esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Arsenic Sulfide Films  
Arsenic sulfide thin films were prepared by using various deposition methods. The obtained films could be used in optical mass memories, hologram recordings, and optoelectronic and solar cell applications. The band gap was 2.6 eV, suitable to be used as an absorber component in thin film solar cells. As\({}_{2}\)S\({}_{3}\) films (of good quality) were deposited onto various substrates (glass and silicon (111) wafers) by using arsenic trioxide or arsenic (III) oxide (As\({}_{2}\)O\({}_{3}\)) and Na\({}_{2}\)S\({}_{2}\)O\({}_{3}\) solution [70].
# An Internet of Things (IoT) Application on Volcano Monitoring
## 2 IoT for Volcano Monitoring
LoRa offers some advantages over Sigfox that are convenient for volcano monitoring, and specifically for the application described in this paper:  
* Endpoints and base stations with LoRa-enabled devices are usually more inexpensive than with Sigfox. This is primarily because is possible to use the same radio device for a receiver on the base and at the endpoint stations
* When true bidirectionality is needed, LoRa is likely the better option because of the symmetric link
* LoRa allows to set up and manage the wireless network at a deeper level than Sigfox. The latter provides an API to interact with the radio module, but no configuration is possible
* LoRa has no data limit to be sent per day, and it is not necessary to pay for a renewable subscription plan for each device  
For the aforementioned reasons, the communication protocol that best fits the needed requirements is LoRa.
# Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion
## 3 Experimental Design and Results Analysis
### Experimental Result Analysis
It shows that the introduction of multi-path cost aggregation in cross-scale cost aggregation and the use of internal left and right consistency check in disparity optimization can significantly improve the processing efficiency of the stereo matching algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Cones** & **Teddy** & **Tsukuba** & **Venus** & **Avg** \\ \hline SGM [11] & 12.82 & 21.16 & 16.94 & 12.72 & 15.91 \\ MGM [12] & / & / & / & / & / \\ OmniSGM [15] & / & / & / & / & / \\ CSCA + BOX [16] & 10.70 & 20.26 & 13.49 & 6.4 & 12.71 \\ CSCA + GF [16] & 8.76 & 16.54 & 13.55 & 4.77 & 10.91 \\ SGM-MSIF (this paper) & 8.18 & 13.33 & 9.61 & 5.78 & 9.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of EMRs in Disc regions of six algorithms (%). \({}^{\prime}\)/\({}^{\prime\prime}\): the results are not available in the references. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Cones** & **Teddy** & **Tsukuba** & **Venus** & **Avg** \\ \hline SGM [11] & 4.30 & 7.84 & 4.56 & 0.98 & 4.42 \\ MGM [12] & 5.36 & 9.51 & 2.53 & 1.27 & 4.67 \\ OmniSGM [15] & 3.78 & 6.62 & 1.48 & 1.07 & 3.24 \\ CSCA + BOX [16] & 3.92 & 9.98 & 4.52 & 0.79 & 4.80 \\ CSCA + GF [16] & 3.25 & 6.41 & 3.44 & 0.40 & 3.38 \\ SGM-MSIF (this paper) & 2.77 & 4.75 & 1.85 & 0.47 & 2.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of EMRs in Non-occ regions of six algorithms (%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Cones** & **Teddy** & **Tsukuba** & **Venus** & **Avg** \\ \hline SGM [11] & 10.33 & 14.64 & 5.80 & 1.80 & 8.14 \\ MGM [12] & / & / & / & / & / \\ OmniSGM [15] & / & / & / & / & / \\ CSCA + BOX [16] & 9.68 & 15.20 & 5.27 & 1.12 & 7.82 \\ CSCA + GF [16] & 8.90 & 11.45 & 3.85 & 0.54 & 6.19 \\ SGM-MSIF (this paper) & 8.75 & 10.90 & 2.62 & 1.11 & 5.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of EMRs in All regions of six algorithms (%). \({}^{\prime}\)/\({}^{\prime\prime}\): the results are not available in the references. Figure 7: EMRs in Non-occ regions.
