esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
## 5 Discussion  
This section provides a summary and analysis of the results. Firstly, we present the visualization window of our open-source TC RTK/INS implementation. Next, we compare the positioning performances of RTK and the TC RTK/INS integration. The introduction of INS improves the positioning continuity and the fixed rate. RTK can provide positioning results at no more than 90 percent of the epochs for both datasets, whereas the integration can provide positioning results at every epoch. The fixed rate increases by 6.1% for the Odaiba dataset and 2.8% for the Shinjuku dataset. However, the integration does not significantly improve positioning accuracy because of the outliers. Then, we compare the positioning performance of the TC RTK/INS integration with and without the outlier resistance. IGG-based RKF uses inflation factors to suppress the outliers. If the inflation factor is too large, RKF will discard the corresponding measurements. RKF generates a significant improvement in positioning accuracy on both datasets. The RMSE has been reduced from 7.95 m to 1.75 m on the Odaiba dataset and from 14.17 m to 10.01 m on the Shinjuku dataset. However, obvious excessive robustness happens in the Shinjuku dataset. Taking this into account, we improve the RKF to prevent excessive robustness. We use MRKF to denote our proposed algorithm. MRKF can be divided into two parts: the CNR-based classification and the constraints. The CNR-based classification aims to maintain some measurements that are incorrectly identified as outliers. The constraints reduce the probability of incorrectly retaining true outliers. MRKF improves the positioning accuracy for the Shinjuku dataset. RMSE has been reduced from 10.01 m to 8.26 m. When it comes to the Odaiba dataset without obvious excessive robustness, MRKF will not reduce the positioning accuracy. RMSE has been changed from 1.84 m to 1.80 m.
## 3 Results and Discussion
### Results for Time Domain
#### 3.2.2 Influence of Porosity  
As discussed in the previous subsection, the effect of porosity on the L(0,1)\({}_{P}\) mode is strong enough to estimate the porosity of the medium surrounding the liquid-filled pipe. Subsequently, we convert the guided waves in the dispersion into the time-domain waveform to interpret the effect of porosity on the guided wave in the liquid-filled pipe embedded in a porous medium.  
The effect of the change of porosity on the time-domain waveform with respect to time in the liquid-filled pipe embedded in infinite and finite porous media is depicted in Figure 9, when the porosity is varied between 0.1, 0.2, and 0.3. Figure 9a shows that as the porosity changes, the positional movement of two waves is not evident, and the dispersion characteristic indicates that there is no large change in the phase velocity, which is consistent with the conclusion obtained from Figure 2. At the external interface of the finite porous medium (Figure 9b), the waveform of the L(0,1)\({}_{P}\) mode moves slightly backward with the increase in the porosity of the porous medium, which is due to the fact that the wave velocity decreases with porosity, as seen in Figure 4b. This also shows that the displacement amplitude of the wave is gradually reduced as the porosity increases.
## 2 Methods
### Selecting a Solution from the Pareto Front  
After evaluating the convergence and diversity metrics using raw objective values, the next step is selecting the most suitable solution from the Pareto front. To ensure fairness when selecting a final solution, normalization is applied to account for differences in objective scales, providing an equal footing for all objectives during the decision-making process. Three strategies guide this selection process:  
* Distance to ideal point, \(z_{ideal}\): The ideal point was calculated relative to each generated population, by determining the best-achieved values for each objective within the solution space for each population. However, for the objective OWAS Lundqvist Index, where a known theoretical range exists (100 to 400) [27], normalization was based on these theoretical limits. For the objectives without known ranges, such as distance walked and area utilization, normalization is performed using the observed minimum and maximum values from the corresponding population [26]. Once the ideal point is established in this normalized space, the Euclidean distance from this ideal point to all solutions on the Pareto front is calculated [18]. In the absence of any other preferences from the decision-maker, the solution closest to the ideal point in the normalized objective space represents a balanced trade-off among objectives and is hence often considered the most desirable solution. This method ensures a balanced selection, focusing on achieving the best possible outcomes across all objectives [27].
