esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Peak-to-average power ratio (PAPR) reduction in multiplexed signals in orthogonal frequency division multiplexing (OFDM) systems has been a long-standing critical issue. Clipping and filtering (CF) techniques offer good performance in terms of PAPR reduction at the expense of a relatively high computational cost that is inherent in the repeated application of fast Fourier transform (FFT) operations. The ever-increasing demand for low-latency operation calls for the development of low-complexity novel solutions to the PAPR problem. To address this issue while providing an enhanced PAPR reduction performance, we propose a synchronous neural network (NN)-based solution to achieve PAPR reduction performance exceeding the limits of conventional CF schemes with lower computational complexity. The proposed scheme trains a neural network module using hybrid collections of samples from multiple OFDM symbols to arrive at a signal mapping with desirable characteristics. The benchmark NN-based approach provides a comparable performance to conventional CF. However, it can underfit or overfit due to its asynchronous nature which leads to increased out-of-band (OoB) radiations, and deteriorating bit error rate (BER) performance for high-order modulations. Simulations' results demonstrate the effectiveness of the proposed scheme in terms of the achieved cubic metric (CM), BER, and OoB emissions.  
clipping and filtering; cubic metric; neural networks; OFDM Article  
1  
[PERSON]; [PERSON], [PERSON].  
OFDM PAPR Reduction via Time-Domain Scattered Sampling and Hybrid Batch Training of Synchronous Neural Networks  
2021  
13 July 2021  
16 July 2021
## 8 Conclusions  
In this study, we verified the appropriateness of a real-time flow-rate estimation method using the DKFs in a tilting-ladle-type automatic pouring machine. The design of this flow-rate estimation method was described in detail. This method was implemented using a laboratory-type automatic pouring machine and experimentally compared with two other flow-rate estimation methods. In the estimation using the derivative of load cell data, we were unable to recognize the flow rate due to large amounts of noise. In the estimation using a visible camera, we were barely able to recognize the flow rate. The noise in the flow rate estimated by DKFs was smallest among the approaches performed in this study. However, it was confirmed that the estimated precision of this method may be degraded by disturbances, such as uncertainty about model parameters. Although the estimated precision can be improved with increasing the covariance of process noise in the liquid height on the pouring mouth in DKFs, the amount of noise in the estimated flow rate also is increased. Accordingly, we consider flow-rate estimation by DKFs to be appropriate for estimating flow rate in a pouring motion with small disturbances. In the practical applications, it might be difficult to maintain the pouring condition with small disturbances because of varying temperature of the molten metal in the ladle and attaching the slag to the ladle. Therefore, we plan to integrate the online model parameters identification to the flow rate estimation system.  
In this study, the sampling interval in the load cell data acquisition was limited to 0.02 s by the specification of the load cell amplifier with the serial communication. The noise in the load cell data might be reduced by the amplifier with a fast sampling rate and the higher order filter. In future work, we will reconstruct the sensing system in the automatic pouring machine for improving the accuracy of the flow rate estimation. Furthermore, we will try to develop the high-precision and robust state estimation system of the automatic pouring machine using the sensor fusion approach such as the integration of the load cell and visible camera.  
Conceptualization, Y.N.; Methodology, Y.N. and Y.S.; Software, Y.N. and Y.S.; Validation, Y.S.; Formal analysis, Y.S.; Investigation, Y.S.; Resources, Y.N.; Data curation, Y.S.; Writing--original draft preparation, Y.S.; Writing--review and editing, Y.N.; Visualization, Y.S.; Supervision, Y.N.; Project administration, Y.N.; Funding acquisition, Y.N. Both authors have read and agreed to the published version of the manuscript.  
