esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Small Scale Experimental Test
### Comparison of Original Image and Improved Image  
The original image was modified using the proposed image processing method. In Step 2, the original image was divided into R, G, and B channels after the colored image was converted to grayscale. Figure 5 confirms that the original image was biased towards a certain pixel value. In Step 3, the shape was deflected to one side (Figure 6) by applying the image histogram equalization technique to the R, G, and B channels. Figure 7 presents the results of the image grayscale stretching technique, which was applied to the R, G, and B channels in Step 4.  
Figure 4: Image shooting path.  
Figure 5: Results of the image separated into R, G, and B channels and the histogram in Step 2: (**a**) Red, green, and blue channels; (**b**) Histograms of red, green, and blue channels.  
After Step 5, the improved image was presented by integrating the R, G, and B channels. Figure 8 shows a comparison of the original and improved images. The interior of the construction safety net in the original images prior to the application of image processing  
Figure 6: Results of the image separated into R, G, and B channels and the histogram in Step 3: (a) Red, green, and blue channels; (b) Histograms of red, green, and blue channels.  
Figure 7: Results of the image separated into R, G, and B channels and the histogram in Step 4: (a) Red, green, and blue channels; (b) Histograms of red, green, and blue channels.  
cannot be sufficiently visualized, including the \"X\" mark and other objects placed on the work platform (Figure 8a). On the contrary, in the improved image with the implementation of the proposed method, the red \"X\" mark was clearly discerned. The overall identification of objects on the work platform apparently improved (Figure 8b). Considering the hard hat, the shape appeared to be more clearly identified, although it was also partially identified in the original image. The hammer that was not visible in the original image appeared somewhat identifiable in the improved image. The shape of the hammer was not clearly identifiable because the color of the working platform and body of the hammer were the same. The measuring tape was also approximately identifiable in the improved images.
# Application Study of Adaptive Tracking Algorithm in Active Noise Control System of Transformer
###### Abstract  
Active noise control (ANC) technology can be able to reduce the low frequency noise effectively, and has been widely applied in limited enclosed equipment, such as cars, aircraft cockpits, and headphones, etc. Compared with the traditional noise reduction methods, ANC technology is a cost-effective method in handling the low-frequency noise of transformers. An experimental ANC system in a virtual substation is designed and constructed. An adaptive signal tracking ANC algorithm is proposed, and a set of ANC experimental prototypes applied to a virtual substation is designed. Based on the proposed algorithm, the distributions of the acoustics and spectral characteristics of transformer noise in substations are analyzed. Experimental results show that the effective noise reduction can be achieved an average of 5 dB(A) in a given region with respect to the absence of the ANC system.  
Adaptive tracking algorithm; transformer noise; acoustic characteristics; active noise control prototype; substation experiment +
Footnote †: journal: Applied  
1
# Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview
## 7 Applications of Nanoencapsulated Natural Antimicrobial Agents
### Solid Food Systems  
Nanoencapsulation technologies are also used in solid food matrices to improve uniform mixing and extend shelf life. [PERSON] et al. [155] used spray-drying method to encapsulate omega-3 unsaturated fatty acid with high-linear corn starch, and added it to dough in different amounts to study its effect on bread quality. The results show that nanoapsules can effectively reduce the oxidation of unsaturated fatty acids during the bread baking process, thereby greatly improving the quality of bread products, and reducing the oxidation of harmful fatty acids and the production of harmful substances during the baking process [155]. [PERSON] and [PERSON] [156] investigated the activity of liposome-encapsulated pediocin AcH in beef slurries. Their results showed that encapsulating pediocin AcH in liposomes can increase the recovery of pediocin activity in heated beef muscle and beef tallow pulp by an average of 27.5% and 28.9%, respectively, compared with similar beef pulp with free pediocin.
# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 3 Myoelectric Signal Classification  
There are many classifiers in the literature, such as Simple Logistic Regression (SLR), Artificial Neural Networks (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-nearest neighbor (KNN), Nonlinear Logistic Regression (NLR), Multi-Layer Perceptron (MLP), and Support Vector Machines (SVM), among others. However, in some cases, such as the ones shown below, the classification of MES with SVM has demonstrated improved performance in terms of accuracy.  
