esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Artificial Neural Network (ANN) Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Phase** & **Structure** & **R2** & **R** & **\(p\)-Value** \\ \hline \multirow{4}{*}{7} & Training & & 0.69 & 0.83 & 0.20 \\  & Validation & [8 1] & 0.66 & 0.81 & 0.67 \\  & Testing & & 0.64 & 0.80 & 0.99 \\  & Total & & 0.64 & 0.80 & 0.46 \\ \hline \multirow{4}{*}{8} & Training & & 0.86 & 0.93 & 0.65 \\  & Validation & [7 2 1] & 0.66 & 0.81 & 0.51 \\  & Testing & & 0.56 & 0.75 & 0.21 \\  & Total & & 0.73 & 0.85 & 0.25 \\ \hline \multirow{4}{*}{9} & Training & & 0.82 & 0.90 & 0.95 \\  & Validation & [8 2] & 0.64 & 0.80 & 0.51 \\  & Testing & & 0.51 & 0.72 & 0.92 \\  & Total & & 0.72 & 0.85 & 0.89 \\ \hline \multirow{4}{*}{10} & Training & & 0.80 & 0.89 & 0.95 \\  & Validation & & 0.63 & 0.80 & 0.14 \\  & Testing & & 0.63 & 0.79 & 0.85 \\  & Total & & 0.69 & 0.83 & 0.51 \\ \hline \multirow{4}{*}{11} & Training & & 0.70 & 0.84 & 0.95 \\  & Validation & [5 1] & 0.64 & 0.80 & 0.35 \\  & Testing & & 0.66 & 0.81 & 0.60 \\  & Total & & 0.66 & 0.81 & 0.67 \\ \hline \multirow{4}{*}{12} & Training & & 0.90 & 0.95 & 0.98 \\  & Validation & [7 2 1] & 0.62 & 0.79 & 0.18 \\  & Testing & & 0.59 & 0.77 & 0.40 \\  & Total & & 0.73 & 0.86 & 0.34 \\ \hline \multirow{4}{*}{13} & Training & & 0.79 & 0.89 & 0.98 \\  & Validation & [5 1 1] & 0.62 & 0.79 & 0.22 \\  & Testing & & 0.51 & 0.71 & 0.65 \\  & Total & & 0.67 & 0.82 & 0.74 \\ \hline \multirow{4}{*}{14} & Training & & 0.85 & 0.92 & 0.99 \\  & Validation & [4 3 3] & 0.63 & 0.79 & 0.71 \\  & Testing & & 0.54 & 0.73 & 0.64 \\  & Total & & 0.74 & 0.86 & 0.72 \\ \hline \multirow{4}{*}{15} & Training & & 0.71 & 0.84 & 0.98 \\  & Validation & [7 2 2] & 0.61 & 0.78 & 0.43 \\  & Testing & & 0.62 & 0.79 & 0.21 \\  & Total & & 0.66 & 0.81 & 0.42 \\ \hline \multirow{4}{*}{16} & Training & & 0.78 & 0.89 & 0.93 \\  & Validation & [7 2] & 0.60 & 0.78 & 0.90 \\  & Testing & & 0.66 & 0.78 & 0.90 \\  & Total & & 0.68 & 0.82 & 0.61 \\ \hline \multirow{4}{*}{17} & Training & & 0.83 & 0.91 & 0.99 \\  & Validation & [5 3 3] & 0.60 & 0.78 & 0.23 \\  & Testing & & 0.58 & 0.76 & 0.89 \\  & Total & & 0.71 & 0.84 & 0.58 \\ \hline \multirow{4}{*}{18} & Training & & 0.76 & 0.87 & 0.98 \\  & Validation & [5 2 2] & 0.61 & 0.78 & 0.29 \\  & Testing & & 0.57 & 0.75 & 0.35 \\  & Total & & 0.66 & 0.82 & 0.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._For the testing sets, the value of the determination coefficients (R\({}^{2}\)) obtained shows that the models developed can explain at least 50% of sample variability. The correlation coefficients in the testing set (0.71 to 0.81) exceed the minimum value of 0.70 indicated in the 2000 version of standard EN 326-2 [65] to accept the test results obtained using a non-standard method. The \(p\)-value exceeds 0.05 in all cases, and therefore, there are no significant differences between the test values and the ANN outputs for a significance level of 95%, as specified in the 2014 version of standard EN 326-2 [64]. This means that all the conditions have been met for the acceptance of the ANNs developed.
# Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals
## 3 Implementation Using the Difference Signals
### Effect of Time Compensation  
The possibilities of damage existence for all eight sensing paths A1B1 to A8B8 in pulse-echo configuration are presented in Figure 12a-h, respectively. These plots were based on the normalized  
Figure 7: Signals for sensing path A2A8: (**a**) undamaged; (**b**) damaged; (**c**) difference; (**d**) coefficient of CWT.  
CWT signals without the time compensation, and it can be noticed that the maximum possibility regions are ahead in time with respect to the actual damage position. The time compensation in all the CWT signals was made based on the discussion in Section 2.2. As an example, the signals for sensing pair A8B8 are presented again in Figure 13. The undamaged, damaged, and difference signals are presented in Figure 13a-c, respectively. The envelopes of normalized coefficient of CWT with time compensation (blue) and without time compensation (red) are presented in Figure 13d.  
The damage possibilities for sensing paths A1B1 to A8B8 are presented again with time-compensated signals in Figure 14a-h, respectively. It can be seen that the maximum possibility regions coincide with the actual damage locations in this case. The aggregate damage images for \(DI_{a}(x_{i},y_{i})\) and \(DI_{m}(x_{i},y_{i})\) using the time-compensated signals are shown in Figure 15a,b, respectively. The results indicate that the time-compensated signals could localize the damage accurately for both the fusion schemes. However, the damage index based on the conjunctive fusion scheme can provide better damage imaging than that on compromised fusion scheme. Therefore, the pulse-echo configuration with damage index based on conjunctive fusion scheme \(DI_{m}(x_{i},y_{i})\) is proposed in this method, and this combination was chosen for the remaining analyses in this paper.  
Figure 8: Signals for sensing path A8B8: (**a**) undamaged; (**b**) damaged; (**c**) difference; (**d**) coefficient of CWT.  
Figure 9: Possibilities of damage existence in single damage scenario for sensing path: (**a**) A2A8; (**b**) A8B8.  
Figure 11: Aggregate damage image for single damage scenario in pulse–echo configuration based on (**a**) compromised fusion scheme; (**b**) conjunctive fusion scheme.  
Figure 12: Possibilities of damage existence in pulse–echo configuration for sensing paths: (**a**) A1B1; (**b**) A2B2; (**c**) A3B3; (**d**) A4B4; (**e**) A5B5; (**f**) A6B6; (**g**) A7B7; (**h**) A8B8.  
Figure 10: Aggregate damage image for single damage scenario in pitch–catch configuration based on (**a**) compromised fusion scheme; (**b**) conjunctive fusion scheme.  
Figure 14: Possibilities of damage existence with time compensation in pulse-echo configuration for sensing paths: (**a**) A1B1; (**b**) A2B2; (**c**) A3B3; (**d**) A4B4; (**e**) A5B5; (**f**) A6B6; (**g**) A7B7; (**h**) A8B8.  
Figure 13: Signals for sensing path A8B8: (**a**) undamaged; (**b**) damaged; (**c**) difference; (**d**) coefficient of CWT with and without time compensation.
