esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Grid Impact
### Demand Response (DR)  
As both the number of PEVs and their concentration in certain urban locations increase, network will be affected if appropriate strategies are not adopted. Demand responses (DR) can provide tools to deal with this issue [128; 142].  
DRs and demand response programs (DRP) are related to the idea that electrical systems will tend to be more efficient if fluctuations in demand are smaller. DRPs are created as incentives for customers to consume less energy at times when market prices are higher, when the electrical system is more stressed, or when it is at risk. For this reason, a large part of the success of a DRP is its predictability in terms of demand; consequently, proposals that are satisfactory to potential clients must be made in economic terms and in schedules [121; 137].  
Examples include Rescheduling charging power, in which a smart charger is necessary to provide variable power to a EVs within predefined limits; rescheduling the charging period, which consists of moving usual hours to other slots accepted by consumers; and power feedback or V2G, as explained above, in which DRPs are articulated by taking advantage of the large amount of stored energy for use at optimal moments in order to stabilize the network. Excellent control of these strategies could allow for high penetration of EVs with little impact on the distribution network. At the local level and focused on EV users, various groups of such vehicles which may be encompassed by buildings or other configuration of a distribution sub-grid can be managed by charging service providers (CSPs), commonly called aggregators. Through supply-and-demand strategies, the optimal objective is for all three actors to achieve financial profit. In the future, clients could access day-ahead or real-time markets without using aggregators as intermediaries [121; 122; 138].  
In order to be successful using the aforementioned strategies, economic dispatch tools should be used to minimize operation costs under electric system safety constraints. Thus, generation should use EVs in V2G mode to stabilize production, while transmission should minimize losses by optimizing EVs location and their charging schedules. At the equipment maintenance level, optimizing the use of EV batteries should be pursued [104; 123; 136].
# MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
## 2 Methods and Principles
### CCA and CSEA Modules
#### 2.2.1 CCA Module  
The structure of the CCA module is shown in Figure 4, which mainly consists of two 2D convolutions and CA. We can enhance the localization effect by adding CA.  
Figure 3: The architecture of the (**a**) CAM and (**b**) SAM attention modules.  
CA reintroduces the channel attention mechanism along with the spatial attention mechanism, which embeds location information into the channel attention in the attention mechanism. The implementation of CA is shown in Figure 5 and can be divided into two main parallel stages. CA pools the input feature maps in both height and width directions with global average pooling to obtain the feature maps in both height and width directions, respectively. We enter the dimensions of the feature layer as [H, W, C], where H and W are the height and width of the feature map, respectively, and C is the number of channels. After the above operations, the feature had dimensions obtained in height and width are and [H, 1, C], respectively. Afterward, the two parallel stages are merged to transpose the height and width to pass a dimension, and then stacking is performed to fuse the height and width features, at which point the feature layer size obtained is [1, H + W, C], followed by convolution, normalization, and ReLU activation function to obtain a feature layer size of [1, H + W, C/r], where r is the scaling factor.  
Figure 4: The architecture of the CCA module.  
Figure 5: The architecture of the CA module.  
The process is bifurcated into two parallel stages, where the dimensions of height and width are segregated into [1, W, C/r] and [1, H, C/r], respectively. Following this, a transpose operation is executed, yielding two distinct feature layers: [W, 1, C/r] and. The application of a Sigmoid function then determines the attention distribution across both the vertical and horizontal dimensions. Finally, merging the original features through multiplication culminates in the implementation of the CA mechanism.
## 4 Simulation Results and Discussion
### Influence on Fault Indicator Configuration Scheme by Environment
#### 4.3.1 Electromagnetic Interference in the Power System  
As mentioned in [28], LoRa adopts Frequency-Hopping Spread Spectrum (FHSS) technology, which not only keeps the characteristics of low power consumption and long communication distance but also improves the anti-interference ability of electromagnetic compatibility (EMC). Moreover, [PERSON] et al. [29] have tested the LoRa technology in the substation, which is in the complex electromagnetic environment, and the test results show that LoRa performed steady communication. Similarly, [PERSON] [30] has proposed a custom hardware platform that is suitable for implementation of Internet of Things networks in Smart Grid systems. The communication range, reliability, and consumption testing results show that NB-IoT technology works well.
