esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
The results are summarized in Table 6. Even if the error from the EXPLO5 fitting is smaller than the one obtained by the proposed method, one should note that JWL coefficients coming from EXPLO5 do not fulfill C] constraints (42). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **NLLS (This Work)** & **ZMWNI** \\ \hline \(A\) (GPa) & 819.0663 & 989.0848 \\ \hline \(B\) (GPa) & 5.5055 & 11.1190 \\ \hline \(C\) (GPa) & 1.5229 & 1.5142 \\ \hline \(R_{1}\) & 4.7769 & 5.1669 \\ \hline \(R_{2}\) & 0.8738 & 1.0458 \\ \hline \(\omega\) & 0.3979 & 0.3979 \\ \hline \(E_{0}\) (J/m\({}^{3}\)) & \(-12.272\) & \(-12.272\) \\ \hline \(Er\) & 0.0352 & 0.1755 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of JWL parameters obtained using NLLS fitting and ZMWNI built-in fitting solver based on [PERSON]’s method. It is worth noting that the value of the detonation energy \(E_{0}\) is the same up to 5 significant digits. \begin{table}
\begin{tabular}{c c} \hline \hline \(\
u\) & \(p\) [GPa] \\ \hline
0.7600 & 27.0045 \\ \hline
1.0000 & 11.7309 \\ \hline
2.2000 & 1.3823 \\ \hline
2.4100 & 1.1088 \\ \hline
4.1000 & 0.3425 \\ \hline
6.5000 & 0.1427 \\ \hline
10.0000 & 0.0685 \\ \hline
20.0000 & 0.0234 \\ \hline
40.0000 & 0.0086 \\ \hline
80.0000 & 0.0033 \\ \hline
160.0000 & 0.0013 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pressure (\(GPa\)) with respect to reduced volume \(\
u\) data from ZMWNI software for RDX with \(\rho_{0}=1630\) [Kg of energetic compound/m\({}^{3}\)]. Chapman–Jouguet (CJ) point values from ZMWNI are \(v_{\rm CJ}=0.465\,\rm cm^{3}/g\), \(p_{\rm CJ}=27.0045\,\rm GPa\), \(\gamma_{\rm CJ}=3.125\). The plot in Figure 3 shows, qualitatively, a good agreement between the results from EXPLO5 and the described algorithm, being the isentropic _pv_ data and the fitted curve practically overlapping. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **NLLS (This Work)** & **EXPLO5** \\ \hline \(A\) (GPa) & 913.1 & 687.214 \\ \hline \(B\) (GPa) & 14.54 & 13.842 \\ \hline \(C\) (GPa) & 1.424 & 1.448 \\ \hline \(R_{1}\) & 4.810 & 4.516 \\ \hline \(R_{2}\) & 1.144 & 1.256 \\ \hline \(\omega\) & 0.288 & 0.294 \\ \hline \(E_{0}\) & \(-10.492\) & \(-10.922\) \\ \hline \(Er\) & 0.028 & 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of JWL parameters obtained using NLLS fitting and EXPLO5. Results for \(E_{0}\) are comparable. \begin{table}
\begin{tabular}{c c c c c c} \hline \(\
u\) & \(p\) [GPa] & \(v\) & \(p\) [GPa] & \(v\) & \(p\) [GPa]_ \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 5: Sixty-nine pressure values (\(GPa\)) with respect to reduced volume \(\
u\) data from EXPLO for PETN with \(\rho_{0}=1760\) [Kg of energetic compound/m\({}^{3}\)]. Chapman–Jouguet (CJ) point values from EXPLO5 are \(v_{\rm CJ}=0.4286\,\rm cm^{3}/g\), \(p_{\rm CJ}=30.386\,\rm GPa\), \(\tau_{\rm CJ}=3.070\).
## 5 Discussion  
### Sample Number Issue and Feature Importance  
Based on the classification results presented above, it is evident that RF outperforms LR and SVM in terms of several evaluation indicators and is also marginally better than DT. However, it should be noted that the classification results may be affected by the different proportions of positive and negative samples in the dataset, which is determined by the setting of the strong earthquake threshold and can impact the prediction outcomes. To investigate this further, we experimented with several magnitudes which were shown in Table 4 as the classification threshold and analyzed the number of positive and negative samples, as well as the difference in classification metrics. It is important to exercise caution when interpreting the accuracy score in cases where there is an imbalance in the number of positive and negative samples. Newer models that are insensitive to class imbalance [44] may help overcome this problem, but this is out of the scope of this study.  