This research was funded by JSPS KAKENHI Grant No. JP25420181.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CFD & Computational Fluid Dynamics \\ SSKF & Steady State Kalman Filter \\ EKF & Extended Kalman Filter \\ DKFs & Decentralization of Kalman Filters \\ DC & Direct Current \\ IAE & Integral Absolute Error \\ TV & Total Variation \\ mTV & Modified Total Variation \\ \end{tabular}
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
## 5 Analysis of Influencing Factors
### New Tunnel Depth  
Take the new tunnel burial depths of 10 m, 12 m, 14 m, 16 m, and 18 m, respectively, for the calculation of the working conditions, which can be obtained under different new tunnel burial depths of the existing tunnel settlement and deformation distribution law, as shown in Figure 12a. As the depth increases, the width of the existing tunnel settlement groove increases and the settlement value gradually decreases. The change curve of the maximum settlement value of each sinkhole curve must be analyzed in relation to the burial depth of the new tunnel, as illustrated in Figure 12b. It is observed that as the burial depth of the new tunnel increases, the maximum settlement value of the existing tunnel decreases. Furthermore, when the burial depth exceeds 14 m, the degree of influence experiences a gradual decline, which occurs when the clear distance between two tunnels is greater than 6 m. This is referred to as the one-dimensional (1D) case. It has been demonstrated that, as the distance between two tunnels is increased, the ground deforestation caused by tunnel excavation is gradually transferred to the ground layer in which the existing tunnel is located. This process also results in a reduction of the induced settlement deformation of the existing tunnel.  
Figure 11: Comparison of settlement values of existing tunnels [29].
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Experimental Procedure
#### 4.4.6 Real-World Testing and Prediction Latency  
The neural network model was tested on real-world shot data collected from live badminton games and training sessions. The dataset consisted of shot attempts performed under actual playing conditions, using mobile eye-tracking devices (Tobii Pro Glasses 2, 100 Hz) and motion capture systems (Vicon Motion Systems, 200 Hz) for data collection. This approach ensured that the shot data accurately represented in-game dynamics, with a mix of smashes, drops, and clears executed by both amateur and elite players.  
Here is how we assessed our model. First, we divided our data into two buckets: training and validation. Then, we added new data to check how good the model was at generalization. The model performed well, showing high accuracy across all shot types, which suggests it is suitable for practical use in badminton. We also checked the prediction times to determine whether we could use it in real-time. From the moment QE and biomechanical data were input into the model to the output of shot accuracy predictions, the average prediction time was approximately 150 milliseconds. This speed meant that the model can provide near real-time predictions, which is very important for live performance tracking.
## 4 Conclusions  
Abnormal Al and Cu element segregation has been discovered in the Al-Cu-Mg-(Zn) MG layers through the PED treatment. Surface element evaporation has been found and proven to be the main reason for such significant surface element segregation. Beneficial to the evaporation of the non-corrosion resisting elements in the MG layers, the average corrosion rates of Al-Cu-Mg-Zn alloys with MG layers were reduced to about 2% of that without MG layers. Additionally, the surface hardness of Al-Cu-Mg-Zn alloys with MG layers was enhanced from 90.086 (\(\pm\)2) HV\({}_{0.1}\) to 537.173 (\(\pm\)7) HV\({}_{0.1}\). As a result, PED treatment provides us a possible way for the element proportion and distribution control in wear- and corrosion-resistant Al-based MG layers.  
Conceptualization, H.D.; Methodology, J.S.; Validation, X.Y.; Investigation, J.Z.; Resources, Z.L. and J.C.; Data Curation, H.F.; Writing-Original Draft Preparation, H.D.; Writing-Review & Editing, H.D.; Supervision, X.Y.; Project Administration, H.D. and X.Y. This research were funded by (A Project of Shandong Province Higher Educational Science and Technology Program) grant number (J17 KA043, J17 KB076); (Natural science foundation of Shandong province, China) grant number (ZR2017 PEM005). This research was support by Caiqiong Li for logistical services.  
The authors declare no conflict of interest.  
