esa-sceva/satcom-chunk-collection huggingface dataset:
## 11 Summary
_Number of sinks_: According to the limited bandwidth of the sink, it may become the bottleneck of the data monitoring and leads to traffic congestion in case of simultaneous packet transmissions to a unique sink. By using multiple sinks, the nodes can have multiple targets to send their data to (Figure 12). Therefore, according to the required routing goal (such as energy or delay), the most appropriate sink can be selected or for performing fault-tolerant data routing, other sinks can be used as alternatives in case of failures [1, 51, 64]. _Data aggregation_: in sensor networks, since sensor nodes might generate significant redundant data, similar packets from multiple nodes can be aggregated so that the number of transmissions would be reduced. When a node receives the results of other nodes, it combines the results, removes the redundant data, aggregates the data into one single packet and sends this packet toward the sink node. This process is continued until the final result is received by the sink node. Data aggregation is one of the most significant techniques which can be used to achieve energy efficiency and traffic optimization in routing operation [47, 52, 60, 65]. For example, in Figure 13, all the nodes with gray colors can aggregate their received data packets. Table 1 illustrates the main goals of some well known and current data routing approaches in WSNs according to their features. Of course, some approaches may have other goals in addition to their main goal, but in this table, only the main goal of the approaches is specified. Figure 12: Using multiple sinks for data routing. Figure 13: Data aggregation in WSNs.
## 4 Results and Discussion
### Scenarios of Implementation  
The versatility of the designed authentication mechanism can be illustrated by the various scenarios and the variety of domains where it can be implemented, which involve a high level of information protection and strict requirements for preserving user confidentiality.  
Figure 6: Emerging attacks on the biometric component.  
A first scenario for the implementation of the proposed authentication mechanism includes the integration of these access protection tools in a single-channel military radio network transmitting data in the HF range as presented in Figure 7. The HF radio subsystem is of particular importance in the military communications infrastructure, as it provides real-time informational support for the various missions in the battlefield that underpin the command and control act, characterized by flexibility, portability, and interoperability. The HF radio stations in the data networks function by allocating radio frequencies in specific working bands and are configured with appropriate cryptographic algorithms to ensure a secure flow of data. Supplementing the safety measures by securing the access of authorized users within the computer system contributes to the efficient management of user identity, so that access to information is ensured only to those who need it, at the right time and in a timely manner.  
Another scenario for the implementation of the designed authentication mechanism, represented graphically in Figure 8, consists of incorporating it into the architecture of a contemporary video surveillance system through which real-time monitoring is carried out, a system that represents a key element in reducing existing risks at the organizational level and optimizing the level of security and safety.  
Figure 8: Securing access within a video surveillance system.  
Figure 7: Implementation of authentication mechanism in HF radio data networks.
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 7 Conclusions and Future Works  
In this paper, the introduction of a troposphere Collins model inside an open source GNSS simulator--called _GPS-SDR-SIM_--for a vehicles environment has been considered. The model introduces an additional signal delay (in form of pseudorange) in order to consider the effects of the troposphere for a more realistic scenario. The experiments have been conducted considering a specific hardware--HackRF One with its original ANT500 antenna, a very precise 10 MHz nooelec oscillator and a smartphone as a GPS receiver placed in vehicles. Three different tests have been carried out considering three different latitude coordinates. The results show how the implemented tropospheric software module introduces an improvement in the system guaranteeing better performance in term of position errors. The introduction of the troposphere model allows to have errors  
Figure 10: Histograms of experimental results on Middle Latitude.  
in latitude/longitude coordinates and in altitude values lesser than those performing experiments with the _GPS-SDR-SIM_ simulator without the correction module.  
For the future works, it could be important to analyze the accuracy positioning characterizing the channel with an appropriate model, such as, for example, using approach based on markovian process [32; 33].  