At the same time, the impact of the small number of overall samples on the classification results should also be taken into consideration. Although the evaluation metric of RF is relatively the highest among the four classification methods, indicating that it is the best classifier in the prediction of strong earthquakes, further verification is necessary to determine its reliability on different datasets due to the small number of samples in the test set and the uneven distribution of positive and negative samples. To better interpret the classification results of RF, the feature quantity was ranked from high to low based on  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Magnitude** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Positive Samples** & **Negative Samples** \\ \hline
5.0 & 0.975 & 0.982 & 0.991 & 0.986 & 549 & 41 \\
5.5 & 0.958 & 0.949 & 0.987 & 0.967 & 386 & 204 \\
6.0 & 0.949 & 0.922 & 0.959 & 0.940 & 258 & 332 \\
6.5 & 0.958 & 0.96 & 0.857 & 0.906 & 141 & 449 \\
7.0 & 0.992 & 0.889 & 1.0 & 0.941 & 60 & 530 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results and sample numbers of the random forest method.  
Figure 12: Feature importance obtained by the permutation importance method.  
their weights in the classification, as illustrated in Figure 13. The top three most important features are T_elaps75, T_elaps7, and dM_lsq, which is consistent with the feature importance of earthquake magnitude prediction and confirms the important role of the number of days since the last large earthquake and the magnitude deficit.  
The poor classification results of LR and SVM in this experiment also suggest the need for further investigation into the potential impact of dataset size and feature distribution on the classification performance. To address this issue, the feature values were standardized and normalized in the dataset, and the classifier parameters were adjusted accordingly. This approach led to an improvement in the overall classification performance.
## 3 Edge-Cloud Collaborative Architecture
### Application Motivating Example: Comprehensive Assessment Workflow for Industrial Robot Monitoring  
The Figure 2 shows a comprehensive assessment workflow of industrial robot monitoring. The first step is to acquire data, where the industrial robot transmits internal and external sensor data to the monitor through cables or optical fibers; the next step is to extract characteristics such as time domain, frequency domain, and time-frequency domain from the sensor data respectively; the next step is to normalize the characteristic data; the next step is to perform condition monitoring and life estimation of the industrial robot respectively; and the last step is to make a comprehensive assessment for industrial robots. The subtasks in the workflow have different data and computational characteristics, for example, the extraction of features in the time domain, frequency domain, and time-frequency domain requires a large amount of data input and simple computation to complete, so such tasks are more suitable for deployment in local or edge servers; while tasks such as industrial robot condition monitoring and life estimation require complex computational models, such as dynamic prediction Neural Networks [11] or Deep Learning Algorithm, to transform the processed characteristics into corresponding metrics, which are more suitable for running in cloud centers or high-performance edge servers. Therefore, industrial robot-monitoring workflows need intelligent algorithms to allocate appropriate computing resources to different types of tasks.
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 1 Introduction
We also enhance the U-Convolutional blocks in ESC-MASD-Net by adding a conformer layer [15]. We evaluate our model using the WHAM! dataset [16], and the results indicate that ESC-MASD-Net outperforms SuDoRM-RF++ in terms of scale-invariant signal-to-distortion ratio improvement (SI-SDRi) scores. This improvement is due to the contribution of each additional component (ResCon, MA, and the conformer layer). These findings suggest that ESC-MASD-Net has the potential to be a solution for speech separation in real-world applications. The main contributions of this study can be summarized as follows. * We propose ESC-MASD-Net, a speech separation framework that achieves state-of-the-art performance while maintaining low computation load and compact model size. * ESC-MASD-Net is built on the SuDoRM-RF++ structure, and it considers the input feature stream's channel, global, and local information by employing a multi-view attention (MA) block. Additionally, the framework uses a residual conformer (ResCon) block that tunes the input channel numbers flexibly for extracting information without any loss due to the residual connection arrangement. Evaluation experiments indicate that adding MA and ResCon blocks significantly improves SI-SDRi. * We investigate the conformer layer structure presented in [15] and propose three different arrangements to adopt the conformer layer in the U-Convolution blocks in ESC-MASD-Net. The evaluation results exhibit that ESC-MASD-Net equipped with the conformer layer achieves superior performance, and thus, the conformer layer is well additive to ESC-MASD-Net. The rest of the paper is organized as follows. Section 2 provides a brief overview of the SuDoRM-RF++ framework, while Section 3 introduces the new ESC-MASD-Net, which incorporates two modules, ResCon and MA, to enhance SuDoRM-RF++. The use of the comformer layer in the U-Convolutional blocks of ESC-MASD-Net is explained in Section 4. Section 5 provides details on the experimental setup, while Section 6 presents the experimental results and discussions. Finally, Section 7 concludes the paper with some remarks.
