esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision-Time Learning and Planning Integrated Control for the Mild Hyperbaric Chamber
## 5 Performance Evaluation  
### Environment Setup  
Main specifications of the MHBC used in the experiment are listed in Table 1.  
The system employs a Siemens 200-Smart embedded computer (Munich, Germany) as the primary unit responsible for acquiring sensor readings and valve status data. These collected data are subsequently transmitted to the host computer for further processing. Concurrently, the embedded computer also executes valve control instructions received from the host computer. The host computer is equipped with an Intel i5 9400F CPU (Santa Clara, CA, USA) and an Nvidia RTX 2080 Ti GPU (Santa Clara, CA, USA). The training and inference processes are accelerated by leveraging the GPU. To protect the valves, control  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technical Specification** & **MHBC** \\ \hline Total chamber volume & 12.4 m\({}^{3}\) \\ Design pressure & 0.155 MPa \\ Maximum operating pressure & 0.150 MPa \\ Cabin temperature & Winter: 20 \(\pm\) 2 \({}^{\circ}\)C Summer: 24 \(\pm\) 2 \({}^{\circ}\)C \\ Cabin humidity & 40–50\% \\ Standard capacity & 4 persons \\ Pressurization rate & 0.004\(\sim\)0.01 MPa/min \\ Depressurization rate & 0.004\(\sim\)0.01 MPa/min \\ Oxygen concentration inside & 21–23\% \\ Air supply pressure & 0.5 MPa \\ Air storage tank capacity & 3 m\({}^{3}\) \\ Air compressor output rate & 0.9 m\({}^{3}\)/min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main specifications of the mild hyperbaric chamber.  
command execution is separated by at least 1 s. The control system computation takes roughly 670 ms per step, thereby adequately satisfying real-time operation.  
The pressures related to the MHBC include four variables: chamber pressure, air storage tank pressure, oxygen source pressure, and vacuum source pressure. The chamber pressure is the average value of multiple pressure sensors located at different positions inside the chamber. The oxygen source pressure and vacuum source pressure are controlled by the configured values of the oxygen concentrator and vacuum pump, respectively. These two pressure values affect system states as action inputs in practice and are, therefore, categorized as action variables. The action variables in the MHBC control system also include: two pressurization control valves in the pressurization pipeline; two depressurization control valves in the depressurization pipeline; one oxygen supply control valve in the oxygen supply pipeline; and one oxygen exhaust control valve in the oxygen exhaust pipeline. As a result, the system consists of two state variables and eight action variables.
# Effect of Amphiphilic Polymer/Nano-Silica Composite on Shale Stability for Water-Based Muds
## 3 Results and Discussion
### Probable Shale Stabilization Mechanism  
A possible shale stabilization mechanism of SMA/SiO\({}_{2}\) could be summarized in view of the research findings from this work and some previous researches. Under the drilling pressure differential, the nanoparticles in SMA/SiO\({}_{2}\) are pressed into the nanopores and cracks in the shale. A plugging layer is formed on the shale, resulting in a lower pressure transmission rate and shale permeability.  
Figure 12: The results of rolling dispersion experiment.  
Figure 13: The results of contact angle test.  
Thus, the excellent plugging performance of SMA/SiO\({}_{2}\) is achieved. In addition, the hydrophilic segments in SMA/SiO\({}_{2}\) can enhance the adsorption between SMA/SiO\({}_{2}\) and the shale. After being adsorbed by SMA/SiO\({}_{2}\), a more hydrophobic shale surface will be obtained due to the hydrophobic segments in SMA/SiO\({}_{2}\), which can further prevent the water invading into the shale. As a result, SMA/SiO\({}_{2}\) exhibits good hydration inhibition. Overall, the novel shale stabilizer SMA/SiO\({}_{2}\) has the advantages of both physical plugging and chemical inhibition.  
In addition, in order to further optimize the molecular structure of SMA/SiO\({}_{2}\) and explore its shale stabilization mechanism, more detailed statistical analysis and refined structural characterization will be carried out in our future work [49; 50].