For this research article all authors have provided the same contribution. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData sharing is not applicable to this article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AEV & Average Environmental Value \\ COTS & Commercial-Off-The-Shelf \\ EGNOS & European Geostationary Navigation Overlay Service \\ GGA & Global Positioning System Fixed Data \\ GLL & Geographic Position--Latitude/Longitude \\ GLONASS & Global\({}^{\text{,}}\)naya Navigatsionnaya Sputnikovaya Sistema \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ GSA & GNSS DOP and Active Satellites \\ GSV & GNSS satellites in view \\ KML & Keyhole Markup Language \\ ITS & Intelligent Transportation System \\ NMEA & National Marine Electronics Association \\ RINEX & Receiver Independent Exchange \\ RMC & Recommended Minimum Specific GNSS Data \\ SBAS & Satellite-Based Augmentation System \\ SDR & Software Defined Radio \\ VANET & Vehicular Ad-Hoc Network \\ VTG & Course Over Ground and Ground Speed \\ WAAS & Wide Area Augmentation System \\ \end{tabular}
# A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning
## 3 Materials and Methods
### Model Selection
#### 3.2.1 Random Forest (RF)  
Random forests or random decision forests are a well-known method for classification and regression tasks and is an ensemble model made of many decision trees. A random subset of data and features are imported to each tree, and trees are fitted on this subset for  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{3}{c}{**Demographics Data**} & \multicolumn{3}{c}{**Behavioral Data**} & \multicolumn{3}{c}{**Grades**} \\ \hline
**Variable** & **Group** & **Count** & **Variable** & **Group** & **Count** & **Variable** & **Group** \\ \hline \multirow{3}{*}{Gender} & Male & 221 & & Less t 1 & 23 & \multirow{3}{*}{First math midterm} & 0–20 \\  & Female & 237 & & 1–2 & 127 & & \\ Age & 15–18 & - & Study time & 2–3 & 177 & Second math & \\ \multirow{3}{*}{Family members} & L & 301 & & 3–4 & 82 & midterm & \\  & S & 157 & & More t 4 & 49 & First science & \\  & Excellent & 118 & Supervision & Yes & 78 & midterm & \\  & Good & 253 & by school & No & 380 & Second science & \\ \multirow{3}{*}{Family relation} & Normal & 56 & Supervision & Yes & 256 & midterm & \\  & Bad & 21 & by family & No & 202 & First language & \\  & Worse & 10 & & Yes & 404 & midterm & \\  & Father & 319 & & No & 54 & Second language & \\ \multirow{3}{*}{Guardian} & mother & 107 & & Yes & 374 & midterm & \\  & other & 32 & & No & 84 & \\  & No formal & 9 & & Yes & 137 & First sports midterm & \\ \multirow{3}{*}{Mother} & Primary. s & 54 & & No & 285 & Second sports & \\  & Educational & High. s & 140 & & Yes & 176 & midterm & \\ \multirow{3}{*}{Background} & Bachelor & 201 & & No & 282 & \multirow{3}{*}{First art midterm} & 0–20 \\  & Graduate & 55 & & Less t 1 & & \\  & No formal & 15 & Usage of & 1–2 & & \\ \multirow{3}{*}{Father} & Primary. s & 86 & each internet, & 2–3 & & Second art midterm & \\  & High. s & 99 & TV, game & 3–4 & & \\ \multirow{3}{*}{Background} & Bachelor & 197 & & More t 4 & & \\  & Graduate & 62 & & 1 & 59 & \\  & Education and social & 152 & & 2 & 91 & \\  & Health & 146 & Free time & 3 & 169 & \\ \multirow{3}{*}{Mother-job} & Services & 61 & & 4 & 104 & \\  & Agriculture and env & 55 & & 5 & 35 & \\  & Engineering & 44 & & 1 & 27 & \\ \multirow{3}{*}{Father-job} & Education and social & 229 & & 2 & 119 & \\  & Health & 153 & Go-out rate & 3 & 146 & \\ \multirow{3}{*}{Father-job} & Services & 34 & & 4 & 90 & \\  & Agriculture and env & 25 & & 5 & 76 & \\ \multirow{3}{*}{Travel duration} & Engineering & 17 & & 1 & 40 & \\  & 0 & 300 & & 2 & 75 & \\ \multirow{3}{*}{Travel duration} & 1 & 120 & Friendship & 3 & 82 & \\  & 2 & 33 & & 4 & 102 & \\ \multirow{3}{*}{Travel duration} & 3 & 5 & & 5 & 160 & \\  & & Absences & 0–10 & & \\  & & Attention & 0–100 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Attributes of the database with value details.  
the training of decision trees in the model. Moreover, RF uses the average votes of trees to make predictions [55].
