esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 DEM-FDM Coupled Model
### Description of Subgrade Settlement  
By fitting observation data from high-speed railway subgrade settlement, it was determined that the shape of the subgrade in a settlement section is similar to a cosine curve [30]. Therefore, a cosine curve was adopted to describe the differential settlement of the bottom of the surface layer of the subgrade in this study, as shown in Figure 5.  
In the settlement section, the expression for subgrade settlement along the longitudinal direction of the railway line can be written as  
\[z=f\bigg{[}\frac{1}{2}-\frac{1}{2}\cos\bigg{(}\frac{2\pi\mathrm{x}}{l}\bigg{)} \bigg{]}, \tag{11}\]  
where \(z\) refers to the value of the settlement, \(f\) refers to the settlement amplitude, \(x\) refers to the position coordinate, \(l\) refers to the settlement wavelength.  
\begin{table}
\begin{tabular}{c c c} \hline
**Components** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Precast slab} & Elastic modulus (MPa) & \(3.55\times 10^{4}\) \\  & Density (kg/m\({}^{3}\)) & 2400 \\  & Poisson’s ratio & 0.2 \\ \hline \multirow{3}{*}{CA mortar layer} & Elastic modulus (MPa) & \(8.00\times 10^{3}\) \\  & Density (kg/m\({}^{3}\)) & 2400 \\  & Poisson’s ratio & 0.2 \\ \hline \multirow{3}{*}{Concrete base} & Elastic modulus (MPa) & \(2.20\times 10^{4}\) \\  & Density (kg/m\({}^{3}\)) & 2400 \\  & Poisson’s ratio & 0.2 \\ \hline \multirow{3}{*}{The surface layer of the subgrade} & Density of gravel (kg/m\({}^{3}\)) & 2300 \\  & Normal contact stiffness between gravel grains (N/m) & \(1.0\times 10^{8}\) \\ \cline{1-1}  & Shear contact stiffness between gravel grains (N/m) & \(1.0\times 10^{8}\) \\ \cline{1-1}  & Sliding Friction coefficient & 0.7 \\ \cline{1-1}  & Rolling friction coefficient & 0.25 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for the DEM-FDM coupled model.  
Figure 4: Gradation curve of the gravel on the surface layer of the subgrade.  
Figure 5: Differential subgrade settlement.
###### Abstract  
On top of the condition-based maintenance (CBM) practice for rotating machinery, the robust estimation of remaining useful life (RUL) for rolling-element bearings (REB) is of particular interest. The failure of a single bearing often results in secondary defects in the connected structure and catastrophic system failures. The prediction of RUL facilitates proactive maintenance planning to ensure system reliability and minimize financial loss due to unscheduled downtime. In this paper, to acquire early and reliable estimations of useful life, the RUL prediction of REBs is formulated into nonlinear degradation state estimation tackled by the combination of the envelope spectral indicator (ESI) and extended Kalman filter (EKF). By fusing the spectral energy of the bearing fault characteristic frequencies (FCFs) in the averaged envelope spectrum, the ESI is crafted to remove the interference from rotor-dynamics and reveal the bearing deterioration process. Once the fault is identified, the recursive Bayesian method based on EKF is utilized for estimating the bearing end-of-life time via the exponential state-space model. The distinctive advantage of the proposed approach lies in its ability to make an early prediction of RUL using a small number of ESI observations, offering an efficient practice for predictive health management at the early stage of bearing fault. The performance of the proposed method is validated using publicly available experimental bearing vibration data across three different operating conditions.  
