esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
It is necessary to optimize clustering processing of communication big data numerical attribute feature information in order to improve the ability of numerical attribute mining of communication big data, and thus a big data clustering algorithm based on cloud computing was proposed. The cloud extended distributed feature fitting method was used to process the numerical attribute linear programming of communication big data, and the mutual information feature quantity of communication big data numerical attribute was extracted. Combined with fuzzy C-means clustering and linear regression analysis, the statistical analysis of big data numerical attribute feature information was carried out, and the associated attribute sample set of communication big data numerical attribute cloud grid distribution was constructed. Cloud computing and adaptive quantitative recurrent classifiers were used for data classification, and block template matching and multi-sensor information fusion were combined to search the clustering center automatically to improve the convergence of clustering. The simulation results show that, after the application of this method, the information fusion performance of the clustering process was better, the automatic searching ability of the data clustering center was stronger, the frequency domain equalization control effect was good, the bit error rate was low, the energy consumption was small, and the ability of fuzzy weighted clustering retrieval of numerical attributes of communication big data was effectively improved.  
cloud computing; communication big data; numerical attributes; fuzzy weighting; clustering +
Footnote †: journal: Computer Science and Technology  
0000-0001-9993-3300]H.H.Ding
# Stability Analysis in Multi-VSC (Voltage Source Converter) Systems of Wind Turbines
## 3 Equilibrium Points Computation and Eigenvalue Analysis in a Multi-VSC System  
First, two dq frames need to be defined in order to describe the dynamics related to the PLL. These are the grid dq frame and the control dq frame, both of which have been analytically introduced in [23]. The converter current \(I_{L}\) and the PCC voltage \(V_{PCC}\) are the variables impacted by the control dq frame. The resulting equations from the linearization between the two dq frames, which express the transformation between the grid and the control dq frame, are shown below (variables in the control dq frame are indicated by the superscript \(c\)):  
\[\Delta V_{PCCd}^{c}=\Delta V_{PCCd}+V_{PCCq,0}\Delta\theta \tag{3}\]  
\[\Delta V_{PCCq}^{c}=\Delta V_{PCCq}-V_{PCCd,0}\Delta\theta \tag{4}\]  
\[\Delta I_{Ld}^{c}=\Delta I_{Ld}+I_{Lq,0}\Delta\theta \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Description** & **Value** \\ \hline \(V_{S}\) & Grid Phase Voltage & 311 V \\ \(f_{n}\) & Rated Grid Frequency & 50 Hz \\ \(V_{DC}\) & DC Link Voltage Reference & 800 V \\ \(L_{F}\) & Filter Inductance & 5 mH \\ \(R_{F}\) & Filter Resistance & 0.1 \(\Omega\) \\ \(C_{F}\) & Filter Capacitance & 10 \(\mu\)F \\ \(f_{su}\) & Switching Frequency & 20 kHz \\ \(f_{S}\) & Sampling Frequency & 20 kHz \\ \(V_{PCCdf}\) & Reference PCC Voltage & 280 V \\ \(P_{nf}\) & Nominal Active Power & 30 kW \\ \(\omega_{FFLDF}\) & Cutoff frequency of FF Voltage & 100 rad/s \\ \(\omega_{AVC,LPF}\) & Cutoff frequency of AVC & 50 rad/s \\ \(K_{LCC0}\) & Default Integral Gain of Current Control & 666.7 \\ \(K_{PCC0}\) & Default Proportional Gain of Current Control & 33.3 \\ \(K_{LPL10}\) & Default Integral Gain of PLL & 0 \\ \(K_{P,PL10}\) & Default Proportional Gain of PLL & 0.1637 \\ \(K_{L,\mu 0}\) & Default Integral Gain of AVC & 10 \\ \(K_{P,\mu 0}\) & Default Proportional Gain of AVC & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: System and Default Control Parameters of the System in Figure 1.  
\[\Delta I_{Lq}^{c}=\Delta I_{Lq}-I_{Ld,0}\Delta\theta \tag{6}\]  
where \(\Delta\theta\) is the phase shift between the two dq frames.  
