esa-sceva/satcom-chunk-collection huggingface dataset:
# Adhesion Evaluation of Asphalt-Aggregate Interface Using Surface Free Energy Method
## 4 Adhesion of Asphalt-Aggregate Interface  
### Adhesion Models of the Asphalt-Aggregate Interface  
Adhesion models of the asphalt-aggregate interface in different conditions are established respectively based on surface free energy theory [27, 28, 29]. The adhesion model of asphalt-aggregate without water (dry condition) is established, and is as shown in Equation (2). \[W_{as}=2(\sqrt{\gamma_{a}^{LW}\gamma_{s}^{LW}}+\sqrt{\gamma_{a}^{+}\gamma_{s} ^{-}}+\sqrt{\gamma_{a}^{-}\gamma_{s}^{+}}) \tag{2}\]  
where \(W_{as}\) denotes the adhesion of the asphalt-aggregate interface, respectively. The adhesion model of the organic additive-asphalt-aggregate system without water (dry condition) is also established as follows:  
\[W_{as}=2(\gamma_{\epsilon}^{LW}+2\sqrt{\gamma_{\epsilon}^{+}\gamma_{\epsilon} ^{-}})-2\sqrt{\gamma_{a}^{LW}\gamma_{\epsilon}^{LW}}+\sqrt{\gamma_{a}^{+} \gamma_{\epsilon}^{-}}+\sqrt{\gamma_{a}^{-}\gamma_{\epsilon}^{+}}-2(\sqrt{ \gamma_{s}^{LW}\gamma_{\epsilon}^{LW}}+\sqrt{\gamma_{s}^{+}\gamma_{\epsilon }^{-}}+\sqrt{\gamma_{s}^{-}\gamma_{\epsilon}^{+}}) \tag{3}\]  
where \(W_{as}\) denotes the adhesion of organic additive-asphalt-aggregate without water; the rest of the parameters are the same as above. The adhesion model of asphalt-aggregate with water (wet condition) can be written as follows:  
\[ \begin{array}{rl}W_{asw}=&-(2\sqrt{\gamma_{a}^{LW}\gamma_{LW}^{LW}}+2\sqrt{ \gamma_{s}^{LW}\gamma_{LW}^{LW}}+2\sqrt{\gamma_{w}^{+}}(\sqrt{\gamma_{a}^{-} }+\sqrt{\gamma_{s}^{-}})\\ &\quad+2\sqrt{\gamma_{-}^{-}}(\sqrt{\gamma_{a}^{+}}+\sqrt{\gamma_{s}^{+}})-2 \gamma_{w}^{LW}-2\sqrt{\gamma_{a}^{LW}\gamma_{s}^{LW}}-4\sqrt{\gamma_{w}^{+} \gamma_{w}^{-}}\\ &\quad-2\sqrt{\gamma_{a}^{+}\gamma_{s}^{-}}-2\sqrt{\gamma_{a}^{-}\gamma_{s}^{ +}})\end{array}  \tag{4}\]  
where \(W_{asw}\) denotes the adhesion energy of asphalt-aggregate with water; the rest of the parameters are the same as above. The adhesion model of organic additive-asphalt-aggregate with water (wet condition) is built and is expressed as: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Items** & \(\gamma_{a}\) & \(\gamma_{a}\)\({}^{\text{LW}}\) & \(\gamma_{a}\)\({}^{\text{AB}}\) & \(\gamma_{a}\)\({}^{\text{+}}\) & \(\gamma_{a}\)\({}^{\text{-}}\) \\ \hline SK-70 & 25.570 & 25.541 & 0.028 & 0.000 & 4.416 \\ SK-90 & 21.512 & 21.082 & 0.430 & 0.016 & 2.915 \\ Sasobit & 40.210 & 38.370 & 1.840 & 0.129 & 6.543 \\ RH & 26.94 & 21.63 & 5.32 & 1.63 & 4.34 \\ Sasobit + SK-70 & 17.353 & 14.709 & 2.643 & 2.023 & 0.863 \\ Sasobit + SK-90 & 24.150 & 24.144 & 0.006 & 0.419 & 0.000 \\ RH + SK-70 & 16.75 & 15.10 & 1.65 & 0.21 & 3.22 \\ RH + SK-90 & 13.17 & 9.91 & 3.26 & 1.60 & 1.65 \\ Limestone & 48.351 & 46.427 & 1.924 & 0.108 & 8.603 \\ Basalt & 53.140 & 52.016 & 1.123 & 2.313 & 0.136 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Surface free energy components of raw materials (mJ/m\({}^{2}\)). \[