## 3 Study on the ECD Model of the Irregular Eccentric Wellbore
### ECD Calculation Model
#### 3.2.2 Frictional Pressure Drop Calculation  
In field cementing operations, the annulus below the wellbore is often eccentric, with the downhole fluid flowing through the eccentric annulus [11]. An eccentric annular flow model is established, where the inner and outer radii of the annulus are \(R_{1}\) and \(R_{2}\), respectively, with the center of the inner circle being \(O_{1}\) and the outer circle being \(O_{2}\), as shown in Figure 7. By establishing a polar coordinate system \((\alpha,\,r)\) with the center of the inner circle as the origin and the radius direction as the ray, the calculation Formula (10) for the annular gap \(R_{i}\) can be obtained.  
\[R_{i}=(1+\cos\alpha)(R_{2}-R_{1})-\frac{1}{2}\bigg{(}\frac{R_{2}-R_{1}}{R_{2}} \bigg{)}\sin^{2}\alpha \tag{10}\]  
where the following variables are used:  
\(\alpha\) is the angle, \({}^{\circ}\);  
\(R_{1}\) is the annular inner radius, m;  
\(R_{2}\) is the annular outer radius, m.  
Figure 7: The eccentric annulus motion model.  
When \(\alpha=0\), the annular gap is at its maximum; when \(\alpha=\pi\), it is at its minimum. The Herschel-Bulkley model with three parameters is more representative of the cement slurry, and its constitutive relationship is shown in Equation (11).  
\[\tau=k\gamma^{n}+\tau_{0} \tag{11}\]  
This Equation is established based on the dynamic resistance balance of the fluid flow in the annulus. The frictional pressure drop in the Herschel-Bulkley model can be derived and represented as follows:  
\[\left\{\begin{array}{l}P_{f}=\Big{(}\frac{1+2\pi}{3\pi}\frac{\epsilon_{0}}{R _{i}\epsilon}\Big{)}^{n}\Big{(}\frac{2 pk}{R_{i}}\Big{)}+\Big{(}\frac{1+2\pi}{1 +n}\Big{)}\frac{2 pk\gamma N}{R_{i}\epsilon}\\ G=1+\frac{1+n}{4\pi}\bigg{(}\frac{3+(R_{1}/R_{2})^{2}}{1+(R_{1}/R_{2})}+\frac{ 1}{n}\Big{)}\Big{(}\frac{\epsilon}{R_{2}-R_{1}}\Big{)}^{2}\\ N=1+\frac{1+n}{4\pi}\bigg{(}\frac{3+(R_{1}/R_{2})^{2}}{1+(R_{1}/R_{2})}+\frac{ 1-n}{n}\Big{)}\Big{(}\frac{\epsilon}{R_{2}-R_{1}}\Big{)}^{2}\end{array}\right. \tag{12}\]  
where  
\(P_{f}\) is friction pressure drop, Pa;  
\(e\) is the eccentric distance, m.  
Based on another researcher's experimental data [25], the friction calculation of the model is proven to be accurate. The length of experimental device annulus is 9 m, and the diameters of the inside and outside pipes are 54.42 mm and 127 mm, respectively. The comparison of model calculations and experimental results is shown in Table 2.
