esa-sceva/satcom-chunk-collection huggingface dataset:
# Equity of Social Health Insurance Coverage for Migrants in Thailand: A Concentration Index Analysis
## 2 Materials and Methods
### Data Management and Analysis  
The analysis was composed of four parts. First, for the SSS data, as the data were arranged in a monthly format while the economic data were stored in a yearly format, we used the median number across months within a year to represent the number of migrants in a respective year. Then we merged the economic data with the yearly migrant data. The study years lasted from 2015 to 2018. The provincial economic data consisted of gross provincial product (GPP) per capita and the GPP per capita sorted by business sectors, namely, agricultural product per capita, industrial product per capita, and service product per capita.  
Second, we conducted descriptive statistics to present the overview of number of migrants in Thailand, number of SSS migrants and the ratio of SSS insurees to all migrants (we named the ratio as SSS coverage). Additionally, we illustrated the descriptive statistics of economic data across provinces in Thailand.  
Third, we performed the association between SSS coverage and each provincial economic level during 2015-2018 by [PERSON]'s rank correlation [12].  
Fourth, we assessed the equity of insurance coverage respective to the provincial economic level by concentration index (CI). The CI is widely recognized as a useful tool to evaluate the degree of socioeconomic related inequality in health. The CI was acquired by regressing \(2\alpha^{2}r\) (\(h_{\mathrm{i}}/\mu\)) on \(r_{\mathrm{i}}\), as proposed by the World Bank [13; 14] where the coefficient \(\beta\) represented the point estimate of the CI, \(\alpha^{2}r\) denoted the variance of the fractional economic rank, \(h_{\mathrm{i}}\) was the health variable of interest (in this case, insurance coverage), \(\mu\) was the mean of \(h_{\mathrm{i}}\), and \(r_{\mathrm{i}}\) was the rank of population unit respective to provincial economic status. A statistical significance was determined at the 95% confidence level. We used Stata software v16.1 (serial license number: 301606241761) for all calculations. The positive value (from 0 to +1) of CI meant the insurance coverage was concentrated amongst the better-off provinces. On the other hand, there would be a negative value (from 0 to \(-1\)) if the insurance coverage was concentrated amongst the less economically well-off provinces. The results were also graphically displayed by concentration curves (CC). The CC would lie above the 45-degree equality line if the CI took a negative value and would lie below the equality line if otherwise.
## 2 Operation Principle
### Frequency Offset Estimation
Substituting Equation (4) into the double-angle formula for \(cos(2\beta_{n})\), we obtain the following equation:  
\[\cos(2\beta_{n})=cos^{2}(\beta_{n})-sin^{2}(\beta_{n})\approx|cos(\beta_{n})|- |sin(\beta_{n})| \tag{5}\]  
The sine term requires further elaboration for clarification. From Equation (4), we can derive the following equation:  
\[sin(2\beta_{n})=2 cos(\beta_{n})\cdot sin(\beta_{n})\]  
Figure 4:0  
Figure 4:1  
Figure 4:2  
Figure 4:3  
From the above derivation, it is evident that to ensure that the expanded \(S_{n}\) does not produce errors, both the sine function and cosine function must be expanded simultaneously. Equation (6) indicates that the sine function is expanded by a factor of \(\sqrt{2}\). To maintain coefficient balance, Equation (5) needs to be multiplied by the coefficient \(\sqrt{2}\) to complete the expansion, resulting in the following formulas:  
Figure 4:4  
Figure 4:5  
Figure 4:6  
Figure 4:7  
Step 2: Perform the 4 th power operation on the trigonometric functions corresponding to the real and imaginary parts. Based on the angle expansion from Step 1, the fourfold increase in the angle can be achieved using the same principle. However, it is important to note that, to balance the coefficients of the real and imaginary parts, only the cosine coefficient needs to be multiplied by \(\sqrt{2}\) in the first step. Figure 4:8  
Figure 4:9  
Finally, to achieve a fourfold increase in the phase angle, we obtain the following equation:  
Figure 3:0  
The preceding steps achieved a fourfold increase in the phase angle. The next phase involves frequency offset estimation and compensation. Since the implementation principles are consistent across all parallel paths, we will focus on detailing the process for a single path. The following formula is used to complete the calculation of the frequency offset value:  
Figure 3:1  
Here, \(f_{k}\) represents the frequency offset estimate for the \(k_{th}\) symbol block, \(M\) is the number of symbols contained in each symbol block, and \(I\) indicates the time intervalscaused by discontinuities in the signal due to QPSK partitioning, which contribute to frequency offset accumulation. To reduce the impact of additive ASE noise, a summation and averaging method is employed across multiple symbols to smooth out the additive ASE noise. The formula for the actual accumulated frequency offset error \(FO\_sum\) is as follows:  
Figure 3:2  
\(FO\_sum\) denotes the actual accumulated frequency offset error. n represents the position of the received signal \(S_{n}\) in the entire received signal, and \(f\) represents the frequency offset of the current symbol.