To evaluate the stability of the converter-based system, the potential equilibrium points need to be identified first through the linearized state-space model. Assessing the system's local stability involves using a linear approximation of this state-space model, represented as  
\[\dot{x}=Ax \tag{7}\]  
In this context, the matrix \(A\), known as the Jacobian matrix, comprises partial derivatives relating to the system at steady operating points. These equilibrium points are established by setting the state equations of the system to \(\dot{x}=0\) and solving for them.  
In Section 3.1, a generalized methodology is analytically described for obtaining the system's equilibrium points based on the number of VSCs connected to the PCC.
## 2 Methods
### Study Tools  
The DCDQ was used in this study to gather information on the research group of children with DCD. The DCDQ [19] is a 15-item questionnaire, where parents rate the motor abilities of their child (from '1'-'does not describe my child' to '5'-'fully describes my child'), culminating in a score ranging from 15 to 75. A score in the DCDQ of up to 46 was suggested as the best cutoff score to identify children with DCD or suspect for DCD ([PERSON] et al., 2009).  
The MABC-2 [20,21] for the age group of 3-6 years old contains 8 tasks. Of these, three tasks evaluate manual dexterity (posting coins, treading beads, and tracing a bicycle trail with a pencil), two tasks evaluate visual-motor coordination (catching a beanbag and rolling a ball into a goal), and three tasks evaluate static and dynamic balance (one-leg balance with each leg, jumping forward, and walking with raised heels). A score up to the 5 th percentile is an indication of motor difficulty, a score between the 5 th and 16 th percentiles indicates suspicion of motor difficulty, and a score above the 16 th percentile indicates normal motor function.  
A portable, non-immersive VR game, designed by an occupational therapist and fitted for children with motor difficulties (Timocco, Tel Aviv, Israel), utilizes a laptop with a webcam and two soft playing balls of different colors, worn on each hand like a glove. The movement of each ball is identified by the camera in real-time, and controls the hand movements of a virtual monkey, displayed on the screen. Each ball controls the mirrored virtual hand. A game named \"Falling Fruit\" was chosen for this study out of a number of available games. During this game, the virtual monkey has to catch a piece of fruit dropping from the top of the screen, and place it in one of two baskets, set near the legs of the monkey. This game was chosen since it requires visual tracking of a moving target (the virtual fruit), reaching towards a moving target, and also movement to a stationary target (the virtual basket). Other games did not include all the aforementioned requirements, e.g., a game of balloon popping did not include movement towards a stationary target. The balloon popping game was therefore used only for practice, in order to get the child acquainted with the system. The computerized game records the total score (number of pieces of fruit placed in the basket--a higher score indicates higher success in the game), and the path length of each hand during the game [22,23].  
Lastly, a thin pressure-measuring pad was taped to the floor. The pad consisted of 256 pressure sensors (M-flex, size 53 cm x 53 cm). The location of the COP was recorded during the trial.
## 4 Experiments and Analysis
### Comparative Analysis of Single-Source Domain and Multi-Source Domain Transfer Learning Task Results
#### 4.3.1 Visual Comparative Analysis of Output Features  
Figure 9 shows the fault feature visualizations of the output from the target domain samples by the last layer of the fault diagnosis network. Like the previous comparison method, the fault feature is reduced to a two-dimensional plane. In the three images in each row, the target domain of the transfer learning task is the same.  
From Figure 9, we can see that the single-source domain transfer learning tasks (a), (b), (d), (e), (g), and (h) all have more overlap problems, and the fault diagnosis network has difficulty in classifying them accurately. In contrast, there is less overlap between categories in the multi-source domain migration learning tasks (c), (f), and (i).
