esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 4 Experiment
### Comparison with Different Traditional Methods
#### 4.3.3 Analysis of Relative Time Delay on Separation Results  
A total of 2000 test samples with a SNR of 5, 10, 15, 20 and 25 dB, power difference between signals of 2 dB, carrier frequencies of 10 MHz and 10.0015 MHz, and relative time delays of 0, 5, 10 and 20.3 us are selected for testing. The decoding accuracy and BER of PASA, TDBSS and MConv-TasNet algorithms are shown in Figures 9 and 10, respectively,  
Figure 8: The BER of PASA, TDBSS and MConv-TasNet algorithms on carrier frequency difference.  
with the power difference and carrier frequency of the signals fixed, SNR varying from 5 to 25 dB, and relative time delay taken as 0, 5, 10 and 20.3 us, respectively.  
The average decoding accuracy of PASA, TDBSS and MConv-TasNet algorithms is 13.65%, 27.07% and 99.93%, respectively, in Figure 9. It can be observed from the figure that PASA and TDBSS algorithm are more influenced by the relative time delay between overlapped signals. The decoding accuracy of both algorithms are increased with the rise of the relative time delay, and they achieve the highest decoding accuracy when the relative time delay is 20.3 us. However, the MConv-TasNet algorithm outperforms them and is less  
Figure 10: The BER of PASA, TDBSS and MConv-TasNet algorithms on relative time delay.  
Figure 9: The decoding accuracy of PASA, TDBSS and MConv-TasNet algorithms on relative time delay.  
affected by the relative time delay. In particular, the MConv-TasNet algorithm is still able to separate signals with high accuracy when the relative time delay is 0 \(\upmu\)s.  
The average BERs of PASA, TDBSS and the MConv-TasNet algorithms are 22.45%, 9.81% and 0.02%, respectively, in Figure 10. The MConv-TasNet algorithm achieves the maximum BER 0.12% when the relative time delay between overlapped signals is 0 \(\upmu\)s and the SNR is 5 dB. The BERs of the algorithm are all 0 when SNR is greater than or equal to 10 dB. The TDBSS algorithm achieves the minimum BER 6.1% at the relative time delay of 5 \(\upmu\)s, while the PASA achieves the minimum BER 12.06% at the relative time delay of 20.3 \(\upmu\)s. Therefore, the TDBSS and PASA are more affected by the relative time delay, while it has less impact on the performance of the MConv-TasNet algorithm.  
In summary, it is verified by semi-real data that the MConv-TasNet algorithm proposed in this paper has a much higher decoding accuracy than the PASA and TDBSS algorithm under different SNR, power difference, carrier frequency difference and relative time delay conditions. When the power difference, carrier frequency difference and relative time delay between overlapping signals are small, the improved algorithm can still separate the signals with high accuracy. Therefore, the single-antenna signal separation method based on the deep learning network has great superiority over the traditional methods, which can better separate signals by learning the feature relationships between overlapping and separated signals.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 5 Performance and Complexity Analysis
### DMRS Search  
Similar to PSS and SSS search, we compare the probability of detection of SS index, referred to as \(P_{D}^{DMRS}\), for different WL and SNRs. As shown in Figure 14, we can observe that WL of 16 offers nearly identical performance to that of SPFL and HPFL architecture. As shown in Table 3, DMRS search architecture with WL of 16 offers 33% reductions in execution time along with significant savings in resource utilization as well.  
For all the results presented in this section, we have used the transmitter PHY with a WL of 16. This is done by observing the power spectral density of the transmitter output and performance of PSS search with SPFL WL for different WLs of transmitter PHY. Corresponding results are not included here to avoid repetition of results.  
Figure 14: Effect of WL on the probability of detection, \(P_{D}^{SSS}\), for DMRS search architecture.  
Figure 13: Effect of WL on the probability of detection, \(P_{D}^{SSS}\), for SSS search architecture.
## 5 Experimental Results
### Field Tests  
The field experiment research was conducted on Chongming Island (Shanghai, China) in November with a tractor serving as the field test platform after the relevant intelligent modification, and the field was a dry rice straw field. The test vehicle was manually driven to the vicinity of the reference path, and the upper navigation computer was started. After that, the upper navigation computer sent the deviation information to the control system at a frequency of 10 HZ, according to the specified message format. According to the throttle calibration, the operating speed was set at 0.85 m/s. The upper navigation computer recorded the deviation data during the experiment at a frequency of 1 HZ, and they were saved in the industrial control computer as a TXT file. Three confirmatory tests were conducted, and the field results are shown in Figures 7 and 8 and Tables 3 and 4.  
