esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Results on CelebAMask-HQ
#### 4.3.2 Visualization of Learned Features  
To further investigate how our proposed modules contribute to semantic segmentation, we visualize the class-specific attention maps produced by CCR-FE and SimFA for a representative input image. As shown in Figure 6, we display the heatmaps corresponding to seven key facial components: background, skin, right eyebrow, left eye, nose, mouth, and hair.  
The top row illustrates the attention responses of the CCR-FE module. It can be observed that CCR-FE effectively captures long-range spatial dependencies and highlights semantically consistent regions, particularly on symmetric structures such as eyebrows and eyes. For example, both eyes and brows exhibit strong bilateral activation, demonstrating the module's ability to model cross-axis spatial relationships, which are essential for precise facial parsing.  
The bottom row shows the heatmaps generated by the SimFA module during the upsampling phase. These visualizations highlight the strengths of boundary-aware refinement and detail enhancement of SimFA. Specifically, SimFA attends more precisely to fine-grained structures such as the mouth, eyes, and nose contours while also suppressing background distractions. This illustrates the ability of SimFA to adaptively modulate features based on both global context and local texture information.  
Figure 5: Visualization examples of the proposed FP-Deeplab model on the CelebAMask-HQ test set.  
Figure 6: Visualizations of class-specific activation maps from CCR-FE (**top**) and SimFA (**bottom**) on representative facial regions. The input image is shown on the left. Each column corresponds to a target semantic class.  
Overall, the visualizations offer intuitive evidence that both CCR-FE and SimFA contribute distinct yet complementary roles: CCR-FE enhances multi-scale contextual encoding during decoding, while SimFA focuses on fine-structure recovery and semantic consistency during upsampling. These properties jointly enable FP-Deeplab to produce high-resolution segmentation with sharp and accurate facial boundaries.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 3 Results
### The Coupled Tank Control Results  
#### 3.2.1 The Coupled Tank Model Linearization  
Table 6 shows the adopted parameters of the CT system.  
If we assume that \(q_{i2}=0\), in relaton (13), then the CT linearized SISO model can be derived as shown in Figure 17.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Value** & **Unit** \\ \hline \(H_{\rm 1s}\) & 2.5 & m \\ \hline \(H_{\rm 2s}\) & 1.5 & m \\ \hline \(a_{1}\) & \(58.088\times 10^{-4}\) & m\({}^{2}\) \\ \hline \(a_{2}\) & \(63.6173\times 10^{-4}\) & m\({}^{2}\) \\ \hline \(a_{3}\) & \(72.3823\times 10^{-4}\) & m\({}^{2}\) \\ \hline \(A_{1}\) & 2.0106 & m\({}^{2}\) \\ \hline \(A_{2}\) & 1.7908 & m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Coupled tank system parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{Ziegler–Nichols Method} \\ \hline \(K_{P}\) & \(K_{I}\) & \(K_{D}\) \\ \hline \(2.135\) & - & - \\ \(1.9215\) & \(0.1537\) & - \\ \(2.5620\) & \(0.3416\) & \(4.8038\) \\ \hline \multicolumn{2}{c}{Takahashi tuning method} \\ \hline \(K_{P}\) & \(K_{I}\) & \(K_{D}\) \\ \hline \(2.135\) & - & - \\ \(1.845\) & \(0.1537\) & - \\ \(2.3912\) & \(0.3416\) & \(4.8038\) \\ \hline \multicolumn{2}{c}{Auto-tuning method} \\ \hline \(K_{P}\) & \(K_{I}\) & \(K_{D}\) \\ \(1.554\) & \(0.147\) & \(1.2445\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: PID controller parameter selection for the ST system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Controller** & \multicolumn{2}{c}{**Rise Time**} & **Overshoot** & **Setting Time** & **Steady-State Error** \\ \hline \multirow{2}{*}{Ziegler–Nichols method} & P & 4.7 s & 45.58\% & 38 s & 0.31 \\ \cline{2-5}  & PI & 5.65 s & 23.2\% & 30.2 s & 0 \\ \hline \multirow{3}{*}{Takahashi method} & P & 4.8 s & 45.76\% & 42 s & 0.32 \\ \cline{2-5}  & PI & 9.2 s & 23\% & 38 s & 0 \\ \cline{2-5}  & PID & 3.2 s & 61.8\% & 46 s & 0 \\ \hline Auto-tuning method & PID & 4.87 s & 14.3\% & 26.7 s & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The parameters of the transient response with PID controllers.  
