esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Establishment of Core Indicator System
### Initial Indicator System
#### 3.1.1 Data Cleaning  
A number of indicators that could not be quantified and those with missing values greater than 5% were eliminated, resulting in an initial screening of 42 evaluation indicators.  
1. Quantitative processing of data  
The qualitative data were quantified by finding relevant information on the internet and combining it with subjective judgment, as shown below.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**User ID**} & **Overall Satisfaction with Voice** & **Whether** & & & & **Commercial** & & & **Hight-Speed** \\  & **Satisfaction** & **Encountered Network** & **Residential** & **Offices** & **Colleges** & **Streets, Subways** & **Areas** & **High-Speed** \\  & **Calls** & **Problems** & & & & **Subways** & & **Areas** & **Railways** \\ \hline
1 & \(10\) & \(1\) & \(-1\) & \(2\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) \\
2 & \(2\) & \(1\) & \(1\) & \(2\) & \(-1\) & \(4\) & \(-1\) & \(-1\) & \(-1\) \\
3 & \(10\) & \(1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(6\) & \(-1\) \\
4 & \(6\) & \(1\) & \(1\) & \(2\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) \\
5 & \(5\) & \(1\) & \(-1\) & \(2\) & \(-1\) & \(-1\) & \(5\) & \(-1\) & \(7\) \\
6 & \(7\) & \(1\) & \(1\) & \(-1\) & \(-1\) & \(-1\) & \(5\) & \(6\) & \(-1\) \\
7 & \(8\) & \(1\) & \(-1\) & \(-1\) & \(-1\) & \(4\) & \(-1\) & \(6\) & \(7\) \\
8 & \(10\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) \\
9 & \(10\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The samples of data.  
1 Whether encountered network problems (yes \(\to\) 1, not \(\to-1\)) 2 4/5G User (2G \(\to\) 2, 4G \(\to\) 4, 5G \(\to\) 5) 3 Phonetic method (VONR \(\to\) 6, EPSFB \(\to\) 5(5G)) (VOLTF \(\to\) 4, CSFB \(\to\) 3(4G), GSB \(\to\) 2(2G)) 4 Whether to care for the user (not \(\to-1\), yes \(\to\) 1) 5 Whether or not the user is a real - name registered user (not \(\to-1\), yes \(\to\) 1) 6 Client star rating logo (unrated \(\to-1\), semi - starred \(\to\) 0) (one-starred \(\to\) 1, two-starred \(\to\) 2, three-starred \(\to\) 3) (Sliver card \(\to\) 4, Gold card \(\to\) 5, Platinum card \(\to\) 6, Diamond card \(\to\) 7)
2. Empty value processing The null values of the indicators after the initial screening were removed because they accounted for less than 5% of the data.
3. Outlier handling The Outbound Traffic Percentage indicator contained two \"#DIV/0\" outliers, which were deleted.
# When Maritime Meets Aviation: The Safety of Seaplanes on the Water
## 4 Discussion
### Broader Implications  
From a broader perspective, this study highlights the intersection of maritime and aviation safety, where dual operational environments create unique challenges for seaplane operations. The increasing trend in private recreational occurrences, particularly those involving amateur-built aircraft, raises concerns about regulatory oversight and safety education in this sector. Addressing these gaps requires collaboration between aviation and maritime authorities to establish integrated safety management systems (SMSs) that consider the dual nature of seaplanes. The findings also underscore the importance of reporting near-misses and minor incidents, which often go unreported but provide valuable insights into potential hazards. Enhanced reporting frameworks could significantly improve the understanding of seaplane safety and inform the development of targeted interventions.  
Mitigating risks from boats and jet skis requires raising boaters' awareness about seaplane operations through community outreach and collaboration with maritime authorities. Regulatory adjustments giving aircraft priority during take-off and landing, along with clear right-of-way rules communicated to both pilots and boaters, can reduce conflicts. Education on maritime regulations is essential for safer shared waterways. Thus, pilots should be trained to recognize high-traffic water areas and apply effective avoidance tactics using standardized protocols.  
