esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Influence of Tunnel Stiffness on Tunnel Responses  
Figure 4 shows the influence of tunnel relative stiffness (\(\beta_{0}/\beta\)) on the response of the twin tunnel system by taking the results of the vertical incident angle \(\gamma=0^{\circ}\) and the tunnel spacing distance \(s/a=5\) as an example. As Figure 4 shows, for vertical incident angle (\(\gamma=0^{\circ}\)), when the tunnel has certain stiffness (\(\beta_{0}/\beta=2\), 5, and 10), the displacement amplitudes of tunnel 1 and tunnel 2 are the same and are all larger than that of the rigid single tunnel [6]. For different dimensionless frequencies, with tunnel relative stiffness (\(\beta_{0}/\beta\)) increases, the displacement amplitudes of tunnel 1 and tunnel 2 are decreasing and approach that of the rigid single tunnel [6]. For example, for \(\eta=1.0\), the displacement amplitudes of tunnel 1 that respectively correspond to tunnel relative stiffness \(\beta_{0}/\beta=2\), 5, 10, and 100 are 1.915, 1.014, 0.587, and 0.509, while the displacement amplitude for the single rigid tunnel is 0.443. The tunnel displacement amplitude can be amplified by about 3.3 times ((1.915\(-\)0.443)/0.443). It also can be seen that, even if the tunnel relative stiffness reaches \(\beta_{0}/\beta=100\), the displacement amplitude of the tunnel is still greater than that of the single rigid tunnel [6].  
Figure 4 also shows an interesting phenomenon. For the incident wave with lower frequency \(\eta=0.25\), the deformation of the tunnel is mainly concentrated at the left and right sides of the tunnel, and the deformation amplitude is small. However, when the frequency of the incident wave is higher, the deformation of the tunnel is not only concentrated on the two sides of the tunnel, but also on the lower part of the tunnel. This phenomenon may be explained as follows. When the frequency of the incident wave is small, the wavelength of the incident wave is long and the diffraction effect of the wave is strong, resulting in the small tunnel displacement amplitude. For higher frequency waves with short wavelengths, it has a strong scattering effect at the lower part of the tunnel, resulting in a large displacement at the lower part of the tunnel. At the same time, the short wave will have an obvious interference effect between the two tunnels, which will also cause a large displacement amplitude at the left and right sides of the tunnel.
## 3 Numerical Results and Discussion  
### Simulator Calibration and Optimization of the BPS Number of Phases  
In order to verify the correct operation of the simulator, initially a SMF transmission system was simulated at 10 GBd using a trace format similar to the one shown in Figure 4a. Figure 5 shows the OSNR penalty as a function of the phase noise, in units of linewidth times the symbol duration, as a function of the number of test phases \(B\in\{8,16,32,64\}\) of the BPS algorithm, for (left) QPSK and 16-QAM modulations and (right) for 32-QAM and 64-QAM modulation formats. Note that in this simulation the phase noise between of transmitter laser and the LO was identical and corresponding to the value in the \(X\)-axis. The OSNR penalties were calculated with respect to the theoretical curves and hence the simulator implementation penalty was about 0.5 dB for QPSK and 16-QAM, and approximately 1 dB for 32-QAM and 64-QAM.  
For all modulation formats, the general trend is that increasing the number of test phases \(B\) reduces the OSNR penalty regardless of the phase noise. However, for sufficiently large values of \(B\) the  
Figure 4: (**a**) Diagram of a simulated data trace, showing the symbols stripped to avoid errors due to circular convolutions and the traning sequence. (**b**) Configuration of the equalizer to optimize the numbers of taps and hence reduce the simulation time.  
