esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Impact of Calculation Temperature  
Calcination treatment plays a critical role in the performance of SSZ-13 membranes since the thermal expansion coefficient of the alumina substrate is +8.7 \(\times\) 10\({}^{-6}\) K (for temperatures ranging from 25 to 800 \({}^{\circ}\)C), while the thermal expansion coefficient for the SSZ-13 membrane layer is \(-\)28.5 \(\times\) 10\({}^{-6}\) K (for temperatures from 25 to 600 \({}^{\circ}\)C, the negative value is due to the removal of OSDA from the framework structure), and this variance may cause defects or cracks during heat treatments, e.g., template removal [49]. To eliminate OSDA from the membrane composition, a calcination process was conducted  
Figure 3: Effect of post-hydrothermal reaction parameters including filling factor (**a**), synthesis time (**b**), synthesis temperature (**c**), and calcination temperature (**d**) on single gas permeation measurement of SSZ-13 membranes.  
at temperatures ranging from 400 to 600 \({}^{\circ}\)C for 6 h with a heating and cooling rate of 0.5 K/min (Table S10). According to the results, elevated calcination temperatures led to an increase in thermal stress and defects in the membrane layer, as evidenced by high SF\({}_{6}\) permeance and diminished ideal CO\({}_{2}\)/CH\({}_{4}\) permselectivity. Additionally, based on Figure 3d, elevated calcination temperatures over 550 \({}^{\circ}\)C, resulted in a loss of preferential CO\({}_{2}\) adsorption properties, as evidenced by increased He and H\({}_{2}\) permeance compared to CO\({}_{2}\). Based on the outcomes regarding this section, the permselectivity of CO\({}_{2}\)/CH\({}_{4}\), CO\({}_{2}\)/N\({}_{2}\), and H\({}_{2}\)/SF\({}_{6}\) were obtained as 121, 20, and 150, respectively.
## 4 Proposed Clustering Technique in Wireless Sensor Networks (WSNs)
### Cluster Formation and Cluster Head Selection
The optimal BER for wireless communication systems is typically set based on the required quality of service (QoS) for the application [25]. In high-reliability applications such as medical devices or critical infrastructure monitoring, a very low BER of \(10^{-9}\) may be considered as optimal. The BER requirements of \(10^{-12}\) to \(10^{-15}\) may be considered optimal in optical systems to ensure error-free transmission over long distances.
# A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications +
## 6 Discussion and Final Remarks  
In this paper, we studied the throughput and energy efficiency of a network where licensed and unlicensed nodes share the same frequency band that is used by the licensed nodes for their transmission. The interference in this model comes from the licensed network on the unlicensed users. There is also the possibility of retransmissions in case the transmitted message is not successfully decoded at the receiver. We studied the optimal throughput in a constrained optimization problem where the interferers locations are modeled using Poisson point process. We then derived the value of the optimal number of retransmissions and SIR threshold that jointly result in the optimal throughput. We showed how increasing the number of nodes and the outage threshold can decrease and increase the optimal throughput respectively and how these cases compared to the two extreme cases of having zero and infinite numbers of retransmissions in the network. It should be mentioned that the \"uncoordinated\" nature (ALOHA-like) of our proposed solution leads to lower throughput while using time-division or frequency-division in order to make the communication orthogonal would improve the situation. We also studied the optimal energy efficiency and how it also decreases by increasing the number of nodes and gets better by having a looser outage threshold. The energy efficiency behavior with respect to network density, SIR threshold and the number of retransmissions was also studied. In addition to the above results, we also show that the approximation used in our previous work for the average number of retransmissions is very precise and has very low error in the range. It should be  
Figure 11: Energy efficiency of the optimal throughput \(EE^{*}\) versus the number of retransmissions \(m\) with different network densities \(\lambda\), \(\alpha=4\) and \(r_{0}=1\).  
noted that in this paper, we did not consider the block length since the analysis is intricate and would add to the complexity of the analysis; however, this important notion will be part of our future work.  
Conceptualization, I.R., H.A., and P.H.J.N.; methodology, I.R.; software, I.R.; validation, I.R., H.A. and P.H.J.N.; formal analysis, I.R.; investigation, I.R.; resources, I.R., H.A., P.H.J.N. and A.P.; writing--original draft preparation, I.R.; writing--review and editing, I.R., H.A., P.H.J.N. and A.P.; supervision, H.A., P.H.J.N. and A.P.; project administration, A.P.; funding acquisition, A.P. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by Strategic Research Council/Aka BCDC Energy (Grant no. 292854), and the Academy of Finland 6 Genesis Flagship (Grant no. 318927) and EE-IOT (grant no. 319008).  
