esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Periodic Noise Attenuation Based on Sparse Representation Using a Noise Dictionary
## 2 Method
### Waveform Estimation by Stacking  
The time window, whose length is \(\widetilde{T}\), is chosen and is represented as \(\widetilde{s}_{i}\). The chosen time window \(\widetilde{s}_{i}\) is approximated as the noise waveform; however, it is affected by white Gaussian noise. A stacking method is widely used to improve the SNR of the seismic profile [19], because it weakens white Gaussian noise and emphasizes seismic waves. We stack the approximate waveforms for an accurate noise waveform:  
\[w_{j}=\sum_{i=1}^{n}\widetilde{s}_{i} \tag{5}\]  
where \(w_{j}\) is the noise waveform on the trace \(h_{j}\) and its length \(\widetilde{T}\) is equal to that of \(\widetilde{s}_{i}\). The waveforms of different traces are similar to the near traces \(h_{1},h_{2},\ldots,h_{m}\) when the traces are interfered with by the same noise source. Therefore, we can stack the similar waveforms \(w_{1},w_{2},\ldots,w_{m}\) along different traces:  
\[w=\sum_{j=1}^{m}w_{j} \tag{6}\]  
where \(w\) is the noise waveform estimated from the traces \(h_{1},h_{2},\ldots,h_{m}\). We note that waveforms \(w_{1},w_{2},\ldots,w_{m}\) obtained by Equation (5) are not in phase. Before stacking, waveforms \(w_{j}(j\
eq 1)\) need to be cyclically shifted to the same phase as \(w_{1}\) by scanning the shift length in an interval \(\left[0,1,\ldots,\widetilde{T}-1\right]\). The suitable shift length corresponds to the maximum correlation coefficient between \(w_{j}(j\
eq 1)\) and \(w_{1}\).
## 4 Conclusions and Future Work  
In this paper, we have shown a novel approach to train convolutional neural networks for cophasing segmented mirrors. Unlike other supervised learning approaches, it does not need the data to be labeled. The maximum of the PSF image of the intersection is used instead. The method is able to correct piston step values in the range \([-\lambda_{0}/2,+\lambda_{0}/2]\). The narrower the capture range, the faster the agent learns. This is why the method is more appropriate for piston fine tuning.  
This technique requires us to apply a circular mask centered at the junction of every three segments to obtain the PSF of that part of the wavefront. However, this optical setup aligned with the intersections is only needed during training. This means that once trained, the agent is able to correct piston misalignments in one single forward pass of the network by using the diffraction image alone.  
The accuracy attained in the predictions of the optical path difference between segments was \(rms=20.04\,\mathrm{nm}\) for a reference wavelength \(\lambda_{0}=700\,\mathrm{nm}\). This measurement at the wavefront suffices for the adaptive optics to be applied [22].  
Quantitative analysis on how seeing variations can influence the training of the RL agent will be treated in future work.  
Conceptualization, [PERSON]-R.; methodology, D.G.-R.; software, D.G.-R.; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON]-R.; resources, [PERSON] and [PERSON]; data  
Figure 5: Accuracy of actions at predicting ground truth piston discontinuities. Capture range of \([-\lambda_{0}/2,+\lambda_{0}/2]\) in continuous line and \([-\lambda_{0}/4,+\lambda_{0}/4]\) in dashed line. The RL agent does not predict the mse value itself, rather it learns how to minimize it through the optimization of the PSF. A horizontal line at \(rms=50\,\mathrm{nm}\) has been drawn. A system with an rms below that threshold is known to have a Strehl ratio of 0.8 for the given wavelength.  
curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by Wooptik, a spinoff company of the Universidad de La Laguna.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PSF & Point Spread Function \\ MDP & Markov Decision Process \\ CNN & Convolutional Neural Network \\ RL & Reinforcement Learning \\ \end{tabular}
# The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications
## 2 Antenna Design Challenges for Leo Satellite Communications
The known performances of the AD8340 and DAC8554 circuits, incorporated in the phase shifters used, have been taken into account and are available in the technical specifications of these circuits [27; 28]. As far as we know, it is the first time that a four-element, three-quarter wavelength phased antenna array system has been successfully realized with the intention of being optimizedfor implementation in LEO satellite reception systems. Regarding the hardware perspective of the proposed antenna array system, the conceptual approach was delineated by the distinctive features of the phased antenna system, including the integration of active hardware components, such as phase shifters and their controller, into a unified module. This allows serial communication with various computing systems (such as PCs, Raspberry Pi, or other SBCs) via a USB serial interface. These components are integrated onto a single solid PCB, mechanically fixed, electromagnetically shielded, and grounded. The signal adder is directly connected to the phase shifters via identical short-length professional connectors, which do not introduce additional attenuation or phase shifts, thus obviating the need for delay lines that could induce significant phase shifts. Furthermore, all hardware is positioned below the ground plane to minimize interference and external electromagnetic influences. The use of appropriate simulations and tests was essential to verify the actual performance of the proposed array.
## 4 Proposed Resource Allocation Scheme  
In this section the working principle and functional methodologies of our proposed system are presented in detail. As the mechanisms used in the _Request Prioritizer (RP)_ and the _QoE and Profit-aware Request Scheduler_ greatly control the system performance, we address these two components in the whole system to enhance the efficiency. Here, the _Request Prioritizer (RP)_ helps to identify the priority of the incoming service requests so that resources can be allocated for a request with higher priority first. The multi-constraint problem of resource allocation for the incoming service requests is formulated as a multi-objective non-linear programming (MONLP) optimization problem in the _QoE and Profit-aware Resource Allocator_. As this is an NP-Hard problem, two heuristic solutions are provided here considering a single objective at a time. Two algorithms called First Fit Satisfaction (FFS) algorithm focusing on the maximization of user QoE while maintaining the unit profit threshold and the First Fit Profit (FFP) algorithm targeting the maximization of service providers' profit while satisfying the QoS requirements are introduced here. As limited computational resources are available for heterogeneous incoming requests with various requirements, these resources are allocated with the help of several scheduling intervals in the whole system. To enhance the system performance, dynamic determination of scheduling intervals for resource allocation is also addressed here.
# A New Constrained State Estimation Method Based on Unscented Hoo Filtering
## 3 Simulation Results  
### A Reversible Reaction Example  
Consider the gas-phase, reversible reaction [22; 23; 24]  
\[2A\overset{k_{r}}{\rightarrow}B \tag{36}\]  
with stoichiometric matrix  
\[v=\left[\begin{array}{cc}-2&1\end{array}\right] \tag{37}\]  
and reaction rate  
\[r=k_{r}P_{A}^{2} \tag{38}\]  
It is assumed that the ideal gas law holds (high temperature, low pressure) and that the reaction occurs in a well-mixed, isothermal batch reactor [24]. From first principles, the model of the system can be discretized as follows:  
\[x_{1,k+1}=\frac{x_{1,k}}{1+2k_{r}\Delta tx_{1,k}},x_{2,k+1}=\frac{k_{r}\Delta tx _{1,k}}{1+2k_{r}\Delta tx_{1,k}} \tag{39}\]  
The state vector is defined as:  
\[x=\left[P_{A}\;P_{B}\right]^{T}=\left[x_{1}\;x_{2}\right]^{T} \tag{40}\]  
where \(P_{A}\), \(P_{B}\) are the partial pressures. \(k_{r}\) represents the rate constant for reaction. The measurement vector is defined as:  
\[y=[1\;1]x \tag{41}\]  
The UHF algorithm is estimated under the constraint state, and the initial values are set as follows [23]:  
\[x_{0}=\left[\begin{array}{cc}3&1\end{array}\right]^{T} \tag{42}\]  
\[\Delta t=t_{k+1}-t_{k}=0.1 \tag{43}\]  
\[k_{r}=0.16 \tag{44}\]  
\[P_{x_{0}}=\left[\begin{array}{cc}36&0\\ 0&36\end{array}\right] \tag{45}\]  
\[Q_{k}=10^{-6}\left[\begin{array}{cc}1&0\\ 0&1\end{array}\right] \tag{46}\]  
\[R_{k}=10^{-2}\left[\begin{array}{cc}1&0\\ 0&1\end{array}\right] \tag{47}\]  
The lower bound of the state is set to \(\left[\begin{array}{cc}0&0\end{array}\right]^{T}\), the upper bound of the state is set to \(\left[\begin{array}{cc}\infty&\infty\end{array}\right]^{T}\), and the initial state is set to \(\hat{x}_{0}=\left[\begin{array}{cc}0.1&4.5\end{array}\right]^{T}\).  
