The correlation coefficient (cc) is a numerical measure of correlation whose values are \([-1,1]\). It is a statistical evaluation to measure the quality of the encryption system. Calculating the correlation coefficient between the two signals expresses the linear relationship between the corresponding signals. Values near \(|1|\) indicate a strong relationship between the two samples, and values near zero indicate the weakest relationship between the two samples. A lower correlation suggests a more secure encryption process, as the encrypted signal is less predictable based on the original data; hence, it cannot possibly be predicted by attackers [41].  
Table 3 indicates the values of correlation coefficient for different number of levels. Based on the results, the minimum value of the correlation coefficient is opposite to the N = 4, and any increase in the levels leads to an increase in the value of the correlation coefficient.
# Track-Bridge Interaction of CWR on Chinese Large-Span Bridge of High-Speed Railway
## 4 Special Problems of Track-Bridge Interaction
### Earthquake Load
The speed-locking device can significantly reduce the displacement of the fixed pier top and the internal force of the pier bottom of the long-span railway continuous beam arch composite bridge [66]. The larger resistance of the ballast bed is beneficial to the seismic resistance of bridge piers [62]. After the small resistance fasteners are laid on the long-span bridge, the maximum longitudinal force of the rail at the beam joint will be effectively reduced during the earthquake [54,61].
## 5 Modeling for Prediction and Optimization  
### PSO-LSSVM Model Building  
In this study, intelligent algorithms were built based on the MATLAB software. Moreover, the RMSE was used as a performance index for modeling. The performance of the prediction and RMSE were negatively correlated. The smaller the RMSE value, the better the prediction effect is. In order to obtain the best prediction performance of the PSO-LSSVM model, the maximum number of iterations (\(I_{\text{max}}\)) and the number of particles in the swarm (\(n_{\text{pop}}\)) of PSO were determined according to the experimental method, and the other parameters were taken empirically. In addition, the inertia weight coefficient (\(\varepsilon\)) was iterated according to formula \(\varepsilon-(\varepsilon-0.4)T/k\). As shown in Figure 6, after 100 iterations, there was no significant change in the fitness curve. Among them, the fitness curve with \(n_{\text{pop}}=20\) had the smallest RMSE at the final steady state and reached stability at approximately 70 iterations. Therefore, \(I_{\text{max}}\) was taken as 100, and \(n_{\text{pop}}\) was taken as 20. The initial parameters of the model are shown in Table 3.  
Figure 5: Two-objective optimization process of MOPSO.  
Therefore, the optimal values of \(\gamma\) and \(\sigma^{2}\) were 17.315 and 0.917, respectively, when PPV was used as the output indicator. Based on the optimized hyperparameters and training set, the regression fit of the PSO-LSSVM model was obtained, as shown in Figure 7. It can be seen that the PSO-LSSVM model fit the PPV better.  
Figure 6: Fitness curve of PPV.  
Figure 7: PSO-LSSVM regression fitting to PPV.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & \(\mathbf{n_{\text{Var }}}\)* & \(\mathbf{c_{1}}\) & \(\mathbf{c_{2}}\) & \(\mathbf{\varepsilon_{0}}\)* & \(\mathbf{n_{\text{pop}}}\) & \(\mathbf{pop_{\text{max }}}\)* & \(\mathbf{pop_{\text{min }}}\)* & \(\mathbf{v_{\text{max }}}\)* & \(\mathbf{v_{\text{min }}}\)* & \(\mathbf{I_{\text{max}}}\) \\ \hline PSO-LSSVM & 2 & 1.5 & 1.7 & 0.9 & 40 & 1000 & 0.01 & 1 & \(-1\) & 100 \\ \hline \hline \end{tabular}
* \(n_{\text{var}}\): number of decision variables; \(\epsilon_{0}\) : initial inertia weight coefficient; \(pop_{\text{max}}\): optimization parameter maximum value; \(pop_{\text{min}}\): optimization parameter minimum value; \(n_{\text{max}}\): maximum update speed; \(n_{\text{min}}\): maximum update speed.  
\end{table}
Table 3: The initial parameters of PSO-LSSVM.