## 2 System Model
### Node Deployment
At the EH side, we have  
\[\zeta_{i}\left(1-\beta_{i}\right)Tr\left[\mathbf{H}_{RS_{i}}\mathbf{F}_{r} \mathbf{R}\mathbf{F}_{r}^{H}\mathbf{H}_{RS_{i}}^{H}+\sigma_{S_{i}}^{2}\mathbf{ I}_{M_{R}}\right]\geqslant\bar{e}_{i}, \tag{7}\]  
where \(\zeta_{i}\in(0,1]\) is the energy conversion efficiency at the energy harvester. It is assumed that for \(\zeta_{i}=1\), here, \(\bar{e}_{i}\) represents the minimum power that should be harvested at \(S_{i}\) and \(\mathbf{R}=\sum_{i=1}^{2}\mathbf{H}_{S_{i}R}\mathbf{F}_{i}\mathbf{F}_{i}^{H} \mathbf{H}_{S_{i}R}^{H}+\sigma_{\mathbf{r}}^{2}\mathbf{I}_{N_{R}}\). Moreover, \(\mathbf{F}_{i}\) and \(\mathbf{F}_{r}\) should satisfy the transmitting power constraints, that is,  
\[Tr\left(\mathbf{F}_{i}\mathbf{F}_{i}^{H}\right)\leqslant p_{i},\ \ \ \ Tr\left(\mathbf{F}_{i}\mathbf{R}\mathbf{F}_{r}^{H}\right)\leqslant p_{r}, \tag{8}\]  
where \(p_{i}\) and \(p_{r}\) are the maximum transmit power supplied by \(S_{i}\) and \(R\), respectively. Since channels in our system are memoryless, we can define that \(\mathbf{y}_{S_{i}}\triangleq\mathbf{y}_{S_{i}}[n]\), \(\mathbf{n}_{i}^{{}^{\prime}}\triangleq\mathbf{n}_{i}^{{}^{\prime}}[n,\tau]\), \(\mathbf{s}_{i}\triangleq\mathbf{s}_{i}^{{}^{\prime}}[n-\tau]\), and \(\mathbf{n}_{r}\triangleq\mathbf{n}_{r}[n-\tau]\), and we assume that \(\sigma_{p}^{2}\ll\sigma_{S_{i}}^{2}\). Thus, (6) can be reformulated as  
\[\mathbf{y}_{S_{i}}=\sqrt{\beta_{i}}(\mathbf{H}_{RS_{i}}\mathbf{F}_{r}\mathbf{H }_{S_{i}R}\mathbf{F}_{\mathbf{\hat{y}}}\mathbf{\hat{s}}_{i}+\mathbf{H}_{RS_{ i}}\mathbf{F}_{r}\mathbf{n}_{r}+\mathbf{n}_{i}). \tag{9}\]
# Thermal Fatigue Life Prediction of Thermal Barrier Coat on Nozzle Guide Vane via Master-Slave Model
## 2 Modeling and Simulation Methods  
### Thermal Fatigue Life Theory of Thermal Barrier Coat  
To establish the TFL prediction model of the TBC, the classical phenomenological life prediction method proposed by [PERSON] et al. [22] was referred to in this paper. According to the theory, the TFL of the TBC is related to the TGO thickness and strain range in the thermal cycle. The experimental correlation of the TGO layer thickness is described as:  
\[\delta=\left\{\exp\left[Q\left(\frac{1}{T_{0}}-\frac{1}{T}\right)\right].t \right\}^{n} \tag{1}\]  
where \(\delta\), \(t\), and \(T\) indicate the TGO thickness, oxidation time, and oxidation temperature, respectively; \(Q\), \(T_{0}\), and \(n\) are the active energy of oxygen atom diffusion in metals, undetermined temperature, and undetermined coefficient, which are 28,230, 1572, and 0.313 at 1320 K, respectively, as determined by the experimental investigation of a plasma sprayed TBC material [13].  
