esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Data Sources and Evaluation Factor Selection
### Analysis of Evaluation Factor Selection  
The formation and occurrence of geological disasters is the result of a combination of factors, and its occurrence is affected by a number of factors such as geological conditions and triggering factors, etc. However, too much selection of evaluation indexes will complicate the calculation process, and there is a correlation between some evaluation indexes, which will lead to bias in the evaluation results [46]. Reasonable selection of evaluation indexes is an important basis for the evaluation of geological disaster susceptibility [47].  
Figure 3: Geological hazard susceptibility evaluation flow chart.  
Based on the research results of previous scholars and the geological environment of the study area, as well as the geological conditions and triggering factors of the disaster, Without considering the differences in the sliding mechanisms of different types of landslides on a case-by-case basis, this study, combined with the actual situation of Tekes County, selects the representative factors that have a greater impact on geological disasters as the preliminary factors and uses the multivariate analysis tool in the ArcGIS software (v.10.8.2) to calculate the correlation matrix of the evaluation factors so as to analyze the covariance of the preliminary factors and obtain the table of correlation coefficients of the various factors. The correlation coefficients of the two groups of factors are shown in the table, and the larger the absolute value of the correlation coefficients of the two groups of factors, the larger the correlation is. In the end, nine evaluation factors, including elevation, slope, slope direction, relief, distance from faults, engineering geological rock group, land use type, average rainfall over the years, and distance from rivers, were selected to construct the evaluation system of the susceptibility to landslides and geological hazards in Tekes County.
## 5 Simulation and Results
### Optimal Solution for a Linear Topology  
As we stated in the previous section, [PERSON] _et al._[3] proposed the optimal scheduling algorithm for convergecast in a linear network topology with a fixed transmission range, and we have applied their algorithm to a general network topology to get the minimum scheduling time. Their algorithm produces \(n\) times if \(n<p\), \(2n-p\) time if \(p\leq n\leq p(p+1)\) and \(2n-2p+n\) / \(p-1\) time if \(n>p(p+1)\), where \(p\) is power (=transmission range). In this section, we show the minimum time if the given number of nodes are in a linear topology. Table 8 and Figure 5 represent the optimal time schedule for the given number of nodes in a linear network topology with fixed power.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{11}{c}{**Node**} \\ \cline{2-13}  & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 \\ \hline
1 & 1 & - & - & - & - & 8 & - & - & - & - & - & - & - & - & - \\
2 & 2  
As Table 8 illustrates, when the power is three and 10 nodes are placed on the line, it needs 17 time slots. If 100 nodes are placed, it needs 226.33 time slots, which is the maximum. As the power is increased, time slots are decreased, as is shown in Figure 5. When the power is 12 or 15, the time slots are 10 with 10 nodes, because the number of nodes are less than the power (\(n<p\)).
## 4 Analysis of Effect Factors
### Effect of the Thickness of Block-Matrix Interface  
Since the effect of BMI on the strength of the SRM is crucial, in addition to the above discussion of the effect of BMI strength on the overall strength, this paper also considers the effect of the thickness of the cemented surface on the UCS of the SRM. Taking advantage of the method of model generation in this paper, three sets of models with different thicknesses of BMI are generated. The thickness of the BMI of these three groups of models is 0.5 mm, 0.6 mm, and 0.7 mm, respectively, models with a VBP of 60%, as presented in Figure 11. While varying the thickness of the BMI, this paper also keeps the positions of rock blocks fixed as a way of avoiding the effect of stone distribution on the strength characteristics.  
Overall, the general tendency of each group is that the UCS declines with the growth of the VBP, as shown in Figure 12. Then, it can be observed that the UCS of the SRM decreases with the increase in the thickness of the BMI. At the same time, it can be clearly shown that the decreasing trend slows down with increasing thickness. It is noteworthy that the overall decline in strength is more significant than before in the second and third set of models when VBP increases from 60% to 70%. As mentioned above, the rule proposed by [PERSON] et al. [33] that block-to-block interlocking contacts are definitely assumed to be almost absent or very few can explain this tendency with more than 60% of VBP.  
Figure 11: Models with VBP of 60% with different BMI thickness: (**a**) 0.5 mm; (**b**) 0.6 mm; (**c**) 0.7 mm.  
Figure 10: The trend in UCS of the SRM with different BMI strength.