The transfer function of the CT model can be described by the following equation:  
\[G(s)=\frac{h_{2}(s)}{q_{i1}(s)}=\frac{K}{(T_{l}s+1)(T_{2}s+1)}e^{-\tau s}. \tag{33}\]  
Using the data presented in Table 6, relation (33), and the model in Figure 17, the linearized transfer function of the CT system is written as as follows:  
\[G(s)=\frac{h_{2}(s)}{q_{i1}(s)}=\frac{0.0039}{(44.84s+1)(196.08s+1)}e^{-s}. \tag{34}\]  
The linearized CT model response is shown in Figure 18.
## 5 Connected Vehicles
### Benefits and Business Opportunities
#### 5.2.8 Infotainment  
CVs will completely disrupt the current situation where in-vehicle entertainment is limited to FM and Bluetooth. The spread of high-speed networks has dramatically increased the speed of information transmission, making it possible for high-bandwidth streaming services to take over the in-vehicle entertainment market, meaning that passengers will have more mainstream digital service options, such as audio-visual interaction [103]. Today, Apple and Google are competing to create a general software platform that adapts to in-vehicle hardware in order to capture the underlying core of that market. In addition, Android Auto allows drivers to access favorite apps, purchase rented media services, and synchronize Google Play music.
## 2 Materials and Methods
### Selection of Machine Learning (ML) Methods for Grain Yield Predictions
**Model 12 (ridge regression)**: Beneficial when dealing with multicollinear features in multi-temporal data, ridge regression applied L2 regularization to stabilize the model and reduce the impact of multicollinearity. **Model 13 (Support Vector Regression)**[60]: Chosen for its capability to capture non-linear relationships between multi-temporal features and crop yields, SVR mapped data into a higher-dimensional space, enabling the identification of non-linear patterns. **Model 14 (XGBoost regression)**[61]: Selected for its effectiveness in various regression tasks, XGBoost regression constructed an ensemble of decision trees using gradient boosting and regularization techniques, offering high predictive accuracy. These ML algorithms were amalgamated to form a comprehensive toolkit capable of analyzing multi-temporal satellite imagery data and accurately predicting crop yields. Through their collective usage, they effectively addressed various challenges encountered in the analysis process, including outlier handling, feature selection, non-linearity, and temporal dependencies.
## Appendix A Scattering Process of Acoustic Laser Mode
### Scattering Process of the Acoustic CPA Mode  
As shown in Figure 1b, for the acoustic CPA mode, the outgoing waves can be neglected, i.e., the outgoing waves are zero. The acoustic field distribution of the corresponding area is expressed as,  
\[\left\{\begin{array}{l l}p_{1}=Ae^{-jk_{x}x}e^{-jk_{y}y}&(x<0)\\ p_{2}=\left(Be^{-jk_{x}^{\prime}x}+Ce^{jk_{x}^{\prime}x}\right)e^{-jk_{y}y}&(0< x<d)\,.\\ p_{3}=De^{jk_{x}(x-d)}e^{-jk_{y}y}&(x>d)\\ \end{array}\right. \tag{14}\]  
According to the relationship between velocity and acoustic pressure \(v=-\frac{1}{\rho}\int\
abla pdt\), the following equations can be obtained,  
