esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Data Results Related to the 1 st Set of Measurements  
By analyzing the data in the frequency range between 1.0 kHz and 10 kHz, different behavior in terms of insertion loss (IL) was found. In particular, Figure 7 shows the results related to configurations 1 to 9 of the acoustic barrier, specifically composed of three rows of type A diameter bars and having the addition of one to three rows of sticks of type B diameter.  
Figure 6 indicates a peak of IL, equal to 18 dB, in a specific frequency band (i.e., 5 kHz), obtained by adding a row of sticks having type B diameter (configuration 2). This behavior was not found by adding a second row of type B diameter sticks (configuration 3), for which a wider attenuation occurred between 5 kHz and 8 kHz, being around 13 dB and having a dissimilar amplitude.  
By having the acoustic barrier composed of four rows of type A diameter sticks (configuration 4), the maximum peak attenuation occurred at 4.0 kHz, equal to 19 dB. By  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Configuration**} & **No. of Rows** & \multirow{2}{*}{**D\({}_{\mathbf{A}}\) (mm)**} & **No. of Rows** & \multirow{2}{*}{**D\({}_{\mathbf{B}}\) (mm)**} & **dr (mm)** & **dr (mm)** & **dr (mm)** \\  & **(Type A)** & & **(Type B)** & & **D\({}_{\mathbf{B}}\) (mm)** & **\(\alpha\) (mm)** & **of Xs—** & **of Xs—** & **of Xs—** \\  & & & & & & **1 st Set** & **2 nd Set** & **3 rd Set** \\ \hline No. 1 & 3 & 15 & - & - & 30 & & & - \\ No. 2 & 3 & 15 & & 30 & & & - \\ No. 3 & & 15 & & 1 & 9 & 18 & & - \\ No. 4 & 4 & 15 & & 30 & & & 800 1 \\ No. 5 & 4 & 15 & & & 30 & & - \\ No.  
adding one row of sticks composed of type B diameter, the attenuation was lowered by 3 dB but it became wider, covering the frequencies between 4 kHz and 10 kHz. In configuration 6, the maximum peak attenuation at 4 kHz was slightly lower than configuration 5, equal to 15 dB at 4 kHz.  
When there were 5 rows of bars having type A diameter, a broadband attenuation was recorded at high frequencies (i.e., between 4.0 kHz and 10 kHz). The addition of one row of type B diameter sticks increased the attenuation proportionally to 1 dB, related to the frequency bands falling into the same range. Furthermore, the addition of the second row of type B diameter sticks highlighted a peak attenuation at 4.0 kHz, equal to 16 dB.  
Overall, an acoustic barrier composed of three rows of type A diameter elements achieved a sound attenuation of less than 10 dB. The addition of only a row of type B diameter sticks had the effect of pronouncing the IL at the 5.0 kHz frequency band, which was more accentuated with the addition of the second row of type B diameter sticks. The best sound attenuation in terms of IL was recorded with the configurations 5 and 9, equal to 16 dB at 4.0 kHz and having comparable values at the bands between 5.0 kHz and 10 kHz. On this basis, the effectiveness of a coupled sound barrier composed of different sizes of scatterer diameter was demonstrated; therefore, the IL covers a broader frequency band gap, having similar characteristics to vehicular road traffic noise.
## 3 Communication Performance  
### Djk16  
We start by describing the 2016 Danjiangkou (DJK16) lake experiment, in which we transmitted QPSK signals from one source to eight receivers. We conducted the DJK16 experiment in Danjiangkou Lake, Henan Province, China, in January 2016. The water depth was 45-50 m. The source was deployed at 20 m below the surface. The receiver array had eight elements that were uniformly  
Figure 3: Hybrid time frequency domain equalizer (HTFDE).  
Figure 2: Basic FDE receiver structure for the single carrier-system.  
spaced at 0.25 m apart and was deployed at depths between 18-20 m. The data were transmitted from the source using QPSK modulation with a carrier frequency of 6 kHz and a bandwidth of 4 kHz. The distances between transmitter and receivers ranged from 4.98 to 5.20 km. The longest distance between different transmitter locations was about 720 m, while the shortest was about 20 m.
