esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Mathematical Study of Flexible-Rigid Rail Vehicle Riding Comfort and Safety
## 5 Results and Discussion
### Running Safety  
To perform the running stability simulation, the tried-and-true rigid-flexible coupled vehicle model and the multi-body model are used. As a result, derailments and a wheel load reduction rate were examined so that the running safety could be assessed. It is known that extreme vehicle hunting may result in the vehicle body colliding laterally with the road. Moreover, it may result in the wheel flange colliding laterally with the rail.  
In the second scenario, there is a risk that the vehicle may be derailed. For this reason, it is essential to research the impact of derailing utilizing models of rigid-flexible coupled vehicles and multi-body models. The Nadal restriction is applied for these specific objectives. The comparison of the data from the simulation with the outcomes of the experiment is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Speed (Km/h)** & \
\begin{tabular}{c} **Rigid-Flexible** \\ **Simulation (RF)** \\ \end{tabular} &
\begin{tabular}{c} **Multi-Rigid** \\ **Simulation (MR)** \\ \end{tabular} & **Experimental (E)** & **\% Error = (\(\frac{E-RF}{E})*100\)** & **\% Error = (\(\frac{E-MR}{E})*100\)** \\ \hline
30 & 2.54 & 2.24 & 2.2 & 13.90 & 0.45 \\ \hline
60 & 2.61 & 2.47 & 2.4 & 8.30 & 2.49 \\ \hline
90 & 2.79 & 2.67 & 2.5 & 8.14 & 3.49 \\ \hline
120 & 3.02 & 2.94 & 2.8 & 5.96 & 3.16 \\ \hline
150 & 3.12 & 3.04 & 2.9 & 5.41 & 2.70 \\ \hline
180 & 3.38 & 3.28 & 3 & 8.68 & 5.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation error of Sperling Ride Index.  
Figure 8: The Sperling Ride Index at the rigid and flexible car body end.  
Table 3 shows that the derailment coefficient for the considered speed varies from 0.104 to 0.170 for the RF simulation and 0.104 to 0.167 for the MR simulation. Field test reports have indicated that the mean derailment coefficient varies from 0.112 to 0.183. Hence, when comparing the experimental results, the % error for RF varies from 5 to 9%, and for MR, it varies from 8 to 14%. Therefore, the mean derailment coefficient of the vehicle body in terms of error for the rigid body dynamics model is more than the modulus value obtained by the rigid-flexible coupling dynamic model. Similarly, for the mean wheel load reduction rate, Table 4 shows the comparison of simulation data with the experimental results.  
Table 4 suggests that the mean wheel load reduction rate for the considered speed varies from 0.124 to 0.221 for the RF simulation and 0.121 to 0.219 for the MR simulation. Field test reports have indicated that the mean derailment coefficient varies from 0.129 to 0.247. Hence, when comparing the experimental results, the % error for RF varies from 3 to 15%, and for MR, it varies from 6 to 14%. Therefore, the mean derailment coefficient and mean wheel load reduction rate of the vehicle body in terms of error for the rigid body dynamics model is more than the modulus value obtained by the rigid-flexible coupling dynamic model.
## 3 Materials and Methods
### Datasets
#### 3.1.4 Street View Imagery  
A systematic grid was established to catalog latitude and longitude in meters, facilitating the retrieval of street view imagery (SVI). By employing these geographic coordinates, images were sourced from the Google Street View Static API, capturing visuals in all four cardinal directions: north, south, east, and west. Figure 3 illustrates the SVI acquired from one of the slum regions in Bandung City, as referenced in the official slum map for the year 2025.  
Random points were created along the network of minor and branch roads, ensuring a roughly equal distribution between slum and non-slum regions based on the modified slum reference data, utilizing the \"random points along line\" function in QGIS. To maintain clarity in deep learning classification, points located at the interface of slum and non-slum areas were omitted. This exclusion specifically pertained to points associated with priority slums identified in SVI images, which included slum areas exceeding 10 hectares. These encompassed urban villages such as Sukaraja, Ciumbuleuit, Sukahaji, Pajajaran, Cijerah, Babakan, Babakan Ciparay, Kopo, Pasirwangi, Jamika, Sukabungah, Kebon Jayanti, Sadang Serang, Cibangkong, and Babakan Asih. Ultimately, the dataset comprised 2904 random points for slum areas and 420 for the database of neighborhood roads throughout Bandung City that were developed to prevent the growth of housing and slum areas, as depicted in Figure 3.  