Note that the partial pressures \(P_{A}\), \(P_{B}\) may allow negative concentration mathematically, whilst this is not physically possible, so we set the lower bounds of \([0,0]^{T}\) for them. On the other hand, the partial pressures can be as large as possible in real applications, so we just set the upper bounds of \([\infty,\infty]^{T}\) for them, which presents no problems because they can converge to their true values quickly if a proper algorithm is utilized. The state of the update is evaluated at each iteration to satisfy the above constraints.  
The estimation results for the unconstrained UHF and the constrained UHF are shown in Figure 2(a), respectively. It can be seen from Figure 2(a) that the unconstrained UHF estimates become negative(meaning negative concentrations), which is physically impossible. The UHF with a constrained projection method can obtain more accurate estimation results. All the constrained UHF estimated values are positive. From Figure 3b, it clear that it takes a longer time for the unconstrained estimate to converge. The constrained UHF converges faster than the unconstrained UHF, and it can converge to the real state at t = 1 s. A summarization of the unconstrained UHF and the constrained UHF are given in Table 1. The results from this example confirm that the proposed constraint handling method in this paper is effective.
## 5 Experimental results and discussion
### Convolutional neural network hyperparameter optimization experiments
#### 5.2.2 Convolutional neural network hyperparameter optimization results and analysis
However, according to the simulation results, we concluded that EE-TGA-CNN and \(E^{3}FA\)-CNN managed to establish the best CNN accuracy after only 15 iterations (please refer to Figure 8); in this case, the computational burden was 129 evaluation function calls (\(8+8\cdot 15+1\)). The methods proposed in this paper had a higher computational burden than the state-of-the-art algorithms presented in [18] and significantly lower computational costs than the evolutionary approaches shown in [17]. As shown above, the proposed metaheuristics were tested only for an MNIST dataset. Moreover, the simulations with different control parameter adjustments could also be performed to further establish the validity and efficiency of the proposed approaches and potentially to obtain better results. This represents one of the most significant threats and limitations of the proposed research. Since  
Figure 9: Comparative analysis with other swarm intelligence approaches for the accuracy. the simulations took a long time to execute, we could not encompass more datasets in one set of the simulations, and we plan do this in some of our future research works in this domain.
# Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
## 7 Conclusions  
In this article, the Wi-SUN channel approximations with multi-data rates were investigated for the residential area following the IEEE 802.15.4g Wi-SUN standard. The received signal strength distribution approximation for the IEEE 802.15.4g Wi-SUN smart grid networks was investigated by Rician distribution curve fitting, and then further improved the accuracy of approximation by the biased function. For all aliases, RMSE parameters of the biased approximation in Table 5 are reduced from the conventional approximation. For instance, on average, RMSE parameters were decreased by about 3.827% and 2.788% in 2440 and 443 MHz frequency bands, respectively. In addition, the K-factor (dB) in the Rician distribution, which represents the ratio of the power contribution by the line-of-sight (LOS) path to the remaining multipaths, in the biased approximation is decreased in all aliases; therefore, it sufficiently shows that the biased Wi-SUN channel model is more accurate than that of the conventional channel model. Hence, the experimental results and channel model could be applied to the Wi-SUN channel simulation for advanced metering infrastructure (AMI) last-mile network planning in smart grids. In future work, the authors will investigate the other biased functions for a better accuracy improvement, and also investigate the utilization of the investigated channel modelling in the smart meter network planning.  