## 5 Simulation Validation and Discussion
### Simulation Setup  
The simulation configuration diagram is shown in Figure 8 and presented in Section 4.1. It represents the system configuration of an ungrounded system linked to a transformerless PV power generation system connected to an IMD and GPT. Specifically, the PV power generation system is connected to the low-voltage ungrounded system, the IMD is linked to the DC line from the PV array to the input side of the power converter, and the GPT is linked to the AC output side of the PV power generation system. Parameters and simulation conditions of each system are summarized in Table 1.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.8 Gyroscope  
Gyroscopes play a critical role in measuring orientation and angular velocity. They work in tandem with accelerometers to provide accurate motion tracking and spatial orientation. Wearable devices and smartphones make extensive use of these sensors, allowing features such as screen rotation and gesture recognition. In healthcare, gyroscopes help to diagnose balance disorders and monitor movement disorders such as Parkinson's disease. Changes in orientation and movement patterns can be indicative of disease progression. Examples include Apple Watches, Samsung Galaxy Watches, and the Huawei Watch GT 3.
# Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
## 3 Experimental Architecture and Results
### Autonomous UAV Mission and Sensor Offloading--Data Traffic Overload  
For this experiment, a UAV runs an autonomous mission performing a circular trajectory. The controller responsible for generating the velocity commands for the UAV's motors is running in a remote edge server, and the communication between the UAV and the edge server is established over a 5G network. Figure 6 depicts the experimental architecture setup. For the intuition behind the UAV model and the utilized controller, the reader is directed to the complete study [18]. The goal here is to test how extensive the degradation of the performance of an autonomous mission is when the uplink of a 5G-UAV is flooded with sensor data far above the maximum throughput of the 5G link. More specifically, the most critical KPI that describes such a mission would be the latency experienced on the application's control and command data packets [5; 9]. The experiment is designed as  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
\begin{tabular}{c} Remote controllers, centralized optimization \\ problems, path planning, localization, \(\dots\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: This table shows the average data rates measured from the ROS bandwidth tool in the application layer for various types of sensor data topics. The rightmost column depicts the potential offloading task.  
Figure 5: Pie chart that describes the allocation for the average sensor data production of a capable 5G-UAV platform considering the available uplink capacity. Note that the achievable throughput requires high signal strength values and low interference.  
follows: the 5G-UAV operates while offloading all its computational processes to a remote edge server. While it executes a mission of a circular trajectory, the remote edge server requests the raw sensor data. The 5G network supports both the communication of the sensor data and the control and command data. The transmission of the additional sensor data, in that case, the raw camera image, starts and stops by the operator's command, and the behavior of the 5G-UAV is observed. Figures 7 and 8 depict the measured latency of the control and command packets in uplink and downlink respectively. Before transmission of the image sensor data is started, the uplink latency has an average value of \(\sim\)11 msec. When the transmission of the image sensor data starts, the latency of the control packets significantly increases and peaks at the value of \(\sim\)365 msec. Similar behavior is observed for the downlink. This means that the controller processes delayed states of the UAV and thus generates commands that correspond to previous states of the UAV. Hence the overall round trip time (RTT) for the packets that correspond to the UAV is significantly larger than the required value. The consequences of this additional latency are also visible in the trajectory of the UAV (see Figure 9).
## 5 Train-to-Ground Communication Architectures with Leaky Waveguide
### On-Train Leaky Lens  
As the wireless channel is assumed to be stationary, the direct coverage method could be considered. The direct-link approach assumes a direct connection between leaky waveguide and UEs. In this solution, different radio coverage strategies are applied for safety-related data and passengers' data separately. On the one hand, the security-related data only needs to be transmitted to the train roof antenna, and then be transmitted to the corresponding on-train equipments through cable links. Thus, the security-related data transmission can be realized by the traditional radio coverage method. On the other hand, the direct wireless link between leaky waveguide and UEs needs to be established for the transmission of passengers' data. Thus, the on-train leaky wave refractive lens (leaky lens) solution is proposed in this subsection, which could cover wireless signals for passengers directly.  
As is shown in Figure 4b, the train roof is opened to install the leaky lens. If the transmitting power is set appropriately, the leaky wave could directly cover the passengers through the leaky lens. When the radio signal passed through the lens, it could be tuned to achieve uniform wireless coverage for passengers.  
In the proposed leaky lens system, the leaky waveguide is located at the focus plane of lens, through which the radiated cylindrical leaky wave could be refracted and then tuned to plane wave. As a result, the antenna gain is increased, whereas the antenna sidelobe and backlobe are decreased. Assume that the lens is generally made of dielectric materials, and its refractive index is greater than 1, which implies a retarded phase shift for the signals passed through lens. Base on this, the shape of the lens could be determined using the optical theories, such as Fermat's principle and Snell law.  