The collection of these randomly generated point locations is referred to as Google street view (GSV) locations. In total, 2904 images were acquired for slum areas, while 420 images were obtained for neighborhood roads in Bandung City residential area, although some GSV locations yielded no images. Each SVI was meticulously reviewed for any anomalies, with images that were excessively dark or captured from within buildings being excluded from the dataset. The final compilation thus included 3324 images representing both slum and non-slum areas.
## 1 Introduction
Consequently, researchers are forced to use the spaces available between buildings, walls, roads, and paths. The separation between the available spaces can be much greater than the minimum recommended distance to design grids or almost 3D electrode arrangements. In addition, inserting the electrodes into the ground may be prohibited due to buried electricity cables, telephone lines, water, gas, and drainage pipes, or simply because the structure being studied is a historical monument. To obviate these problems, unconventional arrangements have been proposed in the past to investigate beneath surfaces that cannot be reached directly by the electrode arrays. In the methods called \"L'-array\" [22] and \"Corner array\" [22], the electrodes are distributed to form an \(L\), exploiting, for example, two perpendicular ways adjacent to a building. However, the tomographies obtained by these methods suffer from a low resolution in the center of the investigated area. A modified version of these arrays, called \"L and Corner\" array has been proposed by [PERSON] et al. [23], considering among other modifications the possibility of extending the survey to a quadrilateral. However, the results show a low resolution when compared with those obtained from a regular grid of electrodes. In this work, the resolution capability of unconventional arrays is studied in situations where it is not possible to distribute the electrodes for optimal coverage, due to logistical impediments that force placing the electrodes only in certain accessible areas. In these cases, in which it is not possible to arrange the electrodes according to a regular and homogeneous distribution, the only solution is to surround the area to be investigated with polygonal electrode arrangements [22; 23]. The potential of areal electrode arrangements on one or more quadrilaterals, contiguous or concentric, is studied to counteract the low resolution afflicting the previously discussed electrode sequences. Furthermore, for these arrangements, the possibilities of choosing electrode array configurations are studied, based both on a low number of current dipoles, to exploit the potential of multi-channel resistivity meters, and on thresholds on the geometric factor, to limit the noise. Apparent resistivity measurements are simulated for the chosen combinations on a three-dimensional model with resistive and conductive blocks and the results are discussed and compared. Finally, a field test is carried out on a waste landfill, characterized by the impossibility of setting up a regular grid of electrodes, for which the access paths to the various sectors of the landfill were exploited to arrange electrodes on approximately concentric paths. The apparent resistivity and induced polarization data were inverted to obtain 3D models, aimed at defining the presence and geometry of any areas with a high concentration of leachate.
## 4 Suggestions for Future Trends
### Adaptive  
Adaptive requires the SAR system have the capability to adopt the optimal strategy according to the dynamic change of the RFI environment. Low resolution SMAP system with 1 MHz signal bandwidth adopts an agile frequency selection scheme to avoid the interference in various time and regions. This may not be applicable for other high-resolution systems. Transmitted waveform could also be adaptively designed according to changing RFI environment [100].  
On the other hand, the artificial learning techniques have proved very successful for various applications in recent years. Initial research show that the introduction of advanced artificial intelligent techniques such as deep learning network perform rather good for interference suppression [101; 102; 103; 104]. After finishing the off-line training step of the artificial neural network from the big data, the on-line detection and mitigation could be realized in a quite efficient way [104]. Its key challenge is that the training database should be large enough to include the RFI cases as much as possible, and thus the trained artificial neural network could capture the essential feature explicitly. This could be achieved by combing the cognitive techniques mentioned in Section 4.1, which makes the long-term accumulation of RFI samples and incremental learning of the RFI environment possible. Therefore, in the foreseeable future, more works about applying the intelligent learning methods for RFI mitigation in SAR system will emerge.
