esa-sceva/satcom-chunk-collection huggingface dataset:
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 3 Atomic Clocks and Time Scale Metrics
### Performance Metrics for Time Scale
#### 3.2.2 Stability  
The stability of a time scale is its ability to maintain a constant scale interval. It quantifies how the frequency value changes over time. The Allan variance, proposed by [PERSON], is commonly used for this purpose. For phase data, it is expressed as [23]  
\[\sigma_{y}^{2}(\tau)=\frac{1}{2(N-2)\tau^{2}}\sum_{i=1}^{N-2}[x(i+2)-2x(i+1)+ x(i)]^{2}, \tag{14}\]and for frequency data as  
\[\sigma_{y}^{2}(\tau)=\frac{1}{2(M-1)}\sum_{i=1}^{M-1}[y(i+1)-y(i)]^{2}, \tag{15}\]  
where \(\tau\) is the smoothing time, often an integer multiple of the measurement time interval.  
Hadamard variance, based on the secondary frequency deviation for frequency data and cubic deviation for phase data, helps to remove linear drift. For frequency data,  
\[H\sigma_{y}^{2}(\tau^{\prime})=\frac{1}{6(M-2)}\sum_{i=1}^{M-2}[y_{i+2}-y_{i+1 }+y_{i}]^{2}, \tag{16}\]  
and, for phase data,  
\[H\sigma_{y}^{2}(\tau^{\prime})=\frac{1}{6\tau^{2}(N-3m)}\sum_{i=1}^{N-3}[x_{i+ 3}-3x_{i+2}+3x_{i+1}-x_{i}]^{2}. \tag{17}\]  
Clock offset data can be directly obtained in observations, and the stability of one time scale is always relative to another. The three-cornered hat method is commonly employed, providing stability relationships among three time scales [24]. If the stability of a reference time scale is significantly better than the measured time scale, the measured stability can be considered as that of the measured time scale. If their stabilities are equivalent, the three-cornered hat method equations can be solved to obtain the stability of each time scale.
## 3 Results and Discussion  
### Microstructural Characteristics  
Figure 4 presents the microstructures of the F, FR, and FRH in the axial and transverse sections. The white and dark regions indicate that the Al matrix and Si precipitations, respectively. A comparison of Figure 4a,d reveals that the materials manufactured through the CCDR process (F) exhibited microstructural differences in different sections. The dendritic structure was partially destroyed and the irregular Si organization aggregated  
Figure 3: **(a)** The erosion test equipment developed by our group, **(b)** the position of the erosion surface (working surface) and the erosion subsurface, **(c)** the cross-sectional view of the specimen carrier and the definition of impact angle, and **(d)** Al\({}_{2}\)O\({}_{3}\) ceramic particles for erosion wear experiments.  
in the transverse section after the cold rolling process in the FR specimen, as shown in Figure 4b,e. However, an inconspicuous change in microstructure of the axial section can be observed through OM. In contrast to the F specimen, FR showed a long strip of Si-rich zone transform into a thicker one caused by stress compression. The FRH specimen shown in Figure 4c,f. Si precipitation was crushed during cold rolling and redissolved into the Al matrix by the solid solution treatment, resulting in round Si particles and an oval-shaped Si-rich zone formation after the aging treatment. An observation by the Si particles through backscattered electronic (BSE) image in microstructure of the axial section is shown in Figure 4g-i.  
The size and distribution of the Si particle influences on the Al matrix play a significant role in the mechanical strength and ductility of Al-Si alloys [34]. Different shapes of Si particles in the microstructure can be changed by F, FR, and FRH. Refining the Si particle can substantially improve the material ductility to satisfy the requirements for industrial application [28].
# Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS
## 3 Methods
### fNIRS Signal Preprocessing  
fNIRS requires careful preprocessing to reduce noise caused by artifacts such as motion and ambient light, which otherwise limit its effectiveness. These artifacts typically manifest as low-frequency fluctuations that closely resemble genuine brain activation patterns, requiring more sophisticated artifact removal and preprocessing strategies to ensure clean signals. Traditional high-pass or Butterworth band-pass filters often fall short in eliminating such noise due to the overlapping frequency components between artifacts and physiological signals.  
To address this challenge, we developed a preprocessing strategy (Algorithm 1) that includes a K-means-based noisy channel identification step. Specifically, we first compute the standard deviation of the raw signal for each individual channel across time. This statistic serves as a robust indicator of overall signal variability: clean channels tend to exhibit moderate and consistent fluctuations, while noisy channels often display excessive or irregular variations.  
