esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Sodium Dodecylbenzenesulfonate by Macroporous Adsorbent Resins
## 3 Results
### Removal of SDBS from a Mixed Solution of SDBS and Cs Ions  
Large amounts of surfactants are used to decontaminate RLWs, and thus large amounts of radioactive complexes are generated during decontamination and decommissioning processes. To simulate such radioactive complexes, we prepared a mixed solution consisting of SDBS and Cs ions (denoted as SDBS/Cs). The effect of the adsorption time on SDBS removal from the aqueous SDBS/Cs solution using various amounts of the MARs is shown in Figure 6. Interestingly, HP20 (20 mg/mL) showed a better SDBS removal efficiency from the SDBS/Cs solution than from the pure SDBS solution: more than 90% of the SDBS was removed in 40 min (Figure 6a). The removal of SDBS from the SDBS/Cs solution by HP20 was noticeably more efficient than that by HP2 MGL (Figure 6b). After 60 min adsorption time, the efficiency of SDBS removal reached equilibrium at 95% (by a 20 mg/mL HP20) after 30 min and at 80% (by a 20 mg/mL HP2 MGL) after 240. SDBS, an anionic surfactant, can form a complex with metal (Cs ions) via an electrostatic interaction between the negatively charged anionic surfactants and the positively charged metal ions [27]. It is assumed that the complex became more hydrophobic than the anionic surfactant alone in aqueous solution, because of charge compensation. Therefore, the increase in the hydrophobicity of SDBS/Cs complexes resulted in better adsorption capacity for HP20 and less efficient adsorption for HP2 MGL..  
In addition, the adsorption of various initial concentrations of SDBS from the SDBS/Cs solution by the MARs was significantly enhanced, compared with the pure SDBS solution (Figure 7). The maximum amount of SDBS adsorbed by the MARs increased more than five times (compared with \(q\) in Figure 5). When using HP20 for the removal of SDBS from SDBS/Cs solution, the maximum adsorption of SDBS observed was as 345 mg/g (for a 10 mg/mL initial SDBS concentration in SDBS/Cs aqueous solution). This is remarkably high removal of SDBS in the presence of metal ions in comparison with a previous report (140 mg/g from SDBS/Ca solution) [25]. Because of the difference in the hydrophobic nature of the resins, HP2 MGL showed inefficient adsorption, compared with HP20.  
To investigate whether Cs ions were removed by the resins from SDBS/Cs solution simultaneously with SDBS, the concentration of Cs ions that remained after adsorption by the MARs was measured. More than 85% of Cs ion removal was attained by both MARs (Figure 8). This result suggests that when the SDBS was removed from SDBS/Cs solution by the MARs, Cs ions were removed together with the SDBS.  
Figure 5: Effect of initial SDBS concentration on the removal of SDBS using (**a**) HP20 and (**b**) HP2 MGL. [adsorbents] = 5 mg/mL; adsorption time = 7 h.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 3 Materials and Methods
### Discriminator Networks  
The standard GAN is used to calculate the probability whether the image is true or fake. To be able to distinguish which images are real and which are generated, we trained a discriminator network. The architecture of the discriminator is depicted in Figure 6, which contains eight convolutional layers. A batch normalization (BN) layer follows all convolutional layers, except for the first convolutional layer. Like the VGG network, the number of channels of the convolutional layer doubles from 64 to 128, and then 128 to 256, and finally 256 to 512. The stride size of the first, third, fifth, and seventh convolutional layers is set to 2, while the stride size of the remaining convolutional layers is set to 1. Moreover, the stride convolution and LeakyReLU activation (\(\alpha=0.2\)) function are used rather than the maximum pooling layer. The main advantage of using stride convolution is to reduce the size of features. At the end of the network, two dense layers and a sigmoid activation function are followed to calculate the probability that the image is authentic.
# Research on the Tooth Surface Integrity of Non-Circular Gear WEDM Based on HPSO Optimization SVR
## 4 Results and Discussion
### Comparison of Model Performance  
In order to describe the relationship between the process parameters and various indicators of surface integrity more accurately, this research used SVR to predict the surface integrity and used HPSO to optimize the parameters of SVR.  
In the prediction process, the sample value used for training should be more than the predicted sample value, so that the prediction result can be more accurate [24]. Therefore, 80% of the above data was randomly selected for training the model, and the remaining 20% was used for prediction testing. In this research, we used HPSO to optimize the SVR model using different kernel functions. The HPSO swarm number was set to 20, iterated 200 times, and the acceleration factor was \(c_{1}\)= \(c_{2}\)= 2. The comparison results are shown in Table 11.  
