esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Scheme
### Message Signing and Verification Phase
#### 4.5.2 Message Verification  
Single Message Verification  
Upon receiving the message signature, the receiving vehicle must check the message and signature authenticity and integrity before accepting it to prevent malicious vehicles from impersonating authentic vehicles and from transmitting false messages. Therefore, each receiver must verify the message signature \(\sigma_{i}\) of the signed message by utilizing this verification process, as follows:  
* Upon receiving the message-signature tuple \(\left\{AID_{i},R_{i},M_{i},T_{i},\sigma_{i}\right\}\), the verifier initially verifies the timestamp of the message. The receiver can accept the message if it is fresh; otherwise, it rejects the tuple.
* The verifier checks whether Equation (5) holds with \(\sigma_{i}.P\). \[\sigma_{i}.P=H_{2}(AID_{i}||R_{i}||M_{i}||T_{i})P_{pub}^{d}+H_{3}(AID_{i}||R_ {i}||T_{i})\] (5) The following step proves Equation (5). \[L.H.S=\sigma_{i}.P\] \[=\big{(}s_{d}.\alpha_{i}+\frac{1}{p}\beta_{i}\big{)}.P\] \[=\big{(}s_{d}.H_{2}(AID_{i}||R_{i}||M_{i}||T_{i})+\frac{1}{P}H_{ 3}(AID_{i}||R_{i}||T_{i})\big{)}.P\] \[=H_{2}(AID_{i}||R_{i}||M_{i}||T_{i})s_{d}.P+H_{3}(AID_{i}||R_{i}|| T_{i})\frac{1}{P}.P\] \[=H_{2}(AID_{i}||R_{i}||M_{i}||T_{i})P_{pub}^{d}+H_{3}(AID_{i}||R_ {i}||T_{i}\] \[=R.H.S.\] Hence, it is verified that Equation (5) is true.  
Batch Message Verification  
Upon receiving a large number of message-signature tuples \(\{AID_{i}^{1},R_{i}^{1},M_{i}^{1},T_{i}^{1},\sigma_{i}^{1}\}\), \(\{AID_{i}^{2},R_{i}^{2},M_{i}^{2},T_{i}^{2},\sigma_{i}^{2}\}\),...,\(\{AID_{i}^{n},R_{i}^{n},M_{i}^{n},T_{i}^{n},\sigma_{i}^{n}\}\) from other vehicles, the verifier can simultaneously verify \(n\) messages. The verifier checks whether Equation (6) holds given \(\sum\limits_{i=1}^{n}(a_{i}.\sigma_{i}).P\).  
\[\sum\limits_{i=1}^{n}\big{(}a_{i}.\sigma_{v_{i}}\big{)}P=\sum\limits_{i=1}^{n} \big{(}a_{i}.H_{2}(AID_{i}||R_{i}||M_{i}||T_{i})\big{)}P_{pub}^{d}+\sum\limits_ {i=1}^{n}\big{(}a_{i}.H_{3}(AID_{i}||R_{i}||T_{i})\big{)} \tag{6}\]
## 4 Discussion  
The results reported in the previous section show that the proposed method has a good level of reliability. In particular, the determination of the maximum value of the electric field is the crucial physical quantity in an emission tests because standards fix limits for it.  
The capability of reconstruction of the emissions in the entirety of the space assures that the maximum amplitude of the radiated emission is correctly detected.  
With respect to the previous formulation of the method [17], there is a reduction of the number of equivalent sources for two reasons. The first one is that they are placed only in the surface surrounding the EUT, while previously they were placed in the entirety of the volume. The second reason is that, due to the application of the equivalence principle, only the tangential electric and magnetic currents are considered. In this way, in each point, only four equivalent sources are considered instead of six.  
The computational time is therefore strongly reduced; the simulations presented in this paper stand for about 16 h using a standard workstation (equipped with two CPU Intel Xeon E5640 2.66 GHz, 24 GB of RAM). Applying the precedent version of the method, the computational time was 24 h, using the same workstation.  
