esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of Precision Current Efficiency Curve Measurement with a Casing-Type Anode
## 5 Results and Discussion  
The current efficiency curves of 304 SS measured by the conventional and proposed methods are shown in Figure 15. It was observed that the value of \(\eta\omega\) measured by the conventional method was significantly smaller than that of the proposed method. When the current density was over 50 A/cm\({}^{2}\), the difference was negligible and the value of \(\eta\omega\) measured by both the methods was maintained at about 1.20 mm\({}^{3}\)/Amin. For current density values smaller than 50 A/cm\({}^{2}\), both the methods showed the same trend. The value of current efficiency increased with the increase in current density. However, the deviation between the conventional method and the proposed method grew with the decrease in current density. The results of the experiment are consistent with the analysis from the simulation. Furthermore, the stability of the measurement process improved by using the  
Figure 14: Experimental fixture and chucks.  
Figure 13: Experimental system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Feed rate \(\upsilon\) & 0.1/0.2/0.3/0.4/0.6/0.8/1.0 mm/min \\ Electrolyte & 20 wt.\% NaNO\({}_{3}\) \\ Inlet pressure & 0.8 MPa \\ Outlet pressure & 0.1 MPa \\ Electrolyte temperature & 25 \({}^{\circ}\)C \\ Applied voltage \(U\) & 10 V \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental conditions.  
proposed measurement method. In the conventional method, the maximum fluctuation of \(\eta\omega\) was 0.042 mm\({}^{3}\)/Amin, while it was 0.030 mm\({}^{3}\)/Amin in the proposed method.  
Figure 16 shows the specimens with a feed rate of 0.1 mm/min. In the conventional constant voltage method, the corrosion of the workpiece on the side was visible and had a level angle of about 11\({}^{\circ}\), which was potentially caused by intense scour and electrochemical corrosion. The rounded corner at the edge of the workpiece was about 0.622 mm. This means that the non-uniform current distribution at the side and edge was present and had a severe influence on the measurement. In the casing-type anode measurement, the condition of the casing was consistent with the workpiece used in the conventional method. However, the mandril showed an entirely different appearance: the sidewall of the mandril maintained the metallic luster and the edge was very sharp. The electric field shielding of the casing contributed to this appearance. These results were also consistent with the analysis from the simulation, meaning that the recorded current of the mandril was less influenced by the side current and edge effects, and the accuracy of the current efficiency curve measurement improved.  
Figure 16: Specimens with a feed rate of 0.1 mm/min.  
Figure 15: The current efficiency curves of 304 SS.
## 3 Results and Discussion
### Co-Drive Mechanism of Ecosystem ET and GPP  
According to the above research we found that there is a strong synchronism between ecosystem evapotranspiration and photosynthesis. Figure 11 illustrates that the simulated ET and GPP have a significant positive liner relation (R\({}^{2}\) = 0.88) within two study years. Except for a consistent seasonal variation with a same peak period at July and August, they also have a similar spatial heterogeneity. Previous research also pointed the consistent diurnal variation between ecosystem water and carbon fluxes ([PERSON], 2016), and [PERSON] (2016) found that the annual accumulated carbon assimilation and evapotranspiration have a stable linear relationship. Except for the impact of soil moisture, another major reason is probably because the driving energies of ecosystem C-water cycle have a synchronous change. Canopy intercepted net radiation for ET and short-wave radiation for photosynthesis have a similar co-linear relation with input solar radiation. Additionally, as a common organ for transpiration and photosynthesis, the growth of leaves synchronously controls canopy ET and GPP fluctuation and this control is hardly affected by soil water and fertilizer conditions ([PERSON], 2016). It also explains the similar spatial distribution between C-water fluxes and regional LAI discussed above. Furthermore, daily temperature and wind speed can also synchronously affect the exchange rate of CO\({}_{2}\) and H\({}_{2}\)O between vegetation and the atmosphere through the control of enzymatic activity, stomatal conductance and vaporizing rate.  