## 5 Conclusions
Due to each of the two optimization models taking the impact of the whole life cycle of the MCS into account, the annual operational cost of the optimized system is significantly reduced compared with that before optimization. The system maintenance cost is an unavoidable part of expenditure in the course of the system's operational life cycle; the optimization has little effect on it. The total cost of the system after optimization is lower than that before optimization, which indicates that the two optimization schemes have good applicability. The research conclusion of this paper verifies the effectiveness of the proposed bi-level multi-objective collaborative optimization model from the perspective of optimization objectives and optimization performance, showing that it can better realize the comprehensive optimization of system planning and operation. However, this study does not address the specific impact of uncertainty in MCS renewable energy, nor does it consider the interconnection between multiple MCSs with energy storage modules and different characteristics. In addition, total carbon emission value considering the production of the renewable elements of the analyzed system is a question worth exploring. Further research on these issues will be undertaken in the future. Conceptualization, C.L. and H.W. ; methodology, C.L. and Z.L. ; software, C.L. ; validation, C.L., S.Y., and Z.L. ; formal analysis, H.W. ; investigation, C.L. and S.Y. ; resources, Z.L. ; data curation, C.L., S.Y., and Z.W. ; writing--original draft preparation, C.L., S.Y., and Z.W. ; writing--review and editing, C.L. and H.W. ; visualization, C.L. ; supervision, H.W. ; project administration, C.L., and Z.W. ; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the Hunan Province NSF project of China with grant number 2020J6072 and grant number 2021 JJ50007, and by the National Natural Science Foundation of China with grant number 51676209 and grant number 22008265. This study did not involve humans or animals. This study did not report any data. The statements made herein are solely the responsibility of the authors. The authors would like to thank the Hunan Province NSF project of China (Grants no. 2020J6072 and 2021 JJ50007) and the National Natural Science Foundation of China (Grants no. 51676209 and 22008265). The authors declare no conflict of interest.
## 4 Effect of Different Highly Heterogeneous LEO Constellations on CIT
This is in line with the variation of the number of LEO satellites in the plan design; however, the number of LEO satellites in the 800 km altitude is the smallest for planD and planE, which leads to the RMS gain of the two plans being also the smallest. Combining the statistical RMS results in Figures 12 and 13, for the enhancement effect of heterogeneous LEO constellations on CIT under the consideration of the launch and maintenance cost of LEO satellites, we demonstrate that planA can effectively improve the accuracy of CIT; i.e., 192 LEO satellites are designed for 300 km altitude, 96 LEO satellites for 500 km altitude, and 60 LEO satellites and 1000 km altitude can adaptively adjust the number of LEO satellites according to the design needs without affecting the CIT results. Figure 12: The RMS distribution of the CIT results from GNSS and different plans, compared with the NeQuick2 model, across altitudes on 19 March 2024.
## 7 Review of Security and Privacy-Preserving Schemes in VANETs
### Group Signatures  
For VANETs, authors in [27] proposed the grouping of vehicles travelling at the same speed and in the same direction. This allows group members to anonymously issue messages and sign them with the group signature on behalf of the group. This also allows extended silent periods for vehicles as only one vehicle in the network transmits at a time. By combining the vehicles into groups, the authors proposed that a vehicle can reduce its V2I transmission, which enhances the anonymity of vehicles. Group signatures allow the distribution of a group public key associated with multiple group private keys. Therefore, an attacker will be able to detect that a message has been sent by a particular group but it will not be able to identify which vehicle sent the message [24].  
In [18], a scheme based on group signatures and short-lived keys, called Temporary Anonymous Certified Keys (TACS), is presented as an efficient way to fulfill the privacy and safety requirements. The scheme suggests a long-term group key stored in the OBU provided by a managing authority which the OBU uses anonymously to prove to the RA that it is a valid OBU and to get a short-term certified key that is only valid in that RA's region. As soon as the OBU moves into a new region, it has to update its TACS by getting a new short-lived certified key from the new RA. This ensures that OBUs update their keys after entering a new region. This ensures short-term linkability and traceability in the long run as the managing authority can be queried by the RA for the real identity of the vehicle.  
Group signatures provide a high level of privacy but revocation becomes a serious problem when the size of the group increases, thereby raising scalability issues. Furthermore, they can be computationally more expensive [28].
