esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The study is still in the preliminary stage, where the analysis of the active compound of EEP is currently under investigation using the High-Performance Liquid Chromatography (HPLC) method. Based on the previous study conducted on the EEP produced through the fermentation of fruit peels, acetic acid and naringenin have been identified as the main components, which we believe will also be detected in our sample. Additionally, other active components and secondary metabolites may be present as well, and the analysis will give a better insight and strengthen our understanding of the exact mechanism of EEP towards the cell. These findings will add more value to the development of EEP-based irrigants for root canal treatment. The EEP in this study was prepared through the conventional fermentation process as described previously by [PERSON] et al. [32] and [PERSON] et al. [25]. This fermentation method does not provide a stringent controlled environment for fermentation, thereby introducing potential batch-to-batch variation. The use of a bioreactor can be considered, as this device provides a stringent environment for the fermentation process, allowing precise regulations of the pH and temperature together with air filtration and other sterilisation capabilities, which will prevent the introduction of unwanted contamination during fermentation. In addition, the present study was conducted in a well-controlled environment and did not replicate the actual clinical scenario of an oral environment. The cytotoxic effects measured were solely based on the cell's response, without considering the involvement of the inflammatory reaction provided by the host defence mechanism [50]. However, the biocompatibility profile of EEP obtained from this study will ethically justify the use of a similar concentration for in vivo studies on animals [69]. Future in vivo studies can be conducted, either by injecting the material into the subcutaneous tissue of the animal to elucidate the inflammatory reaction towards EEP or by replicating the actual root canal treatment protocols in laboratory animals, followed by clinical assessment, radiographic imaging and histopathological evaluation of the periapical tissue reaction [70]. Clinically, since EEP is a fruit-based product, this irrigant might not be suitable to be used on patients with citrus allergies. Although the prevalence is rare [71], precautionary measures should be taken, which may limit its application in clinical settings. Exploration of the antibiofilm activity of EEP at 6.25% against the multi-species bacteria that cause endodontic infection should be investigated in the future. Further, the penetrative ability of EEP and its effect on the microhardness, tensile strength and modulus of elasticity of the dentine should be assessed, either in vitro or in vivo. Additionally, its potential interactions with restorative and obturation materials are yet to be explored. Addressing these questions requires further research efforts in the future.
## 6 Experimental Results and Analysis
### Analysis of Prediction Based on ELM  
To verify that ELM can be used to predict pedestrians accurately, a set of chaotic sequences is used for testing. Chaotic sequences, usually generated by mapping functions such as cat map and logistic map, are commonly used to test the approximation and prediction ability of an algorithm. They have similar properties as random signals, like arbitrary changes with time and non-periodic. ELM used for prediction is based on the framework of n-L-m, where n = 12, L = 20, and m = 1. The first 990 chaotic data points in the txt file are used as training data. Then, the values of chaotic sequences for testing from the 990 th frame to the 1000 th frame are predicted. The vertical axis of Figure 8a is the value of a chaotic sequence. As we can see, the predicted value is very close to the actual value, and the mean absolute percentage error is 6.91%, which means that the ELM can predict the change of data well.  
In the testing video for pedestrian prediction, the number of pedestrians dynamically changes from zero to four. Figure 8b shows the predicted results of the number of pedestrians in the video. Obviously, using ELM to predict the number of pedestrians obtains a good result.