## 4 Discussion
This study provides practical insights for those working with both machine learning and traditional forecasting methods. Researchers who use wind data in this region should adopt the model structures developed for machine learning as well as the model structure established for the traditional method, SARIMA, in their studies. The results obtained in this region confirm the effectiveness of these approaches. More specifically, in studies on predicting wind data with machine learning, the use of WT significantly improves model results. Among the tested models, LSTM-W algorithms outperform other algorithms and serve as an optimal reference in terms of performance criteria. As a result, if successful models are to be established, they should be similar to the LSTM-WM04 model. Another conclusion is that if WT is not used, traditional methods, particularly SARIMA, achieve competitive results comparable to machine learning methods.
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 7 Conclusions  
In this paper, the uplink PC-SCMA system is improved by information coupling and parity check. The systematic PCCA-polar code is encoded as the base code of the PIC polar code. A low-complexity joint iterative detection and SCL decoding receiver is proposed for the SCMA system. The hybrid construction algorithm is designed to construct the extrinsic messages of coupled polar decoder. Based on EXIT idea, we have optimized the weight factor of extrinsic messages construction algorithm and coupling ratio of PIC PCCA-polar code. The simulation results show that, at various code rate and channel configurations, the PIC PCCA-PC-SCMA has the best performance in TBER and CBER. In addition, compared with uncoupled PC-SCMA with SCL decoder, the complexity of PIC PCCA-PC-SCMA is reduced at a high \(E_{b}/N_{0}\).  
Conceptualization, X.W. and Y.W.; methodology, X.W.; software, X.W.; validation, X.W. and Y.W.; formal analysis, X.W. and Y.W.; writing--original draft preparation, X.W.; writing--review and editing, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Technology Research and Development Program of China under Grant 2018 ZX03001022.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(E_{b}/N_{0}\) (dB) & **PIC PCCA** & **PIC CA** & **CA (SCL)** & **CA (SCAN) [17]** \\ \hline \multirow{2}{*}{2.5} & \(\overline{l}\) & 5 & 5 & 5 & 5 \\  & \(\overline{L}\) & 17.83 & 18.27 & 17.78 & 1 \\ \hline \multirow{2}{*}{3} & \(\overline{l}\) & 5 & 5 & 5 & 5 \\  & \(\overline{L}\) & 16.2 & 17.89 & 14.94 & 1 \\ \hline \multirow{2}{*}{3.5} & \(\overline{l}\) & 4.79 & 5 & 4.96 & 5 \\  & \(\overline{L}\) & 11.74 & 14.42 & 11.35 & 1 \\ \hline \multirow{2}{*}{4} & \(\overline{l}\) & 3.85 & 4.39 & 4.42 & 5 \\  & \(\overline{L}\) & 8.08 & 10.1 & 8.45 & 1 \\ \hline \multirow{2}{*}{4.5} & \(\overline{l}\) & 2.91 & 3.34 & 3.55 & 4.95 \\  & \(\overline{L}\) & 6.24 & 7.17 & 6.8 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average number of iterations \(\overline{l}\) and the average list size \(\overline{L}\) over AWGN channel.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Hybrid Cooperative Spectrum Sharing Protocols Based on OMA and NOMA in Cognitive Radio Networks
## 2 System Model and Protocol Descriptions
### Throughput of SUs  
In this section, the throughput of the SU for hybrid cooperative cognitive radio network base on pure OMA is derived. In this network, the SU's packet is transmitted over the channel ST \(\rightarrow\) SR under the interference of PT's transmission in underlay OMA transmission mode. The SU can transmit its own packet only at the state \(S_{2}\). Similar to the situation of OMA and NOMA, the success probability of the SU's packet is equal to \(1-P_{\text{ST}\rightarrow\text{SR}}^{\text{out},x_{S},I}\). The SU's throughput for the hybrid cooperative cognitive radio network base on pure OMA can be written as:  
