esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solutions of Water Inflow for Foundation Pit in Confined Water Stratum
## 4 Verification and Analysis
### Comparison and Analysis between Analytical Solution and Site Measured Data  
Table 1 shows the comparison result of water inflow between the analytical solution and the actual measured values in the project. \(Q_{\rm c1}\) and \(Q_{\rm c}\) in Table 1 are the water inflow of the analytical solution and the measured values at the site from the reference [18], respectively. It is pointed out that \(Q_{\rm c1}\) does not consider the influence coefficient \(\alpha\). \(Q_{\rm c2}\) refers to the analytical solutions in this paper. \(Q_{\rm j}\) is the water inflow calculated by the formula from the specification [23], which does not consider the influence of the water-proof curtain. It can be seen from Table 1 that both \(Q_{\rm c1}\) and \(Q_{\rm c2}\) are much smaller than \(Q_{\rm j}\). This shows that a water-proof curtain can effectively reduce the water inflow in foundation pits. It can be known from the comparison of \(Q_{\rm c1}\) and \(Q_{\rm c2}\) that \(Q_{\rm c2}\) is much closer to the measured value than \(Q_{\rm c1}\). Indicating that the influence coefficient \(\alpha\) has a great impact on the calculation of water inflow, and the calculation results considering the influence coefficient \(\alpha\) are more accurate. \(Q_{\rm c2}\) is slightly larger than the measured value; it is safer for water inflow calculation in actual dewatering projects.  
Figure 7: Parameter sensitivity analysis. (**a**) The variation in water inflow with \(L/M\) for different permeability coefficients; (**b**) the variation in water inflow with \(L/M\) for different thickness of aquifer; (**c**) the variation in water inflow with \(L/M\) for different hydraulic heads.
## 4 Results
### Spatial Representativeness Determined by Relative Coefficient of Variation  
Figure 3 shows the histogram of the \(R_{CV}\) at each scale. To depict the spatial variation at adjacent scales, the \(R_{CV}\) was computed from the CV values at each adjacent scale. The median value was calculated as in Figure 3 to describe the integral spatial variation at each scale. From Figure 3, we can see that \(R_{CV1}\), which represents the \(R_{CV}\) of the first scale, was the largest for all scales. This means that the spatial variation between scale 1 (15 \(\times\) 15 TM pixels) and scale 2 (21 \(\times\) 21 TM pixels) was significant. The median values of \(R_{CV2}\) and \(R_{CV4}\) were small, which means that the effective spatial resolution should have been 630 m or 870 m. The conclusion was partly consistent with that of the t-test on the sill values. However, the step sizes of the scales were different: for scales 1 and 2, the step was 6 TM pixels, but for scales 2 and 3, it was only 2. Hence, the low level of the variation in the sill value and the small value of the \(R_{CV}\) could have been deduced from the small step size. To verify this conclusion, we then reduced the analysis step size and aggregated the TM data at different scales to explore the proper representative scale.
# Formation Mechanism of Metro Rail Corrugation Based on Wheel-Rail Stick-Slip Behaviors
## 3 Theoretical Analysis on the Cause of Rail Corrugation
When the accumulated torque increases to a certain value, the adhesion force of the wheel-rail interface is less than the torsional force generated by the torque at the contact position, the torque will be suddenly be released, and the adhesion state between the wheel tread and rail surface will change into a slip state, that is, the \"stick-slip\" effect between wheel and rail. This process is completed in a very short moment, and it starts over and over again, so as to realize the smooth passing of the wheelset through the curve. As long as there is slip between wheel and rail, the torque on the wheel and rail will be released rapidly, that is, as shown in the curves of b and c in Figure 4, the torsional force (i.e., creep force) will drop to the dynamic friction force (the dynamic friction force is less than the static friction force), at this time, the torsional force is less than the static friction force. If the torque is released completely, the torsional force will return to 0, and the wheel-rail interface will become sticky again. So repeatedly, the wheel makes a lateral torsional stick-slip vibration on the rail surface. When the wheel and rail stick together, the wear of the rail surface is small, that is, the wave peak of the corrugation is formed. When the wheel slips on the rail, the wear of the rail surface is large, that is, the wave trough of the corrugation is formed. At present, the ATO (automatic train operation) mode is basically adopted in the metro train operation, that is, the speed of a section is constant. Therefore, the reciprocating operation of the train will aggravate the development of wavelength-fixed wear. Over time, obvious wavy wear will form on the rail surface. For the straight line, the wheel-rail torsional stick-slip vibration process also exists, but the torsional vibration intensity is weak, the appearance probability is less, so the occurrence possibility of rail corrugation on the straight line is low. Rail corrugation is also found in the straight section during the field measurement, as shown in the eighth line section in Table 2. Because the driving distance difference between left and right wheels on the straight line is relatively small, the rail corrugation of the straight line may be related to the wheel-rail longitudinal torsional stick-slip vibration. In this section, the formation mechanism of rail corrugation is explained from the wheel-rail contact stick-slip theory. In the following, the multibody dynamics method will be used to analyze the relationship between the wheel-rail stick-slip characteristics and rail corrugation, and verify the above formation mechanism of rail corrugation.