## 3 Materials and Methods
### Analysis of Mycotoxins in Salmon Side Stream Materials  
High-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (LC-ESI-qTOF-MS) was employed to investigate the occurrence of mycotoxins in salmon side stream materials. An Agilent 1200-LC system (Agilent Technologies, Palo Alto, CA, USA) equipped with a Gemini(r) column NX-C18 (3 \(\upmu\)M, 150 \(\times\) 2 mm ID) (Phenomenex), as well as a vacuum degasser, binary pump, and autosampler, were used to achieve the chromatographic separations The mobile phases consisted of acidified (0.1% of formic acid) water (A) and acetonitrile (B). A gradient program of 50% B (0-6 min); 100% B (7-12 min); and 50% B (13-20 min) was applied. Samples (5 \(\upmu\)L) were injected at a flow rate of 0.2 mL/min. Mass spectrometry (MS) analysis was carried out using a 6540 Agilent Ultra-High-Definition-Accurate-Mass-q-TOF-MS coupled to the HPLC, equipped with an Agilent Dual Jet Stream electrospray ionization (Dual AJS ESI) interface in positive and negative ionization modes. The operational conditions were as follows: nebulizer pressure (50 psi); capillary voltage (3500 V); fragmenter voltage (160 V); scan range (\(m\)/z 50-1500); drying gas temperature (370 \({}^{\circ}\)C); and nitrogen drying gas flow (12.0 L/min). Automatic MS/MS experiments were performed under the following collision energy values: \(m\)/z 100, 30 eV; \(m\)/z 500, 35 eV; \(m\)/z 1000, 40 eV; and \(m\)/z 1500, 45 eV. For data acquisition and integration, Mass Hunter Workstation software was used.  
The QuEChenRS procedure to extract mycotoxins from fish discards, previously reported by [PERSON] et al. (2018), was applied. Approximately 3 g of salmon samples were mixed with 30 mL of acidified water (2% formic acid) in an orbital shaker (IKA KS 260) for 30 min. Then, 10 mL of acetonitrile were added and the mixture was stirring again for 30 min. Next, 8 g of MgSO\({}_{4}\) and 2 g of NaCl were added to the mixture, vortexed for 30 s and centrifuged at 4000 rpm for 10 min. Afterward, 0.1 g of Octadecyl C18 sorbent and 0.3 g of MgSO\({}_{4}\) were mixed with 2 mL of supernatant. Additional shaking and centrifugation under the same conditions as reported previously were performed. The supernatant was then filtered (13 mm/0.22 \(\upmu\)m nylon filter) and 20 \(\upmu\)L were injected into the LC-ESI-qTOF-MS system.
## 3 Network Intrusion Detection Model Based on CNN and GRU
### Model Structure  
#### 3.2.1 Convolutional Block Attention Module  
The purpose of introducing an attention mechanism in the model is mainly to improve the representational power. It mainly means that we give a larger weight to important features and a smaller weight to unnecessary features. The Convolutional Block Attention Module (CBAM) is a lightweight attention module proposed by [PERSON] et al. in 2018, which contains of two main parts: the channel attention module and the spatial attention module [48].  
The specific structure of the channel attention module in CBAM is shown in Figure 3, which uses the relationship of features between channels to generate a channel attention map to form the input. Afterwards, the spatial information is aggregated using Averagepooling and Maxpooling, respectively, to generate two different sources of spatial information \(F_{avg}^{c}\) and \(F_{\text{max}}^{c}\). The two spatial information sources are fed into a shared network consisting of a multi-layer perceptron and a hidden layer, which in turn generate the required channel attention graph \(M_{c}\), and finally output the feature vector by element summation.  
\[\begin{split} M_{c}(F)=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F)))\\ =\sigma(W_{1}(W_{0}(F_{avg}^{c}))+W_{1}(W_{0}(F_{\text{max}}^{c}) ))\end{split} \tag{4}\]  
where \(\sigma\) represents the sigmoid function, \(W_{0}\) and \(W_{1}\) are the weights of the multilayer perceptron, \(F\) is the input and \(M_{c}\) is the final output.  