Conceptualization, N.T. and C.P.; methodology, N.T. and C.P.; software, N.T. and C.P.; validation, N.T. and C.P.; formal analysis, N.T. and C.P.; investigation, N.T. and C.P.; resources, N.T. and C.P.; data curation, N.T. and C.P.; writing--original draft preparation, N.T.; writing--review and editing, C.P.; visualization, C.P.; supervision, C.P.; project administration, C.P.; funding acquisition, C.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by King Mongkut's University of Technology North Bangkok--contract no. KMUTNB-61-PHD-007.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: ([[https://github.com/deepped/datasets-Wi-SUNChannelModel-energies2022](https://github.com/deepped/datasets-Wi-SUNChannelModel-energies2022)]([https://github.com/deepped/datasets-Wi-SUNChannelModel-energies2022](https://github.com/deepped/datasets-Wi-SUNChannelModel-energies2022)), 17 February 2022).  
The authors declare no conflict of interest.
# Atomic Layer Deposition (ALD) of Metal Gates for CMOS
## 1 Introduction
### Metal Gate and Effective Work Function (EWF)
Aluminum metal has a work function around 4.28 eV and good conductivity, and thus is a candidate of n-type metal gate. Its melting point, however, is quite low (\(\sim\)660 \({}^{\circ}\)C), implying a poor thermal stability. Alloying Al into more refractory metal to form alloy was one of the research directions [43]. Doping Al into PVD TiN, for example, can shift the _EWF_ from the value near the bottom of the Si conduction band to that near the top of the valence band [44] and get different flatband voltages in TiN(Al)/HfO\({}_{2}\)/Si stack. In 2010, [PERSON] et al. reported a work function screening result for gate last process and proposed the combination of PVD TiAl for n-type metal with PVD TiN for p-type metal. The PVD TiAl and PVD TiN in gate stacks with HfO\({}_{2}\) show _EWF_ around 4.1 eV and 4.85 eV. The C-V measurements show that the PMOS using HfO\({}_{2}\)/PVD-TiN and the NMOS using HfO\({}_{2}\)/PVD-TiAl have 1 volt separation in \(V_{\beta}\)[45]. Intel seemed to use this combination for their planar CMOS integration, and got working high-performance devices with large gate leakage current reduction [25].
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 5 Software Simulation
### Validation of Minimum Tracking Error  
In order to verify the validity of the adaptive adjustments of bandwidth and integration time, the simulation result of the phase tracking error of discriminator output is compared with the theoretical minimum tracking error value. Noise is added in previous GPS IF simulation data after 15 s with a rate of 1.6 dB/s to attenuate \(C/N_{0}\), and the initial \(C/N_{0}\) is set as 49.2 dB-Hz. The corresponding phase tracking errors of discriminator output are shown in Figure 9, and the theoretical value is calculated by Equation (34).  
It can be seen from the Figure 9 that the phase tracking error increases with the \(C/N_{0}\) attenuation. The phase tracking error of discriminator output is almost consistent with the theoretical minimum tracking error. It demonstrates that the adaptive INS-aided PLL tracking method can achieve the minimum tracking error for different \(C/N_{0}\) and vehicle dynamic condition by choosing the optimal bandwidth and integration time effectively.
## 1 Introduction
During the interviews, the results of the ticket categorization and the ticket visualization were shared because they show the lifetime and keywords of ticket categories as well as the relationships between categories. Interviewees were asked if our method helps comprehend the project's features. * Does our method improve project comprehension? To answer this question, an experiment compared the comprehension of the target tasks using our method and a traditional method. The target tasks in the experiment were the same as those in the case study. Subjects were asked to perform four tasks: two comprehension tasks for activeness of ticket categories and two comprehension tasks for activeness of features. We divided the 28 subjects into two groups (i.e., the experimental group and the control group) to compare subjects' comprehension correctness with and without the support of our method. * For which tasks is our method effective? To answer this question, we compared the statistical test results of all four tasks. Then we identified tasks with the most significant difference between the two groups. The rest of this paper is organized as follows. Section 2 summarizes related works. Section 3 presents our ticket categorization and visualization methods. Section 4 shows the results of the case study. Section 5 describes the results of the comparative experiment. Section 6 explains the limitations and use cases of our method. Section 7 concludes this paper and provides a future direction. This paper substantially extends our preliminary 6-pages workshop paper presented at IWESEP 2019 [11]. The proposed method to automatically visualize the implemented features and research questions RQ1-RQ2 have been taken from the preliminary paper. This paper added use cases and a comparative experiment to answer two new research questions RQ3-RQ4. Limitations and threats to validity, related works, and explanations of the proposed method are also substantially extended in a well-structured paper format [12].