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 5 Experiments
### Model Performance Comparison
#### 5.3.3 Ablation Study  
To prove the impact of the proposed dynamic convolution on the model performance, we perform an ablation study to compare the model performance when using dynamic convolution and only regular convolution. In this ablation study, only the dynamic convolution part is omitted and replaced with regular convolution blocks. The rest of the model is untouched and kept the same for both the model with dynamic convolution and the model with regular convolution. The ablation study is performed by measuring the model metrics, such as the precision, recall, Dice, and mIoU, on the validation and test sets.  
Both models are trained using the same set of training, validation, and test sets. During training, both models' performances are compared using the validation set. Results from the validation set are shown in Table 6, where it shows the model parameters, precision, recall, Dice, and mIoU metrics. From the table, it shows that both models have a significant difference in the number of model parameters. In the model with dynamic convolution, the model parameters are only 1.3 million, while the model parameters in the model with regular convolution reach up to 15.3 million. Although both models are using a similar channel size, the number of parameters is larger in a regular convolution.  
Table 6 also shows the metrics performance of both models. It is clearly shown that the models can learn the data properly during training, which is shown by the decent metrics value. Both models can achieve an mIoU of more than 0.64, with the model with dynamic convolution achieving lower performance than the model with a regular convolution. The Dice metrics of the models also show that the models are able to generate a similar mask compared with the ground truth mask. Again, the model with regular convolution can show a better Dice coefficient value compared to the model with dynamic convolution. Due to the large number of parameters, which means that the model has a greater number of receptors to learn the characteristics of the data, the model with regular convolution is able to overcome the model with dynamic convolution performance.  
However, in the test set results, which are shown in Table 7, the metrics performance of the model with regular convolution is degraded. Both the Dice and mIoU values of the model with regular convolution are decreased by almost half. Meanwhile, the performance of the model with dynamic convolution remains stable without much difference from the training phase. The model with regular convolution might face an overfitting case, where the model performance degrades significantly in the test phase. Therefore, the model with  
Figure 10: Generated mask by the ThinDyUNet and UNet model on IR image input (**a**) Original RGB image. (**b**) Ground truth segmentation mask. (**c**) ThinDyUNet prediction masks. (**d**) UNet prediction mask.  
dynamic convolution performs better in this case because it can perform a similar result during the training and testing phase.
# Evaluation of Three Auditory-Sculptural Qualities Created by an Icosahedral Loudspeaker
## 5 Experiment 3: Discrimination
### Discussion  
According to Figure 5, S\({}_{2}\) is perceived almost as mainly contoured and, therefore, lies closest to Contour (D7%, C81%, and P12%). We assign this to its far-projecting, static 0\({}^{\circ}\) sound beam with PN(6)  
Figure 5: Results of experiment 3: mean values, 95% confidence regions (gray areas) and 99.9% confidence regions (gray ellipses) of the means.  
(see Table 4), away from the listener. In this way, when the listener is walking around in the room a distinctive listening-position-dependent contour would be observable, which otherwise triggers little or no pronounced depth grading or guidance of attention.  
Listeners rated S\({}_{4}\) closest to Directionality (D80%, C11%, P9%), see Figure 5. It is almost purely directed and consists of a single rotating beam of the sound FG(3) (see Table 4). Its movement is attention-guiding around the surrounding horizontal directions, and obviously it is both clear and smooth enough to avoid depth grading and contour.  
S\({}_{1}\) is still rated as directed at D59% in Figure 5, and yet it already contains impressions of depth grading and contour (D59%, C20%, P22%). It consists of a pair of counter-rotating beams that periodically meet at 0\({}^{\circ}\) and 180\({}^{\circ}\). Both beams use the far-projecting sound FM(5). Accordingly, the position of S\({}_{1}\) should be distinctly front-back oscillating with weakly pronounced transitions over the sides, which obviously yields slight plasticity. We assume that due to the pronounced front-back-oriented extent of its development, S\({}_{1}\) is also rated as slightly contoured.  
S\({}_{3}\) was rated to be plastic by P56% and contoured by C42% as seen in Figure 5, with no attention-guiding share D3%. It might be because of its static front-back beam pair (Table 4) that it was perceived as yielding contour. Moreover, the static beams and the stationary sounds should avoid any temporal attention guidance. The fact that different, clearly discernible sounds FG(3) to 180\({}^{\circ}\) and FM(5) to 0\({}^{\circ}\) were used, could have caused a pronounced plasticity that slightly outweighs contour.  