The specific structure of the spatial attention module in CBAM is shown in Figure 4, which uses the spatial relationships between features to generate a spatial attention map to form the input. Afterwards, two 2D feature maps \(F_{avg}^{s}\) and \(F_{\text{max}}^{s}\) are generated in turn by Averagepooling and Maxpooling, which effectively highlight the information region. The two are finally fused and the desired 2D spatial attention map is generated by standard convolution to output the feature vector.  
\[\begin{split} M_{s}(F)=\sigma(f^{\gamma\times 7}([AvgPool(F); MaxPool(F)]))\\ =\sigma(f^{\gamma\times 7}([F_{avg}^{s};F_{\text{max}}^{s}])) \end{split} \tag{5}\]  
where \(f^{\gamma\times 7}\) represents a convolution operation with filter size \(7\times 7\), \(F\) is the input and \(M_{s}\) is the final output.  
Figure 3: Channel Attention Module.  
CBAM uses a channel attention module and a spatial attention module in turn, enabling the model to learn the features of the channel and spatial axes respectively, as shown in Figure 5. Given the initial feature map \(F\) as input, CBAM will generate 1D channel attention feature maps \(M_{c}\) and 2D spatial attention feature maps \(M_{s}\) in turn. The whole process is shown in Figure 5 and summarized as follows.  
\[\begin{split}& F^{\prime}=M_{c}(F)\otimes F\\ & F^{\prime\prime}=M_{s}(F^{\prime})\otimes F^{\prime}\end{split} \tag{6}\]  
where \(\otimes\) denotes element-wise multiplication, \(F^{\prime}\) is the output after the channel attention module and \(F^{\prime\prime}\) is the final output.
## 3 Results and Discussion
### Volumetric Flow Rate  
The volumetric flow rate per unit length, \(q\), for each cell is shown in Figure 4, calculated over the range \(0.1\leq\varepsilon_{wf}/\varepsilon_{ff}\leq 1.0\). For the \(h=3\) nm cell, \(q\) presents a monotonic, decreasing behavior as the walls vary from being strongly hydrophobic (\(\varepsilon_{wf}/\varepsilon_{ff}=0.1\)) to strongly hydrophilic (\(\varepsilon_{wf}/\varepsilon_{ff}=1.0\)). Moreover, for \(\varepsilon_{wf}/\varepsilon_{ff}=0.1\) and for all cell heights investigated here, \(q\) obtains its maximum value. This is attributed to the existence of slip near the solid boundaries, which facilitates flow and, in the case of narrower channels, is more pronounced [26]. As the cell height increases, e.g., for \(h=21\) nm, the flow rate is stabilized to a constant value within statistical accuracy, without being further affected by the wall/fluid interaction.
## 4 Results and Analysis
### Landing: Analysis
#### 4.4.2 Landing: Ordinal Regression  
In the survey, pilots were asked at what point they start their landing checks. Initially, an ordinal regression analysis was attempted for this question, but the regression did not  
\begin{table}
\begin{tabular}{c c} \hline \hline
**In Your Opinion, at What Point Would It Be the Last Stage** & **Proportion** \\
**to Recover from an Unstabilised Landing?** & **Proportion** \\ \hline Touchdown and rollout & 39\% \\ Maximum float point & 28\% \\ Aim point & 11\% \\
200 ft AGL & 22\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Recovery from an unstabilised landing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Question** & **P\({}_{\mathbf{o}}\)** & **P\({}_{\mathbf{e}}\)** & **Kappa** \\ \hline (36a 2) What actions do you take? 1 & 0.268 & 0.0616 & 0.220 \\ (36b 2) Which aspect of your training do you rely on the most in this situation? 1 & 0.274 & 0.063 & 0.225 \\ (37 2) If you encountered a bounce upon initial runway contact during landing, & & 0.229 & 0.039 & 0.198 \\ what would be your immediate action? & & & \\ \hline \hline \end{tabular}  
* Lead question: During the landing roll, you spot debris on the runway ahead that was not visible on the initial approach. You must quickly decide whether to attempt an evasive manoeuvre or abort the landing. Given this new information, with all other flight conditions remaining the same. 2 Refers to the question number in the survey.  