Figure 6: The experimental platform.  
Figure 8: The lateral errors of the tractor and trailer in three tests: **(a)** Test 1; **(b)** Test 1; **(c)** Test 3.  
Figure 7: The driving trajectories of the tractor-trailer: **(a)** FSMC; **(b)** SMC.  
The tractor could be brought online at a distance of 10-20 m thanks to the FSMC, whose maximum steady-state absolute error was between 0.39 and 0.53 m. However, for SMC, the maximum steady-state absolute inaccuracy of the tractor was between 0.38 and 0.75 m, and it also overshot during the process of getting online because it was constructed based on the constant speed-reaching law. It also led to the overshoot for the trailer and the main reason was the chattering problem. The FSMC could keep the absolute value of the lateral error within 0.09-0.17 m. The experimental results show that, compared with the traditional SMC, the steady-state tracking accuracy of the trailer improved by about 10.5-36.8% and the steady-state tracking accuracy of the trailer improved by about 11.1-50%.
## 3 Results
### Settlement Analysis  
Table 7 presents the soil settling beneath the pipe. Based on the results presented in Table 7, both settlements occur with and without the reinforcement of corrugated steel plate (CSP) and settlement increases with additional embankment filling. Following the installation of CSP in Phase 2, subsequent settlements demonstrated a reduction in settlement magnitude with CSP reinforcement. Figure 11 illustrates that the maximum settlement without reinforcement is 288.31 mm. Figure 12 displays the maximum settlement with CSP reinforcement, measuring 26.91 mm. Consequently, we can infer that CSP effectively reduces maximum settlement by 261.40 mm or 90.64%. This concludes the CSP's effectiveness in mitigating settlement in the surrounding soil around the pipeline. Moreover, we need to implement stability control measures for pipeline protection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phase** & **Safety Factor** & **Safety Factor Minimum** \\ \hline
1 & 5.408 & 1.500 \\
2 & 3.529 & 1.500 \\
3 & 2.860 & 1.500 \\
4 & 3.178 & 1.500 \\
5 & 3.140 & 1.500 \\
6 & 2.510 & 1.500 \\
7 & 2.232 & 1.500 \\
8 & 2.631 & 1.500 \\
9 & 2.691 & 1.500 \\
10 & 2.173 & 1.500 \\
11 &Figure 11: Settlement result on construction phase 13 without reinforcement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Phase**} & \multicolumn{2}{c}{**Settlement on \(-5\) m (mm)**} \\ \cline{2-3}  & **Without Reinforcement** & **Reinforcement** \\ \hline
1 & 1.20 & 1.20 \\
2 & 1.78 & 1.78 \\
3 & 46.67 & 5.52 \\
4 & 69.67 & 6.08 \\
5 & 98.24 & 7.68 \\
6 & 121.81 & 7.77 \\
7 & 133.52 & 7.95 \\
8 & 146.22 & 11.43 \\
9 & 155.50 & 12.26 \\
10 & 165.96 & 13.44 \\
11 & 226.27 & 18.36 \\
12 & 270.69 & 18.66 \\
13 & 288.31 & 20.39 \\
14 & 282.96 & 26.91 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Settlement result.  
Figure 10: Safety factor result on construction phase 10.  
The largest settlement due to causeway embankment with pipe protection is 26.91 mm during Phase 14. The results of this settlement are compared with the design standards issued by the National Standardization Agency of Indonesia: Indonesian National Standard (SNI) 8460:2017 [43]. Referring to Table 33 of Standard SNI 8360:2017 [43], a settlement of 26.91 mm fits into risk category 2, indicating a small risk where deformations may occur but not significant structural damage. Thus, it can be concluded that, based on the magnitude of the settlement, the pipeline construction does not experience significant damage.
# Residential Buildings Complex Boundaries Generation Based on Spatial Grid System
## 4 Algorithm  
The general algorithm used to illustrate and refine the proposed method is presented in Figure 4.  
In the rest of the section, there is a detailed explanation of the steps that have been used in the algorithm and method refinement process.  
The shape of a building layout \(L\) is defined by the label of the array with 20 columns and 20 rows. Each cell of the array represents space of 1 m by 1 m that can be either part of the building--B or part of the empty space around it--E. The number of cells that represent built space \(N\) is the value that corresponds to the desired area of the house. Value \(N\) is the area of the building in square meters. A valid shape should satisfy two constraints first, all B labels are connected into a single shape; second, there are no E labels inside layout \(L\).  
\[\text{Area}(L)=N \tag{1}\]  
The algorithm starts from the simplest case where the floor plan is represented as an array of five columns and five rows where random values describe the overall shape. Next, the transformation of the results into a 10 by 10 matrix with specific rules that are applied. Finally, from previous results, a 20 by 20 array is formed. In this final step sets of rules are applied.  