The authors declare no conflict of interest.
## 3 Theory and Numerical Method  
### Governing Equations  
In Figure 1, the flow because of the pair of primary vortical structures shedding from wingtips induces velocity on the solid surface, \(\mathcal{S}_{\text{b}}\). The effect of crosswind velocity can still be present in the fluid domain (\(\Omega\)) to induce velocity on the ground plane too. Consequently, vorticity is continuously created from the ground plane and, once created, is free to undergo advection under all surrounding influences and diffusion under the action of viscosity. The governing equation for a two-dimensional, incompressible, and unsteady flow is given in terms of the vorticity transport equation of the filtered field [7]:  
\[\frac{\text{D}\overline{\omega}}{\text{D}\text{t}}=\frac{\partial\overline{ \omega}}{\partial\text{t}}+(\overline{\mathbf{u}}\cdot\
abla)\overline{ \omega}=\left(\frac{1}{\text{Re}}+\
u_{\text{t}}\right)\
abla^{2}\overline{ \omega} \tag{1}\]  
where \(\overline{\omega}=\
abla\times\overline{\mathbf{u}}\) is the fluid vorticity (the vorticity is a scalar in two dimensions), \(\overline{\mathbf{u}}\) is the velocity vector, \(\text{Re}\) is the Reynolds number, and \(\
u_{\text{t}}\) is the local eddy viscosity coefficient.  
The Reynolds number is defined as in [21]:  
\[\text{Re}=\frac{\rho\text{U}_{\infty}^{*}\text{b}^{*}}{\mu}=\frac{\Gamma^{*}} {\
u} \tag{2}\]  
where \(\rho\) is the fluid density, \(\text{U}_{\infty}\) is the crosswind velocity, \(\text{b}^{*}\) is the wingspan, \(\mu\) is the dynamic viscosity, \(\Gamma^{*}\) is the circulation strength, and \(\
u\) is the kinematic viscosity (momentum diffusivity).  
The turbulence manifestations are computed through the concept of the local eddy viscosity coefficient (instantaneously computed for every vortex blob), defined as follows [7; 20]:  
\[\
u_{\text{t}}(\text{t})=0.105\,\text{C}_{\text{k}}^{-3/2}\,\sigma_{0_{\text{ k}}}\sqrt{\overline{F}_{2_{\text{i}}}(\text{t})} \tag{3}\]where \(\rm C_{k}=1.4\) is the Kolmogorov constant, \(\sigma_{0}\) is the Lamb vortex core, and \(\rm\overline{F}_{2}\) is the local second-order velocity structure function of the filtered field [5; 20], namely:  
\[\rm\overline{F}_{2_{i}}(t)=\frac{1}{N}\sum_{k=1}^{N}\|\overline{u}_{k}(x_{i})- \overline{u}_{k}(x_{i}+r_{k})\|_{k}^{2}\bigg{(}\frac{\sigma_{0}}{r_{k}}\bigg{)} ^{2/3} \tag{4}\]  
In Equation (4), the summation must be computed by counting the number N of vortex blobs placed inside of an annulus, in which \(\rm{r}_{k}\) establishes the distance among the center of the annulus (center of the i-th vortex core under analysis) and centers of other vortex blobs located between the internal and the external radius of the same annulus [5; 7] (e.g., k-th vortex blob is placed inside it).  
Therefore, the large scales are solved through Equation (1) applied to the cluster of vortex blobs, and the sub-grid scales are modeled from Equation (4) using the concept of the local second-order velocity structure function of the filtered field. The surface roughness model adopted in this work is based on the fundamentals of turbulence modelling, as will be discussed in the next subsection.
# Mechanical Analysis of Ground Sleeve
## 5 Mechanical Analysis of Grouted Sleeve Lapping Connector  
### Bond Stress Distribution between the Bar and Grout  
Figure 14 shows the mechanical equilibrium of the inserted bar in a grouted sleeve lapping connector. The resultant bond stress acting on the inserted bar by the grout from section A to B is denoted as \(F_{b}^{AB}\), and the resultant bond stress acting on the inserted bar by the grout from section B to C is denoted as \(F_{b}^{BC}\). Neglecting the effect of the gap opening displacement [43], it follows that:  
\[F_{b}^{AB}=P-F_{5} \tag{6}\]  
\[F_{b}^{BC}=F_{5} \tag{7}\]  
where \(F_{5}\) is the tensile forces of the reinforcement bar, which was calculated by using the strains measured by SG5.  
Figure 13: Force equilibrium at the middle of the grout in a grouted splice connector.  