\end{table}
Table 14: Results of Landing Fleiss’ kappa (part 2).  
work. This could be due to model specification, scaling, or insufficient data. Table 15 shows the proportion of respondents for each question.  
This reflects that rather than using a \"minute\" timing, many pilots correlate their checks with their circuit position, e.g., downwind.  
Pilots were also asked how often they practise a crosswind landing. Whilst the coefficient for \"hours\" of \(-0.0001\) suggests that the likelihood of practising often decreases with hours, the high \(p\)-value of 0.907 renders these results as not statistically significant; hence, this correlation may not be reliable. However, whilst 61% of pilots did practise these landings every flight, 4% did not practise them at all.
## 4 Results  
### Simulation and Measurement Process  
The duration of each scenario simulation was set to 300 s. Measurements were collected once the network stabilized (Section 3.2.2) and the source-destination node pairs were within the road section. The source-destination node pairs, spaced 1-4 hops apart (Figures 5-11), 5-6 hops apart (Figure 12) and 7 hops apart (Figure 13), were chosen to be spaced by the same number of IP hops for the duration of their exchanges.  
Each result presented is the mean of the results obtained from six simulations, each carried out with a different seed value to avoid the influence of random number generation. The results collected could be entirely obtained upon request. However, due to the content limits of this paper, we focused on the results allowing us to conduct our analysis.
## 3 Results
### Static Two-Legged Balance Performance  
With regard to results on two-legged static balance performance, due to the non-statistically significant training intervention by the sex interaction effect in any CoP parameter, the results refer to the main effect of training intervention and vision and to the training intervention considering the vision interaction effect. Specifically, there was a significant main effect of training intervention only in the anteroposterior CoP sway range (F\({}_{1,38}\) = 7.52, \(p\) = 0.009), with higher values post-training (PRE vs POST measurement: 1.8 \(\pm\) 0.4 vs. 2.5 \(\pm\) 0.3 cm) (Figure 1B). There were no significant differences due to training intervention for the CoP path length (F\({}_{1,38}\) = 0.35, \(p\) = 0.564, Figure 1A) and the sway range in the mediolateral direction (F\({}_{1,38}\) = 2.38, \(p\) = 0.131, Figure 1C). Vision had a significant main effect in the CoP path length (F\({}_{1,38}\) = 30.12, \(p\) < 0.001), with a higher CoP displacement  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **PRE** & **POST** & **\%\(\Delta\)** & **Cohen’s d** \\ \hline Age (years) & 12.6 \(\pm\) 1.6 & 12.7 \(\pm\) 1.6 *** & +0.8\% & 0.10 \\ Maturity offset (years) a  & \(-\)0.76 \(\pm\) 1.46 & \(-\)0.75 \(\pm\) 1.49 & +1.8\% & 0.01 \\ Body mass (kg) & 47.1 \(\pm\) 10.8 & 48.2 \(\pm\) 11.1 *** & +2.3\% & 0.09 \\ Body height (cm) & 157.1 \(\pm\) 13.4 & 158.0 \(\pm\) 13.1 *** & +0.6\% & 0.07 \\ Body mass index & 18.8 \(\pm\) 1.9 & 19.0 \(\pm\) 2.1 1 1 & +1.1\% & 0.10 \\ (kg/m2) & & & & \\ \(\mathrm{{}^{b}}\) Lower limb length & & & & \\ (cm) & 83.5 \(\pm\) 6.8 & 84.3 \(\pm\) 7.2 *** & +1.0\% & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Male and female soccer athletes’ anthropometric characteristics before (PRE) and after (POST) the combined balance and multidirectional pylometric training intervention.  
found for the eyes-closed condition (Figure 1A). No statistically significant differences were found for either the anteroposterior (F1,38 = 0.14, \(p\) = 0.706, Figure 1B) or the mediolateral CoP sway range (F1,38 = 0.54, \(p\) = 0.468, Figure 1C) due to vision restriction. The interaction effect of training intervention and vision was statistically significant (F1,38 = 29.67, \(p\) < 0.001) in the CoP anteroposterior sway range, where athletes had significantly higher CoP sway values after the 6-week training intervention with eyes closed, as compared with the lower increase in the respective values in the eyes-open condition (Figure 1B). There was a non-statistically significant training intervention for the vision interaction effect for the CoP path length (F1,38 = 0.46, \(p\) = 0.500, Figure 1A) and the sway range in the mediolateral direction (F1,38 = 0.20, \(p\) = 0.661, Figure 1C).