## 3 Materials and Methods
### Deep Learning Methods  
As shown in Figure 1a,b, information only moves in one direction in FNN. This requires moving forward from the input node through the hidden node to the output node. There are no cycles or loops in the network. Figure 1a shows an example of a fully connected feedforward neural network with two hidden layers. \"Fully connected\" means that each node is connected to all nodes in the next hierarchy. This work addresses the binary classification problem of normal and pathological speech classification using FNN. In machine learning, classification is a supervised learning method that divides data samples into predefined groups using decision-making features [27]. This study uses two feed-forward layers. After the first layer, there is rectified linear unit (ReLU) activation and,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Diagnosis of Pathological Voices**} & \multicolumn{2}{c}{**Number of Samples**} \\ \cline{2-3}  & **Female** & **Male** \\ \hline Hyperfunctional dysphonia & 97 & 44 \\ Functional dysphonia & 51 & 33 \\ Laryngitis & 30 & 62 \\ Vocal fold polyp & 19 & 25 \\ Leukoglakia & 14 & 27 \\ Vocal fold cancer & 1 & 21 \\ Vocal nodule & 13 & 4 \\ Reinke edema & 27 & 7 \\ Hypofunctional dysphonia & 4 & 10 \\ Granuloma & 1 & 1 \\ GERD & 0 & 3 \\ Contact ulcers & 2 & 22 \\ Subtotal & 259 & 259 \\ Healthy voices & 259 & 259 \\ \hline Total & 518 & 518 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the voice samples used in the study.  
after the last layer, softmax activation occurs, as shown in Figure 1b [28]. The parameter values used are shown in Table 2.  
CNN is similar to a typical neural network, as shown in Figure 2. It consists of neurons with learnable weights and biases. Each neuron receives some inputs, performs a dot product, and optionally follows it with a non-linearity. The entire network represents one differentiable score function, i.e., from the raw image pixels at one end to the class scores at the other end. Finally, there are activation functions, such as softmax, in the fully connected layer [28, 29]. This work used two convolutional layers and two feed-forward layers. Dropout with a probability of 0.05 and batch normalization was applied to all layers except the feed-forward layers. Max-pooling and average-pooling operations were performed between the convolutional layers to downsample the intermediate representations over time, and to add some time invariance into the process. The details are provided in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Loss function & Tanh \\ Optimization algorithm & SGD + Momentum \\ Regularization & L2 \\ Mismatch propagation & BPTT \\ Minibatch size & 100 samples \\ Learning rate & 0.01 exponential attenuation \\ Loss function & Cross-entropy \\ Weights for samples & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the FNN.  
Figure 1: FNN architectures. (**a**) FNN with two hidden layers [27]; (**b**) FNN with two hidden layers in detail [28].
# Research on the Simulation Method of HTTP Traffic Based on GAN
## 5 Evaluation
### Evaluation Results  
#### 5.4.1 Statistical Similarity  
We used the following three metrics to statistically measure the similarity of the generated data:  
* Difference in pairwise correlation (Diff. Corr.): this metric was used to measure the difference in correlation between two sets of data. We first calculated the correlation coefficients between the columns of the synthetic data and the real data separately. This metric had a range of \([-1,1]\), where higher values indicated stronger correlation.
* Wasserstein distance (WD): for continuous data columns in the dataset, we used the Wasserstein distance to measure the distributional difference between the data. A smaller value indicated a smaller difference and higher similarity between the generated data and the real data. This metric was chosen as one of the evaluation indicators due to its relatively stable characteristics.
* Jensen-Shannon divergence (JSD): for categorical variables in the generated data, we used the JSD as a measure of similarity. This metric quantified the similarity between two probability distributions and had the range \([0,1]\). JSD was a symmetric measure of similarity.  
From Table 5, it can be observed that our proposed SE-GAN achieved the best performance in the comparison. Regarding the Corr parameter, the results of the AE-GAN, which adopts an AE structure, were second only to our SE-GAN. Our SE-GAN outperformed the AE-GAN by 16% in terms of feature similarity in generated data. This was attributed to the utilization of Swin Block modules in SE-GAN, which placed more emphasis on the local and global relationships compared to the simple, fully connected layers used in AE-GAN. As a result, SE-GAN exhibited higher feature similarity in terms of the Corr metric.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Conditions** & **Model Used** \\ \hline Operating System & Ubuntu 20.04 \\ CPU & 17-8700 \\ GPU & RTX-2080 \\ Memory & 32 G (3200 Hz) \\ Disk & PM981 \\ Python Version & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment environment.  
Regarding the WD metric, our SE-GAN also demonstrated the best performance, with our model achieving a 38% lower WD value compared to OCT-GAN. This metric adequately demonstrated the effectiveness of our proposed model in terms of similarity. Similarly, in the last parameter JSD, our model achieved an 18.7% lower value compared to OCT-GAN, which was based on the transpose convolutional structure. This was because we employed an additional classifier to train our model, enabling our model to allocate more attention to the classification data module.
