esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This work reports the synthesis of SnIn\({}_{4}\)S\({}_{8}\) photocatalyst and its efficient application in degradation of ethiofencarb carbamate, which is a commonly used toxic insecticide. The catalytic degradation was carried out under visible-light irradiation, reaching 98% removal of ethiofencarb in 24 h. The optimal catalytic loading for ethiofencarb removal was found to be 0.5 g L\({}^{-1}\) of SnIn\({}_{4}\)S\({}_{8}\), and the optimal pH was 3. It was also found that common inorganic anions that generally exist in natural water systems such as Cl\({}^{-}\) and NO\({}_{3}\)\({}^{-}\) can slightly slow down the kinetics of this photodegradation process. Furthermore, the mechanism of the photocatalytic degradation of ethiofencarb was investigated. \({}^{\bullet}\)OH and \({}^{\bullet}\)O\({}_{2}\)\({}^{-}\) radicals were found to be the principal active species in this reaction. Nine intermediate products were identified, and plausible transformation routes were proposed involving the cleavage of either the carbamate group or the C-S bond of ethiofencarb. The SnIn\({}_{4}\)S\({}_{8}\) material was found to be stable under visible-light irradiation for three successive runs. The photocatalyst was efficient for practical removal of ethiofencarb from environmental water samples (lake and river water), demonstrating its prospect in wastewater treatment.  
The following are available online at [[https://www.mdpi.com/article/10.3390/nano11051325/s1](https://www.mdpi.com/article/10.3390/nano11051325/s1)]([https://www.mdpi.com/article/10.3390/nano11051325/s1](https://www.mdpi.com/article/10.3390/nano11051325/s1)), Figure S1: Adsorption of ethiofencarb on SnIn\({}_{4}\)S\({}_{8}\) at different pH conditions. Experimental conditions: ethiofencarb concentration 10 mg L\({}^{-1}\); SnIn\({}_{4}\)S\({}_{8}\) concentration 0.5 g L\({}^{-1}\); Figure S2: Mass spectra of intermediates formed during the photodegradation of ethiofencarb after they were separated by GC/MS method.; Table S1: Characteristics of the real water sample.  
Conceptualization, C.-S.L. and C.-C.C.; funding acquisition, C.-S.L.; investigation, C.-C.C., J.S., K.P., V.B.G., S.T. and S.-Y.W.; project administration, C.-S.L.; supervision, C.-S.L.; writing--original draft preparation, C.-S.L. and C.-C.C.; writing--review and editing, C.-S.L., J.S. and V.B.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of the Republic of China (MOST 108-2113-M-025-001).  
Data Availability StatementData is contained within the article and Supplementary Materials.  
We acknowledge the support by the Khalifa University of Science and Technology under Award No. RC2-2018-024.  
The authors declare no conflict of interest.
## 4 Experiments
### Impact of the AEGCN Layers  
In order to verify the influence of the number of AEGCN layers on the model, we used different layers of AEGCN on the Lap14 dataset to compare its effects. Accuracy is still used as the evaluation indicator, and the experimental results are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Aspect** & \multicolumn{2}{c}{**Attention Visualization**} & **Prediction** & **Label** \\ \hline \multirow{3}{*}{AEGCN} & \multirow{3}{*}{food} & \multicolumn{2}{c}{Dalicious} & \multirow{3}{*}{food but terrible} & \multirow{3}{*}{Positive} & \multirow{3}{*}{Positive} \\  & & & environment & & \\ \cline{3-5} \multirow{4}{*}{AEN} & \multirow{3}{*}{son} & His & lovely & son is always & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table}
Table 4: Visual analysis cases of ASGCN, AEN, and AEGCN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Twitter** & **Lap14** & **Resti4** & **Resti5** & **Rest16** \\ \cline{2-5}  & **Acc** & **Acc** & **Acc** & **Acc** & **Acc** \\ \hline AEGCN-BERT & 75.99 & 80.37 & 84.46 & 83.92 & 89.61 \\ \hline w/o attt & 75.12 & 79.13 & 83.45 & 83.14 & 88.36 \\ w/o MHIA\({}^{1}\) & 74.77 & 78.91 & 83.11 & 82.73 & 88.13 \\ w/o MHIA\({}^{2}\) & 73.43 & 78.31 & 82.11 & 81.52 & 86.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study results of accuracy (%) on five datasets.  