Figure 10: Simulated spatial patterns of annual total GPP for 2013 (**a**) and 2014 (**b**) in the Upper Heihe watershed.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 7 Limitations and Challenges
### Audio Splicing  
There is much research on detecting fake news via text analysis or fake images. However, research should also be extended concerning fake audio shared with fake images/videos posted over social media. Recently, many fake images shared did not have text; instead, manipulated audio is shared to convey long messages and gather more attention. Fake audio detection will yield good research and help mitigate fake news during elections or pandemics wherein such fake audio is majorly shared.
## 3 Experimental Data and Measurement Methods  
This section presents the empirical data to train and test the artificial neural networks. Furthermore, the measurement methods to calculate movement quantities (headway and speed) are described. To investigate pedestrian speed, we used a dataset from experiments conducted in Palestine [12]. Single-file experiments were performed at the Arab American university in Palestine, with a total of 47 participants (26 females and 21 males). Several experimental runs were performed that focused on the influence of gender on pedestrian movement. Side view videos were captured using a digital camera for different numbers of pedestrians (densities) and various gender compositions. The experimental dataset includes the 1D trajectories recorded in different time frames and the gender information of each pedestrian (male and female). In the Palestine experiments, the data were obtained after performing several runs for pedestrians walking with the same gender composition (homogeneous: females alone and males alone) or mixed (heterogeneous: males and females walking together) (see Figure 1). Our analysis will utilize the mixed-gender (UX, N = 20, 24, 30), female (UF, N = 20), and male (UM, N = 20) experiments where N is the number of pedestrians in each run. In Figure 2, we see the trajectories of pedestrians in UX experiments over time. We notice the emergence of stop-and-go waves for high densities (N = 30), which means that the pedestrians start to adjust their positions to avoid collision.  
The same measurement method as [26] is used to calculate the individual speed and headway for pedestrians walking at each time frame. The speed of the pedestrian \(i\) is calculated at time \(t\) as follows:  
\[v_{i}(t)=\frac{x_{i}(t+\Delta t/2)-x_{i}(t-\Delta t/2)}{\Delta t}, \tag{4}\]  
where \(\Delta t\) is a short time constant (10 frames, 0.4 s) and \(x_{i}(t)\) is the \(x\) coordinate of pedestrian's \(i\) position at time \(t\). We use the small value of \(\Delta t=0.4\) s to smooth the trajectories in order to avoid fluctuation of the pedestrian's step [27].  
The headway is defined as the distance between a pedestrian \(i\) and its predecessor \(i+1\):  
\[h_{i}(t)=x_{i+1}(t)-x_{i}(t), \tag{5}\]  
Figure 1: Snapshots from Palestine experiments. (**Left**): UM experiment, N = 20. (**Right**): UX experiment, N = 24.  
where \(x_{i+1}\) and \(x_{i}\) are the \(x\) coordinates of predecessor and subject pedestrian at time \(t\), respectively.  
These calculated movement quantities and associated pedestrian information are utilized as inputs to the FFNN. Table 1 shows the descriptive statistics of the input and the output data we fed into the FFNNs. The first column presents the inputs (subject, predecessor, and follower pedestrian's headway distances) and the output (subject pedestrian speed).
