esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Data Analysis  
Image processing was conducted by ImageJ (NIH), NI-IMAQ (National Instruments; Austin, TX, USA), and JPIV ([[http://www.jpiv.vennemann-online.de](http://www.jpiv.vennemann-online.de)]([http://www.jpiv.vennemann-online.de](http://www.jpiv.vennemann-online.de))). The 2000 raw images were divided into two groups (group A and group B) with a time interval of 1 ms. Average background images were generated by the ImageJ plug-in \"average image filter\" for group A and group B respectively, and then subtracted from raw images accordingly. In order to increase the particle density in one single image, each group was further divided into 20 smaller groups with 50 images. After applying the median filter and the math min filter, the overlap function of ImageJ was introduced to generate one single image from 50 images, and 20 images were output in both group A and group B separately. While signal-to-noise ratios were significantly increased by these operations, the dynamic boundary was missing, which could be compensated for by NI-IMAQ. After setting up the parameters in JPIV (multi pass, interrogation window size, region of interest, between passes), average ensemble correlation was selected to calculate the velocity profile. A typical image of velocity profile around an adherent cell calculated from micro-PIV images is shown in Figure 1C.  
Local shear rates and upstream shear rates defined previously were calculated at each PMN-to-TC position state separately. Equations (2) and (3) were used to calculate bulk-flow Reynolds numbers and cell Reynolds numbers,  
\[\text{Re}_{chamber}=\frac{2\rho vWH}{\mu(W+H)} \tag{2}\]  
\[\text{Re}_{cell}=\frac{\rho\dot{\gamma}R^{2}}{\mu} \tag{3}\]  
where \(\rho\) is the density of flow; \(\mu\) is the viscosity of flow; \(v\) is the average velocity in the chamber; \(W\) is the width of the chamber; \(H\) is the height of the chamber; \(\gamma\) is the local shear rate of the cell; and \(R\) is the cell radius.
## 6 Results and Discussion
### Test Using ISCXIDS2012
#### 6.1.1 Effect of Data Splitting  
The effect of splitting on DDoS detection was investigated using train-test-validate split in three different ways: (60-20-20), (70-15-15), and (80-10-10), as shown in Table 4.  
Figure 3: Accuracy results for (**a**) the CNN-autoencoder model, and (**b**) the BDLSTM-autoencoder model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Loss** & **Accuracy** & **Val. Loss** & **Val. Accuracy** \\ \hline ANN-Autoencoder & 0.5842 & 0.6612 & 0.5641 & 0.6484 \\ CNN-Autoencoder & 0.1027 & 0.9554 & 0.5907 & 0.6279 \\ BDLSTM-Autoencoder & 0.0388 & 0.9935 & 0.0624 & 0.9930 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of tests using the ISCXIDS2012 dataset for three models.  
In Table 4 and Figure 4a-f, the tests found that splitting was not very influential on the results, as no significant differences are found in accuracy and other metrics. However, 60-20-20 splitting achieved relatively more accuracy and a more stable result.
## 3 Results
### The Experimental Preparation
#### 3.1.4 Parameter Selection  
The parameters of the comparison method are selected according to the optimization method of the corresponding paper. The k-nearest neighbor algorithm adjusts the parameter \(k\) from \(\{3,5,7,9,11,13,15\}\) for LS, UDFS, and MCFS. For LS, the bandwidth parameter \(\sigma\) of the Gaussian kernel is set to a constant value of 10. For SCFS, as stated in the original article by [PERSON] et al., the parameter \(\gamma\) is held constant at 106, and the values of the other parameters are adjusted from \(\left\{10^{-4},10^{-2},10^{0},10^{2},10^{4}\right\}\). Other weight parameters in DUFS and UDFS are searched from \(\left\{10^{-6},10^{-5},10^{-4},\ldots,10^{4},10^{5},10^{6}\right\}\).  