## 4 Numerical Model and Materials
### Model Validation and Convergence Study  
In this section, to validate the effectiveness and reliability of the refined numerical model of the anchor joint, a comparison study is conducted on the mechanical behavior of the anchor joint during the assembly and tension deformation. Specifically, the results obtained from the model shown in Figure 8 are calibrated against those recorded in the tests conducted by [PERSON] et al. [38]. In their tests, a single anchor joint was used, and the reinforced concrete segment was simulated by the metal bases. During the tests, the anchor joint was first assembled. After that, the tensile load was imposed on the assembled anchor joint to investigate its tensile performance. For the verification analysis, the anchor joint is first in a separate state as shown in Figure 9a. Then, the anchor joint will be assembled with a thrust action, illustrated in Figure 9b, which are totally the same with those in tests. In the process mentioned above, a displacement-force curve can be drawn. Similarly, another relationship between the displacement and force can be gained during the tension deformation.  
Meanwhile, a convergence study is also necessary to determine the reasonable mesh size according to the corresponding accuracy of the models with different mesh results, so that a good balance can be reached between the computational cost and precision. Looking at this target, three models with small mesh size, medium mesh size, and large mesh size are established for comparison. The experimental and numerical results during the assembly and tension deformation of the anchor joint are shown in Figure 10.  
Figure 9: Boundary and loading conditions. (**a**) Step 1. (**b**) Step 2.  
During the convergence study, the small mesh size has a basic dimension of 1 mm for the rot and sleeve, while that for the lantern ring is 3 mm. The medium mesh size has a basic dimension of 3 mm for the rot and sleeve, while that for the lantern ring is 5 mm. For the large mesh size, the basic dimensions of the rot, sleeve, and lantern ring are 5 mm, 5 mm, and 8 mm. As the concrete segment is not the main component in the model, its mesh size remains unchanged during the convergence study. From Figure 10, it can be found that the numerical results agree well with the experiment results in both the assembly and tension deformation process, whichever mesh size is adopted. The comparison between experimental and numerical solutions provides evidence that the numerical model has the satisfactory reliability. Thus, all of the three examined mesh sizes can be used for further study of the mechanical behavior of the anchor joint. Since using the medium mesh size shows the best computational efficiency according to the calculation process, the medium mesh size is finally employed in the subsequent analysis.  
Figure 10: Comparison between experimental and numerical solutions. (**a**) Displacement-force curve during assembly process. (**b**) Joint opening-force curve during tension deformation process.
## 6 Bit Error Rate in Single-Bit Sampling Transceivers
As we defined the carrier sequence \(C_{\text{seq}}\) to be in-phase with the incoming data sequence, a bit error occurs if a symbol of '0' is detected, so that the bit error rate is given by:  
\[\text{BER}=\left(\sum_{s=1}^{3}P_{\text{out}}\left(s\right)\right)+\frac{1}{2} \cdot P_{\text{out}}\left(4\right)\,, \tag{40}\]  
In summary, the bit error rate of a single-bit sampling receiver can numerically be determined by computing the probability density function at the sampling points for a given set of noise parameters, which allows for deriving conditional probabilities from (35), sequence probabilities from (36), output sum probabilities from (39), and finally the bit error rate from (40).