## 5 Experiments and Results
Figure 8 shows the comparisons of the three methods in the validation process. The mean of the error using the proposed method was 0.2388 mm. The maximum of the error was 0.5930 mm and the standard deviation was 0.18 mm. These results were similar to the results of the calibration process. Using the 100 random set \(Q_{randVal}\) for the calibration, the KM had the highest of the mean of errors at 2.18 mm; the lowest of the mean of errors was 0.75 mm and the average was about 1.06 mm. The SKCM had the highest of the mean of errors at 2.71 mm; the lowest of the mean of errors was 0.67 mm and the average was about 0.99 mm. From these results, it was clear that the proposed method had a better effectiveness in contrast to the KM and SKCM methods. Figure 7: Result of the calibration process.
## 4 Results and Discussions
### Parametric Effects Discussions
#### 4.2.2 Improved Pseudo-Dynamic Parametric Analysis  
In Figure 8, the effects of several typically improved pseudo-dynamic factors on the safety for two-stage rock slopes as \(\mathrm{B/H}\) varies are depicted. It can be seen that FS gradually decreases as the horizontal seismicity factor \(k_{h}\), velocity of transverse wave propagating \(V_{s}\), seismic period \(T\), and damping ratio \(D_{r}\) increase, respectively. But their reductions are not linear, i.e., it is as the value of the seismic pseudo-dynamic parameters change at smaller values that FS decreases more. For example, fixing \(\mathrm{B/H}=3\), FS reduces by 35% as \(k_{h}\) increases from 0.1 to 0.2, while FS decreases by less than 10% as \(k_{h}\) increases from 0.4 to 0.5, as shown in Figure 8a. The findings demonstrate that the smaller magnitude of aftershocks is additionally significant for the slope reinforcement engineering. Then, Figure 9 exhibits the interactions of the horizontal seismicity coefficient \(k_{h}\) with the rest of the modified pseudo-dynamic parameters. At arbitrary \(k_{h}\) (\(0.1\leq k_{h}\leq 0.5\)), the variations of FS with the unit change for \(V_{s}\), \(T\), and \(D_{r}\) are all minor and do not exceed 10%.
## 3 Results
### Characterization of MG-Loaded Mesoporous Silica
#### 3.2.3 Fourier Transform Infrared Spectroscopy (FTIR)  
The formation of magnolol-loaded silica composites is demonstrated by FTIR spectroscopy. Figure 3 shows the FTIR spectra of bare SBA-15 silica and silica with modified surface (APTES, MG, APTES/MG). The absorption spectrum of MG shows absorption bands in the range 3340-2663, 1649-1015 and 997-418 cm\({}^{-1}\)[51].  
As the MG curve shows, the intense absorption band attributed to the hydroxyl stretching vibration occurs at 3138 cm\({}^{-1}\). The presence of allyl C=C stretching vibration is determined by the absorption band at 1637 cm\({}^{-1}\), while C=C aromatic stretching is characterized by the 1494 cm\({}^{-1}\) absorption band. The broad peak in the range 3340-2663 cm\({}^{-1}\) includes the stretching vibration of aliphatic C-H, C-O and Ar-H bending vibration.  