For the proposed algorithm DGA, it is necessary to preset six parameters. These parameters include the graph nearest neighbor k, the weight regularization parameter \(\lambda\), the dual-graph regularization parameter \(\beta\) and \(\alpha\), the auto-encoder hidden layer m, and the learning rate \(\gamma\). Similar to other algorithms, the value of the neighbor number k is adjusted starting from \(\{3,5,7,9,11,13,15\}\). Then, the parameters \(\lambda\), \(\beta\), and \(\alpha\) are optimized from \(\left\{10^{-6},10^{-5},10^{-4},\ldots,10^{4},10^{5},10^{6}\right\}\), while \(\gamma\) is optimized from \(\left\{10^{-6},5\times 10^{-5},10^{-5},5\times 10^{-4},10^{-4},\ldots,10^{4}, 5\times 10^{4},10^{5},5\times 10^{5},10^{6}\right\}\). Additionally, the parameter m is selected from the set \(\left\{5,\ 50,\ 100,\ 150,200,250,300\right\}\).  
We employ the k-means clustering algorithm to assess the performance of each approach mentioned above, where the value of k is the number of classes in each dataset. For each dataset, the selected features range from 10 to 300, with an interval of 10. Firstly, the feature set is acquired through the utilization of diverse feature selection algorithms. Subsequently, the obtained feature set is subjected to clustering using the k-means algorithm. Analyze the clustering results of each approach and the best clustering outcome is reported.
## 3 Results
### Membrane Fouling Characterization  
To more accurately characterize membrane fouling after the addition of an electric field to RO, SEM was employed for membrane fouling characterization.  
Figure 8a presents the condition of a new membrane, showing a smooth surface free from pollutants. Figure 8b-e depict the membrane surface after the addition of pollutants, indicating the presence of pollutants.  
Figure 8b,c demonstrate the impact of Ca2+ on the fouling of the RO membrane in the absence and presence of an electric field, respectively. It can be seen from the figures that the surface fouling of the membrane is relatively loose in the presence of an electric field, whereas the fouling is dense in the absence of an electric field. Given that the test water was artificially prepared, with the NaCl solution combined with only CaCl2, it can be assumed that the pollutants are primarily CaCl2. The SEM morphology analysis results align with the aforementioned observations and the increased electric field alters the trajectory of Ca2+, making it less likely to be deposited on the surface of the film.  
Figure 8d,e showcase the impact of HA on the fouling of the RO membrane in the absence and presence of an electric field, respectively. The pores of the membrane are  
Figure 8: The effect of Ca2+ or HA on RO membranes was characterized by SEM in the presence or absence of an electric field. (**a**) The unused RO membrane, (**b**) effect of Ca2+ with no electric field, (**c**) effect of Ca2+ with electric field, (**d**) effect of HA with no electric field, (**e**) effect of HA with electric field.  
blocked by pollutants. However, increasing the electric field of the membrane leads to less dense pollutant deposition, making it easier to remove from the surface. Conversely, without the electric field, the fouling is denser and more resistant to cleaning. These findings are consistent with the filtration results, indicating that the electric field better inhibits and slows down membrane fouling.
# Effect of Plate Curvature on Heat Source Distribution in Induction Line Heating for Plate Forming
## 5 Conclusions  
In this paper, we used an FEM calculation to investigate the effect of plate curvature on heat source distribution and strain distribution, defining the influence range of plate curvature. The following conclusions were obtained:  
1. The finite element method, which divides the whole problem into three parts in sequence, can reflect the deformation of the actual process very well.
2. When the coil width is less than 100 mm, it can be considered that the effect of plate curvature on heat source distribution and strain distribution is not apparent.
3. When the coil width is over 100 mm, the plate curvature affects the heat generation distribution. As the curvature increases, the Joule heat generation in the center of the heating area is reduced by up to 21%, and the plastic strain is obviously reduced.
4. A more significant deformation and strain can be obtained in a single-pass heat line with a wider heat source. A wider coil can achieve a higher processing efficiency.  
