esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Compression Strength  
The compression strength of low \(M_{\rm w}\) PLA materials increased starting from the preparation step through sterilization to degradation after 12 weeks in PBS (Figure 5). An exception occurred in a degraded composite comprising Sr-rich HA, however, the standard  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & \multicolumn{2}{c}{**Preparation**} & \multicolumn{2}{c}{**Sterilization**} & \multicolumn{2}{c}{**Degradation**} \\ \cline{2-7}  & \(M_{\rm w}\)**[Dalton]** & \(M_{\rm w}\)**/\(M_{\rm n}\)** & \(M_{\rm w}\)**[Dalton]** & \(M_{\rm w}\)**/\(M_{\rm n}\)** & \(M_{\rm w}\)**[[PERSON]]** & \(M_{\rm w}\)**/\(M_{\rm n}\)** \\ \hline Low \(M_{\rm w}\) & 668,964 & 2.2 & 146,814 & 2.6 & 110,554 & 2.7 \\ Control & & & & & & \\ Low \(M_{\rm w}\) & 657,872 & 2.4 & 141,291 & 2.6 & 116,469 & 3.0 \\ Whiskers & & & & & & \\ Low \(M_{\rm w}\) & 624,991 & 2.2 & 167,082 & 2.4 & 116,423 & 2.8 \\ Whiskers-Zn & & & & & & \\ Low \(M_{\rm w}\) & 638,310 & 2.4 & 156,717 & 2.6 & 120,735 & 3.3 \\ Low \(M_{\rm w}\) & 633,119 & 2.1 & 156,802 & 2.5 & 119,868 & 3.0 \\ Rods-Sr & & & & & & \\ \hline High \(M_{\rm w}\) & 1,034,000 & 2.3 & 164,076 & 2.5 & 123,425 & 3.5 \\ Control & & & & & & \\ High \(M_{\rm w}\) & 1,031,000 & 2.5 & 162,828 & 2.7 & 119,578 & 3.3 \\ Whiskers & & & & & & \\ High \(M_{\rm w}\) & 1,059,000 & 2.5 & 170,110 & 2.5 & 122,374 & 3.0 \\ High \(M_{\rm w}\) & 1,142,000 & 2.3 & 167,232 & 2.7 & 121,381 & 2.7 \\ High \(M_{\rm w}\) & 1,061,000 & 2.9 & 166,265 & 2.7 & 118,867 & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(M_{\rm w}\) and dispersity index of PLA in composite after preparation, sterilization and degradation.  
deviation was higher than the prepared and sterilized counterparts, suggesting a similar raising trend.  
Among the high \(M_{\mathrm{w}}\) PLA composites the raising trend was observed only for composites with Sr rods included or whiskers added alone (Figure 5). In the case of other samples, there was impossible to determined clear dependence between compressive strength and the process step. With respect to the range of values of the standard deviation, one can say that the compression strength was very similar prior and after sterilization and degradation in the control, whiskers, whisker-Zn or rods filled composites.
## 3 Results and Discussion
### Adsorption Mechanism Study
#### 3.5.2 Comparison of SiPMA-TEPA with Other Materials  
Furthermore, the SiPMA-TEPA adsorbent was compared with other materials for the treatment of uranium (Table 3). The results showed that SiPMA-TEPA had a relatively high adsorption capacity and an extremely fast adsorption equilibrium time. SiPMA-TEPA exhibited excellent adsorption properties.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**adsorbent** & \(\mathbf{Q_{\text{max}}}\) **(mg/g)** & \(\mathbf{t_{\text{e}}}\) **(min)** & **Temperature (K)** & **Reference** \\ \hline SBA/EnSA & 105.3 & 1675 & 298 & [52] \\ MHO & 106.4 & - & 293 & [53] \\ FMHO & 133.3 & 480 & 293 & [53] \\ SAER & 124 & 8 & 298 & [30] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the adsorption performances of SiPMA-TEPA and various solid materials for uranium removal.  
Figure 12: Schematic of the adsorption and desorption mechanisms of uranium from environmental water using the SiPMA\(-\)TEPA adsorbent.
## 4 Result and Discussion
### Evaluation of IFCBs with GLONASS-Only PPP  
Figure 2 illustrates the IFCB variability during DOY 227-297, 2017 for satellite R02 at five representative stations (DRAO, ROAP, MIZU, BOR1 and BRUX). Both IFCBs range from \(-20\) to 22 ns, depending on different signal frequencies and stations. It indicates that IFCBs of the two strategies are similar in tendency, while different in values.  
