esa-sceva/satcom-chunk-collection huggingface dataset:
## 3. Results and Discussion
### Experimental Results  
#### 3.1.1. Curvature Radii  
The principal meridian radii of the anterior and posterior corneal surfaces were obtained from the Pentacam data. The variations in the average radius of each meridian on both corneal surfaces with the changes in pressure are shown in Figure 1. The variations were taken from an initial common pressure of 2.47 kPa.  
Figure 1. Variations in the average meridian radii for both corneal surfaces with changes in pressure. Rfa = flattest radius of the anterior corneal surface; Rsa = steepest radius of the anterior corneal surface; Rfp = flattest radius of the posterior corneal surface; Rsp = steepest radius of the posterior corneal surface.  
Increases in pressure led to higher radii values, and the cornea became flatter with each increment in pressure and steeper with the lowering pressure values. One important fact is that the final average anterior surface radius approximately equaled the initial one, while the posterior surface had a smaller final average radius compared with the initial one. This had consequences for the optical power of the entire cornea.  
Apical displacement (corneal vertex displacement) is normally used as a corneal deformation measure [21; 22; 23; 24; 25]. The authors of these papers calculated the curvature radius from the apical displacement measures and geometric simplifications and obtained lower values when the pressure increased and higher values when the pressure dropped. However, the studies that applied the Scheimpflug technology for inflation tests [26; 27; 28] reported similar results to those obtained herein.
## 3 Experiments
### Evaluation Metrics  
To evaluate our results quantitatively, four popular evaluation metrics of object detection are calculated, i.e., Precision \(P\), Recall \(R\), F\({}_{1}\)-Measure \(F_{1}\), and average detection time \(T\). Precision and Recall are often a pair of contradiction measures. Generally speaking, when the Precision is high, the Recall tends to be low. Therefore, the comprehensive evaluation metric F\({}_{1}\)-Measure is needed. Higher  
Figure 7: Detection results in 5 challenging scenes: (**a**) large ripples, (**b**) strip ripples, (**c**) near-view reflections, (**d**) circular ripples, (**e**) far-view reflections. From top to bottom, the results are from: spatial-based texture detection, frequency-based saliency detection, and combination.  
value indicates better performance of the detection model. The calculation methods of \(P\), \(R\), and \(F_{1}\) are as follows:  
\[\left\{\begin{array}{l}P=\frac{TP}{TP+FP}\\ R=\frac{TP}{TP+FN}=\frac{TP}{G}\\ F_{1}=\frac{2P}{P+R}\end{array}\right., \tag{13}\]  
where \(TP\) and \(TN\) denote the number of true positives and true negatives, respectively, while \(FP\) and \(FN\) denote the number of false positives and false negatives, respectively. \(G\) is the number of ground truths, equal to the number of all positives. In our testing set consisting of 120 source images, the value of \(G\) is 140 rather than 120, because some testing images contain multiple floating objects.  
In our experiments, a result was considered a true positive only when the IoU calculated from the bounding box and its ground truth was higher than 0.8. This threshold guarantees the practicalities of the testing methods.
## 3 Multi-Index Synthetical Evaluation Method of Bond Properties
### Evaluation for Synergistic Bond Property of Unit Weight Steel Fibers  
The reinforcement of steel fiber on concrete is dependent on the type, volume fraction, and distribution of fibers [1; 2; 3; 4]. The bond properties of a single fiber could not reflect the synergistic action of steel fibers in concrete. Thus, the unit weight of steel fibers is supposed as a unit of measurement to comprehensively express the synergistic bond properties in this paper.  