## 2 Time-Frequency Representations for Vibration Signals
### Derivatives of the Time-Frequency Representation  
In addition to the bi-dimensional representation, dynamical features are also calculated. Such features are the first and second derivatives of the 2D MFCC array. The bi-dimensional representation of the MFCC is denoted as \(c_{m,k}\). However, the derivative calculation is performed column-wise. The derivative FIR filter is denoted \(\Theta\) with an odd length \(N=2p+1\). The first derivative for a column of the MFCC is denoted as \(\Delta_{m,k}\) is calculated as:  
\[\Delta_{m,k}=\sum_{r=-p}^{r=p}c_{m-r,k}\widehat{\Theta}_{r} \tag{9}\]  
where \(\widehat{\Theta}=\Theta/\|\Theta\|\) is the normalized filter. Calculation of Equation (9) is performed for all columns of the MFCC array (\(0\leq k<K\)), to obtain 2D derivative representation \(\Delta_{m,k}\).  
Similarly, the second derivative denoted \(\Delta\Delta_{m,k}\) is calculated from \(\Delta_{m,k}\) using the same procedure described by Equation (9) using a derivative FIR filter \(\Phi\). This type of derivative filter is Finite Impulse Response (FIR), and the choice of the length of the filter is a trade-off between the accuracy of the derivative approximation and noise amplification. A discussion concerning FIR derivative filters' design and length selection is presented in [49]. The selection of the FIR derivative filters will be presented in Section 5.
## 4 Experimental Results and Analysis
### Experimental Analysis
In sum, the proposed DDMF method performs well in terms of all the evaluation indexes in the SARS, Genepath, and mouse networks and comes in second place in the yeast network. Specifically, the proportion of the important nodes in the yeast network is relatively large. Therefore, some errors may occur in calculating the distance distribution of important nodes. In general, the proposed DDMF method achieves excellent performance in real and complex network datasets, especially in terms of the evaluation of precision and AUC. It is obviously better than several comparative algorithms. At the same time, the selected datasets come from different fields. The results indicate that the DDMF method is characterized by high precision and wide applicability in mining relatively important nodes in networks. Figure 2: Precision rate results in four networks: (**a**) SARS network; (**b**) Genepath network; (**c**) Mouse network; (**d**) Yeast network.
# Forging Prawn and Salmon Flavours with Non-Animal-Based Ingredients
## 3 Results and Discussion
### Odour Qualities of Prototypes  
The odour quality of the final prawn (P + DHA + PFM) and salmon (S + DHA + SFM) prototypes was evaluated through olfactory assessment, with detailed results provided in Supplementary Materials S2. Samples in their TS and FTS states, representing typical shelf and pre-consumption statuses, were selected for the assessment. For the prawn prototypes (Figure 4A), a significantly (\(p<0.05\)) stronger grilled note was perceived after frying, aligning with the observed increase in N- and S-containing compounds associated with grilled aromas in the volatile analysis. Interestingly, a significant decrease in the strength of marine odour was also noted after frying. Since the formulation did not include typical marine odour-contributing compounds (e.g., bromophenols) apart from algal DHA, this decrease might be attributed to the odour-masking effect of the grilled notes. For the salmon prototypes (Figure 4B), no statistically significant differences in the assessed odour attributes were observed, which could be explained by the relatively similar volatile profiles of S + DHA + SFM before and after frying (Figure 1). Overall, for both prawn and salmon prototypes, the changes in odour quality before and after frying were minimal. This aligns with previous findings indicating that the volatile profile changes in commercial PB seafood alternatives before and after cooking are minor compared to authentic seafood [17].  