The phenomenological TFL model is  
\[N=\left[\left(\frac{\Delta\varepsilon_{f_{0}}}{\Delta\gamma_{i}}+a\Delta \varepsilon_{i}\right)\left(1-\frac{\delta}{\delta_{c}}\right)+\left(\frac{ \delta}{\delta_{c}}\right)\right]^{b} \tag{2}\]  
where \(N\) indicates the number of cycles; \(\Delta\gamma_{i}\) and \(\Delta\varepsilon_{i}\) are the shear strain range and axial strain range at risk point in the \(i\)th thermal cycle, respectively; \(\Delta\varepsilon_{f_{0}}\) is the critical tensile strain range; \(\delta_{c}\) is the critical TGO thickness; and \(a\) and \(b\) are unknown parameters in Equation (2).  
Regarding \(\Delta\varepsilon_{f_{0}}=0.087\) and \(\delta_{c}=0.058\) mm for the plasma sprayed TBC material [14], Equation (2) is considered as a time-dependent oxidation-induced cracking process accompanied by oxidation failure at the mixed TC/TGO layer with respect to the peel off process of the TBC. [PERSON]'s linear cumulative damage model [22] was used to compute cyclic life in this paper.  
In cumulative theory, the damage caused by one cycle is assumed to be \(D_{m}=1/N_{m}\). The total damage \(D\) caused by multiple cycles is \[D=\sum_{m=1}^{k}D_{m}=\sum_{m=1}^{k}\frac{1}{N_{m}} \tag{3}\]  
where \(m\) denotes the number of cycles; \(N_{m}\) is the life under \(m\) cycles; and \(k\) indicates the maximum number of cycles. When the damage \(D\) is larger than 1, the TBC is considered to have failed.
# Synthesizing 3D Gait Data with Personalized Walking Style and Appearance
## 5 Experimental Results
### Visual Demonstration of Factor Variations  
We first demonstrate the ability of our method to change a variety of factors, which may significantly affect the accuracy and robustness of state-of-the-art recognition methods. The factors include:  
**View variation.** As mentioned before, our paradigm can easily generate gait data under various camera positions (see Figure 4a).  
**Scene variations.** Our gait generator also supports the changes in the scenes, from simple lab environment to a complex real street scenario (see Figure 4b).  
**Human appearance variation.** The user can semantically control the shape of the human model by adjusting attributes like weight (Figure 4c, top). The user can also change the accessories and clothes on a person (Figure 4c, bottom).  
**Lighting variation.** We can also simulate different lighting conditions. This is particularly common for surveillance cameras on the street (see Figure 4d).  
**Motion variations.** Unlimited motion data of walking, running, and jumping can be synthesized from the PCA motion space.  
In extreme cases, multiple factors may arise simultaneously. For example, a person may wear different clothing and walk in different scenes, which may introduce occlusions by the external environment (e.g., the trolley in Figure 4e).
## 3 Multi-UGV Path Planning Modeling
### Environment Settings  
Before path planning, it is first necessary to model the environment in which UGV is located. The quality of environmental model construction affects the efficiency of path planning and determines the success or failure of path planning to a certain extent.  
The research background of this paper is the smart industrial park, the environment in the industrial park has the characteristics of relatively simple traffic scenes, low vehicle speed, and mostly structured roads. Therefore, this paper uses the raster method based on the global map environment modeling method to construct the environment for the simulation experiment. The environment in which the agent is run is quantified to form some small cells in the size of the UGV projection as a mesh map of the environment [52]. In a raster map, the operating environment of the UGV is evenly broken into cells of the same size, and depending on the size of the environment, the corresponding grid map is \(a\times b\). The representation of the raster graph is shown in Figure 1.  
In the figure, the yellow grid is the starting point of UGV, the red grid is the target point of UGV, and the black grids are the obstacles in the industrial park, such as buildings and green belts. The obstacles in the industrial park are inflated, the black grids are represented in the grid map, and the white grids are the road that can be freely passed in the industrial park.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 2 State-of-The-Art FC-AE-1553 Space Network
### Scheduling Problem in FC-AE-1553 Space Network  
The completed outdoor access network in the system is designed as a relatively independent bus-topology network with multiple branches. Communication between terminals across different outdoor access networks requires an uplink connection to the access network bridge. From there, the communication is routed through the backbone network to reach different access network bridges. Finally, the communication is downlinked to the intended terminals through another outdoor access bridge (Figure 2d).  
In terms of physical space, the distance between two terminals may not be significant. However, due to the distinct networks they belong to, communication between two access network terminals in the original network architecture necessitates the involvement of bridges and the backbone network to establish a multi-level network. This approach consumes network bandwidth along the entire path and increases the system's failure rate due to the complexity of the multi-level routing.  
Figure 2: Typical traffic flow. (**a**) NTs within the backbone network. (**b**) NTs within the same access network. (**c**) NTs between the backbone and access networks. (**d**) NTs between different access networks.  