Recently, [PERSON] et al. [8] made a performance evaluation of several classifiers, using EMG signals to predict five levels of knee angles. They used 15 features per each of the four measured muscles, combining time and frequency features with four auto-regressive (AR) coefficients. The evaluated classifiers were LDA, NB, KNN, and SVM with different kernels, of which the quadratic kernel of SVM performed the best classification accuracy of \(93.07\pm 3.84\%\). In addition, in the study, the EMG signals were segmented in five different window sizes with various overlapped window schemes, and the best result was achieved with the overlapping of 500 ms and 250 ms window sizes.  
Furthermore, EMG signals can be used for finger movement recognition. [PERSON] and [PERSON] [6] compared SVM against LDA and NB in the classification of 15 different finger movements from 15 subjects. They utilized Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) as feature selection algorithms. MAV, ZC, SSC, and WL were the features extracted (see Appendix A). Remarkably, they achieved more than 95% effectiveness without feature selection using the SVM and 4% less with 16 features considered using PSO and ACO.  
EMG signals also allow the diagnosis of muscular dystrophy disorder. [PERSON] and [PERSON] [25] compared ANN with SVM in the classification of EMG signals to identify this disorder by using a wavelet-based decomposition technique. The results show the classification accuracy of an available clinical EMG database, of 140 samples, with 95% effectiveness using a polynomial kernel of fifth order in an SVM.  
A low-cost mechatronics platform for the design and development of robotic hands was proposed by [PERSON] [14], by comparing the SVM with other classifiers, such as ANN, LDA, and SLR.  
For the database, the subjects performed six different hand movements, and from those signals, the time-domain features were extracted, such as MAV, ZC, SSC, WL, MAVS, VAR, RMS, WAMP, and fourth-order AR coefficients (see Appendix A). With these features, they ensembled five groups to demonstrate their influence. Finally, they applied different kernel functions in the classification with SVM and reached the best result with a linear kernel and normalized data of 92.8% in the group with MAV, SSV, WL, ZC and fourth-order AR coefficients.
# The Role of Cryptogams in Soil Property Regulation and Vascular Plant Regeneration: A Review
## 3 Effects of Cryptogam on Vascular Plants Regeneration
### Physical Barrier  
Cryptogam often covers the surface of the soil like a \"carpet\", prevents seeds and sunlight from reaching the soil, and then affects vascular plant regeneration. In a desert study, moss crust restricted seed entry into the soil and significantly reduced seed bank density (4-23 seeds per 0.005 m\({}^{2}\)). After the moss crust was destroyed, the seed bank density returned to bare soil level (>60 seeds per 0.005 m\({}^{2}\)) [32]. [PERSON] studied the germination ability of five plant seeds in different ground cover patches and found that the seeds with the largest size were significantly inhibited in germination because the moss layer prevented them from reaching the suitable substrate for germination, while the remaining seeds were small and could get through the moss layer. As such, germination was not affected [102]. [PERSON]'s study also found that moss significantly inhibited the germination of grass seeds, with seed size directly proportional to the inhibition effect, but moss did not inhibit the growth of seedlings [103]. [PERSON]'s research showed that moss layers not only accumulate more _Emmenopterys henryi_ seeds than bare layers but also reduce the germination rate of seeds [104]. In [PERSON]'s study, the physical barrier and shading effect of moss significantly inhibited seed germination and the seedling growth of four herbaceous plants [35].
## 4 Simulation Results and Attack Test
### Information Entropy Analysis  
One of the important features to show the randomness of the image and measure the encryption scheme is the information entropy, which reveals the confusion degree of the image. The equation that calculated the information entropy is displayed below:  
\[\text{H}(\text{m})=-\sum_{i=0}^{L}p(m_{i})\log_{2}p(m_{i}) \tag{9}\]  
where \(p(m)\) represents the probability of symbol \(m\), which is the source of the information. H(m) shows the value of the entropy, and \(L\) is the amount of grayscale pixels of the image. The theoretical value of information entropy H is 8.  
With the growth in the entropy of the image, much more uncertainty is also shown. The closer it approaches to the ideal value 8, the harder it is for the attackers to divulge information from the image. Table 5 reveals the enthalpy values of the four images and their corresponding encrypted images. From the values in the table, we can see that after the encryption, the entropy for all four cipher images is quite close to the ideal theoretical value and is entirely different from the values in the relevant plaintext image, which means that the algorithm proposed here has significantly disordered the pixels in the digital image.