Figure 15 illustrates the ratios of \(F_{b}^{AB}\) and \(F_{b}^{BC}\) to loads of 25 kN, 50 kN, and the ultimate load (\(P_{u}\)). As we can see from Figure 14, for all of the specimens under loads of 25 kN and 50 kN, the ratios of \(F_{b}^{AB}\) to the respective load were larger than 50%, whereas the ratios of \(F_{b}^{BC}\) to the respective load were smaller than 50%, which was in accord with the bond stress distribution variation law of the bond pull-out test [44] (as shown in Figure 16a,b). Even a very small load caused some slip, and a high bond stress developed near the loaded end. However, the upper part of the bar was left completely unstressed, as shown in Figure 16b. When a higher load was applied, the slip at the loaded end increased, and both the high bond stress and slip extended deeper into the specimen.  
At the ultimate load, the ratios of \(F_{b}^{AB}\) and \(F_{b}^{BC}\) to the respective load were generally near 50%. This result can be explained by Figure 16b. When the slip almost reached the unloaded end, and the maximum resistance had nearly been reached, the bond stress close to the unloaded end was almost equal to the loaded end, which resulted in \(F_{b}^{AB}\) being nearly equal to \(F_{b}^{BC}\).  
It can be concluded that the bond stress distribution around the inserted bar in the grouted sleeve lapping connector was similar to that around a single bar anchored in concrete.
# Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack
## 4 Experiments
### Simulated Experiments
#### 4.1.3 Performance of Detection Algorithm Based on Multi-Layer CPSFS  
In this part, we carry out feature detection using \(L\)-layer CPSFS on simulated raw images, in order to test the performance of the proposed method in scenes with different depth ranges. Specifically, different plane targets are placed perpendicular to the optical axis successively, so that the targets will form multiple imaging planes with plenoptic disc radius range \(\{R_{gt}|R_{gt}=-2-i,i=1,2\ldots,8\}\). We carry out feature detection with \(L\)-layer CPSFS and observe the change of detection results with regard to the change of \(R_{gt}\).  
For each value of \(R_{gt}\), 20 independent feature detection experiments are conducted on 20 simulated raw images with different textures. In order to reflect the relation between the number of layer in CPSFS and the detected results, we change the value of \(L\) and repeat the experiment 3 times. The specific value of \(L\) is chosen from \(\{2,4,8\}\) and the corresponding plenoptic disc radii of the refocus planes used in CPSFS are \(\{-3,-4\}\), \(\{-3,-4,-5,-6\}\), and \(\{-3,-4,-5,-6,-7,-8,-9,-10\}\), respectively. Note that \(d=1/2,\theta_{R}=5\), during the experiment.  
The detected results are shown in Figure 10. As one can see, the more layers used in CPSFS, the better the detection results, but the calculation time will increase as well. In practice, we can first determine the approximate range of the plenoptic disc radius according to the working range of the focused plenoptic camera. Then, the CPSFS can be layered accordingly. The detected results of the same raw image using 2-layer CPSFS and 8-layer CPSFS are illustrated in Figure 11.  
At this point, we can conclude that the proposed feature detection algorithm based on multi-layer CPSFS is precise, effective, and time-efficient. In the actual process, the calculation efficiency can be further improved by reasonably layering of CPSFS.  
Figure 9: Example of detected results on simulated raw image using proposed algorithm based on 2-layer CPSFS with \(R_{gt}=-4,\Delta R=2\). Note that different colors in figure are used to distinguish the features projected by diverse plenoptic disc data.