## 5 Inspection and Weighted Compensation of Multi-Sensor Data
### Weighted Correction for Variance  
When the ZigBee-GPS/BDS multi-sensor positioning system was used to locate the navigation path of the unmanned ship at different positions in the same space, the measurement noise of each sensor followed Gaussian distribution [24]. The variance of the \(i\)th positioning data can be expressed as  
\[\widetilde{\sigma}_{i}^{2}=E(x_{i}^{\text{ZGB}}-x_{i}^{t})^{2} \tag{34}\]  
In the real navigation situation of the multi-sensor positioning system of the unmanned ship, since the true positioning value \(x_{i}^{t}\) in Equation (34) is unknown, the arithmetic mean value \(\overline{x}_{i}\) of the positioning data in Equation (32) was taken as the unbiased estimate of the true value \(x_{i}^{T}\), namely  
\[\sigma_{i}^{\prime 2}=D(x_{i}^{\text{ZGB}}-\overline{x}_{i}) \tag{35}\]  
In order to improve the credibility of data samples, the multi-sensor positioning system in this paper measured the position of the unmanned ship \(m\) times, and integrated the \(m\) times of positioning data into a data set. The \(j\)th positioning value of the \(i\)th sensor information was \(x_{ij}\), and the sensor positioning data \(x_{i}^{\text{ZGB}}\) was replaced in Equation (35) with \(x_{ij}\) to obtain the information variance of the data set, which can be expressed as  
\[\widetilde{\sigma}_{i}^{2}=\frac{1}{m}\sum\limits_{j=1}^{m}{(x_{ij}-\overline{ x}_{i})^{2}} \tag{36}\]  
where \(j=1,2,\ldots,m\).  
According to the variance of the data collected by the sensor, the fusion weight \(\kappa_{i}\) of the fault data was defined as follows:  
\[\kappa_{i}=\frac{{{{\left(\overline{\sigma}_{i}^{2}\right)}^{-1}}}}{{\sum \limits_{i=1}^{n}{\frac{1}{\overline{\sigma}_{i}^{2}}}}} \tag{37}\]  
\[\sum\limits_{i=1}^{n}{\kappa_{i}}=1 \tag{38}\]  
According to the obtained fusion weight \(\kappa_{i}\), the multi-sensor fault data was weighted and fused.  
\[\widetilde{\mathbf{x}}(k)=\widehat{\mathbf{x}}^{o}(k)\mathbf{\kappa} \tag{39}\]  
where \(\widetilde{\mathbf{x}}(k)=\{\widetilde{x}_{1}(k),\widetilde{x}_{2}(k),\ldots, \widetilde{x}_{n}(k)\}\) is the multi-sensor positioning data after variance weighting, \(\widetilde{\mathbf{x}}^{o}(k)=\{\widetilde{x}_{1}{}^{o}(k),\widetilde{x}_{2}{}^{o }(k),\ldots,\widetilde{x}_{n}{}^{o}(k)\}\) is the multi-sensor fault data, and \(\mathbf{\kappa}=\{\kappa_{1},\kappa_{2},\ldots,\kappa_{n}\}\) is the fusion weight, \(i=1,2,\ldots,n\).  
By weighting the variance of the fault data, a certain amount of information correction was realized, and the fault-tolerant performance of the data fusion algorithm was improved. Even if the ZigBee signal had measurement deviation due to environmental interference, or the GPS/BDS signal was weakened or lost due to environmental occlusion, data samples with high credibility based on the proposed method could still be obtained.
## 6 Experimental Results and Discussion
### Pitch Angles of the Horizontal Blades  
Figure 11 shows the coefficient of power against the tip speed ratio for various pitch angles of the horizontal blades of the CAWT. All the curves in Figure 11 show a similar trend where the \(C_{\mathrm{P}}\) increases with increase in TSR; the \(C_{\mathrm{P}}\) reaches its peak value at a certain TSR (between 0.89 and 1.16) and then decrease with increase in TSR. The results showed that the \(10^{\circ}\) pitch angle has the highest \(C_{\mathrm{P}}\) values compared to the other ones. Therefore, for this study, \(10^{\circ}\) pitch angle is considered as the optimum pitch angle for the horizontal blades of the CAWT. At \(10^{\circ}\) pitch angle, the angle of attack is optimum, therefore, the flow along the blade is laminar which is the condition for the generation of lift force that maximizes the torque produced and improved the power output for the \(10^{\circ}\) pitch angle. When the pitch angle was increased above \(10^{\circ}\) (i.e., \(15^{\circ}\)), the \(C_{\mathrm{P}}\) values decreases significantly (which results in lower \(C_{\mathrm{P}}\) values for the CAWT). Again, the performance of the CAWT further decreases when the pitch angle is decreased below \(10^{\circ}\). This shows that the pitch angle of the horizontal blades has significant effect on the performance of the CAWT.  
