esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Detection of the Robot Mounting Release from the Base Using Machine Learning Algorithms
### Testing Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Evaluation** & **Wide** & **Narrow** & **Trilayered** & **Bilayered** & **Medium** & **RUSBoosted** & **Bagged** & **Subspace** \\
**Metric** & **Neural** & **Neural** & **Neural** & **Neural** & **Neural** & **Trees** & **Trees** & **KNN** \\ \hline Accuracy & 98.47\% & 98.47\% & 97.97\% & 97.97\% & 97.29\% & 96.95\% & 96.61\% & 92.71\% \\ Precision & 98.00\% & 97.68\% & 97.03\% & 97.03\% & 96.37\% & 96.66\% & 95.41\% & 87.80\% \\ Recall & 98.99\% & 99.33\% & 98.99\% & 98.99\% & 98.32\% & 97.31\% & 97.98\% & 99.33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Calculated evaluation metrics. Figure 10: Confusion matrixes for selected models. As we mentioned at the beginning of the section, the Wide Neural Network (WNN) model shows significantly better accuracy (98.47%) compared to the Subspace KNN model (92.70%), indicating that the WNN is more reliable in correctly classifying both anomalies and normal sequences. WNN has also a higher precision (98.00%), which means that it better minimizes false positives compared to Subspace KNN, which has a lower precision (87.76%). This suggests that Subspace KNN may more often mislabel normal sequences as an anomaly sequence. Both models perform well in the recall, with Subspace KNN slightly outperforming WNN (99.32% vs. 98.99%). This indicates that both models are highly effective in detecting anomalies, with KNN being slightly better at avoiding false negatives. In case of specificity WNN excels here with a specificity of 97.95%, which is much higher than the 86.01% for Subspace KNN. This means that the Subspace KNN model is more prone to false positives and more likely to mislabel anomalous sequences as normal. Based on the above, the WNN model trained using the vibration signal metrics chosen in 5.3.3 performs well in accuracy, precision, and specificity, making it more reliable in detecting true anomalies and minimizing false positives. While Subspace KNN performs slightly better in recall, indicating its strength in detecting almost all true normal sequences, the model suffers from lower accuracy and specificity, indicating a higher rate of classifying anomalous samples as normal. This factor is crucial in practical implementation, as much more expense may be incurred just by incorrectly fitting anomalous data into the normal category. For this specific anomaly detection task, the WNN model appears to be a more balanced and accurate choice overall.
## 3 Results  
### Effect of PBMT-sMF in Non-Oxidizing Environment  
#### 3.1.1 Biochemical Effects of PBMT-sMF  
To assess the impact of PBMT-sMF on cellular viability, C2C12 cells were exposed to varying energy doses, and the MTT assay was conducted three hours post-irradiation. An overall significant difference was found between treatment groups, F (6,7) = 89.13, \(p\) < 0.0001 (Figure 1). Specifically, 211 J and 302 J caused a significant decrease in viability (69.50 \(\pm\) 0.71%; \(p\) = 0.002 and 54.00 \(\pm\) 5.66%; \(p\) = 0.001, respectively), and under the dose of 121 J it was observed a trend toward reduction. However, 9 J, 30 J, and 60 J did not exhibit significant differences in viability compared to the control group. Therefore, the three higher doses were discontinued from the study.  
Regarding assays for NO (Figure 2A), dsDNA release (Figure 2B), and ROS (Figure 2C) levels, no significant effects of PBMT-sMF were observed compared to the control group.  
A significant increase in COX activity was observed at doses of 9 J (153.34 \(\pm\) 8.29; \(p\) = 0.004), 30 J (149.26 \(\pm\) 0.28; \(p\) = 0.006), and 60 J (181.89 \(\pm\) 10.38; \(p\) = 0.001), compared to the negative control (F (3,4) = 52,37, \(p\) < 0.001; Figure 3).  
Based on these results, only the highest (60 J) and lowest (9 J) doses of PBMT-sMF were selected for further molecular analyses using flow cytometry.  
Figure 1: Effects of Photobiomodulation Therapy combined with Static Magnetic Field (PBMT-sMF) on the viability of C2C12 myoblast cells after 3 h of irradiation. * Statistical difference determined by analysis of variance (ANOVA) and [PERSON]’s post hoc test (\(p\) < 0.05) versus control. J = Joules.