The algorithm can be applied for generating buildings that are square-shaped and have a maximum area of 400 square meters. It can give results for houses that are in the range of 40 to 360 square meters.
# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 4 Results
### Comparative Results on HSI Target Detection
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **AUC\({}_{(P_{r},P_{D})}\)** & **AUC\({}_{(\tau,P_{D})}\)** & **AUC\({}_{(\tau,P_{F})}\)** & **AUC\({}_{BS}\)** & **AUC\({}_{SNPR}\)** & **Running Time** \\ \hline
**CEM**[14] & 0.9993 & 0.5005 & 0.0225 & 0.9768 & 22.18 & 0.007 \\
**SID**[18] & 0.9489 & 0.0800 & 0.0050 & 0.9439 & 13.43 & 0.09 \\
**MF**[54] & 0.9992 & 0.5435 & 0.1286 & 0.8706 & 4.22 & **0.023** \\
**DSE**[32] & 0.9900 & **0.5670** & 0.0525 & 0.9375 & 10.78 & 18.78 \\
**BLTSC**[41] & 0.9990 & 0.3671 & 0.0008 & 0.9982 & 445.18 & 2.57 \\
**OS-VAE**[45] & 0.9992 & 0.3381 & 0.0005 & 0.9987 & 645.46 & 2.7 \\
**Proposed** & **0.9994** & 0.3547 & **0.0002** & **0.9992** & **1742.73** & 0.47 \\ \hline \hline \end{tabular} Note: The best results are highlighted in bold.  
\end{table} 
Table 3: Performance comparison of different methods across various AUC metrics on the Gulport dataset.
# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 3 Relative Strengths and Limitations of Existing Techniques
### Approaches
#### 3.1.6 Unobstructed, Beneath-Vegetation Flood Versus Urban Flood
At present, the major challenge to an operational system for urban flood mapping is still data availability. Ultra-fine resolution data (~1 m) such as TerraSAR-X ([[https://directory.eoportal.org/web/eoportal/satellite-missions/t/terrasar-x](https://directory.eoportal.org/web/eoportal/satellite-missions/t/terrasar-x)]([https://directory.eoportal.org/web/eoportal/satellite-missions/t/terrasar-x](https://directory.eoportal.org/web/eoportal/satellite-missions/t/terrasar-x))) and COSMO-SkyMed ([[http://www.e-geos.it/cosmo-skymed.html](http://www.e-geos.it/cosmo-skymed.html)]([http://www.e-geos.it/cosmo-skymed.html](http://www.e-geos.it/cosmo-skymed.html))) are less accessible than free datasets.
## 2 Problem Formulation  
Assuming that \(m\) (\(m\) = 1, 2, 3,...) fault states exist in a system while \(n\) (\(n\) = 1, 2, 3,...) tests are designed for the system, the dependency model-based multiple fault diagnosis problem can be formulated as follows:  
\[\Re=\left\{F,P,T,FT,R\right\}, \tag{1}\]  
where  
\[\left\{\begin{array}{l}F=\left\{f_{1},\cdots,f_{m}\right\},\\ P=\left\{p\left(f_{1}\right),\cdots,p\left(f_{m}\right)\right\},\\ T=\left\{t_{1},t_{2},\cdots,t_{n}\right\},\\ FT=\left[ft_{ij}\right]_{m\times n^{\prime}}\\ R=\left\{r_{1},r_{2},\cdots,r_{n}\right\}.\end{array}\right. \tag{2}\]  
All the parameters in Equations (1) and (2) are interpreted as follows:  
* \(F\) means the set of independent fault states in the system. \(f_{l}\) (\(1\leq l\leq m\)) represents the \(l\)st fault state;
* \(P\) means the set of the prior probability among all the fault states that happens independently; \(p\left(f_{i}\right)\) (\(1\leq i\leq m\)) represents the prior probability of the fault \(f_{i}\) happens alone;
* \(T\) means a finite set consisting of all the available tests in the system. Here, assume that each test result is reliable with an output in the form of a binary number;* \(FT\) represents the dependency matrix of fault test results, of which the expression is defined as follows: \[FT=\left[\begin{array}{cccc}ft_{11}&ft_{12}&\cdots&ft_{1n}\\ ft_{21}&ft_{22}&\cdots&ft_{2n}\\ \vdots&\vdots&\cdots&\vdots\\ ft_{m1}&ft_{m2}&\cdots&ft_{mn}\end{array}\right]_{m\times n}.\] (3) \(FT\) is a matrix with a scale of \(m\times n\). The element \(ft_{ij}\) denotes the correlation between a fault state \(f_{i}\) in \(F\) and a test \(t_{j}\) in \(T\). If \(ft_{ij}=1\), then the fault state \(f_{i}\) can be tested by the test \(t_{j}\). If \(ft_{ij}=0\), then the test \(t_{j}\) fails to know the situation about the fault state \(f_{i}\). In this way, in Equation (3), the rows in the matrix \(FT\) denote the set of fault types, while the columns denote the set of tests;
* \(R\) is a set of the observation results according to the tests in the system, e.g., \(r_{1}\) is the observation result of the test \(t_{1}\). For each test \(t_{n}\), the corresponding observation result \(r_{n}\) will be only one of the following two states:
* 'Pass', denoted by '0'. The system passes the test;
* 'Fail', denoted by '1'. The system fails to pass the test.  