* Distance from nadir point, \(z_{nadir}\): The nadir point, representing the worst-achieved values across all objectives on the Pareto front, is also calculated in the normalized space. As with the ideal point, normalization of objectives is handled according to known theoretical ranges (for the OWAS Lundqvist Index) or observed data ranges (for distance walked and area utilization). The Euclidean distance from the nadir point to each Pareto front solution is then computed [18]. The solution furthest from the nadir point may be preferable in scenarios where avoiding poor performance across any objective is critical. This method identifies robust and risk-averse solutions but may sacrifice some potential for optimal performance in favor of avoiding the worst-case outcomes [28].
* Distance along the vector joining ideal and nadir points, \(d\): A third technique that combines aspects of the previous approaches involves projecting the obtained solutions onto the vector joining the ideal and nadir points. The projection that is closest to the ideal point, and hence furthest from the nadir point, is often selected as a compromise solution. The distance from the ideal point along this vector is given by the equation (where \(\mathbf{s}\) represents the solution being evaluated on the Pareto front):  
\[d=\frac{\left((s-z_{ideal})\times(z_{nadir}-z_{ideal})\right)}{\|z_{nadir}-z_{ ideal}\|}\,, \tag{3}\]
## 3 Results
### Evaluation of the Chemical Shift Derived from the JCS
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Compound**} & **High Field and JCS** & **R-Factor** & **R-Factor** \\  & **\({}^{19}\)F \(\delta_{\text{iso}}\) (ppm)** & **(\({}^{19}\)F \(\delta_{\text{iso}}\) = 0 ppm) (\%)** & **(correct \({}^{19}\)F \(\delta_{\text{iso}}\)) (\%)** \\ \hline Benzene + Hexafluorobenzene & \(-\)163.2 & 92 & 14 \\ \hline Monofluorobenzene & \(-\)112.9 & 10 & 9 \\ \hline
1,2-Difluorobenzene & \(-\)138.4 & 26 & 13 \\ \hline
1,3-Difluorobenzene & \(-\)109.9 & 31 & 12 \\ \hline
1,4-Difluorobenzene & \(-\)119.6 & 28 & 11 \\ \hline
1,2,4-Trifluorobenzene & \(-\)143.5 & 54 & 24 \\  & \(-\)115.7 & & \\ \hline
1,2,4,5-Tetrafluorobenzene & \(-\)139.7 & 83 & 19 \\ \hline  & \(-\)162.5 & & \\ Pentafluorobenzene & \(-\)154.2 & 90 & 15 \\  & \(-\)139.2 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of \(R\)-factors for simulation of the JCS accounting for chemical shift. Figure 7: Impact of chemical shift on the simulated spectrum of a hexafluorobenzene + benzene mixture (1:1). The red spectrum is the simulated spectrum assuming \({}^{19}\)F \(\delta_{\text{iso}}\) = 0 ppm. The black spectrum is the experimentally acquired JCS, which was well fitted in simulations with a \(\delta_{\text{iso}}\) = \(-\)163.2 ppm (Figure 3a). The hexafluorobenzene chemical shift at these microtesla (\(\mu\)T) fields is a discernable shift of approximately \(-\)0.34 Hz.
# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
In [24], grid-based DBSCAN is proposed for clustering objects in radar data. The method is not specially designed for PBR and its model is simple. In [25], ENM (ellipsoid norm method) is proposed to promise optimal result in passive multi-static location. It focuses on finding the nearest grid point in grid-based method. However, it only works in the noise-free scenario and is not suitable for dense clutter environment. Thus, based on the acquisition geometry of PBR, we firstly propose a non-uniform polar grid construction method. In addition, with the help of the grid platform, a false-alarm clutter separation method is proposed based on dilation morphology. The combination of these two steps is the whole algorithm we proposed in this paper. The remaining part is organized as follows. Section 2 illustrates the geometrical relationship of PBR and describes the proposed non-uniform polar grid construction method. Section 3 describes the grid-based clutter suppression method and its parallel iteration scheme. Section 4 describes both simulated data experiment results and field data experiment results. Section 5 is the conclusion.