## 2 Materials and Methodology
### Effect of CO\({}_{2}\) Concentration (10%, 18%, and 25%)  
To evaluate the effect of CO\({}_{2}\) concentration on CO\({}_{2}\) capture, seven RAs were submitted to forced carbonation over time in a climatic chamber with the following exposure  
Figure 4: Weight loss due to decarbonation before and after forced carbonation, considering the time at which maximum CO2 uptake was achieved for each RCA at 23, 40, and 60 \({}^{\circ}\)C.  
Figure 5: Weight loss due to decarbonation, before forced carbonation and after; the maximum CO\({}_{2}\) uptake for CA-L was measured at temperatures of 23, 40, and 60 \({}^{\circ}\)C.  
conditions: 23 \({}^{\circ}\)C, 60% RH, and 10-25% CO\({}_{2}\). Figure 6 presents the values of the maximum CO\({}_{2}\) content captured as a function of the cement paste content for each RA at 10%, 18%, and 25% CO\({}_{2}\) concentrations. The captured CO\({}_{2}\) content varied from 0.7% to 34.8%, with the highest capture rate of 34.8% observed in RCA-IW1 with a CO\({}_{2}\) concentration of 25%. In general, the results indicate that reducing the CO\({}_{2}\) concentration to 18% and 10% hinders the amount of CO\({}_{2}\) captured. This suggests that higher concentrations of CO\({}_{2}\) might facilitate CO\({}_{2}\) capture due to increased interaction with the cement paste. The same tendency is reported by other authors [13; 47; 48].  
The increase in CO\({}_{2}\) concentration promoted more CO\({}_{2}\) capture (Table 6). In the case of MRA, the reduction in CO\({}_{2}\) concentration below 25% resulted in less CO\({}_{2}\) captured and a longer carbonation period required to achieve maximum CO\({}_{2}\) capture. The results for the MRA indicate that higher concentrations are more beneficial for carbonating lower-\"quality\" recycled aggregates. Higher concentrations will create higher driving forces (more molecules of CO\({}_{2}\)) for the carbonation reaction, achieving greater carbonation even in materials that are less susceptible to carbonation. Regarding RCA, in general, the same trend occurred. The greater difference in this capture is from 25% to 18% CO\({}_{2}\) concentration. However, when the concentration is reduced from 18% to 10%, the CO\({}_{2}\) capture does not decrease [38]. For CA-L, no significant difference in CO\({}_{2}\) capture was noticed with a reduction in CO\({}_{2}\) concentration from 25% to 10%. In conclusion, a gas concentration of 25% is generally the most favourable situation based on all the analysed examples.
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Networking
#### 3.3.1 Sensor Network  
This category includes the most used technologies on the deployment of applications. The most important subcategories are Wireless protocols (LPWAN and LR-WPAN protocols like LoRa, NB-IoT, and ZigBee), WiFi, and Cellular technologies (3G/4G). Other subcategories include Bluetooth and other wired technologies (i.e., Ethernet, USB, and serial communications).  
The following table (Table 5) contains two major columns--sensor network and user network--which represent the type of networking technology used by a sensor or user to connect to the infrastructure of a Smart Parking Solution. Previous columns have been divided into types of networking connectivity. Wireless protocol indicates that a wireless IoT protocol was described in the paper reviewed, if so, the \"Specific Protocol\" column is filled with the name of the used protocol. Then, if a paper matches a specific column, black bullet \"\(\blacklozenge\)\" appears otherwise a blank space. Only the papers that specified a type of networking technology were considered in this table.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 2 Materials and Methods
### Discriminant Factor
#### 2.3.4 Permutation Entropy (PE)  
Permutation entropy introduces the idea of permutation. Before calculating the distance value of time series, permutation entropy rearranges the series from large to small, and then calculates the complexity between the rearranged series.  
The following process can intuitively reflect the calculation process and difference of the above four entropies.  
In Figure 3:  
(1) The time series: \(\{X_{1},X_{2},\cdots,X_{n}\}\)  
(2) Taking \(m\) as the window size, the time series is decomposed into \(k=n-m+1\) series: \(X_{i}(t)=\{x_{i}(t),x_{i+1}(t),\cdots,x_{i+m-1}(t)\}\)  
(3) Calculate the distance value between each series and all \(k\) series:  
\[d_{ij}=max\Big{\{}x_{i+k}(t)-x_{j+k}(t)\Big{\}}\ \ (k=0,1,\cdots,m-1) \tag{12}\]  
(4) List the above data in Table 1. \(d\) means the distance value between each series, and \(D\) means the fuzzy membership degree between each series.  