We analyzed the behaviors of the IFCBs in two steps. We differenced IFCB1 and IFCB2 firstly. The mean absolute value of IFCB1-IFCB2 over all satellites in DOY 227-297 is then computed for each station, as shown in Figure 3. We can see that the quality of the IFCBs for two strategies are not equal. The bias is approximately 0.5 ns. From the analysis, it is essential to estimate the IFCBs using GLONASS PPP for international time transfer. The impact of IFCBs on GLONASS PPP international time transfer with different strategies is numerically analyzed in the next section.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Descriptions** \\ \hline IFCB0 & Neglecting IFCBs \\ IFCB1 & Estimating IFCBs for each frequency number \\ IFCB2 & Estimating IFCBs for each GLONASS satellite \\ \hline \hline \end{tabular}
\end{table}
Table 5: GLONASS IFCBs processing schemes.  
Figure 2: IFCB series of five stations for satellite R02: (**a**) is the IFCB1 series, and (**b**) is the IFCB2 series.
## 2 Materials and Methods
### Post-Processing to Identify Optimal Conditions  
Based on the results of each of the 24 independent simulations, we investigated the optimal conditions through TI stimulation implementation. First, we selected two pairs of electrodes for individual electric field formation. The electrode pair consisted of an active electrode and a reference electrode. The active electrodes of each pair were selected at a different height of the row. When two electric fields formed through an electrode pair are superpositioned through field subtraction, no current flows through the reference electrode. This process was repeated to form independent electric fields, E1 and E2.  
Subsequently, we used the following formula to implement the TI stimulation:  
\[E_{total}=||E_{1}+E_{2}|-|E_{1}-E_{2}|| \tag{1}\]  
This was calculated for all possible electrode combinations, and the optimal electrode setting was determined using the peak ratio (PR) value as an index to consider targeting ability and energy transfer efficiency [32,33].  
\[\text{PR}=\frac{E_{target}}{E_{total}} \tag{2}\]  
\(E_{target}\) is the average value of the E-field in the nerve area, and the \(E_{target}\) of the individual target area was calculated by calculating the coordinate values considering the position and size of the subject's nerve. \(E_{total}\) is the average value of the E-field of the entire area including the target area. To minimize numerical error, the PR value was calculated using the average value, rather than the maximum value used in the previous formula. By adjusting the electrode positions and current intensities, which we suggested as optimization factors, we determined that the highest PR value condition was the optimal condition. A post-processing algorithm to automatically determine optimal parameter settings was written using MATLAB R2020b. This algorithm calculated the PR values for all possible electrode pair combinations, which then resulted in the highest PR value condition. This post-processing was performed on all subjects.
## 1 Introduction
Moreover, there have been efforts to detect stress levels during computer usage using physiological indicators such as electroencephalography (EEG), blood volume pulse, and pupil diameter during these operations [46; 47; 48]. However, to the best of our knowledge, few studies have examined the operations of game controllers. Thus, by examining the quantitative relationship between controller operation sounds and game performance, it is possible to predict performance based solely on the level of the sounds with regard to the operations of the controller. However, the relationship between the sounds of the controller during operation and player performance remains unclear. This study aimed to clarify the relationship between the sound of esports players' controller operations and their objective, as well as subjective metrics, including their emotional state and game performance. Initially, we investigate the sounds of the controller of players exhibiting various skill levels in _Super Smash Bros. Ultimate_ (SSBU), developed by Nintendo [49]. In addition, the association between the control sounds of skilled esportsplayers and their day-to-day variations in game performance and emotional state were investigated. Our research questions are as follows:  
* How do the sound characteristics of game controller operations reflect players' gaming skills? * To what extent can the maximum sound pressure level (\(L_{\text{max}}\)) of controller operation sounds predict players' psychological states and in-game performance?
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 1 Introduction
### Frame Work  
This section shows, in brief, the novelty of our work and difference between it and the related previous work. Furthermore, it explains clearly our framework. For once, an imaging VLC-MIMO technique based on traditional FFT-OFDM has been proposed by [PERSON] et al. [22] with a raw 1.2 Gbps bit rate for various channels. The technique used the dual property of the space/frequency domain pre-equalization scheme. Towards the aim of Signal to Noise Ratio (SNR) and Bit Error Rate (BER) improvement, a pre-space domain equalization (Pre-SDE) is performed after the Pre-FDE at a target BER of \(10^{-3}\). In comparison with the system which uses only Pre-FDE, a communication coverage improvement by 52.6% is achieved in our system.  