## 2 Materials and Methods
### Network Architecture
In this work, a dual-discriminator was utilized for maintaining the consistency of input images and ensuring the plausibility of out-painted regions, as shown in Figure 4. Each sub-discriminator had 5 convolution layers with a ReLU activation function and 1 flatten layer, and the sub-discriminators shared 1 cropping layer, 1 concatenation layer, and 1 dense layer. The cropping layer divided a generated image into out-painted and non-truncated regions using the input image of the generator. The divided regions were separately processed by the sub-discriminators during network training. This strategy enables to reduction of the deformation of non-truncated regions and improves the reality of out-painted regions because the sub-discriminators were independently optimized for discriminating each region. The outputs from both the sub-discriminators were merged and compressed via the concatenation and dense layers for binary classification. A detailed summary of the discriminator architecture is presented in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Kernel Size** & **Dilation Rate** & **Activation Function** & **Output Channel** \\ \hline Conv. & 5 \(\times\) 5 & - & ReLU & 64 \\ Conv. & 4 \(\times\) 4 & - & ReLU & 128 \\ Conv. & 4 \(\times\) 4 & - & ReLU & 256 \\ Conv. & 4 \(\times\) 4 & - & ReLU & 512 \\ Conv. & 4 \(\times\) 4 & - & ReLU & 512 \\ Dilated Conv. & 4 \(\times\) 4 & 4 & ReLU & 512 \\ Dilated Conv. & 4 \(\times\) 4 & 8 & ReLU & 512 \\ Dilated Conv. & 4 \(\times\) 4 & 16 & ReLU & 512 \\ Dilated Conv. & 4 \(\times\) 4 & 32 & ReLU & 512 \\ Conv. & 4 \(\times\) 4 & - & ReLU & 512 \\ Conv. & 4 \(\times\) 4 & - & ReLU & 512 \\ Deconv. & 4 \(\times\) 4 & - & ReLU & 256 \\ Deconv. & 4 \(\times\) 4 & - & ReLU & 128 \\ Conv. & 4 \(\times\) 4 & - & ReLU & 64 \\ Conv. & 4 \(\times\) 4 & - & Sigmoid & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed configuration of the generator used for the proposed out-painting network.
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP  
The CoAP has found its application in the various fields. It is being used to provide the interoperability and integration services, authentication and authorization services, streaming services and so on. The implementation of CoAP in various domains has enabled us to achieve secure, fast, and efficient communication between devices. This section briefly summarizes some applications of CoAP and the services it provides.
## 6 Experimental Results and Analysis
### Regression Results of Non-Directional Online Behavior and Score  
The experimental purpose of the polynomial regression model [32] is to fit the curve trend between performance and online behavior by training the features of the user's non-directional online behavior in the user profile, and take the regression curve as the analysis model so as to predict the student's academic performance according to the user's non-directional online behavior.  
We calculated the students' average score \(AV_{score}\) and daily average flow \(AVE_{flow}\) as the training data. After many times of training, when the degree was 7, the fitting effect was the best. Figure 5a shows the quartic polynomial regression model for score and net flow. The X-axis is the average daily net flow (MB), the Y-axis is the score, and the flow-score regression model is  
\[R_{f-s}=-19x^{7}-2.197E-15x^{6}+5.87E-12x^{5}-8.004E-9x^{4}+5.899E-6x^{3}\]  
Figure 4: Online time preference by clustering.  
By calculating the average score \(AV_{score}\) and daily average online time \(AVE_{length}\) as the training data, after many experiments, when the degree was 4, the fitting effect was the best. Figure 5b shows the cubic polynomial regression model of time and score. The X-axis is the daily average online time (minutes), the Y-axis is the performance, and the time-score regression model is  
\[R_{t-s}=-7.633E-10x^{4}+1.196E-0.6x^{3}-0.0005505x^{2}+0.04846x+82.68\]  
The experimental results show that the longer users are online or the more traffic they use, the lower their academic performance is. This proves that users' irregular online behavior will harm their learning. Through the analysis of the model, we can find out whether users are addicted to the network in time, and draw the attention of teachers or schools to this in order to formulate relatively strict online behavior management strategies for them.