## 2 Material and Methods
### Sample Preparation  
The sandstone block that was used in this study was fresh, un-weathered, and without obvious fractures. All the tested samples were cored from the same sandstone block and then processed into cylinders with a nominal diameter of 50 \(\pm\) 0.5 mm and a height of 100 \(\pm\) 1 mm. According to the ISRM recommendations, the samples were ground with a tolerance of 0.02 mm for flatness at both ends, and deviations from perpendicular to the axis did not exceed 0.1% rad. To minimize the uncertainty of test results, the samples were selected before the F-T test: first, the samples with visible defects were excluded; then, the samples with similar P-wave velocities (3229 \(\pm\) 50 m/s) were used by ultrasonic P-wave velocity (UPV) tests; finally, a total of 24 sandstone samples were selected and randomly divided into eight subgroups, with three samples in each subgroup. The subgroups F0, F1, F2, F3, F4, F5, and F6 represented sandstone samples with 0, 5, 10, 15, 20, 25, and 30 F-T cycles, respectively. Subgroup F7 was used to study the effect of F-T action on porosity, P-wave velocity, and the dry density of the samples.  
Figure 1: Location of the Xinjiang province from where the sandstone samples were obtained: (**a**) Location in China; (**b**) Detailed map of Xinjiang Province, China; (**c**) Actual view; (**d**) Satellite map of sampling point; (**e**) Samples. Red circle: rough location of the tested sandstone.  
Figure 2: X-ray diffraction results for the tested sandstone.  
Before the F-T testing, all the samples were left to dry in an oven (110 \({}^{\circ}\)C) until the mass remained constant and then cooled to room temperature in a dry environment (Figure 4), while the dry mass, P-wave velocity, and volume of the sample were measured. Then, all the samples were kept in distilled water and saturated by the vacuum equipment with the fixed vacuum pressure of 100 kPa and the saturation time of 12 h [21]. Next, the samples were submerged completely in distilled water under vacuum conditions (total 24 h). Finally, the initial physical properties were obtained. The average values are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & \(\mathbf{V_{\text{p}}}\) **(m/s)** & \(\mathbf{\rho_{d}}\)**/(g\(\cdot\)cm\({}^{-3}\))** & \(\mathbf{\rho_{\text{sat}}}\)**/(g\(\cdot\)cm\({}^{-3}\))** & \(\mathbf{\omega}\)**/(\%)** & \(\mathbf{\eta}\)**/(\%)** \\ \hline Range & 3193–3257 & 2.412–2.415 & 2.521–2.526 & 4.48–4.62 & 4.32–4.51 \\ Average & 3229 & 2.414 & 2.523 & 4.56 & 4.37 \\ SD & 33.005 & 0.00125 & 0.00235 & 0.135 & 0.122 \\ \hline \hline \end{tabular}  
* Notes: \(V_{\text{p}}\), P-wave velocity; \(\rho_{d}\), dry density; \(\rho_{\text{sat}}\), saturation density; \(\omega\), water absorption; \(\eta_{\text{t}}\), effective porosity; SD refers to the standard deviation.  
\end{table}
Table 1: The physical parameters of the selected samples.  
Figure 4: Experimental procedure and equipment.  
Figure 3: Thin section photomicrographs. (**a**) 0 F-T cycle; (**b**) 15 F-T cycles.
## 5 Results and Discussion
In case of DNN too, we can observe from our results table that using combined features of MFCCs and DWT, we can get higher results by efficiently utilizing the information present in the heart sound signal. The improved accuracy is due to usage of completely different domain signals (MFCCs and DWT) together. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifiers** & **Features** & **Sensitivity** & **Specificity** \\ \hline \multirow{3}{*}{Centroid displacement based KNN} & MFCCs & 81.98\% & 93.5\% \\  & DWT & 92.0\% & 97.9\% \\  & MFCCs + DWT & 97.6\% & 98.8\% \\ \hline \multirow{3}{*}{DNN} & MFCCs & 86.8\% & 95.1\% \\  & DWT & 91.6\% & 97.4\% \\  & MFCCs + DWT & 94.5\% & 98.2\% \\ \hline \multirow{3}{*}{SVM} & MFCCs & 87.3\% & 96.6\% \\  & DWT & 92.3\% & 98.4\% \\ \cline{1-1}  & MFCCs + DWT & 98.2\% & 99.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sensitivity and Specificity calculated for each classifier performance based on the Figure 6 confusion matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**DWT** &
\begin{tabular}{c} **KNN** \\ **(Centroid Displacement Based)** \\ \end{tabular} & **SVM** & **DNN** \\ \hline Accuracy & 91.8\% & 92.3\% & 87.8\% \\ Average F1 Score & 98.3\% & 99.1\% & 95.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result based on DWT Features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MFCCs** &
\begin{tabular}{c} **KNN** \\ **(Centroid Displacement Based)** \\ \end{tabular} & **SVM** & **DNN** \\ \hline Accuracy & 80.2\% & 91.6\% & 86.5\% \\ Average F1 Score & 92.5\% & 86.0\% & 96.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Improved Result based on MFCCs Features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MFCCs + DWT** &
\begin{tabular}{c} **KNN** \\ **(Centroid Displacement Based)** \\ \end{tabular} & **SVM** & **DNN** \\ \hline Accuracy & 97.4\% & 97.9\% & 92.1\% \\ Average F1 Score & 99.2\% & 99.7\% & 98.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result based on MFCCs and DWT Features.