With some similarity to S\({}_{1}\) but clearly separated, the listeners rated S\({}_{5}\) as more plastic (D40%, C17%, P43%). According to Table 4, S\({}_{5}\) also contains two beams rotating in opposite directions, but with PN(6) and FM(5) as clearly distinguishable sound material, together with the additional static component FG(3). Both rotating sounds differ in projection distance and character, which is assumed to be the cause for the strong pronunciation of depth grading (plasticity) and attention guidance (directionality) in the results. The contour of S\({}_{5}\) is comparable to the one of S\({}_{1}\).
## 2 Phase Spectrum Simulation by Modified Fractional Brownian Motion
### Calculation of the Hurst Index and Variance from the Phase Difference  
The simplest way to identify the Hurst index \(H\) and variance \(\sigma_{\Delta\psi}^{2}\) from observed ground motion is to calculate the phase difference \(\Delta\psi\) for several circular frequency interval, \(\Delta\omega\ =\ kd\omega,\ \left(k\ =2^{m}\,,\ m\ =\ 0,\,1,\,2,\,\cdots\right)\) as defined by Equation (6).  
\[\Delta\psi\left(\Delta\omega,\omega\right)\ =\ \psi\left(\omega+\Delta\omega \right)-\psi\left(\omega\right) \tag{6}\]By keeping \(\Delta\omega\) constant, we can calculate the variance of \(\Delta\psi\) using the hypotheses of the ergodic and stationary characteristics of the phase difference process as follows:  
\[\sigma_{\Delta\psi}^{2}\left(\Delta\omega\right)=\ \frac{1}{\omega_{E}-\Delta \omega}\int\limits_{0}^{\omega_{E}-\Delta\omega}\Delta\psi(\Delta\omega\,,\, \omega)^{2}d\omega \tag{7}\]  
where \(\omega_{E}\) is the upper bound of the circular frequency used to calculate the phase. Take \(m\) from 0 to 25 and calculate the variance of phase difference for each step. We obtained 25 variances of phase difference \(\sigma_{\Delta\psi}^{2}\left(\Delta\omega\right)\) from each circular frequency interval \(\Delta\omega\). As an example, Figure 2 shows the relationship between the variance of the phase difference and circular frequency interval obtained from the acceleration record during the Landers earthquake. The information of this event is shown in Table 1 record number 9. In this figure we only show the calculated result with the red circles on a common logarithmic scale. A linear regression equation is obtained by a least-squares fitting for determining the values of \(H\) and \(\sigma_{0}^{2}\). From this regression line, we can obtain the following relationship between \(\sigma_{\Delta\psi}^{2}\left(\Delta\omega\right)\) and \(\Delta\omega\):  
\[\sigma_{\Delta\psi}^{2}\left(\Delta\omega\right)\quad=\ \sigma_{0}^{2}(\Delta \omega)^{2H} \tag{8}\]  
where \(\sigma_{0}^{2}\) is the value of \(\sigma_{\Delta\psi}^{2}\left(\Delta\omega\right)\) at \(\Delta\omega\ =\ 1\). From Figure 2 we can obtain \(\sigma_{0}^{2}=36.413\) and \(2H=1.589\).  
Based on the confidence interval analysis of identified parameters with one sigma the all parameters have accuracy of three-digit level, therefore we changed the effective digit of parameters are within the three digits.  
Figure 2: Relationship between the variance of the phase difference and circular frequency interval.