When comparing the two final formulations after frying (Figure 4C), most odour attributes showed similar perceived intensities. However, the prawn prototype (P + DHA + PFM) exhibited significantly (\(p<0.05\)) stronger cooked crustacean and grilled notes. This suggests that fortifying the formulation with FAAs and betaine can effectively enhance crustacean-like flavour attributes in PB products. On the other hand, a significantly stronger cooked salmon odour was not perceived in the DHA-fortified formulation (S + DHA + SFM). This finding may imply that the characteristic salmon odour profile involves a more complex combination of odourants, beyond those derived solely from algal n-3 PUFAs.  
Figure 4: Comparison between odour perception of thermal stablised (TS, green) and fried thermal stablised (FTS, red) P + DHA + PFM prototypes (**A**), TS (green) and FTS (red) S + DHA + SFM prototypes (**B**), and FTS P + DHA + PFM (blue) and FTS S + DHA + SFM prototypes (yellow) (**C**). Significant (\(p<0.05\)) differences in perception were tested using the Mann–Whitney test and are marked with “_raw_”.
# Reverse Circulation Drilling Method Based on a Supersonic Nozzle for Dust Control
## 2 Description of the Suction Process and Nozzle Structure  
The reverse-circulation drilling method aims at dust control, which utilizes the suction and entrainment performance of a supersonic nozzle with no need for an additional precipitator. As shown in Figure 1, compressed air flows into the supersonic nozzle and the flushing nozzle, which are located on the drill bit body towards the center passage and the borehole bottom, respectively. The flushing nozzle is designed to cool the working face and suspend rock debris. The motive air from the supersonic nozzle induces ambient air upward, and converts the pressure energy to kinetic energy, resulting in a decrease of pressure in the center passage. The difference of pressure between the center passage and the annulus space causes the suction of a lot of air into the center passage. By this process, the cuttings and dust are carried into the center passage by the air and collected by a dust bag at passage outlet.
# Assessing the Performance of the Multi-Beam Echo-Sounder Bathymetric Uncertainty Prediction Model
## 4 Results and Discussion
### Trends Visible in the Measured Standard Deviation  
Before comparing the modeled and measured uncertainties, the latter is presented in Figure 3 for measurements with varying pulse lengths (shown with different colors) and a water depth of (a) 10 m and (b) 30 m to obtain an insight into the expected effect of different parameters.  
1. As the depth increases, generally, the vertical uncertainty increases (compare Figure 3a and Figure 3b), except for the longest pulse length.
2. An increase in the pulse length leads to an increase in the depth uncertainty due to the deterioration of the range resolution and the increase in the contribution of the baseline decorrelation; see [21].
3. In general, lengthening the pulse results in using the amplitude detection for a larger range of beam angles around nadir. As an example, compare the transition point of around \(\pm 15\,\mathrm{\SIUnitSymbolOhm}\) for a water depth of 30 m and a pulse length of 134 us to that of around \(\pm 4\,\mathrm{\SIUnitSymbolOhm}\) for the same water depth and a pulse length of 54 us. The reason for this change in the transition point for varying pulse length is the changing nature of the phenomenon referred to as the baseline decorrelation with the pulse length [21]. For beam angles close to nadir and long pulse lengths, the entire main beam is ensonified at any one instant implying that the scattered return is received from a wide range of angular directions corresponding at least to the width of the main beam. Thus, the estimate of the phase difference zero-crossing (in the interferometry step) becomes uncertain. Therefore, the amplitude detection is used for the bottom detection. Another contributing factor to the quality of the zero-crossing estimate is the Signal-to-Noise Ratio (SNR) where longer pulse lengths has higher values of the SNR due to the increased energy of the transmitted signal. Based on the results presented, it could be seen that the increased SNR for longer pulse lengths did not compensate for the increased uncertainty due to the baseline decorrelation (blue markers are always above the others).  
Figure 2: Mean value of the vertical uncertainty for different patch sizes in the across-track direction (squares with varying colors) for a water depth of 10 m (gray line) and 30 m (black line) and a pulse length of 54 μs.