## 5 Short-Baseline Instantaneous RTK Performance with Smartphones in Lying Down and Upright Positions  
In the previous section, we showed in a zero-baseline setup that the internal smartphone antennas are sensitive to interference from nearby surfaces, even inside a RF shielding box, and that using the upright position rather than having the smartphones lying down will benefit the RTK positioning performance. This is true in particular for the S20 smartphones. We namely observed S20 DD phase residuals with linear trends and non-zero mean values when the smartphones were lying down, which became absent when the smartphones were upright. To further validate whether these effects are also present for short-baselines, we analyse in the following the DD phase residuals and RTK positioning performance with the use of both external and internal antennas (cf. Figures 3 and 4).
## 2 Sparse Representation
### Sparse Coefficient Solution  
The sparse solution problem is to solve the dictionary learning objective function Formula (4) and minimize the non-zero term in the coefficient vector, which is a zero-norm optimization problem. Traditional OMP generally uses the greedy iterative method to select the column vector of the learning dictionary to maximize the association with the over complete dictionary vector [35; 36]. The OMP algorithm needs to set a threshold as the termination condition to control the number of atoms in the selected dictionary. If the threshold is too small, the signal will lose some useful features, and the signal with a too large threshold will increase the interference of noise, so it is difficult to set the most effective threshold in practical application. When the bearing fault occurs, the impact characteristics have a certain regularity: the larger the amplitude of the transient impact component is, the weaker the noise is, and the greater the kurtosis is. In order to solve the problem that it is difficult to determine the iterative termination condition of the orthogonal matching pursuit algorithm (OMP), this paper adopts the maximum kurtosis principle as the iterative termination condition of the OMP algorithm. When solving the sparse coefficient, the kurtosis of each iteration is calculated respectively. When the kurtosis of the signal reaches the maximum value, the iteration is stopped, which can retain the impact component to a large extent and reduce the noise interference. The steps are as follows: (Algorithm 2).  
```
Input: Sample \(x\), Sparsity \(k\), Training Dictionary \(D=\{d_{1},d_{2},d_{3},\ldots,d_{N}\}\). Step 1: Initialize the residual \(r_{0}=x\), support the index set \(\Lambda_{0}=\varnothing\), \(k=1\). Step 2: Find the supporting index\(\lambda_{k}=\underset{i=1,2,\ldots,N}{\arg max}\Big{\langle}r_{k-1},d_{j} \Big{\rangle}\Big{|}\). That is, find out the maximum value in the inner product of the residual \(r_{k-1}\) and the atomic \(d_{j}\) in the dictionary \(D\). Step 3: Update index set \(\Lambda_{k}=\Lambda_{k-1}\cup\lambda_{k}\). Tag and find the reconstructed atomic collection \(D_{k}=\big{[}D_{k-1},X_{\lambda_{k}}\big{]}\) in the dictionary \(D\). Step 4: Use the least square method to solve the sparse coefficient matrix \(\alpha=arg\ min\|X-D_{k}\alpha\|_{2}^{2}\). Step 5: Update residual: \(r_{k}=x-D_{k}\alpha\). Step 6: Calculate the kurtosis of the signal, \(k=k+1\). Stop the iteration when the kurtosis of the signal reaches the maximum; otherwise, return to step 3. Output: Support index set \(\Lambda_{k}=\Lambda_{k-1}\), sparse coefficient \(\alpha_{k}\).