\[\begin{array}{l}\pi_{\text{SU}}^{\text{OMA}}=\Big{(}1-P_{\text{ST} \rightarrow\text{SR}}^{\text{out},x_{S},I}\Big{)}\pi_{\text{S}_{2}}\\ =\frac{\big{(}1-P_{\text{ST}\rightarrow\text{SR}}^{\text{out},x_{S},I}\big{)} P_{1}}{1+2P_{1}+P_{2}+P_{3}-P_{1}P_{2}}=\frac{F}{\Lambda}\,\end{array} \tag{40}\]  
where the closed-form of \(A\) is the same as Equation (26). Subsiting Equations (30) and (37) into Equation (40), we can obtain the closed-form of \(F\):  
\[\begin{array}{l}F=\Big{(}\frac{\rho_{1}\lambda_{SS}}{\rho_{1}\lambda_{SS}+ \varphi_{1}\lambda_{SS}+\varphi_{P}\lambda p_{P}}\Big{(}1-exp\Big{(}-\frac{ \rho_{\text{th}}}{\rho_{1}\lambda_{SP}}\Big{)}\Big{)}+\frac{\rho_{\text{th}} \lambda_{SS}}{\varphi_{S}\lambda_{SP}\rho_{P}}exp\Big{(}-\frac{\rho_{\text{th} }}{\rho_{1}\lambda_{SP}}+DC\Big{)}\cdot Ei(-CD)\Big{)}\\ exp\Big{(}-\frac{\varphi p}{\rho_{P}\lambda p_{P}}-\frac{\varphi_{\text{th}}}{ \rho_{1}\lambda_{SS}}\Big{)},\end{array} \tag{41}\]  
where \(C=\frac{\varphi_{SP}\lambda p_{P}+\rho_{1}\lambda_{SS}}{\rho_{1}\lambda_{SS} \lambda_{PR}}\) and \(D=\frac{\varphi_{S}\lambda_{SP}+\rho_{\text{th}}\lambda_{SS}}{\varphi_{S} \lambda_{SP}\rho_{P}}\).
# Smoothed Particle Hydrodynamics Simulation of Orthogonal Cutting with Enhanced Thermal Modeling
## 5 Results & Discussion
### Application: Orthogonal Metal Cutting
#### 5.2.1 Force Prediction and Chip Shape
Nevertheless, the community of SPH cutting simulations typically consider an average error of force prediction to verify their numerical results and not the maximum value, see, e.g., in [17, 20, 39]. One way to rectify this situation is increasing the spatial resolution (i.e., decreasing the discretization size), as shown in [19]. High-resolution SPH simulations of metal cutting are (arguably) only possible with high-performance computing and parallel processing. This topic is outside the scope of this paper, thus not taken into consideration here. Another way to mitigate this issue in predicting the thrust force is to enhance the contact and friction model. This is because the thrust force is mainly affected by the frictional behavior at the rake face (particularly if \(\gamma=0^{\circ}\)). The following sensitivity analysis will provide some deeper insight into this subject matter. Previous studies such as those in [19, 40] have already shown that the force prediction in metal cutting simulations is mainly affected by the choice of the flow stress parameters (e.g., the five unknown constants in \(\sigma_{y}^{\text{JC}}\) in Equation (9)) and friction coefficients. This statement justifies the marginal difference between the SPH reference and the SPH present data provided in Figure 6, where all material and friction parameters are the same, and only the thermal modeling approach is different (Please note that the signed percent errors  
Figure 5: Evolution of predicted forces compared to the experimental data. displayed between parentheses in the table of Figure 6 are obtained by comparing the simulated forces against the experimental values given on the first row). It also clarifies the reason why increasing \(\mu\) from 0.35 to 0.65 decreases the average error of thrust force from 15% to only 1%. Given the measured forces in Figure 6, the friction coefficient corresponding to the experimental \(F_{c}\) and \(F_{t}\) is equal to 0.66, whereas \(\mu=0.35\) was initially taken for the SPH simulation. Therefore, two additional simulations are carried out using \(\mu=0.50\) and \(\mu=0.65\) to gain further insights. Figure 6 shows a quantitative comparison of these simulated forces in a bar chart, where the present thermal model is used. As expected, the effect of \(\mu\) on \(F_{t}\) is higher than \(F_{c}\), resulting in the best prediction of \(F_{t}\) when \(\mu=0.65\). It is because the friction model in this cutting geometry with a rake angle of 0 degrees has the most significant influence on the magnitude of \(F_{t}\). Although not very significant, the choice of \(\mu\) was found to have impact also on the simulated chip shapes.