## 4 Conceptual Framework: DDPLM  
### Framework Overview  
The Data-Driven Personalized Learning Model (DDPLM) is the fusion of linguistic, pedagogical, and computational disciplines devised to address the subtlety of contemporary language education requirements. This model combines the accuracy of big data analytics with the kinetics of language learning, providing a methodical yet dynamic method of creating individualized learning programs. The core of DDPLM is its ability to utilize huge volumes of educational data and convert them into useful outputs that directly change language learning processes [32].  
Fundamentally, DDPLM is underpinned by a layered analytical model that reviews student data from different perspectives, including proficiency levels, learning rates, and interaction patterns. This approach makes it possible to discern individual learning journeys and helps deliver content and learning strategies that meet specific learner needs. The framework uses sophisticated algorithms operating on learner data in real time, ensuring the learning environment morphs with the learner's progress.  
The Set of DDPLM is built to be flexible and scalable and can be used in all educational settings and learners' populations. It incorporates several key components, including intuitive user-centric design modules [33]. The components operate in unison to ensure a continuous learning experience that is both interesting and successful.  
Data collection in DDPLM is also comprehensive in that it captures a wide spectrum of learner behaviors, including direct inputs such as test responses and writing samples, to indirect behaviors, such as time spent on tasks and navigation patterns. Therefore, this all-encompassing dataset is indispensable for creating a thorough insight into learner behaviors and preferences.  
The model's analytical engine uses a machine learning approach to reveal complicated patterns inherent in the data, which gives a view of how the learners are performing and why certain instruction strategies work while others fail. This analysis provides the background supporting the creation of predictive models that forecast learner needs and tailor instructional content accordingly [34].  
Content adaptation in DDPLM is fluid and influenced by continuous data analysis. Learning resources and activities are modified to provide the learner with appropriate levels of challenge, thereby maximizing learning progression. The content is non-static but changes according to the learner's interactions and attainment levels, preserving educational relevance and effectiveness.  
By its integrative design, DDPLM intends to change traditional language learning into a systematic, personalized, and data-driven form that promotes virtual learning and the learning of individual differences.
## 4 Proposed Method for Notch Fatigue Analysis
### Model Verification and Analysis  
In the present study, the new critical distance length was obtained by coupling NPC and TCD. The length of the non-propagating crack of the proposed model was determined by the crack closure effect. Therefore, the standard ASTM [48] was used to determine the opening stress coefficient of the crack closure effect in the present study, as shown in Equation (10).  
\[\lambda=\frac{\sigma_{op}}{\sigma_{\max}}=0.424+0.561R-0.394R^{2}+0.409R^{3} \quad R\geq 0 \tag{10}\]  
Taking the stress within the new critical distance as the effective stress, the fatigue limit prediction result of the proposed theory can be obtained. Figure 10 compares the prediction results of the proposed model and conventional TCD. As can be seen from Figure 10, all predictions of the proposed model fall within the \(\pm 10\%\) scattering band, while the conventional TCD-LM falls within the \(-20\%\) range, TCD-PM falls within the \(-30\%\) range. Obviously, the prediction result of the proposed model is better than that of the traditional critical distance method. The result can also be explained from a microscopic perspective. [PERSON] [34, 35] have illustrated that the fatigue limit refers to the stress level required to overcome the strongest propagation barriers, which will be determined by microstructural barriers such as grain boundaries, twin boundaries, phase boundaries and pearlite bands. However, machining of components destroys the structure of the grains near the notch and produces some overlapping/branched microcracks. The complete closure near the tip for the overlapped/branched crack path shields the crack tip from the applied stress [49], producing a phenomenon in which the interaction of adjacent or surrounding microstructures and crack grains affects crack propagation [50, 51]. In a recent study, [PERSON] et al. [52] also obtained similar conclusions through their studies and pointed out  
Figure 9: Schematic illustration of plasticity-induced crack closure model. (**a**) crack tip initial position; (**b**) Position of crack tip when non-propagating crack stops growing.  
that the notch fatigue limit depends on the interaction between the initial effective driving force and crack closure. Therefore, considering the crack closure phenomenon caused by short cracks at the notch root in the framework of the critical distance method is conducive to improving the accuracy of fatigue prediction.
