esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Video Rate Identification
This angle-dependent behaviour can directly impact of the BER, by the activation of unwanted channels and leading to a wrong corresponding binary code. To compensate for such a frequency-shift impact, we must increase the channels width \(\delta\!f\). According to Expressions (8) and (9), the number \(N\) of channel decreases, as well as the coding capacity. In order to fix a limit acceptance angle, we plot in Figure 9 the impact of the incident angle on the ultimate coding capacity based on the tag structure previously described. Figure 8: BER (%) calculated by using the 100 signatures of the tag, versus the acquisition duration. Figure 7: (a) BER versus the value of decision threshold, computed from different acquisition times. (b) Evolution of the decision threshold in function of the acquisition time for minimum BER (blue curve), and optimal decision threshold calculated from (black line). Considering the first analysis, the initial coding capacity of 8-bits remains unchanged since the incidence angle remains smaller than 12\({}^{\circ}\). However, in the case of a larger capacity around 60 dB, the angular acceptance drops down to 5\({}^{\circ}\). Let noticed that for a coding capacity from about 25 bits, such angular acceptance keeps roughly constant.
## 3. Results  
### Significance and Correlation between Mixing Ratio of Materials and Sensory Properties  
Table 4 displays the coefficients of the quadratic regression equation, encompassing the linear, quadratic, and cross-product terms for hardness (\(Y_{1}\)), redness (\(Y_{2}\)), and sensory texture (\(Y_{3}\)), along with their significance, contingent on the mixing ratio of squid (A), soy protein isolate (B), and surimi (C), the primary constituents of CSPSP.  
In the case of hardness (\(Y_{1}\)), significance was observed in five types of terms: primary terms \(X_{1}\) and \(X_{2}\), secondary terms \(X_{1}\)\({}^{2}\) and \(X_{2}\)\({}^{2}\), and the cross term \(X_{1}X_{2}\) (\(p<0.05\)). This can be expressed by the equation: \(Y_{1}=27,\!189-7574X_{1}-4286X_{2}+3770X_{1}\)\({}^{2}+2500X_{2}\)\({}^{2}\)\(+2387X_{1}X_{2}\) (R\({}^{2}=0.973\), \(p\)-value = 0.000). For redness (\(Y_{2}\)), significance was noted in three terms: the primary term \(X_{1}\) and the secondary terms \(X_{1}\)\({}^{2}\) and \(X_{2}\)\({}^{2}\). This can be represented by the equation: \(Y_{2}=10.667+1.455X_{1}-0.812X_{1}\)\({}^{2}-0.630X_{2}\)\({}^{2}\) (R\({}^{2}=0.878\), \(p\)-value = 0.000). In the case of sensory texture (\(Y_{3}\)), only four terms--the primary terms \(X_{1}\) and \(X_{2}\) and the secondary terms \(X_{1}\)\({}^{2}\) and \(X_{2}\)\({}^{2}\)--were found to be significant (\(p<0.05\)). This is expressed by the equation: \(Y_{3}=4.333+0.1332X_{1}-0.2259X_{2}-0.5917X_{1}\)\({}^{2}-0.7167X_{2}\)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{\(Y_{1}\)} & \multicolumn{2}{c}{\(Y_{2}\)} & \multicolumn{2}{c}{\(Y_{3}\)} \\ \cline{2-7}
**Parameters** & **Coefficient** & \(p\)**-Value** & **Coefficient** & \(p\)**-Value** & **Coefficient** & \(p\)**-Value** \\ \hline Constant & 27,189 & 0.000 & 10.667 & 0.000 & 4.333 & 0.000 \\ \(X_{1}\) & \(-7574\) & 0.000 & 1.455 & 0.001 & 0.1332 & 0.035 \\ \(X_{2}\) & \(-4286\) & 0.000 & \(-0.111\) & 0.590 & \(-0.2259\) & 0.005 \\ \(X_{1}X_{1}\) & 3770 & 0.001 & \(-0.812\) & 0.016 & \(-0.5917\) & 0.000 \\ \(X_{2}X_{2}\) & 2500 & 0.008 & \(-0.630\) & 0.040 & \(-0.7167\) & 0.000 \\ \(X_{1}X_{2}\) & 2387 & 0.019 & 0.267 & 0.370 & \(-0.0250\) & 0.719 \\ \hline \end{tabular} \(X_{1}\), A/(B + C); \(X_{2}\), B/C; A, RCS mantle; B, isolated soy protein paste; C, surimi paste; \(Y_{1}\): hardness (\(\times 1000\) N/m\({}^{2}\)), \(Y_{2}\): redness, \(Y_{3}\): sensory texture (points).  