It must be remarked that the computation of the matrix \(Z_{ni}\) is done only once for the considered equivalence surface. This is the most burdensome operation, whereas the rest of the code needs only a few minutes to run. In this direction, the equivalent surface might be extended to the surface of the entire working volume of the RC. This would allow consideration of any EUT, regardless of its geometry; in this way the \(Z_{ni}\) computation would represent a type of chamber calibration for the application of the described method.  
Finally, using parallel computing, it is possible to calculate \(Z_{ni}\) for many frequencies simultaneously.
## 3 Multi-Source Subdomain Adaptation Model
### Network Optimization  
The calculation formula for classification \(loss_{class}\) is as follows.  
\[loss_{class}=\sum\limits_{j=1}^{N}E_{x\sim X^{s}}J(C_{j}(H_{j}(F(x_{i}^{s_{j} }))),y_{i}^{s_{j}}) \tag{10}\]  
Figure 4: Domain adaptation.  
In Formula (10), \(loss_{class}\) represents the classification loss of \(N\) source domains, \(C_{j}\) is the classifier trained in the source domain of the \(j\)th group, \(J\) is a cross-entropy function, \(H_{j}\) is a specific domain feature extraction network, and \(F\) is a shared feature extraction network.  
In the multi-source classifier, the classifier learned by each set of data may have differences in the prediction of target domain samples. Therefore, a loss function that minimizes the differences between different classifiers has been proposed [31], as shown in Formula (11).  
\[loss_{disc}=\frac{2}{N\times(N-1)}\sum_{i=1}^{N-1}\sum_{j=i+1}^{N}E_{x\sim X ^{t}}\big{|}C_{j}(H_{j}(F(x^{t})))-C_{i}(H_{i}(F(x^{t})))\big{|} \tag{11}\]  
There are three optimization objectives in the process of network training:  
1. Minimize the classification loss function, \(loss_{class}\), of the source domain dataset;
2. Minimize the difference loss, \(loss_{disc}\), between different classifiers;
3. Minimize the domain invariant, \(loss_{immd}\), of the source and target domain datasets. Then, the expression of \(loss\) is as follows:  
\[loss=loss_{class}+\lambda(loss_{disc}+loss_{immd}) \tag{12}\]  
where \(\lambda\) is the equilibrium hyperparameter.
# Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
## 2 Methodology
### Data Processing: Time-Distribution of THD Excursions  
While our dataset has no information about whether events (e.g., voltage drops, rapid voltage changes, interruptions, or earth faults) occur, we can nevertheless try to understand how the largest excursions (outliers) of harmonic power behaves. In other words, we wish to determine how the largest values of harmonic power are distributed in time. Do they occur regularly? Do they cluster together? Does their distribution depend on the time-scale?  
We characterize the time-distribution of excursions by determining the fractal dimension \(\mathcal{D}\) of a downsampled and binarized THD signal. For each minute, we determine whether any of the samples therein have a value exceeding the 99 percentile of the (four-year) THD distribution. If it does, the minute is tagged as containing an outlier (and vice versa). We then calculate \(\mathcal{D}\) by box-counting (and slope-fitting) the binarized time-series [69]. This method essentially asks how many boxes \(N(s)\) of a given size \(s\) (the time-scale) are required to completely cover the binary signal. The fractal dimension is the slope \(\mathcal{D}\) of the power-law \(N\propto s^{-\mathcal{D}}\). We compute \(\mathcal{D}\) from least-squares regression of \(\log_{10}(N(\log_{10}s))\). For a given time-scale \(s\), \(\mathcal{D}=1\) indicates that excursions are uniformly distributed. Conversely, \(\mathcal{D}<1\) indicates temporal clustering of excursions. We consider a range \(s_{\text{min}}\leq s\leq s_{\text{max}}\) with \(s_{\text{min}}=300\) s (five times the time-resolution of our binary signal) and \(s_{\text{max}}=292\) days (a fifth of the four year measurement period).