Figure 5: (**a**) Hydrochloric acid (HCl) corrosion of the Al-Cu-Mg-Zn alloy with and without an MG layer; microhardness of the Al-Cu-Mg-Zn alloy without (**b**) and with (**c**) MG layers; (**d**) weightlessness of the Al-Cu-Mg-Zn alloy with and without MG layers in 10 vol % HCl solution.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 2 Related Work
Nodes have been assigned different categories based on their functionalities. The head node is selected, and it is responsible for gathering data from the neighboring nodes. Head nodes are distributed over the network, and they collect the data from respective neighboring nodes and forward the data to an AUV, which is taking adata-gathering tour. The selection criteria of the head node and its placement are not discussed, which leads to non-uniform energy consumption. The rapid depletion of energy leads to decreased NLT. In [5], the authors used an AUV working as an MS. It effectively saves energy by reducing the transmission range of nodes. The tour points are predefined for data gathering from the network. Due to drastic environmental conditions, nodes are mobile and randomly changing their positions; therefore, the authors called them the probabilistic neighborhood of the AUV stop. The AUV finds these probabilistic neighborhoods of its tour stops for a specific time period, and this is called the probe interval. In the next step, the AUV identifies the nodes and creates a communication schedule for these nodes. The AUV waits at each stop until all data are collected from the neighbors. As the neighbors are changing due to the probabilistic nature, nodes consume non-uniform energy, and some nodes deplete more quickly; also, the end-to-end delay of the scheme increases.
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
## 5 Performance Evaluation
### Simulation Study  
Simulation experiments were conducted to increase the number of UAVs constituting the UAV network and to implement the scenarios in which the network topology changes through mobility. In the simulation experiments, we verified the performance of \(ASRAN\) through network simulator NS3. Figure 7 shows the simulation topology. The Operating_UAV transmits data to the GCS through the UAV network. The UAV network constructed the transmission path based on the DSDV routing algorithm. During data transmission, Routing_UAV 2 and Routing_UAV 3 exchanged their position. In this environment, we compared the throughput of \(ASRAN\) and the most commonly used TCP algorithm, TCP-NewReno. The average transmission rates measured in this scenario are shown in Table 3. As shown in Table 3, the \(ASRAN\) has a higher throughput than the TCP-NewReno. Figure 8 shows throughput fluctuations of typical simulation experiments. Especially in Figure 8b, \(ASRAN\) and TCP-NewReno show the same disconnect time. This is because both experiments use the same routing algorithm and the routing algorithm changes the transmission path at approximately the same time after detecting the UAV2 mobility. After the transmission is disconnected, the transmission path was recovered via the UAV 4 and the transmission was resumed. Even in this environment, \(ASRAN\) showed higher average throughput. Also, Jain's fairness index was 0.996, indicating that the \(ASRAN\) generally maintained fairness. As a result, \(ASRAN\) has overall better performance than TCP-NewReno, even in environments where data is transmitted over unstable UAV networks due to mobility.  
Figure 6: Throughput comparison of simultaneous experiment in heavy traffic environment.  
Figure 7: Simulation topology.
# Eigenmode Wavefront Decoupling Algorithm for LC-DM Adaptive Optics Systems
## 2 Principle of the LC-DM AO System
### Working Principle  
The incident interfered beam obtained using a turbulence simulator is a broad spectrum beam of the 400-1700 nm waveband. The TTM and DM are situated at the front end of the optical path and are connected in series with the WFS to form the closed-loop control system. The LC is situated in the rear of the optical path and the WFS. The LC optical path works in the open-loop control system. First, the full wavelength incident beam passes through the TTM to correct the tilt aberration, and the DM is used to correct the low-order aberration to eliminate the wavefront aberration in the 950-1700 nm infrared waveband. The 950 nm beam is reflected through the beam splitter and enters the infrared imaging system. The transmitted beam is split by the 700 nm Polarized Beam Splitter (PBS), and after correction through the DM, the 400-700 nm band beam with the most comprehensive  
Figure 1: Block diagram of the LC–DM AO system.  
aberration information enters the WFS for residual aberration measurements. The low-order distortion residuals and high-order distortion aberrations are decomposed to the DM and LC, respectively, in the front and rear optical paths, respectively. In addition, the short wavelength band of 700-950 nm enters the LC, and the remaining high-order aberrations are corrected according to the feed-forward signal of the WFS. Finally, diffraction-limited high-resolution imaging is obtained in a visible camera.