## 5 Experimental Section
### Comparison of Experimental Results  
The proposed RSII model was compared with eight contrast algorithms on three public datasets and the experimental results are shown in Table 3. The bold text in Table 2 represents the best experimental results. \(\Delta\) represents the relative percentage improvement of the RSII model compared to the optimal benchmark model. The comparison results show that both indicators were excellent on all three datasets. This also verified that the proposed model was effective. Through the analysis of the experimental results, we can draw the following conclusions.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MI**} & \multicolumn{2}{c}{**OP**} & \multicolumn{2}{c}{**DM**} \\ \cline{2-7}  & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** \\ \hline DeepCoNN & 0.622 & 0.557 & 0.682 & 0.718 & 0.783 & 1.055 \\ D-Attn & 0.644 & 0.862 & 0.618 & 0.746 & 0.653 & 0.840 \\ NARRE & 0.640 & 0.839 & 0.658 & 0.797 & 0.683 & 0.876 \\ CARL & 0.709 & 0.880 & 0.624 & 0.742 & 0.681 & 0.884 \\ TransNets & 0.784 & 1.162 & 0.661 & 0.718 & 0.825 & 1.197 \\ MPCN & 0.571 & 0.739 & 0.728 & 0.749 & 0.764 & 1.017 \\ MPCAR & 0.670 & 0.858 & 0.615 & 0.682 & 0.660 & 0.857 \\ OURS & **0.482**1  & **0.539**1  & **0.553**1  & **0.575**1  & **0.619**1  & **0.777**1  \\ \hline \hline \(\Delta\) & 15.59\% & 3.23\% & 10.08\% & 15.69\% & 5.21\% & 7.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of evaluation for different models.  
First, we found that the models with the ID information (NARRE, MPCAR, CARL and RSII) performed better than those without the ID information (DeepCoNN, D-Attn and MPCN). This showed that introducing the ID information could to some extent correct the prediction error caused by the reviews. Therefore, the ID information should be introduced when designing a recommender algorithm based on reviews.  
Second, compared with all the other models, the experimental results showed that the RSII model performed the best. This indicated that the dynamic fusion of the target review semantics and the ID information could enrich the prediction features and improve the recommendation performance, also demonstrating the effectiveness of the RSII model.  
Thirdly, compared with the MPCAR model, the RSII model was superior. Additionally, the application scope of RSII model was larger than that of the MPCAR model. This showed that as long as the model had the ability to generate target reviews semantically and dynamically fuse the review and the ID information, it could not only expand the application scope of the model, but it also had excellent recommendation performance.
# Practical Performance Analyses of 5G Sharing Voice Solution
## 2 Motivation and Contribution  
The focus of this study is the call setup delay and handover delay for VoLTE, EPS fallback, and VoNR performance in the 5G sharing network.  
Global operators face many challenges in 5G network development, with insufficient coverage, difficult construction and high investment, and high operating expenses. Most operators worldwide are in the 5G NSA deployment phase right now [27]. This study first proposes a sharing architecture for 5G access networks in response to the real problem of slow progress in 5G construction faced by global operators.  
Regardless of which network configuration solutions, voice service demand is a key aspect of 5G network construction [28]. Providing stable and high-quality voice services to users is an important part of the 5G experience. The performance of VoLTE, EPS fallback, and VoNR is the key issue to ensure a better voice experience in NR networks. The relevant parameters are given in [29; 30]. However, the existing voice solutions research is only up to 4G networks, e.g., [11]. 5G network-sharing studies are only limited to the architectural level, e.g., [31]. There is no research yet on voice solutions for the full network evolution phase from NSA to SA for 5G sharing networks.  
The contribution in this paper is in three-fold. Firstly, we present voice solutions for different 5G sharing network evolution stages, including VoLTE in the NSA stage, EPS fallback in SA early stage, and VoNR in SA mature stage. Secondly, the theoretical analysis of each involved solution is presented, giving the main concepts and their potential results for deployment in current 5G sharing networks. We compare and analyze the call setup time delay of three voice solutions. The handover process involved is highlighted. The call flow and relevant call setup KPIs are established and analyzed in detail for the redirection and handover strategies. Thirdly, this study performs practical verification and analysis by building test environments based on mainstream equipment vendors in the first actual commercial 5G sharing networks. The best practices and optimization techniques for VoLTE, EPS fallback, and VoNR are provided based on live network performance and the targeted end-user experience. This is also the first study of sharing voice solutions in different evolutionary stages of 5G in the world, analyzing and verifying from theoretical and experimental perspectives.