(5) For ApEn and SampEn, the threshold \(F=r\times SD\) is defined. The number of the distance value in each row of the table which is greater than the threshold \(F\) needs to be counted, which is used to calculate the ratio of \(C_{i}^{m}(t)\) to the total number. Then, it uses \(k\) different \(C_{i}^{m}(t)\) to calculate its average value:  
\[\Phi_{\text{ApEn}}^{m}(t)=\frac{1}{n-m+1}\sum_{i=1}^{n-m+1}C_{i}^{m}(t) \tag{13}\]  
\[\Phi_{\text{SampEn}}^{m}(t)=\frac{1}{n-m}\sum_{i=1}^{n-m+1}C_{i}^{m}(t) \tag{14}\]  
The above two equations are the average equation for calculating the ApEn and SampEn.  
For the FE, it calculates the fuzzy membership \(D_{ij}\) according to the distance vaule \(d_{ij}\), and counts the number of each row greater than the threshold to calculate its ratio to the number (\(n-m\)).  
\[D_{ij}^{m}=\mu\left(d_{ij}^{m},n,r\right)=e^{(\frac{-(\mu_{ij}^{m})^{2}}{j})} \tag{15}\]  
\[\Phi_{\text{FE}}^{m}(t)=\frac{1}{n-m}\sum_{j=1}^{n-m}\left[\frac{1}{n-m-1}\sum _{j=1,j\
eq i}^{n-m}D_{ij}^{m}\right] \tag{16}\]  
(6) Increase the window size by 1 and repeat the above operations to obtain ApEn, SampEn, and FE. The calculation results are as follows:  
\[\text{ApEn}(t)=ln\Phi_{\text{ApEn}}^{m}(t)-ln\Phi_{\text{ApEn}}^{m+1}(t) \tag{17}\]  
\begin{table}
\begin{tabular}{l|l l l l l l|l  
\[\text{SampEn}(t)=ln\Phi_{\text{SampEn}}^{m}(t)-ln\Phi_{\text{SampEn}}^{m+1}(t) \tag{18}\]  
\[\text{FuzzyEn}(t)=ln\Phi_{\text{FE}}^{m}(t)-ln\Phi_{\text{FE}}^{m+1}(t) \tag{19}\]  
The calculation of PE is slightly different. Through decomposing the original data into \(k\) series, each series is rearranged in ascending order and mapped into a symbol sequence: \(X_{i}(t)^{*}=\left\{x_{i+(j_{1}-1)L}(t)\leq x_{i+(j_{1}-1)L}(t)\leq\cdots\leq x _{i+(j_{m}-1)L}(t)\right\}\).  
Calculate the number of each symbol sequence. Then, divide it by the total number of different symbol sequences as the probability of the symbol sequence \(\left\{P_{1},P_{2},\cdots,P_{k}\right\}\), where \(k\leq m!\). Then, calculate the permutation entropy through probability:  
\[H(m)=-\sum_{j=1}^{K}P_{j}lnP_{j} \tag{20}\]
###### Abstract  
Providing rapid access to emergency medical services (EMS) within the \"golden time\" for survival is important to improve the survival rate of emergency patients. This study analyzes the accessibility of EMS based on driving speed changes following real-time road traffic conditions by time to estimate vulnerable areas for EMS and survival rates of emergency patients. The key results of the network analysis based on real-time road speed and this evaluation of vulnerable areas by village level across South Korea reveal the different characteristics of urban and rural areas to access emergency medical facilities. In urban areas, road traffic congestion during rush hour delays the patients' access time to EMS. In contrast, in rural areas, the long geographical distance to an emergency medical facility is a hurdle for receiving care from an EMS during the \"golden time\" because emergency medical facilities are mostly located in urban areas. The existing standard to assess vulnerable areas of EMS accessibility is based on the speed limit of roads, but the time may be underestimated because the speed limit alone does not reflect the real road conditions. The study results show that an effective way to increase the survival rate is receiving immediate first aid treatment, which means that the government should continuously train the public to perform cardiopulmonary resuscitation (CPR) as well as install automated external defibrillators (AEDs) in populated places, and train the public to use them. Reducing assess time to emergency medical centers in urban areas and providing additional manpower to help with first aid in rural areas are reasonable ways to improve the survival rate of emergency patients.  