The mechanical anchoring of hooked-end steel fiber is mainly on the end hook, and embedded length has little influence on its bond properties [32]. Therefore, the synergistic bond strength indices of per unit weight steel fiber, including the debonding load \(L_{\rm d}\), the bonding load \(L_{\rm m}\), and the residual bond load \(L_{\rm r}\), are proposed for hooked-end steel fibers. Their calculation equations are listed as follows:  
\[L_{\rm d}=\frac{P_{\rm d}}{4}\times N_{\rm sf} \tag{13}\]  
\[L_{\rm m}=\frac{P_{\rm max}}{4}\times N_{\rm sf} \tag{14}\]  
\[L_{\rm r}=\frac{P_{\rm r}}{4}\times N_{\rm sf} \tag{15}\]  
The mechanical anchoring of the crimped steel fiber, indentation steel fiber, milling steel fiber, and large-end steel fiber are almost linear with the embedded length. Therefore, the synergistic bond strength indices of per unit weight steel fibers, including the debondingload on per embedded length \(L_{\text{dl}}\), the bonding load on per embedded length \(L_{\text{ml}}\), and the residual bond load per embedded length \(L_{\text{rl}}\), are proposed with the equations as follows:  
\[L_{\text{dl}}=\frac{P_{\text{d}}}{4I_{\text{f,em}}}\times N_{\text{sf}} \tag{16}\]  
\[L_{\text{ml}}=\frac{P_{\text{max}}}{4I_{\text{f,em}}}\times N_{\text{sf}} \tag{17}\]  
\[L_{\text{rl}}=\frac{P_{\text{r}}}{4I_{\text{f,em}}}\times N_{\text{sf}} \tag{18}\]  
The synergistic expended energy indices of per unit weight steel fibers, including the debonding work \(UW_{\text{d}}\), the slipping work \(UW_{\text{p}}\), and the pull-out work \(UW_{\text{r}}\), are proposed for all types of steel fiber in this study as follows:  
\[UW_{\text{de}}=W_{\text{d}}/4\times N_{\text{sf}} \tag{19}\]  
\[UW_{\text{max}}=W_{\text{p}}/4\times N_{\text{sf}} \tag{20}\]  
\[UW_{\text{res}}=W_{\text{r}}/4\times N_{\text{sf}} \tag{21}\]  
The equations of synergistic toughness indices of per unit weight steel fibers are the same as those of a single fiber. The debonding energy ratio \(R_{\text{d}}\), slipping energy ratio \(R_{\text{dp}}\), and pull-out energy ratio \(R_{\text{pr}}\) are also suitable to evaluate the synergistic energy dissipation capacity of per unit weight steel fiber.
# Multidisciplinary Design Optimization of Cooling Turbine Blade: An Integrated Approach with R/ICSM
## 3 Multidisciplinary Design and Optimization of Turbine Stage Blade
### Optimization Model Description  
This article aims to enhance the efficiency of the turbine 41 cross-section through multidisciplinary design optimization of the cooling blade in the first-stage turbine. Drawing on previous design experience, this study identified design variables, including the radius at the leading edge radius \(R_{q}\), inlet geometric angles \(\beta_{q}\), outlet geometric angle \(\beta_{h}\), inlet wedge angles \(W_{q}\), turning angle \(\delta\), blade stagger angle \(\gamma\), and axial chord length \(S\), for each section. It is crucial to mention that, due to installation considerations involving blade and tenons, the axial chord length of the blade root section was excluded from the scope of design variables. To mitigate potential adverse effects on downstream components caused by the optimization of the high-pressure turbine blade, it is imperative to improve the efficiency of the high-pressure turbine 41 section while maintaining a constant gas flow rate \(W_{41}\) and total expansion ratio \(\pi\). Additionally, it is essential to ensure that the outlet flow angle \(\alpha\) of the high-pressure turbine falls within a reasonable range. Simultaneously, it is imperative to ensure that the maximum von Mises stress \(\sigma_{\text{eq,max}}\) on the blade remains below a specified threshold \(0.9\sigma_{0.1}\) to guarantee the blade's safe and reliable operation. Therefore, parameters \(W_{41}\), \(\pi\), \(\alpha\) and \(\sigma_{\text{eq,max}}\) were selected as constraint parameters.  