These per-channel standard deviation values are then fed into a K-means clustering algorithm with \(k=2\), which partitions the channels into two groups--representing low-variance (clean) and high-variance (noisy) channels. Channels assigned to the high-variance cluster are considered noisy and are excluded from subsequent processing steps.  
The remaining channels are then filtered using a Butterworth band-pass filter with low and high cutoff frequencies of 0.002 Hz and 0.1 Hz, respectively, to retain relevant hemodynamic components. Finally, the signals are normalized using z-score normalization. The output of this full denoising and preprocessing pipeline is illustrated in Figure 2.  
Figure 1: The MMAPA framework design of our model consists of two sections: feature extraction and a fusion module. Facial video representation vectors are extracted from videos using MARLIN Encoder [15]. At the end, all features were fused using an attention-based layer.  
Figure 2: As a result of noise filtering, the signal became clearer, and outliers were removed. Sampling of 50 Hz is used in the experiment.  
```
0:\(n\geq 0\)
0:\(y=x^{n}\)
1:for each channel in channelsdo
2:ifchannel is noisy then
3:\(signal\gets load\_signal()\)
4:\(X\gets diff(signal)\) {diff is equal to \(signal[i+1]-signal[i]\)}
5:\(sttd\gets standard\_deviation(X)\)
6:\(X[X>=sttd]=0\) {Set \(X[i]\) to 0 if \(X[i]\geq sttd\)}
7:endif
8:endfor
9:return\(y\)
```  
**Algorithm 1** Artifact Noise Filtering Algorithm
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 4 Results
#### Comparison with Other Machine Learning Models  
In this section, we present comparative experimental results to demonstrate the validity and effectiveness of the proposed MLP model over other ML alternatives. The random forest (RF) classifier was tuned with a depth of 3 and 100 estimators, keeping default values for other parameters. From the experimentation, we noticed that although increasing the maximum depth of the tree improved the model's accuracy, it was still below than the accuracy of other models. Then, we tried with a support vector machine (SVM) classifier with a hinge loss function and trained for 2000 iterations; here, we noted that an increase in the number of iterations did not lead to better accuracy. We also tested a K-nearest neighbors (KNN) model, where we found that k = 3 led to the best results, since larger values of k led to lower accuracies. The next model we tried was a gradient boosting (GB) classifier, which built an additive model that led to very good results. To achieve this, it was tuned with 100 estimators and a maximum depth of 3. The best accuracy was achieved with a learning rate of 0.0125, with higher learning rates leading to underfitting. Figures 13 and 15 show the comparison between the proposed model and the other machine learning alternatives all coded using the SkLearn library [37]. From this experimentation, we concluded the following:  
1. The MLP model had the best performance for the training, validation, and test sets, as shown in Figures 13 and 15.
2. Although the MLP achieved the best results, the GB model achieved an accuracy only 0.4% lower than that achieved by the MLP.
3. The MLP model required more time to complete the training stage than other simpler models; nonetheless, once trained, the time it took to make the estimation with the test data was very brief, approximately 0.02 s, as shown in Figure 14.
4. We demonstrated that the MLP model can be implemented with limited computational resources and with data gathered from a low-cost transceiver. Despite these disadvantages, the MLP model was capable of making accurate estimations. In addition, for industries in developing countries, the application of these technologies can help the transition into Industry 4.0.