Figures 9 and 10 represent the \(R^{2}\) and MAPE values obtained in Table 10 as bar graphs. Figures 9 and 10 clearly show that the performance of the rbf kernel function was better than the poly kernel function and sigmoid kernel function for the surface roughness, surface residual stress, and surface microhardness models, which indicates that the model fitting using the rbf kernel function was better. Therefore the rbf kernel function was the best choice for modelling the surface roughness, surface residual stress, and surface microhardness in the WEDM machining of the non-circular gear.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Kernel Name**} & \multicolumn{2}{c}{**Surface**} & \multirow{2}{*}{**Surface Residual Stress**} & \multicolumn{2}{c}{**Surface**} \\  & & **Roughness** & & & **Microhardness** \\ \cline{2-7}  & \(\mathbf{R}^{2}\) & \(\mathbf{MAPE}\) & \(\mathbf{R}^{2}\) & \(\mathbf{MAPE}\) & \(\mathbf{R}^{2}\) & \(\mathbf{MAPE}\) \\ \hline poly & 0.9624 & 5.4115 & 0.9716 & 2.3867 & 0.9533 & 0.7808 \\ rbf & 0.9967 & 1.2761 & 0.9992 & 0.4151 & 0.9941 & 0.3017 \\ sigmoid & 0.9347 & 4.9032 & 0.9357 & 3.4287 & 0.9926 & 0.4168 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison kernel function.  
Figure 8: Main Effects Plot for Surface Microhardness.  
Therefore, this research used the above HPSO to optimize the SVR model using the rbf kernel function. Figure 11 shows a comparison between the predicted and actual values of surface roughness, surface residual stress, and surface microhardness, where \(R^{2}\) = 0.9978, \(MAPE\) = 0.4534 for surface roughness, \(R^{2}\) = 0.9483, \(MAPE\) = 3.1673 for surface residual stress, \(R^{2}\) = 0.9786, \(MAPE\) = 0.4779 for surface microhardness. Therefore, using the HPSO-optimized SVR model allowed achieving the prediction of surface roughness, surface residual stress, and surface microhardness with high performance.  
Figure 10: Performance comparison of kernel function based on MAPE.  
Figure 9: Performance comparison of kernel function based on \(R^{2}\).
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 4 Results and Discussion
### Multi-View Color Consistency Evaluation
#### 4.2.1 Subjective Evaluation  
Ten original images of the same area were automatically mosaicked by the use of MCM Zhipin software [33] and are shown in Figure 12. For the original mosaic, there were obvious differences in color and brightness in both the single images and between the different images. The mosaic border lines were also clearly visible. After consistency processing, the Wallis method achieved color consistency in a few areas and a slight improvement in visual effect. However, there were still some evident boundaries in the results, and the brightness distribution was uneven. In contrast, the proposed method completely eliminated the uneven lighting. There were no visible mosaic boundaries, and the entire Antarctic ice shelf appeared in good uniform color.  
Figure 11: Statistics of the original images and the results after processing. The mean, standard deviation, and average gradient of F1, F2, and F3 are shown separately.  
In order to further analyze the handling of mosaic boundaries by the proposed method, the details of two mosaic results are shown in Figure 13. In the mosaic result for the original images (Figure 13a), the upper sub-image and the lower sub-image were easily distinguishable, and the mosaic line was clear. The result of the Wallis method did not show a visible boundary, but the brightness and contrast were abnormal in Figure 13b. The mosaic image produced by the proposed method showed a good performance in uniformity of brightness and color, and there was no visible mosaic line in Figure 13c.  
Figure 12: Comparison of the mosaic results for the group images processed by the Wallis method [12] and the proposed approach. (**a**) Mosaic results for the original images. (**b**) Mosaic results for the images processed by the Wallis method. (**c**) Mosaic results for the images processed by the proposed method.