In summary, the optimization model is represented by the following formula:  
\[\max: \eta\] (8) w.r.t. : \[\left\{\begin{array}{l}R_{q1},\beta_{q1},\beta_{h1},W_{q1},\delta_{1}, \gamma_{1},S_{1}\\ R_{q2},\beta_{q2},\beta_{h2},W_{q2},\delta_{2},\gamma_{2},S_{2}\\ R_{q3},\beta_{q3},\beta_{h3},W_{q3},\delta_{3},\gamma_{3}\end{array}\right.\] \[\text{s.t.}: \left\{\begin{array}{l}80\leq\alpha\leq 90\\ |\sigma_{\text{eq,max}}|\leq 0.9\sigma_{0.1}\\ \left|\frac{W_{41}-W_{41,\text{bas}}}{W_{41,\text{bas}}}\right|\leq e_{1}\\ \left|\frac{\pi-\tau_{\text{bas}}}{\pi}\right|\leq e_{2}\\ x^{l}\leq x\leq x^{u}\end{array}\right.\]  
In the formula, \(e_{i}(i=1,2)\) represents a small constant, \(x\) denotes the design variable vector, and \(l\) and \(u\) denote the lower and upper limits of the design variable \(x\), respectively. Subscripts 1, 2, and 3 denote the blade tip section, blade center section, and blade root section, respectively, while the subscript _bas_ denotes the relevant parameters of the initial scheme of the high-pressure turbine.
## 5 Experiments
### Diagnosis by SK-Based Envelope Analysis  
In the three procedures of the proposed method in this paper, the prewhitening based on linear prediction is indispensable. If we removed this step, we could get the envelope order demodulation algorithm based on the SK. However, the following analysis shows that removing prewhitening is unacceptable.  
Figure 24 shows the analysis results for the fault data of the intershaft bearing's outer ring using the envelope order demodulation algorithm based on SK. Figure 24a is the kurtogram. The SK shows that the center frequency of the optimum demodulation band is 2000 Hz, and the bandwidth is 800 Hz. In other words, the optimum demodulation band is [1600,2400] Hz. Figure 24b shows the envelope order spectrum. In this spectrum, an order of 2.8 is in the dominant position, which is almost the same as the results discussed in Section 5.3. An order of 15.9, corresponding to the fault in the bearing's outer ring, is not extracted.  
Similarly, Figure 25 shows the analysis results for the fault data of the inner ring. Figure 25a shows the kurtogram. The SK shows that the center frequency of the optimum demodulation band is 12,800 Hz, and the bandwidth is 25,600 Hz. In other words, the optimum demodulation band is [0,25600] Hz. Figure 25b shows the envelope order spectrum. An order of 1 and its harmonic are the dominant components, which is similar to the results discussed in Section 5.3. An order of 18.1, corresponding to the fault of the bearing inner ring, is not extracted.  
Figure 23: Envelope order spectrum of inner ring fault (envelope demodulation based on adaptive filtering).  
Figure 22: Envelope order spectrum of outer ring fault (envelope demodulation based on adaptive filtering).  
The above analysis shows that the envelope order demodulation algorithm based on SK does not work well in extracting the weak fault features of the intershaft bearing.
# RotJoint-Based Action Analyzer: A Robust Pose Comparison Pipeline
## 5 Experiment  
### Subjective Pose Similarity Assessment  
To qualitatively validate the effectiveness of the RotJoint pipeline in estimating pose similarity, we conducted a subjective pose similarity assessment to evaluate the similarity of pose pairs. To ensure fairness, we applied the same weight parameter configuration when calculating similarity for the same body parts. The only difference was that one method used the ground truth directional vector of the 3D skeleton, while the other used RotJoints.  
We collected 500 representative pose samples from the Human3.6M [21,47] dataset's test samples, each containing 120 consecutive frames. From these, we created 1000 pose pairs. We selected 50 qualified participants for the experiment, all with normal vision and expertise in sports and medical fields. Ensuring the participants' conditions and the experimental setup met strict standards was crucial for this subjective testing. Poses were displayed on a 65-inch ultra-high-definition monitor, and participants rated the quality  
Figure 4: Model overview. To enhance the model’s robustness and generalization capabilities while simulating the increased attention on partial joint information in real-world scenarios, we apply a joint masking mechanism to each motion sequence, followed by linear embedding to obtain the class token and frame tokens. The resulting sequence of vectors is then fed into a standard Transformer block. The structure of the Transformer block is inspired by ViT [45].  
of pose samples using a score range from 0 to 1, with completely identical (1) and very dissimilar (0). The numeric values in parentheses represent the labels.  