\end{table}
Table 4: Estimated coefficients of the fitted quadratic polynomial equations for dependent variables based on the results of the t-statistic.  
(R\({}^{2}\) = 0.959, \(p\)-value = 0.000). Table 5 presents the correlation between the independent and dependent variables necessary to optimize the mixing ratio of the main ingredients of CSPSP. To optimize the mixing ratio of these main ingredients, in the case of hardness (\(Y_{1}\)), all primary, secondary, and cross terms were significant (\(p\) < 0.05). For redness (\(Y_{2}\)) and sensory texture (\(Y_{3}\)), only the primary and secondary terms were significant (\(p\) < 0.05).
# Gaze Estimation via Strip Pooling and Multi-Criss-Cross Attention Networks
## 5 Conclusions  
In this paper, we propose a novel network architecture, referred to as SPMCCA-Net. We designed this model by incorporating strip pooling and the criss-cross attention mechanism for gaze estimation to address the insufficient ability of existing models to capture long-distance dependencies between the eyes from full-face images and to reduce the impact of redundant information in face images that may interfere with gaze prediction. We further improved model accuracy and enhanced the stability and convergence speed of model training by adopting smooth L1 loss as regression loss. The experimental results with the Gaze360 and RT-Gene datasets demonstrate that the newly designed network outperforms existing gaze estimation methods, with some dependence on the choice of different regression coefficients.  
Funding acquisition, S.D.; investigation, C.Y., S.D., C.X., W.P. and X.L.; methodology, C.Y. and S.D.; project administration, C.Y.; resources, S.D. and C.X.; software, S.D.; supervision, S.D. and C.X.; writing--original draft, C.Y.; writing--review and editing, C.Y., S.D., W.P. and C.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Natural Science Foundation (423026), the National Natural Science Foundation of China (Grant No. 61906017, 62102033, 62171042, 61871028, 62272049, 62006020), the Beijing Municipal Commission of Education Project (No. KM201911417001, KM202111417001), the Project of Construction and Support for high-level Innovative Teams of Beijing Municipal Institutions (No. BPHR20220121), the Beijing Advanced Talents Great Wall Scholar Training Program (CIT&TCD20190313), the R & D Program of the Beijing Municipal Education Commission (KZ202211417048), and the Collaborative Innovation Center of Chaoyang (Grant No. CYXC2203). Scientific research projects of Beijing Union University (ZK10202202, BPHR2020 DZ02, ZK40202101, ZK120202104).  
Not applicable.  
The authors declare no conflict of interest.
# Cutting Blade Measurement of an Ultrasonic Cutting Machine Using Multi-Step Detection
## 3 Curvature-Based Binarization Method
### Background Extraction  
In the binary image, the accurate extraction of the cutting blade area is difficult due to the existence of fine noise around the cutting blade and the mounting portion, which is used to fix the cutting blade. Therefore, for accurate detection of the cutting blade, the mounted part is also treated as the background, which is first extracted and then removed by using the morphological filter. The morphological filter includes erosion, dilation, opening, and closing computations. The opening and closing operations, \(\tilde{y}(f)\) and \(\tilde{\phi}(f)\), can be given by using the Equation (8):  
\[\begin{array}{l}\tilde{y}(f)(x)=\delta(\varepsilon(f))(x)\\ \tilde{\phi}(f)(x)=\varepsilon(\delta(f))(x)\end{array} \tag{8}\]  
where \(\varepsilon(f)\) and \(\delta(f)\) represent erosion and dilation, respectively [13]. In this paper, the background region is extracted by using the closing operation.  
The cutting blade image obtained using the proposed binarization method is shown in Figure 7a. The background image extracted by using the closing operation is shown in Figure 7b. This image is also composed of the mounted portion of the cutting blade. The image of the cutting blade obtained by  
Figure 5: **(a)** Acquired image; **(b)** gray values line profile; **(c)** curvature values line profile; **(d)** proposed binary image; **(e)** comparison of the gray values of the acquired and binary images along the defined line profile.  