predictive maintenance; remaining useful life; rolling-element bearings; degradation estimation; Bayesian methods; extended Kalman filter; nonlinear state estimation
## 3 Experimental Results and Discussions
### Main Amplifier and Suppression of SRS effect
The pulse width of amplifying systems was 600 ps, corresponding to 109 MW pulse peak power. The beam images of this amplifying system are shown in Figure 9. The beam images were measured with a commercial CCD setting at a position which was about 1 m away from each amplifier. Figure 8: Pulse temporal width of: (**a**) the oscillator (**b**) via the amplifying system. Figure 6: Obtained spectrum of the maximum laser output. Figure 7: Pulse spectrum of: (**a**) the oscillator (**b**) via the amplifying system. The laser beam of pre-amplifier had good Gaussian distribution. With the increment of pulse energy in the main amplifier, beam center intensity gradually increased due to self-focusing, which could lead to little beam deterioration, as illustrated in Figure 10. The output beam of the oscillator was near diffraction limitation with M\({}_{x}^{2}\)\(\times\) M\({}_{y}^{2}\) = 1.28 \(\times\) 1.20. The M\({}^{2}\) for the horizontal and vertical axes via the amplifying system were 1.50 and 1.57, respectively. The results show the amplifier system still kept good beam quality using diode-end-pumped Nd:YVO\({}_{4}\) amplifier architecture when running at 1 kHz. A 9 \(\times\) 9 \(\times\) 20 mm\({}^{3}\) type I LBO (\(\theta\) = 90\({}^{\circ}\), \(\phi\) = 10.8\({}^{\circ}\)) crystal was used for extra-cavity frequency doubling. Maximum pulse 532 nm green light energy of 40.5 mJ was obtained at the maximum pump energy. The conversion efficiency was 61.8% and corresponding green light power was 40.5 W. The one-hour power stability has thus been tested, and 0.28% RMS was achieved, as shown in Figure 11. Figure 12 shows beam quality of green light were 1.26 and 1.25 for the horizontal and vertical axes. The beam distribution is also showed as the color image insets in Figure 12. The beam quality of green light was better than that of fundamental light, which is because the nonlinear process during frequency doubling can filter out the stray light with low peak power. Figure 5:0 Energy stability of amplified pulses from the whole amplifier. Figure 5:1 Beam quality factor M\({}^{2}\) of (**a**) the oscillator (**b**) via the amplifying system. Figure 5:2 Beam images of each stage. (**a**) pre-amplifier; (**b**) stage 1 in main amplifier; (**c**) stage 2 in main amplifier; (**d**) stage 3 in main amplifier.
## 2 Materials and Methods
### Procedures to Synchronization  
To synchronize the positional data with the passing event data, an algorithm is proposed to correct the potential delay between events and positional data, thereby improving the identification of the moment of the passing actions. To achieve this objective, the distance travelled by the ball is first calculated, followed by the estimation of its velocity. This enables the detection of speed variation, which in turn allows for the identification of the actual moment the pass occurs.  
To accurately identify passing actions, we developed a rule-based algorithm that combines ball tracking data with event annotations system. The process begins by monitoring ball speed to detect potential passes. An event is flagged when the ball's speed exceeds 8 m/s (meters per second) and is preceded by an increase above 5 m/s, ensuring that only sharp and deliberate changes in velocity are considered. To prevent multiple detections of the same event, the algorithm applies a temporal filter: if several speed threshold crossings occur within 0.4 s, only the first instance is retained. In addition to speed thresholds, the algorithm detects moments where the ball undergoes a sudden and significant change in velocity, which often signals a purposeful action like a pass or shot. For each of these moments, a unique sequence ID is generated. To confirm whether the detected action is indeed a pass, the algorithm compares the timestamp of the identified event with the closest \"Pass\" label from the event data. If the time difference between the two is less than 0.5 s, the event is classified as a valid pass; otherwise, it is discarded. This approach allows for more accurate identification of passing actions by combining mechanical features of ball movement with contextual information from annotated event data (Figure 1).  
At this point, a potential pass is identified when the ball's speed exceeds 8 m/s, preceded by an acceleration above 5 m/s, conditions that typically signal a deliberate ball displacement. However, these kinematic thresholds are not exclusive to passes; other actions such as shots, long clearances, or even fast goal kicks may also satisfy these criteria, as actions in which the ball radically changes its speed.  
To refine detection, the algorithm implements a two-stage filtering process. First, it eliminates redundant detections by retaining only the first qualifying event within 0.4 s.  
Figure 1: Flowchart of the algorithm.  
Then, to validate whether the detected action is indeed a pass, the algorithm queries the event dataset for the nearest labeled \"Pass\" event and compares its timestamp to the detected event. If the time difference is less than 0.5 s, the event is classified as a valid pass; otherwise, it is discarded. As such, while the algorithm does incorporate a basic validation mechanism via event matching, its reliance on speed thresholds means that without the event data, it may not reliably distinguish between ball actions with similar mechanical profiles.