It can be seen from the above figure that when the number of AEGCN layers exceeds two, the model performance begins to decline. Due to the limitations of the dependency tree itself, when the number of AEGCN layers is too large, a lot of noise is also updated to the representation of the last layer, which has a negative impact on the performance of the model.
## 3 Results and Discussion
### D Surface Analysis
The samples belonging to code group E are characterized by surface parameters with the following values: \(Sq\) = 0.414 \(\upmu\)m, \(Ssk\) = \(-\)0.184 \(\upmu\)m, \(Sku\) = 2.5 \(\upmu\)m, \(Sp\) = 1.12 \(\upmu\)m, \(Sv\) = 0.714 \(\upmu\)m, \(Sz\) = 1.84 \(\upmu\)m, \(Sa\) = 0.33 \(\upmu\)m, \(Spd\) = 1942 1/mm\({}^{2}\), and \(Spc\) = 2602 1/mm. For the parts belonging to code group C, the surface was milled on a numerical milling machine with feed parameters \(f\) = 150 mm/rev, cutting speed \(vc\)\(\approx\) 39.6 m/min, and cutting depth \(ap\) = 0.3 mm. The code group F was characterized by surface parameters the following values: \(Sq\) = 0.237 \(\upmu\)m, \(Ssk\) = \(-\)0.180 \(\upmu\)m, \(Sku\) = 3.23 \(\upmu\)m, \(Sp\) = 0.794 \(\upmu\)m, \(Sv\) = 0.847 \(\upmu\)m, \(Sz\) = 4.64 \(\upmu\)m, \(Sa\) = 0.187 \(\upmu\)m, \(Spd\) = 1452 1/mm\({}^{2}\), and \(Spc\) = 584 1/mm. For the parts belonging to code groups A, B, and D, the surface was made by turning on a numerical lathe with feed parameters \(f\) = 0.02 mm/rev and cutting speed \(vc\)\(\approx\) 100 m/min. The typical roughness parameters, such as \(Sq\), \(Ssk\), \(Sku\), \(Sp\), \(Sv\), \(Sz\), \(Sq\), and \(Spc\), are strongly dependent on a variety of parameters, which can be classified into primary and natural surface roughness effects. The primary surface roughness effect is contributed by the machining parameters (cutting speed, feed rate, depth of cut, nose radius, cutting time, coating, etc. ), type of machining, and tool geometry. Meanwhile, the natural surface roughness effect is contributed by the machine tool and uncontrolled variation in the machining process, such as tool wear, dynamic unbalance of machining system (stability and stiffness of the machine tool--cutting tool--workpiece system), cutting fluid, chip formation, and workpiece (workpiece deflection, material characteristics) [32; 33; 34; 35]. It is found that within the machining parameters, the feed rate has the most significant effect on surface roughness, followed by the nose radius and cutting time. An example of the influence of a parameter change on roughness is a comparison of the values of the Sa parameter for code groups A and B. The difference in the feed rate between the groups is 0.1 mm/rev, which results in a reduction in the Sa parameter by less than 40%. Figure 6: Contour map and isometric view of samples. (**a,b**) Coded A, (**c,d**) coded B, (**e,f**) coded C.
# Insights into Membrane Damage by \(\alpha\)-Helical and \(\beta\)-Sheet Peptides
## 3 Results
### Water Permeation
Figure 2B shows the number of water permeation events in three independent simulations performed using M03 (gray-black lines) and M04 (pink-red) peptides. In these simulations, five peptides were added to the system every 1 us, accounting for the same amino acid concentration of added peptides as in panel A. All three simulations performed with the M03 peptide resulted in pore formation characterized by a large increase in the number of water permeation events at \(\sim\)2.5 us and \(\sim\)4.5 us. The pore from the simulation represented by the black line in panel B is depicted in panel III, showing peptide accumulation in the form of a cylindrical channel that spans both leaflets of the membrane. This channel is filled with water in its interior. In stark contrast, simulations performed with M04 peptides did not account for a sudden increase in water permeation events. Accordingly, we found that the peptides, at the end of the simulation (panel IV), were mostly dispersed on the membrane, without piercing it. Thus, despite the _perfect_ segregation of polar-nonpolar  
residues to different faces in an \(\alpha\)-helix, which is a feature of many membrane active peptides, the M04 sequence has a significantly lower potential to damage lipid membranes than M03. Notice, however, that in one of the M04 simulations (red line), the number of water permeation increased by a small amount at time \(\sim\)2.5 us. This suggests that M04 peptides also have the potential to damage lipid membranes, although much less than M03.