To address these challenges, the system has been enhanced by incorporating the optical wireless communication (OWC) function at the node terminals. With this extension, nodes in the access network can now communicate directly with each other without the need for relaying through the bridge and backbone network.  
By enabling direct communication between nodes across the access network, the OWC function eliminates the reliance on the bridge and backbone network for inter-node communication. This improvement significantly reduces network bandwidth usage and mitigates the higher failure rate associated with the multi-level path.
## 2 Materials and Methods
### MIMO OFDM Communication and Radar Sensing  
#### 2.4.1 MIMO OFDM Communication  
In the MIMO OFDM transmission system, the transmit antenna is \(N_{\rm t}\) and the receive antenna is \(N_{\rm r}\). Basically, it consists of an MIMO encoder, \(N_{\rm t}\) transmitter, serial to parallel conversion, inverse fast Fourier transform (IFFT), and CP insertion. At the receiver, there is a parallel to serial converter with CP remover, synchronized in time and frequency, and a decoder for channel estimation. The channel can be assumed to be Rayleigh flat fading or realistic channel. The main advantages of OFDM MIMO are high flexibility for link adaptation, high spectral efficiency, and simple implementation [16; 17].  
The MIMO channel is generally expressed as  
\[y_{i}[k]=\sum_{l=0}^{N}h_{i,l}[k]s_{l}[k]+w_{i}[k], \tag{16}\]  
where \(y[k]\) is the output of \(k-\)th sample, \(h[k]\) is the filter, \(s[k]\) is the data of \(k-\)th sample, and \(w[k]\) is the AWGN.
## 3 Proposed Methods
### Adaptive Pre-Distortion System  
In this paper, as shown in Figure 3, we aim to enable optimal pre-distortion compensation for all LEDs at the time of transmission by estimating the nonlinear characteristics of LEDs at the receiver and feeding back the nonlinear coefficients representing these characteristics to the transmitter as FBI. After channel equalization, we estimate nonlinear coefficients obtained by IFFT on the received signal \(y_{k}\), and obtained by IFFT on the signal after detection \(\hat{x_{k}}\). From now on in this work, \(y_{k}\) and \(\hat{x_{k}}\) used in coefficient estimation will be referred to as reference signals, and \(N\) is the number of reference signals used.  
Figure 2: LED driver circuit.  
In this work, we use the least squares method (LSM) and median-based method (MBM) as nonlinear coefficients estimation techniques. Specifically, the implementation of pre-distortion using the inverse function of the nonlinear characteristics with the nonlinear coefficients fed back by the FBI enables optimal compensation for all LEDs in the system.
Sensors_**2012**, _12_, 7634-7647; doi:10.3390/s120607634  
_Sensors_  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
_Article_  
A New Inertial Aid Method for High Dynamic Compass Signal Tracking Based on a Nonlinear Tracking Differentiator  
**[PERSON] *, [PERSON] and [PERSON]**  
College of Mechanical Engineering and Automation, National University of Defense Technology,  
Changsha 410073, Hunan, China; E-Mails: wenqiwu_lit(r)hotmail.com (W.W.);  
tt_kanghua(r)hotmail.com (K.T.)  
* Author to whom correspondence should be addressed; E-Mail: guoyao.cn(r)gmail.com;  
Tel./Fax: +86-731-8457-6305.  
_Received: 14 May 2012; in revised form: 1 June 2012 / Accepted: 1 June 2012 / Published: 7 June 2012_  
**Abstract:** In Compass/INS integrated navigation systems, feedback inertial navigation solutions to baseband tracking loops may eliminate receiver dynamic effects, and effectively improve the tracking accuracy and sensitivity. In the conventional inertially-aided tracking loop, the satellite-receiver line-of-sight velocity is used directly to adjust local carrier frequency. However, if the inertial solution drifts, the phase tracking error will be enlarged. By using Kalman filter based carrier phase tracking loop, this paper introduces a new inertial aid method, in which the line-of-sight jerk obtained from inertial acceleration by a nonlinear tracking differentiator is used to adjust relevant parameters of the Kalman filter's process noise matrix. Validation is achieved through high dynamic Compass B3 signal with line-of-sight jerk of 10 g/s collected by a GNSS simulator. Experimental results indicate that the new inertial aid method proposed in this paper is free of the impact of the receiver dynamic and inertial errors. Therefore, when the integrated navigation system is starting or re-tracking after losing lock, the inertial error is absent from the navigation solution correction that induces large drift, and the new aid method proposed in this paper can track highly dynamic signals.  