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 6 Experiment and Results
### Experimental Results and Analysis
#### 6.3.2 Visualization of Attention Positions  
In a detailed analysis of the impact of the global and local attention mechanisms on signal detection, we visualize the attention features of the TATR model during the inference stage. Specifically, we display the feature maps of the last layer of the ResTA backbone and the weighted summation of the last self-attention weights in the encoder and decoder, leveraging position encoding, on the spectrogram. The results are shown in Figure 11. At higher SNRs, the TATR network effectively concentrates attention on the positions where TT&C signals appear, focusing on both the peak positions and the signal envelope within the bandwidth. When the SNR is lower, such as at \(-15\) dB, signal features are almost overwhelmed. TATR manages to capture useful contextual information around the signal spectrum to learn relevant knowledge for detection. By visualizing the abstract-level feature representations learned by the network on the spectrogram, we robustly demonstrate that the proposed TATR, leveraging the global attention capability of triplet attention and the contextual local attention of Transformer, achieves high-precision detection of TT&C signals, enhancing the interpretability of the network.  
Figure 10: Performance of various indicators for TATR at different SNRs: (**a**) [EMAIL_ADDRESS], (**b**) [EMAIL_ADDRESS]:0.95, (**c**) [EMAIL_ADDRESS], (**d**) AR. TR represents the TATR model without the triplet attention (TA) module.
###### Abstract
The sensor layout is optimized to accommodate various driving postures, ensuring accurate data collection. The EVI system assigns a unique identifier to each vehicle, linking it to the physiological data of different drivers. By combining the driver physiological data with the vehicle's operational environment data, a comprehensive multi-source data fusion system is established for a driving state evaluation. Secondly, a deep learning model is employed to analyze physiological signals, specifically combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN extracts spatial features from the input signals, while the LSTM processes time-series data to capture the temporal characteristics. This combined model effectively identifies and analyzes the driver's physiological state, enabling timely anomaly detection. The method was validated through real-vehicle tests involving multiple drivers, where extensive physiological and driving behavior data were collected. Experimental results show that the proposed method significantly enhances the accuracy and real-time performance of physiological state monitoring. These findings highlight the effectiveness of combining EVI with multimodal biometric recognition, offering a reliable means for assessing driver states in intelligent driving systems. Furthermore, the results emphasize the importance of personalizing adjustments based on individual driver differences for more effective monitoring.
###### Abstract  
As the core component for fracturing plug anchoring, dissolvable ball seat (DBS) slip performance directly determines the success of fracturing operations. However, frequent failures, such as tooth structural fractures, casing damage, and the slip breaking off entirely, compromise DBS reliability during high-pressure fracturing. This study investigates DBS slip anchoring performance through finite element analysis (FEA), anchoring performance tests, and structural optimization. We established a comprehensive safety and performance assessment framework incorporating strength criteria, peak contact pressure, and anchoring uniformity. Comparative stress analysis of nail-type versus block-type slip systems revealed superior performance in block-type configurations, demonstrating more uniform slip-casing interfacial stress distribution. To further enhance the anchoring performance of the block-type slip, a structural parameter analysis was conducted to identify critical factors influencing anchoring capability, with tooth apex angle and inclination angle determined as key parameters. The influence laws of these parameters on anchoring performance were systematically investigated. Subsequently, a multi-parameter optimization methodology was employed to optimize the structural configuration of the block-type slip. The optimization results revealed that an optimal slip tooth apex angle of 80\({}^{\circ}\) or 85\({}^{\circ}\) and an inclination angle of 70\({}^{\circ}\) enhance the safety and anchoring reliability of the dissolvable ball seat slip while providing a theoretical framework for future slip structure design improvements. At present, the new structure of the soluble ball seat structure proposed in this paper has been successfully applied in some oil fields. Field tests show that the anchoring efficiency has been significantly improved. This research not only provides a theoretical framework for the design of sliding structures, but also offers reliable technical support for the efficient development of deep oil and gas resources.  