# A Filter-Based and Parallel Unknown Tag Identification Protocol in Open RFID Systems
## 3 System Model  
### Assumption and Problem  
This paper considered an open RFID system consisting of one reader, \(N\) known tags, \(M\) unknown tags, and some missing tags. All these tags were within the interrogating range of this reader. The IDs of all known tags (including missing tags) were already stored in the back-end database of the server, but all IDs and \(M\) unknown tags were unavailable in the server. The known tag set is denoted as \(\mathbb{K}\), i.e., \(\mathbb{K}=\{t_{1},t_{2},\ldots,t_{i},\ldots,t_{N}\}\), and the unknown tag set is denoted as \(\mathbb{U}\), i.e., \(\mathbb{U}=\{t_{u_{1}},t_{u_{2}},\ldots,t_{u_{i}},\ldots,t_{u_{M}}\}\). The intersection of sets \(\mathbb{K}\) and \(\mathbb{U}\) is null, i.e., \(\mathbb{K}\cap\mathbb{U}=\emptyset\). Each tag has a unique ID, and all tags are equipped with the identical hash generator \(H(\cdot)\). The missing tag set is a part of the known tag set \(\mathbb{K}\). Figure 1 presents an overview of an open RFID system with unknown tags and missing tags. There are a large number of known tags and numerous unknown tags in the interrogation of the reader. Table 1 shows the notations used in this paper. The objective of our protocol is to collect the information of the intact IDs of all unknown tags from the total tag set \(\mathbb{K}\cup\mathbb{U}\) and eliminate the influence of false positives.
## 3 Applications of Crosslinked PEI
### Adsorption of Heavy Metal Ions and Adsorption Models
However, poly(ethylene glycol) diglycidylI ether-, 1,4-butanediol diglycidylI ether-, and EGDE-crosslinked materials have also given respectable adsorption values for Hg\({}^{2+}\), Cu\({}^{2+}\), and Au\({}^{3+}\), as summarized in this review. In particular, materials that have chelating cites such as NH\({}_{2}\) and OH groups work well as adsorbents for metal ions and other pollutants. Equally important is the desorption efficiency and the reusability of the crosslinked materials. While most of the materials discussed can be regenerated by chemical treatments, a 2,4,6-trichloro-1,3,5-triazine (TCT)-crosslinked PEI material reported by [PERSON] et al. [55] is among the substances with the fastest regeneration kinetics (1 min) and an almost 100% regeneration efficiency. The material worked fine for up to 10 adsorption and desorption cycles. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**PEI/Composite** & **Crosslinker Type** & **Analytes Removed** & \multicolumn{2}{c}{**Removal Efficiency (mg g\({}^{-1}\))**} & \multicolumn{2}{c}{**Adsorption Isotherm/ Kinetics**} & **Ref.** \\ \hline PEI/Fe\({}_{3}\)O\({}_{4}\)/corncob & Glutaraldehyde & Cu\({}^{2+}\) Pb\({}^{2+}\) & 459.4 & Langmuir equation & [9] \\ \hline PEI & Glutaraldehyde & Au\({}^{3+}\) Pd\({}^{2+}\) & 2575 & Sips model & [65] \\ \hline  & & Cr\({}^{+6}\) Ni\({}^{2+}\) & 1.43 & & \\ PEI & ECH & Mn\({}^{2+}\) & 1.31 & & \\ PEI & ECH & Pb\({}^{2+}\) & 1.26 & - & [21] \\  & & Zn\({}^{2+}\) & 1.57 & & \\  & & Hg\({}^{2+}\) & 0.88 & & \\ \hline A sulfonated crosslinked PEI & - & Hg\({}^{2+}\) & 1.36 & \begin{tabular}{p{56.9 pt}} Freundlich isotherm/pseudo-second-order \\ \end{tabular} & [66] \\ \hline PEI cryogels & Poly(ethylene glycol) & & 1000.2 & - & [17] \\  & diglycidylI ether, 1,4-butanediol diglycidylI ether, and glutarac aldehyde & Cu\({}^{2+}\) & \
\begin{tabular}{p{56.9 pt}} 118.1 \\ (highest values from the Langmuir model) \\ \end{tabular} & - & [17] \\ \hline A sulfonated crosslinked PEI & - & SeO\({}_{3}\)\({}^{2-}\) & 3.17 &
\begin{tabular}{p{56.9 pt}} The Freundlich isotherm/pseudo-second-order \\ \end{tabular} & [68] \\ \hline PEI & 2,4,6-Trichlor-1,3,5-triazine & Au\({}^{3+}\) & 1073.0 & Pseudo-second-order & [55] \\ \hline PEI & EGDE & Au\({}^{3+}\) & 943.5 & Langmuir & [43] \\ \hline PEI-coated polysulfone/_Escherichia coli_ biomass composite fiber & Glutaraldehyde & Pd\({}^{2+}\) & 216.9 & - & [69] \\ \hline PEI/Ca\({}^{2+}\)-Alginate hydrogel & Glutaraldehyde & Au\({}^{3+}\) & 2300 & - & [71] \\ \hline PEI/HPMC & Glutaraldehyde & & 333 & \\ PEI only & Glutaraldehyde & Cu\({}^{2+}\) & 272 & Pseudo-second-order & [31] \\ PEI only & Glyoxal & & 222 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PEI-based materials, crosslinkers, and applications in metal removal from aqueous solutions.