## 5 Simulation Results
### Network Performance Comparison
For UAV-assisted VANETs, UAVs are flexibly deployed and freely moved in three-dimensional (3-D) space, making it easier to convert multi-hop forwarding into single-hop relay, thus reducing communication delay and improving transmission reliability. Therefore, we can find that through a proper deployment of the UAV in VANETs, the path connectivity and the network comprehensive performance are improved, which can provide more users with the reliable QoS.
## 3 The Proposed DSCA-MC-CR Technique  
The proposed network includes two systems, i.e., the privileged PU system and the CR system. In this regard, the proposed DSCA-MC-CR technique assumes no change in the infrastructure of the existing PUs privileged system that legally owns the spectrum. Thus, the proposed technique uses an underlay DSA model that has no interference effect on the PUs RF bands due to the usage of spreading orthogonal codes by the SU proposed system. In addition, the proposal includes two models, i.e., model 1 and model 2.  
The SU system encoder and decoder of both models have a bit length \(L=8,16\) in sequence, which encodes-decodes the SU bits by using Hadamard matrices \(H_{8}\) and \(H_{16}\), which include 8 and 16 orthogonal vectors each. The propagation channel is assumed to be Gaussian. Then, another analysis is performed through the practical fading channel (i.e., the Rayleigh channel). Finally, the proposed models use the OFDM modulation scheme so that model 1 and model 2 are applicable for one-two-way communication applications. The channel gain level of the desired signal is \(\in\{0.7:0.95\}\), and the channel gain level of the interference devices is \(\in\{0.2:0.3\}\).
## 5 Conclusions  
In view of the low accuracy of blade fault identification, it is proposed to use multi-source data to identify faults and broaden the source of fault information. At the same time, the 1 DCNN-BiLSTM-AdaBoost algorithm model is innovatively proposed for fault type classification. Through the comparison of model performance, it is found that the 1 DCNN-BiLSTM algorithm has a better effect than the use of 1 DCNN, BiLSTM, and other models alone. And after multi-channel data fusion, the performance of the 1 DCNN-BiLSTM model is further improved. At the same time, through experimental verification, the AdaBoost algorithm has a good optimization effect on weak classifiers. After multiple runs, the improvement effect on the model is about 2-10%. The model proposed in this paper uses a large amount of data, which can meet the actual needs of wind turbine blade fault diagnosis. Although the proposed 1D CNN-BiLSTM-AdaBoost model achieved satisfactory performance on the ZENODO dataset, several challenges remain when deploying it in real-world wind turbine systems. These include data diversity, sensor configuration differences, real-time processing requirements, and system integration complexities. Future work will focus on expanding data sources, exploring domain adaptation techniques, and developing online update mechanisms to enhance the model's robustness and generalization capability. These efforts aim to provide more reliable technical support for intelligent maintenance and fault diagnosis in practical applications.  
In the future, this research is expected to have a profound impact on intelligent monitoring and maintenance of wind energy and other renewable energy systems, facilitating a transition toward data-driven and intelligent operations. Further studies could focus on enhancing the system's adaptability to varying operating conditions and integrating emerging technologies such as IoT and big data analytics. These efforts aim to develop a more comprehensive, real-time, and efficient fault diagnosis framework, providing robust support for the safe and stable operation of renewable energy generation.  
Writing--original draft preparation, K.M.; software, Y.Y.; formal analysis, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the R&D Program of Beijing Municipal Education Commission (Grant No. KM202211232014).  
The authors do not have permission to share the data.  
The authors declare no conflicts of interest.