# Multiple Kernel Stein Spatial Patterns for the Multiclass Discrimination of Motor Imagery Tasks
## 3 Experimental Setup  
### Dataset IIa from BCI Competition IV (BCICIV2a)  
To test our proposal, we use the dataset IIa of the BCI Competition IV, provided by the BCI Laboratory at the Graz University of Technology ([[http://www.bbci.de/competition/iv/](http://www.bbci.de/competition/iv/)]([http://www.bbci.de/competition/iv/](http://www.bbci.de/competition/iv/))). The dataset BCICIV2a comprises EEG trials from nine subjects while executing four motor imagery tasks, namely, left hand, right hand, foot, and tongue. The participants imagined each movement 72 times following a visual cue, resulting in \(N=288\) trials. For each trial, twenty-two EEG electrodes (\(C=22\)) distributed over the scalp (as seen in Figure 0(a)) recorded the brain activity at a sampling frequency of 250 Hz during six seconds. The trial started with an auditory signal, followed by a black fixation cross, that warned the subject for the upcoming cue. An arrow pointing towards the movement during 1.25 s indicated the task to perform. The trial ended four seconds after the fixation cross disappeared as Figure 0(b) shows. A break period of about 1.5 s allows the participant to rest before starting the next trial. Taking into account the cue onset and the short-lasting imagined movement, the methodology classifies each recording only using the period between 2.5 and 4.5 s, that is, \(T=500\) samples.
## 3 Materials and Methods
### Classification Methods
#### 3.4.2 Random Forest  
Based on Decision Trees (DTs), Random Forest (RF) is regarded as an advanced approach. The basic principle of RF is to integrate individual DTs by combining random subspace feature selection and bagging. Randomness is used in two stages by the RF model. First, it chooses subsets of the original dataset at random. Second, feature subsets that are derived from the original full feature dimensions are chosen at random. As a result, there is less association between DTs in the forest. After a voting process, the input sample is designated as the class with the most votes, and the final decision is made [56].
# A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & **RW1** & **RW2** & **\% Error** & **LW1** & **LW2** & **\% Error** \\ \hline
1 \({}^{*}\) & 56.73 & 57.88 & 1.86 & 61.59 & 61.12 & \(-\)0.60 \\
2 \({}^{*}\) & 56.72 & 57.07 & 1.13 & 61.03 & 60.75 & \(-\)1.35 \\
3 \({}^{*}\) & 56.56 & 58.84 & 2.56 & 61.54 & 61.09 & \(-\)0.67 \\
1 \({}^{**}\) & 59.93 & 60.34 & \(-\)2.58 & 58.14 & 57.82 & 3.05 \\
2 \({}^{**}\) & 60.45 & 60.80 & \(-\)1.78 & 57.19 & 57.06 & 1.53 \\
3 \({}^{**}\) & 60.57 & 60.97 & \(-\)1.56 & 56.60 & 56.63 & 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Half court lap distance tracking results, comparing the IMU derived output with the known court markings. (Note: \({}^{*}\) denotes clockwise travel. \({}^{**}\) denotes anticlockwise travel. Perimeter of the court 14 m \(\times\) 15 m, Wheelbase = 0.87 m, therefore the internal wheel should travel 56.26 m and the external should travel 61.73 m). Figure 4: Trial 2, half court lap performed in a clockwise direction starting and finishing at the origin (0,0) with the error visually depicted by the incompleteness of the loop. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & **RWx** & **RWy** & **Error Distance (m)** & **LWx** & **LWy** & **Error Distance (m)** \\ \hline
1A \({}^{*}\) & \(-\)0.12 & 0.82 & 0.88 & 0.57 & 0.0
## 3 Modeling Test Cases
### Heating with Electric Current  
Mathematically, heating with an electric current can be reduced to the same 3D heat conduction problem of heating a body by internal heat sources, as described above. In practice, electric current stimulation provides lower power release (400 W per linear meter of pole in our case) and thus should operate for a longer time (10 min) to ensure detectable temperature signals. Figure 10 shows synthetic IR images of dynamic temperature distributions. The images consist of two sections: the upper half-image is related to the above-ground section of the pole, while the bottom half-image exhibits the temperature distributions on the pole section buried in moistened soil. Rebar damage has been modeled by 20% rebar cross section thinning separated from the concrete surface layer by a 1 mm thick air gap. Defect-free reinforcement is characterized by excess temperature of about 10 \({}^{\circ}\)C (Figure 10a),  
Figure 8: Temperature signal (\(\Delta T\)) vs. time (\(\tau\)) in the case of inductive heating at some characteristic surface points (model in Figure 6, steel reinforcement at 10 mm depth, points of interest shown in Figure 7, 1—defect-free case, 2—defective case, reinforcement cross section diminished by 25%, 3—same as (2) but additional 1 mm thick air-filled delamination).  
