esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer-Generated Holography Methods for Data Page Reconstruction Using Phase-Only Medium
## 3 Methods for Synthesizing Phase Fourier CGH of Data Page
### Bipolar Intensity Method  
The bipolar intensity method is based on the modeling of holographic recording of complex light fields in the off-axis Leith-Upatnieks scheme [48]. Firstly, an interference pattern between the object and reference beams should be simulated. Let \(\Gamma\) be the hologram of object \(O\), and let \(R\) be the reference beam. Then the transmission function of the calculated hologram can be described mathematically as:  
\[\Gamma(\xi,\eta)\propto|A_{R}(\xi,\eta)+A_{O}(\xi,\eta)|^{2}=|A_{R}|^{2}+|A_{O }|^{2}+A_{R}^{*}A_{O}+A_{R}A_{O}^{*} \tag{4}\]  
where \(A_{R}\) and \(A_{O}\) are complex amplitudes of the reference and object beams in the hologram plane correspondingly, * is a sign of the complex conjugation.  
Since the calculation is performed numerically, \(|A_{R}|^{2}\) and \(|A_{O}|^{2}\) can be excluded. Then the transmission function of the obtained hologram takes the form:  
\[\Gamma(\xi,\eta)=2\text{Real}[A_{R}^{*}A_{O}] \tag{5}\]  
where \(\Gamma(\xi,\eta)\) is a real sign-variable function, and \(Real[]\) is a function of calculating the real part of the complex numbers. It can only be implemented using a medium with bipolar amplitude modulation, which is real-axis amplitude modulation with a variable sign. However, a hologram can be implemented on a medium with unipolar modulation by adding an amplitude bias:  
\[\Gamma(\xi,\eta)\prime=2\text{Real}[A_{R}^{*}A_{O}]+C \tag{6}\]  
where C is the amplitude bias. It is usually a positive value defined as:  
\[C\geq-\text{min}_{(\xi,\eta)}[\Gamma(\xi,\eta)] \tag{7}\]  
The fringe pattern model (Equation (6)) can be displayed by phase-only SLM. This procedure is similar to the well-known technique of hologram bleaching. The expression for the CGH model, in this case, can be written as:  
\[\Gamma(\xi,\eta)_{ph}=\exp\left(j\frac{\zeta}{\text{max}_{(\xi,\eta)}\left[ \Gamma(\xi,\eta)^{\prime}\right]}\Gamma(\xi,\eta)\prime\right) \tag{8}\]  
where \(\zeta\), is the modulation depth coefficient. This hologram can be implemented on a device with a unipolar phase modulation type. An example of fringe patterns obtained using the bipolar intensity method is shown in Figure 2a. The fragment size is 128 \(\times\) 128 pixels. The number of phase levels is 256. The black pixels correspond to the 0 phase shift. White pixels provide a 2n phase shift. We can see that the majority of pixels are gray. They correspond to intermediate phase levels.  
Three diffraction orders can typically be obtained in the light field reconstructed by bipolar intensity hologram: zero-order--a bright point at the center of the light field; \(-1\) order, which is the object image itself; and \(+1\) order, which is a conjugate image. The main drawback of this type of CGH is the impossibility of reconstructing the object image located on the optical axis of the read-out scheme.
## 3 Methodology  
This section explains the methodology applied in this work and how the feature selection for the classification problem was performed using genetic algorithms. Figure 1 shows a diagram of the feature-selection process and the generation of the classification model.  
In order to apply the methodology, we need to select three main items:  
* The machine-learning technique.
* The dataset, which defines the features.
* The FS method, and if it applies, the parameters of the genetic algorithm.  
From the original dataset, a curation process is performed, which also defines the initial dataset to be used. After that, the FS process is applied, and the selected features will be used to train the ML algorithm. The best classification ML model will be chosen after analyzing the results, their accuracy, and the number of false negatives.  