```  
**Algorithm 2** The steps
# DRL-Based Load-Balancing Routing Scheme for 6G Space-Air-Ground Integrated Networks
###### Abstract  
Due to the rapid development of the space-air-ground integrated network (SAGIN), a satellite communication system has the advantages of wide coverage and low requirements for a geographical environment and is gradually becoming the main competitive technology for 6G. The low-earth-orbit (LEO) satellite network has the characteristics of low transmission delay, small propagation loss, and global coverage, and its exploration has become the main research object of contemporary satellite communications. However, traditional routing algorithms cannot adapt to the characteristics of the high dynamics and load-balancing requirements of LEO satellite networks. In this paper, a load-balancing routing algorithm for LEO satellites based on Deep Q-Network (DQN-LLRA) is proposed by using deep reinforcement learning. Making use of the model obtained by the DQN training, satellite nodes can select the best routing results according to the delay, bandwidth, and queue utilization of the surrounding satellite nodes. The simulation and analysis show that the path load obtained by the proposed algorithm is low. Compared with the Q-learning-based algorithm, this algorithm reduces the maximum queue utilization rate of the routing path by 5%, reduces the average queue utilization rate of the routing path by 13%, and effectively balances the load in the network.  
low earth orbit; satellite routing algorithm; deep reinforcement learning; high dynamics; load balancing 2023152801. [[https://doi.org/10.3390/rs15112801](https://doi.org/10.3390/rs15112801)]([https://doi.org/10.3390/rs15112801](https://doi.org/10.3390/rs15112801))
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 6 Performance Evaluation and Benchmarking
### Metrics for Evaluating VLC-Based IPS
#### 6.1.2 Precision  
Precision measures the consistency of the estimated positions. It indicates how closely the estimated positions are clustered together. MSE is a commonly metric that measures the average squared difference between the estimated and true positions. The metric can be formulated as  
\[\epsilon=\frac{1}{N}\sum_{i}^{N}(\mathbf{p}_{est,i}-\mathbf{p}_{true,i})^{2} \tag{7}\]  
where \(N\) is the total number of samples and the subscript \(i\) represents the \(i\)-th measurement.
## 3 Upper Bounds on the Number of Channels
### Upper Bounds on the Number of Channels
This is equivalent to prove that every vertex of every subgraph of \(G\) has a degree at most \(D-1\). In other words, we prove that for every subset of link set \(LS\), there exists link \(l_{ij}\) such that there are at most \(D-1\) links that interfere with link \(l_{ij}\). Similar to the proof of Theorem 2, we also show that the nodes interfering link \(l_{ij}\) will all fall in a region that can be covered by two identical sectors with radius \(R_{i}\) and angle \(\theta_{m}\). As shown in Figure 6, for any link \(l_{kl}\) that interferes with node \(X_{j}\), at least one node of that link must fall in the interference range of \(X_{j}\). The other node of such link \(l_{kl}\) must fall into region \(ABCDEFG\) with the bold border. We illustrate that this region with the bold border can be covered by two identical sectors with radius \(R_{i}=2\) and angle \(\theta_{m}\). We place these two sectors as follows. First, we put a sector horizontally. By calculating the coordinates of point \(A\) and point \(B\), we can prove that points \(A\) and \(B\) are falling into the first sector. Then we put the second sector contiguous to the first sector. By calculating the coordinates of points \(C\), \(D\) and \(E\), we can prove that points \(C\), \(D\) and \(E\) fall into the second sector. Similarly, we can prove that points \(F\) and \(G\) fall into the first sector. So, the region \(ABCDEFG\) can be covered by the two sectors. Since the region \(ABCDEFG\) can be covered by two identical sectors with radius \(R_{i}\) and angle \(\theta_{m}\), by definition of the node density, the number of nodes in region \(ABCDEFG\) is at most \(2D\). Those \(2D\) nodes can form at most \(D\) links in this area. Other than link \(l_{ij}\), there are at most \(D-1\) links that can interfere with link \(l_{ij}\). Therefore, every vertex in subgraph \(H\) (the gray area in Figure 6) of \(G\) has a vertex with degree at most \(D-1\). From Lemma 2, the conflict graph can be colored by using \(D\) colors. Figure 6: The proof of Theorem 4.