## 3 Methodology
### Structured Model Reconstruction  
#### 3.3.1 Model Reconstruction  
Figure 9b shows the line segments are incomplete due to missing point clouds. The lines are extended to form the enclosed floorplans (shown in green lines in Figure 10). Lines in the enclosed floorplans have topology and the point clouds in the segmented rooms have semantic information. Thus, the line segments and point clouds with individual rooms are can be used as constraint conditions in building the two-dimensional floorplan using multi-label graph cuts. The labelled Point clouds are projected on the 2D polygon floorplan is shown in Figure 10. Each cell is assigned a label from set \(\{l_{1},\dots,l_{Nrooms},l_{out}\}\), which includes one label for each room plus an additional label \(l_{out}\) for the outer space. Each line cell is assigned a label from the initial \(\{l_{1},\dots,l_{Nrooms},l_{out}\}\). The labelled line segments are used to segregate cells from adjacent region, of which cell labels should be different. Our approach differs from [PERSON]'s work [PERSON] (1999); [PERSON] (1999) such that the line segments are projected directly on the floorplan and divided into 2D line cells to improve the accuracy and efficiency of the model. With the approach expressed as an energy minimization function [PERSON] (1999), the 2D polygons and lines are globally optimized to build the floorplan model, which are then extended to the estimated the floor and ceiling heights from segmented surfaces to build 3D room models. Figure 11 shows that the reconstructed model can better retain the details of indoor scene.  
Figure 9: Clustering similar lines. (**a**) The line groups project onto the cluster line. (**b**) The final main lines.
# Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
## 4 Examples
### Comparison of the FWNN with BP Using Synthetic Data with Three Noise Variants
However, as the SNR decreases, the performance index decreases, as expected, while the % accuracy remained mostly constant for the FWNN system as against a reduction in the value obtained for the BP network. The average time for the FWNN operation was recorded to be 0.40 s; although this is generally twice the time required for the BP operation, it is still fast enough for real-time operation. This operation time can, however, be slower if the value of N is increased drastically. In general, it is found that the FWNN yielded the best PSNR values and achieved performance improvements in all kinds of noise levels. Furthermore, the comparison of the average processing time of the image recorded in Table 1 indicates that the methods have quite a small computational cost.
# Metal Mobility in Afforested Sites of an Abandoned Zn-Pb Ore Mining Area
## 3 Results
### Metal Speciation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic** & **Unit** & **Paryz I** & **Paryz II** & **Paryz III** \\  & **(n = 27)** & **(n = 13)** & **(n = 13)** \\ \hline
**fraction \textless{}0.063 mm** & & & & \\ min-max & [wt.\%] & 1.92–70.70 & 4.71–78.60 & 2.29–69.30 \\ _median_ & & _46.25_ & _25.50_ & _27.70_ \\ (25 th; 75 th percentile) & & (25.35; 60.48) & (10.30; 44.40) & (5.08; 45.60) \\ \hline
**Cd** & & & & \\ min-max & [mg\(\cdot\)kg\({}^{-1}\)] & 1.72–309.70 & 2.88–741.90 & 2.23–223.60 \\ _median_ & & 48.07 & 33.65 & 40.76 \\ (25 th; 75 th percentile) & & (4.32; 90.88) & (5.30; 209.30) & (4.86; 62.58) \\ \hline
**Cu** & & & & \\ min-max & [mg\(\cdot\)kg\({}^{-1}\)] & 0.10–49.58 & 2.89–67.76 & 2.25–104.32 \\ _median_ & & _8.11_ & _12.29_ & _8.77_ \\ (25 th; 75 th percentile) & & (4.32; 18.33) & (4.54; 19.78) & (3.86; 14.51) \\ \hline
**Pb** & & & & \\ min-max & [mg\(\cdot\)kg\({}^{-1}\)] & 55.8–8582.8 & 43.3–4798.4 & 54.4–2333.8 \\ _median_ & & 1345.5 & 1325.2 & 506.1 \\ (25 th; 75 th percentile) & & (258.2; 2185.1) & (119.7; 2968.5) & (114.2; 1559.7) \\ \hline
**Zn** & & & & \\ min-max & [mg\(\cdot\)kg\({}^{-1}\)] & 134.0–30,305.0 & 181.2–31,816.0 & 119.3–16,762.0 \\ _median_ & & _5604.0_ & _7520.0_ & _1338.0_ \\ (25 th; 75 th percentile) & & (524.4; 8337.8) & (378.0; 15,908.0) & (536.6; 8531.0) \\ \hline
**Mn** & & & & \\ min-max & [mg\(\cdot\)kg\({}^{-1}\)] & 103.0–1305.0 & 133.0–637.8 & 1.0–16.0 \\ _median_ & & 478.3 & 451.9 & _7.4_ \\ (25 th; 75 th percentile) & & (257.2; 600.3) & (299.6; 489.7) & (2.8; 12.8) \\ \hline
**Fe** & & & & \\ min-max & [mg\(\cdot\)kg\({}^{-1}\)] & 609–46,194 & 12,685–29,413 & 10,585–22,187 \\ _median_ & & 16,231 & 16,419 & 14,946 \\ (25 th; 75 th percentile) & & (12,574; 17,657) & (14,679; 18,287) & (13,251; 17,911) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metals and silty-clay fraction concentration in the soil from profiles at the Paryz location.