## 4 Discussion
The measurement of the effect of HU treatment and the impact of induced hypoxia on RBCs at specific time points were evaluated, demonstrating the capability of time-dependent monitoring. Our results show a significant increase, decrease and even non-significant change in transit time, which all fit with the dynamic nature of the changes induced by hypoxia and HU treatments of SCD patients. A recent multicenter study of hydroxyapatite in SCD patients revealed increased deformability of RBCs for patients taking HU, measured using ekracytometry [37]. However, HU had other effects, such as changes in oxidative stress, nitrite levels and the nitric oxide synthase signaling pathway [30]. The blockage of the MMM inlet in the case of HU-treated RBC samples in our study opens the way for broader monitoring of the impact of HU treatment and other drugs as well as drug candidates on the circulatory system. The MMM method has the advantage of providing surrogate visualization of events as they occur in the microcirculation, and the measurements closely mimic actual events in human circulation. Therefore, it has the potential of increasing understanding and expediting research into the benefits or otherwise intervention of agents under research and drug development.
## 5 Results and Discussion
### Selective Hardening  
To explore new possibilities in the design space, S-SWIFT-R (selective hardening) was applied to the ln2 program. This test program was chosen over the others because it is the algorithm with more approximated versions. This means a wider design space to explore in detail jointly with selective hardening. Three additional versions of the program were obtained by hardening two of the registers that achieved the highest level of protection with SWIFT-R. The three new versions correspond to the protection of the R12 register, the protection of the R13 register and the protection of the two registers at the same time (R12 and R13).  
The results of the tests carried out on the selective versions of the ln2 program are shown in Figure 13. The different versions can be seen using groups of bars. The first group on the left corresponds to the fully hardened version of the program. The others correspond to selective versions. As in the previous case, the percentage of error in the results is represented by color. Likewise, the y-axis represents the standardized MWTF in the logarithmic scale, and the different hardening versions can be seen on the x-axis.  
Figure 13: Normalized mean work to failure (MWTF) metric for the ln2 program: Complete and selective hardening using S-SWIFT-R, jointly with the percentage of error in the results (color bar).  
It can be seen that the highest MWTF corresponds to the selective version, where only the R13 register hardened. Therefore, it can be affirmed that reliability depends on the protected registers. In this case, although R13 is not the register most affected by faults, it is the one that best results in terms of reliability that can be delivered when protected. The results show that, by protecting only the R13 register, higher reliability is obtained than even by applying total hardening. The obtained MWTF by hardening the R13 register of the most approximated version is 31.47\(\times\), while with the full hardening the MWTF is 24.37\(\times\) for the same version. When only the R12 register is protected, a normalized MWTF of 30.2\(\times\) can be reached, while if R12 and R13 are jointly protected, a maximum normalized MWTF of 28.49\(\times\) is obtained.  
With these results, we can observe that not necessarily protecting more registers in a microcontroller implies greater reliability. Sometimes the overheads generated by hardening several registers are so high that the fault coverage does not compensate for the additional exposure time that the hardened system will have.  
Experiments show that it is possible to explore new possibilities in the design space by combining complete and selective hardening strategies with approximate computing techniques. This allows the designer to choose from a wide range of fault tolerance solutions, with a different trade-off between reliability, performance, and accuracy of results.
## 2 Materials and Methods  
### Study Area and In Situ Network Measurements  
The QTP is the highest plateau in the world, and contains a unique ecohydrological and geographical environment [47]. It covers approximately 250 million km\({}^{2}\), ranging from 26.5\({}^{\circ}\) to 40\({}^{\circ}\)N in latitude and 73\({}^{\circ}\) to 105\({}^{\circ}\) in longitude. As shown in Figure 1, most of the region is at a high elevation, and the average altitude is 4000 m above sea level [48]. The environmental characteristics of the QTP are alpine, arid, and anoxic, making the plateau's ecological environment extremely fragile and sensitive. The combination of its high elevation and unique atmospheric, water, and energy circulation creates a series of 'high-cold' vegetation types distributed over the QTP [49; 50]. For nearly half a century, the QTP has had a trend of warming and humidification, and frequent hydrometeorological change has been observed in this region [51].  