## 2 Nanodiamonds Synthesis Techniques  
It is well known that the stable allotrope of carbon at ambient pressure and room temperature is graphite. As can be seen in the carbon phase diagram of Figure 1, diamond requires extreme conditions of temperature and pressure to be formed. For instance, natural diamond may be formed in the Earth's mantle, 140-200 km below the surface [26], where the temperature and pressure are in the range of 900-1400 \({}^{\circ}\)C and 4.5-6 GPa, respectively [27]. Once the diamond phase is formed, the transition back to graphite at ambient conditions is avoided by the high energy barrier for phase transition. Indeed, even if graphite is thermodynamically favored (energy difference of 0.02 eV per atom between diamond and graphite), a 0.4 eV energy barrier must be overcome to move from sp\({}^{2}\) to sp\({}^{3}\) chemical bonds. This makes diamond a metastable phase, as it is thermodynamically unstable, but the transition kinetics to graphite is prohibitive. Currently, many different methods for artificial synthesis of NDs are available [1]. In the following sections, the three main techniques commercially available are described: detonation, chemical vapor deposition (CVD), and milling of high-pressure, high-temperature (HPHT) micro-sized diamonds.
# Imperceptible-Visible Watermarking to Information Security Tasks in Color Imaging
## 4 Experimental Results
### Robustness against Common Image Processing Attacks  
To evaluate the performance of the proposed algorithm against malicious or non-malicious image processing attacks, an image is watermarked individually by the three proposed watermarks. As example, Table 7 shows the QR watermarked image after being attacked by the common image processing attacks listed in Table 3. Table 8 shows the visual and numerical robustness results obtained from the images watermarked by the three mentioned watermarks. For the experimental evaluation, the quality of the exhibited watermarks is performed in terms of the NC and BER, and the watermarked images have been attacked by the maximum attack strength that the watermarks can resist; in this way, for watermarked images by QR, a maximum attack strength allows the QR code information decoding to watermarked images by FI or UNAM, a maximum attack strength also allows recognizing the watermark; the specification to each attack is also provided. The obtained results show the robustness of the proposed algorithm against Filter, Noise contamination, Sharpening, Blurring, and Histogram Equalization; in all those cases, visual results of the exhibited watermarks can be recognized easily by the HVS. Numerical results in terms of NC and BER show diverse values nearest to 1 or 0, respectively, which can be considered as the maximum or minimum degradation to recognize and/or decode the watermark pattern. Obtained results from the QR watermarked image show that the watermark can be exhibited with enough quality to be decoded after being attacked; however, its compression robustness is limited due to the QR watermark characteristics and its error correction level; in this way, the QR is only recognized after compression attacks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Attack**} & \multicolumn{2}{c}{**Filter**} & \multicolumn{2}{c}{**Noise Contamination**} \\ \cline{2-5}  & **Average** & **Median** & **Gaussian** & **Impulsive** & **Gaussian** \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Attacked \\ Image \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} **Sharpening** \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} **Blurring** \\ \end{tabular}  } & \multirow{4}{*}{\ \begin{tabular}{c} **Histogram** \\ **Equalization** \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} **Compression** \\ **JPEG** \\ \end{tabular}  } \\ \cline{1-1} \cline{5-5}  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \cline{1-1} \cline
Similar to the results reported in [24], Table 8 shows that watermarks such as owner logotypes FI and UNAM are more robust against common image processing attacks than QR logotypes, as can be observed in the compression evaluation where a UNAM watermark is recognized after being compressed by JPEG and JPEG2000 with a quality factor QF:75
# Efficient Knowledge Distillation for Brain Tumor Segmentation
## 5 Ablation Study  
### Effectiveness of Distillation Methods  
The proposed distillation methods, CD and KD, are collectively referred to as CK, and a large number of experiments were carried out on the BraTs2018 dataset to verify the effectiveness of CK. Figure 2 shows some visual examples, which show that our method corrected some of the students' mistakes and made the predicted mask closer to the ground truth.  