After all participants rated the pose sequences, we calculated the mean opinion score for the pose pair similarity. Each test session lasted 50 min, followed by a 10 min break to reduce dizziness from prolonged screen use. Each participant spent a total of 180 min on the task. The results are shown in Figure 5. Using a third-order polynomial fitting method, curves are fitted to the data, revealing that the RotJoint-based approach demonstrates higher consistency with human evaluations. The Pearson correlation coefficient is calculated, showing a value of 0.745 for the RotJoint-based method and 0.487 for the 3D skeleton-based method. This indicates that the RotJoint-based approach aligns more closely with human perception of pose similarity, making it a more accurate measure according to human cognitive standards.
## 3 System Requirements for BVLOS Flight Operations  
As shown in Figure 2, additional FTS and FPV systems are installed on the UA and integrated with its built-in autopilot and R/C control systems. Four RF links are used during BVLOS flight operations: a 900 MHz C2 link is used for the communication between the autopilot and the GCS; a 2.4 GHz C2 link is used for R/C manual control; a 5.8 GHz video link is used for real-time airborne video transmission, and a 978/1090 MHz ADS-B link is used for cooperative air traffic detection. During normal operations, the FTS checks the healthy status of the 900 MHz and 2.4 GHz links. If both links are lost for more than two minutes, the FTS will terminate the engine to prevent the UA from flying away. At ranges beyond the visual range of the manual pilot (approximately 500 m) and within the R/C link range (approximately 2000 m), the FPV video system can help the manual pilot control the aircraft to fly home (using manual R/C control) if the autopilot system fails. At ranges beyond the R/C link range, the FPV system allows ground control station operators to visually monitor the progress of the flight and the status of the onboard systems.
# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 4 Problem Solution
### Aggregating Businesses
#### 4.2.1 MDP Element Group  
* We define the elements of the MDP as follows: 1. State space (\(s_{t}\)): \(s_{t}=\{V,B_{1},\ldots,B_{u},\ldots,B_{v},\ldots,B_{M}\}\), where \(B_{i}\) represents the current business volume of satellite \(S_{i}\). 2. Action space (\(a_{t}\)): \(a_{t}=\{a_{1},\ldots,a_{v},\ldots,a_{V}\}\). Each action \(a_{k}\) denotes assigning business \(\mathbb{B}_{uj}\) to satellite \(S_{v}\). 3. Transition probability (\(P(s_{t+1}\mid s_{t},a_{t})\)): The probability of assigning business \(\mathbb{B}_{uj}\) to satellite \(S_{v}\) is represented as follows: \(P(B_{v}+B_{uj}\mid B_{v},a_{v})\). This denotes the probability of satellite \(S_{v}\) transitioning from state \(B_{v}\) to state \(B_{v}+B_{uj}\) after action \(a_{v}\). 4. Reward function (\(R(s_{t},a_{t})\)): The reward for assigning business \(\mathbb{B}_{uj}\) to satellite \(S_{v}\) is defined as \(R(B_{v},a_{v})\). 5. Discount factor (\(\gamma\)): \(\gamma\) is a discount factor between 0 and 1 that balances the importance of current and future rewards.
## 5 Results and Discussion
### Sensitivity Analysis
#### 5.3.2 FE Time Step and Triangulation  
Estimates were produced using time step sizes of \(h=0.025\), \(0.05\), and \(0.1\,\mathrm{ms}\) and triangulations with \(172\), \(205\), and \(263\) elements over the same geometry. For direct comparison, the estimates were computed using an importance sampling ensemble size of \(50\),\(000\).  
Figure 8a shows that, on average, the relative error (as defined above) in the estimates is very similar whether the triangulation involves \(263\) or \(205\) elements. In fact, all estimated parameter values differ by \(0.1\%\) or less for the two triangulations. This difference is likely due to numerical error resulting from the sample-based nature of importance sampling. In contrast, the lower density mesh introduces a larger error for all step sizes, but rapidly improves with decreasing step size.  