## 1 Introduction
Attention mechanism was proposed in 2017 [41], which has brought widespread attention. Attention mechanism reweights the input features in order to highlight important parts and suppress unimportant parts, which has widely used in natural language processing [42], computer vision [43], and also aroused AMC's interest, [PERSON] [44] combined attention mechanism and one-dimension convolution module to propose an One-Dimensional Deep Attention Convolution Network (OADCN) network for different modulation types with different channel coding mode. Nevertheless, attention mechansim is only applied to assign weights for spatial features from CNN model, it still has broad application prospects. Softmax function [45] is the most popular classification function in AMC. Softmax function can amplify the difference between outputs. However, there are also many output functions for neural network. Cosine-similarity-based function has become popular recently [46]. Existing literature rarely considers cosine-similarity distance for AMC problem. As a consequence, this paper proposed the idea of applying cosine similarity softmax called Additive margin softmax (AM-softmax) [47] which is widely utilized in metric learning to AMC problem. In this paper, we propose a hybrid parallel module which concatenates spatial features and temporal features simultaneously, utilizing attention mechanism to reweight all features. At first, a hybrid parallel feature extraction module is designed for extracting features in parallel. CNN and GRU are applied to extract spatial features and temporal features, respectively. Then the two types of features are concatenated in the channel dimension. Different attention mechanisms are applied to assign weights for features. Squeeze-and-Excitation (SE) block and multi-head attention mechanism are put into use for assigning weights for features in channel dimension and feature dimension respectively. The iterative Attentional Feature Fusion (iAFF) module is utilized for weights assignment in the residual shortcut structure. We use SE block and multi-head attention mechanism to construct hybrid parallel feature extraction module and build a residual structure with hybrid parallel feature extraction module and iAFF module. Finally, we flatten the output features, apply multi-head attention mechanism to each point in features, and send the features to AM-softmax to calculate the cosine-similarity distance. To our knowledge, this is the first paper utilizing cosine similarity softmax in the AMC domain. The remainder of the paper is organized as follows: In Section 2, we will show the details of our proposed method. In Section 3, we evaluate the performance of the proposed model with simulation results. Finally, we summarize the content of this paper in Section 4.
# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 4 Experiments and Discussion
### Experimental Setup  
#### 4.1.1 Datasets  
Our proposed method was tested on simulated noisy datasets including SVHN [27], MNIST [28], CIFAR10, CIFAR100 [29], and one real dataset, Clothing1M [30]. SVHN is derived from real-world images featuring door numbers in Google Street View, encompassing 10 classes with images sized 32 \(\times\) 32. It consists of 7325 images for training, 26,032 images for testing, and an additional 531,131 less challenging samples for training data. The MNIST handwritten digit dataset is a fundamental digital classification dataset, featuring 10 classes of grayscale images sized 28 \(\times\) 28. It comprises 60k digital images for training and an additional 10,000 digital images for testing. CIFAR10 and CIFAR100 datasets are both composed of images sized 32 \(\times\) 32, featuring 60k training images and 10k test images. The key distinction lies in the number of classes: CIFAR10 comprises 10 classes, whereas CIFAR100 encompasses 100 classes. Clothing1M comprises 14 classes of clothing images totaling 1M, sourced from various online shopping platforms, incorporating numerous mislabeled samples. Furthermore, the dataset comprises 50,000 training images, 14,000 validation images, and 10,000 testing images, all labels with accurate labels. Moreover, experiments were conducted on a noisy dataset with a long-tailed distribution [31], wherein the training data exhibited class imbalance following a long-tailed distribution pattern. To simulate this distribution, the quantity of training samples for various classes was reduced. Both the MNIST and SVHN datasets were employed for this setup. Furthermore, asymmetric noise was introduced to generate a noisy long-tailed dataset. Table 1 outlines the dataset details for all experiments conducted.
## 7 Conclusions  
This study investigates a new technique for detecting and distinguishing external and internal power transformer defects. This method, which uses a mix of DWT and PNN, has been presented. The DWT has been used to deconstruct high-frequency fault signal components. To compare the DWT maximum coefficient and the maximum ratio of the division algorithm, the PNN was divided into two training cases. In various circumstances, different fault kinds and locations on both the main and secondary sides of the power transformer are considered. The average accuracy of the DWT maximum coefficient cases is more than 96.67 percent, which is more satisfactory. The simulation results demonstrated that the PNN and BPNN are effectively implemented and perform fault detection with satisfactory accuracy. The PNN method's maximum coefficients algorithm has the highest accuracy, with an internal fault detection accuracy of 100 percent and external fault detection accuracy of 96.67 percent. The maximum ratio technique gives an accurate analysis of the nearly comparable PNN and BPNN approaches; the BPNN method is significantly greater. The BPNN method has a 191.49 percent overall accuracy, while the PNN approach has a 189.63 percent overall accuracy. External fault detection accuracy is 93.34 percent for both the PNN and BPNN approaches, while internal fault detection accuracy is 98.15 percent for the BPNN method and 96.30 percent for the PNN method. However, the PNN method is most suitable for detecting internal and external faults, and the maximum coefficient algorithm is the most effective at detecting the fault. Although the suggested approach produces satisfactory results, the overall accuracy indicates that it has to be improved in order to attain higher precision. This strategy, on the other hand, will be useful in the power transformer's differential protection scheme. The method will be improved in the future so that the transformer's magnetizing inrush current can be determined.  