SM datasets measured in situ, which can be downloaded from [[https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)]([https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)) (accessed on 21 June 2022), were collected from three study sites (Naqu, Ngari, and Maqu). They belong to the CTP-SMTMN (Soil Moisture and Temperature Monitoring Network on the central TP) [52] and the Tibet-Obs (Tibetan Plateau Observatory of Plateau-Scale Soil Moisture and Soil Temperature) [53; 54] observatory systems. Naqu has a cold, semi-arid climate and land cover dominated by alpine meadows. The Ngari region is also characterized by alpine meadows, but with a cold, arid environment. The Maqu region has a cold, humid climate and dense vegetation cover. The primary characteristics of these networks are summarized in Table 1. More details can be found in the above references. In this study, ground measurements were used for two purposes: (1) examining the spatial representativeness of measurements collected by each in situ SM sensor within the three networks, and (2) evaluating the eight fine-RES SM datasets generated from the four GMs by combining and non-combining with the WT technique.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Networks** & **Ngari** & **Naqu** & **Maqu** \\ \hline Datasets & Tibet-Obs & CTP-SMTMN & Tibet-Obs \\ Location in QTP & West & Central & Northeast \\ Total nodes & 18 & 57 & 20 \\ Measured SM depth & 5 cm & 0–5 cm & 5 cm \\ Measured time & 15 min/day & 30 min/day & 15 min/day \\ interval & & & \\ Used time coverage & & April 2016-September 2016 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the basic information of the three in situ networks.
## 4 EE Improvement Issues for Each Component
### Precoding  
For LSASs, as mentioned beforehand, there are two representative precoding schemes, ZF and MF precoding. Regarding power consumption, ZF consumes more than MF. However, the difference is small enough to not particularly affect overall power consumption. Figure 6 shows the difference in the power consumption of ZF and MF precoding using (14).  
As observed, the power consumption difference between ZF and MF is only around 4 W even though we use 600 TX antennas. TP is one of the important factors that constitute EE. Figure 7 shows the TP difference between ZF and MF. For the simulations in this subsection, we assume perfect channel state information with no RS overhead to show the clear difference of ZF and MF. As can be seen, using ZF precoding gives 68% (17.5 (bps/bps) \(\rightarrow\) 29.4 (bps/bps)) improvement at \(N_{t}=600\) compared to using MF precoding.  
Based on Figures 6 and 7, we present the EE difference between ZF and MF in Figure 8. ZF shows much better EE and the difference is more dominant as the number of TX antennas increases. When \(N_{t}=600\), ZF gives 56.73% (85.5 ((bps/W)/(bps/W)) \(\rightarrow\) 134 ((bps/W)/(bps/W))) better EE than MF. The analysis in this subsection supports the claim that ZF is much better than MF for both the TP and EE perspectives. However, in some applications, MF is much more useful than ZF. For example, in distributed TX antenna systems, to use ZF precoding, the distributed TX antenna systems need to know the channels of other distributed TX antennas. In this case, the backhaul burden could be too  
Figure 6: \(P_{BB}(W)\) as increasing \(N_{t}\), when using ZF and MF precoding with \(K=0.1N_{t}\).  
high to use ZF. MF does not requires the channel information of all distributed TX antennas. For this reason, MF is still a valuable option.
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 2 Related Works
### Head Pose Estimation  
Proctoring involves identifying whether an audience is looking straight at the computer screen or not, in order to assess their behavior, attentiveness, and integrity during online conduct. This is often achieved by analyzing the head pose, or the Euler angles (yaw, pitch, and roll) of the head in 3D space. Many studies have focused on estimating head pose, especially from visual data, for a variety of applications, including driver fatigue detection [37; 38; 39]. Head pose has also been studied for proctoring purposes in previous research [40; 41; 42].  
Deep learning-based approaches for estimating head pose can be found in previous research [38; 43; 40]. The authors of [44], for instance, used a CNN [45] to detect the head pose. However, they took a different approach; they z-score standardize the yaw and pitch to accommodate different settings based on an assumption that they follow normal distribution, as in the case of face landmarks in this paper. Other studies have used non-deep learning approaches, such as localized gradient orientation (LGO) histograms with support vector regressors (SVRs), to detect not only head pose but also position in 3D space [39]. In another proctoring solution [42], the head pose was estimated using an extended version of the Constrained Local Model.  
The survey in [46] documents several methods for estimating head pose, including geometric methods that use facial landmarks. For example, ref. [41] described a method for calculating head pose from cylindrical and ellipsoidal face models (proposed in [47]).  
An example of a method for estimating head pose using facial landmarks can be found in the work of [48]. They used Euler angles to estimate head pose by solving the Point-n-Perspective (PnP) problem with methods such as POSIT (Pose from Orthography and Scaling with ITerations). This solution requires a reference 3D face model and camera parameters, and is most optimal when the real camera parameters and the input face closely resemble the references [45; 49]. Another study [37] used Efficient PnP (EPnP) for head pose estimation in driver fatigue detection.  