# Secured Real-Time Machine Communication Protocol
## 3 Methodology-SRMCP Protocol Design
### Phase and Communication Procedure
#### 3.1.3 Data Link Phase  
The \"Data Link Phase\" facilitates the creation of the Data Link (DL). All messages exchanged during this phase are encrypted with the shared key using the AES-GCM algorithm. The procedures are as follows:  
1. **Step 1**: The client sends a Data Link Request (DL-Req) to the server, requesting the server to open a new TLS listener for a specified duration.
2. **Step 2**: The server opens a new TLS listener on a random port number with its x509 certificate. The port number is then sent to the client via a Data Link Response (DL-Res).
3. **Step 3**: The client attempts to establish a TLS connection to the server using the provided port number. If the client does not connect to the new TLS listener within the specified time indicated in Step 1, the server closes the TLS listener. Otherwise, the server closes the TLS listener once the client has successfully established the connection as a DL.
4. **Step 4**: The client repeats Step 1 multiple times to create several DLs.
## 6 Appendix-Spatial Resolution
On the other hand, if we assume \(\varphi_{s}\) equal to \(180^{\circ}\) (or around it), when \(\theta_{s}\) increases starting from \(0^{\circ}\), the \(\rho_{gr}/\rho_{gr}{}^{back}\) ratio decreases and is  
Figure 7:1 Figure 7:2  
Having assumed that the two spacecraft fly at the same height, we can make the simplifying hypothesis that the velocity vectors have the same amplitude (_i.e._, \(v_{T}\!=v_{R}\)), thus obtaining:  
Figure 7:3 (22a) where: \[ \begin{array}{l}F(\theta_{i},\theta_{s},\varphi_{s},\delta_{T},\delta_{R} )\!=\!\cos^{2}\theta_{i}(\cos^{\delta}\theta_{i}\cos^{2}\delta_{T}+\sin^{2} \delta_{T})\!+\!\cos^{2}\theta_{i}[1\!-\!\sin^{2}\theta_{i}(1\!+\!\cos^{2} \theta_{s})\!\cos^{2}\!(\varphi_{s}-\delta_{R})]+\\ +2\cos\!\!\theta_{i}\cos\!\!\theta_{s}[\cos^{\delta}\theta_{i}\cos\!\delta_{T }\cos\!\delta_{R}+\!\sin\!\delta_{T}\sin\!\delta_{R}-\!\sin^{2}\theta_{i}(\cos ^{2}\theta_{i}\cos\!\delta_{T}\cos\!\varphi_{s}+\!\sin\!\delta_{T}\sin\! \varphi_{s})\!\cos(\varphi_{s}-\delta_{R})]\end{array}  \tag{22b}\]  
The final expression for the azimuth resolution of a bistatic system is derived by replacing (22) in (14):  
\[\rho_{a}=\!\frac{H\ \lambda/\!\varepsilon_{\mathrm{int}}}{v_{T}\sqrt{F( \theta_{i},\theta_{s},\varphi_{s},\delta_{T},\delta_{R})}} \tag{23}\]  
The azimuth resolution of a conventional monostatic SAR is given by:  
\[\rho_{a}^{\mathit{back}}=\frac{D}{2}\approx\frac{H\ \lambda/\!\varepsilon_{ \mathrm{int}}}{2v_{T}\cos\theta_{i}} \tag{24}\]  
where \(D\) is the dimension of the antenna. Therefore, the \(\rho_{a}/\rho_{a}^{\mathit{back}}\) ratio turns out to be given by (2a).
## 5 Calculations and Evaluation
### Evaluation of Rebar Recovery Rate after High-Frequency Induction Heating  
The following steps consist of measuring the amount of concrete remaining on the reinforcing steel after high-frequency induction heating and evaluating the recovery rate of concrete by heating (reinforcement quality). For high-frequency induction heating, the concrete around the reinforcing steel was weakened by the rapid temperature increase of the surface of the reinforcing steel, and the difference can be quantitatively evaluated to provide basic data on the reutilization rate after demolition and separation.  