## 3 Distortions Induced by Low Cost
### Linear Impairments: Limited Bandwidth and Chromatic Dispersion  
The high-speed PAM4 system suffers from severe bandwidth limitation due to the consideration of low cost. After bandwidth limited devices, the received signal with low-pass filtering effects can be expressed as:  
\[y_{k}=I_{k}+\sum_{\begin{array}{c}n=0\\ n\
eq k\end{array}}^{\infty}I_{n}x_{k-n}+v_{k} \tag{1}\]  
where \(x_{k}\) and \(y_{k}\) is the input signal and output signal at the \(k\)-th sampling instant, respectively. The first term \(I_{k}\) represents the desired information symbol while the second term donates the ISI. The last term \(v_{k}\) is the additive Gaussian noise variable at the \(k\)-th sampling instant. To investigate the performance impact of bandwidth limitation, a typical measured system frequency response is shown in Figure 2a [60]. The 3 dB bandwidth of the total transmission system is approximately 7.5 GHz, which is far below the required bandwidth for 64 Gbaud PAM4 signal. The attenuation of amplitude frequency is about 15 dB at the Nyquist frequency. In the insets (I) of Figure 3a, it can be observed that the optical eye diagram is confusing, and even four levels of PAM4 signal are unable to be detected. After compensation, a great improvement is achieved in the quality of the eye diagram, which reveals the tremendous influence of limited bandwidth. As shown in the blue part of Figure 2b, the receiver sensitivity penalty is measured as a function of the bandwidth for PAM4 system in simulation [19]. The received optical power at BER = \(3.8\times 10^{-3}\) for a bandwidth of 35 GHz is employed as reference. It can be seen that approximately 3.3 dB received power is lost as the bandwidth decreases from 35 GHz to 15 GHz. Moreover, the receiver sensitivity penalty increases in speed when the bandwidth limitation becomes severe. The other simulation for PAM4 transmission is depicted in the black part of Figure 2b, where the bandwidths of the components are varied uniformly [61]. The total bit rate is fixed at 96 Gbps, while the 3 dB bandwidths of DAC, modulator, PD and ADC are changed from \(-30\) to \(30\%\) simultaneously. The obtained BER performance is degraded as the bandwidth reduces, which confirms that the narrow bandwidth is one of the factors that deteriorate the performance of PAM4 optical links.  
When PAM4 is used in the C-band, the CD tolerance is considered as one key drawback, which critically impairs the high-frequency components of the signal. The typ  
Figure 2: (**a**) Frequency response in band-limited system and eye diagrams (I) before and (II) after bandwidth compensation (redrawn after [60]). (**b**) Effect of bandwidth to system performance (redrawn after [19; 61]).  
CD in terms of the optical signal-to-noise ratio (OSNR) for PAM4 is shown in Figure 3, where the OSNR penalty is about 1 dB for dispersion within \(\pm\)100 ps/nm [62]. Meanwhile, the eye diagrams for 56 Gbps PAM4 signal after 0 km and 20 km transmission are shown in Figure 3b,c, respectively. A qualitative comparison between these two eye diagrams reveals that CD brings serious degradation in signal quality.
## 6 Atmospheric Gaseous Losses Profile for Aircraft-Stratospheric Basestation (SB) Link
### Long Flight Handover Scenarios
The minimum atmospheric attenuation exists when the aircraft is directly underneath the SB where the atmospheric layer thickness is minimum between them. The performance of this case is shown in Figure 15 as the minimum line-of-sight (LOS) distance performance. Figure 15 shows a huge dynamic range of signal attenuation between an aircraft and SB especially at high frequencies for the case of maximum LOS distance between SBs. The dynamic range is very important as it defines the requirements of the receiver and transmitter circuits for proper signal reception and affects the power control and consumption of the aircraft. Table 1 displays the signal dynamic range due to atmospheric attenuation only at the operating 5G frequencies. The performance of tangential LOS handover is acceptable even at high frequencies where the dynamic range in this case is only 2.634 dB at 50 GHz and increases to 19.454 dB at 66 GHz. However, the dynamic range of atmospheric attenuation is very small at lower frequencies where it is only 0.522 dB at 40 GHz and is negligible at 28 GHz and lower frequencies. On the other hand, a special care for applying handover at maximum LOS link distance should be performed especially at high frequencies greater than 40 GHz. The handover process is impossible at maximum LOS case for the 50 and 66 GHz where the dynamic range is very large due to the huge atmospheric attenuation which is more than 110 dB. The other lower frequencies could be used for SB networks performing handover at the maximum LOS distance. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Frequency (GHz)**} \\ \cline{2-9}  & **3.5** & **6** & **10** & **26** & **28** & **40** & **50** & **66** \\ \hline Minimum atmospheric attenuation (dB) & 0.005 & 0.005 & 0.005 & 0.011 & 0.012 & 0.035 & 0.176 & 1.305 \\ Tangential LOS handover (dB) & 0.071 & 0.074 & 0.080 & 0.166 & 0.187 & 0.522 & 2.634 & 19.454 \\ Maximum LOS handover (dB) & 2.755 & 2.969 & 3.538 & 15.130 & 13.650 & 25.786 & 110.860 & 751.363 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Signal dynamic ranges due to atmospheric gaseous attenuation only for SB @ 20 km high and cruise altitude of 11 km high. Figure 15: Atmospheric gaseous attenuation for three different handover scenarios.