## 2 Experimental Setup
### DBD Actuator Construction  
The DBD actuator is composed of two copper electrodes separated by a dielectric layer and arranged in an asymmetric manner, as shown in Figure 2. There is a slight overlap between the upper and lower electrodes in order to encourage uniform plasma generation [10]. The encapsulated electrode has a width of 10 mm while the exposed electrode is 4 mm wide. The length of the actuator over the model span, which corresponds to the length of the exposed electrode, is 300 mm. The dielectric barrier is composed of four layers of Kapton tape. Each layer has a thickness of 70 \(\upmu\)m, i.e., 25 \(\upmu\)m Kapton with a 45 \(\upmu\)m adhesive layer. The plasma forms in the upstream direction over the encapsulated electrode, as shown in Figure 2. Thus, the first disturbance encountered by the flow is the plasma itself. The upstream edge of the exposed electrode was located at a distance of 4.5 mm along the chord from the leading edge of the model (at \(x/c=0.03\)). As the occurrence of plasma on an airfoil surface induces only a relatively small effect on the airflow, it is clear that, to gain any significant advantage from its use, considerable leverage should be provided by the flow sensitivity. Many experimental studies [15; 20; 21; 22] have been performed with the aim of determining the optimal location of a NS-DBD actuator on such a profile. These studies have consistently shown that the maximum effect is achieved when the electrodes are positioned such that the discharge zone is located just upstream of the separation point.
## 5 Results and Discussion  
### Main Results  
Table 1 presents the results for a music genre classification task, where different methods are evaluated with three different acoustic features. First, we compare the performance of many existing classification models, with ResNet performing the best. So, we chose it as the backbone of our network. Then, the experiments show that our proposed LGNet has overwhelming advantages over the other models. Our LGNet uses a similar network backbone as ResNet, on which only a few simple linear layers are added. Our strategies can bring a 6.66% (CQT) to 8.16% (Mel) performance improvement. In addition, we find that the number of MLP layers also has an impact on the results. The related research is detailed in Section 4.3.  
In addition, we plot a confusion matrix to further illustrate the detailed performance of our proposed LGNet on each genre. For the confusion matrix, darker colors indicate that the model has a greater probability of making corresponding predictions. In Figure 4, we observe that classic music and metal music are very well classified, while music with the genre of country and rock is harder to distinguish. Overall, the performance of the automatic classification system based on LGNet is satisfactory; it can make correct predictions for most of the music tracks.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Accuracy (STFT)** & **Accuracy (Mel)** & **Accuracy (CQT)** \\ \hline Naive Bayes & 50.48 & 54.45 & 51.90 \\ SVM & 62.25 & 64.36 & 64.99 \\ LSTM & 60.14 & 61.44 & 60.79 \\ Bi-LSTM & 61.48 & 62.01 & 61.41 \\ FCN & 73.55 & 74.39 & 68.86 \\ FCN-LSTM & 72.59 & 73.70 & 71.28 \\ ResNet & 74.60 & 74.55 & 72.51 \\ \hline LGNet-2 MLP & 82.43 & 82.36 & 79.17 \\ LGNet-4 MLP & 82.24 & 82.71 & 79.55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main results on the filtered GTZAN dataset. All reported results are the accuracy on the test set.  
Now, let us turn our attention to acoustic features. We observe that models based on the Mel spectrogram outperform models based on other acoustic features in most cases. It shows that applying the filter bank with a high resolution at low frequencies is an effective method. In addition, the STFT spectrogram is also a well-behaved feature that sometimes could outperform the Mel spectrogram (74.60% vs. 74.55% and 82.43% vs. 82.36%). We also observe that models based on CQT spectrograms could not achieve a competitive performance. According to our inference, CQT spectrograms contain more frequency bins, including some less informative frequency bins that are not discriminative for classification.
## 4 Results of the Optimizations
### Conventional Solution  
The Conventional Solution is considered as the reference solution, assuming that all thermal energy is produced by BOI, all cooling energy is produced by electric CC and all electricity is bought from the grid. Furthermore, the thermal and cooling energy can be stored in separated energy storages. The optimization is performed adopting a reduced superstructure where only BOI and CC can be installed (Figure 5).  