# An Improved Artificial Bee Colony for Feature Selection in QSAR
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \(Limit\) & **Mean \(Q^{2}\pm Std\)** & **Mean \(RMSE\pm Std\)** & **Mean \(NUM\pm Std\)** \\ \hline \multirow{6}{*}{Artemisinin} & 10 & 0.7164 \(\pm\) 0.0057 & 0.835 \(\pm\) 0.0083 & 43.25 \(\pm\) 4.2221 \\  & 50 & 0.7678 \(\pm\) 0.0018 & 0.7555 \(\pm\) 0.0029 & 32.56 \(\pm\) 4.6042 \\  & 100 & 0.7716 \(\pm\) 0.002 & 0.7493 \(\pm\) 0.0032 & 33.29 \(\pm\) 5.3073 \\  & 150 & 0.7721 \(\pm\) 0.0016 & 0.7485 \(\pm\) 0.0026 & **32.5 \(\pm\) 4.489** \\  & 200 & 0.7726 \(\pm\) 0.002 & 0.7477 \(\pm\) 0.0032 & 32.74 \(\pm\) 5.3459 \\  & \(\infty\) & **0.7731 \(\pm\) 0.0019** & **0.7468 \(\pm\) 0.0031** & 32.99 \(\pm\) 5.5204 \\ \hline \multirow{6}{*}{BZR} & 10 & 0.5283 \(\pm\) 0.0051 & 0.7542 \(\pm\) 0.0041 & 30.82 \(\pm\) 3.6828 \\  & 50 & 0.5714 \(\pm\) 0.0051 & 0.7189 \(\pm\) 0.0043 & **23.09 \(\pm\) 2.0797** \\  & 100 & 0.5724 \(\pm\) 0.0053 & 0.7181 \(\pm\) 0.0044 & 23.26 \(\pm\) 2.1112 \\  & 150 & 0.5733 \(\pm\) 0.0052 & 0.7173 \(\pm\) 0.0044 & 23.91 \(\pm\) 2.1182 \\  & 200 & 0.5758 \(\pm\) 0.0049 & 0.7152 \(\pm\) 0.0041 & 23.99 \(\pm\) 1.8395 \\  & \(\infty\) & **0.5759 \(\pm\) 0.005** & **0.7150 \(\pm\) 0.0042** & 24.22 \(\pm\) 1.7557 \\ \hline \multirow{6}{*}{Selwood} & 10 & 0.8626 \(\pm\) 0.0177 & 0.2743 \(\pm\) 0.0178 & 17.83 \(\pm\) 2.8035 \\  & 50 & 0.9321 \(\pm\) 0.0009 & 0.1913 \(\pm\) 0.0014 & 14.07 \(\pm\) 0.5366 \\ \cline{1-1}  & 100 & 0.9326 \(\pm\) 0.0023 & 0.1925 \(\pm\) 0.0033 & **13.86 \(\pm\) 0.9849** \\ \cline{1-1}  & 150 & 0.9337 \(\pm\) 0.0035 & 0.1908 \(\pm\) 0.0053 & 14.2 \(\pm\) 0.8646 \\ \cline{1-1}  & 200 & 0.9337 \(\pm\) 0.0043 & 0.1908 \(\pm\) 0.0066 & 14.28 \(\pm\) 0.9543 \\ \cline{1-1}  & \(\infty\) & **0.9338 \(\pm\) 0.0045** & **0.1906 \(\pm\) 0.0068** & 14.33 \(\pm\) 1.3185 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of ABC-PLS-1 with different values of \(Limit\) on three datasets.