The FTIR spectra of the pure, functionalized and MG-loaded silica show an intense broad absorption band at 3600-3200 cm\({}^{-1}\) corresponding to the free -OH stretches vibration. This wide absorption peak and the vibration of Si-OH groups overlap, the entire broad peak of the area being generally attributed to the influence of the hydrogen bonds. In the curves of functionalized and magnolol-loaded silica samples, the peak is lightly modified because the functionalization process and magnolol molecules block active sites and leave smaller amounts of free Si-OH groups on the silica surface. This is an indication that the silica surface has been successfully functionalized with  
Figure 2: SEM images for: (**a**) pure SBA-15 silica (C); (**b**) functionalized SBA-15 silica with APTES (Cf); (**c**) magnolol loaded in SBA-15 silica (C-MG) and (**d**) magnolol loaded in functionalized SBA-15 silica with APTES (Cf-MG).  
APTES molecules, and/or further grafted with magnolol species evidenced by the shoulder close to 3200 cm\({}^{-1}\) wavenumber. At wave values >1240 cm\({}^{-1}\), no modification is identified. [52].  
The grafting of -NH\({}_{2}\) groups on the pore wall surface is proved by the IR band at 1645 cm\({}^{-1}\) with a lower intensity shoulder at 1558 cm\({}^{-1}\). The bands are assigned to the asymmetric and symmetric deformation modes of the -NH\({}_{2}\) groups fixed on the silica surface. [53].
# Performance and Parameter Sensitivity Analysis of the PEMFC Flow Channel with Porous Baffles
## 4 Results and Analysis
### Sensitivity Analysis of the New Channel Parameters  
Figure 9 shows the variation of the average current density with the thickness and baffle porosity of the PEMFCs with four baffles at 0.4 V. The influence of the baffle porosity on the average current density of the PEMFC varies in function of thickness. When the thickness of the baffle is 1.28 mm, and the porosity of the baffle increases from 0.3 to 0.8, the average current density of the PEMFC increases from 1.238 A/cm\({}^{2}\) to 1.298 A/cm\({}^{2}\), i.e., an increase of 7.428%. When the porosity of the baffle is constant, the influence of the baffle thickness on the performance of the PEMFC increases and decreases twice, forming two peaks and valleys. At a voltage of 0.4 V and baffle porosity of 0.5, the first peak of the average current density is at 1.239 A/cm\({}^{2}\), at a thickness of 0.2497 mm, whilst the second peak is at 1.308 A/cm\({}^{2}\), at a thickness of 0.8 mm. The valley is at 1.232 A/cm\({}^{2}\) and at a thickness of 0.3224 mm.  
Figure 8: Two-dimensional cloud diagram: oxygen concentration distribution of (**a**) the GDL cathode in a straight channel model and (**b**) the GDL cathode in a straight channel model with four porous baffles at 0.4 V.  
Figure 10 shows the effect of the number of baffles on the performance of the PEMFC for a porosity of 0.3, with a baffle thickness of 0.8 mm, and a voltage of 0.4 V. Figure 10 shows that the performance of the PEMFC increased approximately linearly with the increase in the number of baffles. When the number of baffles increased from 3 to 9, the average current density of PEMFCs increased from 1.1301 A/cm\({}^{2}\) to 1.1766 A/cm\({}^{2}\), i.e., an increase of 4.11%.
## 3 Direct CSI SVM Modulator  
The direct CSI (or VSI) SVM space vector modulation requires considerably more computational power for both simulations and more complex hardware implementation. The advantage of this approach is the flexibility in vector placement, compared to the lack of flexibility in the previous case. In the available literature, various CSI strategies have been discussed. In this paper, the space vector modulation and the sequence are shown in Figure 5 below [34]. The space vectors and switching sequences that form the basis of direct CSI SVM modulation are shown in Figure 5a. It represents the arrangement of current vectors in the hexagonal plane and their switching states, while Figure 5b indicates the different combinations of switches required to generate these vectors. The goal here is to obtain the desired current vector in the appropriate switching order, which is critical for efficient modulation.