emergency medical service; accessibility; vulnerability; survival rate; golden time; real-time; network
# _Applied_
## 3 Results  
### Isolation of Riboflavin over Production LAB from Kimchi  
Kimchi is a fermented vegetable dish made from cabbage or radish, with a high content of group B vitamins [42; 43; 44]. Various LABs, including _L. plantarum_, _L. fermentum_, _L. sakei_, _Leuconstoc (Lc.) mesenteroides_, _L. brevis_, _L. curatus_, and _Lactococcus (Lc.) lactis_ have been identified in Kimchi [45; 46; 47]. Therefore, in this study, 26 Kimchi samples collected from different regions of Korea were selected as raw materials for the screening of riboflavin-producing LAB.  
About 250 LAB were isolated from Kimchi (data not shown), of which 46 formed colonies in the riboflavin-free CDM. As a result of 165 rRNA sequencing of 46 LAB, 33 _L. plantarum_, seven _Lc. lactis_, three _L. fermentum_, two _L. paracasei_, and one _L. farciminis_ were identified. When LAB, which formed colonies on CDM plates, were inoculated into riboflavin-free CDM broth, only 24 strains grew. Among the LAB that could be grown in CDM, 18 strains produced riboflavin, and five had natural resistance to roseoflavin (Table S1). Strains that formed colonies in the riboflavin-free CDM containing 150 ug/mL of roseoflavin were four of _L. plantarum_ (No. 2, No. 3, No. 9, and No. 14) and one of _L. fermentum_ (No. 21). Colonies formed on CDM plates containing roseoflavin have been found to change the color of the medium from white to yellow during liquid culture in riboflavin-free CDM (Figure S1). This can be a qualitative indicator of riboflavin overproduction [19]. After measuring the vitamin B2 content of No. 2, No. 3, No. 9, No. 14, and No. 21, No. 9 was found to produce the highest riboflavin at 1.85 \(\pm\) 0.20 mg/L. There was no significant difference in FAD and FMN levels between strains (Figure 2A). To confirm that the riboflavin production characteristics of above strains were maintained even in the presence of riboflavin, strains were cultured in CDM (0.1 mg/L riboflavin contained) and MRS brofts for 24 h, and then the amount of vitamin B2 (riboflavin and its cofactors) was measured. MRS is a medium that is commonly used for LAB culture and contains riboflavin [20; 48]. Strain No. 9 produced high riboflavin even in the presence of riboflavin, which was in contrast to the remaining strains (Figure 2B). In addition, strain 9 produced 5.06 \(\pm\) 2.05 mg/L of riboflavin in MRS broth, while the other strains produced little riboflavin in MRS broth (Figure 2C). LAB strain No. 9 showed the highest riboflavin production among the selected strains and was able to produce riboflavin even in a growing environment with riboflavin. Therefore, this strain was named _L. plantarum_ HY7715 (HY7715), and the following experiment was performed.
# Discrete Data Rate Adaptation for Wireless Body Area Networks
## 5 Evaluation
### Multiple Runs  
The proposed algorithms were tested on generated networks. Five test series were generated; each of them consisted of ten cases. The number of nodes varied in the different series; there were 5, 10, 15, 20, and 25 nodes in the different series. We examined the utilities of the solutions as a function of the number of nodes. Figure 5 shows the average utilities of each test series. The figures compare the results of the proposed algorithms, namely the greedy algorithm and the dynamic programming on the routing tree. Dynamic programming and integer linear programming methods always find the exact optimum. According to the chart, the greedy algorithm produces similar results to dynamic programming. The accuracy of the approach rounding the solution of the continuous model is low even if the continuous model is solved optimally. We obtained similar results for applying the solution with maximum throughput.  
Table 2 shows the accuracy of the proposed algorithms. The accuracy of a method in a test series is defined as the ratio of the measured and optimal overall utilities. Although the greedy method does not guarantee the optimum, it approximates well it in the test series.  