This paper, however, presents an algorithm that estimates face orientation using two degrees of freedom (horizontal and vertical) for proctoring purposes. A similar approach for detecting face orientation can be found in a previous study [50], which used five separate AdaBoost classifiers to classify five discrete face orientations (left, right, up, down, straight) directly from raw pixels. In contrast, the authors of this paper used only a single nose landmark to evaluate continuous horizontal and vertical orientations. The algorithm is reliable as long as the face landmark detection is accurate, which is ensured by the underlying MediaPipe FaceMesh solution [31], which has been demonstrated to be accurate and is widely used in production.
# A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks
## 4 Problem Formulation  
For comprehensively optimizing the energy efficiency of HetNets, the parameters such as SI-SBSs, ABS, and CRE should be jointly considered. The optimization problem is modeled by setting the  
where \(f\) function is unknown, \(\beta\) represents the CRE parameter, \(|S|\) represents the number of SBSs activations.
# An LLC Converter with Capacitive Insulation
## 3 Design Considerations
### Loss Breakdown Analysis  
The existing commercial 150 W LLC module from Asian Power Device Inc. (Taipei, Taiwan) is used as a reference in this paper. In order to make a fair comparison, the proposed prototype in this paper will adopt the same controller IC and MOSFETs as a reference. The proposed prototype only miniaturizes the magnetic component and replaces the resonant capacitor \(C_{r}\) using the film capacitor with high-side and low-side capacitors using Y1 capacitors. Due to the much lower capacitance of Y1 capacitors than that of film capacitors, the resonant tank parameter is redesigned to enhance the output power with higher switching frequency. The current of primary winding and the current of secondary winding can be calculated as follows:  
\[i_{L(\text{pk})}=\frac{n\cdot V_{o}}{4\cdot L_{m}\cdot f_{r}}=0.285\text{ A}, \tag{13}\]  
\[i_{Lr(\text{rms})}=\sqrt{\frac{V_{o}^{2}}{8}\cdot\left[\left(\frac{n}{2\cdot L _{m}\cdot f_{r}}\right)^{2}+\left(\frac{\pi}{n\cdot R_{o}}\right)^{2}\right]}=0. 819\text{ A}, \tag{14}\]  
\[i_{Ns1(\text{rms})}=i_{Ns2(\text{rms})}=I_{o}\cdot\frac{\pi}{4}=3.535\text{ A}, \tag{15}\]  
\[i_{Co(\text{rms})}=\sqrt{\left(I_{o}\cdot\frac{\pi}{2\sqrt{2}}\right)^{2}-I_{ o}^{2}}=4.838\text{ A}, \tag{16}\]  
The magnetic flux of \(L_{m}\) also can be calculated as in (17). \[B_{max\cdot Lm}=\frac{L_{m}\cdot i_{Lm(\text{pk})}}{N_{P}\cdot A_{e(T1)}}=0.27 4\text{ T}, \tag{17}\]  
Based on the manufacturability of the \(T_{1}\), the \(N_{P}\) is determined to be made with 28 turns of Litz wire with a diameter of 0.1 mm and 30 strands. \(N_{S1}\) and \(N_{S2}\) are both made with two turns of Litz wire with a diameter of 0.1 mm and 120 strands. Eventually, the DC resistance of each winding is measured to be \(DCR_{NP}=9.2\text{ m}\Omega\) and \(DCR_{NS1}=DCR_{NS2}=164\text{ }\upmu\Omega\) so as to obtain the copper loss of \(T_{1}\), as shown in (18) and (19). The core loss of \(T_{1}\) is also obtained from (20) with core characteristics: \(V_{e(T1)}=2480\text{ mm}^{3}\) and \(P_{CV(T1)}=1800\text{ kW}/\text{m}^{3}\). \[P_{copper\cdot Np}=DCR_{NP}\left(\frac{i_{Lr(\text{pk})}}{\sqrt{2}}\right)^{2}=6. 16\text{ mW}, \tag{18}\]  
\[P_{copper\cdot Ns}=DCR_{Ns1}\left(i_{SR(\text{rms})}\right)^{2}+DCR_{Ns2}\left( i_{SR(\text{rms})}\right)^{2}=17\text{ mW}, \tag{19}\]  
\[P_{core\cdot T1}=V_{e(T1)}\cdot P_{CV(T1)}=4.464\text{ W}, \tag{20}\]  
Figure 5: Voltage gain curves at 10% load and 100% load.