## 4 Results and Discussion
### Developed Models
#### 4.1.4 ELM  
With respect to the experimental results shown in Table 10, the optimum ELM model developed had 12 neurons in the hidden layer with a sigmoid activation function. Thus,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of Neurons** \\ **in Hidden Layer** \\ \end{tabular} } & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \cline{3-6}  & & \(R\) & \(MSE\) & \(R\) & \(MSE\) \\ \hline
7-10-1 & 10 & 1.0000 & \(1.0244\times 10^{-21}\) & 0.2794 & 83.2970 \\
7-15-1 & 15 & 1.0000 & \(1.97866\times 10^{-22}\) & 0.9293 & 2.2797 \\
7-20-1 & 20 & 1.0000 & \(5.7607\times 10^{-24}\) & 0.9008 & 2.4538 \\
7-24-1 & 24 & 1.0000 & \(1.5760\times 10^{-23}\) & 0.8714 & 5.0448 \\
7-28-1 & 28 & 1.0000 & \(4.9485\times 10^{-25}\) & 0.7352 & 6.7391 \\
7-30-1 & 30 & 1.0000 & \(3.6328\times 10^{-26}\) & 0.7831 & 5.6888 \\
7-34-1 & 34 & 1.0000 & \(5.7972\times 10^{-20}\) & 0.8136 & 8.5761 \\
7-38-1 & 38 & 1.0000 & \(9.7338\times 10^{-26}\) & 0.5893 & 7.7243 \\
7-40-1 & 40 & 1.0000 & \(1.6407\times 10^{-25}\) & 0.6707 & 16.7280 \\ \hline \hline \end{tabular}
\end{table}
Table 7: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Interaction Order**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-4}  & \(R\) & \(MSE\) & \(R\) & \(MSE\) \\ \hline Zero Order & 0.9924 & 0.1548 & 0.9895 & 0.2605 \\ First Order & 0.9944 & 0.1145 & 0.9953 & 0.1038 \\ Second Order & 0.9940 & 0.1220 & 0.9923 & 0.1506 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of Different MARS Models.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Basis Function**} & \multicolumn{1}{c}{**Equation**} \\ \hline BF1 & max (0, D - 850) \\ BF2 & max (0, 850 - D) \\ BF3 & max (0, D - 550); \\ BF5 & max (0, MC - 96.764); \\ BF6 & max (0, 96.764 - MC) \\ BF7 & max (0, D - 1750) \(\times\) BF5; \\ BF10 & max (0, MC - 119) \(\times\) BF3; \\ BF11 & max (0, 119– MC) \(\times\) BF3; \\ \hline \hline \end{tabular}
\end{table}
Table 9: The Relationship Between Basis Functions and their Related Equations.  
having a structure [7-12-1] that represents seven inputs with 12 neurons in the hidden layer and one output.
## 3 Detection Precision and Sensitivity Analysis
### Analysis of Detection Sensitivity for QD under Spread-Spectrum Technology
That is, when the spread-spectrum gain is 1023, the received optical power required for spread-spectrum communication drops from approximately \(-47.1\) dBm to \(-62.6\) dBm. Therefore, by modulating the PRNsof different depths to expand the spectrum of the transmitted signal, SNR gain can be obtained, thereby improving the sensitivity of the detector.
###### Abstract  
The use of seawater in horticulture is underestimated. Although pure seawater is harmful to most living plants, diluted seawater could represent a promising integration to meet the crop's nutrient and water requirements. In the current trial, we compared the effects of moderate and high concentrations of seawater and a comparable NaCl solution on a salt-tolerant (_Tetragonia tetragonioides_) and a salt-sensitive (_Lactuca sativa_) crop grown in hydroponics. We tested the hypothesis that, due to its mineral composition, diluted seawater would result in a less stressful growing medium than NaCl. We observed that diluted seawater resulted in a less detrimental growing medium compared to an EC-comparable NaCl solution, with remarkable differences between the salt-tolerant and the salt-sensitive species. While the growth rates in _Tetragonia_ did not vary between the two types of stress, diluted seawater led to a higher FW and DW biomass yield in the salt-sensitive lettuce compared to the NaCl treatment. Moreover, NaCl reduced the water consumption and water productivity in _Tetragonia_. In lettuce, NaCl-treated plants demonstrated lower water use efficiency and water productivity compared to the EC-comparable seawater treatment. Physiological parameters and the concentration of mineral elements, phenolics and proline also demonstrated that, due to different mineral composition, seawater is a less stressful growing medium compared to a NaCl solution at comparable EC.  