# A New Click-Through Rates Prediction Model Based on Deep&Cross Network
## 4 Experimental Results  
### The Datasets  
The proposed FO-FTRL-DCN mode for CTR prediction was evaluated on the famous benchmark of iPinYou [[https://contest.ipinyou.com/](https://contest.ipinyou.com/)]([https://contest.ipinyou.com/](https://contest.ipinyou.com/)). We have carried out a large number of comparative studies and analysis. The iPinyou dataset consists of real advertising logs released by iPinYou company in 2013, which includes four types of log records: bidding, exposure opportunity, click, and conversion. Among them, exposure opportunity and click log can be used for CTR prediction. In the empirical studies, each data record is a corresponding exposure record, including the features, such as the users' own information, the relevant information of the advertising contexts and the advertising, and the final click data. In these experiments, the exposure samples of the first 7 days were used as the training set, and the exposure samples of the next 3 days were used as the test set. In the experimental settings, we use the advertising and click logs of four advertisers: 1458, 3358, 3386, and 3427 in the iPinYou data to establish four datasets of advertising and click, respectively. Table 1 shows the sample statistics of these four datasets.
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 6 Filter Selection  
The filter architecture uses four resource sharing edge filters that operate simultaneously to filter the edges of two 8 \(\times\) 8 blocks of pixels. Each 8 \(\times\) 8 block's filtering decision is made in accordance with the \(\beta\), t\({}_{\text{c}}\) parameters and in accordance with the threshold values of the two adjacent pixel blocks. The following criteria are employed for selecting the type of filter:  
1. If the BS value is zero, then no filtering is performed.
2. If the BS value is nonzero, then Equations (4)-(8) are computed; and if Equation (8) is not satisfied, then no filtering is performed. \[dp0=abs(p_{2,0}-2p_{1,0}+p_{0,0})\] (4)  
Figure 12: Flow diagram to perform quality assessment.  
\[dp3 = abs(p_{2,3}-2p_{1,3}+p_{0,3}) \tag{5}\] \[dq0 = abs(q_{2,0}-2q_{1,0}+q_{0,0})\] (6) \[dq3 = abs(q_{2,3}-2q_{1,3}+q_{0,3})\] (7) \[dp0+dp3+dq0+dq3<\ \beta \tag{8}\]
3. If Equation (8) is satisfied then Equations (9)-(16) are computed to decide a strong or weak filter. If Equations (11)-(16) are satisfied, then a strong filter is used. \[dpq0 = dp0+dq0\] (9) \[dpq3 = dp3+dq3\] (10) \[dpq0 <\beta/8\] (11) \[dpq3 <\beta/8\] (12) \[abs(p_{3,0}-p_{0,0})+abs(q_{0,0}-q_{3,0})<\ \beta/8\] (13) \[abs(p_{3,3}-p_{0,3})+abs(q_{0,3}-q_{3,3})<\ \beta/8\] (14) \[abs(p_{0,0}-q_{0,0}) < 2.5t_{c}\] (15) \[abs(p_{0,3}-q_{0,3}) < 2.5t_{c}\] (16)
4. If any one of Equations (11)-(16) are not satisfied, then Equations (17)-(20) are computed and if Equation (20) is satisfied, a weak filter is used. \[dp0+dp3 < \beta/16\] (17) \[dq0+dq3 < \beta/16\] (18) \[\delta_{0} =\ 9(q_{0,0}-\ p_{0,0})-3(q_{0,1}-\ p_{0,1})\gg 4\] (19) \[abs(\delta_{0}) < 10t_{c}\] (20)
5. If Equation (20) is not satisfied then the filtering process is skipped (no filter).
# FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
## 3 Experimental Results and Analysis
### Execution Time  
Compared with other existing techniques. The simulation result shows that it takes about 1.231 ms to detect edges of the image at an operating frequency of 50 MHz, which is sufficient for real-time applications.  
As far as we know, the existing FPGA-based approaches implemented the Canny algorithm, and only the computation time for detecting edges is presented in papers [6; 7; 8; 9]. The  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Edge Detection Methods** & **Image Size** & **FPGA Device** & **Used Slices** & \begin{tabular}{c} **Memory Used** \\ **(KB)** \\ \end{tabular} & \begin{tabular}{c} **Max Freq.** \\ **(MHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Time (ms)** \\ \end{tabular} &
\begin{tabular}{c} **Norm.** \\ **Time (ms)** \\ \end{tabular} \\ \hline
[6] & \(512\times 512\) & Xilinx Virtex-5 & 4553 & 192 & 292 & 0.57 & 3.338 \\
[7] & \(360\times 280\) & Altera Cyclone & - & - & 27 & 2.5 & 1.44 \\
[8] & \(512\times 512\) & Xilinx Virtex-5 & 23904 & 16184 & 250 & 0.15 & 0.774 \\
[9] & \(512\times 512\) & Xilinx Virtex-5 & 17928 & 278 & 272 & 0.13 & 0.54 \\ proposed & \(512\times 512\) & Altera Cyclone & 3303 & 261 & 56.7 & 1.231 & 1.231 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of resource utilization and computation time.  