In this paper, through VLC, we propose a wavelet-OFDM, instead of FFT-OFDM [22], for both the space and frequency domain pre-equalization technique for the first time. In the proposed system, due to the nature of WT overlapping symbols, the CP is eliminated. The removal of CP, which can be used to avoid multipath besides the Inter-Symbol Interference (ISI), enables the WT to make full use of its spectral efficiency. This leads to improving system performance in terms of the coverage contour and data rate. In this evaluation, we executed a Monte Carlo protocol to efficiently estimate an indoor VLC-MIMO performance characterized by N-channels. The obtained results reveal that the suggested system significantly enhances the SNR performance. Moreover, a performance comparison is performed between the suggested system based on the discrete Meyer (Dmey) wavelet (DWT-OFDM) and the FFT-OFDM for imaging VLC-MIMO systems for both the space and frequency domain pre-equalization technique. This comparison reflects the superiority of the DWT-OFDM proposed system over the FFT-OFDM one, in terms of the coverage contour for transmission and data rate. Thus, the suggested system can be a good applicant for light fidelity (Li-Fi) systems.  
The remainder of this paper is organized as follows. Section 2 introduces the system model and numerical analysis of the proposed system involving all equations that are used to estimate technique performance. The simulation results are displayed and discussed in Section 3. Section 4 is devoted to the main conclusions.
# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 3 Proposed Fusion Network for Change Detection with Panchromatic Imagery
### Dual-Prediction Post-Processing for Change Detection  
During the inference stage, post-processing was introduced using dual-prediction for change detection. In the counting rule, binary hypotheses can be passed to a fusion center, which then decides which one of the two hypotheses is true [37]. The proposed algorithm employed a hard-logical rule using an AND and OR operation with the same probability output thresholds to predict a changed pixel. This aimed to validate and ensure the change detection based on the proposed fusion network outputs (\(D\) and \(O\)). There were two steps to applying this post-processing. First, an intersection operation was employed to obtain a strict prediction and avoid false positives. Assume that (\(m~{}\times~{}n\)) images (T) will be tested using the proposed fusion network, resulting in an (\(m~{}\times~{}n\)) change map (\(M_{1}\)). This prediction was conducted by sliding in the raster scan order, as shown in Figure 4. The inputs (\(I^{1}\) and \(I^{2}\)) with the central pixel position, x and y, were assigned to the proposed fusion network to generate the values of \(D\) and \(O\). If \(D\) and \(O\) identified a changed pixel, then \(M_{1}(x,y)\) was set to a value of 1; otherwise, it was set 0. This was performed for the entire image T.  
Then, the second prediction was performed to ensure the first prediction, as shown in Figure 5. Let us assume that \((m~{}\times~{}n)~{}M_{2}\) was a change map for the second prediction. Initially, a prediction noise was investigated by analyzing the local information from \(M_{1}\) by computing \(Nb\), as defined by:  
\[Nb(x,y)=\sum_{i=x-\frac{x}{2}}^{x+\frac{y}{2}}\sum_{j=y-\frac{y}{2}}^{y+\frac{y }{2}}M_{1}(i,j). \tag{9}\]  
where \(Nb(x,y)\) computes the local information \(M_{1}(x,y)\) using a \(q\times q\) window. If the value of \(Nb(x,y)\) is greater than the input size \(s\) (40) divided by 4, then the second prediction is applied, otherwise, \(M_{2}(x,y)\) is assigned to 0. A union operation was operated from \(D\) and \(O\) for the second prediction. When it returned the changed pixel, \(M_{2}(x,y)\) was assigned a value of 1, otherwise, it was assigned a value of 0. The final change map was obtained based on the result of \(M_{2}\).  
Figure 4: First prediction flowchart.  
Figure 5: Second prediction flowchart.