## 2 Results
### Identification of VOCs Produced by B. atrophaeus Strain HF1  
An SPME-GC/MS approach was employed to identify VOCs produced by strain HF1. In total, 27 potential VOCs were identified when analyzing 5-day-old strain HF1 samples (excluding the same VOCs in control samples), including alcohols, esters, ketones, alkanes, ethers, acids, and aldehydes (Table 1).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Family** & **Compound** & **CAS** & **Molecular Formula** & **RT (min)** & **Area (\%)** \\ \hline Alcohols & Methanethiol & 74-93-1 & CH\({}_{4}\)S & 1.87 & 7.69 \\  & 2-pentanol, 3-methyl- & 565-60-6 & C\({}_{6}\)H\({}_{14}\)O & 11.87 & 8.49 \\  & (\(\pm\))-5-methyl-2-hexanol & 111768-09-3 & C\({}_{7}\)H\({}_{16}\)O & 14.32 & 16.56 \\  & 2-heptanol, 6-methyl- & 4730-22-7 & C\({}_{8}\)H\({}_{18}\)O & 17.01 & 16.67 \\  & 2-heptanol, 5-methyl- & 54630-50-1 & C\({}_{8}\)H\({}_{18}\)O & 17.39 & 14.24 \\  & 1-hexanol, 4-methyl- & 818-49-5 & C\({}_{7}\)H\({}_{16}\)O & 19.15 & 5.50 \\  & 2-furanmethanol & 98-00-0 & C\({}_{5}\)H\({}_{6}\)O\({}_{2}\) & 24.10 & 11.15 \\  & Octaethylene glycol & 5117-19-1 & C\({}_{16}\)H\({}_{34}\)O\({}_{9}\) & 40.10 & 4.03 \\ \hline Esters & Methyl thiolacetate & 1534-08-3 & C\({}_{3}\)H\({}_{6}\)OS & 6.62 & 19.37 \\  & Butanoic acid, 2-methyl-, ethyl ester & 7452-79-1 & C\({}_{7}\)H\({}_{14}\)O\({}_{2}\) & 6.89 & 9.99 \\  & Butanoic acid, 3-methyl-, ethyl ester & 108-64-5 & C\({}_{7}\)H\({}_{14}\)O\({}_{2}\) & 7.38 & 26.80 \\  & S-methyl 3-methylbutanethioate & 23747-45-7 & C\({}_{6}\)H\({}_{12}\)OS & 11.84 & 8.49 \\  & Iberverin & 505-79-3 & C\({}_{5}\)H\({}_{8}\)NS\({}_{2}\) & 26.35 & 3.84 \\  & Methyl methoxyacetate & 6290-49-9 & C\({}_{4}\)H\({}_{8}\)O\({}_{3}\) & 29.88 & 3.75 \\ \hline Ketones & Bis(4-fluorophenyl)methanone & 345-92-6 & C\({}_{13}\)H\({}_{8}\)F\({}_{2}\)O & 2.04 & 7.21 \\  & 3-methyl-2-butanone & 563-80-4 & C\({}_{5}\)H\({}_{10}\)O & 3.72 & 33.41 \\  & Methyl Isobutyl Ketone & 108-10-1 & C\({}_{6}\)H\({}_{12}\)O & 5.46 & 6.64 \\  & 4\({}^{\prime}\)-methylvalerophenone & 1671-77-8 & C\({}_{12}\)H\({}_{16}\)O & 13.48 & 1.28 \\  & Hydroxyacetone & 116-09-6 & C\({}_{3}\)H\({}_{6}\)O\({}_{2}\) & 14.68 & 3.95 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SPME-GC/MS volatile profile of _B. atrophaeus_ strain HF1.  
Figure 7: The effect of _B. atrophaeus_ strain HF1-derived VOCs on _V. pyri_ hyphae membrane permeability. The hyphae were stained with 1 \(\upmu\)M SYTOX green at 24 h after fumigation with 100 \(\upmu\)L of 1 \(\times\) 10\({}^{8}\) CFU mL\({}^{-1}\) of strain HF1 cell suspension. Bacteria-free liquid NA medium fumigation served as CK. Bar: 20 \(\upmu\)m. Bright: bright-field, green fluorescence: green fluorescence-field, merged: merged images of bright-field and green fluorescence-field.