# A Low-Power Read-Decoupled Radiation-Hardened 16T SRAM for Space Applications
## 3 Simulation Results and Analysis
### Write Ability Comparison  
The write static noise margin (WSNM) has been used as a design metric to evaluate the write ability of SRAM cells. However, recent studies suggest that a more reliable indicator of write capability is the word line write trip voltage (WWTV) [9]. To measure WWTV, a write simulation is performed by supplying the desired data to the bit lines. WWTV is defined as the voltage difference between VDD and WL at the point where the storage nodes Q and QB intersect during a write operation. Cells with longer write access times (WAT) require more time to flip the stored data, which causes Q and QB to intersect at a higher WL voltage level--resulting in a lower WWTV. Therefore, there is an inverse relationship between WWTV and WAT across different cells. As a result, the ranking of cells based on WWTV (Figure 8) exactly mirrors the inverse of their WAT (Figure 6) ranking.  
Figure 6: Comparison of WAT between the proposed LDRH16T and previous works.  
According to the simulation results, the proposed LDRH16T cell achieves a higher WWTV than all other comparison cells except for SAW16T. This indicates that the LDRH16T offers strong write capability while maintaining radiation-hardened features.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 5 Experiment and Analysis
### Infrared Image Test Evaluation
#### 5.3.1 Common Filtering Algorithm Evaluation  
In this section, to verify the performance of our proposed algorithm, first of all, two common smoothing filters are applied to the infrared image. We selected infrared images with weak texture information and severe stripe noise [9]. The algorithm includes 1D Gaussian filtering and Guide filtering and then compare it with our proposed method.  
We set the smoothing parameter of the 1D Gaussian filter to \(\gamma=0.4\), and the filtering window size of the 1D Gaussian filter to 3. Since the window value of the single guided filter is small, there will be a large amount of residual stripe noise, so the filter window for guided filtering is set to h = 0.5H and the regularization parameter \(\varepsilon\) is set to 0.2\({}^{2}\). For our own method, the wavelet basis function of wavelet decomposition is set as db1, the variance of vertical component column equalization is \(\varphi=5\), the regularization parameter \(\varepsilon\) of guided filter is set as 0.2\({}^{2}\), and the filtering window h is set as 0.3H. The comparison results are shown in Figure 11. It can be clearly seen that the Gaussian filter fails to distinguish the texture information from the noise, which reduces the gradient of the whole image and results in image blurring. Guided filter has a better effect of striping, but it can also blur the texture information. Our proposed method eliminates stripe noise while simultaneously retaining image details.
###### Abstract  
Knowledge about the permeability of surrounding rock (salt rock and mudstone interlayer) is an important topic, which acts as a key parameter to characterize the tightness of gas storage. The goal of experiments that test the permeability of gas storage facilities in rock salt is to develop a synthetic analogue to use as a permeability model. To address the permeability of a mudstone/salt layered and mixed rock mass in Jintan, Jiangsu Province, synthetic mixed and layered specimens using the mudstone and the salt were fabricated for permeability testing. Because of the gas \"slippage effect\", test results are corrected by the Klinkenberg method, and the permeability of specimens is obtained by regression fitting. The results show that the permeability of synthetic pure rock salt is \(6.9\times 10^{-20}\) m\({}^{2}\), and its porosity is 3.8%. The permeability of synthetic mudstone rock is \(2.97\times 10^{-18}\) m\({}^{2}\), with a porosity 17.8%. These results are close to those obtained from intact natural specimens. We also find that with the same mudstone content, the permeability of mixed specimens is about 40% higher than for the layered specimens, and with an increase in the mudstone content, the Klinkenberg permeability increases for both types of specimens. The permeability and mudstone content have a strong exponential relationship. When the mudstone content is below 40%, the permeability increases only slightly with mudstone content, whereas above this threshold, the permeability increases rapidly with mudstone content. The results of the study are of use in the assessment of the tightness of natural gas storage facilities in mudstone-rich rock salt formations in China.  
gas storage; material science; rock permeability; synthetic rock salt testing; Klinkenberg method +
Footnote †: journal: Applied Physics Letters
# Robust Control for a Slung-Mass Quadcopter Under Abrupt Velocity Changes
## 1 Introduction
### Contributions  
* This paper presents a robust control law to reduce oscillations caused by a slung load on a quadrotor and to stabilize a slung load in abrupt velocity changes.