## 4 Results
### Cross-Sensor Comparison of Water Quality Products
The matchup plots for Chl-a and CDOM retrievals are shown only for PRISMA vs. Sentinel-2 (Figures 11 and 12) for brevity whereas the average R and RMSD values are reported in Table 4. For TSM retrievals, the average R values are 0.85, 0.97, and 0.85 with average RMSD of 1.2, 0.6, and 1.2 g/m\({}^{3}\) for PRISMA vs. Sentinel-2, PRISMA vs. synthesized Sentinel-2, and synthesized Sentinel-2 vs. Sentinel-2, respectively. Moreover, the biases are close to unity. Note that CDOM retrievals from either PRISMA or Sentinel-2 images were not successful for June 2020 and the estimates converged to zero values for most of the pixels. This might be related to a very low (and therefore hardly detectable) concentration of CDOM. Thus, the results of CDOM retrievals for June 2020 are excluded from our analyses. The average value of the compensating parameter for sun-glint and atmospheric artifacts (\(g_{dd}\)) and the residuals of spectrum matching through the inversion (\(\Delta\)) are reported in Table 5. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**PRISMA vs. Sentinel-2**} & \multicolumn{3}{c}{**PRISMA vs. Synthesized**} & \multicolumn{3}{c}{**Synthesized Sentinel-2 vs.**} \\  & \multicolumn{3}{c}{**Sentinel-2**} & \multicolumn{3}{c}{**Sentinel-2**} \\ \hline  & **R** & **RMSD** & **MAD** & **Bias** & **R** & **RMSD** & **MAD** & **Bias** & **R** & **RMSD** & **MAD** & **Bias** \\ \hline TSM & 84.8 & 1.2 g/m\({}^{3}\) & 1.26 & 1.12 & 97.5 & 0.6 g/m\({}^{3}\) & 1.18 & 1.05 & 84.8 & 1.2 g/m\({}^{3}\) & 1.21 & 1.09 \\ \hline Chl-a & 64.5 & 1.2 mg/m\({}^{3}\) & 1.39 & 0.83 & 84.6 & 0.8 mg/m\({}^{3}\) & 1.31 & 0.81 & 67.3 & 1.3 mg/m\({}^{3}\) & 1.27 & 0.93 \\ \hline CDOM & 75.0 & 0.11 m\({}^{-1}\) & 1.32 & 1.24 & 85.9 & 0.05 m\({}^{-1}\) & 1.26 & 1.17 & 76.1 & 0.13 m\({}^{-1}\) & 1.19 & 1.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The agreement analyses among the retrieved constituents in terms of average R and RMSD. Figure 11: Agreement analyses among the Chl-a derived from PRISMA and Sentinel-2 images in terms of R and RMSD. Figure 12: Agreement analyses among the CDOM derived from PRISMA and Sentinel-2 images in terms of R and RMSD. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PRISMA** & **Synthesized Sentinel-2** & **Sentinel-2** \\ \hline \(g_{dd}\) (sr\({}^{-1}\)) & 0.26 & 0.25 & 0.04 \\ \hline \(\Delta\) (sr\({}^{-2}\)) & \(1.4\times 10^{-4}\) & \(2.4\times 10^{-4}\) & \(1.9\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average \(g_{dd}\) and \(\Delta\) derived through the inversion.
## 2 Related Works
### gNB Parameters Relevant in Low-Latency, High-Throughput Scenarios  
To evaluate and optimize both latency and throughput, we focused on the gNB configuration parameters that are most impactful in industrial 5G scenarios. Our analysis was guided by insights from the literature, as well as suggestions provided directly by the base station manufacturer through their baseline configuration sets.  
The channel bandwidth was selected as a key parameter, as doubling it nearly doubles throughput [33]. Subcarrier spacing and numerologies were also included; higher numerologies reduce latency due to a shorter symbol duration [14; 15; 37; 38]. The impact of the transmitter-to-base-station distance on latency, shown in [38], was approximated in our setup using programmable attenuation.  
Amarisoft documentation [39] provided several baseline configurations targeting either low latency or high throughput. Each configuration included a recommended set of tuning parameters selected by the manufacturer as the most influential. We therefore prioritized parameters such as the uplink/downlink symbol allocation, PRACH configuration, defined vs. automatic CSI-RS, Scheduling Request period [40], RX to TX latency, TRS, SSB, and the number of PDCCH candidates in the UE-specific search space (USS) [41], as these were directly derived from those vendor-provided baselines. The importance of these parameters is supported by recent studies. For example, reducing the SR Period directly minimizes uplink latency, which is crucial for URLLC applications [38]. Increasing the number of USS candidates enhances scheduling flexibility and control channel robustness. This supports higher throughput and reliability in dynamic 5G scenarios [42].  
This set of parameters served as the foundation for our experimental campaign, which was conducted in the LAB PL5G testbed environment [43]. We evaluated both the original baseline configurations and our own parameter modifications, which were motivated by the preceding literature review and the vendor's technical documentation, in order to assess their effectiveness and identify the most influential tuning options in practical deployment scenarios.
# Study on SPH Viscosity Term Formulations
## 4 Results I--Analysis of Model Accuracy through Benchmark Tests
### Accuracy under Uniform Distribution of the Particles  
To examine the second-order derivative of the polynomial function, i.e., \(\Delta f(x,y)=\
abla^{2}f=12(x^{2}+y^{2})\), Figure 2 gives the comparisons between the analytical and numerical results computed by the different SPH viscosity force models, at \(x=0.5\) m and \(y=(0,1)\) m, under uniform particle distribution with a particle size \(dx=0.05\) m. As shown in Figure 2, all the four viscosity methods led to a very good agreement with the analytical results when the particles are uniformly distributed.  