# Equity of Social Health Insurance Coverage for Migrants in Thailand: A Concentration Index Analysis
###### Abstract  
Thailand is attempting to implement an effective health insurance scheme to cover all migrant workers in the country. One of the remarkable policies is the Social Security Scheme (SSS). This study aims to assess the equity of SSS coverage among migrant workers in Thailand, sorted by types of businesses (agriculture, services and industrial sectors) and Gross Provincial Product (GPP) per capita. A secondary data analysis on time series cross-sectional data was employed. The dataset comprised: (1) the number of migrant insurees under the SSS; (2) the volume of migrant insurees in formal and informal sectors; and (3) provincial economic level and provincial population by years from 2015-2018. Descriptive statistics, [PERSON]'s rank correlation, and concentration index analysis on the ratio of SSS insures to all migrants ranked by GPP per capita and business types from 2015-2018 were performed. Results showed that the ratio of SSS insures to all migrants increased from 38.5% to 58.9%. [PERSON]'s correlation found a positive relationship between the SSS coverage and GPP per capita and business types in 2018. The statistical significance (\(p<0.001\)) was found only in the industrial sector (\(r_{\text{s}}=0.346\)). Significant CIs were found in SSS coverage in the industrial sector in 2016 (CI = 0.147, \(p<0.001\)), and SSS coverage in the industrial sector in 2017 (CI = 0.137, \(p<0.001\)). In conclusion, the trend of CIs displayed a movement towards zero for all business types. This implied that the distribution of migrants in SSS shifted toward the equitable distribution across provinces in Thailand. A possible explanation was a major change in Thai politics in 2014 and a change in employment legislation for migrants in 2017. To expand the insurance coverage, the government should use the CI as a guide to consider specific provinces or target specific economic sectors as a priority to expedite the insurance enrolment for migrants.  
equity; migrant workers; income-related inequality; inequality decomposition; social security scheme; concentration index; concentration curve; Thailand +
Footnote †: journal: Journal of Environmental Research and Public Health
## 3 Results
### Analysis of Turbulent Kinetic Energy (TKE) in Flow Field  
Figures 3 and 4 show the effect of the nozzle diameter on the turbulent strength of the jet, respectively. The equation for the turbulent strength is as follows [34]:  
\[TKE=\frac{1}{2}(\overline{u^{\prime 2}}+\overline{v^{\prime 2}}) \tag{8}\]  
where \(u^{\prime}\) and \(v^{\prime}\) represent the fluctuation velocity in the flow direction and normal direction, respectively. Figure 3 evidently shows that under a jet pressure of 0.36 MPa and a nozzle cone angle of \(115^{\circ}\), the nozzle diameter has a significant influence on the turbulence kinetic energy (TKE) of the flow field. As the nozzle diameter increases, the intensity of shear disturbances in the flow field along the z-direction gradually declines. Both the magnitude and extent of the TKE in the flow field decrease along the x-direction, suggesting an enhanced stability of the jet outcome. However, the TKE in the flow field does not exhibit a consistent pattern of change along the central axis. Notably, the TKE is significantly higher than in the other three test conditions when the nozzle diameter is 1 mm, highlighting the intense entrainment effect on the internal flow field of the jet (as seen in Figure 4a). The degree of fluctuation in TKE data also suggests a pronounced interaction between the internal jet flow and the surrounding air when the nozzle diameter is 1 mm (Figure 4b). The TKE exhibits a distinct multi-peak structure at the jet outlet. Specifically, the flow field's TKE displays three maxima and two minima with a 1 mm nozzle diameter, indicating relatively poor stability. Conversely, the TKE decreases significantly with a 2 mm nozzle diameter, exhibiting a peak-free and uniformly distributed pattern. When the nozzle  
Figure 2: (**a**) Flow velocity profile of the jet; (**b**) the transverse distribution of the velocity at the top position of the jet; (**c**) the transverse distribution of flow velocity at the middle position of the jet; (**d**) the transverse distribution of flow velocity at the bottom position of the jet.  
diameters are 4 mm and 6 mm, distinct double-peak structures reappear on both sides of the flow field. The extreme values of TKE in the flow field initially decrease and then increase as the nozzle diameter increases. The range of fluctuation and the gradual change pattern of TKE data indicate that the flow fields corresponding to nozzle diameters of 2 mm and 4 mm exhibit the best stability.