## 3 Validations of the TLBM Approach
### Wall Temperature Distribution  
It is known that heat can be removed through evaporation. Since stronger evaporation occurs in the thin-film evaporating region, a local cooling effect can be expected. Based on this idea, the authors of [5] investigated the wall-temperature distribution of an evaporating micro channel. The wall was heated at constant power in their experiments. The structure shown in Figure 7 is employed to simplify the problem. The domain size in the computation is \(67\times 200\) (\(x\times y\)) in lattice unit. At the inlet of the liquid, we assume that the net mass flow is zero. To this end, we simply use \(f_{2}=f_{4},f_{5}=f_{7},f_{6}=f_{8}\). At the vapor outlet, the Zou-He boundary condition is employed for the flow field. In the flow field, for the solids nodes, i.e., nodes representing the solid walls, bounce-back rule is adopted, which also applies to all the simulations presented in this paper. For the temperature field, the non-equilibrium extrapolation method is used in both inlet and outlet. Besides, the solid walls are heated at the heat flux \(8\times 10^{-4}\), and thus the constant heat flux boundary condition is considered. Other parameters in the model are \(G=0.92\), \(\beta=0.06\), and \(\rho_{\rm s}=1.9\) (the resulting intrinsic contact angle is about \(15^{\circ}\)). The comparison of TLBM simulations and the experimental data extracted from [5] is given in Figure 8, where \(X\) denotes the normalized distance ranging from intrinsic meniscus region (\(X=0\)) to adsorption region (\(X=1\)). Thus the thin-film evaporating region locates in the middle. The temperature is normalized via \((T-T_{\rm min})/(T_{\rm max})\), where \(T_{\rm min}\) and \(T_{\rm max}\) are the minimum and maximum values in the temperature field, respectively. The results shows that the TLBM simulations agree well with the reported experimental data. It is noticed that there is a sudden drop of temperature in the wall temperature distribution, which implies that the intensive evaporation occurs in this region. In our simulations, it is validated that for the same computational domain, mesh resolution larger than \(50\times 150\) is fine enough to obtain grid-independent numerical results.  
Figure 7: Illustration of side walls and wetting liquid: black lines are side walls, blue region represents liquid, above white region is vapor; the two side walls are heated for evaporation.
## Appendix B The Introduction of the PTMM  
To illustrate the distinction between the TMM and the PTMM, consider the series pipeline system depicted in Figure 11. The total length of this pipeline system is represented as \(L\), which is constructed by connecting \(N\) sub-pipe ends, each with a length of \(L_{i}\).  
For the classical TMM, the global transfer matrix of a piping system is obtained by the series multiplication of the transfer matrices of its individual pipeline segments, as follows:  
\[\mathbf{\Phi}(l_{N},s)=\mathbf{T}_{global}(s)\mathbf{\Phi}(l_{0},s)\] (A11)  
\[\mathbf{T}_{global}(s)=\mathbf{T}(L_{N},s)\ldots\mathbf{T}(L_{2},s)\mathbf{T} (L_{1},s)\] (A12)  
When the total length of the pipeline is considerable, \(\mathbf{T}_{global}\) may become singular at high frequencies, resulting in instability phenomena.  
The PTMM substitutes the global transfer matrix with a node-coupled matrix, thereby mitigating the numerical instability of matrix elements, as illustrated in Equation (A13):  
\[\begin{bmatrix}\mathbf{D}_{s}(s)&0&0&0&0\\ \mathbf{T}(L_{1},s)&-\mathbf{I}&0&0&0\\ 0&\mathbf{T}(L_{2},s)&-\mathbf{I}&0&0&0\\ 0&0&0&\ddots&\ddots&0\\ 0&0&0&0&\mathbf{T}(L_{N},s)&-\mathbf{I}\\ 0&0&0&0&0&\mathbf{D}_{e}(s)\end{bmatrix}\begin{bmatrix}\mathbf{\Phi}(l_{0},s) \\ \mathbf{\Phi}(l_{1},s)\\ \mathbf{\Phi}(l_{2},s)\\ \vdots\\ \mathbf{\Phi}(l_{N-1},s)\\ \mathbf{\Phi}(l_{N},s)\end{bmatrix}=\begin{bmatrix}\mathbf{Q}_{s}(s)\\ 0\\ 0\\ \vdots\\ \mathbf{Q}_{e}(s)\end{bmatrix}\] (A13)
# HS-SPME Gas Chromatography Approach for Underivatized Acrylamide Determination in Biscuits
## 3 Results and Discussion
### Selection of SPME Fiber for Acrylamide Headspace Extraction  
A total of 6 SPME fibers coating were tested: PDMS, PA, CW/DVB, CAR/PDMS, PDMS/DVB, and DVB/CAR/PDMS, covering different coating characteristics (Table S2). Since several GC-FID analyses were performed, an example of a chromatogram obtained for a concentration of 5 mg/vial was included in Figure S1. The best result for AA extraction was obtained for CW/DVB fiber (Figure 2).  