The objective of a GA is to find the best solution of a problem through the iterative transformation (using crossover and mutation operators [21]) of an initial set of potential solutions (population). For each solution (individual), its performance (fitness) is evaluated, and, based on this value, the fittest ones will have a higher probability of passing to the next iteration. After a certain number of iterations, one of the candidates will be selected as the solution to the problem.  
Four key concepts need to be considered when designing GAs:  
* The encoding of the problem.
* The size of the population and the initialization method.
* The selection method including the fitness function.
* The processes by which the changes are introduced in the next iteration, including the probabilities and parameters [22].  
After the execution of the GA, the fittest individual of the last generation represents the set of features (variables) selected to train the ML models.
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 4 Assessment of the Adaptive Fault Tolerance Mechanism
### Outcomes
Hence, we can confirm that it meets its specifications (see Section 3). When coverages are smaller (that is, when the transient fault is not tolerated by the EPB3932 code), we can see percentages of failures of between 5% and 10%. Note the special case of the 2\(\times\) ATr column, where the detection coverage is 100%. Remember that the EPB3932 code can detect all double adjacent faults (except those that include the EPB, which are corrected). Thus, we can conclude that our proposal works properly, and the AFT mechanism is able to detect and correct the errors specified in each ECC; and when the number of errors corrected in a  
Figure 9: Influence of faults in Plasma_ADAPTIVE core. Combined (intermittent + transient) faults. Intermittent fault model: ISA with L\({}_{\text{burst}}\) in the range [10, 20], t\({}_{\text{A}}\) in the range [10T, 100T] and t\({}_{\text{I}}\) in the range [10T, 100T]. bit reaches the configured threshold (\(\alpha_{\text{TH}}\)), it considers that the bit is suffering an intermittent error, and reconfigures itself to adapt to the new fault hypothesis. In this way, the adaptive system tolerates intermittent and combined (intermittent+transient) faults, as the significant reduction of failures and latent errors demonstrates.
## 4 Cost Function to Be Minimized
### Cost for the Land to Be Purchased to Install the Cable  
Compared with the previous references to OSCaR [35; 36; 37], the capital cost for purchasing the land required to install a superconducting cable in its tunnel and the land around it, corresponding to the buffer zone, to comply with the law's tolerable magnetic field limits is added in Equation (14). It is computed as follows:  
\[C_{land}=c_{land}\ L_{line}\ 2\cdot max\Big{(}R_{field_{max}},R_{install} \Big{)} \tag{27}\]  
where \(c_{land}\) is the cost per unit surface for the land at the installation site. For long lines crossing differently urbanized and costly areas, an average cost or a more discretized calculation approach should be considered.  
\(R_{field_{max}}\) is the farthest radial location from the central axis at which a magnetic field produced by the cable and exceeding the permitted field level persists. For three-phase AC concentric geometries, the magnetic field practically cancels out beyond the conductor, even though the currents in the three phases are not perfectly balanced. Even if a strict magnetic field threshold is adopted to evaluate the buffer zone (i.e., 3 uT), \(R_{field_{max}}\) nearly corresponds to the cable's outer radius. In such scenarios, the radial distance necessary for tunnel excavation and cable installation (\(R_{install}\)) is used to calculate the required land to be purchased in Equation (27). However, for cables with different designs, whether superconducting or not, \(R_{field_{max}}\) might be greater than \(R_{install}\), and thus the first should be employed in Equation (27). \(R_{field_{max}}\) can be computed by checking the magnetic field at increasing radial distances from the conductor, such as by replacing the term \(R_{meank,z}\) in Equation (9).  
The authors considered it relevant to include this index in the cost analysis to ensure a fairer comparison with other cable technologies. Indeed, one potential economic (and social) benefit of coaxial superconducting lines may be their reduced land usage to comply with magnetic field constraints, in comparison, for instance, with the large land footprint required for conventional overhead or underground high-power lines.