Figure 16: Comparison of processing efficiency.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Thickness (mm)** & **Width = 60 mm** & **Width = 100 mm** & **Width = 140 mm** & **Width = 180 mm** \\ \hline out-of-plane strain in & 20 & 0.0572 & 0.1147 & 0.0946 & 0.1136 \\ the X direction & 16 & 0.0496 & 0.1076 & 0.1201 & 0.1301 \\ out-of-plane strain in & 20 & 0.0896 & 0.113 & 0.1748 & 0.2657 \\ the Y direction & 16 & 0.0647 & 0.0883 & 0.1357 & 0.2255 \\ in-plane strain in the & 20 & 0.0712 & 0.1257 & 0.1723 & 0.2836 \\ X direction & 16 & 0.0757 & 0.1438 & 0.1977 & 0.2584 \\ in-plane strain in the & 20 & 0.0222 & 0.0437 & 0.0821 & 0.1427 \\ Y direction & 16 & 0.0227 & 0.0505 & 0.1058 & 0.1753 \\ \hline \end{tabular}
\end{table}
Table 5: Maximum relative variation.  
When the coil width is less than 100 mm or the curvature is less than 0.2 m-1, the effect of the plate curvature is negligible. A compromise is to use a heat source distribution under an intermediate curvature for the calculation to reduce the deviation caused by a curvature change.  
[PERSON] designed the experimental plans, contributed to the discussions, and reviewed the paper; [PERSON] carried out the experiments, performed the numerical simulations, and wrote the paper; [PERSON] assisted in the experiment and reviewed the paper; [PERSON], [PERSON] and [PERSON] investigated the processing process in the shipyard. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology of the People's Republic of China (Grant No.: 2012 DF80390) and Ministry of Industry and Information Technology of the People's Republic of China (Grant No.:2016545).  
The authors declare that they have no conflicts of interest to report regarding the present study.
# Detection and Localization of Failures in Hybrid Fiber-Coaxial Network Using Big Data Platform
###### Abstract  
Modern HFC (Hybrid Fiber-Coaxial) networks comprise millions of users. It is of great importance for HFC network operators to provide high network access availability to their users. This requirement is becoming even more important given the increasing trend of remote working. Therefore, network failures need to be detected and localized as soon as possible. This is not an easy task given that there is a large number of devices in typical HFC networks. However, the large number of devices also enable HFC network operators to collect enormous amounts of data that can be used for various purposes. Thus, there is also a trend of introducing big data technologies in HFC networks to be able to efficiently cope with the huge amounts of data. In this paper, we propose a novel mechanism for efficient failure detection and localization in HFC networks using a big data platform. The proposed mechanism utilizes the already present big data platform and collected data to add one more feature to big data platform--efficient failure detection and localization. The proposed mechanism has been successfully deployed in a real HFC network that serves more than one million users.  
big data; failure detection; failure localization; HFC networks; network management Article Detection and Localization of Failures in Hybrid Fiber-Coaxial Network Using Big Data Platform  
10.390/electronics10232906  
10.390/electronics10232906
## 5 Experiments and Discussion
### Performance Analysis and Implementation  
This section explains the experimental results obtained from the proposed RMSDC technique, as shown in Figure 6.  
To evaluate the performance of our proposed model in comparison with recent frameworks for statistical downscaling of climate variables, we utilize two metrics: root-mean-square error (RMSE) and mean absolute difference (referred to as bias).  
While our current research considers fifteen temporal inputs, incorporating additional temporal factors may enhance the model's performance. However, we are constrained in selecting a lag for this study due to computational limitations.  
Figure 6: RMSDC based on convolutional LSTM structure.  
Figure 7 depicts the best forecasting results with the lowest RMSD MSE. Despite the lowest MSE, Figure 7 shows a significant discrepancy at the beginning and halfway. However, as seen in Figure 7, the forecasting results are approaching the initial value as the period ends.  
We can contrast the plot behavior of training and testing losses. Figure 8 demonstrates that the training and testing losses decreased, with no rise in the testing loss at this level. Furthermore, because the testing loss was decreased, we could continue training without testing our training data. As a result, no overfitting happened.  
All of the climatic variables including friction, state, air temperature, latitude, longitude, water, speed, and the data from seven sensors are represented as channels on the left.  
The number of kernels employed in a particular block is indicated by the numbers in each block's label, followed by the block's name and activation.  