# Transitional SAX Representation for Knowledge Discovery for Time Series
## 4 Experiments
### Information Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{Lighting2} & \multicolumn{2}{c}{SpaceShuttle} & \multicolumn{2}{c}{ECG2} \\ \hline \(n\) & **SAX** & **SAX-TM** & **SAX** & **SAX-TM** & **SAX** & **SAX-TM** \\ \hline
16 & 0.00071 & 0.00757 & 0.00169 & 0.06058 & 0.00143 & 0.2883 \\
32 & 0.00047 & 0.00722 & 0.00128 & 0.05236 & 0.00060 & 0.2563 \\
64 & 0.00057 & 0.01095 & 0.00260 & 0.05413 & 0.01102 & 0.2491 \\
128 & 0.00129 & 0.01344 & 0.0 signal [20]. A higher value of information loss and a lower value of KL-divergence imply that the reconstruction preserves a large quantity of information while reducing complexity. Hence, we prefer a representation method with lower IEC. For intuitive understanding, we graphically compare SAX with the proposed method using one representative coffee dataset, providing only performance summaries for the others. Figure 4a shows the raw time series together with representations by SAX and SAX-TM for the coffee dataset. SAX and SAX-TM affordably follow up the shape of the raw signal. In Figure 4b, the information loss of SAX-TM is higher than that of SAX. This means SAX-TM lost much more information than SAX. However, the KL-divergence value of SAX-TM is lower than that of SAX, as shown in Figure 4c. The tendency is also preserved in the IEC scores shown in Figure 4d. We used twelve datasets in total, including the coffee one, to see the amount of useful information preserved in terms of the information loss, KL-divergence, IEC score, and 1 NN classification error. The comparative results between SAX and the proposed method are shown in Table 4, where \(N\) is the data length. We applied the same parameter settings for SAX as in [20] and applied the combination of parameters from Table 2 for SAX-TM. Figure 4: Performance comparison of SAX and the proposed method on the coffee dataset. Overall, in Table 4, information loss of SAX is lower than that of SAX-TM. That is, SAX loses smaller quantities of information than SAX-TM. However, the KL-divergence values of SAX-TM are mostly lower than those of SAX: the number of lower KL-divergence for SAX-TM (7/12) is larger than that for SAX (5/12). This tendency is preserved in the IEC score. Even the average IEC scores for SAX-TM are lower than those for SAX. That is, SAX-TM loses less useful information than SAX. Nevertheless, the 1 NN classification error of SAX-TM is considerably lower than that of SAX. By appending transitional information to the original SAX, we obtained substantial gains in accuracy.
## 3 Method
### PPO and SAC Algorithms
```
Input: \(\theta_{1},\theta_{2},\Phi\) \(\theta_{1}\leftarrow\theta_{1},\theta_{2}\leftarrow\theta_{2}\) \(\triangleright\) Initial parameters \(\mathrm{D}\leftarrow\Phi\) \(\triangleright\) Initialize target network weights for each iteration do \(\triangleright\) Initialize an empty replay pool for each environment step do \(\mathrm{a_{t}}\ \sim\ \pi_{\Phi}(\mathrm{a_{t}}\,|\mathrm{s_{t}})\) \(\triangleright\) Sample action from the policy \(\mathrm{s_{t+1}}\ \sim\ \mathrm{p(s_{t+1}}\,|\mathrm{s_{t}},\mathrm{a_{t}})\) \(\triangleright\) Sample transition from the environment \(\mathrm{D}\leftarrow\mathrm{D}\cup\{(\mathrm{s_{t}},\mathrm{a_{t}},\mathrm{r( s_{t}},\mathrm{a_{t}}),\mathrm{s_{t+1}})\}\) \(\triangleright\) Store the transition in the replay pool end for for each gradient step do \(\theta_{1}\leftarrow\theta_{1}-\lambda_{\mathrm{Q}}\widehat{\
abla_{\theta_{1 }}}\mathrm{I_{Q}}(\theta_{1})\) for \(i\in\{1,2\}\)\(\triangleright\) Update the Q-function parameters \(\mathrm{\phi_{i}}\ \leftarrow\phi_{1}-\lambda_{\mathrm{Q}}\widehat{\
abla_{\theta}} \mathrm{I_{\pi}}(\Phi)\) \(\triangleright\) Update policy weights \(\mathrm{\alpha}\leftarrow\alpha-\lambda_{\mathrm{Q}}\widehat{\
abla_{\mathrm{Q }}}\mathrm{J}(\mathrm{\alpha})\) \(\triangleright\) Adjust temperature \(\overline{\theta_{i}}\leftarrow_{\mathrm{r}}\theta_{i}+(1-\tau)\overline{ \theta_{i}}\) for \(i\in\{1,2\}\)\(\triangleright\) Update target network weights end for Output: \(\theta_{1},\theta_{2},\phi\)
```  
**Algorithm 2**Soft Actor\(-\)Critic  
This paper used the above PPO and SAC algorithms because digital football-simulation games include organic content that can produce various results according to the rules. To learn the rules of this content, learning algorithms that were compatible with the rules and policies were needed. This paper selected PPO and SAC as representative algorithms based on policy optimization. In addition, the reason for comparing the two algorithms is that they are policy-based algorithms, but there is a difference in learning methods between the two algorithms in that PPO is a general policy-optimization method while SAC is a greedy policy-based method, so significant results will possibly be derived.
# MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph
## 4 Experimental Results and Analysis
### Performance Comparison  
#### 4.2.1 Quantitative Evaluation  
To verify the effectiveness of the model, we compare MTGNet with nine trajectory prediction models. As shown in Table 1, MTGNet achieves the best performance in three evaluation metrics on the Argoverse 2.0 dataset, outperforming nine state-of-the-art multi-modal trajectory prediction methods.