To address the issue of insufficient training quality, the minimum mandatory course durations could be extended, and mandatory proficiency checks could be implemented, as well as using detailed checklists to ensure that pilots meet required standards before flying solo. To combat any decline in skills due to overreliance on technology, training programs should balance advanced system use with manual flying techniques, emphasizing hands-on experience. Updating the Seaplane Rating document to cover unsafe scenarios and spreading training over diverse conditions would better prepare pilots. Incorporating real-world flying exercises, simulated emergencies, and theoretical-practical integration should be mandatory, as this could further enhance pilots' readiness. Additionally, offering insurance discounts for pilots completing extra safety-related courses could incentivize further training and improve overall safety standards.  
To summarize the actionable recommendations proposed in this study, Table 7 presents the key areas for intervention, along with corresponding measures.
## 3 Results
### Effect of F-T Cycles on the Functional Groups in S/S Soil  
Figure 6 shows the FTIR spectra of S/S Pb-Zn-Cd composite HM-contaminated soil under six F-T cycle conditions (0, 3, 7, 14, 30, and 90 cycles). According to their own spectra, a large amount of literature data, and standard Fourier infrared spectrogram, the types of functional groups in each condition were distinguished.  
The locations of the characteristic peaks of the FTIR spectra under the six F-T conditions are roughly the same, indicating that within 90 F-T cycles, the types of target substances (soil components, binder components, hydration products, etc.) detected in the S/S Pb-Zn-Cd composite HMs-contaminated soil did not change.  
Seven hundred and ninety-seven per centimeter, and Seven hundred and eighty-seven per centimeter are caused by Si-O-Si stretching vibrations, which are typical double peaks of quartz minerals, which is an important part of the soil itself, and the added binders. The peaks at 516 cm\({}^{-1}\) and 463 cm\({}^{-1}\) are the Si-O vibration absorption peaks of the gel [52; 53]. There are two main reasons for the existence of the characteristic peak of colloid. One is that the effective component of cement, tricalcium silicate, and dicalcium silicate, generates a large amount of hydrated calcium silicate colloid after hydration; the other is that the hydration product of cement, calcium hydroxide, continues to react with lime and fly ash to form colloids [54]; among them, there was residual calcium hydroxide, and the characteristic peak appears in 1651 cm\({}^{-1}\). The existence of colloids has made a great contribution to the S/S of HMs, such as the large amounts of adsorption of HM ions, and the filling of soil pores, which hinders the flow of HMs. The formation of calcium hydroxide also underwent a substitution reaction with HM ions, causing HM ions to precipitate.  
Eight hundred and seventy-four per centimeter and one thousand four hundred and twenty per centimeter are caused by carbonate stretching vibration. These two obvious characteristic peaks appeared in the FTIR spectra under every F-T condition (0, 3, 7,14, 30, and 90 F-T cycles). This is mainly produced by the entry of carbon dioxide into the atmosphere and is affected by the pH of the soil. There is a huge relationship with the formation of HM carbonate-bound fraction.
## 3 Aerodynamic Interactions
### Bridge-Deck Model  
#### 3.2.1 Flow Transition Promoting Effect  
In Figures 3 and 4, the instantaneous flow structures of the bridge-only model in \(\alpha=[-16^{\circ},\,12^{\circ}]\) are depicted in the middle columns. Based on previous studies [31; 35; 36], the lower half bridge-deck is characterized by leading-edge vortex shedding (LEVS) in \(\alpha=[-16^{\circ},\,-12^{\circ}]\), impinging leading-edge vortices (ILEV) in \(\alpha=[-10^{\circ},\,-8^{\circ}]\), and trailing-edge vortex shedding (TEVS) in \(\alpha=[-6^{\circ},\,12^{\circ}]\). However, the corresponding \(\alpha\) ranges of the above three typical flow patterns (i.e., LEVS, ILEV, and TEVS) in the train-bridge case are \(\alpha=[-12^{\circ},\,-8^{\circ}]\), \(\alpha=[-6^{\circ},\,-4^{\circ}]\), and \(\alpha=[-2^{\circ},\,12^{\circ}]\) as shown in the right columns of Figures 3 and 4, respectively. In other words, the critical \(\mid\alpha\mid\) of the above three typical flow patterns transiting from one to another on the lower half bridge-deck is reduced by about \(4^{\circ}\) with the presence of the train model. Moreover, Figure 6 can quantitively re-confirm the above flow transition promoting phenomenon. By comparison, the pressure distribution in \(x_{p}=7.3\)\(\sim\)\(14.1\) at \(\alpha_{i}\) in the train-bridge case is in line with that at \(\alpha_{i}-4^{\circ}\) in the bridge-only case. That is to say, the critical \(\mid\alpha\mid\) of the flow transition of the lower half bridge-deck model is reduced by about \(4^{\circ}\) with the presence of the train model.  