Figure 9: Temperature signal (\(\Delta T\)) vs. time (\(\tau\)) in the case of inductive heating at some characteristic surface points (model in Figure 6, steel reinforcement at 20 mm depth, points of interest shown in Figure 7, 1—defect-free case, 2—defective case, reinforcement cross section diminished by 25%, 3—same as (2) but additional 1 mm thick air-filled delamination).  
while the presence of an air gap diminishes surface temperature signals in the above-ground bottom section of the pole by about 3-4 \({}^{\circ}\)C (Figure 10b).  
Figures 11 and 12 present graphs of both surface temperature and temperature time derivatives for three test cases, respectively (see the figure caption). Qualitatively, the temperature evolutions look similar to those in Figures 8 and 9, and the temperature values conditioned by total released energy are close to the case of inductive heating. The modeling shows that the temperature maximums appear at about the same times over rebars of different thickness but a 1 mm thick air gap in a pole with a 55 mm thick wall diminishes the temperature evolution rate up to 3-fold with regard to non-defect areas (Figure 12). Hence, variations in the temperature evolution rate (temperature derivative) can be used for identifying air
## 3 Analysis and Discussion of Results  
The results from index properties (\(v_{p}\), \(v_{s}\), \(\rho_{d\gamma}\), \(\rho_{sat}\) and \(n\)) and strength (UCS) are shown in Table 1. The mean values of \(\rho_{d\gamma}\), \(\rho_{sat}\) and \(n\) were 2.43 g\(\cdot\)cm\({}^{-3}\), 2.47 g\(\cdot\)cm\({}^{-3}\) and 4.05%, respectively. The \(n\) values range from 0.7 to 8.1%, a fairly wide interval, due to the heterogeneity in the pore distribution and the traverting petrofabrics [38; 39], which is in line with the UCS values, whose average value was 91.72 MPa but in the 25-29 test specimens the UCS values became 70 MPa on average to 120 MPa, coinciding with this decrease in \(n\).  
The increase in dry and saturated primary velocity \(\Delta v_{\text{P}}\) was 369 m\(\cdot\)s\({}^{-1}\) on average, from which a clay mineral content ECC practically zero, has been deduced [40]. The average dry \(v_{p}\)/\(v_{s}\) ratio is 1.84, within the range 1.8-2 obtained by [PERSON] et al. [38] for traverting and 1.6-2.3 by [PERSON] [41], for consolidated sediments.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Specimen** & \begin{tabular}{c} **Diameter,** \\ **D (mm)** \\ \end{tabular} & \begin{tabular}{c} **Height,** \\ **H (mm)** \\ \end{tabular} & \begin{tabular}{c} **Porosity,** \\ **n (\%)** \\ \end{tabular} & \begin{tabular}{c} **Density,** \(\rho\) \\ **(g\(\cdot\)cm\({}^{-3}\))** \\ \end{tabular} & \begin{tabular}{c} **P-Wave Velocity,** \\ **v\({}_{p}\) (km\(\cdot\)s\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **S-Wave Velocity,** \\ **vs. (km\(\cdot\)s\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Uniaxial** \\ **Compressive** \\ **Strength, UCS (MPa)** \\ \end{tabular} \\ \cline{3-10}  & & & & **Dry** & **Sat** & **Dry** & **Sat** & **Dry** & **Sat** \\ \hline Traverting 1 & 31.090 & 59.470 & 3.65 & 2.48 & 2.52 & 5.310 & 5.505 & 3.109 & 3.030 & 75.28 \\ Traverting 2 & 30.970 & 59.550 & 4.09 & 2.37 & 2.41 & 5.085 & 5.505 & 3.243 & 3.046  
The dry \(v_{p}\) values were correlated with the UCS of the rocks using the simple and multivariate regression methods. The experimental data set was used to generate empirical equations developed to estimate the UCS of traverting from the independent variables \(v_{p}\), \(\rho_{dry}\) and \(n\). In simple regression analyses, models were generated to individually relate the independent variables (\(v_{p}\), \(\rho_{dry}\) and \(n\)) to the dependent one (UCS) by means of simple linear regression models, exponential models, potentials and quadratics through a logarithmic transformation, while in multivariate regression analyses, all variables considered in the sampling that are significant, and that contribute to explain the variability of the response were included. The analytical model, the coefficient of determination (R\({}^{2}\)) and the \(p\)-value were calculated, to study and compare the goodness of fit of the models generated for each regression.