Figure 5: Phase noise penalties for a single-mode fiber (SMF) transmission system (1 mode) and the BPS algorithm for (**left**) quadrature phase-shift keying (QPSK) and 16-quadrature amplitude modulation (QAM) modulation formats and (**right**) 32-QAM and 64-QAM modulation formats.  
penalty does not reduce any further. In addition, constellations with fewer number of symbols require less number of test phases \(B\). These results approximately agree with the ones in Figure 7 of [18] and confirm the correct operation of the simulator, and in particular of the equalizer-CPR loop which is the most critical component. Furthermore, to reduce the complexity of the equalizer-CPR loop a value of \(B\) as low as possible is desirable. \(B=32\) was chosen for all subsequent simulations in this work, which introduces a small penalty for all the modulation formats investigated.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 4 Experimental Simulation and Analysis
### Model Complexity Analysis
Its time complexity is similar to that of the TVAE algorithm, but due to the addition of the fault detection time overhead denoted by \(O(N_{s}*M)\), its runtime is slightly slower compared to the TVAE algorithm. As shown in Figure 9, when increasing the length of data packets and in the presence of malicious attacks, the TVAE algorithm can still maintain itself within an acceptable range. Therefore, the time consumption brought about by its security assessment module is not significant.
# Simulation-Based Evaluation of V2X System with Variable Computational Infrastructure
## 3 Scenario 3  
In this case, the most effective scenario in terms of losses involves a high average speed for mRNAs following the on-call route. Detailed examination of each graph pair leads to the following conclusions:  
Figure 8: Average percentage of losses at 10 iterations (on-call mRSU): (a) average speed \(30\) km/h; (b) average speed \(50\) km/h.  
Figure 7: Average percentage of losses at 10 iterations (floating mRSU): (a) average speed \(30\) km/h; (b) average speed \(50\) km/h.  
Figure 9: Average percentage of losses at 10 iterations (cluster): (a) average speed \(30\) km/h; (b) average speed \(50\) km/h.  
In the scenario without mRSUs, lower speeds result in lower losses in the Hata model. This occurs because OBUs at lower speeds have sufficient time to receive responses to most of their requests while remaining within the coverage area of a specific RSU. In contrast, when considering the 3 GPP path loss model, the impact of speed on losses is negligible. We believe this is due to the higher signal attenuation characteristics inherent to the 3 GPP model.
2. The use of mRSUs proves to be justified, as both models demonstrate a reduction in losses regardless of the average vehicle speed. At higher speeds, the Hata model shows a decrease in the percentage of losses when mRSUs are used, which is not observed in the 3 GPP model. This is likely due to the same reasons outlined above.
3. The reduction in losses by introducing mRSUs into the system is less significant than anticipated during the modeling phase. Despite achieving the best results with mRSUs, the improvement in terms of losses is not substantial under conditions of uniform map coverage by RSUs. A potential reason for the limited efficiency increase is the choice facing OBUs: which entity to interact with. The introduction of mRSUs into the coverage area offloads some connections onto them, causing current connections with RSUs to break and be replaced by mRSU connections. This leads to unplanned losses. Some of these broken connections might have been completed successfully with a longer average waiting time. Furthermore, an mRSU to which an OBU connects continues moving irrespective of the main traffic flow, potentially causing their routes to diverge and generating additional losses.  
An essential system parameter is stability. A stable simulation system is expected to reach an equilibrium state in terms of losses. The developed system is stable, as shown in Figure 10. The horizontal axis represents system operation time, while the vertical axis represents the percentage of losses. A stable pattern emerges after an average of 100 s, suggesting that calculation times for obtaining statistics can be reasonably reduced.
# Electromagnetic Particle Algorithm for Beam-Wave Interaction in Traveling Wave Tube of Symmetry
## 3 Space Charge Effect  
Some early analysis models ignored the effect of space charge, which may be acceptable for wavelet amplitude, but it cannot be ignored for large amplitude signals close to saturation. The early space charge model follows the wavelength of a disk or a ring and usually only has an approximate space charge field. The total force acting on the electron is the sum of the forces generated by the circuit field and the space charge field. However, in our proposed algorithm, PIC technology (i.e., space grid) is used to model the space charge field, as shown in Figure 2.  