Similar trends are observed in Figure 8b where decreasing step size or increasing mesh density results in a decrease in uncertainty. The observed decrease in uncertainty with decreasing time step and increasing mesh density results from having a higher fidelity numerical model that is more sensitive to the parameter values; that is, small changes in the parameter values will have a larger impact on  
Figure 8: The average (**a**) relative error and (**b**) relative uncertainty of the estimates as a function of time step size for various triangulations. Blue with the plus markers: \(172\) elements; red with circle markers: \(205\) elements; black with diamond markers: \(263\) elements.  
Figure 7: (**a**) Relative error of the estimates; and (**b**) relative uncertainty for increasing ensemble size. Solid blue line: average; dashed red line: maximum.  
the simulated data [33]. As a result, the uncertainty of the estimates will decrease as the fidelity of the model increases. However, as the time step decreases and mesh density increases the computational cost grows exponentially. Thus, examining how the estimated uncertainty behaves as the fidelity of the model increases becomes quickly infeasible.
# Photonic Systems and Devices for Linear Cell Radar
## 2 RoF System  
For future mobile services, such as beyond 5G or 6G, a huge number of antenna units are required to provide high-speed and low-latency radio services with large coverage, because available radio frequency resources are limited. Thus, the networks have many interfaces between optical and radio signals. Figure 2 shows a schematic of an interface between optical and radio links using a conventional digital transmission link. This interface is for radio-wave radiation. For radio-wave reception, we could use a similar setup. The interface should have an OE conversion device, digital signal processor (DSP), and radio front-end (FE). Data streams demodulated from optical signals generated by an optical transmitter (Tx) should be converted into radio-waveforms by the DSP. The reduction of complexity, power consumption, and latency in such interfaces is very important to minimize the system operation cost of future mobile networks. One possible solution for this issue is to use RoF, where a RoF Tx generates an optical signal for waveform generation at the radio FE, as shown in Figure 3.  
The optical signal carries IF (intermediate frequency) or RF (radio frequency) signal waveforms for radio signal generation. In IF-over-fiber systems, radio FEs should have local oscillators (LOs) for frequency conversion. For advanced modulation formats, we need low phase noise and stable sinusoidal forms as LO signals. RoF can also be used for the distribution of such sinusoidal waveforms to antenna units [9]. Figure 4 shows an example of RoF systems for radio-wave reception. Radio-waves received by an antenna were fed to the radio FE for frequency conversion or amplification. The dynamic range of RoF links is smaller than in electric conventional radio systems, so the proper design of automatic gain control (AGC) at the radio FE is very important to obtain high-performance radio  
Figure 1: Schematic of a radio-over-fiber (RoF) system.  
transmission. Waveform deformation of optical signals in the fibers and radio signals in the air would be comprehensively compensated by optical coherent signal detection with DSP.
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 2 Fault Analysis of DFIG-Based Wind Farm and Its Verification
### Application of Conventional Distance Protection
#### 3.1.1 Phase-to-Ground Fault  
For the case of the single-phase ground fault outlined in Section 2.3.1, the fault currents of the wind farm side are dominated by the zero-sequence current, the frequency of which is 50 Hz. According to the real fault waveform data, the impedance trajectory calculated by the Fourier algorithm is shown in Figure 7.  
Figure 7: The phase B impedance trajectory of the wind farm side when a phase B ground fault occurs on the transmission line connected to a DFIG-based wind farm.  
In Figure 7, the blue line is the impedance trajectory measured by conventional distance protection. Zone 1 and Zone 2 are the polygon action areas of wind farm side instantaneous and time delay instantaneous distance protection on Line 1, respectively. 6.75 and 14.45 \(\Omega\) are the instantaneous and time delay instantaneous distance protection setting values of the reactance, respectively. 42.46 \(\Omega\) is the setting value of the ground resistance. Referring to Figure 4, the length of Line 1 is 22 km and the actual fault point is 21 km away from Bus 1 (the corresponding reactance is about 9 \(\Omega\)). Thus, the impedance trajectory is beyond Zone 1 and lies in Zone 2. From the magnified view of the impedance trajectory, the measured reactance is stable finally and close to 9 \(\Omega\) during the fault. That is to say, the conventional ground distance protection of the wind farm side is relatively reliable.