PDMS/DVB and DVB/CAR/PDMS fibers showed the worst results, although they also contain a DVB coating. This coating polymer has been described to be more suitable for  
Figure 2: AA peak area (expressed as arbitrary units) obtained as function of different tested fibers. PDMS—polydimetylsiloxane; PA—polyacrylate; CAR—carboxen; DVB—divinylbenzene; CW—carbowax. Different letters represent values that are significantly different (\(p<0.05\)).  
medium- and high-molecular weight analytes [33; 34], which may explain the inefficiency for AA, a low-molecular weight compound. PDMS coating is recommended for non-polar compounds [34], which explains its low capacity towards AA. The PDMS/DVB has been used for AA extraction using HS-SPME, but only after AA silylation [25]. The best results were obtained using CAR/PDMS and CW/DVB fibers. These two fiber coatings are bipolar and adsorption-type (Table S2). CAR is a porous carbon polymer with micro-, meso-, and macropores of 6-50 A. When blended with PDMS [35; 36], it has been suggested for low molecular weight (C2-C12) compounds extraction [33; 34; 35; 37] with good results for high polarity ones, including the Maillard intermediates [38]. Therefore, the 75 \(\upmu\)m CAR/PDMS fiber coating thickness recommended for the compounds in the molecular weight range of 30-225 Da [37], was considered suitable for AA (71 Da). Indeed, CAR/PDMS fiber exhibited higher extraction efficiency (\(p<0.05\)) compared to other fibers, although slightly lower than CW/DVB. Similar results were observed when using these fibers for direct immersion sampling [24]. Although CW/DVB fiber showed better results than CAR/PDMS (\(p<0.05\)), as CW/DVB fiber is no longer available in the market, CAR/PDMS fiber was chosen as the best option for AA extraction using HS-SPME. The use of a polar fiber, such as PA was also considered. However, the liquid polyacrylate polymeric coating was degraded by propanol, the solvent used to promote the transfer of AA to the vial headspace. Based on all these observations, the optimization of SPME extraction conditions were performed for CAR/PDMS fiber.
## 3 Results
### Cohesion \(c\) and Friction Angle \(\varphi\)  
To better understand the effect of remolding water content \(w\) on shear strength \(q_{\max}\), the cohesion \(c\) and friction angle \(\varphi\), which determined the magnitude of \(q_{\max}\), were calculated using the critical state method [35]:  
\[\sin\varphi\ =\ \frac{3M}{6+M} \tag{1}\]  
\[c\ =\ \frac{S(3-\sin\varphi)}{6\cos\varphi} \tag{2}\]  
where \(M\) and \(S\), respectively, correspond to the slope and intercept of the critical state line established in the \(p\)-\(q\) plane. \(p\) represented the bulk stress.  
The results of calculated \(c\) and \(\varphi\) are plotted in Figure 9a,b as a function of \(w\), respectively. As can be observed, when remolding water content \(w\) rose, cohesion \(c\) increased,  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Slope \(k\) (\(w\leq 27.5\%\))** & **Slope \(k\) (\(w>27.5\%\))** \\ \hline \(q_{\max}\) at \(\sigma_{3}\) = 200 kPa & 0.54 & 1.07 \\ \(q_{\max}\) at \(\sigma_{3}\) = 700 kPa & 0.46 & 0.89 \\ \(q_{\max}\) at \(\sigma_{3}\) = 1200 kPa & 0.38 & 0.80 \\ \(c\) & 0.42 & 1.38 \\ \(\varphi\) & 0.65 & 0.98 \\ \(E_{0}\) & 0.33 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Slopes of the variation of mechanical parameter.  