In order to realize the difference calculation of spatial coordinates and considering the orthogonal and cross-linked relationship of electromagnetic fields in space, in the spatial grid, the field components in each coordinate axis direction are separated by half a grid space step, so the same field components are separated by one space step: the electric field and the magnetic field are separated by half a time step in time sequence, so that [PERSON]'s equation can form an explicit difference equation after being dispersed, and then the electromagnetic problem can be solved iteratively in time after the initial value is given.  
Assume that the radial distribution of the perturbed space charge field variable (such as \(Ex\)) is of the form \(J_{0}(k_{vt}t)\), where \(k_{vt}=2.4/r_{b}\), \(r_{b}\) is the beam radius. This mode indicates that there is a conductor at the edge of the beam. Then, we use [PERSON]'s equation to  
Figure 2: Spatial Network Model.  
calculate the disturbing space charge potential \(\phi\), density \(\rho\) and the cylinder is shown in Formula (3):  
\[\frac{\partial^{2}\phi}{\partial x^{2}}-k_{vt}^{2}\phi=-\frac{\rho}{e_{0}} \tag{3}\]  
In the formula \(\phi(x,r,\theta)=\phi(x)J_{0}\). The next section discusses the finite difference decomposition of this equation using the PIC technique.  
In Equation (3), choosing to propagate as \(e^{i(kx-\omega t)}\), one can obtain  
\[\phi(k)=\frac{1}{e_{0}}\frac{1}{k^{2}}\bigg{(}\frac{k^{2}}{k^{2}+k_{vt}^{2}} \bigg{)}\rho(k) \tag{4}\]  
This equation shows the usual space charge reduction factor, where the effective plasma frequency is \(W_{q}=RW_{q}\),  
\[R\Big{(}k_{pl},k_{vt}\Big{)}\equiv\bigg{(}\frac{k_{pl}^{2}}{k_{pl}^{2}+k_{vt}^ {2}}\bigg{)}^{1/2}\leq 1 \tag{5}\]  
This coefficient gives a simple explanation for the reduction of the axial field and the presence of the transverse field. The \"best\" choice of \(k_{vt}\) depends on the type of slow-wave circuit chosen. As mentioned above, if the slow-wave circuit is a drift tube located at the edge of the beam, we choose to use \(k_{vt}\), which is probably the best choice for a coupled-cavity circuit.
First, the measured sEMG and joint angle data set is augmented using amplitude adjusted Fourier transform to generate the surrogate data while consistency is being sustained. Next, we visualize the measured sEMG and joint angle data as one heatmap by setting a horizontal and a vertical axis to be the time and the channels of the measured data, respectively. Then, we utilize the heatmap as an input data to CNN and then investigate the classification accuracy via CNN varies according to the merging location of the joint angle, e.g., in-between sEMG channels (just as a separate bar) or at the bottom of all sEMG channels. Furthermore, the measured sEMG and joint angle are also visualized with the patches of a Hadamard product matrix made of the joint angle and each sEMG channel, which is believed that, at the first glance, spatio-temporal features become challenging to be recognized. Finally, we perform statistical analysis to substantiate the main effects of the human-eye friendly image manipulation and the level of data augmentation as well as the interaction effect between those two. To our best knowledge, the idea of the merging of muscle physiology and joint kinematics as well as the human-eye friendly image manipulation have not yet been fully studied in a field of CNN-based classification. Therefore, the contribution of this study can be summarized as follows:  
* We propose a novel approach for upper arm gym-workout classification by imputing a relationship between muscle physiology and joint kinematics via CNN feature extraction. * We introduce a data augmentation technique for time series, present various visualization methods according to human-eye friendly image manipulation, and statistically analyze the CNN classification performance based on experimental evaluations. * The outcomes from this study can be utilized to advance the state of the art in the problem of developing exercise monitoring systems by providing the level of data augmentation and the visualization method which guarantees the best CNN classification performance. The rest of the paper is organized as follows: the proposed approach for gym-workout classification is introduced in Section 2; specifically, our approach is explained through corresponding subsections as follows: system architecture, experimental setup and procedure, data augmentation technique, post data-processing, and is culminated to the main idea about imputing a biopotential-kinematic relationship and performing classification via CNN. In Section 3, the classification result is reported in terms of training loss and test accuracy, which is followed by statistical analysis. In Section 4, significant outcomes and findings are discussed. Lastly, Section 5 will be the conclusion of this paper.
# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
## 2 NDT Architecture for the RAN
### Data Repository  
The _data repository_ module is responsible for collecting data from the real RAN environment and storing it to feed the RAN NDT. This allows the NDT to have an accurate and real-time representation of the real RAN but also provides historical data to be exploited by the models in the NDT.  
Types of data included in the repository can be related to configurations, operational states, topology, traces, key performance metrics, etc. The data may also vary in the level of detail (i.e., packet level, time slot level, user level, etc.) as well as in the time scale in which it is gathered (e.g., milliseconds, seconds, minutes, etc.). This will depend on the purpose of the NDT instance (e.g., training a RL model at the MAC layer operating at millisecond time scales or a RRM policy operating in the order of minutes) and the required data by the different models in the NDT.
## 4 Experimental Analysis
### Evaluation Protocols  
In all conducted experiments, the evaluation protocol for the MAI dataset involves the selection of four samples from each individual for inclusion in the training set, with the remaining samples designated for the test set. This protocol results in a training dataset comprising 400 samples (i.e., 100 \(\times\) 4) and a test dataset comprising 300 samples (i.e., 100 \(\times\) 3). In contrast, the experimental evaluation protocol for the AWE dataset entails the selection of six samples per individual for the training set, while the persistent samples are allocated to the test set. Consequently, this protocol yields a training dataset comprising 600 samples (i.e., 100 \(\times\) 6) and a test dataset comprising 400 samples (i.e., 100 \(\times\) 4).  
The experimentation process encompasses augmented configurations on datasets for the purpose of ear recognition. Various transformations are applied to the original images to generate additional variations. The transformations applied to the training images include the following:  
- Normalization using the mean and standard deviation.  
- Random rotation of the image by \(-\)20 and +20 degrees.  
- Application of a Gaussian blur filter to the image.  
- Adjustment of the hue, saturation, contrast, and brightness of the image within specified range values.  
- Horizontal flipping of the image by 50%.  
Within our models, online data augmentation is employed, where images undergo pre-processing, augmentation during runtime, and are subsequently stored after the operation. The training dataset is augmented by randomly applying different transformations to each image. This ensures that each training epoch processes distinct variants of the same image, with all training images undergoing four transformations throughout the learning process.
## 3 Materials and Methods
### Calibration of Signal Conditioner  
Signal conditioner is usually required to convert analogue voltage signals to readable signals for digital devices (DT80 dataTaker) by the linearization process [30]. The converted signals are, however, a signal with a very low span; in other words, small output range. For this reason, the conditioning of the sense-element output begins with a gain stage which is usually prone to various sources of amplifier errors such as offset, gain and nonlinearity errors. The errors can be worsened or improved based on the way the conditioned output signal is calibrated before transmission in the monitoring system [24; 29].  
To this effect, the signal condition was calibrated to reduce errors initiated by the analogue signal non-idealities and as such improving the accuracy of the output sensor. Moreover, the input and output circuitry was isolated from the signal-conditioning circuits to scale down the input voltage (\(\mathrm{V_{in}}\)) of 120 V to output voltage (\(\mathrm{V_{out}}\)) of 10 V. Figure 9 illustrates the signal conditioning process.  