## 3 Results and Discussion
### Mechanical Properties  
Depicted in Figure 9a-d are the mechanical properties such as the tensile strength (TS), elongation at break (EB), modulus at different percentage elongations and the shore-A hardness of NR/CR-CV, NR/CR-CVMF\({}_{3}\), NR/BIIR-CV and NR/BIIR-CVMF\({}_{3}\) molded at 170 \({}^{\circ}\)C as per their \(T_{90}\). NR/CR-CV shows a tensile strength of 2.85 MPa with a breaking elongation of 511%. However, the TS of NR/CR-CVMF\({}_{3}\) was 174% higher than NR/CR-CV. Moreover, both the EB and modulus at different percentage elongations of NR/CR-CVMF\({}_{3}\) were also considerably higher than NR/CR-CV. Similarly, the TS of NR/BIIR-CVMF\({}_{3}\) was 107% higher than the corresponding NR/BIIR-CV. The modulus of NR/BIIR-CVMF\({}_{3}\) at different percentage elongations were also significantly higher than NR/BIIR-CV. The hardness of the blends cured with CV and CVMF\({}_{3}\) is represented in Figure 9d. The knowledge of the hardness of a rubber material is very essential, particularly when it is used in seals and gaskets. For sealing applications, the rubber compounds should be soft enough for better sealing ability, yet hard enough to sustain the loading force.  
Figure 8: The solvent uptake of (**a**) \(T_{90}\) cured NR/CR-CV and 8 min cured NR/CR-CVMF\({}_{3}\) (**b**) \(T_{90}\) cured NR/BIIR-CV and 5 min cured NR/BIIR-CVMF\({}_{3}\).  
Generally, rubbers with a tightly cross-linked network structure exhibit a high resistance to indentation. From Figure 9d, it is clear that the hardness of both the NR/CR-CVMF\({}_{3}\) and NR/BIIR-CV MF\({}_{3}\) were significantly improved compared to their respective NR/CR-CV and NR/BIIR-CV.  
The above-mentioned mechanical properties were also evaluated after molding NR/CR-CVMF\({}_{3}\) up to 8 min and NR/BIIR-CVMF\({}_{3}\) up to 5 min. It is important to note that the \(\Delta M\) value of the \(T_{90}\) cured NR/CR-CV and the 8 min cured NR/CR-CVMF\({}_{3}\) are the same. Similarly, the \(\Delta M\) value of the \(T_{90}\) cured NR/BIIR-CV and the 5 min cured NR/BIIR-CVMF\({}_{3}\) are also the same. Interestingly, the TS of NR/CR-CVMF\({}_{3}\) cured up to 8 min was around 191% higher than the \(T_{90}\) cured NR/CR-CV. Similarly, the TS of 5 min cured NR/BIIR-CVMF\({}_{3}\) was 230% higher than the \(T_{90}\) cured NR/BIIR-CV. Moreover, the EB, modulus at different percentage elongations and shore-A hardness of the 8 min cured NR/CR-CVMF\({}_{3}\) and the 5 min cured NR/BIIR-CVMF\({}_{3}\) were considerably higher than the \(T_{90}\) cured NR/CR-CV and NR/BIIR-CV, respectively. These mechanical property data gives additional support for the enhanced compatibilization between NR and CR (or BIIR) with CVMF via Diels-Alder reaction.
# Model Test Study on a Shield Tunnel Adjacent to Pile in the Sub-Clay of a Coastal Area
## 2 Design of Model Test System Crossing Existing Piles by Shielding  
### Model Test System of Crossing Existing Piles by Shielding  
A plane strain shield tunneling model test system was designed to realize automatic pushing and tail grouting. It is mainly composed of four parts: the test platform, the shield grouting module, the loading module and the monitoring module (Figure 2).  
Figure 1: Surrounding rock zone division by shield.  
Figure 2: The model test system (**a**) the overall test device; (**b**) part of test device.  
The size of the model chamber is 520 mm \(\times\) 200 mm \(\times\) 520 mm (length \(\times\) width \(\times\) height). In order to reduce the boundary effect, the experiment adopts the transparent tempered SiO\({}_{2}\) glass material; 5 cm thickness can ensure that the surface deformation is less than 0.2 mm. The simulation prototype is the shield engineering project of Tianjin Metro Line 1. The diameter of the circular section is 6390 mm, the geometric similarity ratio is 1:100, and the circular hole with 80 mm diameter is opened in the front part of the chamber with a peripheral angle steel reinforcement and an outer alloy steel support frame. The test system is shown in Figure 2.  