Figure 6: Images of the tiny segments of the cutting blade for thickness comparison for **(a)** the acquired; **(b)** Otsu method; and **(c)** proposed method.  
removing the background shown in Figure 7b from the image shown in Figure 7a is given in Figure 7c. Various fine noises exist in this image, which can lead to inaccurate measurement. An enlarged portion of the cutting blade near the mounted portion, which contains such fine noise, is shown in Figure 7d.  
In order to remove these noises, the proposed method used the opening operation. The cutting blade image after removing the noise using the opening operation is shown in Figure 8a. Usually, the regions of the top and bottom parts (near the mounted portion and the near the tip) of the cutting blade are more immune to noise. The enlarged view of the regions, \(N_{1}\) and \(N_{2}\), which enclose the mounted portion and the tip of the cutting blade are shown in Figure 8b,c, respectively. However, in this noise removal process, some data is lost in the \(N_{2}\) region, which results in inaccurate information about the tip of the cutting blade. Some measures are needed to restore the lost tip data in order to secure the accurate cutting blade image.
## 4 Experiment and Security Analysis
### Comparison with the Current State-of-the-Art Works  
In this section, we compare the current state-of-the-art works with our proposed scheme in Phase-2, weighted modulus reversible data hiding (WMRDH), which conceals the three secret messages. The comparison is due to the fact that most of our competitors embed a single level of secret message. To provide a fair comparison, we set \(k=5\) in our experiment, indicating that we conceal a 5-ary secret message to produce dual stego images. We compare our scheme with previous works, including [PERSON] et al. [18], [PERSON] et al. [20], [PERSON] et al. [22], [PERSON] et al. [26], [PERSON] and [PERSON] [30], and [PERSON] et al. [31]. Table 16 shows the average PSNR values of each test image (marked with \"*\"), the embedding ratio in bpp, and the embedding efficiency. Note that the embedding efficiency (EE) represents the embedding ratio (bpp) over the image distortion in mean square error (MSE), thereby signifying the overall message-embedding performance, i.e., the higher, the better. The embedding ratio of the proposed method is about 1.161 bpp, the average PSNR (Peak Signal-to-Noise Ratio) is 50.35 dB, and the embedding efficiency is 1.935. [PERSON] et al. [18] have a high embedding ratio, but small PSNR values and small embedding efficiency. This means that their method conceals a large number of messages but produces significant image distortion as well. Our algorithm has higher embedding efficiency, thus outperforming [PERSON] et al. 's. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Image** & **Type** & **BER(DUAL)** & **BER(SB)** & **BER(WM)** & **PSNR** & **VSI** & **SSIM** & **Q-Index** \\ \hline Kodim07 & S\&P (1\%) & 1.588\% & 1.312\% & 4.995\% & 27.23 & 0.99211 & 0.94924 & 0.99926 \\ Kodim23 & S\&P (5\%) & 7.803\% & 6.482\% & 22.615\% & 20.85 & 0.95783 & 0.92022 & 0.99713 \\ Baboon & Cropping (96) & 5.752\% & 4.789\% & 6.143\% & 22.73 & 0.99223 & 0.96217 & 0.97417 \\ Lena & Cropping (128) & 10.188\% & 8.532\% & 10.837\% & 21.62 & 0.98961 & 0.94171 & 0.95237 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The bit error rate and image quality under the salt-and-pepper and crop attacks. [PERSON] et al. 's [20] embedding ratio is similar to our method, but ours has much higher PSNR (e.g., 46.84 vs 50.35 in the Lena test image), thereby indicating less image distortion. Although the average of PSNR in [20] is about 46.85 dB, the dual stego images generated have significantly different PSNR values. The embedding ratio of [PERSON] et al. [22] is 1.00 bpp, and the average PSNR is 49.10 dB. The statistics show that our scheme performs better than [22], both on the embedding capacity and the image quality. The embedding ratio of [PERSON] et al. [24] is 1.2413 bpp, and the average PSNR is 48.71 dB. The statistics show that our scheme performs better than their schemes in the aspect of the embedding efficiency. In addition, our algorithm offers
# Advanced and Potential Methods for Extraction of Bioactive Compounds from Avocado Peel--A Review
## 5 Potential Techniques for Extraction of Bioactive Compounds from Avocado Peel
### High Voltage Electric Discharge Plasma (HVED)  
Another new technology, high voltage electric discharge plasma (HVED), is a unique and exciting alternative to extraction technology. HVED is a general term for pulsed mode plasma systems. However, it is most commonly called \"corona discharge\" since it resembles a crown around the cathode wire when utilizing a pulsed DC power supply. This technology, which uses pulsed rapid discharge voltages (electric field intensity ranging from 20 to 80 kV/cm), is based on the mechanism of electrical breakdown in liquid, which causes physical and chemical processes that affect both cell walls and membranes while also releasing intracellular components [89]. Moreover, during the photonic dissociation of water, HVED forms heat and confines plasma, which emits UV light and -OH radicals. HVED will produce shockwaves and pyrolysis due to electrohydraulic cavitation at the same time [90, 91].  