**Keywords:** high dynamics; Compass/INS integrated navigation system; inertial aiding; nonlinear tracking differentiator
## 5 Discussion
### Errors Induced by TM Albedo Estimation  
In this study, TM albedos derived by direct estimation were utilized as albedo observation data. The TM albedos were used to adjust model-based predictions to bring them as close as possible to actual measured values. The quality of the TM data directly affects the accuracy of the final estimations. Previous researchers have thoroughly validated TM albedos derived by direct estimation [18]. In this study, we also compared the TM albedo with the ground measured albedo for several FluxNet sites. Figure 9 shows that the surface albedo estimated from TM images is highly accurate (maximum error \(<0.03\)) and can be used as the input observation value for the data assimilation algorithm.  
The accuracy of inversion is affected by the accuracy, quantity, and time distribution of TM data, where quantity and time distribution complement each other. In this study, we used L1T data with less than 10% cloud contamination for our estimations. There were approximately 5-11 TM data points available at the selected sites, which was sufficient for albedo estimation by data assimilation. If the quantity of available TM data in one year falls below 5 points, the albedo during the time series may not be updated for long periods of time, and the accuracy of the estimations degrades. Several high-quality observations are needed to ensure accurate estimations. Other high-resolution satellite images such as HJ1A/B or Sentinel-2 can be supplemented to resolve this problem for those areas where high-resolution observations are limited.
## 3 Results and Discussions  
### Consistency Analysis between GNSS, GRACE and GGFC  
In this section, we interpret non-tidal deformations in vertical component of GNSS time series by comparing with the GRACE and GGFC derived VD series for the selected 186 stations. In our data analysis, CSR provides monthly GRACE SHC solutions since 2002, GGFC load models include atmospheric, non-tidal ocean and terrestrial water storage loading, and the GGFC NTOL and CWS data are available only for times before 2013. To eliminate the impacts of sampling rate differences and time span (temporal resolution), we unify the sampling rates (all data aiming to monthly solutions) and time span (2002/03-2012/12) of GNSS, GRACE, and GGFC solutions, and analyze the consistency among these datasets. Before correcting IG2 results with the corresponding results from GRACE and GGFC solutions, the IG2 GNSS time series are detrended.
# Study on the Steepness of Landslide-Induced Wave and Nonlinear Motion of Container Ships
## 5 The Ship Motion Characteristics Caused by Landslide Surges
### The Influence of the Landslide Volume on Ship Roll Characteristics  
By studying the influence of landslide volume on ship rolling motion characteristics the amplitude of the ship rolling motion was determined, and some theoretical guidance for the safe binding of shipping containers was provided. It can be seen in Figure 10 that the sliding mass with different dimensions slides into the water at high speeds and carries out an energy exchange with the water body. During the course of high speed sailing, the ship roll nonlinearity is obvious.  
The water mass particles implicate each other. The water body on the right side of the landslide rises rapidly and forms a reverse entrapment wave. Part of the energy of the landslide-induced surge causes the water quality point to oscillate around the equilibrium position, and the other part of the energy spreads forward and interacts with the ships in the channel. The rolling motion of the ship shows obvious non-linear, large-scale, and high-frequency motion. The amplitude of the rolling motion of the ship increases rapidly. With the attenuation of the surge energy along the landslide, the amplitude of the rolling motion of the ship also decreases gradually. It gradually reaches a stable and safe state by its own restoring moment. Additionally, the sliding mass with different magnitudes sliding into the water has different influences on the ship's rolling motion. The larger the amount of the landslide, the faster the propagation speed of the landslide surge, the earlier the ship's rolling motion, and the larger the amplitude of the ship's rolling motion. For actual engineering disaster prevention, slopes should be strengthened properly to prevent unstable, large-area slope slides that threaten the navigation safety of passing ships.  
When the landslide volume is \(1\times 1.5\times 0.4\) m (length \(\times\) width \(\times\) thickness), the maximum roll value of the ship is about 17 degrees, which is within the range of large dip rolls and meets the specification  
Figure 10: Rolling motion of the ship by different volumes of landslide.  
requirements of the technical rules for the legal inspection of ships. With the increase of time, the energy of the landslide-induced surge gradually dissipates, the rolling motion turns into a slight oscillation motion, and the ship is in a relatively safe sailing state. At this point, the ship should pay attention to the change in external forces on the side of the ship in the navigation process, to prevent an impact load that is too large and that would result in a large roll movement of the ship, affecting the navigation safety of the ship. In the actual process, the height of the center of gravity should be lowered to increase the ship's initial stability.