Figure 6 shows the ratio of the solutions with the exact optimum to all test cases. It shows that the proposed greedy method finds the exact solutions in most cases. Although rounding the optimal continuous solution and the throughput optimization often find the exact optimum, they fail to find the optimum in many cases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Nodes** & **5** & **10** & **15** & **20** & **25** \\ \hline greedy & 96.8 & 100 & 96.7 & 100 & 100 \\ DP & 100 & 100 & 100 & 100 & 100 \\ ILP & 100 & 100 & 100 & 100 & 100 \\ cont. + rounding & 65.5 & 100 & 73.5 & 84.8 & 97.6 \\ throughput & 100 & 88.7 & 89.6 & 81.3 & 91.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy (in %) of the proposed data rate adaptation algorithms.  
Figure 5: Utility values of the proposed data rate adaptation algorithms.
# A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
## 2 OTFS-BFDM System  
### OTFS-BFDM System Model  
We assume that an OTFS-BFDM transmission frame occupies \(M\) sub-carriers and \(N\) time slots. The sub-carrier spacing is \(\Delta f\) and the duration of a time slot is \(T\), where \(\Delta f=1/T\). When \(D[m,n]\) is the n-th sub-symbol on the m-th sub-carrier in the TF domain, the corresponding DD domain signal \(d[l,k]\) (e.g., 4 QAM symbol) is quasi-period with delay period \(\tau=1/\Delta f\) and Doppler period \(v=1/T\). Figure 1 indicates the OTFS-BFDM system model.
## 1 Introduction
Using these methods, the calculated power supply reliability under a given system time is no longer a constant, but a variable value expressed as a probability density curve and its corresponding confidence interval, which helps the designers to fully grasp all the scenarios of the power supply reliability including worst, best, most likely, etc. Furthermore, if the confidence interval of system reliability is large, it means that the system reliability is not stable in the operation stage and may face high uncertain risk [37]. However, there is little research on how to help designers effectively reduce such uncertain risks in the design stage. For this reason, this paper proposes to establish an importance measure index/indicator to measure the contribution of component failure rate uncertainty to system reliability uncertainty under a given system time, so that components with high contribution to system reliability uncertainty can be quickly locked based on the ranking of index. Then, in combination with the case studies, the strategy of reducing the uncertainty of system reliability effectively in the system design stage by using the proposed index is put forward. The rest of this paper is arranged as follows. Section 2 summarizes the theoretical basis of aircraft power system reliability. In Section 3, based on the variance decomposition theory, the importance measure index to measure the uncertainty of component failure rate to the uncertainty of system power supply reliability is proposed, and the detailed calculation steps and pseudo-codes are given. In Section 4, taking a typical serial-parallel system and an aircraft power supply system as an example, the correctness and effectiveness of the proposed method are discussed, and a criterion is proposed to help designers effectively reduce the uncertainty of system reliability in the design stage. Section 5 presents summary and prospects.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 3 Communication System Model
### Performance Analysis
#### 3.2.3 Bit Error Rate  
In this paper, it is assumed that the modulation is QPSK. According to the definition given in the literature [53], the BER of the OFDM can be expressed as  
\[P_{BER}=Q(\sqrt{\frac{2\sum|H_{c}|^{2}\epsilon_{Z}}{N_{0}}}) \tag{25}\]  
where \(N_{0}\) is the noise spectral density of the AWGN channel, \(\epsilon_{Z}\) is the total transmitted signal energy for the Z-OFDM signals, which was given by  
\[\begin{split}\epsilon_{Z}&=\int_{-\infty}^{+\infty} |Y_{z}(f)|^{2}df\\ &=\int_{-\infty}^{+\infty}|Y_{z}(\frac{f}{D})|^{2}df\\ &=D\int_{-\infty}^{+\infty}|Y_{z}(\frac{f}{D})|^{2}d\frac{f}{D}\\ &=\epsilon D\end{split} \tag{26}\]  
Combining Equations (25) and (26), the probability of error of the Z-OFDM system is [46; 54]  
\[P_{BER}=Q(\sqrt{\frac{2\sum|H_{c}|^{2}\epsilon D}{N_{0}}}) \tag{27}\]  
Figure 5 shows the BER of Z-OFDM at different values of factor \(D\) over the AWGN channel. At a BER of \(10^{-3}\), Z-OFDM achieved a 3 dB gain compared to FFT-based OFDM when \(D=2\). It also achieved a 6 dB gain when \(D=4\) and an 8 dB gain when \(D=6\). It also shows that the BER of the Z-OFDM system decreased as the factor \(D\) increased.