\[\left\{\begin{array}{l l}\overset{-1}{v}_{1}=[\frac{k_{x}}{\omega\rho_{0}} \overset{-}{v}_{x}+\frac{k_{y}}{\omega\rho_{0}}\overset{-}{v}_{y}]Ae^{-jk_{x} x}e^{-jk_{y}y}&(x<0)\\ \overset{-}{v}_{2}=[\frac{k_{x}^{\prime}}{\omega\rho_{0}}\overset{-}{v}_{x}+ \frac{k_{y}}{\omega\rho_{0}}\overset{-}{v}_{y}]Be^{-jk_{x}^{\prime}x}e^{-jk_{ y}y}+[\frac{-k_{x}^{\prime}}{\omega\rho_{1}}\overset{-}{v}_{x}+\frac{k_{y}}{ \omega\rho_{1}}\overset{-}{v}_{y}]Ce^{jk_{x}^{\prime}x}e^{-jk_{y}y}&(0<x<d)\,. \\ \overset{-}{v}_{3}=[\frac{-k_{x}}{\omega\rho_{0}}\overset{-}{v}_{x}+\frac{k_{ y}}{\omega\rho_{0}}\overset{-}{v}_{y}]De^{jk_{x}(x-d)}e^{-jk_{y}y}&(x>d)\\ \end{array}\right. \tag{15}\]  
By matching the boundary conditions at \(x=0,\ d\), we get  
\[\left\{\begin{array}{l}A=B+C\\ A\frac{k_{x}}{\rho_{0}}=B\frac{k_{x}^{\prime}}{\rho^{\prime}}-C\frac{k_{x}^{ \prime}}{\rho^{\prime}}\\ Be^{-jk_{x}^{\prime}d}+Ce^{jk_{x}^{\prime}d}=D\\ \frac{k_{x}^{\prime}}{\rho^{\prime}}Be^{-jk_{x}^{\prime}d}+\frac{-k_{x}^{ \prime}}{\rho^{\prime}}Ce^{jk_{x}^{\prime}d}=\frac{-k_{x}}{\rho_{0}}D\\ \end{array}\right.. \tag{16}\]  
Let \(\eta=\frac{k_{x}}{\rho_{0}}/\frac{k_{x}^{\prime}}{\rho^{\prime}}=\frac{\rho^ {\prime}k_{x}}{\rho_{0}k_{x}^{\prime}}\), the dispersion relation for the CPA mode can be solved as  
\[\left(\frac{1-\eta}{1+\eta}\right)^{2}=\frac{e^{-jk_{x}^{\prime}d}}{e^{jk_{x} ^{\prime}d}}. \tag{17}\]
# Beat the Heat: Syscall Attack Detection via Thermal Side Channel
## 4 Discussion  
### Anomaly Detection  
In this paper, we propose _BeatTheHeat_, a technique that performs anomaly detection through the thermal sensors of the CPU, aiming to distinguish between an authentic Linux command and an augmented one, while a lot of noise appears on the CPU.  
In comparison with the initial study, we can see a similar difference in the average temperature of the commands, between 0.5 and 1 degrees Celsius. However, the correlation between the patterns of the original and augmented command is higher in this study. In the case of _ls_ command, in this paper, we have a higher correlation of 0.85, instead of 0.73 as in the Hot-n-Cold paper [2], while for the _chmod_ command, the correlation was 0.96 instead of 0.83. These results are better due to the increased number of traces for the thermal analysis used in this paper. Hence, a good correlation (above 0.80) leads to the conclusion that the commands are very similar in their pattern, in both their forms (i.e., original and augmented), which makes it possible to detect an attack taking in consideration their temperature values. An abnormal (i.e., higher) temperature for a specific command leads to the conclusion that it has additional code inserted.  
As we already stated in the results section, more accurate results were achieved using the _D-150_ system. We attribute these results to the year of manufacture of the processor and the larger number of cores. Furthermore, we can see a difference between _D-151_ and _D-152_, with better results for the first one, the year of production being again a reason for better results, in our opinion.  
This technique is directed towards any system that uses an Intel CPU; however, it can also be extended to any other device, including IoT devices that have and use thermal sensors, from which the temperature can be retrieved.