Figure 4: Wireless access solutions for vactrain: (**a**) Mobile relay station and (**b**) Leaky lens [42].
# Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning
###### Abstract
This allows for the immediate integration of updates and reduces the impact of slower devices. For instance, high-end devices may train a full model, while resource-constrained devices might train a pruned version with fewer parameters. The server then applies techniques like layer-wise [12; 14] or hierarchical aggregation [15] to reconcile and merge updates from heterogeneous models. This approach ensures effective global model updates despite diverse device capabilities, providing a robust and scalable solution in real-world deployments where data are collected from a wide variety of devices. Despite the advancements in FL for heterogeneous devices, existing ECG classification methods lack efficient adaptation to device heterogeneity while maintaining model accuracy and scalability. Most prior approaches have assumed homogeneous client capabilities or struggled with optimizing resource-constrained participation, limiting the robustness of FL in real-world ECG applications [16; 17]. To bridge this gap, this research explored heterogeneous FL techniques specifically tailored for ECG classification. We implemented and extended the HeteroFL framework to optimize model performance across devices with different computational capacities. Specifically, we utilized the MIT-BIH Arrhythmia ECG dataset [18] and integrated the model slimming technique from HeteroFL [12]. This technique adjusts the neural network's width, enabling resource-limited devices to participate effectively in training without compromising the global model's accuracy. By adapting the model width to each device's capabilities, our approach ensures that even low-power devices contribute meaningfully to the FL process, enhancing scalability, efficiency, and generalization in ECG classification. The main contributions of this paper are as follows:  
* Extending HeteroFL to ECG classification by implementing model slimming, enabling efficient training across heterogeneous devices. * Demonstrating the effectiveness of heterogeneous FL for ECG classification using the MIT-BIH Arrhythmia dataset. * Analyzing the effects of device heterogeneity on model convergence and generalization to better understand its implications for FL in ECG classification. The rest of this paper is structured as follows: Section 2 discusses existing ECG classification methods, ranging from centralized to decentralized approaches. Section 3 describes the methodology and materials used for the experiments. Section 4 outlines the experimental settings, including dataset preprocessing strategies, model configurations, and performance evaluation metrics. Section 5 presents the results and analysis of different heterogeneous configurations. Finally, Section 6 concludes the paper with key findings and future research directions.
# SoftRec: Multi-Relationship Fused Software Developer Recommendation
## 4 Experiment and Discussion
### User Study and System Design (RQ5)
#### 4.3.1 Effectiveness of Our Approach  
The results of the questionnaires are shown in Figure 5, We can see that for all surveyed projects, SoftRec's recommendation results are significantly superior to other approaches. For SoftRec, The average values of precision, recall and ndcg in these study projects Workflow, DI, DataViz, ACAP and APM are 0.4721, 0.7868, 0.5102, respectively. Compared with other four approaches, the values of precision, recall and ndcg of SoftRec are 26.33%, 12.64% and 15.63% higher on average, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**GitHub Dataset**} & \multicolumn{3}{c}{**GitLab Dataset**} \\ \cline{2-7}  & **Precision** & **Recall** & **ndcg** & **Precision** & **Recall** & **ndcg** \\ \hline SoftRec & 0.4673 & 0.7633 & 0.4727 & 0.4929 & 0.7701 & 0.5109 \\ \hline SoftRec’ & 0.3113 & 0.5742 & 0.2971 & 0.2935 & 0.5707 & 0.3419 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the SoftRec with and without the multi-relationship fusion (\(k=5\)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**k**} & \multicolumn{2}{c}{**precision**} & \multicolumn{2}{c}{**recall**} & \multicolumn{2}{c}{**ndcg**} \\ \cline{3-8}  & & **fullUpdate** & **fastUpdate** & **fullUpdate** & **fastUpdate** & **fullUpdate** & **fastUpdate** \\ \hline \multirow{6}{*}{github} & 1 & 0.6991 & 0.7103 & 0.2801 & 0.2912 & 0.2911 & 0.3133 \\  & 2 & 0.6622 & 0.6819 & 0.3106 & 0.3262 & 0.283 & 0.3123 \\  & 3 & 0.5523 & 0.5756 & 0.5344 & 0.5445 & 0.4391 & 0.4423 \\  
Because we recommend developers within the scope of each project, the developer-developer collaboration relationships, developer-task interaction relationships, and task-task association relationships are very close. This leads to higher recommendation accuracies compared with those achieved on the sparse datasets.