## 5 Discussion
### Key Findings and Implications
The adoption of blockchain apps, as evidenced by the strong path coefficient (0.591), is positively influenced by the recognition of its advantages, such as secure data management, anti-doping compliance, and transparent ticketing (Figure 3). Ticketing systems, as highlighted, utilize blockchain-based platforms to eliminate counterfeit tickets, ensure fair pricing, and enable secure resale markets. This result corresponds to the high loading factor for \"Smart Tickets\" (0.89 in football and 0.73 in basketball) in Table 1, demonstrating its pivotal role in fostering adoption. These findings align with previous research that highlights blockchain's ability to address critical vulnerabilities in centralized systems [7; 8]. Second, blockchain applications were shown to have a moderate but positive association with athletes' performance at both national and European levels (path coefficient: 0.410). The integration of decentralized technologies facilitates better data-driven decision-making, performance tracking, and injury prevention--key factors that contribute to improved outcomes. All the loading factors for different blockchain apps have high loadings (above 0.819 across contexts), reflecting the role of blockchain in managing sensitive player data, preventing injury, and supporting staff with comprehensive records. These findings extend the existing knowledge by empirically demonstrating blockchain's role in enhancing measurable athletic performance metrics (Figure 3). Third, the cluster analysis revealed variations in the perceptions of blockchain benefits across professional roles. While the athletes and coaches expressed stronger agreement with blockchain's advantages, the sports managers and administrators exhibited more neutral attitudes. This divergence highlights the need for tailored strategies to improve stakeholder understanding and the adoption of blockchain technology. Effective communication of blockchain's value proposition can bridge this gap and encourage wider acceptance (Figure 4). Figure 4: Cluster final centers by profession. Even though the findings are consistent with international trends, certain limitations should be acknowledged. The performance indicators are self-reported and may be influenced by subjective interpretation. Also, the exclusive use of quantitative methods limits the depth of understanding regarding how and why blockchain is adopted. These limitations may affect the generalizability and external validity of the findings and suggest the need for complementary qualitative or longitudinal research in the future.
# Shading on Photovoltaic Collectors on Rooftops
## 7 Conclusions  
The deployment of PV collectors on rooftops may encounter shading by adjacent collectors and by obscuring structures on the rooftops, resulting in a reduction in the generated electric energy. Increasing the row distance between the collector rows or increasing the distance from the obscuring structures (walls and fences) to reduce or avoid shading may not be an option because of limited rooftop area. The present study develops mainly analytical expressions for distances between walls and fences and PV collector rows for horizontal, inclined, and saw-tooth roofs. These expressions may also be applied to PV fields on the ground. Based on Figures 5, 6, 8 and 10, the following conclusions may be drawn: (1) Shorter row-to-row distances between the PV collectors are needed for steeper inclined roofs. (2) Larger collector inclination angles require larger row-to-row distances. (3) Shorter distances are needed from walls and fences to the PV collectors for steeper inclined roofs; however, higher walls and fences require larger distances. (4) Shading losses decrease with an increase in the distance from obscuring objects; however, higher obscuring objects increase the shading losses. On fenced roofs, the shadow on the collectors depends on the fence height and azimuth, on the distance between the collectors and the fence, and on the collectors' inclination angle. Higher annual incident irradiation may be obtained on sloped triangular roofs for PV modules deployed with angles pointing south-east and south-west, rather than due south.  
Conceptualization and methodology, J.A.; software, A.A. All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflict of interest.
# High Optical Feedback Tolerance of a Detuned DBR Laser for 10-Gbps Isolator-Free Operation
## 3 Results
### Modulation Performance under Feedback  
#### 3.2.1 Signal-to-Noise Ratio of the Eye Diagram  
To investigate the influence of the wavelength detuning on the modulation performance of the DBR laser under direct modulation, the SNR of the modulated signal was first examined. The two DBR lasers mentioned above were directly modulated by using a 10.3125 Gbps pseudo-random bit sequence (PRBS) with a pattern length of \(2^{15}-1\) under different feedback levels. The extinction ratio (ER) was set to a relatively small value of 2 dB to demonstrate the clear dependence of the SNR on the wavelength detuning. Higher ERs also showed a similar trend, but not as significant as the low ER case. The feedback level was set to a moderate level of \(-15\) dB. The SNR of the eye diagram was measured by a sampling oscilloscope. The variation of SNR versus bias current is shown in Figure 5.  
