esa-sceva/satcom-chunk-collection huggingface dataset:
# Correcting Ski Resort Trajectories Extracted from Video
## 2 Perspective Correction
### Refinement of the Correction Coefficient  
In the previous section, we assumed that the correction coefficients were constant because we only chose two reference points for the correction. However, in reality, the width and slope of the hill are not uniform, necessitating additional reference points to account for these variations. To address this, we selected more reference lines to determine more accurate correction coefficients, making the correction more representative of real-world conditions. In this approach, the correction coefficient is no longer constant, but varies with position on the slope.  
Figure 5: Flowchart of the model.  
Figure 6: Original vs. corrected trajectory.  
The reference system was expanded to include five lines to account for slope irregularities and variations in terrain. Five lines were chosen as a balance between computational efficiency and accuracy. By adding more reference lines, we were able to derive position-dependent correction coefficients that reflect finer variations in slope geometry.  
To derive the equation for the dependence of \(\alpha\) on the position on the slope, we need to collect more data points. After obtaining six additional points, we can perform a regression analysis. The results of this analysis can be seen in Table 3, which illustrates how the correction coefficient \(\alpha\) varies with the variation in the \(y\)-coordinate.  
Both linear and nonlinear regression analyses were applied to test their effectiveness in capturing the variations in correction coefficients. Linear regression provides a simpler model that approximates global trends, while nonlinear regression (e.g., polynomial regression) can capture finer variations at different vertical positions.  
The data from Table 3 are plotted in Figure 8. Performing a linear regression analysis we find  
\[\alpha(y)=-0.0012\cdot y+0.1958 \tag{7}\]  
for the dependence of the correction coefficient on the \(y\)-coordinate.  
Considering Figure 9, a non-linear regression analysis seems to be more appropriate. We utilized a polynomial regression of the form  
\[\alpha=\beta_{0}+\beta_{1}\cdot y+\beta_{2}\cdot y^{2} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Points** & **Width (in Pixel)** & \(y\)**-Coordinate** & \(alpha(y)\) \\ \hline
1, 2 & \(p_{12}=181.95482\) & 155.4505 & 0.0316 \\
6, 7 & \(p_{67}=261.4292\) & 130.05 & 0.0338 \\
8, 9 & \(p_{89}=316.269\) & 111.9395 & 0.0451 \\
3, 5 & \(p_{35}=392.048\) & 93.47985 & 0.0982 \\
10, 11 & \(p_{1011}=418.945\) & 82.603 & 0.109 \\ \hline \end{tabular}
\end{table}
Table 3: Scaling factor \(\alpha\) determined at different positions (see Figure 7).  
Figure 7: Screenshot with more points from PeTrack working platform.  
and obtain  
\[\alpha=0.000023\cdot y^{2}-0.006583\cdot y+0.502847\,. \tag{9}\]
###### Abstract
[30] investigated reducing the hub vibratory loads of a lift-offset rotor using IBC (individual blade pitch control) in high-speed forward flight. As an ideal green vehicle, electric helicopters use electric power as the power source for the propulsion system, which has the advantages of energy conservation, environmental protection, low vibration, and high efficiency, and is expected to be widely used in future general aviation [31; 32; 33; 34; 35]. In terms of electric helicopters, [PERSON] of the United States has established a variety of power system models, including electric power [36], by studying the electric refitting of R22 helicopters [37] and by systematically analyzing the characteristics of lithium polymer batteries, hydrogen fuel cells, and motors. The flight performance of an electric R22 helicopter is calculated and analyzed. [PERSON] et al. [38] developed a global optimum envelope method for balancing performance improvements in hover and various freestream velocities. [PERSON] et al. [39] studied the mechanism of the variable-speed rotor affecting the rotor aerodynamic performance by means of dimensionless parameter analysis, and reveals the various effect mechanisms under the different flight speeds and takeoff weights. [PERSON] et al. [40] studied the aerodynamic performance of a variable-speed coaxial helicopter. The results show that the variable-speed coaxial helicopter has low required power when it is less than the forward flight speed corresponding to the hovering rotor speed. The variable-speed rotor has proven to be a promising means to improve helicopter performance. [PERSON] et al. [41; 42] proposed a novel transient balancing technology for the identification and diagnosis of transient dynamic balancing of aero-engine rotor systems. Most research on rotor dynamic balance are aimed at traditional helicopters that work at a fixed speed. There have been few investigations into the dynamic balance of variable-speed rotor helicopters. The purpose of this paper is to investigate the influence of rotor adjustment parameters on the dynamic balance of rigid variable-speed rotors at different speeds. The experiment was conducted in the helicopter of the Key Laboratory of Ministry of Education at Beihang University. The experiment was carried out using two scale-model rigid rotorswith the same geometry. Rotor adjustment parameters involve counterweight, pitch, and trailing-edge flap. The experimental data are measured and analyzed, respectively, through vibration analyzer installations and an all-phase fast Fourier transform. Through a waterfall diagram of the auto-power spectrum and amplitude and phase diagram of 1 \(\Omega\) vibration acceleration, the influence of adjustment parameters on fuselage vibration at different speeds is analyzed.