# Numerical Simulation Research on Aerodynamic Characteristics during Take-Off Phase in Ski Jumping
## 2 Methodology
### Research Methodology
#### 2.2.1 Simulation Model
To obtain more accurate results, the RNG k-\(\varepsilon\) PANS turbulence model is utilized, with its governing equations expressed as follows:  
\[\frac{\partial(\rho k_{u})}{\partial t}+\frac{\partial\big{(}\rho U_{j}k_{u} \big{)}}{\partial x_{j}}=\frac{\partial}{\partial x_{j}}\Bigg{[}\alpha_{k} \bigg{(}\mu+\frac{\mu_{u}}{\sigma_{u}}\bigg{)}\frac{\partial k_{u}}{\partial x _{j}}\Bigg{]}+P_{ku}-\rho\varepsilon_{u} \tag{1}\]  
\[\frac{\partial(\rho\varepsilon_{u})}{\partial t}+\frac{\partial\big{(}\rho U_ {j}\varepsilon_{u}\big{)}}{\partial x_{j}}=\frac{\partial}{\partial x_{j}} \Bigg{[}\alpha_{k}\bigg{(}\mu+\frac{\mu_{u}}{\sigma_{u}}\bigg{)}\frac{ \partial\varepsilon_{u}}{\partial x_{j}}\Bigg{]}+C_{\varepsilon 1}^{*}P_{ku} \frac{\varepsilon_{u}}{k_{u}}-C_{\varepsilon 2}^{*}\rho\frac{\varepsilon_{u}^{2}}{k_{u}} \tag{2}\]  
In the equations, \(U_{j}\) represents the resolved velocity field, \(t\) denotes time, \(\rho\) represents fluid density, \(\mu\) denotes the dynamic viscosity coefficient, \(\mu_{u}\) represents the turbulent viscosity coefficient, \(f_{k}\) represents the unresolved turbulent kinetic energy ratio, \(f_{\varepsilon}\) represents the unresolved turbulent kinetic energy dissipation rate ratio, \(k_{u}\) represents the unresolved local time-averaged turbulent kinetic energy, and \(\varepsilon_{u}\) represents the unresolved local time-averaged turbulent kinetic energy dissipation rate, where  
\[f_{k}=\frac{k_{u}}{k} \tag{3}\]  
\[f_{\varepsilon}=\frac{\varepsilon_{u}}{\varepsilon} \tag{4}\]  
\[\mu_{u}=\rho C_{\mu}\frac{k_{u}^{2}}{\varepsilon_{u}} \tag{5}\]  
\[\sigma_{u}=\frac{f_{k}^{2}}{f_{\varepsilon}} \tag{6}\]  
\[C_{\varepsilon 2}^{*}=C_{\varepsilon 1}^{*}+\frac{f_{k}}{f_{\varepsilon}}(C_{ \varepsilon 2}-C_{\varepsilon 1}^{*}) \tag{7}\]  
\[\eta=\big{(}2S_{ij}\cdot S_{ij}\big{)}^{1/2}\frac{k_{u}}{\varepsilon_{u}} \frac{f_{\varepsilon}}{f_{k}} \tag{8}\]  
\[S_{ij}=\frac{1}{2}\bigg{(}\frac{\partial U_{i}}{\partial x_{j}}+\frac{ \partial U_{j}}{\partial x_{i}}\bigg{)} \tag{9}\]  
\[\frac{\partial(\rho k_{u})}{\partial t}+\frac{\partial\big{(}\rho U_{j}k_{u} \big{)}}{\partial x_{j}}=\frac{\partial}{\partial x_{j}}\Bigg{[}\alpha_{k} \bigg{(}\mu+\frac{\mu_{u}}{\sigma_{u}}\bigg{)}\frac{\partial k_{u}}{\partial x _{j}}\Bigg{]}+P_{ku}-\rho\varepsilon_{u} \tag{1}\]0  
The values of the constants in the model are \(C_{\mu}=0.0845\), \(\alpha_{k}=\alpha_{\varepsilon}=1.39\), \(C_{\varepsilon 1}=1.42\), \(C_{\varepsilon 2}=1.68\), \(\eta_{0}=4.377\), and \(\