(1) Experimental Methods  
A 30-mm thick reinforced concrete specimen was manufactured for each water binder ratio and heated for 360 s under high-frequency induction heating. Following the heating, the reinforcing bars and concrete lumps were separated using the hammering method, with the separated reinforcing bars being placed in a 100 mL beaker and the laboratory solution (10% hydrochloric acid solution) filled up to 100 mL. After 24 h, the reinforcing steel was collected, the remaining solution was dried at 100-110 \({}^{\circ}\)C, and the remaining powder was weighed. In addition, experiments were conducted using reinforcing bars of the same length, as described above, and the amount of powder dropped from the reinforcing bars in addition to the residual concrete by the laboratory solution was measured. Subsequently, the average amount of powder dropped from 10 reinforcing bars was calculated, and the result provided a measurement of only the concrete residue. The experimental factors and some photographs are presented in Table 8 and Figure 19.  
Figure 18: Rebar extraction experiment after induction heating.  
2. Results and considerations  
An experiment was conducted using a 30 mm-thick reinforced concrete specimen of D10 with the ratio of the water binder as a factor. Figure 20 shows the results of the rebar recovery experiment using induction heating. The higher the water-binder ratio, the higher was the recovery rate. The water binder ratio increased by approximately 17% and 7% in the case of 40% and 50%, respectively. For the same reinforcement diameter and heating distance, it was determined that higher the water-binder ratio, larger the range of evaporation of free water due to induction heating, and hence, greater the range of voids around the reinforcement.
# Low Power QC-LDPC Decoder Based on Token Ring Architecture
## 4 Experimental Results
The submatrix size \(P\) corresponds to the number of processing units (see Figure 4), and at the same time it affects the number of clock cycles \(L_{c}\). As a result, the decoder throughput, but also power loss, is strongly affected by \(P\). On the other hand, the average energy consumption \(E_{d}\) is supposed to be independent of \(P\). However, we still observed \(E_{d}\) dependent to some extent on \(P\), which reveals the energy efficiency of the proposed implementation is dependent on \(P\). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Decoder Element**} & \multicolumn{2}{c}{\(P_{d}\) **[mW]**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Irregular**} & \multicolumn{2}{c}{**Regular**} \\ \hline Communication support module & 0.19 & 0.48 \\ Memory \(L(c|y)\) & 12.77 & 5.21 \\ Initialization module & 6.03 & 2.03 \\ Memory \(Q\) & 0.27 & 0.42 \\ Control node computing unit & 31.64 & 17.99 \\ Memory \(R\) & 0.13 & 0.17 \\ Bit node computing unit & 47.46 & 12.84 \\ Memory \(X\) & 4.5 & 2.62 \\ Verification of control equations module & 3.76 & 1.1 \\ Communicating the results module & 0.05 & 0.05 \\ Clock tree & 71.44 & 23.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of energy losses of elements of regular and irregular decoders.
## 2 Data and Methods  
### Reference Data  
#### 2.1.1 Sentinel-1 SAR Data  
The Sentinel-1 satellite was designed by the European Space Agency (ESA). It performs C-band imaging in the four exclusive modes of strip map mode (SM), interferometric wide-swath mode (IW), extra wide-swath mode (EW), and wave mode (WV). The Sentinel-1 SAR data in the IW and EW modes have a large swath width of hundreds of kilometers and can provide data for a wide range of incidence angles. In this paper, we collected about three thousand Sentinel-1 ground range detected (GRD) high-resolution images in the IW (pixel spacing is 10 m \(\times\) 10 m) and EW modes (pixel spacing is 25 m \(\times\) 25 m) under dual polarization (HH + HV, VV + VH) with incidence angles ranging from 32\({}^{\circ}\) to 45\({}^{\circ}\). We collected data in the Southern Ocean for the past four years and data near Australia for the past two years.  
Since a Sentinel-1 image is too large, we first cropped the entire Sentinel-1 image and obtained a 10 km \(\times\) 10 km sub-image every 50 km \(\times\) 50 km. Then, in order to obtain the wind and swell information corresponding to the Sentinel-1 data, we matched the 10 km \(\times\) 10 km sub-images with the time/space interpolated sea surface winds and ocean waves obtained from the fifth-generation reanalysis (ERA5). The ERA5 is produced by ECMWF and provides a detailed record of the global atmosphere, land surface, and ocean waves from 1950 onwards [18]. Finally, we obtained about 59,788 matching pairs.