Recent research verified HVED as a practical extraction approach with a considerable improvement in yield due to sample cell disturbances and improved mass transfer during electrical breakdown [92]. Another study discovered that HVED is a pre-treatment strategy that improves pectin recovery from sugar beet pulp without changing the structure or chemical makeup of the pectin [93].  
The application of HVED for extracting bioactive compounds from avocado peel has not yet been carried out up till now. However, the extraction of proteins and polyphenols from olive pits has been successfully conducted, and it was proven that HVED was faster than UAE or PEF [94]. This method preserved more proteins and polyphenols during processing [95]. PEF and HVED, as well as other electrically assisted extractions, are less thermally damaging and are more effective for particular compound extraction. Extracts can be obtained in less time and at lower temperatures [96; 97]. Thus, this technique may potentially extract bioactive compounds from the avocado peel.
# Mode Shape-Based Damage Detection Method (MSDI): Experimental Validation
## 3 Results and Discussion
### Effect of Damage Level at a Single Damage Location  
The first two damage cases at a single damage location were analyzed to determine the effect of the damage level when using the MSDI method. Damage cases DC1 and DC2 at element E8 simulated two levels of damage, \"small\" (depth of 2 cm) and \"moderate\" damage (depth of 3 cm). The damage location was determined by the MSDI method and presented as a colored graphical representation in Figure 6 for the first two damage cases. The MSDI values were calculated according to Equation (4). The peak values indicate the location of the damage on the plate in 3D view, and the damaged area is presented in 2D view. The exact location of the discrete damage, the element E8, was accurately identified. Moreover, the increase from small to moderate damage was followed by the increase of the MSDI value.  
all coefficients participate with the maximum value 1 when damage is detected [30] because there are no measurement uncertainties. The comparison of experimentally obtained values is presented in Figure 7 for the first 16 mode shapes of the plate for damage cases DC1 and DC2. Some of the mode shapes for both damage cases were inconsistent (Figure 7a, mode shapes 11, 13, 14; Figure 7b, mode shapes 11, 13, 14, 15) in the undamaged and damaged states as a result of noise in the measurement signal and some other uncertainties (experimentally unrepeatable boundary conditions). It is shown that only a few mode shapes participated with values close to 1 (Figure 7a, mode shapes 1, 4, 6), whose weighting factor \(\Delta\alpha_{kl}\) on modal displacements (Equations (5) and (6)) was sufficient for damage localization. Mentioned uncertainties are challenging to simulate or predict by numerical analysis. Thus, it is essential to emphasize the importance of validating damage detection methods on the basis of experimental results.
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
## 3 Results and Discussion
### Spatial Distribution of Cloud Fraction  
MODIS and CALIOP observed similar patterns for cloud fraction in Eastern China--the cloud fraction decreased from south to north (Figure 9a,b). Both of them observed the highest cloud fraction over Eastern China in summer and over the southern region in spring. The CTH was the highest in summer and lowest in winter (Table 1). For the annual averaged cloud fraction, CALIOP was 0.04 higher than MODIS (Table 1).  
Annually, MODIS observed more clouds in regions of central Eastern China (including Beijing-Tianjin-Hebei-Shandong-Henan) and the northeastern corner (Figure 9c), which may be due to the high PM\({}_{2.5}\) (Figure 9d), high AOD (Figure 9e), and high SZA (Figure 9f).  