The shield module consists of a welded steel frame and can provide a 6 kN thrust range of the putter composition, and the pushing speed is 1 mm/s, which can realize the automatic push driving. The grouting module is mainly composed of a diaphragm pressure pump which can provide 2 kPa grouting pressure. The preparation of slurry for the modified Vaseline material has good cement and mobility ability after heating and the grouting rings have a solid-state solid and similar soil formation. The loading system consists of the force ring and the range 2000 kg hydraulic jack, which completes the initial consolidation of the layers at each stage of the each loading of 60 kN, corresponding to the surface applied surface force of 1 kPa. The test monitoring modules are composed of contact type (dial gauge, earth pressure cell, strain gauge) and non-contact observation (camera, 1 cm grid, PIV software, etc.).  
The main observation index: load value, soil surface subsidence, soil displacement, soil stress value, bending moment and displacement, pile failure process. The earth pressure and layered settlement monitoring systems are shown in Figures 3 and 4. The micrometer accuracy of the test system is 0.001 mm, and the corresponding actual displacement accuracy is 0.1 mm, which meets the test requirements.
## 1 Introduction
In the above data, winds are implemented in two wave numerical models, that is, WAVEWATCH-III (WW3) [30], which was developed by the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA), and Simulation Wave Nearshore (SWAN) [31], which was developed by the Delft University of Technology. It was revealed in [32] that WW3, using 1\({}^{\circ}\) gridded CCMP winds that are assimilated by ERA-Interim reanalysis winds and observations, had the best performance in wave simulation for the Western Pacific Ocean. However, two aspects of employing those winds must be noted: (1) the inevitable error in the meteorological numerical model and (2) the under-estimation in the TCs [33]. These problems could be solved by using remote-sensed wind products that are regarded as real observations. Moreover, whether waves are predictable by numeric wave models using fusion wind derived from radiometers and scatterometers and their levels of accuracy are interesting questions to explore. Up to now, a modeling wave is usually forced by the result of a meteorological model, and remote-sensed products have been implemented for sea surface dynamics prediction by using artificial intelligence methods [34; 35]. The applicability of fusion wind from radiometers and scatterometers to force numeric wave models has not been reported; therefore, an experiment utilizing multiple satellites is carried out in order to classify this issue. The remainder of this paper is organized as follows: Section 2 presents the satellite products, i.e., the wind data from ASCAT, HY-2B, and WindSAT; the wave data from Jason-3; and the observation data from the National Data Buoy Center (NDBC) buoys of the NOAA. The methodology for the fusion of the multiple satellite-measured winds during the period November 2019 to October 2020 is presented in Section 3. Additionally, the model settings of the numeric wave models (i.e., WW3 and SWAN) are briefly introduced in Section 3. The validation of the fusion wind and simulated waves against the NDBC buoy data is exhibited in Section 4, and the hindcasting waves are evaluated with the products from the HY-2B and Jason-3 altimeters in this section. Section 5 presents the conclusions and prospects.
## 1 Introduction
### Image Deconvolution  
Image deconvolution is a mathematical technique used to improve the resolution or clarity of images by reversing the effects of blurring and distortion. These distortions arise from the instrument's PSF, atmospheric effects, or other turbulence during observation. Deconvolution aims to reconstruct a more accurate version of the original image by using prior knowledge of the PSF and other known properties of the telescope. There are several methods for image deconvolution, each with specific advantages and limitations. Iterative methods, such as the Richardson-Lucy algorithm [15; 16], apply successive approximations to recover the image, and they are particularly effective in dealing with noisy data. Fourier-based methods [17] work in the frequency domain to perform deconvolution efficiently, but they can be more sensitive to noise. Wavelet-based deconvolution [18] is often used for images with structures and edges, allowing for better edge detection and detail recovery. More recently, machine learning techniques [19] have become very common, using deep learning models trained on known PSFs and image pairs to achieve results that are comparable to or better than those from deconvolution methods (but these new techniques are heavily dependent on the availability of training data).  
Applications of image deconvolution have appeared in diverse fields. In astronomy, deconvolution is used to sharpen images of distant stars, galaxies, and various other celestial objects captured by ground-based and space-based observatories. In medical imaging, deconvolution improves the quality of microscopy and radiology scans, aiding in more precise diagnoses. It is also widely used in remote sensing, surveillance, and increasingly in consumer photography to enhance image quality and detail.
## 7 Proportional--Integral--Derivative (PID) Controller Enhanced with Deep Learning (DL)
### Design of Multi-Layer Deep Learning for PID Tuning
The combination of traditional PID control and deep learning enables the system to handle complex control tasks more effectively, improving both accuracy and efficiency.