dissolvable ball seat; slip; safety and performance assessment; anchoring performance test; multi-parameter optimization +
Footnote †: journal: Applied Sciences  
Article
# Compensating Data Shortages in Manufacturing with Monotonicity Knowledge
## 4 Results and Discussion
### Informed Machine Learning Models for Laser Glass Bending
After these calculations on a 1D subset, the full 2D dataset of the considered laser glass bending process with its 25 data points was used. The results are shown in Figure 4. Again, part (a) of the figure displays an unconstrained Gaussian process regressor for comparison. The RBF kernel contained one length scale parameter per input dimension, and sklearn correctly adjusted these hyperparameters using L-BFGS-B. I.e., the employed length scales maximize the log-likelihood function of the model. Nevertheless, the model is unsatisfactory because it exhibits a bump in the rear right corner of the plot, contradicting the monotonicity knowledge. Figure 4b shows the 2D monotonic projection of the GPR with the monotonicity requirements (16) with respect to \(T_{\text{f}}\) and \(n_{\text{c}}\). It was calculated according to the Appendix A on a rectangular grid \(G\) consisting of 40\({}^{2}\) points (40 values per input dimension). The resulting model looks generally reasonable, and in particular, satisfies the monotonicity specifications, but it exhibits kinks and plateaus. The most conspicuous kink starts at about \(T_{\text{f}}=546\,^{\circ}\)C, \(n_{\text{c}}=50\) and proceeds towards the front right. The rearrangement method by [23] was not used for comparison here because for small datasets in \(d>1\), it does not guarantee monotonicity. \begin{table}
\begin{tabular}{l c} \hline \hline
**Monotonic Regression Type** & **RMSE** [\({}^{\circ}\)] \\ \hline projection [24] & 1.3822 \\ rearrangement [22] & 1.8432 \\ SIAMOR & 1.1598 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Root-mean-squared deviations (RMSE) of the monotonic regression models from the training data for laser glass bending (1D). Figure 4c displays the corresponding response surface of a polynomial model of the form (1) with degree \(m=7\) trained with SIAMOR. For \(m=7\) there are \(N_{m}=36\) model parameters. The discretization \(X^{0}\) was initialized with a rectangular grid using five equidistant values per dimension. The algorithm converged in iteration 11 with 69 final constraints. The resulting model is smoother than the one in Figure 4b, and it predicts the training data more accurately. Indeed, the corresponding RMSE values are \(1.2518^{\circ}\) for projection and \(0.6607^{\circ}\) for SIAMOR.
# Experimental Study of a Piezoelectric De-Icing System Implemented to Rotorcraft Blades
## 4 Results
### Rotating Blade  
For the rotating blade experiments, tests were performed at the conditions described in Section 2.3 for the angles of attack of 0\({}^{\circ}\) and 6\({}^{\circ}\), respectively. The small difference in angle of attack resulted in a very similar ice accumulation at the leading edge (Figure 11). The only difference was observed underneath the profile, where a very thin layer of brittle ice was accumulated.  
Delamination was obtained for only two modes during static (without blade rotation) tests. In rotation, frequency sweeps at low sweep rates (below 70 Hz/s) up to 4000 Hz were first performed to assess if and at what frequencies a completed de-icing of the ice layer could occur. Due to voltage offsetting, the voltage was limited to 500 Vpp for all actuator phasing tests, except with all actuators excited in phase. The voltage phasing configurations tested in rotation were the same as for the static (without blade rotation) tests (see Section 4.2). For the phasing configurations where the voltage was limited to 500 Vpp, no de-icing could be obtained. The very low rotation speed and small radius of the setup did not generate sufficient force to help the system de-ice the profile. When all piezoelectric actuators were activated in phase, the voltage could be increased up to 750 Vpp, from \(-250\) V to 500 V, by offsetting the voltage to 125 V. At this higher voltage, de-icing was obtained at the frequency corresponding to mode 5. De-icing tests were repeated for this configuration (sweeping frequency of mode 5, all actuators in phase) four times at a 6\({}^{\circ}\) angle of attack (AOA). At 0\({}^{\circ}\), during the third test, a connection to the actuator broke, and tests had to be stopped. Table 5 presents the voltage at de-icing in rotation. De-icing results were similar at the 0\({}^{\circ}\) and 6\({}^{\circ}\) angles of attack (AOA), which was expected due to the similarities in ice accumulation. Complete de-icing of the leading edge was obtained for all tests performed. In most tests, complete delamination and ice shedding were obtained after two consecutive frequency sweeps around the resonant mode. During the first sweep, de-icing of the first and last thirds of the blade's leading edge occurred, and de-icing of the center of the blade occurred during the second sweep (Figure 12). In one of the tests, de-icing of the first two-thirds of the blade was obtained during the first sweep (Figure 13a), and de-icing of the rest of the blade occurred during the second sweep (Figure 13b). The results were similar at 0\({}^{\circ}\) and 6\({}^{\circ}\). However, due to the angle of attack, a slightly longer ridge of ice was obtained under the profile at 6\({}^{\circ}\).  