Figure 21: Edge map: (**a**) original image; (**b**) Canny edge detection with a fixed threshold; (**c**) proposed Canny edge detection.  
computation time and resource utilization results of these Canny FPGA implementations and our Canny FPGA implementation are shown in Table 2. Since these approaches are implemented for different image sizes and operated at different clock frequencies, their results have to be normalized for a fair comparison. The rightmost column of Table 2 gives the normalized computing time, where the normalization is done with respect to an image of size 512 \(\times\) 512 and an FPGA clock frequency of 50 MHz. Although the execution time of this algorithm is twice that of reference [6], we use fewer logical resources. In addition, since reference [6] uses a fixed threshold, this paper adopts an adaptive threshold technology, so the detection effect in this paper is better than that in reference [6]. In comparison to the algorithm in this paper, the Canny edge detection algorithm based on block structure in [8; 9] substantially decreases detection time but greatly increases resources. As a result, the technique suggested in this research is better suited for platforms with limited hardware resources.  
Table 3 illustrates a run time comparison of GPGPU implementation and proposed FPGA implementation. Even though the proposed FPGA implementation uses adaptive threshold value and operates at a lower frequency, it is faster than GPGPU implementation. The GPU configuration details are given below.
## 2 Continuous Formulation of Topology Optimization Problem Statements and Sensitivities
### Regularization and Penalization  
Regarding this particular work and implementation, it is important to note two adopted considerations. The first one is using two penalization factors applied to the material stiffness and stress. These factors aim at making intermediate design solutions uncompetitive and, in turn, promoting black-and-white solutions.  
The material stiffness is penalized by the SIMP penalization parameter \(P=3.0\), in accordance with [80], when using continuous design variables (Equation (8)). In this implementation, this factor is also applied to the other material properties, as:  
\[\mathbb{C}(\rho)=\rho^{P}\mathbb{C}_{0} \tag{8}\]  
where \(\mathbb{C}_{0}\) is the material stiffness of a fully solid element. Note that \(\mathbb{C}^{\prime}(\rho)=P\rho^{P-1}\mathbb{C}_{0}\) for this type of stiffness penalization.  
The stress is also penalized according to a factor equal to \(\rho^{\beta}\), with \(\beta=\frac{1}{2}\), as adopted in [53], following the initial proposal by [PERSON] et al. [95] with the exponent suggested in [96]. This penalization leads to a non-physical stress for intermediate design densities, but not for black-and-white solutions, and tends towards 0 when the design density decreases, avoiding singularity problems [53]. Thus, \(\sigma_{a}\) is defined as the amplified stress, described by the following expression:  
\[\sigma_{a}(\rho)=\rho^{\beta}\sigma(\rho) \tag{9}\]  
with:  
\[\sigma(\rho)=\mathbb{C}_{0}\bigtriangledown^{s}u(\rho) \tag{10}\]  
where \(\sigma(\rho)\) is the stress vector, written in Voigt notation. Therefore, the von [PERSON] amplified stress norm is \(\sigma_{a}^{VM}=(\sigma_{a}M\sigma_{a})^{\frac{1}{2}}\), with \(M\) being the von Mises matrix operator.  
The second consideration is the use of a regularization approach, where removed or void elements will maintain a minimum design density, \(\rho_{min}=0.01\) by default, which contrasts with the complete element removal. In the literature, these regularization techniques may be referred to as \"soft-kill\" and \"hard-kill\" approaches [17]. The regularization approach was introduced in the code implementation to avoid the stiffness matrix becoming singular, as well as allowing a constant mesh in the finite-element model during the topology optimization loop [17; 53].