Figure 11: Coefficient of power against tip speed ratio for different pitch angle.  
Figure 10: Dynamometer Controller.
## 6 Result and Discussion  
### Performance Analysis Experiments  
This section presents experimental results and discussions of the performance analysis and optimisation of WRF processing in relation to Kunpeng 920. It mainly focuses on MPI + OpenMP analysis, scalable performance analysis, and hotspot function analysis, as well as data presentation and discussion of experimental results on Libamath, domain decomposition, tile division, and IO optimisation.  
Table 1 shows the different combinations of MPI + OpenMP showing unused performance when the total number of cores used is fixed. The numbers in the first row represent the total number of cores used, the first column represents the number of threads used, and the number of processes used is the total number of processes divided by the number of threads. At 8, 16, 32, 64 and 128 cores, the best performance combinations occur when the number of threads used is a multiple of four. This is mainly related to the architecture of Kunpeng, in which every four cores form a CCL with a built-in shared cache. When the number of threads is a multiple of four, four threads in the same CCL can use the shared memory and the data transfer cost for threads in OpenMP is relatively small. Thus, the best performance is obtained.  
As shown in Table 2, the single iteration step runtime of the WRF is 4.49 s when using 64 cores and 0.66 s when using 512 cores. Thus, the performance of the WRF is accelerated by a factor of 6.8 with respect to 64 cores at 512 cores, with a parallel efficiency of 85%. As demonstrated by the experimental data, WRF shows good scalability performance on the Kunpeng 920 processor platform.
## 2 Results
### Sequencing and Assembly  
To investigate the effects of light and darkness treatments on monoterpene synthesis at the transcriptional level, two mRNA libraries were constructed for transcriptome sequencing, comprising a total of 24 samples. After quality filtering, clean data volumes ranged from 6.66 Gb (D3) to 8.97 Gb (D4), with an average error rate of 0.01%. In addition, the Q20 of all the libraries exceeded 98.78% (L4), while the Q30 surpassed 96.7% (L4). The percentage GC content was approximately 46% (Table 2). These results confirmed the high quality of the transcriptome data (Table S4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatment** & **Total Reads** & **Total Map** & **Unique Map** & **Multi Map** & **Proper Map** \\ \hline L1 (Light-0 h) & 50,905,702 & 88.78 & 75.63 & 13.14 & 68.22 \\ L2 (Light-6 h) & 47,260,316 & 86.40 & 71.14 & 15.26 & 62.72 \\ L3 (Light-12 h) & 49,269,064 & 86.93 & 71.52 & 15.42 & 62.60 \\ L4 (Light-18 h) & 49,721,182 & 86.28 & 73.65 & 12.64 & 64.60 \\ D1 (Dark-0 h) & 46,314,858 & 87.58 & 79.38 & 8.20 & 71.16 \\ D2 (Dark-6 h) & 48,571,858 & 89.14 & 80.91 & 8.23 & 72.70 \\ D3 (Dark-12 h) & 48,092,412 & 88.91 & 80.59 & 8.32 & 72.77 \\ D4 (Dark-18 h) & 55,470,416 & 88.69 & 80.6 & 8.08 & 71.98 \\ \hline \hline \end{tabular} Note: Total reads: number of clean reads; Mapped reads: number of reads on \(L\). _cubba_ genome and their proportion in clean reads; Multi map: the number of reads matched to the unique position of genome; Proper map: comprehensive comparison rate.  
\end{table}
Table 1: Sequence alignment results of sample sequencing data and reference genome.  
Figure 3: The effects of darkness treatment (D1–D4) on monoterenoid content in \(L\). _cubba_ fruits. Except for linalool, the contents of other monotereponoids were significantly decreased at the D3 stage (Dark-12 h), whereas those of neral and geranial showed a further decrease at the D4 stage (Dark-18 h). D2, D3, and D4 are compared with D1. All data are expressed as means \(\pm\) SD of three biological replicates (* \(p<0.05\)).  