Figure 12: (a,b) Complete de-icing after a test at 6\({}^{\circ}\) AOA.  
Figure 13: (a) Partial de-icing after first sweep and (b) complete de-icing after second sweep.
## 4 Results and Discussions
### Bit Error Rate  
A very important metric that allows us to evaluate the performance of the complete UM-VLC system is the BER. By using this metric we can find the number of bits that have been altered by the effects of the mining environment, including the effect of scattering by dust particles. This evaluation is carried out using Monte Carlo simulations and integrating the features of the UM tunnel and the UM-VLC channel model. the LED transmission is based on the PHY-I mode together with the On-Off Keying (OOK) modulation, which are described in the IEEE 802.1.5.7 standard. On the other hand, the PD reception employs asynchronous symbol-by-symbol detection [25; 26].  
Figure 8 shows the BER curves obtained for different values of \(N\) applied to the hemispherical distribution of dust particles in the UM-VLC scenario and the UM-VLC reference scenario [12]. It is important to emphasize that the curve obtained from the UM-VLC reference scenario is the one that presents the best performance, due to the fact that the implemented scattering model is basic and unrealistic (2D disk-shaped distribution model) [12]. However, if we compare the curves determined with the UM-VLC model using the hemispherical distribution of dust particles, we can clearly see in Figure 8 that the BER curve with the best performance is the one that implements a value of \(N=40\). This result was quite expected by all the factors analyzed in the previous subsections. On the other hand, for values of \(N=150\) and \(N=200\), the system presents the worst performance in terms of BER. This result allows to reinforces the criterion of the dependence of the UM-VLC system performance in terms of the number of dust particles that exist in the proposed hemispherical distribution model. Finally, it was verified that the performance of the UM-VLC system not only depends on the scattering phenomenon, but also on the other physical variables proper to the general UM-VLC channel model.  
Figure 7: Empirical CDF and distribution of the RMS Delay spread in the UM-VLC scenario with (**a**) 40, (**b**) 60, (**c**) 100, (**d**) 150, (**e**) 200 dust particles in the hemispheric area, and (**f**) UM-VLC reference scenario, Reprinted from ref. [12].
# Time-Varying Communication Channel High Altitude Platform Station Link Budget and Channel Modeling
## 3 HAPS Link Budget and Channel Modeling
### Holistic Applicability Analysis  
#### 3.3.1 Analysis in the Femtocell  
In the process of practical application, clustering has also proved to be an effective method to reduce interference in the femtocell. This method is widely used in ad hoc wireless networks. However, if this method is applied to a femtocell network, it is first necessary to know the number of clusters. The shared registered spectrum band is then divided into \(m\) sub-bands, each of which uses a sub-band. Since there are different numbers of femtocells in each cluster, this means different rate requirements. Therefore, the bandwidth allocation of each sub-band will directly affect the performance of the femtocell network. If the number of clusters is large, the bandwidth allocated by each cluster is relatively small. This will not meet the minimum requirements of femtocells. However, when the bandwidth allocated by each cluster is relatively large, the interference between the femtocells cannotbe effectively reduced. Therefore, a new method is needed to determine the number of clusters and the bandwidth allocated for each cluster. This will be the next step in this paper.