As a sensitivity test, Table 1 gives the mean errors defined by Equation (21) for the different particle numbers computed by the four different viscosity force models. Figure 3 further gives the convergence rate in the form of the total particle number \(N\) and the numerical error \(Err\), in which \(k\) refers to the convergence ratio. That is to say, \(k=1\) indicates the numerical model can achieve the first-order convergence, while \(k=2\) the second-order convergence. Both Table 1 and Figure 3 are provided based on the uniform particle distributions. According to Table 1, the accuracy of VISC-01 is the lowest, while VISC-03 and VISC-04 can achieve the highest accuracy, and VISC-02 lies somewhere in the between. This conclusion is further consolidated by Figure 3, which clearly shows the convergence rate of each viscosity force model. It is well indicated that VISC-01 and VISC-02 are around the first-order accuracy, while VISC-03 and VISC-04 can obtain the second-order.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Particle No.** & **VISC-01** & **VISC-02** & **VISC-03** & **VISC-04** \\ \hline
20 \(\times\) 20 & \(5.00\times 10^{-5}\) & \(1.68\times 10^{-5}\) & \(4.65\times 10^{-6}\) & \(4.65\times 10^{-6}\) \\
40 \(\times\) 40 & \(9.81\times 10^{-6}\) & \(4.24\times 10^{-6}\) & \(2.37\times 10^{-7}\) & \(2.37\times 10^{-7}\) \\
60 \(\times\) 60 & \(4.08\times 10^{-6}\) & \(1.84\times 10^{-6}\) & \(4.38\times 10^{-8}\) & \(4.38\times 10^{-8}\) \\
80 \(\times\) 80 & \(2.22\times 10^{-6}\) & \(1.02\times 10^{-6}\) & \(1.34\times 10^{-8}\) & \(1.34\times 10^{-8}\) \\
100 \(\times\) 100 & \(1.40\times 10^{-6}\) & \(6.45\times 10^{-6}\) & \(5.39\times 10^{-9}\) & \(5.39\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean errors with different particle numbers and viscosity calculation methods under the uniform particle distribution. VISC, Viscosity Force Model.  
Figure 2: Comparisons of analytical and numerical results computed by using different viscosity force models with the uniform particle distribution: (**a**) VISC-01; (**b**) VISC-02; (**c**) VISC-03; and (**d**) VISC-04. SPH, Smoothed Particle Hydrodynamics; VISC, Viscosity Force Model.
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
###### Abstract  
In today's digital world, no one and nothing is safe from potential cyberattacks. There is also no 100% protection from such attacks. Therefore, it is advisable to carry out various studies related to the effects of the different cyberattacks on the performance of the specific devices under attack. In this work, a study was carried out to determine how individual TCP DoS attacks affect the parameters of VoIP (Voice over IP) voice and video streams. For the purpose of this work, a model of a simple IP network has been created using the GNS3 IP network-modeling platform. The VoIP platform used was Asterisk Free PBX. Tools from Kali Linux were used to implement the individual TCP DoS attacks; IP-network-monitoring tools and round-trip-delay-measurement tools were also used. The proposed study is applicable to multiple VoIP platforms wherein voice and video traffic are passed/processed by the VoIP server. From the obtained results, it was found that Asterisk Free PBX is very well secured against TCP DoS attacks, which do not affect the platform performance or the parameters of the voice and video streams. The values of the observed parameters, such as jitter, packet loss, round-trip delay, etc., are very far from the maximum allowable values. We also observed a low load on the CPU and RAM of the system during the whole study.  
Asterisk Free PBX; DoS attacks; GNS3; jitter; modeling of IP networks; network monitoring; packet loss; VoIP; voice streams; video streams Article
###### Abstract  
This paper presents a comparative study focused on a modal parameters estimation of specimens manufactured by the FDM technique using a fixed embedded vibrometer based on the laser Doppler principle and roving hammer-impact method. Part of this paper is devoted to testing a fixed circular plate with a honeycomb infill pattern while varying the number of excitation points (DOFs), the number of analysis lines of fast Fourier transformation (FFT), and the locations or numbers of reference degrees of freedom (REFs). Although these parameters did not significantly affect the values found for the natural frequencies of the structure, there were changes in the estimates of the mode shapes (affected by the low number of DOFs), in the height and sharpness of the peaks of the CMIF functions (caused by the increased number of FFT lines), and in the number of identified modes (influenced by the chosen location(s) of REFs), respectively. Subsequently, the authors compared the results of experimental modal analyses carried out under the same conditions on three circular plates with honeycomb, star, and concentric infill patterns made of PLA. The results confirm that specimens with honeycomb or star infill patterns have a higher stiffness than those with concentric infill patterns. The low values of the damping ratios obtained for each structure indicate a strong response to excitation at or near their natural frequencies.  