In spring and summer, CALIOP observed more clouds than MODIS due to the low COT (Figure 9g), which is consistent with clouds detected by CALIOP but not by MODIS having a low COT (Figure 2f). The averaged cloud fraction of CALIOP is 0.13 and 0.11 higher than that of MODIS, and the averaged CTH of CALIOP is 2.28 km and 0.87 km higher than that of MODIS in spring and summer, respectively. That may be caused by MODIS not observing clouds detected by CALIOP with a higher CTH (Figure 2c) and lower COT (Figure 2f).  
Figure 8: The variation of the proportion of consistent (solid lines) and non-consistent (dashed lines) MODIS and CALIOP cloud detection with SZA for different cloud types over (**a**) dark, (**b**) bright, and (**c**) water surfaces, respectively, and the variation of r_MODcloud_CALfree for each cloud type with SZA over (**d**) dark, (**e**) bright, and (**f**) water surfaces, respectively.  
Figure 9: Annual and seasonal averages of (**a**) CALIOP cloud fraction (CF); (**b**) MODIS CF; (**c**) the CF difference between MODIS and CALIOP; (**d**) PM\({}_{2.5}\) (\(\mu\)g m\({}^{-3}\)); (**e**) AOD; (**f**) SZA; and (**g**) COT. Each row shows different parameters, and each column shows different seasons. Locations 1–5 in (**a**) Annual show Beijing and Tianjin cities, and Hebei, Shandong, and Henan provinces.  
The distribution of the annual cloud fraction difference is similar to that in autumn and particularly in winter (Figure 9c). In autumn and particularly in winter, the haze pollution in the center of Eastern China and the high SZA in the north may induce MODIS to observe more clouds than CALIOP. Both haze pollution and high SZA in the northeastern corner caused MODIS to observe more clouds than CALIOP there. The averaged MODIS cloud fraction over the whole of Eastern China is 0.08 higher than the CALIOP-observed cloud fraction in winter (Table 1). The MODIS CTH was very low in winter (3.66 km) due to its misclassification of aerosols as clouds; it is 2.17 km lower than CALIOP CTH (Table 1).
## 9 Efficiency Analysis
### Efficiency Comparison of Several Communication Methods
The results show that the transmission method whose network layer is IP protocol has the highest average transmission efficiency, and the average throughout capacity can reach more than about 400 Mbps. However, it has no additional security means and is prone to network attacks. Under the condition that only the transmission integrity is guaranteed, the average throughout capacity of the communication methods of the AH protocol and the ESP protocol is the same. The average throughout capacity of the MHC method is about 5% higher than that of the communication methods of the AH and the ESP protocols. The reason is that the MHC method directly uses the message hash chain for data authentication, and reduces the overhead of signature and verification through chain signature and authentication technology. However, the AH and ESP protocols require a two-stage key negotiation process before transmission. These two protocols renegotiate new parameters before the end of the life cycle of the second stage. At the same time, the negotiation process is expensive, and the processing speed of the messages is not significantly improved compared with the MHC method. After the negotiation is completed, these two communication methods have a slightly lower average throughout capacity than the MHC method. In unit time, the average throughout capacity of the MHC method is 4.96% higher than that of the AH protocol communication method and 5.70% higher than that of the ESP protocol communication method. Finally, under the condition that the transport layer is the TCP protocol, the average throughout capacity of the MHC method is about 23.5 times higher than that of the IP protocol and the packet-by-packet signature authentication method. We also compared the transmission efficiency of the AH protocol communication method and the MHC method by recording the time it takes for both parties to transmit fixed-length data. We did not use the ESP protocol as a comparison object because the AH protocol has no encryption function and fewer irrelevant fields, making it easier to compare the transmission efficiency with the MHC method. Therefore, it is better to use the AH protocol as a comparison object instead of the ESP protocol. The experimental conditions were kept consistent with the above experimental conditions. In particular, the life cycle of the SA in the AH protocol's first stage is 86,400 s, and that of the SA in the second stage is set, respectively, at 2 min, 5 min, 10 min, and 60 min. In the experiment, the lengths of the data transmitted by the two communicating parties, respectively, were 1 G, 5 G, 10 G, and 50 G. The average value of five experiments is used to record the experimental results of each group. The experimental results are shown in Table 4.