## 4 Results
### Verify the Downscaled SIF Result
#### 4.2.2 Correlation Analyses between SIF and MODIS GPP  
Numerous studies have established a linear relationship between SIF (W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\)) and GPP (g C m\({}^{-2}\) a\({}^{-1}\)) [43; 59; 60; 61; 62]. To verify the accuracy of the result, the MOD17A2H  
Figure 4: The scatter diagram of the GOSIF (W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\)) and resampled downscaled SIF (W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\)) (the black line denotes the fitting).  
Figure 3: The GOSIF (W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\)) and downscaled SIF (W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\)) result in May 2018. (a) The GOSIF value; (b) the downscaled SIF value based on the RF method.  
GPP product was selected. The MODIS GPP data were obtained by the maximum value synthesis method in May 2018 using 8-day products. The MODIS GPP was resampled to both 1 km and 0.05-degree spatial resolution using the pixel aggregate resampling method. The correlations between MODIS GPP, downscaled SIF, and GOSIF were subsequently calculated (Figure 5).  
Figure 5 compares the correlations among the value of GOSIF, downscaled SIF and MODIS GPP. The correlation coefficients (R) at different spatial resolutions were 0.68 and 0.74, respectively. The MODIS GPP and downscaled SIF result (R = 0.74) showed a stronger correlation than GOSIF (R = 0.68). This finding substantiates the credibility of the downscaled results.
## 2 Materials and Methods  
### Patient Population  
This retrospective study included 89 patients who underwent surgery for degenerative disorders of L2-S1 using the Bioflex system and an interbody cage. This study was approved by the institutional review board of our hospital and our Institutional Regional Board, and the requirement for informed consent was waived in view of the retrospective nature of this study. From 2006 to 2010, a total of 462 patients underwent surgery. Among them, 146 patients (31.6%) had clinical data available for more than 10 years, and 89 patients (19%) had radiologic data. The surgical indications are identical to those of lumbar fusion surgery in degenerative conditions: severe radiating pain that persists despite more than 3 months of conservative treatment and the corresponding evidence of degenerative stenosis, degenerative spondylolisthesis, or spondylolytic spondylolisthesis. Patients under 18 years of age, with less than 10 years of follow-up, or with conditions other than degenerative diseases, such as infection, tumor, trauma, or congenital anomalies, were excluded from this study. Patients diagnosed with severe coronal deformities or osteoporosis were also excluded from this study. The average age of the patients at the time of surgery was 53.5 years, with females comprising 67% of the cohort. The average number of surgical levels involved was 1.4 \(\pm\) 0.6, the follow-up period averaged 13.3 years (ranging from 10 to 17.5 years), and the mean body mass index (BMI) was 25.0 \(\pm\) 2.9 kg/m\({}^{2}\). The distribution of conditions was as follows: 24 patients (27%) had degenerative stenosis, 21 (23.6%) had degenerative spondylolisthesis, and 44 (49.4%) had spondylolytic spondylolisthesis (Table 1). The post hoc power analysis indicated that with an effect size of 89, this study had a power of 91% to detect significant differences with an alpha level of 0.05. This suggests that the sample size of 89 patients was sufficient to achieve adequate statistical power for the primary outcome measures.  
\begin{table}
\begin{tabular}{l c c} \hline  & **Value** & **Percentage (\%)** \\ \hline Age (years) & 53.5 & \\ Gender & & \\ Female & 60 & 67.4\% \\ Male & 29 & 32.6\% \\ Follow-up period (years) & 13.0 & \\ Body mass index (kg/m\({}^{2}\)) & 25.0 \(\pm\) 2.9 & \\ Disease & & \\ Degenerative stenosis & 24 & 27\% \\ Degenerative spondylolisthesis & 21 & 23.6\% \\ Spondylolisthesis & 44 & 49.4\% \\ \hline \end{tabular}
\end{table}
Table 1: The demographic and clinical characteristics of the patients.