# Age and Source of the Jinjingzui Skarn-Type Gold Deposit in the Southeastern Hubei Province, China
## 5 Discussion
### Origin of Ore-Forming Materials  
Based on this study, \(\delta^{34}\)Sv-PDB(%) values of pyrite in the Jinjingzui Au deposit range from \(-\)2.4% to \(-\)8.4%, with an average value of \(-\)3.1% (\(n\) = 6). These values coincide closely with the distribution of previously determined \(\delta^{34}\)Sv-PDB data for mantle-derived fluids (\(\pm\)3%) [36]. Studies conducted previously reported that the sulfur isotopic components were generally uniform in the adjacent skarn Cu-Au deposit in Edong ore clusters [37]. Collectively, these sulfur isotopic compositions are suggested to indicate a deep magmatic source for the sulfur contained within the minerals of the Jinjingzui ore deposit.  
Lead isotopic tectonic patterns can distinguish the sources of lead in different tectonic environments. In \({}^{207}\)Pb/\({}^{204}\)Pb-\({}^{206}\)Pb/\({}^{204}\)Pb and \({}^{208}\)Pb/\({}^{204}\)Pb-\({}^{206}\)Pb/\({}^{204}\)Pb diagrams (Figure 12), the data points fall near the lower crust and orogenic belt regions. The lead isotope evolution line has a high slope, intersecting with the crust-mantle lead isotope evolution line at a high angle, indicating that the lead provenance of Jinjingzui deposit has characteristics of a mixed source of upper mantle and lower crust.  
Previous research suggested that the Re content from molybden with a mantle component has a significantly higher content than those derived from a crustal source [34; 39]. Table 4 shows the Re content in molybden from selected gold-bearing skarn deposits in the Edong ore clusters. The Jinjingzui gold deposit Re concentrations in molybden are significantly higher than other ore samples in the district (847.91-2018.58 \(\times\) 10\({}^{-6}\)), which infers that a higher mantle component is involved in the mineralization. These data, combined with the sulfur and lead isotopic composition from the Jinjingzui Au deposit, indicate that metallic components have a deep source and possibly originated from intermediate magmatism during the Yanshanian period.  
Figure 12: Evolution curve (**a**) and tectonic environment discrimination diagrams (**b**) of \({}^{207}\)Pb/\({}^{204}\)Pb -\({}^{206}\)Pb/\({}^{204}\)Pb and \({}^{208}\)Pb/\({}^{204}\)Pb\({}_{-206}\)Pb/\({}^{204}\)Pb (after [38]) A-Mantl; B-Orogenic belt; C-upper crust; D-lower crust; LC-lower crust; UC-upper crust; OR-Orogenic belt; OIV-volcanic rocks from Ocaemic island.
# Objective Prediction of Human Visual Acuity Using Image Quality Metrics
## Appendix A The Structural Similarity Index (SSIM)  
The SSIM is a metric proposed by [PERSON] and [PERSON] based on the combination of the luminance and contrast distortion and the loss of correlation between the pixels of the image [8]. The analytical expression of SSIM is shown in expression (A1),  
\[SSIM(X,Y)=\frac{\big{(}2\mu_{x}\mu_{y}+C_{1}\big{)}\big{(}2\sigma_{xy}+C_{2} \big{)}}{\Big{(}\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\big{)}\Big{(}\sigma_{x}^{2}+ \sigma_{y}^{2}+C_{2}\Big{)}},\] (A1)  
being \(\mu_{x}\) and \(\mu_{y}\) the mean values of the luminances of the two compared \(X\) and \(Y\) images, \(\sigma_{x}\) and \(\sigma_{y}\) are the standard deviations of the luminances and \(\sigma_{xy}\) is the covariance between the two images. The values \(C_{1},\ C_{2}\) and \(C_{3}\) are constants used to avoid instability when the denominator or denominators approaches zero and are given by:  
\[C_{1}=(K_{1}L)^{2};\ \ \ C_{2}=(K_{2}L)^{2};\ \ \ and\ \ \ C_{3}=C_{2}/2,\] (A2)  
\(L\) is the dynamic range of the image (\(L=255\) for 8-bit/ pixel grayscale images), and \(K_{1}\) and \(K_{2}\) are two scalar constants with a value less than unity, usually 0.01 and 0.03.  