## 2 Materials and Methods
### Construction of Complex Networks from EEG Signals Based on Visibility Graph of Ordinal Patterns  
The signal from each channel in EEG can be treated as a time series \(\{x_{i}\}\), where \(\mathbf{i}~{}=~{}1,2,\ldots N\). Initially, the phase space reconstruction method is used to reconstruct this time series into a sequence [4] using embedding dimensions \(d\) and time delay \(\tau\). The resulting sequence can be written as follows:  
\[v_{j}=\left(x_{j},x_{j+\tau},~{}x_{j+2\tau}\right)\,j=1,2,~{}3,\ldots,L \tag{1}\]  
where \(L\) = \(N-(d-1)~{}*~{}\tau\) and denotes the number of partitions \(v_{j}\) in the resulting sequence.  
Subsequently, each partition \(v_{j}\) is mapped into an ordinal pattern \(O^{(i)}\) = (\(\pi_{0}\), \(\pi_{1}\), \(\pi_{2}\), \(\ldots\), \(\pi_{d-1}\)) where \(\pi_{i}\) \(\in\) {0, 1, 2, \(\ldots\), \(d-1\)} (\(\pi_{i}\)\(\
eq\)\(\pi_{j}\) if \(i\)\(\
eq\)\(j\)). Specifically, the indices of each element in the partition \(v_{i}\) = (\(x_{i}\), \(x_{i+\tau}\), \(x_{i+2\tau}\), \(\ldots\), \(x_{i+(d-1)\tau}\)) are rewritten to \(v_{i}=\left(x_{i+\pi_{0}},~{}x_{i+\pi_{1}},~{}x_{i+\pi_{2}},\,\ldots,~{}x_{i+ \pi_{d-1}}\right)\), according to the ascending order of the values of elements in the partition \(v_{i}\):  
\[x_{i+\pi_{0}}\leq x_{i+\pi_{1}}\leq x_{i+\pi_{2}}\cdots\leq x_{i+\pi_{d-1}}, \forall x_{i+\pi_{k}}\in v_{i}\;\textit{and}\;\pi_{k}=\{0,1,2,\cdots,d-1\} \tag{2}\]  
For example, taking the \(\{18,9,5,11\}\) as a partition, it can be mapped to an ordinal pattern \(\{2,1,3,0\}\).  
Finally, we introduce a metric for quantifying the ordinal patterns, denoted as the ordinal pattern number (OPN) [4]. Its formulation is articulated as follows:  
\[\textit{OPN}\left(O^{(i)}\right)=\textit{Inv}\left(\pi_{0}\right)\times(d-1 )\,!+\textit{Inv}\left(\pi_{1}\right)\times(d-2)\,!+\cdots+\textit{Inv}\left( \pi_{d-2}\right)\times(1)\,!+1 \tag{3}\]  
where (\(\cdot\)!) denotes the factorial function, and \(\textit{Inv}\) (\(\pi_{i}\)) represents the inverse number of each element \(\pi_{i}\) in the ordinal pattern \(O^{(i)}\) = (\(\pi_{0}\), \(\pi_{1}\), \(\pi_{2}\), \(\ldots\), \(\pi_{d-1}\)). In accordance with Equation (3), the minimum value of the _OPN_ is 1, which corresponds to the permutation \(\pi\) = (0, 1, 2, \(\ldots\), \(d-1\)) in ascending order, the maximum value of the _OPN_ is \(d!\), and descending order is \(\pi\) = (\(d-1,\ldots\), 2, 1, 0).  