# Deep Learning Applied to Scenario Classification for Lane-Keep-Assist Systems
## 3 Time Series Classification Using Convolutional Neural Networks (CNN)
### Residual Connections  
To produce models that generalize well on highly complex data, the number of layers need to increase. By creating deeper models, and by using appropriate regularization, we assure that the models learn the necessary features to produce appropriate outputs. However, training deep networks represents a challenge because of the vanishing gradient problem. The gradients from the output layers need to be propagated through the whole network which makes the gradients closer to the input part of the network infinitely small, saturating and degrading the model.  
[PERSON] et al. [32] have proposed skip connections, as shown in the residual block in Figure 3. The skip connection allows the network to learn a residual mapping more easily instead of the original underlying mapping, circumventing the gradient vanishing problem, as gradients can more easily flow through the network. However, [PERSON] et al. [33] have done research on the residual connections and argue that the skip connections allow the network to create better prediction as they effectively allow information to pass through different pathways.  
The skip connection introduced in the residual block is basically an identity connection added to the convolutional layers and can be expressed with the following equation:  
\[res_{x}=x+F(x), \tag{4}\]  
where \(x\) is the layers input and \(F(x)\) presents an ensemble of operations, in our case convolutional layers followed by an activation function.  
Figure 3: Building blocks of the CNN network. The residual block with dilated separable convolutions is shown on the left. The separable convolutional layers with stride \(s=2\) for dimensionality reduction is shown on the right.  
Figure 2: Visual representation of the dilated convolutional layers with different dilation factors \(d\). It can be seen how the last layer has a wide receptive field.
## 2 Fast Deploying Monitoring System
### FDMS in Bage Landslide
The differential working model requires a fixed base station on the stable rocks 1 km away from the moving Beidou receivers, as shown in Figure 7. The installation of the Beidou receivers is shown on the left side of Figure 8a. The traditional method is to fix the supporting stake with concrete, but it takes several days for the concrete to harden. Here, no concrete was used. The Beidou receiver supporting stake was fixed on a steel plate, which was nailed to the bottom of a 1 m deep hole. To make the foundation firmer, the metal battery box was put on the steel plate as a counterweight. This installed method is time saving comparing to the traditional method. The Beidou receiver is designed in accordance with the IP67 (ingress protection rating 67). It is dust-proof, rain-proof, and snow-proof. The installation of the crack meter is shown on the left side of Figure 8b. The crack meter was connected to a metal pole which was buried 1 m deep in the slope at one side of the ground crack. A pull wire drawn from the crack meter was fixed on the metal pole at the other side of the ground crack. The pull wire could extend as the ground crack expands, and so the width of the crack can be determined by measuring the change of the pull wire. The measuring range of the crack meter was 5 m, and the accuracy was 0.5 mm. Figure 4: Location of the Baige landslide. Figure 5: Photograph of the Baige landslide and details of the sliding surface. Figure 6: Geological cross-section of Baige landslide (line A\({}^{\prime}\)-A).