The Multi-Scale Structural Similarity Index (MSSSIM)  
[PERSON], [PERSON] and [PERSON] developed a multiple-scale _SSIM_, _MSSSIM_[9] in order to incorporate image details into different image resolutions. In this metric both the reference image and the distorted image are taken as input signals. As in the _SSIM_, they compare luminance, contrast and correlation between images. The system repeatedly applies a low-pass filter and decreases the resolution of the filtered image by a factor 2. The authors called the original image as Scale 1, and the most distorted image as Scale \(M\) (usually \(M=5\)), which is obtained after M-1 iterations. The comparison between luminance is calculated only on the scale \(M,\ l_{M}(X,Y)\). However, the contrast and correlation between images are obtained as in SSIM, but each time the image is distorted. Both are denoted by \(c_{j}(X,Y)\) and \(s_{j}(X,Y)\), respectively, and \(j\) is the number of performed iterations. Therefore, MS-SSIM is calculated by combining measurements from different scales using Equation (A3).  
\[MS-SSIM(X,Y)=[l_{M}(X,Y)]^{\alpha_{M}}.\prod_{j=1}^{M}[c_{j}(X,Y)]^{\beta_{j}}s_{ j}(X,Y)]^{\gamma_{j}},\] (A3)  
where we use the exponents \(\alpha_{M}\), \(\beta_{j}\) and \(\gamma_{j}\), originally obtained by [PERSON] et al. [9], to adjust the importance of each component.
###### Abstract  
A quad, small form-factor pluggable 28 Gbps optical transceiver design scheme is proposed. It is capable of transmitting 50 Gbps of data up to a distance of 40 km using modulation signals with a level-four pulse-amplitude. The proposed scheme is designed using a combination of electro-absorption-modulated lasers, transmitter optical sub-assembly, low-cost positive-intrinsic-native photodiodes, and receiver optical sub-assembly to achieve standard performance and low cost. Moreover, the hardware and firmware design schemes to implement the optical transceiver are presented. The results confirm the effectiveness of the proposed scheme and the performance of the manufactured optical transceiver, thereby confirming its applicability to real industrial sites.  
PAM4; QSFP28; optical transceiver; PCB design; firmware design Article  
Design and Implementation Scheme of QSFP28
## 3 Test Results
### Effect of Water on AE Characteristics
#### 3.2.2 Water Induced Variation in Microscale Crack Type  
[PERSON] et al. [55] suggested a method to classify the crack type based on two AE parameters, namely the rise-time-to-amplitude (RA) ratio and the average frequency (AF, defined as counts/duration time). Generally, tensile cracks have higher AF and lower RA values while shear cracks possess lower AF and higher RA values. In this study, this method was adopted to identify the failure pattern. Figure 7 depicts the distribution of probability density of AF-RA data. The areas in red indicate higher density regions and the areas in blue represent lower density regions.  
From Figure 7, we can clearly observe that for the dry sandstone, more AE signals are distributed above the diagonal and the maximum density region below the diagonal, meaning that it failed in a tensile-dominated mixed mode. However, for the saturated sandstone, the overwhelming majority of cracks concentrate under the diagonal, which suggests that its failure was characterized by shear fracture. This phenomenon indicates that the water presence transformed the sandstone failure type into shear mode, which agrees with previous studies on gabbro [20] and coal [19]. The change of the failure mode was possibly caused by the water-induced frictional reduction, which will be discussed latter.  
Similarly, for the saturated granite, the areas in dark red move to the part below the diagonal, signifying that there were more shear microcracks after water saturation. However, under dry and saturated conditions, the majority of cracks for the marble were located in the tensile region. The presence of water seemed to not affect the failure mode of the marble.  
Figure 7: Probability density of rise time-to-amplitude ratio-average frequency (RA–AF) data for dry and saturated rocks.