## 1 Introduction
### Analysis of Gas Occurrence and Migration Law in Coal and Rock Mass  
As a type of porous medium, coal contains complex pore and fissure structures, which determine the occurrence characteristics of gas. The occurrence state of gas in coal is mainly divided into the free state and adsorption state. The absorption state and water-soluble state of gas also exist, but the content is small. Gas migration can be divided into three processes: adsorption desorption, diffusion, and seepage. In the original coal, free gas and adsorbed gas will transformed into each other, and the quantity value between them is maintained within a certain range, which is in a dynamic equilibrium state of adsorption and desorption. In the process of coal mining, coal will be affected by external forces and deform. At this time, there will be a large number of cracks formed in the coal due to the failure of loads, leading to a continuous drop in gas pressure. When the gas pressure in the coal decreases to the critical desorption pressure, the internal gas will break away from the coal and transform into a free state. As a result, the amount of adsorbed gas gradually decrease, and the amount of free gas will gradually increase. This process will lead to a difference in gas pressure in the coal and the free surface's atmosphere pressure, resulting in gas pressure differences. Under the action of the pressure difference, the free gas percolates outward, and the gas content inside and outside the coal changes, resulting in a gas concentration difference. Due to the different internal and external concentration gradients, gas will flow from high to low concentrations, and this process is called diffusion motion. After that, the migration form of gas in coal fissures becomes seepage state, which follows [PERSON]'s law.
# Analysis and Application of Selected Control Charts Suitable for Smart Manufacturing Processes
## 2 Theoretical Background
### NP-Cusum  
This NPCC is based on the Mann-Whitney statistic [44; 45]. It is supposed that the quality characteristic \(X\) has a mean \(\mu\). For every measurement is computed the deviation \(y_{i}=x_{i}-\mu\), and then it is transformed into values \(I_{i}\) using Formula (7):  
\[I_{i}=\left\{\begin{array}{ll}1\ if\ \ y_{i}>0\\ 0\ if\ \ y_{i}\leq 0\end{array}for\ i=1,2,\ldots,n.\right. \tag{7}\]  
Let \(MW_{j}\) be the total number of positive deviations for every subgroup.  
The standardized Mann-Whitney statistic \(SMW_{j}\) is computed using Formula (8):  
\[SMW_{j}=\frac{MW_{j}-E_{0}\left(MW_{j}\right)}{\sqrt{var_{0}\left(MW_{j}\right)}} \tag{8}\]  
In the case of a two-sided NP-CUSUM, statistics \(S_{j}^{+}\) and \(S_{j}^{-}\), computed according to Formulas (9) and (10), are recorded into the control chart:  
\[S_{j}^{+}=\max\{0;S^{+}{}_{j-1}+SMW_{j}-K\} \tag{9}\]  
\[S_{j}^{-}=\min\{0;S^{-}{}_{j-1}+SMW_{j}+K\} \tag{10}\]  
\[S\_0=0 \tag{11}\]  
\[K=\Delta/2 \tag{12}\]  
where \(\Delta\) is the size of the shift that should be detected as soon as possible. The control limits are set as follows:  
\[UCL=H,\ LCL=-H \tag{13}\]  
For parameters \(K\) and \(H\):  
\[H=h\cdot\sigma,K=k\cdot\sigma \tag{14}\]where \(\sigma\) is the standard deviation of the statistic used for the creation of NP-CUSUM, and \(k\) and \(h\) are standardized forms of parameters \(K\) and \(H\), respectively.  
In general, the values of parameters \(K\) and \(H\) should be set in a such way that the combination of \(k\) and \(h\) will lead to the required value of ARL(0) [44]. For instance, when the required ALR(0) = 370, the combination of \(k\) = 0.5 and \(h\) = 4.77 is quite good [22].