We used UNet and AttUNet as our teachers and DeepResUNet and UNet++ as our students. Table 1 lists the performance of our distillation method, CK, for different combinations of teacher and student networks. The experimental results showed that the knowledge distillation architecture proposed in this paper played a positive role in improving the segmentation accuracy of the student network. After learning from UNet, DeepResUNet had maximum improvements of 1.25, 0.72 and 2.84 in ET, WT and TC Dice scores, respectively, and 0.08, 0.07 and 0.16 in Hausdorff distance, respectively. Meanwhile, after learning from the teacher AttUNet, the Dice score of the student network DeepResUNet in ET broke from 78.44 to 80.22, and the Dice score of the student network UNet++ in ET broke from 78.47 to 80.48. Thus, we honored DeepResUNet as the most outstanding student. To more visually demonstrate the effectiveness of our method, the obtained experimental results were compared with the currently popular networks in this experiment using AttUNet and UNet++ as the teacher network and the student network, respectively. [PERSON] et al. [24] proposed HybridResNet, using resnet blocks for downsampling and normal double convolution blocks for upsampling. Compared to UNet, ResNet blocks helped to improve the performance of the network, so we also included [24] in our comparison network. In Table 2, it can be seen that the UNet++ using our method achieved the crown while keeping the lower parameters.  
Figure 2: The segmentation results were obtained by applying our distillation method in four cases (**a–d**) of the BraTS validation dataset. ET—yellow, TC—yellow+red, WT—yellow+red+green. The teacher was UNet and the student was DeepResUNet.
# A Super-Resolution Reconstruction Driven Helmet Detection Workflow
## 1 Introduction
The region-based convolutional neural network (RCNN) combines the selective search algorithm and convolutional neural network to detect targets, which makes for great improvements in accuracy and speed compared with traditional methods [5]. An RCNN is a two-stage detection network, the speed of which is difficult to meet in real construction engineering. The you only look once model (YOLO) realizes faster detection based on the improvement of the feature extractor backbone. Due to their significant improvement of detection performance, they replace traditional methods in a short time. Actually, most  
Figure 1: The example of high-resolution images acquired from digital camera and the degraded low-resolution images obtained from the wireless channel due to the constraint of the available transmission bandwidth. (**a**) high-resolution images; (**b**) low-resolution images. modern object detection architectures are developed and validated on prepared datasets, which consist of high-quality images. Although they achieve good accuracy on those well-captured images, their performance still decreases quickly as the input quality decreases. In other words, the design of detection architectures assumes that the input image quality meets the demand. However, in our application scenario, when these models are applied on detecting helmets from images obtained through moving cameras, which are degraded in quality, it is hard to prevent the performance crashing. To solve this problem, we propose a super-resolution reconstruction driven helmet detection workflow to improve detection accuracy under poor image quality. The main contributions of the paper are as follows. 1. We propose an end-to-end helmet monitoring system, which implements a super-resolution reconstruction driven helmet detection workflow. It works well with poor input image quality and is easy to collaborate with any kinds of image acquisition device, including a wireless web camera or UAV. 2. We propose to train a super-resolution model with combination loss of \(l_{1}\) and contextual loss, which enhance its accuracy. We train the super-resolution reconstruction model and the detection model iteratively from scratch to achieve final results. 3. Validations are performed on both a public dataset as well as the realistic dataset obtained from a practical construction site. The results show the proposed workflow achieves a promising performance and surpasses the competing methods.
# Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation
## 3 Results
### Decision Tree Classification
#### 3.2.1 Univariate Classification  
The performance measures, as well as the selected window numbers for the three univariate decision trees, are presented in Table 2, and the corresponding ROC curves and AUC-values are shown in Figure 5. Firstly, a combination of the slope inside window 1 and 6 performs the best for the decision tree based on T\({}_{\text{nose}}\). In total, the condition of 13 out of 19 drivers was classified correctly.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Welch’s** & \multicolumn{2}{c}{**T\({}_{\text{nose}}\)**} & \multicolumn{2}{c}{**T\({}_{\text{wrist}}\)**} & \multicolumn{2}{c}{**HR**} & \multicolumn{2}{c}{**SSS**} \\ \cline{2-9}
**ANOVA** & **D** & **ND** & **D** & **ND** & **D** & **ND** & **D** & **ND** \\ \hline dF & 9 & 8 & 8 & 8 & 8 & 7 & 9 & 8 \\ F & 11.8 & 0.5 & 7.9 & 0.9 & 15.7 & 3.5 & 54.2 & 1.9 \\ \(p\)-value & \textless{}0.001 & 0.820 & \textless{}0.001 & 0.493 & \textless{}0.001 & 0.008 & \textless{}0.001 & 0.097 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the [PERSON]’s ANOVA tests. The “degrees of freedom” for each test are indicated by dF. F indicates the corresponding F-statistic. “D” refers to the group of drowsy drivers and “ND” to the non-drowsy drivers.  