Baseline Models: The LSTM, RNN, CNN, CONV-LSTM, and RMSDC baseline models are used to compare the empirical results. All of the baseline models, including LSTM, which have produced better outcomes than in [56], had their hyperparameters optimized. The RNN, CNN, CONV-LSTM, and RMSDC models' optimum hidden state dimension values are 32, 32, and 64. This configuration has roughly 18,931; 19,190; and 58,774 trainable parameters for the LSTM, RNN, and CNN, respectively.
# Development of a Magnetostrictive FeNi Coated Surface Acoustic Wave Current Sensor
## 3 Technique Realization  
### Sensor Chip  
Two SAW delay lines with two photolithographically defined 100 nm Al interdigital transducers (IDTs) separated by a 2.5 mm length of propagation path were reproducibly fabricated on a same piezoelectric YZ LiNbO\({}_{3}\) wafer. The launch and receive SPUDT pairs were set to 60 and 30, respectively. The acoustic aperture is 100\(\lambda\) (\(\lambda\) stands for the wavelength corresponding operation frequency of 150 MHz: 23.19 \(\upmu\)m). Following the preparation of delay line patterns, a SiO\({}_{2}\) thin film (50 nm) was deposited on the SAW device surface to protect the electrodes in process of FeNi coating by using the plasma enhanced chemical vapor deposition (PECVD). Prior to FeNi coating, the SiO\({}_{2}\) film surface was cleaned of any contaminants by a routine cleaning procedure involving rinsing in acetone solution, a DI water rinse and drying by N\({}_{2}\). Then, the magetostrictive FeNi film with various aspect ratio (1:1, 1.5:1 and 2:1) was deposited onto the sensing device surface by RF magnetron sputtering (JGP-560C magnetron sputtering equipment) and overlay technique from a FeNi target (nominal composition). The base pressure is 2 \(\times\) 10\({}^{-4}\) Pa. The Ar was used as the sputtering gas, and corresponding pressure is set to 1 Pa. The power density of the target was 400 V (DC) \(\times\) 80 mA/p (35 mm)\({}^{2}\). Various FeNi thicknesses (300, 500 and 700 nm) were obtained by controlling the sputtering time. The specific technological process is depicted in Figure 6. Figure 7 is the picture of the developed FeNi-coated sensor chip, and FeNi coating was indicated by the dark area. The surface morphology of FeNi coating was observed by JSM5600-Lv scanning electron microscopy (SEM) (JEOL, Tokyo, Japan), as shown in Figure 8. It can be seen that the prepared FeNi film are densely and uniformly distributed. To make the comparison, 500 nm FeCo film with aspect ratio of 2:1 was also deposited onto the similar SAW device surface to form a FeCo-coated sensor.  
Figure 6: The technological process of SAW current sensor chip. IDT: interdigital transducer; PECVD: plasma enhanced chemical vapor deposition.  
Figure 7: The physical picture of developed sensor chip, FeNi thickness: 500 nm, aspect ratio: 1:1.  
Next, the FeNi-coated SAW sensor chips with various FeNi thickness (300, 500 and 700 nm) were characterized by using the network analyzer, as shown in Figure 9. With increases in FeNi thickness, the operation frequency of the sensor chip decreases owing to the mass loading effect from the FeNi coating. The measured operation frequency and insertion loss of the sensor chip with 300, 500 and 700 nm FeNi (with aspect ratio of 2:1) are 149.9 MHz and 5 dB, 149.7 MHz and 7 dB, 149.6 MHz and 8 dB, respectively.