In summary, to solve the dependency model-based multiple fault diagnosis problem is to locate a suspicious set of fault \(X\subset F\) based on the fault-test dependency matrix \(FT\), the prior probability set of the fault states \(P\) and the set of the observation results \(R\) in a system. After that, all the fault states in the suspicious set \(X\) will be ranked according to the priority in which each fault may happen, which will be based on the prior probability. The following maintenance work of a system will be accomplished based on the rankings of the fault states.
## 4 Performance and Economic Feasibility of a Retaining Wall Displacement Measurement System
### Economic Feasibility of System
#### 4.2.2 Setting of LCC Analysis Variables and Assumptions
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Input Cost**} & \multicolumn{3}{c}{**Conventional Method**} \\ \hline
**Item** & **Unit Cost** & **Quantity** & **Cost** \\ \hline \multirow{8}{*}{Labor cost} & Intermediate technician & $160.95/person & 0.12 person & $22.53 \\  & Special worker & $132.33/person & 0.17 person & $25.14 \\  & Ordinary worker & $105.46/person & 0.23 person & $28.47 \\  & Borer & $141.36/person & 0.23 person & $38.17 \\  & Construction machine operator & $168.06/person & 0.11 person & $18.49 \\  & Labor cost per unit boring depth (\$/m) & & $122.02/m & \\ \hline \multirow{8}{*}{Material cost} & Single-core barrel (HX \(\times\) 1.0 m) & $138.69/box & 0.01 box & $1.39 \\  & Metal crown bit (HX) & $37.96/box & 0.025 box & $0.95 \\  & Drive pipe head (HX) & $43.80/box & 0.01 box & $0.44 \\  & Drive pipe shoe (HX) & $36.50/box & 0.01 box & $0.36 \\  & Drive pipe (HX \(\times\) 1.0 m) & & 0.01 box & $0.91 \\  & Material cost per boring depth (\$/m) & & $4.04/m & \\ \hline \multirow{2}{*}{Equipment cost} & Boring equipment (54 HP) & $28.55/h & 0.88 h & $25.13 \\  & Equipment cost per unit boring depth (\$/m) & & $25.13/m & \\ \hline \multicolumn{2}{c}{Work cost per unit boring depth (\$/m) = labor cost + material cost + equipment cost} & \multicolumn{2}{c}{$151.19/m} \\ \multicolumn{2}{c}{Boring cost (\$) = work cost per unit boring depth \(\times\) boring depth \(\times\) number of inclinometers} & \multicolumn{2}{c}{$1511.92} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Boring cost for the conventional method. The LCC analysis period was set at 10 years, considering the system component with the longest service life. Accordingly, the cash flow diagram was derived, as shown in Figure 15. The inflation rate in Korea over the past 10 years [38] and the bank deposit interest rate [39] were used as discount rates for the LCC analysis. The average inflation rate over the past 10 years was used for the LCC analysis, and a sensitivity analysis was conducted regarding the discount rate for the inflation and bank deposit interest rates range (0.90%-3.43%) over the past 10 years (Figure 16).