## 2 System  
### System Configuration  
In this study, trajectory estimation is performed using a group of sensors: a triaxial acceleration sensor, triaxial angular rate sensor, and triaxial geomagnetic sensor. These sensors are commonly installed in mobile devices. This work is an extended and refined version of research initially presented at the IEEE International Conference on Consumer Electronics--Taiwan [20]. The findings presented here include additional analysis and expanded discussion that goes beyond the scope of the original conference presentation.  
In this study, PDR is divided into five steps: attitude estimation, step detection, step length estimation, step direction estimation, and position estimation. The estimation method is varied based on whether the subject is walking with a mobile device held in front of the user's body (hand-held walking) or walking while swinging their hands to the side (hand-swinging walking). Figure 1 shows an overview of the proposed system configuration. The alphabets in Figure 1 indicate the order of processing.  
The process flow in Figure 1 is shown below.  
* Acquire acceleration, angular velocity, and geomagnetic data from sensors
* Attitude estimation using acceleration, angular velocity, and geomagnetic data
* Perform step detection based on acceleration, angular velocity, and attitude estimation results
* Estimate step length based on acceleration, angular velocity, and step detection results
* Estimate step direction based on step detection and attitude estimation results
* Perform position estimation based on the results of step detection, step length estimation, and step direction estimation  
Development Environment  
The device used in this study was a Google Pixel 4 with Android version 12. Each estimation was performed by an application developed using Unity. The sampling frequency of each sensor was 60 Hz. While some terminals can be set to a sampling frequency higher than 60 Hz, due to the specification that Unity processes at each drawing timing, we set the sampling frequency to 60 Hz, which is a common frame rate. Noise and offsets are problematic during the actual estimation process using sensors; thus, it is necessary to adjust the obtained data before performing each estimation.
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
## 5 Conclusions  
In this paper, a 12-bit 1 GS/s pipelined ADC was developed, which was manufactured in a 40 nm CMOS process. The reasonable and improved working timing is provided to maximize the output-settling time of operational amplifier, which can make the op-amp output more complete. The clock generation circuit, along with DLL, is explored to generate accurate a duty of timing for each stage, which can avoid the 2-GHz external clock mentioned by previous studies on pipelined ADCs. Based on PQT, a four-port comparator is adopted for this ADC to obtain a bit error rate below \(10^{-15}\), less than that in a comparable lookahead pipelined ADC. The offset of the comparator is analyzed by equations and simulated by the Monte Carlo simulation. For calibration strategies to reduce the sensitivity to mismatches and errors, automatic calibration of the residual curve is explored, which can effectively to correct residual curve. Finally, this ADC is tested and measured to obtain relatively good performance. It exhibits an SNDR of 57.3 dB and an SFDR of 78.54 dB for a 227 MHz input signal at 1 GS/s under the acceptable FoM with the help of the proposed timing.  
Conceptualization, F.X., Z.L. and X.G.; methodology, F.X., Z.L. and X.G.; software, F.X., H.J. and X.G.; validation, F.X., X.G. and H.J.; formal analysis, F.X. and Z.L.; investigation, F.X. and X.G.; resources, [PERSON] and J.W.; data curation, H.J.; writing--original draft preparation, F.X.; writing--review and editing, H.J., J.W. and X.G.; visualization, D.W.; supervision, J.W. and D.W.; project administration, J.W. and D.W.; funding acquisition, D.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"Strategic Priority Research Program\" of the Chinese Academy of Sciences (Grant XDC07020400).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & [35] & [36] & [37] & [38] & [39] & **This Work** \\ \hline SFDR (dB) \({}^{*}\) & - & 73.1 & 75.51 & - & - & 78.54 \\ \hline Area (mm\({}^{2}\)) & 4 & 0.54 & 0.9 & 0.032 & 0.71 & 2.16 \\ \hline Power(mW) & 72 & 24.8 & 18.15 & 5.4 & 74 & 97.6 \\ \hline FoM (pi/step) & - & 45 & 27.71 & 22.2 & 339 & 172.9 \\ \hline \end{tabular} \({}^{*}\) Input frequency is around Nyquist frequency. FoM\({}_{\text{Walden}}=\text{power}/\left(\text{F}_{\text{S}}\times 2^{\text{ENOB}}\right)\).  
\end{table}
Table 2: _Cont._  
Figure 18: The ADC performance before and after calibration versus the input frequency. SNR/SFDR of this ADC versus frequency of the input signal with and without calibration at 1 GS/s.