# Numerical Investigations of Pile Group Foundations under Different Pile Length Conditions
## 4 Settlement Analysis  
### Pile Settlement  
Figure 3 illustrates the settlement of a group foundation with piles of equal length and a group foundation with piles of different lengths under a static uniform load of 100 kN. The figure reveals that the largest settlement differences occur at pile 1, pile 6, and pile 9, measuring 2.5 mm, 1 mm, and 1.42 mm, respectively. Specifically, at the location of pile 1, the settlement of the 54 m-long pile is smaller compared to that of the 70 m-long pile.  
Figure 2: (**a**) the slope model; (**b**) the pile model.  
Conversely, at the location of pile 9, where the lengths of the piles are the same, the settlement is smaller in the group foundation with equal-length piles. While the settlement values for other pile positions exhibit a significant degree of overlap, the overall trend suggests that piles with different lengths experience smaller settlements compared to piles with the same length.  
The slope and pile arrangement configuration leads to a specific relationship between the cap and the soil. The back half of the cap is in direct contact with the soil, allowing for the transfer of vertical load from above the cap to the slope surface. As a result, this portion of the soil experiences compression. On the other hand, the front half of the cap is not in contact with the soil, and therefore there is no compression occurring in the soil in this area. This difference in soil compression may be the reason for the slightly higher settlement observed in the back row of piles compared to the front row.
# An Approach for Deployment of Service-Oriented Simulation Run-Time Resources
## 6 Experiment Evaluation
### Experiment Results
#### 6.4.2 Effectiveness of Constraint Handling Policy  
The SRR deployment problem is an optimization problem with constraints. When solving this problem, certain rules can be set to ensure that the obtained solution does not violate the constraints. However, this approach is ineffective for constraints like the one shown in Equation (14). Therefore, additional constraint-handling methods, which have already been discussed in Section 5.1.1, need to be introduced into our algorithm. To validate the effectiveness of the constraint-handling approach used in LGASRD, we conducted experiments using some of the previous experimental settings. In contrast to previous experiments, we reduced the resource capacity of edge servers to one-tenth of their original capacity to simulate a resource-constrained scenario. Previous experiments' results have shown better performance for three evolutionary algorithms, while Random, TServiceD, and TSD\({}^{2}\) performed poorly and generated infeasible solutions under certain settings. Therefore, we will not discuss them further in this section.  
As shown in Figure 7, in most cases, KMNSGA and NSGA-II can only generate a fraction of feasible solutions, or even produce entirely infeasible solutions, as illustrated in Figure 7a. In contrast, LGASRD is capable of producing feasible solutions in all scenarios, with the minimal time cost among the three algorithms. This clearly demonstrates the effectiveness and significance of the constraint-handling mechanism we employed.  
In Figure 8, we present a comprehensive depiction of the time costs of solutions obtained from three algorithms in resource-constrained scenarios. The training simulation system's time cost is the summation of service communication time, service queuing time, and user interaction time. We can observe that the time spent on queuing at MS instances is relatively small compared to the data transmission time incurred due to the system's response to user-initiated service requests. Moreover, these two types of time costs do not exhibit significant increments with the increase in the granularity of service requests, the number of servers, and simulation users. In contrast, the time resulting from user interaction tends to increase significantly with the increase in the number of edge servers and users. This is evident. If micro-services are deployed properly, users may experience low service latency. However, as the number of simulation members increases, it inevitably leads to an increase in the frequency of interactions between users, resulting in a significant growth in user interaction time.  
Figure 6: Algorithm performance comparison under experimental settings J, K and L.