# Delamination Assessment in Composite Laminates through Local Impulse Excitation Technique (IET)
## 1 Introduction
[PERSON] et al. [25] recently showed that vibrational methods can be successfully adopted for delamination detection. They performed experimental and numerical investigations on composite beams and plates clamped at one side and delaminated at the other, with different ratios of delamination length with respect to the total structure length. The results show a reduction in the resonances with an increasing delamination size. For example, for delamination with length corresponding to 30% of the structure length, the reduction in the resonance frequencies was about 5%. It is worth noting that by investigating the global vibrational response, both the delaminated and undamaged portions of the material participate in the vibrations, thus limiting the sensitivity only to high-level damage. By confining the vibrational response only to a region of interest as in the local-IET, the presence of damage can be more easily detected. In this paper, the goal is to assess the sensitivity of a local-IET in the presence of delamination within the inspected region through Finite Element (FE) analyses. The FE model of the local-IET test has been identified through a modal displacement analysis on undamaged composite laminates to replicate the boundary conditions provided by the isolating device. The vibrational response of a glass fiber composite with local delamination has been experimentally assessed with the local-IET and compared to the FE results to validate the numerical model. Finally, the FE model has been exploited to perform a sensitivity analysis, where the size of the delamination, its location within the device and position of the impulse are investigated.
## 4 Discussion of Grout Diffusion and Reinforcement
### Grout Vertical Diffusion Range  
The area surrounding a grouted pile, which is formed by the interaction of grout and soil, comprises two distinct regions: grout accumulation at the grouting outlet and grout splitting diffusion along the pile body. For vertical grout diffusion, Figure 15 displays the results of grout diffusion measurements along vertical lines for different grouting forms. In pile T26, the grout diffusion height is 34 cm (7.4 D) above the pile tip, while, in S26, the diffusion range extends almost from the side outlet to the pile end and reaches 17 cm above the side outlet. The height of grout diffusion downward is significantly greater than that of upward diffusion, whereas the diffusion height upward is lower than that of the tip-grouted pile. This is because a weak stress zone is formed at the pile-soil interface under the influence of soil unloading, causing the grout to preferentially split and spread downward under gravity. The results of side-grouted pile TS26 are similar, with a notably smaller height of upward diffusion. And the grouting fluid used for tip and side grouting is connected. The results of each pile subjected to tip grouting under different pressures are shown in Figure 16. Notably, the return height of grout in tip-grouted piles varies significantly with grouting pressure. Concretely, when the grouting pressure is 0.5, 0.6, and 0.7 MPa, the grout return height is 30, 34, and 45 cm (6.5 D, 7.4 D, and 9.8 D), respectively. In contrast, the diffusion range of S25 and S27 is similar to that of S26. Although it is generally believed that factors such as pile diameter, depth, grouting pressure, and grout type affect the diffusion height of grout, the test results indicate that soil unloading also has a substantial impact on grout diffusion range, particularly in the context of side-grouted piles. It is worth noting that there is a strong correlation between the grout diffusion height and the range of lateral friction enhancement. Taking pile TS26 as an example, the measured coverage of grout extends from 14 cm above the side grout outlet to the bottom of the pile. Meanwhile, the lateral friction enhancement segment of the pile is from approximately 30 cm above the side grout outlet to the bottom of the pile. Considering the precision of 25 cm for the height interval of the side friction regions, the range of grout diffusion and the range of side friction enhancement overlap significantly.  
Figure 18: Pile side grout body of pile TS26.
## 5 Conclusions  
A novel T* revise attenuated travel time inversion method is introduced for Q inversion. The accuracy of attenuated travel time, which is key in attenuated travel time tomography, is improved by the proposed method. Thus, more accurate Q results can be inverted with more details of the longitudinal and transverse variations of Q profiles. The proposed method improves the stability of the inversion results by using multiple reflected wave information to simultaneously invert the multiple attenuation travel times. The logarithm ratio of amplitude spectra of reflections in the overburden and in the second layer are used for attenuated travel time difference between the two layers calculation; also the two reflections in the overburden are used for attenuated travel time difference in the overburden calculation. In addition, the proposed method uses frequency components with good S/N for calculation and avoids the effect of choice of an optimistic frequency range in conventional method, which will introduce error of \(Q\).  