## 5 Conclusions  
In the paper, we present a novel algorithm of extracting a specific mode component for non-contact inspection of pipeline defects. The proposed VSME is based on the fact that a wide-band signal can be converted into a narrow-band signal by demodulation method. Then, the problem of wide-band signal decomposition is expressed as an optimal demodulation problem, which can be solved by an improved ADMM. This algorithm does not need a parametric model, which makes it more suitable for practical problems. To verify the performance of VSME, we used this algorithm to analyze a complex simulation signal. Simulation results showed that VSME is more effective than VMD and EWT in extracting a specific mode signal, even in the case of modes overlap severely. Finally, we applied this method to the actual pipeline defect detection, and compared the detection results at different lift-off distances, which showed that it has a better anti-noise performance. Further work will be focused on improving the robustness of VSME in separating severely crossed component signals and on the applications of pipeline detection in more complex environment.  
**Author Contributions:** Methodology, validation, and writing--original draft preparation, H.J.; writing--review and editing, and supervision, X.W.; validation, investigation, and data curation, Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China, grant number 2017 YC0805005-1, and the Science and Technology Program of Beijing Municipal Education Commission, grant number KZ201810005009.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 12: Defect detection results of pipeline at different lift-off distances in laboratory. (**a**) Lift-off distance is 0.5 m; (**b**) Lift-off distance is 1.0 m; (**c**) Lift-off distance is 1.5 m; (**d**) Lift-off distance is 2.0 m. Six blue boxes from left to right are the defect locations on pipeline, which are axial crack, transverse crack, square groove, through hole, blind hole, and 45-degree crack in turn.
## 2 Materials and Methods
### EMG Data Processing  
The raw sEMG data were collected using Delsys EMGworks Acquisition software (version 4.7.3.0), which features a common mode rejection ratio exceeding 80 dB at 60 Hz and an amplification factor of 1000, with a bandpass filter applied. The data were then analyzed using the Delsys EMGworks Analysis software. Density analysis was performed on 100 s task movements. For the measurement of maximal isometric contraction of key pinch, the middle 3 s from 3 s after the start of the exercise was selected for analysis. For muscle power comparison, the middle 80 s of the 100 s scissors opening/closing task was analyzed.  
EMG power spectra were acquired using a fast Fourier transform. To determine the variation in the MF during the activity, a PSD curve was obtained [29] using Equation (1), and then the MF was calculated using Equation (2).  
\[\text{PSD }(f)=|\text{X}(f)|^{2}/\text{N} \tag{1}\]  
\[\text{CPS }(f_{\text{med}})=0.5\times\text{CPS }(f_{\text{max}}) \tag{2}\]  
Signals were amplified using a Delsys main unit amplifier, and EMG power spectra were continuously recorded by a personal computer.  
The muscle output data were expressed as root mean square (RMS) values of the EMG signals, calculated using a 1 s window. The EMG amplitude signal was digitally processed into RMS values with a 1 s window prior to analysis. The peak RMS amplitude during the MVC was determined using the EMG power spectrum. To assess the maximum RMS amplitude during the MVC, a 1 s window was utilized, moving in 100 ms increments across the MVC. For the scissors opening and closing task, the RMS values were calculated and normalized by the peak RMS recorded for each muscle's MVC, representing the relative percentage of MVE. The entire period was segmented into events and analyzed in 1 s epochs.
## 3 Formal Definition and Security Model  
### Formal Definition  
We assume that there exist \(n\) distinct signers, named \(ID_{1}\), \(ID_{2}\),..., \(ID_{n}\), to authenticate a message \(m\) by cooperatively generating a multi-signature \(m\sigma\). The signer \(ID_{i}\) is denoted as \(signer_{i}\).  