Table 3 shows the optimal configuration of the conventional solution, obtained by minimizing the total annual cost. The table reports the energy peaks of each single user. It can be noted that the boilers installed in each unit are of smaller sizes compared with the thermal peaks, as all units are provided with a proper sized thermal storage. On the other hand, the sizes of the compression chillers correspond to the cooling peaks as the cooling storages are not included in the optimal solution.  
Table 4 shows the economic and environmental results of the optimization performed for the conventional solution.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**User** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Electric Peak (kW) & 189 & 270 & 110 & 54 & 101 & 36 & 628 & 148 & 315 \\ Thermal Peak (kW) & 410 & 655 & 296 & 591 & 246 & 238 & 1847 & 2084 & 1425 \\ Cooling Peak (kW) & 150 & 458 & 115 & 0 & 138 & 91 & 2087 & 0 & 435 \\ Boiler (kW) & 294 & 479 & 217 & 418 & 205 & 179 & 1623 & 1673 & 1153 \\ Comp. Chiller (kW) & 150 & 458 & 115 & 0 & 138 & 91 & 2087 & 0 & 435 \\ Thermal storage (kW) & 544 & 375 & 312 & 766 & 173 & 298 & 690 & 2251 & 1564 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal configuration of the conventional solution.  
Figure 5: Superstructure of the conventional solution.  
The total annual cost of the conventional solution is 2622 k\(\epsilon\) per year, and it is made up of about 93% of operation costs (costs for thermal energy and electricity), 6% of investment costs and 1% of maintenance costs. The results show also that the hospital contributes to the total annual cost by about 50%, as it is the largest energy consumer. The table shows also the environmental results and that they depend basically on the energy consumption of each user. The energy balances are reported in Table 5. The thermal energy produced by BOI is slightly higher than the thermal energy required by the users, because of the heat losses in the TS. It should also be noticed that, as can be expected, the thermal and cooling energies produced by the components are all used by the users without any waste.  
The results presented in this section, and obtained by minimizing the economic objective function of the conventional solution, will be used as a reference for the evaluation of the forthcoming optimizations.
## 2 Materials and Methods
### Data Processing  
All raw data collected were cleansed and formatted. The workflow is as follows.  
1. Finalising the changing rates of stock index data every two consecutive trading days, and addressing six data sources in Section 2. Suppose there are two consecutive trading days, \(i_{d1}\) is the stock index with the closest value on the first day, \(i_{d2}\) is the value on the second day, and \(c_{i}\) represents the changing rate of the stock index of those two values accordingly; then, \[c_{i}\ \frac{i_{d2}-i_{d1}}{i_{d1}}*100\%\] (1)
2. Settling the stock index value changing rate data table. C indicates the collection of \(\{c_{1},c_{2},\ldots,\ c_{m},\ldots,\ c_{n}\}\), in which \(c_{n}\) is the changing rate of the last two consecutive trading days in 2020, and \(c_{m}\) stands for the median value as a result.
3. Managing the inflation of the changing rate data table. To dig into values that represent'significant' differences, we defined that changing values more prominent than the median values have 'obvious' differences. However, for showing clear comparisons, a set of specific values of \(R=\{r_{1},r_{2},r_{3},r_{4},r_{5},r_{6}\}\) has been defined to inflate the discrepancies, that is, we only kept the changing rate values larger than specific values for further analytics in experiments. Say \(c_{m1}\) indicates the median changing rate value of SSEC, and \(r_{1}\) is the chosen inflation assessment of SSEC, then only values more significant than \((c_{m1}*r_{1})\) are kept for analysing the relationship between the pandemic and the stock market index of SSEC. Please refer to Table 1 for relevant \(R\)-_values_, which were determined through experiments for showing the apparent discrepancy.  
Discovering the connection between COVID-19 events and stock market data. Based on data finalised from step (3), we generated graphs that correspond to COVID-related social events and stock market shifting. Here, we used dates to build connections accordingly. We then integrated two data types from Section 2, which involves event details, dates, and changing stock market trends from SSEC, HSI, SZI, CSI500, TWII, and VXFXI. There were 19 records kept for analysis purposes.