Figure 8: Variation of maximum deviator stress \(q_{\max}\) with \(w\).  
while friction angle \(\varphi\) exhibited a decreasing trend. In addition, similar to the case of \(q_{\text{max}}\), the increasing trend of \(c\) and decreasing trend of \(\varphi\) could be divided into two stages with \(w=27.5\%\) as the boundary. Moreover, for both \(c\) and \(\varphi\), the slope within the range of \(w=27.5\)-\(35\%\) was larger than the slope within the range of \(w=19\)-\(27.5\%\). Specifically, regarding \(c\), the slopes in the intervals of \(w=27.5\)-\(35\%\) and \(w=19\)-\(27.5\%\) were \(1.38\) and \(0.42\), respectively. For \(\varphi\), the corresponding slopes were, respectively, \(0.65\) and \(0.98\).
## 4 Results and Discussion
### SDL Model Implementation on Datasets Type-2: Reflection Wave Propagation Simulation
Figure 7: _Cont._  
Figure 7: Line scans of reflected wave propagation with different geometrical domains: the FE and SDL modeled simulations are compared, quantitatively, by extracting the displacement values in each frame at x = 15 mm and y = 0 to 40 mm for different time instances, shown in Figure 6. **(a)** The line scans are extracted from Figure 6 for the curved domain, **(b)** shows the line scans are extracted from Figure 6 for the T-shaped domain, and **(c)** represents the line scans extracted from Figure 6 for the triangular domain. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Line Scan**} & \multirow{2}{*}{**Datasets of**} & \multicolumn{4}{c}{**MAE**} & \multicolumn{4}{c}{**RMSE**} & \multicolumn{4}{c}{**MAPE (\%)**} \\ \cline{3-16}  & & \multicolumn{4}{c}{**Sequence of Images at**} & \multicolumn{4}{c}{**Sequence of Images at**} & \multicolumn{4}{c}{**Sequence of Images at**} & \multicolumn{4}{c}{**Sequence of Images at**} \\ \cline{3-16}  & & **T-1** & **T-2** & **T-3** & **T-4** & **T-5** & **T-1** & **T-2** & **T-3** & **T-4** & **T-5** & **T-1** & **T-2** & **T-3** & **T-4** & **T-5** \\ \hline \multirow{3}{*}{Amplitude} & Curved & 0.00 & 0.00 & 0.00 & 0.01 & 0.01 & 0.00 & 0.10 & 0.13 & 0.06 & 0.06 & 0.00 & 55.9 & 48.3 & 36.6 & 26.6 \\  & T-shaped & 0.00 & 0.00 & 0.00 & 0.01 & 0.00 & 0.00 & 0.00 & 0.06 & 0.08 & 0.18 & 0.00 & 0.00 & 78.7 & 73.0 & 74.0 \\  & Triangular & 0.00 & 0.01 & 0.01 & 0.01 & 0.00 & 0.00 & 0.06 & 0.07 & 0.07 & 0.19 & 0.00 & 57.8 & 67.4 & 131 & 71.4 \\ \hline \multirow{3}{*}{Time of Flight} & Curved & 0.00 & 0.50 & 0.86 & 0.07 & 0.29 & 0.00 & 1.16 & 1.51 & 0.27 & 0.85 & 0.00 & 1.05 & 1.36 & 0.20 & 0.90 \\  & T-shaped & 0.00 & 0.00 & 0.43 & 0.79 & 1.86 & 0.00 & 0.00 & 0.76 & 1.16 & 2.54 & 0.00 & 0.00 & 1.16 & 2.10 & 4.32 \\ \cline{1-1}  & Triangular & 0.00 & 1.14 & 0.23 & 1.64 & 0.64 & 0.00 & 1.75 & 0.48 & 2.56 & 1.04 & 0.00 & 5.37 & 1.19 & 5.34 & 1.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Reflected wave propagation with different geometrical boundaries: the SDL model predicted simulation is compared with FE simulations. The MAE, RMSE, and MAPE methods are used for calculating the line-scan amplitude and time of flight (TOF) difference between SDL and FE, on the sequence of images at different time instances, shown in Figure 7.  
Once trained, the SDL model takes approximately 180 s to generate the 1040 frames in one simulation sequence, for 13 us total simulation time, with the time interval of 0.0125 us. The conventional FE solver takes 3600 s, using the same computer processor. Hence, we can infer that the proposed SDL model could simulate reflected wave propagation simulation for different geometrical boundaries with reduced computational requirements than FE simulation.