Figure 4: Average trend of T\({}_{\text{nose}}\), T\({}_{\text{wrist}}\), heart rate (HR), and Stanford sleepiness scale (SSS) in drowsy and non-drowsy subjects. (**a**) T\({}_{\text{nose}}\) in drowsy subjects, (**b**) T\({}_{\text{nose}}\) in non-drowsy subjects, (**c**) T\({}_{\text{wrist}}\) in drowsy subjects, (**d**) T\({}_{\text{wrist}}\) in non-drowsy subjects, (**e**) HR in drowsy subjects, (**f**) HR in non-drowsy subjects, (**g**) SSS in drowsy subjects, (**h**) SSS in non-drowsy subjects.  
Additionally, in case the classification was solely based on the information from window 6, this resulted in a sensitivity, specificity, and accuracy of 90.0%, 66.7%, and 79.0%, respectively. Secondly, when analyzing the temperature measured at the wrist, the combination of windows 2 and 4 came out on top. As mentioned, the data of 18 drivers were available. All participants that did not experience drowsiness were classified correctly, as well as 7 out of 9 \"drowsy\" drivers. For T\({}_{\text{wrist}}\) we did not observe an increase in the performance when the data of only one window were used. Third, the decision tree based on the information of the heart rate inside window 6 resulted in an overall accuracy of 70.6%. Here, these results are based on the data of 17 driving simulations.
## 3 Methods
### Multiscale Prediction Model  
#### 3.2.1 Ensemble Empirical Mode Decomposition  
EEMD is an improved EMD method based on noise-assisted analysis [37]. The principle is to decompose the signal n times by EMD and add white noise with fixed variance and zero means to the initial signal in each decomposition process. By using the property that white noise has zero means to offset the noise effect and taking the integrated mean result as the final result, the modal mixing problem of the EMD algorithm has been effectively solved, ensuring the automatic distribution of the signal in the appropriate time scale.  
The EEMD algorithm decomposes complex non-stationary signals into intrinsic mode function (IMF) components at various time scales. Compared to the initial signal, the fluctuation amplitude of each IMF component is reduced, and more precise forecasting results can be obtained by analyzing the IMFs separately. This method has significant advantages in extracting the fluctuation patterns of complex sequences and amplifying the precision of the prediction model. The EEMD algorithm consists of the following specific steps:  
1. Add white noise sequence \(w_{i}(t)\) to the initial signal \(x(t)\) to obtain new signal \(X_{i}(t)\): \[X_{i}(t)=x(t)+w_{i}(t)\] (4) where \(i\) is the number of white noise additions.
2. The EMD decomposition of \(X_{i}(t)\) is performed to obtain the sum of the IMF components of each order and the residual component \(r_{es}\) after decomposition: \[X_{i}(t)=\sum_{j=1}^{n}c_{ij}(t)+r_{es}\] (5) where \(c_{ij}(t)\) is the \(j\)th IMF component derived from the decomposition after adding white noise for the \(i\)th time, and the value of \(j\) is in the range of \(1\sim n\).
3. Repeat the above two steps \(M\) times and add the IMF components obtained each time, then calculate the mean value as the result: \[c_{j}(t)=\frac{1}{M}\sum_{i=1}^{M}c_{ij}(t)\] (6) where \(c_{j}(t)\) is the \(j\)th IMF component average obtained after EEMD decomposition of the initial signal.
## 4 Result and Analysis
### Research Validation
#### 4.4.4 Test Statistics  
Table 5 shows the test results using the Wilcoxon test. We calculated the value of \(p\) by passing two groups in the Wilcoxon test. We considered Ant as a target project and observed the accuracy with other source projects i.e., Ivy, Camel, Synapse, Velocity, Lucene, Poi, Xerces, Xalan, and Log4j. One of the groups of the observation contained values of measure without applying hybrid feature selection and another observation contained values of the measure after applying hybrid feature selection.