experimental modal analysis; modal parameters; laser doppler vibrometry; FDM; PLA; infill pattern; circular plate
# A Two-Echelon Electric Vehicle Routing Problem with Time Windows and Battery Swapping Stations
## 4 Algorithms for the 2E-EVRPTW-BSS
### Solution Decoding  
The main task for decoding is to determine the full solution including two echelons. Considering the latest arrival time at each satellite, the first echelon routes can be constructed based on those of the second echelon in this paper. The demand delivered by the EVs from each satellite and the latest arriving time of the ICVs at each satellite can also be derived according to all the second-echelon routes departing from and returning to it. Based on the simplified least-cost insertion heuristic, the generation of the first-echelon routes can be described as follows [25].  
Step 1. According to the second-echelon routes, determine the latest arrival time of ICVs (\(t_{s}\)) and the required demand for each satellite;  
Step 2. Back-and-forth routes are created for each satellite whose demand is larger than capacity \(Q_{1}\) (delivery split). That is, if the demand of satellite \(s\) is larger than \(Q_{1}\), construct a back-and-forth route (0, \(i\), 0), and this process will continue until the remaining demand of the satellite is smaller than \(Q_{1}\);  
Step 3. After Step 2, all satellites visited by the second-echelon routes are sorted in ascending order of the latest arriving time \(t_{s}\);  
Step 4. Select an ICV, insert sequentially the visited satellites to generate its route until the arrival time of the ICV is later than \(t_{s}\) of the last inserted satellite or the load of the ICV exceeds \(Q_{1}\). Then, select the next ICV to repeat the inserting process until all the satellites are visited. The first-echelon routes are thus constructed, and a complete solution is derived (as indicated in Figure 2).
# PDDQN-HHVBF Routing Protocol Based on Empirical Priority DDQN to Improve HHVBF
## 4 PDDQN-HHVBF Protocol
### Selection of the Optimal Relay Node
The larger \(r_{sen}\), the more the residual energy of the node, which means that the node is used less frequently. In addition, subsequent use of nodes with large \(r_{sen}\) also does not lead to energy exhaustion and end of lifetime of M-UWSNs. In addition, it is specified that the virtual pipeline radius \(R\) is fixed, the size of \(\alpha\) is determined by \(d\) and \(\theta_{j}\), and the value of \(\alpha\) is inversely proportional to \(d*sin\theta_{j}\). When \(d*sin\theta_{j}\) is smaller, \(\alpha\) is larger. The shorter the transmission distance between nodes, the smaller the collision probability. When the transmission distance \(d\) is constant, the smaller the \(\theta_{j}\), the closer the candidate relay node is to the Sink node, and the higher the probability of successful data forwarding. \(Num_{can}\) is the number of candidate relay nodes. The larger \(Num_{can}\) is the more relay nodes can be selected, and the less possibility of routing holes due to ocean current movement. Therefore, the strategy of the PDDQN-HHVBF protocol proposed in this paper is to find candidate relay nodes with large \(\alpha\), large \(r_{sen}\), and large \(NUM_{can}\) for routing and forwarding. Therefore, the reward function [32] is calculated by the above \(\alpha\), \(r_{sen}\), \(NUM_{can}\), where the reward function \(R(s_{i},a_{i})\) is generated by each state and action pair of the candidate relay node. The return value is set as Formula (17):  
\[R(s_{i},a_{i})=c\alpha+br_{sen}+aNum_{can} \tag{17}\]  
where \(a\), \(b\), \(c\) are the weight parameters of \(\alpha\), \(r_{sen}\), \(Num_{can}\), respectively. The neural network trains convergence parameters through training iterations. Since the return value of other nodes is between 0 and \(c\), and the \(r_{sen}\) of the Sink node is always set to be 1, the original setting of this value is that when the node can directly transmit data to the Sink node. In this way, nodes will be preferentially transmitted to the Sink node, so as to avoid data looping. Therefore, the node avoids selecting nodes with lower residual energy while reducing the number of detours. More residual energy \(r_{sen}\), higher \(\alpha\), and more \(Num_{can}\) is the strategy when looking for relay nodes.