## 2 Bilinear and Weibull/IPL General Background
### Fatigue Model  
When mechanical elements are subject to cyclic loading, a fatigue analysis is required. The analysis requires determining the cycles until the material fails. Its graphical representation is the Wohler curve, also known as the S-N curve, where the stress (\(S\)) is plotted against the logarithm of the cycles to failure (\(N\)) [13]. The relationship between applied stress and the number of cycles is given by the Basquin model:  
\[\log(S)=A\cdot\log(N)+B \tag{1}\]  
where \(A\) is the slope and \(B\) is the ordinate to the origin. Its application to bilinear behavior is as follows.
## 3 System Model  
As shown in Figure 1, we consider an uplink cellular network with one BS, \(M\) CUs and \(N\) D2D pairs. The \(m\)-th CU is denoted by \(C_{m}\) where \(m\in\mathcal{M}=\{1,2,\cdots,M\}\). The \(n\)-th D2D pairs is denoted by \(D_{n}=(D_{n}^{t},D_{n}^{t})\) where \(n\in\mathcal{N}=\{1,2,\cdots,N\}\), \(D_{n}^{t}\) represents the \(n\)-th D2D transmitter and \(D_{n}^{t}\) is the \(n\)-th D2D receiver. Furthermore, there exist \(L\) orthogonal subchannels denoted by \(\mathcal{L}=\{1,2,\ldots,L\}\) in the system and the bandwidth of each subchannel is \(B_{l}\). The Wi-Fi network has \(W\) users operating on \(5\) GHz unlicensed band, and the set of the Wi-Fi users is denoted by \(\mathcal{W}=\{1,2,\cdots,W\}\). Moreover, there exist \(L_{u}\) subchannels with bandwidth \(B_{u}\) to support each Wi-Fi user to communicate with the access point (AP), and the set of the subchannels is denoted by \(\mathcal{U}=\{1,2,\ldots,U\}\).
###### Abstract  
In an effort to address the problem of the insufficient accuracy of existing orbital angular momentum (OAM) detection systems for vortex optical communication, an OAM mode detection technology based on an attention pyramid convolution neural network (AP-CNN) is proposed. By introducing fine-grained image classification, the low-level detailed features of the similar light intensity distribution of vortex beam superposition and plane wave interferograms are fully utilized. Using ResNet18 as the backbone of AP-CNN, a dual path structure with an attention pyramid is adopted to detect subtle differences in the light intensity in images. Under different turbulence intensities and transmission distances, the detection accuracy and system bit error rate of basic CNN with three convolution layers and two full connection layers, i.e., ResNet18 and ResNet18, with a specified mapping relationship and AP-CNN, are numerically analyzed. Compared to ResNet18, AP-CNN achieves up to a 7% improvement of accuracy and a 3% reduction of incorrect mode identification in the confusion matrix of superimposed vortex modes. The accuracy of single OAM mode detection based on AP-CNN can be effectively improved by 5.5% compared with ResNet18 at a transmission distance of 2 km in strong atmospheric turbulence. The proposed OAM detection scheme may find important applications in optical communications and remote sensing.  
Orbital angular momentum; mode detection; fine-grained image classification; attention pyramid; atmospheric turbulence
# Operation Margin of the ITER Central Solenoid During the Plasma Scenario
## 5 Result and Discussion
### Performance of the Conductor During the Plasma Scenario  
#### 5.1.1 Temperature Evolution in the Pancakes  
The temperature evolution in the bundle of the CICC is shown in Figure 10 for pancake #39 of module CS1U. Three locations are selected, corresponding to the inlet (\(x\) = 0 m), the middle point (\(x\) = 73.75 m) and the outlet (\(x\) = 147.5 m) section of the pancake. This pancake exhibits a peak temperature (7.4 K) at the outlet with respect to the other pancakes.  
The temperature profile at the middle point of the pancake exhibits a peak at \(t\) = 300 s, while this peak is observed after about 6 min at the outlet. This delay is the transient time necessary for the helium in the conductor to flow along half of the pancake, corresponding to about 15 min to run through the 147.5-m-long pancake.  
A further temperature increase is observed between 700 s and 1100 s in the middle point. This secondary temperature rise, although less pronounced than the major one, is related to the increase in losses corresponding to the end of plasma.  
It is worth noting that, despite the amount of losses deposited in the inner turns of the modules, the cold circulator is able to maintain fresh helium at the inlet. A limited temperature increase (below 5.8 K) is observed at the inlet section of the pancake. After a transient of about 30 s, the temperature at the inlet is re-established at about 4.5 K.  