Figure 5: Profiles of flow approaching the train model at various angles of attack: (**a**) Normalized mean velocity in streamwise direction; (**b**) Turbulence intensity in streamwise direction; and (**c**) Turbulence intensity in vertical direction.  
Upon comparison with the study of [34], the flow behaviors around the lower half bridge-deck model are almost the same as when the train model is mounted on the upstream and downstream tracks, suggesting the flow transition promoting effect is insensitive to the occupied track variation.
## 2 Results
### Expression Patterns of AsA Metabolism Genes  
To explore in depth the molecular difference in AsA metabolism between Issai and Ana, the expression profiling of transcripts involved in AsA biosynthesis was analyzed based on the transcriptome data. The results (Figure 6, Supplementary Table S3) showed that several transcripts-encoding enzymes involved in the predominant AsA biosynthetic pathway (L-galactose pathway) were largely up-regulated in Issai. For instance, the transcript Cluster-4820.52093 and Cluster-4820.44393, both encoding GGP, expressed 2.6 and 4.6 folds higher, respectively, in Issai than in Ana. The other key enzymes in this pathway including GMP, GME, GalDH, and GalLDH were also significantly up-regulated in Issai. Moreover, the expression levels of transcripts encoding pectinesterase (PME) and poly-galacturonase (PG) involved in the early steps of D-galacturonate biosynthesis pathway were also significantly enhanced in Issai. Our results suggested a higher flux of AsA biosynthesis in Issai than in Ana. However, notably, the expression of transcripts encoding MIOX was significantly repressed in Issai. In addition, the transcripts encoding AO, APX, and glutathione reductase (GR) involved in the AsA-GSH cycle were also up-regulated in Issai. Two transcripts encoding MDHAR were found to be up- and down-regulated in Issai, respectively. The FPKM values of transcripts involved in the AsA metabolism pathway are shown in Supplementary Table S3.  
Figure 6: Schematic of AsA metabolism pathway in Ana and Issai. Expression patterns of DEGs are represented by log2 FPKM value and shown as a heatmap at the side of each step. The cells of the heatmap from left to right represent the expression level in Issai and Ana, respectively. The full name of the abbreviated genes is listed in the Table S3.
## 5 Numerical Model Update and Validation  
Bending strain and corresponding bending moments are the key parameters for the design of any structure. The bending strain response for particular elements is easier to obtain from a centrifugal test. The numerical model is updated with the bending strain of the wall from the centrifugal test along the depth. The updated soil parameters from the response-surface model are adopted in the numerical model for updating. Figure 9 shows the updated numerical model results. The bending strain of the wall became negative where struts were connected with the wall. The walls were tied with the baseplates that provided fixed tip conditions, so the negative bending strain occurred at the bottom of the wall. The soil in the top layers had the lowest elasticity, and minimal strain occurred in the topsoil layer. However, wall bending strain values at seven particular points were obtained from this updated numerical model for comparison with the centrifugal test.  
A comparison of the magnitude of the bending strain from the updated numerical model and centrifugal test data is shown in Figure 10. The strain gauges were set at a total of seven locations in the centrifuge; corresponding point responses were taken from the numerical model. In the first segment, the location of those particular points is shown. In the third segment, the differences in the magnitude for the seven points in the centrifuge and numerical model are shown. This showed a good agreement among them with an overall 5.57 (\(\times 10^{-6}\)) difference in magnitude.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Updated** & **Previous Study** & **Remark** \\ \hline Density (Kg/m\({}^{3}\)) & 1446.46 & 1450 & [PERSON] et al. [47] \\ Frictional angle (\({}^{\circ}\)) & 40.89 & 41 & [PERSON] et al. [47] \\ Stiffness modulus number & 409.09 & 350–450 & [PERSON] [48] (fine sand) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of updated soil parameters with the previous study.  
Figure 9: Response (bending strain) of the updated numerical model.  