## 3 Experimental Set-Up  
### Apparatus and Schematic  
Figure 3 shows the fuel cell stack and the schematic of single cell distribution. Figure 3a is the 10 single-cell stack and the test platform. The stack, composed of 10 single cells, uses hydrogen as a fuel at the anode, and oxygen as the oxidant at the cathode, and the heater band is used to maintain the temperature. Figure 3b shows the position of the inlet and outlet. The experiment and simulation focused on the fastest power attenuation single cell in the vehicle fuel cell stack.  
The fuel cell test system was made up of test elements, as shown in the test schematic in Figure 4. The test system power was up to 10 kW. There were two exit load systems in this experiment. On the one hand, the H&H ZS electronic load was used for providing load current. It takes advantage of the programming controls, and its control models are constant current, constant voltage, and constant power. On the other hand, RXN-3010D was utilized as a constant-current source in the measurement of membrane electrode parameters. The rotameter, pressure sensor, humidification sensor, and temperature sensor were used to detect the flow rate, pressure, humidification, and the temperature of the gas, respectively. Gas was humidified by a bubble humidifier. The fuel cell system was cooled by water cycle cooling. The pressure was controlled by the back-pressure valve at the  
Figure 3: (**a**) Fuel cell stack and test platform; (**b**) Single cell distribution.  
outlet. In order to prevent flooding and to ensure the inlet temperature, water-gas separators and a heater band were installed at the inlet. Normally, the temperature of the heater band is usually about 5 \({}^{\circ}\)C above the inlet temperature, to prevent gas cooling and liquefaction. Moreover, in order to ensure experimental safety, the alarm valve turns on and test bench is stopped immediately once hydrogen leakage exceeds the alarm threshold.
## 3 Results
### Attenuation Estimation  
Since the DCT performs better than the DFT, as demonstrated in the previous two sections, in estimating the attenuation coefficients from CS ultrasound data, we selected a DCT basis matrix. In these simulations, CS data was randomly sampled from original RF data at a 50% ratio. Diffraction compensation (DC) was provided by the uniform attenuating phantoms as references depicted in Figure 3c,d.  
Figure 7a shows the variation of the estimated spectral centroid with respect to the depth dimension of the ROI, for the p12 numerical phantom. The beam focusing applied alleviates the original signal's decay in the axial direction, as shown by the blue dotted line, making estimation of attenuation properties less accurate. A more representative relationship between the centroid and depth, shown as the red solid line, is obtained after applying DC, enabling accurate estimation of attenuation coefficients, as shown in Figure 7b. In spite of the fact that the ROI in the p12 phantom is brighter than the background in the B-mode image (due to its larger backscatter intensity), at 0.5 dB/cm/MHz, the attenuation coefficients estimated are, on average, the same as the coefficients estimated for the background.  
A similar simulation was conducted with the m12 numerical phantom. The results are shown in Figure 8. Similar to the behavior noted for the p12 phantom, the attenuation coefficient estimated for the ROI following the application of DC (Figure 8b) is the same as that of the background, despite its darker appearance. These two cases demonstrate that spectral analysis of CS data can provide important signal characteristics that are unavailable from time-domain representations.  
As before, for a quantitative comparison of the performance of the traditional and proposed frameworks in frequency-domain analysis, we calculated the RMSE of the attenuation coefficients estimated for the p12 phantom at compression rates varying from 50% to 90%. All resulting RMSE values were normalized with respect to the largest value, which was again obtained at a 90% compression rate, using the traditional framework. The results of this analysis are summarized in Table 2, which shows that the proposed framework produces much smaller RMSEs than the traditional method for lower compression rates, with the difference decreasing as the compression rate is increased. We posit that this  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline \multirow{2}{*}{DFT} & Traditional & 0.625 & 0.718 & 0.836 & 0.94 & 1 \\  & Proposed & 0.63 & 0.73 & 0.839 & 0.946 & 1 \\ \hline \multirow{2}{*}{DCT} & Traditional & 0.197 & 0.411 & 0.612 & 0.825 & 0.982 \\  & Proposed & 0.156 & 0.366 & 0.545 & 0.778 & 0.955 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Normalized root mean square error (RMSE) of results of envelope detection.
# A Low Torque Ripple Direct Torque Control Method for Interior Permanent Magnet Motor
## 1 Introduction
Moreover, it is known that torque ripple can be brought down with higher switching frequency [10], but this would also increase switching loss. Thus, another target of the proposed method is to achieve torque ripple reduction while keeping a low switching frequency.