## 1 Introduction  
\"As GPS further penetrates into the civil infrastructure, it becomes a tempting target\".  
\"Information on the capabilities, limitations, and operational procedures [of spoofers] would help identify vulnerable areas and detection strategies\" [1].  
As the demand for precision positioning grows around the world and spreads across various industries, initiatives are being taken to increasingly protect GNSS receivers from intruders of all kinds, from unintentional industrial interference to advanced GNSS spoofing systems. The timing and cost of these forthcoming satellite navigation safety efforts are difficult to decipher due to the large number of new signals and constellations being deployed. However, it is safe to say that the newly designed anti-jamming and anti-spoofing GNSS systems open up new opportunities for innovative technologies.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 5 Numerical Results
### Tolerated Delay and Overdimensioning  
Figure 9 gives a comparison of the theoretical and empirical values of \(N_{t}\) for a typical cell that guarantee an average delay of 1 ms.  
From the figure, it can be seen that the \(N_{t}\) derived by our statistical tool is always larger than the empirical \(N_{t}\), i.e., it is overdimensioning. The excess in the radio-resource provided by the theoretical model ensures that the expected data rate is achieved and the access to the cellular IoT network does not exceed the predefined average access delay.  
In Figure 10, it shows the relationship between the total number of required RRs and the maximal access delay \(\tau_{max}\) with \(\lambda_{a}=5.5\) nodes/km\({}^{2}\) and \(\lambda_{b}=0.5\) nodes/km\({}^{2}\). Assuming that \(\tau_{max}\) ranges from 0.5 to 4 ms, a larger \(\tau_{max}\) means that the node can wait longer for access to the network. The average access delay \(\tau\) of the active nodes must not exceed the maximal delay \(\tau\leq\tau_{max}\). In general, a lower total number of RRs is required to response to the demands of active sensor nodes with a larger \(\tau_{max}\).
# Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control
###### Abstract  
New upper limb prostheses controllers are continuously being proposed in the literature. However, most of the prostheses commonly used in the real world are based on very old basic controllers. One reason to explain this reluctance to change is the lack of robustness. Traditional controllers have been validated by many users and years, so the introduction of a new controller paradigm requires a lot of strong evidence of a robust behavior. In this work, we approach the robustness against doming/doffing and arm position for recently proposed linear filter adaptive controllers based on myoelectric signals. The adaptive approach allows to introduce some feedback in a natural way in real time in the human-machine collaboration, so it is not so sensitive to input signals changes due to doming/doffing and arm movements. The average completion rate and path efficiency obtained for eight able-bodied subjects doming/doffing five times in four days is 95.83% and 84.19%, respectively, and for four participants using different arm positions is 93.84% and 88.77%, with no statistically significant difference in the results obtained for the different conditions. All these characteristics make the adaptive linear regression a potential candidate for future real world prostheses controllers.  