saline agriculture; seawater irrigation; New Zealand spinach; lettuce; salt stress; seawater salt stress; NaCl salt stress; hydroponics; salt tolerance
## 3 Results and Discussion
### UV-Absorption Studies  
The protein-ligand interaction gives information about the structural changes in the complex formed during the interaction and the protein alone. Static quenching leads to changes in the structure, and hence, the protein spectra on interaction with its ligand. Based on our results, the static quenching was predicted between BSA and ETB. The \(\lambda\)max of BSA was at 280 nm because of \(\pi-\pi^{\star}\) transitions in the aromatic amino acids (tryptophan, tyrosine, and phenylalanine). On the interaction of the BSA with ETB, the \(\lambda\)max shifted to 330 nm (Figure 5A) [31; 41]. The hyperchromicity at 280 nm also suggested complex formation between ETB and BSA.  
The BSA-ETB spectra were also recorded for each RUT, NAR, and SIN to observe if any difference in the spectra of BSA occurs in the presence of these flavonoids (Figure 5B-D). The BSA showed hyperchromicity on interaction with ETB in the presence of RUT, NAR, and SIN. The spectra for the ternary system of BSA-ETB were significantly different from those of the BSA-ETB spectra binary system. Hence, the binary and the ternary-system complexes formed were completely different from one another. Further comparing the ternary systems amongst themselves, the spectra differed from each other and thus inferred that complexes formed between BSA-ETB in the presence of RUT, NAR, and SIN were also different.
# EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
## 4 Experimentation and Results
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Value** & **Comments** \\ \hline Generations & 50 & If the max depth for the evolved trees is relatively small, then more generations are not needed, since the solution size is restricted and the overall search stagnates. \\ Population size & 100 & A small population was found to be sufficient to evolve good solutions with larger populations, reaching the same performance. \\ Population initialization & Full & Since the search space can be huge, very small individuals are not beneficial to quickly explore the solution space. \\ Population init max depth & 5 levels & A good compromise between structure diversity and complexity. \\ Initial features & 3 & Variable \(r\). \\ Search operators & Inter-crossover, inter-feature crossover, intra-crossover, mutation, feature insertion, feature removal & Each value corresponds to each operator in the same order as the above list. \\ Initial probabilities for search operators & 0.2 & This only applies to the inter-crossover operator. \\ Delta values for decay function & 0.2 & Since individuals can have several features, big values for the allowed depth are not desired. If a child tree is above this limit after the genetic operation, then it is discarded and a reproduction operation is performed instead. \\ Terminal set & Tournament, size 5 & A small tournament size provides a richer diversity in the population. \\ Ellism & Only best individual survives & Asures that the best solution is not lost from generation to generation. \\ \hline \end{tabular}
\end{table} 
Table 1: ”FFGP parameters used in the experiments. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l l} \hline  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline \multirow{4}{*}{**Parameter**} & Accuracy & 83.5 & 81.6 & 86 & 81.5 & 83 & 82.5 & 79.3 & 84.9 & 81.5 & 80.7 & 81.9 & 83.5 & 75.8 \\  & \(\kappa\) & 0.70 & 0.68 & 0.77 & 0.63 & 0.72 & 0.56 & 0.72 & 0.77 & 0.64 & 0.49 & 0.77 & 0.69 & 0.63 \\  & \(p\)-value & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** \\ \hline \multirow{4}{*}{**Parameter**} & Accuracy & 82.9 & 82.7 & 58 & 96.5 & 95 & 60.2 & 89.3 & 46 & 82.8 & 87.4 & 91.2 & 63 & 88.6 \\  & \(\kappa\) & 0.70 & 0.65 & 0.16 & 0.90 & 0.88 & 0.30 & 0.79 & 0.01 & 0.72 & 0.81 & 0.78 & 0.24 & 0.75 \\  & \(p\)-value & **0.0000** & **0.0000** & 0.3346 & **0.0000** & **0.0000** & **0.0023** & **0.0000** & 0.9702 & **0.0000** & **0.0000** & **0.0000** & 0.1379 & **0.0000** \\ \hline \end{tabular}
\end{table} 
Table 2: ”FFGP training and testing performance for each LOOCV fold in terms of its accuracy (%), kappa coefficient (\(\kappa\)), and \(p\)-values for kappa statistical tests. Bold values indicate that observed agreement between true and predicted labels is not accidental.