## 2 Methods
### Block-Based PSInSAR Processing  
#### 2.2.1 Sub-Block PS Candidate Point Selection and Optimization  
In block-based PSInSAR processing, traditional methods perform relative radiometric calibration independently for each sub-block, which may lead to a misjudgment of the same physical pixel in overlapping regions between adjacent sub-blocks due to differences in the calibration coefficients. Specifically, the radiometric calibration mean difference between sub-blocks causes discrepancies in the calibrated intensity values at the same location, which, in turn, affects the calculation of the amplitude deviation value \(D_{A,k}(i)\), leading to a contradiction in the \"selection-rejection\" of PS candidate points in the overlapping region. To solve this issue, we designed a global consistency optimization framework based on the candidate point selection in the overlapping regions. First, each sub-block \(k\) undergoes independent radiometric calibration, and an initial set of candidate points \(p_{k}^{\text{init}}\) is selected based on the amplitude deviation threshold \(\tau_{D}\) and the average coherence threshold \(\tau_{\gamma}\). The mathematical definition is as follows:  
\[P_{k}^{init}=\big{\{}i\big{|}D_{A,k}(i)<\tau_{D},\overline{\tau_{k}}(i)>\tau_{ \gamma}\big{\}} \tag{3}\]  
where \(D_{A,k}(i)\) represents the amplitude deviation value of pixel \(i\) in sub-block \(k\); reflecting the temporal stability of the intensity; and \(\overline{\tau_{k}}(i)\) represents the temporal average coherence, which measures the level of phase noise.  
Figure 2: Illustration of adaptive partitioning. Green rectangles represent sub-blocks, blue striped areas indicate overlapping regions, the red rectangle marks the first sub-block, and the yellow rectangle represents the last sub-block.  
Then, for each pair of adjacent sub-blocks \((k,l)\), the union of PS candidate points in the overlapping region \(R_{kl}\) is extracted, denoted as \(O_{kl}\), which is defined as  
\[O_{kl}=\left(P_{k}^{init}\cup P_{l}^{init}\right)\cap R_{kl} \tag{4}\]  
By forcing the merging of candidate points in the overlapping region, consistent selection results for the same physical location are ensured across different sub-blocks. Finally, the candidate point set for sub-block \(k\), \(P_{k}^{\text{final}}\), is updated using  
\[P_{k}^{final}=\left(\bigcup_{l\in\mathcal{N}(k)}O_{kl}\right)\cup\left(P_{k} ^{init}\smallsetminus\bigcup_{l\in\mathcal{N}(k)}R_{kl}\right) \tag{5}\]  
where \(\mathcal{N}(k)\) represents the set of sub-blocks adjacent to sub-block \(k\).  
The candidate point selection and optimization strategy in this section effectively eliminates the local bias introduced by block-wise calibration through the global fusion of redundant candidate points, ensuring the continuity of the spatial distribution of candidate points and providing a consistent input for subsequent parameter calculation.
# Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards
## 1 Introduction
### Contribution  
In this paper, through a detailed analysis of the BSM structure and NIST PQC-DSA, we present two methodologies that enable accurate verification even for large signature sizes (especially Crystals-Dilithium) in V2V communications. First, we propose a \"simple split way\" that uses order-bit to guarantee the correctness of PQC-DSA during transmission and reception. The second method, \"split way for Crystals-Dilithium\" split packets based on the characteristics of the Crystals-Dilithium signature structure without the need to receive and sort all the signature blocks received. Our method allows the signature verification process to proceed immediately upon receipt of the split signature block. In the autonomous driving environment, one local car needs to be acutely aware of the location information of all remote cars around it. In other words, the local car effectively needs to proceed through the several signature verification processes in parallel. Our \"split way for Crystals-Dilithium\" has the advantage of being able to set the verification logic for each signature packet from the perspective of parallelism. Additionally, the signature verification process can be started on any packet received, reducing the latency incurred during the verification. We performed simulations with V2 Verifier. We proceeded to experiment with an ARMv8-based OBU actually used in an autonomous-driving environment. We also simulated signature generation and verification using the latest optimization work of PQC-DSA proposed for the ARMv8 platform and analyzed the simulation results in detail.  