Through RNA sequencing and analysis, a total of 41,822 genes were detected. Among them, a total of 37,721 genes were expressed in the transcriptome, with the transcript length distribution shown in Figure 4. Genes with lengths of 501-1500 bp accounted for the largest proportion of the transcriptome (16,304; 43%), followed by those with lengths greater than 2000 bp (9231; 24%) (Figure 4A). The correlation coefficient of the 24 samples from light and darkness treatments is shown in Figure 4B. The higher the correlation coefficient, the better the quality between samples. The overlapping area shows the number of genes common to both light and darkness samples (Figure 4C). Differential expression analysis was performed using DEG2 seq with a false discovery rate threshold adjusted cutoff of <0.05 and a fold change cutoff of \(\,\mathrm{l}\log\)2(FoldChange)\(\,\mid\,\geq 1\). Figure 4D presents the numbers of upregulated and downregulated DEGs across the four sampling times (L1 vs. D1, L2 vs. D2, L3 vs. D3, and L4 vs. D4) under light and darkness treatments.
# Defining the Influence of Fatigue Protocol on Kinematic Parameters of Ippon Seoi Nage
###### Abstract  
To achieve technical efficiency in a judo fight, it is necessary to know the technical details of each throw. The tempo of a judo fight is highly intense, and it is necessary for the competitor to be physically very well prepared. Considering the tempo, an important factor in the fight is the influence of fatigue, which can significantly affect the performance of a technique. The aim of this research is to determine the kinematic parameters and the influence of the fatigue protocol during the performance of ippon seoi nage (ISN) throw. The sample of participants consists of 30 young judokas (17.02 \(\pm\) 0.91 year; 72.81 \(\pm\) 6.52 kg; 178.60 \(\pm\) 5.60 cm). The following variables were observed with Xsens Awinda kinematic system: the angle of the left shoulder (L_shou_A); the angle of the right shoulder (R_shou_A); the difference in the height of the pelvis in the sukuri phase (Pel_tsu_H); the difference in the height of the pelvis in the kake phase (Pel_kake_H); hand velocity (Hand_V); head position (Head_pos). The participants performed three ISN throws before and after the fatigue protocol. A statistically significant difference (MANOVA) was found between the measurements (F = 8.47; \(p\) = 0.00). Additionally, observing the differences between individual variables (ANOVA), a significant difference was found in all measured variables, with a statistical significance of \(p<0.05\). This research presents key kinematic parameters that had not yet been observed in this manner. Furthermore, based on the established differences, it can be concluded that the participants significantly disrupt the performance of the ISN throw technique in situational, i.e., fight, conditions.  
Keywords:hand throw; judo; kinematic analysis +
Footnote †: journal: Applied
# Control Chart Concurrent Pattern Classification Using Multi-Label Convolutional Neural Networks
## 5 Conclusions  
In this study, a multi-label classification method was developed using CNN architectures to classify control chart concurrent patterns. A series of experiments were conducted using datasets from previous studies to evaluate the performance of the proposed method.  
Figure 11: Application of online monitoring.  
The results reveal that a multi-label CNN performed better than other multi-label classifiers that were compared. The superior performance of a multi-label CNN can be partly explained by its translation invariance property. This feature allows CNN to maintain high accuracy when basic constituents of concurrent patterns may not appear at the same point in time. The traditional classifiers take raw data as inputs and each observation is treated as an individual feature. This makes traditional classifiers very sensitive to the location of features. This study also used a set of industry data to illustrate the applicability of the proposed CNN to online monitoring. The results reveal that the proposed multi-label CNN can be applied to online monitoring and can correctly identify concurrent patterns. The results reveal that that multi-label CNN has the potential to classify concurrent control chart patterns. The results of this study contribute to the further realization of smart SPC.  
This study has several limitations that can be addressed in future research. First, the proposed multi-label CNN was compared with traditional classifiers that used raw data as inputs. In future work, it is worth evaluating the traditional multi-label classifiers using features as inputs. Second, this study considered the case when the constituents of the concurrent pattern may not appear at the same time. In practical application, the change positions of each individual patterns may be more complicated than those considered in this study. Future research should investigate more sophisticated situations when non-random patterns may have different change points and change magnitudes. Finally, this study applied a single threshold to all labels. Future research should consider applying thresholding techniques, such as multi-thresholds, to improve the performance of multi-label classification.  
Conceptualization, C.-S.C.; Methodology, C.-S.C.; Software, C.-S.C. and P.-W.C.; Formal analysis, C.-S.C., P.-W.C. and Y.H.; Investigation, C.-S.C. and P.-W.C.; Data curation, P.-W.C. and Y.H; Writing-Original Draft Preparation, C.-S.C., P.-W.C. and Y.H.; Writing-Review and Editing, C.-S.C.; Supervision, C.-S.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Ministry of Science and Technology, R.O.C. (grant number 109-2221-E-155-020-).  
Not applicable.  
The simulated datasets used in this study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.