* The robust technique is based on a sliding mode controller that compensates for disturbances associated with the trajectory that the vehicle follows to carry the load from one location to another.
* The main characteristic of this algorithm is to obtain the slung-mass angular position and velocity measurements (\(\alpha\), \(\dot{\alpha}\)) to provide feedback and achieve its stabilization on the vertical axis in order to improve the performance by reducing the oscillations in outdoor real-time experiments.  
The present paper is organized as follows: Section 2 presents the system modeling and robust control design, which includes the modeling of the slung-mass quadcopter and design of robust control laws for vertical and longitudinal dynamics. The experimental setup is described in Section 3. Sections 4 and 5 contains the simulation and the experimental results, respectively. Finally, in Section 6 the conclusions are presented.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{(a)} \\ \multicolumn{11}{c}{Franz Josef Terminal Point} \\ \multicolumn{11}{c}{Model Index Predictors} \\ \hline
1 & CO2 & & & & & & & & & & & \\ \hline
2 & & CO2 Global\_Mean & & & & & & & & & & \\ \hline
3 & & CO2 Average\_TMAX Global\_Mean & & & & & & & & & & \\ \hline
4 & & Average\_PRCP CO2 Average\_TMAX Global\_Mean & & & & & & & & & & \\ \hline \multicolumn{11}{c}{Subsets Regression} \\ \multicolumn{11}{c}{Model} & R- & Adj. R- & \multicolumn{11}{c}{Spare} & Pred. R- & C(p) & AIC & SBIC & SBC & MSEP & FFE & HSP & APC \\ \hline
1 & 0.58 & 0.57 & 0.56 & 19.07 & 599 & 338 & 607 & 3495 & 38.82 & 0.43 & 0.44 \\ \hline
2 & 0.64 & 0.63 & 0.61 & 1.93 & 577 & 319 & 587 & 2871 & 32.59 & 0.36 & 0.39 \\ \hline
3 & 0.65 & 0.63 & 0.61 & 2.21 & 577 & 319 & 590 & 2847 & 32.65 & 0.36 & 0.39 \\ \hline
4 & 0.65 & 0.64 & 0.61 & 5.00 & 555 & 308 & 569 & 2779 & 33.77 & 0.39 & 0.39 \\ \hline \multicolumn{11}{c}{(b)} \\ \multicolumn{11}{c}{Franz Josef Area} \\ \multicolumn{11}{c}{Model Index Predictors} \\ \hline
1 & \multicolumn{11}{c}{Global\_Mean} \\ \hline
2 & \multicolumn{11}{c}{Average\_TMAX Global\_Mean} \\ \hline
3 & \multicolumn{11}{c}{Average\_PRCP Average\_TMAX Global\_Mean} \\ \hline
4 & \multicolumn{11}{c}{Average\_PRCP CO2 Average\_TMAX Global\_Mean} \\ \hline \multicolumn{11}{c}{Subsets Regression} \\ \hline Model & R- & Adj. R- & Pred. R- & C(p) & AIC & SBIC & SBC & MSEP & FPE & HSP & APC \\ \hline
1 & 0.34 & 0.31 & 0.24 & 4.74 & 389 & 315 & 393 & 4,213,050 & 174,460 & 7043 & 0.77 \\ \hline
2 & 0.43 & 0.38 & 0.27 & 3.02 & 387 & 314 & 392 & 3,792,603 & 162,351 & 6616 & 0.72 \\ \hline
3 & 0.47 & 0.40 & 0.26 & 3.25 & 387 & 315 & 393 & 3,667,008 & 62,052 & 6687 & 0.72 \\ \hline
4 & 0.48 & 0.38 & 0.20 & 5.00 & 389 & 317 & 396 & 3,805,281 & 173,374 & 7270 & 0.77 \\ \hline \multicolumn{11}{c}{(c)} \\ \multicolumn{11}{c}{Gorner Terminal Point} \\ \multicolumn{11}{c}{Model Index Predictors} \\ \hline
1 & CO2 & & & & & & & & & & \\ \hline
2 & & CO2 Average\_TMAX & & & & & & & \\ \hline
3 & & Average\_PRCP CO2 Average\_TMAX Global\_Mean & & & & & & \\ \hline \multicolumn{11}{c}{Subsets Regression} \\ \hline Model & R- & Square & Adj. R- & Pred. R- & C(p) & AIC & SBIC & SBC & MSEP & FFE & HSP & APC \\ \hline
1 & 0.93 & 0.93 & 0.93 & \(-\)2.00 & 333 & 61 & 341 & 174 & 1.85 & 0.0195 & 0.07 \\ \hline
2 & 0.93 & 0.93 & 0.93 & \(-\)0.82 & 334 & 62 & 344 & 174 & 1.87 & 0.0197 & 0.07 \\ \hline
3 & 0.93 & 0.93 & 0.93 & 1.18 & 336 & 64 & 349 & 176 & 1.91 & 0.0202 & 0.08 \\ \hline
4 & 0.92 & 0.92 & 0.91 & 5.00 & 323 & 65 & 338 & 172 & 2.00 & 0.0223 & 0.09 \\ \hline \end{tabular}