**Theorem 5**.: _A typical IBMS scheme is always made up of six algorithms, that is, \(\boldsymbol{Setup}\), \(\boldsymbol{Extra}\), \(\boldsymbol{Sign}\), \(\boldsymbol{Verify}\), \(\boldsymbol{MSign}\), and \(\boldsymbol{MVerify}\). We describe each of them as follows._  
* _Setup__\((mpk,msk)\leftarrow\boldsymbol{Setup}\) (\(1^{k}\)). The algorithm is controlled by the_ key generator center _(_KGC_). The_ **KGC** _generates the system's master public keys_ \(mpk\) _and master secret keys_ \(msk\) _when it is given the security parameter_ \(k\)_._
* _Extra__\(sk_{ID}\leftarrow\boldsymbol{Extra}\) (\(mpk,msk,ID\)). The algorithm is also controlled by the_ **KGC**_, given_ \(msk\)_,_ \(mpk\) _and a user's identity_ \(ID\)_, such as a string. It returns the private key_ \(sk_{ID}\) _through secure channels._
* _Sign__\(\sigma\leftarrow\boldsymbol{Sign}\) (\(mpk,sk,m,ID\)): The signer uses its private key_ \(sk\)_, the identity_ \(ID\)_, and the message to be signed_ \(m\) _to generate a signature_ \(\sigma\) _on_ \(m\)_._
* _Verify__\(\{0,1\}\leftarrow\boldsymbol{Verify}\) (\(mpk,ID,m,\sigma\)): The algorithm takes the signer's identity_ \(ID\)_, the data_ \(m\)_, and a candidate signature_ \(\sigma\)_. If_ \(\sigma\) _is a valid signature, it returns_ \(1\)_. Otherwise, it returns_ \(0\)_._
* _MSign__\(m\sigma\leftarrow\boldsymbol{MSign}\) (\(mpk,sk,m,IDSet\)). The signer with the private_ \(sk\) _joins in the multi-signing algorithm, which needs additional parameters, including a message_ \(m\) _and an identity set_ \(IDSet=\{ID_{1},ID_{2},...,ID_{n}\}\) _containing all the identities of the signers. After several rounds of interactive communication,_ **MSign** _generates a multi-signature_ \(m\sigma\)_._
* _MVerify__\(\{0,1\}\leftarrow\boldsymbol{MVerify}\) (\(mpk,IDSet,m,m\sigma\)). The algorithm returns_ \(1\) _if_ \(m\sigma\) _is a valid multi-signature on the message_ \(m\) _by authenticating the signers in_ \(IDSet\)_._  
_Correctness._ When all of the participating signers honestly and correctly execute the algorithm \(\mathbf{MSign}\) using the private keys, derived from the algorithm \(\mathbf{Extra}\), each of the signers will end the algorithm by obtaining a local multi-signature \(m\sigma\) such that  
\[\mathbf{MVerify}(IDSet,m,m\sigma,mpk)=1\]  
where all \(mpk\) and \(msk\) are generated by the algorithm \(\mathbf{Setup}\) and \(IDSet\) includes \(n\) identities \(ID_{1},ID_{2},...,ID_{n}\) for any messages \(m\in\{0,1\}^{*}\).
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
###### Abstract  
Regarding access point (AP) overload and performance anomaly which is caused by mobile terminals with different bitrates, a joint AP association and bandwidth allocation optimization algorithm is presented in this paper. Meanwhile, load balancing and proportional fairness are analyzed and formulated as an optimization model. Then, we present a Fair Bandwidth Allocation algorithm based on clients' Business Priority (FBA-BP), which allocates bandwidth based on the bandwidth demand of clients and their business priority. Furthermore, we propose a Categorized AP Association algorithm based on clients' demands (CAA-BD), which classifies APs by different types of clients and chooses an optimal associating AP for a new client according to AP categories and the aggregated demand transmission time that are calculated by the FBA-BP algorithm. The CAA-BD can achieve load balance and solve the performance anomaly caused by multi-rate clients coexisting. The simulation results show that our proposed algorithm obtains significant performance in terms of AP utilization, throughput, transmission delay and channel fairness in different client density levels compared with the categorized and Strong Signal First (SSF) algorithms.  