Following the aforementioned time series transformation and employing the phase space reconstruction method and ordinal pattern quantization, the time series with a data length of N is transformed into a symbol sequence with a length of L. Subsequently, utilizing the visibility graph method [4], the resulting symbol sequence is mapped into a graph network. To clearly illustrate the proposed method, which constructs a network, the basic process of constructing a complex network from a time series is shown in Figure 1(a). Figure 1(b) presents the proposed method for time-series mapping to the OPN of network nodes.
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 4 Results
### Test Set Comparison Experiment  
In this study, QP-DETR and DETR are used for detection on the test set. Figure 9 shows the results for some of the test images, with the QP-DETR results in the left column and the DETR results in the right column. The numbers on the detection boxes indicate the confidence level, which represents the probability that the model considers the box to be a QP event.  
When comparing the two algorithms, it can be seen that both models can successfully identify the more obvious different types of QP events. However, the confidence level of QP-DETR is significantly higher than that of DETR, as shown in Figure 9a-h. For QP events with a weaker intensity and less obvious texture features, DETR shows a higher rate of missed detections, while QP-DETR can still successfully detect these events, as shown in Figure 9i-l.  
These results indicate that the improved model, QP-DETR, has stronger feature extraction capabilities. It can highlight the necessary texture features in a complex background, reduce the missed detection rate of QP events, and thus prove the effectiveness of the model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**EfficientNetV2** & **ECA** & **DefAttention** & **MI-DefAttention** & **Params/M** & **mAP\({}_{0.50.95}\)** & **mAP\({}_{0.5}\)** \\ \hline - & - & - & - & 41.28 & 0.458 & 0.914 \\ \(\surd\) & - & - & - & 34.27 & 0.452 & 0.916 \\ \(\surd\) & \(\surd\) & - & - & 34.27 & 0.467 & 0.920 \\ \(\surd\) & \(\surd\) & \(\surd\) & - & 34.27 & 0.476 & 0.923 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 34.27 & 0.480 & 0.923 \\ \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments. \(\surd\) indicates that the corresponding improvement was made in the model, - no improvement was made.
## 2 Mathematical Methods and Experimental Details
### Experimental Details  
The platform (Figure 3) included a servo driver with a built-in Xilinx Spartan-6 FPGA and a GS equipped with a moving-magnetic motor with a grating position detector. The Spartan-6 FPGA had a 20-MHz crystal oscillator and multiple I/O interfaces. The DAC was a DAC8551 chip with a small volume, low power dissipation, low time delay (clock frequency: 30 MHz), and high resolution produced by Texas Instruments (TI, Dallas, Texas, USA). The power amplifier was an LM3886 chip produced by National Semiconductors (NS, Santa Clara, California, USA). The grating signals were processed using an ic-NQC chip manufactured by iC Haus, which is a 13-bit Sin/D convector with a signal calibration function. The position signals were transmitted to the computer through an RS232 serial port. Software Xilinx ISE Design Suite 14.7 with abundant IP resources was used in the experiment.  
The experiment was carried out indoors at 25 degrees Celsius, and the power amplifier chip in the experiment platform had been heat-dissipated (smeared with heat-dissipated silicone grease).