The remainder of this paper is structured as follows: Section 2 introduces PQC-DSA and V2 Verifier. Section 3 describes our simulation environment, the code we used, and the proposed methodology for applying PQC-DSA to V2 Verifier. Results and discussion of our simulation are presented in Sections 4 and 5 before we conclude the article in Section 6.
## 4 Tool Application to Evaluate Pollutant Emissions
### Emissions Calculation  
The values of pollutant concentration measured by the emission sensors are the basis for the calculation of emissions, which are calculated through the following formula:  
\[\text{{{{E}}}mission}\left[\frac{\text{g}}{\text{kWh}}\right]=10^{3}\frac{ \dot{m}_{exh}\text{{{{E}}}mission}_{concentration,dry}\mu k_{dtw}}{P_{eng}} \tag{32}\]  
Here, two new quantities are introduced:  
* \(k_{dtw}\) : dry to wet coefficient, used to convert a dry quantity to a wet one by means of the following formulation provided in [16]: \[\begin{array}{l}k_{dtw}\\ =\left(1\ +\ 0.005\ \times\ 11.9164\frac{Fuel_{mix\ cont\ H}}{Fuel_{mix\ cont\ C}}( \text{CO}_{2\ concentration\ +\ CO}_{concentration})\right.\\ \left.+\frac{1.608H_{a}}{1000+1.608H_{a}}+0.00794\right.\\ \left.-0.01\frac{0.5\times 11.9164\frac{Fuel_{mix\ cont\ H}}{Fuel_{mix\ cont\ C}} \text{CO}_{concentration}(\text{CO}_{2\ concentration}+\text{CO}_{concentration})}{( \text{3 CO}_{2\ concentration}+\text{CO}_{concentration})}\right)^{-1}\end{array}\] (33)
* \(\mu\) : component specific factor, equal to the ratio between gas component density (Table 2) and exhaust gas density. The NO\({}_{\text{X}}\) Technical Code [16] provides specific tables containing \(\mu\) values (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **NO\({}_{\text{X}}\)** & **CO** & **HC** & **CO\({}_{2}\)** & **O\({}_{2}\)** & **CH\({}_{4}\)** \\ \hline \(\rho_{component}\) & \multirow{2}{*}{2.053} & \multirow{2}{*}{1.250} & \multirow{2}{*}{-} & \multirow{2}{*}{1.9636} & \multirow{2}{*}{1.4277} & \multirow{2}{*}{0.716} \\ \(\left[\text{kg/m}^{3}\right]\) & & & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Gas component density.  
Formula (32) for the calculation of emissions is applicable to all the pollutant emissions except for nitrogen oxides, due to their dependency on ambient air conditions. A coefficient that takes into account ambient air temperature and moisture is introduced as follows:  
\[\begin{array}{l}k_{humNax}\\ =\frac{1}{1-0.012(H_{\mathrm{a}}-10.71)-0.00275(t_{1}-24.85)+0.00285(T_{3}-T_{3 }nom)}\end{array} \tag{34}\]  
where \(T_{3\leavevmode\
obreak\ \mathit{nom}}\) is the nominal air receiver temperature. The coefficient \(k_{hum\leavevmode\
obreak\ \mathit{NO}_{\mathrm{X}}}\) applies to the moisture range \(0-25\mathrm{g}_{\mathrm{H_{2}O}}/\mathrm{kg}_{air,dry}\).  
As a result, the NO\({}_{\mathrm{X}}\) emissions can be calculated as dry quantities with the formula:  
\[\mathrm{NO}_{\mathrm{X}}\Big{[}\frac{\mathrm{g}}{\mathrm{kWh}}\Big{]}=10^{3} \frac{NO_{X\leavevmode\
obreak\ concentration}k_{\mathit{flux}}k_{hum \leavevmode\
obreak\ \mathit{NO}_{\mathrm{X}}}\tilde{n}_{\mathit{cch}}\mu_{NO _{\mathrm{X}}}}{P_{\mathit{eng}}} \tag{35}\]  
The NO\({}_{\mathrm{X}}\) emissions calculated as wet quantities can be obtained omitting the coefficient \(k_{\mathit{dtw}}\) in the previous Formula (35).