The predicted responses were checked through the corresponding actual responses from the numerical model. Figure 11 shows the linear relationship among them. The response-surface model predictions were quite close to the actual numerical responses, with a difference of less than 0.5%.
# Synthetical Modal Parameters Identification Method of Damped Oscillation Signals in Power System
## 3 Validation of SMPI with Simulated Signals
### Modal Parameters Identification with SSI and Prony  
In this section, the modal parameters of DLFO signal A are identified in respect of SSI and Prony approaches. Firstly, SSI is adopted to calculate the frequencies and damping ratios of \(c_{EMD}(t)\) and the results are displayed in Table 2.  
As shown in Table 2, there are four model orders identified by SSI, including one false mode (the last one). And only frequencies and damping ratios have been recognized from each model order, lacking other modal parameters. Subsequently, it is necessary to identify more modal parameters of \(c_{EMD}(t)\), i.e., amplitudes and phase angles in another way. After Prony analysis, the frequencies, amplitudes, and phase angles of the first 99 modes are attained in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nodes** & **Frequency (Hz)** & **Damping Ratio** \\ \hline
1 & 0.3318 & 0.0589 \\
2 & 0.7807 & 0.0258 \\
3 & 1.0072 & 0.1119 \\
4 & 2.0134 & 0.0104 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modal parameters of signal A identified with SSI approach.  
Figure 5: Effective IMFs and a residual signal after denoising.  
Figure 6: Comparison of the synthesized signal by EMD with signal A.  
Although Prony has identified other more modal parameters, amplitude, and phase angle in addition to frequency, compared with SSI analysis, there were still a large number of false modes.  
To avoid missing dominant modes, the number of model orders is set relatively higher in this study, so the false modes are generated owing to the over-estimation of model order, which is difficult to determine in SSI and Prony. Therefore, it is needed to extract the true modes from the trivial modes through a novel method.  
With respect of Tables 2 and 3, SSI has recognized 4 modes while 99 modes have been found by Prony. Although false modes exist in Tables 2 and 3, the true modes of DLFO exist as well. Besides, identified frequencies are both displayed in these two tables except for damping ratios, amplitudes, and phase angles. Therefore, the key to seek for each true model order is to match two sets of the results of identified modal parameters in Tables 2 and 3 according to the principle of the similar frequency. This work will be completed in the next subsection.
# Study on Passenger Comfort Based on Human-Bus-Road Coupled Vibration
## 2 Human-Bus-Road Coupled Vibration System
### Human-Bus-Road Model
#### 2.1.3. 3D Human-Bus Model  
The bus model is comprised of five rigid bodies, i.e., one for the bus body and four for the four wheels, as shown in Figure 1. The bus body is assigned three degrees of freedom with respect to its gravity center: the vertical displacement (\(Z_{b}\)), the rotation about the width-axis (pitch angle \(\theta\)), and the rotation about the length-axis (roll angle \(\varphi\)). Each wheel as one rigid body is assigned one degree in the vertical displacement (\(Z_{wi}\)). There are 7 DOF in total for the bus [14; 15]. In order to consider the spatial effect of passengers on the bus, a refined bus body model with seat arrangement is created, as shown in Figure 1c, which is applicable to study the response of passengers in different locations on the bus. The human body is connected to the bus body using the spring-damper system. It is worth to note that the road surface roughness is the main source for the vehicle vibration in the human-bus-roadvibration. Therefore, the flexibility of the bus body and the engine vibration is not considered in this study, although they are also factors affecting passenger comfort.  
According to the D'Alembert principle, the equation of motion for the bus is given as  
\[[M_{b}]\Big{[}\ddot{Z}_{b}(t)\Big{]}+[C_{b}]\Big{[}\dot{Z}_{b}(t)\Big{]}+[K_{b}] [Z_{b}(t)]=\Big{\{}F_{b}{}^{G}+F_{br}(t)+F_{bh}(t)\Big{\}} \tag{5}\]  
where \([M_{b}]\), \([C_{b}]\), and \([K_{b}]\) are the mass, damping, and stiffness matrixes of the bus, respectively; \([Z_{b}(t)]\) is the displacement vector of the bus; \([F_{br}(t)]\) is the vector of the wheel-road contact forces on the bus; \([F_{bh}(t)]\) is the vector of contact forces between the bus body and the human body; \(F_{b}^{G}\) is the self-weight vector of the bus.  