The ability of the system to sustain stable temperature conditions at the inlet, and to efficiently dissipate the thermal load along the pancake, ensures robust performance under the nominal working conditions of the CS.
# Phenomenologies in Hypersphere Soliton and String Photon Models
## 5 Conclusions  
In summary, we have formulated the HSM to discuss the Dirac quantization in the first-class formalism and the predictions of baryon physical quantities in HSM. To be specific, we have evaluated the intrinsic pulsating frequencies of the baryons. To accomplish this, we have exploited the first-class Hamiltonian possessing the WOC and quantized the hypersphere soliton in the HSM. Here, we have noticed that the profile function in the soliton energy of the hypersphere soliton satisfies the two first-order differential equations to attain the BPS topological lower bound in the soliton energy. Next, we have evaluated the baryon physical quantities such as baryon masses, magnetic moments, charge radii and axial coupling constant. Shuffling the baryon and transition magnetic moments, we have constructed the model independent sum rules. The intrinsic frequency for more massive particles also has been shown to be greater than that for less massive ones. Explicitly, we have evaluated the intrinsic frequencies \(\omega_{N}=0.87\times 10^{23}\) s\({}^{-1}\) and \(\omega_{\Delta}=1.74\times 10^{23}\) s\({}^{-1}\) of the nucleon and delta baryon, respectively, to yield the identity \(\omega_{\Delta}=2\omega_{N}\).  
Next, making use of the Nambu-Goto string action and its extended rotating bosonic string theory, we have formulated the SPM to find the energy of the string configuration, which consists of the rotational and vibrational energies of the open string. We have then investigated a photon intrinsic frequency by exploiting the string, which performs both rotational and pulsating motions. Here, we have interpreted the string as a diameter of a solid spherical shape photon. Explicitly, we have found that the intrinsic frequency of the photon is comparable to those of the baryons such as nucleon and delta baryon. In the SPM, we also have evaluated the photon size given by the string radius, which is approximately 21% of the proton magnetic charge radius. It will be interesting to search for strong experimental evidence for the photon size, which could be associated with the manifest photon phenomenology such as the photoelectric effect, Compton scattering and Raman scattering, for instance. Assuming that the SPM exploited in this paper could be a precise description for the photon, the photon intrinsic frequency \(\omega_{\gamma}=9.00\times 10^{23}\) s\({}^{-1}\) and photon size \(\langle r^{2}\rangle^{1/2}(\text{photon})=0.17\) fm could be fundamental predictions in the extended object phenomenology, similar to \(\omega_{N}\), \(\omega_{\Delta}\) and the charge radii in the hypersphere soliton model.  
[PERSON] was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, NRF-2019R11A1A01058449.  
No data have been used in the work.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 4 Measurement of Fruit External Quality  
The proportion of fruit with external defects is another critical parameter in the marketing of fruit. Fruit packings employ manual and machine vision technology in the sorting of fruit for such defects, creating product lines (e.g., first, second and third grades).  
Figure 10: Fruit mass (g) estimated using lineal dimensions temporally up to harvest for mango, cv. Honey Gold. A linear regression model has been fitted to data between 32 and 11 days before harvest (dotted line): slope = 3.41 g/day, intercept = 604 g, R\({}^{2}\) = 0.99.  
Machine vision was first used in general color sorting of fruit on packlines in the 1970s, and was first used for major defects on fruit in the 1990s [101]. Modern packlines allow the operator to collect images of defects and effect training 'on the go'. A progression from the use of hard coded features for object segmentation to use of machine learning in these systems has been reviewed by [PERSON] et al. [102] and [PERSON] and [PERSON] [103]. [PERSON] et al. [104] provide a description of a trainable smart phone-based system, also intended for use in a postharvest setting.  
The pre-sorting of fruit before the final grading operation can improve the efficiency of the final grading, and earlier knowledge of the proportion of quality graded can inform marketing decisions. The machine vision techniques used on-line can be extended into the orchard, for the assessment of grade proportions of fruit while on-tree, albeit under less controlled imaging conditions. The in-field assessment of the proportion of fruit grades can be used to inform the in-field consolidation of harvest bins with similar-quality fruit. In another step, [PERSON] et al. [105] report on the development of an in-field pre-sorting machine in which machine vision is employed on a harvest aid to separate fruit by quality into separate field bins.  
In another use case, a machine vision system was developed for counting dropped citrus fruit on trees infected with Huanglongbing disease, with the classification of fruit on the basis of the extent of decay ([PERSON] et al. [106]). The application was intended to inform management for the control of the disease.