AP association; bandwidth allocation; load balancing; WLANs +
Footnote †: journal: Future Internet
# Interactions between Two Deformable Droplets in Tandem Fixed in a Gas Flow Field of a Gas Well
## 3 Results and Discussion
### Interactions between Two Droplets in Tandem
#### 3.3.2 For Separating Spaces between Droplets of 2D, 3D, and 4D  
In order to analyze the influence of the separating space on the interaction between the droplets, droplets separated by spaces of 2D, 3D, and 4D were simulated under gas velocities of 0.68, 1.36, 1.78, and 2.36 m/s, with corresponding Weber numbers of 1, 4, 6.8, and 12.1, respectively. Figure 7 shows the changes of the drag coefficient over time with different separating spaces between droplets. From the simulation results, we know that the drag coefficient of the downstream droplet increased as the space between the droplets increased, and the final drag coefficient increased up to zero. The difference in the drag coefficient between the downstream and the isolated droplets decreased with the increase in the space between the droplets. When the droplet spacing increased to 4D, the difference was reduced and could be ignored, indicating that the sheltering effect from the upstream droplet had gradually vanished.  
[PERSON] and [PERSON] (1990) [17] and [PERSON] and [PERSON] (1993) [18] examined the hydrodynamics of droplets arranged in tandem with Reynolds numbers smaller than 200. They found that the drag coefficients decreased with the Reynolds numbers and the droplet spacing. Separating Spaces of 6D was found to be a critical value to consider or not to consider the effect of the upstream droplet on the drag coefficient of the upstream droplet. When the droplet spacing increased to 6D, the drag coefficient of the downstream droplet was not affected by the upstream droplet anymore. However, in present study, such separating spaces was found to be 4D, which is smaller 6D.The discrepancy between the two values could be attributed to the effect of the Reynolds numbers. The Reynolds numbers in the present study were much larger than in previous studies, and the length of the wakes from the upstream droplet was gradually shortened, especially in the case of Re = 2063 and We = 12.1, where the gas stream around the droplet developed into a turbulent flow.
# Travel-Time Inversion Method of Converted Shear Waves Using RayInvr Algorithm
## 4 Crustal S-Wave Velocity Inversion
### Initial Models and the Iteration Termination Conditions  
The initial S-wave velocity model is obtained from dividing the P-wave model by the set crustal Vp/Vs ratios. The five initial S-wave velocity models originated from starting Vp/Vs ratios of 1.71, 1.73, 1.75, 1.78, and 1.81 are shown in Figure 9. The Vp/Vs ratios varying from 1.71 to 1.81 represent the gradual change from felsic to mafic [27], which is reasonable for the geological features of the study area. Output Vp/Vs resulting from the division of P- and S-wave velocities are shown in Figure 9,f,i,l,o. The best theoretical calculation model is achieved using a starting Vp/Vs ratio of 1.81, and the RMS is 187 ms. However, low Vp/Vs is abnormally (the blue area in Figure 9) observed at the edge and 150 km of the model--such a model is unreasonable. The inversion is terminated after two iterations when the starting Vp/Vs is 1.73. Comprehensive analysis is done of the RMS and output Vp/Vs, and the best inversion model (Figure 9) is produced from the starting Vp/Vs of 1.75 (Figure 9). The final inversion model has a relatively good fit with the RMS of 193 ms, \(\chi 2\) of 1.718, and there is no local anomaly.  
Generally, the optimum model is characterised by the RMS and \(\chi 2\) tended to 0 and 1, respectively. Therefore, the RMS and \(\chi 2\) should be taken into consideration if inversion is to be terminated. In addition to having a good travel-time fit, the final model should also trace as many rays as possible. If most of the rays are not traced, then this model should not be adopted. It is very important to establish the necessary conditions to terminate the inversion iteration, because this process requires manual interaction. After several iterations, the \(\chi 2\) has a value of \(<\)2 and the variation range is within 0.2, so the \(\chi 2\) is not taken into account here. We employed the number of tracing phases (NTP) and the RMS as the criteria for termination of inversion iterations (Figure 10). The NTP and RMS gradually decreased, as the adjustment of the model (sometimes the NTP will increase), and the variation range of the NTP is mainly within 30. After three to six iterations, the NTP sharply decreased with no obvious variation in the RMS (red dots in Figure 10); this means that the inversion can be terminated at this time.