For the purpose of comfort evaluation, a human biomechanical model with two vertical DOF is adopted for each passenger, as shown in Figure 2. The biomechanical model is created based on the relationship between the main resonance frequency and the mechanical characteristics of the human body [16; 17].  
The equation of motion for all the passengers on the bus is expressed as  
\[[M_{h}]\Big{[}\ddot{Z}_{h}(t)\Big{]}+[C_{h}]\Big{[}\dot{Z}_{h}(t)\Big{]}+[K_{h} ][Z_{h}(t)]=\Big{\{}F_{h}{}^{G}+F_{hb}(t)\Big{\}} \tag{6}\]  
Figure 1: Three views of the bus model: (**a**) side view; (**b**) front view; (**c**) top view.  
Figure 2: Two degrees-of-freedom (DOF) human biomechanical vibration model.  
where \([M_{h}]\), \([C_{h}]\), and \([K_{h}]\) are the mass, damping, and stiffness matrices of the human, respectively; \(\{Z_{h}(t)\}\) is the displacement vector of the human body; \([F_{hb}(t)]\) is the vector of the contact force between the bus body and the human body; \(F_{h}^{G}\) is the gravity force vector of the human. If \(n\) passengers are considered, the rank of the matrix or vector is \(2n\).
## 2 Related Works  
Atmospheric duct interference (ADI) poses a substantial challenge to the performance, coverage, and quality of service in contemporary wireless communication systems, particularly in TD-LTE and 5G Radio networks. To address these issues, a diverse body of research has explored various ADI detection and mitigation techniques. Existing studies have analyzed signal processing-based methods, machine learning and deep learning frameworks, hybrid algorithmic strategies, and simulation-based evaluations. Each of these methodologies contributes valuable insights into the nature of ADI and the effectiveness of different mitigation strategies under realistic deployment conditions.  
The following subsections review the key contributions within each of these methodological categories. By analyzing the strengths, limitations, and empirical results of the proposed approaches, we aim to identify existing research gaps and motivate the development of more robust and adaptive interference mitigation systems suitable for next-generation wireless networks.
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 3 Modifications of Information Criteria for the Case of Hankel Noise
### Algorithm  
Algorithm 1 describes how to compute BIC versions of the proposed criteria for white and red noise cases.  
**Input**: Time series \(\mathsf{X}\), window length \(L\), rank \(r\), type of IC (TRMAT or SVD), indicator CHECKSTAT if the stationarity check is needed, significance level \(\alpha\) for checking stationarity, indicator NOISETYPE.  
**Result**: Value of IC.  
1. Apply SSA with \(L\) and \(r\) to \(\mathsf{X}\) of length \(N\).
2. Calculate the residuals in the matrix (\(\mathsf{D}_{r}\)) and time-series (\(\mathsf{D}_{r}\)) forms.
3. Calculate \(\hat{\sigma}^{2}\) by (9) if IC = TRMAT or by (8) if IC = SVD.
4. If NOISETYPE = RED: * Estimate \(\phi\) by (12) using matrix residuals \(\mathsf{D}_{r}\) (with weights if IC = TRMAT; Remark 2). * If CHECKSTAT = TRUE and the stationarity of \(\mathsf{D}_{r}\) is rejected with significance level \(\alpha\) or if \(\phi>1\), set \(\phi=1\) and return IC \(=-\infty\). * Recalculate \(\hat{\sigma}^{2}\leftarrow\hat{\sigma}^{2}(1-\phi^{2})\);
5. Calculate the number of parameters \(k\) by (10).
6. Return IC \(=-N\log\hat{\sigma}^{2}-k\log N\) (4).  
**Algorithm 1** Calculation of TRMAT and SVD  
For MDL and white noise, the values of the criterion are calculated by (6). If the noise is red, then \(\hat{\sigma}^{2}\) in (6) is recalculated with the substitution of \(\hat{\sigma}^{2}(1-\phi^{2})\) instead of \(\hat{\sigma}^{2}\).  
**4. Numerical Experiments**  
Let us numerically compare the considered methods and study their accuracy as a function of the noise level.