## 4 Results
### Effects of Stochastic Perturbations  
The evolutionary paths of the parties under different random perturbation intensities are shown in Figure 7. In the absence of random perturbation factors (\(\sigma=0\)), the strategy probabilities of X, Z, Y, and U converge to 1 after 69, 53, 48, and 34 iterations, respectively. The slopes of the curves reflect the evolutionary rates, which are 0.007, 0.009, 0.010, and 0.015, respectively. Compared with the base case (Figure 5), in the absence of random perturbation factors (\(\sigma=0\)), the evolutionary paths of the agents are more stable and evolve more quickly. When the value of random perturbation increases (\(\sigma=0.7\)), the strategy probability changes significantly, and the times for the first x, y, z, and u to stably converge to 1 are 156, 142, 148, and 82, respectively. The mean values of x, y, z, and u are 0.57, 0.50, 0.65, and 0.84, respectively, while standard deviations are 0.20, 0.23, 0.21, and 0.17, respectively. Compared with the base case (Figure 5), the mean value decreases and the standard deviation increases. This indicates that the system is unstable when the external environment changes drastically, and the group's willingness to reduce emissions is not high. Starting from the initial fluctuation, the system reaches (1, 1, 1, 1) for the first time after approximately 156 iterations. When the system iterates about 370 times, it reaches the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Strategy Probability** & \multicolumn{3}{c}{**Mean**} & \multicolumn{3}{c}{**Standard Deviation**} & \multicolumn{3}{c}{**Median**} \\ \hline
**Agent** & **Max** & **Min** & **Mean** & **Max** & **Min** & **Overall** & **Max** & **Min** & **Mean** \\ \hline X & 0.96 & 0.79 & 0.93 & 0.24 & 0.08 & 0.15 & 0.99 & 0.86 & 0.98 \\ Y & 0.98 & 0.76 & 0.96 & 0.32 & 0.08 & 0.17 & 1.00 & 1.00 & 1.00 \\ Z & 0.97 & 0.84 & 0.96 & 0.24 & 0.08 & 0.14 & 1.00 & 0.98 & 1.00 \\ U & 0.98 & 0.92 & 0.97 & 0.15 & 0.06 & 0.10 & 1.00 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of repeated trials.  
Figure 5: Strategy evolution pathways of the four parties in the base case.  
Figure 6: Strategy evolution pathways in the base case (repeated trials).  
stable state of (1, 1, 1, 1) for the second time, which indicates that the system is robust and the PBSC has some adaptability. In either case, the Dismantling and Recycling Party (U) reaches the steady state first and is least affected by random disturbance factors.
# Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum
## 2 Theoretical Basis for Gear Wear Monitoring  
### Gear Vibration Signal Model for Tooth Wear  
For a healthy gear set, the vibration signal model can be expressed as the summation of a group of amplitude and frequency modulated sinusoidal components [22] shown in Equation (1):  
\[x(t)=\sum_{k=1}^{K}X_{k}\cos(2\pi kf_{m}t+\varphi_{k}) \tag{1}\]  
where \(f_{m}=zf_{r}\) is the meshing frequency, which is the product of tooth number \(z\) and the shaft rotating frequency \(f_{r}\), \(X_{k}\) and \(\varphi_{k}\) are the amplitude and phase of the \(k\)th meshing harmonic; and \(k\) is the order of meshing frequency harmonic.  
Gear wear results in changes on the tooth's working surface. As wear proceeds, the worn surfaces of the gears can be regarded as faults deviating from the perfect gear surfaces. In turn, it will produce amplitude modulation \(a_{k}(t)\) and frequency modulation \(b_{k}(t)\) (AMFM) effects on gear mesh vibrations [23]. Such modulation is periodic with the shaft rotation frequency, and can be written as [24]:  
\[a_{k}(t)=\sum_{n=1}^{N}A_{kn}\cos(2\pi nf_{r}t+\alpha_{k}) \tag{2}\]  
\[b_{k}(t)=\sum_{n=1}^{N}B_{kn}\sin(2\pi nf_{r}t+\beta_{k}) \tag{3}\]  
where \(A_{kn}\), \(B_{kn}\) are the amplitudes of the modulation function, and \(\alpha_{k}\) and \(\beta_{k}\) are the respective initial phases of the \(n\)th modulation component.  
Substituting these modulating components into Equation (1), the modulated vibration signal model is given by  
\[x(t)=\sum_{k=1}^{K}X_{k}[1+a_{k}(t)]\cos[2\pi kf_{m}t+\varphi_{k}+b_{k}(t)] \tag{4}\]  
Therefore, considering the nonlinear modulation effects with the worn gear rotating frequency \(nf_{r}\) as the modulating frequency, and with the corresponding meshing frequency \(kf_{m}\) as the signal carrier frequency, a series of sidebands will appear at the frequency locations of \(kf_{m}\pm nf_{r}\) (\(k\), \(n=1\), \(2\), \(3\),... ) in the Fourier spectrum [2; 17; 22]. From the analysis of vibration spectra, we can detect and locate the worn gear by monitoring the presence of amplitude changes of sidebands at the above mentioned frequency locations.