## 4 Numerical Methodology and Simulation Setup
### Numerical Setup  
In order to assess the predictive capability of the adopted numerical framework and to subsequently carry out numerical sensitivity studies, the generic SCR channel is discretized by fully conformal hexahedral mesh with ca. 1.7 M control volumes. As pointed out in [46], instead of considering the extended 25 D (see Section 2), only 1 D channel length is considered while the fully turbulent inlet boundary condition is insured by a digital filtered inlet method, as proposed by [PERSON] et al. [46]. This allows downsizing the computational domain and thereby reduces considerably the computational cost. The resulting turbulent inlet velocity profile for the case 1 (C1) is depicted in Figure 10 for two arbitrary instances suggesting a fully developed inflow condition. The AdBlue spray is represented by approximately 600,000 computational parcels for each injection event. The simulations are performed on the 2 xIntel(r) Xeon(r) Platinum 9242 Processor (3.8 GHz) using domain decomposition method available in OpenFOAM. Thereby, the computational time for 1 s of physical time with one injection event is estimated at approximately 255 CPU hours for the C1 configuration. In this way, by running a parallel computation with 60 processors, the simulation of 1 s of physical time takes ca. 4:15 h. Considering the associated computational cost and the fact that AdBlue film buildup takes relatively longer physical time with many subsequent injection events (50-100), the present numerical study is carried out only for the C1 configuration.
## 5 Enhancement of Brillouin Stokes Signal (II): Influence of Core Diameter and Fiber Length
### Effects of Long Fiber Length  
According to Equations (1) and (2), to employ long PFGI-POFs is another way to reduce \(P_{\text{th}}\) and to enhance the Stokes signal. Figure 8 shows the measured BGS of 80-m and 200-m PFGI-POFs with 62.5-\(\upmu\)m core at \(P_{\text{in}}\) of 5, 10, 15, and 20 dBm. Much larger Stokes signals (\(\sim\)7 dB higher at \(P_{\text{in}}\) of 20 dBm) than those of the 5-m PFGI-POF shown in Figure 6 were observed.  
There was, however, almost no difference between the BGS of the 80-m PFGI-POF and that of the 200-m PFGI-POF, only with a slight discrepancy of the BFS caused by the room-temperature difference. This means that the incident light is considerably attenuated after propagation for 80 m in the PFGI-POF. In order to estimate this effect quantitatively, the effective PFGI-POF length \(L_{\text{eff}}\) was plotted as a function of actual length \(L\) as shown in Figure 9, where \(L_{\text{eff}}\) gradually approaches 18 m  
Figure 7: Relative Stokes power as a function of pump power; comparison between PFGI-POFs with 62.5-\(\upmu\)m and 120-\(\upmu\)m core diameters.  
(\(P_{\mathrm{th}}\)\(\sim\) 13 W) with the increasing \(L\). Thus, we proved that employing a PFGI-POF longer than \(\sim\)50 m is not an effective way to enhance the Stokes signal at 1.55 \(\upmu\)m.  
According to Equations (1) and (2), as the core diameter decreases, the Brillouin threshold \(P_{\mathrm{th}}\) also becomes lower. When \(P_{\mathrm{in}}\) is higher than \(P_{\mathrm{th}}\), SBS is induced and consequently the Stokes signal is exponentially enhanced [7]. Here, under the rough assumption that the multimode nature, NA, refractive index, and loss do not change with core diameters, we calculated \(P_{\mathrm{th}}\) of a 50-m PFGI-POF with 10-\(\upmu\)m core diameter (hypothetical; \(A_{\mathrm{eff}}\) = 17.4 \(\upmu\)m\({}^{2}\)) to be 2.22 W (\(=\) 33.5 dBm). This value is more than one order magnitude higher than the pump power of several tens to hundreds of mW commonly used in BGS characterization in silica SMFs [39]. Even when the PFGI-POF is treated as an SMF (_i.e._, \(b\) = 1 [7] but \(A_{\mathrm{eff}}\) becomes larger [35] in Equation (1)), this difference cannot be compensated. Thus, it seems to be difficult to reduce \(P_{\mathrm{th}}\) of PFGI-POFs down to the same level of that of long silica SMFs by decreasing the core diameter, which is due to the limited effective length of 18 m associated with the high propagation loss at 1.55 \(\upmu\)m.  
Figure 8: BGS dependence on pump power \(P_{\mathrm{in}}\); comparison between 80-m PFGI-POF (solid line) and 200-m PFGI-POF (dotted line).  
Figure 9: Calculated effective fiber length _vs._ fiber length.