## 3 Results
### Uncertainty Analysis under Different Noise Conditions
#### 3.2.2 Delta K  
The ROC curves of \(\Delta K\) are shown in Figure 11. This parameter performs well when the noise is low (\(-20\) dB), as shown in Figure 11a. The ROC curve is close to the top left corner, which indicates that the prediction reaches a high performance. The TPR is approximately 0.6 even when the water saturation is high (98% and 99%). When the noise is \(-10\) dB, the performance of ROC is worse, as shown in Figure 11b. The lowest TPR is 0.75 when the fluid is saturated hydrocarbon, whereas the TPR values range from 0.4 to 0.6 when the water saturation is higher than 80%. The lowest TPR reduces to 0.6 in the \(-7\) dB noise situation (shown in Figure 11c) when the fluid is saturated hydrocarbon. For cases of high water saturation, the TPR is only 0.2 to 0.4. The evaluation metrics are plotted in Figure 11. The parameters are close to one when the water saturation is low, and the noise is \(-20\) dB, as shown in Figure 11d. The results illustrate that the \(\Delta K\) parameter has good performance when the noise is low. In the scenario where the noise is \(-10\) dB, as shown in Figure 11e, the parameters reduce to different degrees. The precision remains above 0.9, whereas the others range mainly between 0.7 and 0.9. When the noise is \(-7\) dB, the parameters reduce further, as shown in Figure 11f. The precision is between 0.85 and 0.9, whereas the others range from 0.65 to 0.75.  
The comparison of the AUC curves in the scenarios where the noise is \(-20\) dB, \(-10\) dB and \(-7\) dB is shown in Figure 12. The performance of \(\Delta K\) decreases with increasing noise. The approximate ranges of the AUC are 0.97 to 1, 0.8 to 0.9 and 0.75 to 0.8 when the noise is \(-20\) dB (blue), \(-10\) dB (red) and \(-7\) dB (yellow), respectively.  
Figure 10: The AUC curves of the FF\({}_{\text{new}}\) in the different noise scenarios: \(-20\) dB, \(-10\) dB and \(-7\) dB, respectively.
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 7 Conclusions  
This paper provides an FH-DOWR method to improve the ranging and time synchronization accuracy between satellites. To ensure the continuity of carrier phase measurement values as much as possible, we adopt distance and speed extrapolation to extrapolate the tracking loops of multiple frequency points. In this case, the signal can remain in a tracking state for a long time without going through the acquisition process, which effectively improves the tracking stable time and loop stability. In addition, polynomial fitting is applied to the carrier phase measurement results to reduce carrier phase measurement errors caused by frequency hopping.  
The FH-DOWR system provides coarse ranging results based on PN codes and removes larger carrier phase integer ambiguity. Then, the integer ambiguity resolution method is used to provide carrier phase ranging results without integer ambiguity. Finally, at a CNR of 40 dB-Hz, the ranging error in the Ka frequency band is better than 0.247 mm. As the CNR increases, the measurement accuracy further improves. The FH-DOWR model achieves a high measurement accuracy 21.7 \(\upmu\)m at 70 dB-Hz with a 95% probability of successful deblurring. The PN code ranging accuracy at 70 dB-Hz is 3.7 mm, which is an improvement of two orders of magnitude. Although the FH-DOWR method has high ranging accuracy, reaching the micrometer level, it requires additional hardware resources for multiple frequency tracking. Therefore, we will further increase resource consumption in the future. In addition, we will increase the frequency points of FH-DOWR signals and analyze their anti-interference performance. We will also comprehensively consider the impact of increasing frequency points on ranging performance.  
Conceptualization, [PERSON] and H.W.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and H.W.; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and H.W. visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2022 YFB3904402).  
Data Availability Statement: Data are contained within the article.