Keywords:linear filtering; prostheses control; biomedical engineering; rehabilitation; myoelectric signals; adaptive filters +
Footnote †: journal: Applied Sciences
# SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks
## 4 Evaluation
### Parameter Analysis
#### 4.6.2 Statistical Features  
In the model design section, we introduce the details of the 56-dimensional feature vectors. We compare this design with V-Fuzz, which uses all of the instructions. According to the Software Developer's Manual [15] released by Intel in March 2020, we set a total of 280-dimensional vectors for the latter. Table 7 (_Statistical Size_) and Figure 10b show the recall and the corresponding training time comparison generated by the two cases. We find that, although the recall obtained for the latter is slightly higher, the time costs are greatly increased. We consider spending 50% more time for less than 1% improvement to be illogical, so we use the 56-dimensional solution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Semantic Content**} & \multirow{2}{*}{**Word Embedding Size**} & \multicolumn{2}{c}{**Recall**} & \multirow{2}{*}{**Time (s)**} \\  & & & **DEV** & & **TEST** \\ \hline RoBERTa-s & \(\langle s\rangle\) & 64 & 0.7655 & 0.7585 & 16.145 \\ RoBERTa-o & opcode & 64 & 0.7515 & 0.751 & 16.12 \\ RoBERTa-32 & \(\langle s\rangle\) + opcode & 32 & 0.763 & 0.7575 & 13.41 \\
**RoBERTa-64(SAViP)** & \(\langle s\rangle\) + opcode & **64** & **0.782** & **0.7785** & **18.04** \\ RoBERTa-128 & \(\langle s\rangle\) + opcode & 128 & 0.791 & 0.7845 & 22.685 \\ RoBERTa-256 & \(\langle s\rangle\) + opcode & 256 & 0.813 & 0.8065 & 36.415 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Language model experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Value**} & \multicolumn{2}{c}{**Recall**} & \multirow{2}{*}{**Time (s)**} \\  & & **DEV** & & **TEST** \\ \hline \multirow{2}{*}{**Statistical Size**} & **56** & **0.782** & **0.7785** & **18.04** \\  & 280 & 0.789 & 0.7845 & 27.535 \\ \hline \multirow{4}{*}{**Depth**} & 0 & 0.743 & 0.7405 & 13.66 \\  & 1 & 0.766 & 0.7665 & 16.865 \\  & **2** & **0.782** & **0.7785** & **18.04** \\  & 3 & 0.769 & 0.767 & 19.89 \\  & 4 & 0.753 & 0.752 & 21.475 \\  & 5 & 0.75 & 0.742 & 22.3 \\ \hline \multirow{4}{*}{**Iteration**} & 1 & 0.7425 & 0.74 & 14.895 \\  & 2 & 0.7665 & 0.7615 & 16.45 \\ \cline{1-1}  & **3** & **0.782** & **0.7785** & **18.04** \\ \cline{1-1}  & 4 & 0.7725 & 0.775 & 19.305 \\ \cline{1-1}  & 5 & 0.778 & 0.7775 & 21.03 \\ \hline \multirow{2}{*}{**Self-Loop**} & **True** & **0.782** & **0.7785** & **18.04** \\ \cline{1-1}  & False & 0.744 & 0.7415 & 14.355 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameter experiments.
## 3 Analysis of Accuracy and Performance
### Analysis of VLF Amplitude and Phase Characteristics
#### 3.2.4 The Influence of Electron Density Variation on Amplitude and Phase  
The electron density was amplified by 20 times and 10 times, respectively, and compared with the standard parameters to study the influence on the amplitude and phase of the electromagnetic wave when the electron density was amplified. The amplitude and phase-variation curves are shown in Figure 11a,b. Figure 11a shows the variation curves of the Er amplitude value with the vertical distance when the vertical dipole source is placed on the ground under different electron densities, and Figure 11b gives the variation curves of the phase of Er of the vertical dipole source placed on the ground under different electron densities with the vertical height distance. For the electron density, it gradually increases with the increase in height, which will cause the phase of the electromagnetic wave to be abnormal at the same time; that is to say, the greater the electron density, the greater the impact. When it reaches a certain extent, it will cause the phase of the electromagnetic wave to be inconsistent. However, the simulation found that in the low ionosphere region, with the increase in the electron density, the amplitude value of the electric-field intensity basically does not change. This may be due to the small thickness of the ionosphere used in the simulation. Currently, the IRI international reference ionosphere is being studied to verify the correctness of this phenomenon.  
The change in phase is influenced by both electron density and collision frequency. At lower altitudes, the values of electron density and collision frequency are insufficient to cause a change in the phase of electromagnetic waves. When reaching around 60 km, they begin to affect the VLF electromagnetic waves.  
Figure 10: (**a**) The amplitude variation curves of sinusoidal electromagnetic waves of different frequencies within 1–500 km; (**b**) the phase-variation curves of sinusoidal electromagnetic waves of different frequencies within 1–90 km.