## 2 Preliminaries and Literature Review
\[(\lambda+2\lambda+3\lambda\ +\cdots+\ n\lambda+\lambda_{F})P_{0}=\frac{\mu}{n}P_{ 1}+\frac{\mu}{n-1}P_{2}+\frac{\mu}{n-2}P_{3}+\ \cdots\frac{\mu}{n-(n-2)}P_{n-1}+\mu P_{n}\] (1) \[\left(\frac{\lambda(n)(n-1)}{2}+\lambda_{F}\right)\!P_{0}=\mu\!\left(\!\frac {1}{n}P_{1}+\frac{1}{n-1}P_{2}+\frac{1}{n-2}P_{3}+\ \cdots\frac{1}{n-(n-2)}P_{n-1}+P_{n}\right)\] (2) and \[P_{0}+P_{1}+P_{2}+\ldots+P_{n}=1\] (3)  
Appendix A shows the detailed solving of balance equations. Equations (4) and (5 describe the generalized value of \(P_{i}\). \[P_{i}=\ q_{i}^{-1}P_{0}\!\left[\sum_{j=1}^{i}j\!\left[\!\prod_{k=1}^{i-1}\!\! \left[\prod_{l=1}^{k-1}q_{l}^{-1}\right]\!\right]\!\right] \tag{4}\]  
where,  
\[q_{i}^{-1}=\left(\frac{\lambda(n-i)(n-i+1)}{2}+\lambda_{F}+\frac{\mu}{n-i+1} \right)^{-1} \tag{5}\]  
In the following, a few baseline performance measurements of primary interests in VBAVBS with \(\lambda_{F}\) are shown. * \(L_{Q}\): The average number of customers (i.e., equivalently the average number of transactions) in the queue (i.e., the block currently being mined) [19] with the new \(P_{i}\) \[L_{Q}=\sum_{i=0}^{n}iP_{i}\] (6)
* \(W_{Q}\): The average amount of time a customer (i.e., equivalently, a transaction) in the queue (i.e., the block currently being mined) [19]. \[W_{Q}=\frac{L_{Q}}{\lambda}\] (7)
* \(W\): The average amount of time a customer (i.e., equivalently, a transaction) in the system (i.e., the transaction pool in the blockchain) [19]. \[W=W_{Q}+\frac{1}{\mu}\] (8)
* \(L\): The average number of customers (i.e., equivalently, the average number of transactions) in the system (i.e., the transaction pool in the blockchain) [19]. \[L=\lambda W\] (9)
# Modeling and Simulation of Sky Survey
## 4 Design of the Model Solving Algorithm
### Prearrangement Algorithm  
After the statistics of inefficient time periods and difficult observation sky areas, it is necessary to prearrange the program to both cases to ensure that the inefficient time periods and difficult observation sky areas can be arranged effectively during the overall sky survey arrangement. Algorithm 3 gives the arrangement algorithm for the inefficient time period, and we obtain the arrangement sequence \(ArrOrder_{1}\). Since the arrangement algorithm for the difficult sky area is similar to it, we do not show it in detail, and we obtain the arrangement sequence \(ArrOrder_{2}\) for the difficult sky area correspondingly.
# Further Advances in Atrial Fibrillation Research: A Metabolomic Perspective
## 2 Application of Metabolomics in Atrial Fibrillation
### Metabolomic Analysis of Liver Markers with Atrial Fibrillation  
Several studies have linked higher circulating levels of liver enzymes, which are markers of liver damage, with an increased risk of AF [11; 20; 21; 22; 23; 24]. However, the mechanism that relates high levels of bile acids and the risk of AF is not very clear yet. The reason is that both glycolithocholate sulfate and glycolocholenate sulfate can be found at elevated levels in the context of liver diseases [20,21]. However, [PERSON] et al. [11] found that markers of liver damage and function are independent from associating the two bile acids with a higher risk of AF. Thus, this suggests that there might be different pathways under this association [10].  
The aforementioned studies are reinforced by previous assays performed in mice's ventricular muscle [10,11] that had evidenced the occurrence of arrhythmias in relation to abnormal levels of bile acids due to negative inotropic effects. Moreover, other studies in neonatal rat cardiomyocyte culture have observed the same effect, where bradycardia and a loss of synchronous beats are caused by the conjugation of bile acids with taurine [22,23].  
In addition, intrahepatic cholestasis of pregnancy, characterized by elevated maternal serum bile acid levels, has been associated with the appearance of fetal cardiac arrhythmias [24]. [PERSON] et al. [25] studied possible arrhyththmogenic effects of bile acids in the adult human heart. They deduced that increasing concentrations of bile acids conjugated with taurine and glycine are responsible for increased extra-arrhythmic contractions in the myocardium.