\end{table} 
Table 1: Multiple Regression Models. (**a**) Terminal Point of Franz Josef as Response. (**b**) Area of Franz Josef as Response. (**c**) Terminal Point of Gorner as Response. (**d**) Area of Gorner as Response.
## 2 Modeling of Pieoresistive Behavior  
### Nonlinear Piezoresistive Behavior on Plain-Woven CFRP  
In CFRP, electrical current can flow via carbon fibers [20]. The arrangement of the fibers inside the CFRP determines the electrical resistance. This is also the reason why the woven CFRP has different electrical characteristics with unidirectional CFRP. Figure 1 shows a schematic illustration of fiber direction in plain-woven CFRP. The fibers initially have a certain waving position, which depends on manufacturing process quality. Under tensile loading, fibers in wrap direction change their arrangement from waving to straightening prior to tightening. In contrast, under tensile loading, fibers in the wert direction approach to fibers in wrap direction and spread along the wrap direction. The rearrangement of these fibers may cause the electrical resistance changes differently compared to unidirectional CFRP. Fiber distribution in the weft direction plays an important role in determining overall electrical resistance.  
During the unloading process, both fibers in wrap and weft directions may not return back to the initial waving position, especially after imposing high tensile loading, which causes high strain conditions. Depending on the rearrangement of those fibers, nonlinear behavior and residual electric resistance might appear during the unloading process. This behavior must be evaluated accurately to improve the design process of the SHM for the woven CFRP.
## 4 Discussion  
### Effects of Void Ratio Changes on Damping Ratio  
An investigation was also conducted to examine the effects of cyclic loading on the void ratio of the tested samples. The minimum and maximum void ratio (i.e., \(e_{\text{min}}\) and \(e_{\text{max}}\)) variations against the number of cycles based on the \(R\), \(S\) and \(\rho\) parameters are provided in Figure 18, Figure 19 and Figure 20, respectively. Note that the circle diameters in these figures represent the magnitude of the void ratio parameter. The findings demonstrate that with an increase in the number of loading cycles, and thus a consequent rounding of the sand grains, the void ratio between the grains reduces. This can be attributed to the fact that more rounded and spherical particles possess a higher packing capability, which leads to a reduction in the volume of voids (and an increase in the contact levels achieved between the particles). This increase in inter-particle interaction leads to higher energy dissipation during cyclic loading, which is reflected in the higher damping ratio (with an increasing number of cycles).  
Figure 17: Correlation performance of the best SVM model to predict the damping ratio for (**a**) training and (**b**) testing datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Training Dataset** & **Testing Dataset** \\ \hline MAE (\%) & 0.716 & 0.831 \\ MSE (\%) & 0.761 & 1.302 \\ RMSE (\%) & 0.872 & 1.141 \\ MSLE (\%) & 0.006 & 0.003 \\ RMSLE (\%) & 0.079 & 0.057 \\ \(R^{2}\) (-) & 0.973 & 0.892 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Overall performance of the best SVM model to predict the damping ratio for both training and testing datasets.  
Figure 18: Effects of cycling loading on (**a**) \(e_{\text{min}}\) and (**b**) \(e_{\text{max}}\) based on the roundness \(R\) parameter.