The overburden effect comes from the attenuation effect of different travel paths in the overburden. This cumulative attenuation effect cannot be eliminated and leads to error in Q estimation in the conventional LSR method. In the proposed method, the overburden effect is avoided by accounting for the travel time differences in reflections in each layer. The effectiveness is proved by synthetic data and field data application. Q compensation migration can give more reliable data using the Q results from the proposed method. The proposed method assumes that the velocity is known before Q inversion. For further studies, velocity and Q inversion can be considered and updated simultaneously.  
Conceptualization and methodology, Z.J.; writing--original draft preparation, Z.J. and R.W.; writing--review and editing, Z.J. and R.W.; supervision and funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Comparison of Q compensation migration gathers by using the Q results of (**a**) the conventional method and (**b**) the proposed method.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 42274173.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors are grateful to the National Natural Science Foundation of China (grant no. 42274173) for supporting this research. Thanks also are due to [PERSON] for his valuable suggestions and discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 System Architecture: Edge-FLGuard
### Federated Learning Orchestration  
The federated learning mechanism enables edge nodes to collaboratively train a global model without compromising privacy. The federated training process unfolds across the following stages:  
1. Global Initialization: The FL coordinator distributes a common model (e.g., pre-trained on public data) to all the participating edge nodes.
2. Local Training: Each edge node fine-tunes the model using its local dataset for a predefined number of epochs (\(E\)).
3. Model Update: Instead of raw data, only model weight updates or gradients are sent back to the coordinator.
4. Secure Aggregation: The server uses FedAvg or robust alternatives like trimmed mean or Krum to merge updates into a new global model.
5. Model Distribution: The updated model is redistributed to edge nodes for the next round. Edge-FLGuard supports asynchronous training, partial client participation, and non-IID-aware clustering, ensuring scalability and robustness in the presence of client churn or data skew.
## 2 The Procedures of Forecasting Strategy
### Adaptive Neuro-Fuzzy Inference System (ANFIS)
\[\mathrm{O_{2,j}}=w_{j}=\mathrm{\mu_{A_{i}}(x_{1})*\mu_{B_{i}}(x_{2})*\mu_{C_{ i}}(x_{3})} \tag{3}\]  
**Layer 3**: The output of \(\mathrm{j^{th}}\) node at the third layer is called as normalization of the firing strength (\(\overline{w_{j}}\)) where the firing strength of the respective \(\mathrm{i^{th}}\) rule is divided with the summation of all rules' firing strength as shown in Equation (4). \[\mathrm{O_{3,j}}=\overline{w_{j}}=\frac{w_{1}}{w_{1}+w_{2}} \tag{4}\]  
**Layer 4**: In the fourth layer, the \(\mathrm{j^{th}}\) node calculates the contribution of the \(\mathrm{i^{th}}\) rule to the overall output. This expression is shown in Equation (5) where \(\mathrm{p_{i}}\), \(\mathrm{q_{i}}\) and \(\mathrm{r_{i}}\) denote the consequent parameters. \[\mathrm{O_{4,j}}=\overline{w_{j}}*\mathrm{f_{i}}=\frac{w_{1}}{w_{1}+w_{2}}( \mathrm{p_{i}x+q_{i}x+r_{i}x}) \tag{5}\]  
**Layer 5**: The last process is the defuzzification process where each node produces a single output from the total contribution of each rule as shown in Equation (6). \[\mathrm{O_{5,j}}=\sum{(\overline{w_{j}}*\mathrm{f_{i}})} \tag{6}\]  
In the training process of the ANFIS model, two important parameters, the premise parameters and consequent parameters, must be determined optimally to increase the model performance. The ANFIS utilizes gradient descent backpropagation to estimate the premise parameters and the least squares estimation technique is employed to evaluate the values of consequent parameters. The training process of the ANFIS model is done iteratively until the network is converged. After that, the testing samples comprised of panel temperature data, tilted radiation data and global radiation data are inserted to the ANFIS model to forecast the future values of solar power.