## 3 Results and Discussions
### Transcriptomic Differences between L. paracasei Zhang from SSF and LSF  
We used the BGISeQ-500 platform to analyze the differences at the transcriptional level between solid-state fermented and liquid-fermented bacteria. The number of reads obtained from platform sequencing was shown in Table S1. The alignment between reads and reference genome (including genes) was shown in attached Table S2. Correlation analysis results of different replicates showed (Figure S3) that the correlation coefficients of samples from different replicates in the same group were all above 0.96, indicating a good correlation between different replicates in the same group, while the correlation coefficients among different groups were all below 0.56, indicating a low correlation between different groups. The results of principal component analysis also showed (Figure S4) that the three replicate samples in the same group were clustered together, and that they were obviously distinguishable from those in the different groups, indicating that there was an obvious analyzable difference between the groups. Transcriptional analysis revealed a total of 970 differentially expressed genes (DEGs, fold change > 2.0, \(p\) value < 0.001 LSF vs. SSF) (Figure S2), of which 687 DEGs were down-regulated and 283 DEGs were up-regulated. The GO and KEGG analyses of DEGs were performed, and the results were shown in Figures S5 and S6. The major metabolic differences were shown in Table S3 and Figure 5.
# Investigation into Recognition Technology of Helmet Wearing Based on HBSYOLOX-s
## 4 Experiments and Analysis
### Comparison with Other Models  
To verify the reliability of the improved YOLOX algorithm, we primarily compared the mainstream target detection algorithms at one stage with the algorithm proposed in this paper, including fast SSD, YOLOv3, YOLOv4, YOLOv5-s, YOLOX-s, and the HBSYOLOX-s algorithm proposed in this paper; the results of these comparisons are shown in Table 3.  
Comparing the precision, recall, F1, mAP, and FPS values of each approach, as shown in Table 3, demonstrates that the improved HBSYOLOX-s algorithm described in this study is primarily due to SSD, in addition to other models from the YOLO family. YOLOX's precision and recall are more significant than YOLOv5's due to its innovative use of decoupling heads. As a result, the technique outlined in this paper improves the YOLOX model, which has achieved superior results by substituting modules such as HorNet, BiFPN, and SIOU, substantially enhancing the model's performance indices. The model's precision, durability, and detection speed have improved for wider practical use.
## 3 Processing Flow
### Dictionary Training
_Step 3_: Implement the K-SVD algorithm on the phase \(\varphi_{\text{diff}}^{h}\) to train the over-complete dictionary **D**. _Step 4_: Separate out the linear phase \(\varphi_{c}^{l}\) and higher-order phase \(\varphi_{c}^{h}\) from the phase \(\varphi_{c}\), and get sparse coefficients \(\mathbf{a}_{i}\) of \(\varphi_{c}^{h}\) by the OMP algorithm. _Step 5_: Obtain the compensation phase \(\hat{\varphi}_{c}\) by adding the phase \(\hat{\varphi}_{c}^{h}\) denoised by Equation (16) and the phase \(\varphi_{c}^{l}\). _Step 6_: Obtain the accurate compensation phase \(\varphi_{\text{ac}}\) by compensating the phase \(\varphi_{\text{cal}}\) and Doppler compensation phase \(\varphi_{\text{dop}}\) to the interpolated phase \(\hat{\varphi}_{c}\).
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Exploiting FPGA Features  
FPGAs provide unique opportunities for power optimization due to their reconfigurability, customizability, and fine-grained control. These features allow designers to tailor FPGAs precisely to their application's needs, resulting in energy-efficient designs. They offer an opportunity to optimize specific parts of a design by leveraging hardware capabilities such as clock gating, power gating, and dynamic partial reconfiguration (DPR) [120]. These gating techniques not only enable the selective activation or deactivation of components but also facilitate the design of fine-grained power modes. On the other hand, DPR allows for the time-shared deployment of hardware components, further enhancing resource and energy efficiency.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Strategy** & **Description** & **Advantages** & **Constraints** \\ \hline \multirow{2}{*}{Event-driven approach} & Activation of hardware components in response to specific events, rather than running them continuously. & Avoid wasteful energy expenditure during idle periods. & Introduces latency associated with activation time and sharing of resources. \\ \hline \multirow{2}{*}{Duty cycle optimization} & Optimizes average power consumption by balancing active and idle periods at predetermined intervals. & Balance the on and off periods to optimize power consumption. & Introduces latency associated with activation time and sharing of resources. \\ \hline \multirow{2}{*}{TDM of hardware components} & Share the hardware components multiple tasks in a time-sliced manner. & Minimize switching activities. & Could compromise reliability if a true event is missed. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimization strategies for energy-efficient task scheduling.