## 5 Case Study
### Contour Reconstruction of Test Targets  
According to the proposed procedure, it is necessary to position the \(P_{i}\) tentative points in the image to identify the \(ROI_{P_{i}}\). This positioning requires locating an identifiable part origin in the digital image. In this work, test targets were provided with a reference hole at the center of the target layer. This hole is identified in the digital image using the Hough algorithm [48], obtaining the pixel coordinates of its center, which serves as a valid coordinate origin for the tentative points. The theoretical coordinates of the \(N\) tentative points are then transformed into pixel coordinates through the application of LDA, aligning them with the image of the manufactured part.  
In this work, a total of 720 tentative points were used. Given that the selected test geometry was a circumference, the angular resolution for the dimensional and geometric characterization of the contour was \(0.5^{\circ}\). Since the reference database was constructed with a \(15^{\circ}\) resolution, an interpolation procedure was applied. Accordingly, each pixel (\(n\)) of the \(GRP_{H,\theta}\) corresponding to angle \(\theta\) was calculated from the \(n-th\) element of the adjacent \(GRP_{H,\theta}\) vectors defined for the preceding and subsequent angles. Figure 16 shows an example of the interpolation between \(GRP_{H_{2},150}\) and \(GRP_{H_{2},165}\) to construct an interpolated \(GRP_{H_{2},160}\).  
For each \(P_{i}\), an \(ROI_{P_{i}}\) is defined according to the orientation rules outlined in Section 3. In this work, the length of the ROIs (\(m\)) was calculated using Equation (5).  
\[m=2\cdot\|GRP_{H,\theta}\|-1=2\cdot n-1=101 \tag{5}\]  
Consequently, each ROI was 1.069 mm long. The procedure then extracted all the n-sized \(CP_{j}^{P_{i}}\)s, and Equation (4) was applied to determine each candidate's similarity to the \(GRP_{H,\theta}\). By repeating this process for all tentative points, a cloud of contour points was obtained, providing a discrete representation of the target layer's contour.  
Figure 16: Example of the interpolation procedure.  
Figure 15: Digitized sample specimens with heights, from left to right, \(H_{8}\), \(H_{6}\), \(H_{4}\), and \(H_{2}\).
## 4 Results  
After quantification of glacier area and locating its terminus, a univariate time series for the changes in Franz Josef and Gorner glacier's terminal point and area were created from the collected Landsat image sequences. The climate data provides the independent variables for both the multiple regression and the GAM while the area, and location data are used as the response variable. Multiple regression and GAM were used for modeling the glacier variations. As depicted in Figure 8, it can be observed that the two glaciers have differing behaviors over the time. Franz Josef having a period where the glacier was advancing while Gorner was in a continuous state of retreat during the same period (1985-2010). The optimal multiple regression based on AIC for modeling Franz Josef variations was a combination of local temperature, global temperature, CO\({}_{2}\), and average precipitation for terminal point as response, and only local temperature and average precipitation for area as response (Table 1). Local temperature and CO\({}_{2}\) were identified as the impactful factors by multiple regression to model Gorner glacier variations using terminal point, while with glacier area as response, local temperature and global temperature were only impactful predictors identified by the model (Table 1). The optimal GAM for modeling Franz Josef variations was a combination of local temperature, CO\({}_{2}\), and average precipitation using either terminal point or glacier area as response (Table 2). Local temperature, average precipitation, and CO\({}_{2}\) were identified as the impactful factors by GAM to model Gorner glacier variations using terminal point, while with glacier area as response the global temperature was also included in the model (Table 2). Figure 8: (**Top**) Time Series for the terminus variations 2000 to 2021 (pixel resolution is 30 meters). (**Bottom**) Time series for the area variations 2000 to 2021.
## 3 Results and Discussion
### Class Incremental Learning Method
For tests using the Darkshell family, with 1049 examples in the dataset, as an incremental class, the initial average accuracy obtained by the model was 86.52% and at the end of the second step of incremental training, with the new class added to the model, was 81.56%, therefore showing a loss of accuracy of 4.96%, but 19.52% higher compared to the accuracy of the model trained without transfer learning with the reduced set of examples. The recall in the initial stage of the model with 60 families was 81.67% and rose to 85.88% after the second stage of incremental training. There was a gain in this metric of 4.31%, and a gain of 10.84% when compared to the trained model without transfer of learning. The F1 score in the initial stage was 82.77% and became 82.45% at the end of the second stage of incremental class training, showing a loss of 0.31%, but there was a gain in the model trained from scratch, without the incremental process of 17.15%.