# The Cut-Off Frequency of High-Pass Filtering of Strong-Motion Records Based on Transfer Learning
## 4 Application of the Trained Network Models
### Comparison to Results of the SNR Method  
As described in the introduction, the SNR method is widely used in obtaining high-pass cut-off frequencies of strong-motion records [17; 23; 24; 25; 26]. This section compares the high-pass cut-off frequencies obtained via the traditional method in this paper with those obtained using the SNR method. The method proposed by [PERSON] et al. [24] was used to obtain a high pass cut-off frequency, and this paper used an SNR greater than 3. The strong-motion record before the arrival of the P-wave was used as a noise window (Figure 10). The P-wave arrival pickup algorithm proposed by [PERSON] et al. [47] was used here. The FAS of strong-motion records and noise was smoothed (the Konno-Ohmachi method [48]), and the smoothed noise FAS was linearly scaled by 4 times (the green dotted line in Figure 11). The point where the zoomed noise smoothing FAS intersects the ground motion smoothing FAS was considered the high-pass cut-off frequency (indicated by an arrow in Figure 11). The choice of a scaling factor equal to four ((signal + noise)/noise) implied an SNR equal to three as the criterion for the choice of the high-pass cut-off frequency.  
Figure 12 shows the displacement curves for filtering at high-pass cut-off frequencies obtained using the traditional method, the method of this paper, and the SNR method, respectively. For this record, there was a significant drift in the filtered displacement curve using the high-pass cut-off frequency based on the SNR method, indicating that the high-pass cut-off frequency used was inappropriate.  
Figure 11: Illustrative plot for selecting filter corner frequencies.  
Figure 10: An example of recorded ground motion and noise (AKBAW–n.0212o88 mof.BNE).  
Then, 300 records were randomly selected from the strong-motion database in this paper, and the high-pass cut-off frequencies obtained using the traditional and SNR methods were compared. Records without intersections (at 0-1 Hz range, as shown in Figure 13) between noise smoothing FAS and record smoothing FAS were removed, and 184 records could be obtained with high-pass cut-off frequencies via the SNR method. Figure 14 shows the high-pass cut-off frequencies obtained via the SNR method for 184 records.  
It can be seen from Figure 14 that the high-pass cut-off frequency obtained via the SNR method was generally smaller than that obtained using the traditional method. This indicates that the displacement time series curves obtained by filtering the high-pass cut-off frequency obtained using the SNR method were mostly unqualifie  
Figure 14: The obtained high-pass cut-off frequency (unit: Hz). (**a**) SNR method (**b**) Proposed method.  
Figure 12: Displacement curves after filtering using different cut-off frequencies.  
Figure 13: FAS of fHP cannot be obtained via SNR method (AKPWL-n.1000 hyfh.HNN.–V1c).  
the displacement time series curves obtained via filtering with high-pass cut-off frequency obtained using this paper's method were qualified.
# Recent Progress in Synthesis and Photonic Applications of Two-Dimensional Bismuthene
## 3 Production of 2D Bismuthene  
Effective fabrication methods to realize high quality bismuthene are the key to realizing devices with outstanding performance. It is well known that top-down and bottom-up methods are the two main strategies for synthesizing 2D materials. In general, the top-down strategy for fabricating 2D bismuth includes mechanical exfoliation, liquid-phase exfoliation, chemical exfoliation, etc. [45; 46]. Common bottom-up fabrication methods include chemical vapor deposition (CVD) [22], pulsed laser deposition (PLD) [17], molecular beam epitaxy (MBE) [47], and electron beam (e-beam) evaporation [48]. In this section, several methods for the production of bismuthene will be described, and the advantages and drawbacks of each are also discussed.