Conceptualization, A.N. and N.P.; methodology, N.P.; software, S.T.; validation, C.P., S.A. and M.L.; formal analysis, S.B.; investigation, V.P.; resources, P.C.; data curation, A.N.; writing--original draft preparation, S.A.; writing--review and editing, C.P. and N.P.; visualization, S.Y.; supervision, S.B.; project administration, S.Y.; funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Rajamangala University of Technology Rattanakosin Research Fund. Institutional Review Board StatementNot applicable. Informed Consent StatementNot applicable. Data Availability StatementData is contained within the article. This work was supported in part by the Rajamangala University of Technology Rattanakosin Research Fund. Conflicts of InterestThe authors declare no conflict of interest.
# Assessment of Active Wheelset Steering System Using Computer Simulations and Roller Rig Tests
###### Abstract  
The paper is created within a project which aims to design a system of active wheelset steering for an electric four-axle locomotive. The wheelset steering system enables reduction in forces acting in the wheel-rail contacts in a curved track and consequently a reduction in wear and maintenance costs of both vehicles and rails is achieved. The project consists of three main parts: computer simulations, scaled roller rig experiments, and field tests. The paper is focused on the fundamental aspects of the first and the second part on the project. Track curvature estimation based on the rotation of the bogies towards the car body is proposed and assessed by computer simulations across varying track radiuses, vehicle speeds, and friction conditions. The scaled roller rig has been innovated in order to simulate bogie run in a curved track with uncompensated value of lateral acceleration and instrumented with a system of measurement of lateral wheel-rail forces. The experimental bogie has been equipped with systems of active wheelset steering and measurement of axle-box forces. The experiment setup, newly developed and applied systems of forces measurement and wireless signal transmission, and results of the first experiments are described in detail. Performed computer simulations and scaled roller rig experiments show that active wheelset steering is effective and practically implementable method of reducing guiding forces acting between railway vehicle wheels and rails in a curved track.  
active wheelset steering; roller rig; wheel-rail contact forces; guiding forces Article
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
## 1 Introduction
Combining the advantages that frequency domain notching does not require prior information and frequency point estimation is more accurate, and the time domain notching can completely eliminate interference, then avoiding their respective shortcomings; (3) Aiming at the problem that the frequency signal acquisition speed is not fast enough and the problem led by information symbol rate is not an integer multiple of the pseudo-code rate while using the PMF-FFT algorithm [3; 4], the three-loop tracking technology with the bit synchronization loop is introduced into the tracking loop to increase the acquisition speed, achieve a large doppler frequency shift, enough acquisition sensitivity and precise loop control [7; 8]; (4) Combining data demodulation, RS decoding technology and positioning solution algorithm and based on the FPGA digital processing board, the JFM/K325T chip is used to design the FPGA, thereby realizing the complete design simulation of the low-orbit satellite broadcast signal processing terminal.
###### Abstract  
Microwave-based all-analogue (orthogonal) subcarrier multiplexing (SCM) permits a direct processing of baseband data at Gbit/s while achieving low power consumption, low latency, low cost, and tolerance to dispersion. A key figure of merit in any SCM link is the sensitivity in the receiver, which depends on the transmitter, the link and the receiver. By analysing the impact of the nonlinearities of an optical IQ modulator in the presence of optical noise, sensitivities are mathematically estimated as a function of the optical modulation index (OMI) at the transmitter. The results are verified with simulations achieving a good agreement with the mathematical model. The theoretical model provided can be employed as a tool to predict the best achievable sensitivities and the optimum OMI in broadband SCM and orthogonal SCM links.  
subcarrier multiplexing; orthogonal subcarrier multiplexing; optical IQ modulator; direct detection +
Footnote †: journal: Applied  
Article