Figure 8: Set-up of signal conditioning using voltage divider approach  
The power supply unit (PSU) in Figure 9, ranges from \(-\)15 V to 15 V. The 120 V is fed directly via the input side of the isolation board to the DC to DC converter which comes out as 10 V from the isolator. To linearise the sensor, the 120 V input from the PV arrays was initially set at 80% of 120 V (full scale) reading 96 V in the multimeter and the span was gradually adjusted until it reached 8.0 V output. On the other hand, the span was set to the lowest value 20% of 120 V (full scale), reading 24 V in the multimeter, the span was equally readjusted to 2.0
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 2 The Equivalent Layers of Coal Seam Gas Emission
### The Basic Gas Emission of the Equivalent Layer  
The basic gas emission of an equivalent layer, also known as the basic quantity, represents a quantitative evaluation of the typical gas emission from a specified equivalent layer. This parameter is solely dependent on the inherent rock properties of the coal seam and its immediate roof and floor strata. The determination of the basic quantity relies on a rigorous statistical analysis of historical monitoring data gathered from mining operations in production areas where gas emission is minimally or not at all influenced by external factors.  
It is important to note that the equivalent range of mining and excavation activities can vary for the top and bottom plates, resulting in distinct basic quantities for each.
###### Abstract  
To address issues such as uneven airflow distribution, reduced dust collection efficiency, and different filter bag lifespans in multi-chamber bag filters, computational fluid dynamics (CFD) simulations were employed to analyze the internal flow field and propose optimization strategies. The results revealed that, although there were differences in velocity and pressure fields across the chambers, the overall distribution patterns showed certain similarities. In the original filter model, disordered airflow from the high-velocity inlet led to significant discrepancies in airflow distribution, especially along the chamber sidewalls, top corners, and central regions, resulting in considerable variation in the airflow handled by individual filter bags. Furthermore, high velocities at the bottom of the filter bags caused severe wear, complicating bag detection and replacement. Therefore, an optimization approach involving three vertical descending deflectors in the hopper was proposed. Five optimization schemes were developed to improve airflow organization before it reached the filtration area. To validate the feasibility of the proposed design and the accuracy of the numerical model, experiments were conducted using a 1:10 scaled-down model. According to the simulation results, the optimal scheme reduced the inlet and outlet pressure drop by 11.2%, while improving airflow uniformity by 40-50%, leading to a more balanced airflow distribution and increased operational efficiency.  
bag filter; computational fluid dynamics (CFD); deflectors; filtration; structure optimization +
Footnote †: journal: Applied Sciences
# Test Center Location Problem: A Bi-Objective Model and Algorithms
## 4 Solution Approach for Test Center Location Problem  
The test center location problem entails the concurrent minimization of two distinct objectives. Existing literature delineates the NP-hardness of each of the objectives, k-median and k-balance problems (see [4] and [16] respectively). The identification of Pareto-optimal solutions, comprising two extreme solutions that represent optimal outcomes for each objective, intimates the intrinsic difficulty of solving the bi-objective optimization problem. Consequently, we contend that solving the bi-objective problem is commensurate in complexity to addressing either the k-median or k-balance problem in isolation. It follows that any algorithm capable of discerning at least the extreme Pareto-optimal solutions of the bi-objective problem would inherently resolve the optimal solution for both k-median and k-balance. Consequently, the exploration of approximation and heuristic methods becomes invaluable. In this section, we first introduce an \(\epsilon\)-constraint approach capable of yielding a single Pareto optimal solution per execution. Furthermore, we propose a finely-tailored, efficient bi-objective hill-climbing approach designed to discover a set of _non-dominated_ solutions. Given the local-search nature of this approach, it is important to note that these non-dominated solutions may or may not represent the real Pareto optimal solutions. However, through extensive simulations and comparisons with the Pareto optimal solutions obtained via the \(\epsilon\)-constraint approach, we affirm that the majority of the non-dominated solutions either belong to the Pareto-optimal set or exhibit remarkable proximity to the Pareto-optimal fronts.