When the bias current was below 30 mA, all SNRs were poor. As the current increased, the overall SNR improved. For Laser 1, the improvement before the mode hopping point was monotonous. The higher the current was, the higher the SNR would be. After the mode hopping, the SNR dropped suddenly by 3 dB. This is consistent with the prediction from the CW critical feedback value. For Laser 2, the SNR increased to 5 dB before the mode hopping point of 27 mA, followed by a sudden drop at the blue-detuned region. Then with the increase in the pump current, the SNR increased back from 3.5 dB to 6 dB, which was also consistent with the static performance shown in Figure 4b.  
The result shows that the wavelength detuning has a similar influence on the eye diagram SNR as that of the CW case under optical feedback. The OFT performance of a directly modulated DBR laser should depend on the wavelength detuning of the laser and can be roughly inferred from the critical feedback value measured at the CW condition.
## 5 Discussion  
The proposed methodology has limitations that affect the quality, accuracy, and time of creating a 3D model. The main factors that affect the limitations in the presented methodology and, as a result, the accuracy of the obtained models, are presented below:  
1. Software and hardware factors--in order to obtain a high-quality 3D model, the method requires a modern smartphone that supports video recording in 1920 \(\times\) 1080 (pixels) resolution. The video format can be both vertical and horizontal. It is important to note the method's and algorithms' demand of photogrammetry to computing resources. Depending on the specific CPU and GPU model, there are different processing and 3D model creation times. In addition, it is necessary to use video cards (GPUs) that support CUDA technology. The optimal number of frames sufficient to obtain a complete and high-quality 3D model varies from 50 to 150;  
2. Human factor--two people should be involved in the reconstruction process. The first person whose foot is to be reconstructed should lie on a flat surface, holding the foot with their hands in space. The foot should be strictly in the center of the frame and remain fixed throughout the video shooting. The second person records the video of the first participant at a certain angle. In this case, limitations may be low stabilization due to camera shake when shooting without using a tripod; the height of the shooter: a tall person will find it uncomfortable to bend down to maintain the correct angle when shooting; reluctance to comply with the requirements due to a lack of time to read the requirements;  
3. The conditions of the placement, where the shooting was taken--in order to obtain high-quality data, it is important to avoid glare, highlights, and shadows in the camera lens and to choose a contrasting background. It is also important to provide the necessary free space in the room, because the shooter should move strictly in a circular path and travel around the foot. The distance from the camera lens to the foot can vary from 30 to 70 cm.
## 3 Proposed Methodology
### Data Communication Phase  
By gathering data packets at a nearby header node, this will help to mitigate interference. A composite data packet is sent by this node to the endpoint. Based on maximum residual power, CHs are chosen. CHs connect to both the sink node or with CH neighbors and the forward data packets, while handheld sinks are in dispersed areas to gather data with minimal energy consumption. If the RSSI reaches its predefined limit stated as either low or high, the sender node tunes its TPL accordingly, i.e., if the RSSI is below the limit, it switches to the next power level; on the other hand, if the RSSI is above the high limit, the power level will degrade to a level. The RSSI limits are fixed at the start of the data communication phase and it depends on the channel condition. The process of the proposed technique is depicted in Figure 3, where n is the number of nodes, R is the region, r is the rGR which is donated searching the number of sparsity and density regions of the network.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Node numbers & 100 \\ Nodes’ initial energy & 5 J \\ Data rate & 16 Kbps \\ Band width & 16 Kbps \\ Center frequency & 30 kHz \\ Size of packet & 50 Bytes \\ Reception power packet & 0.1 W \\ Transmission power packet & 2 W \\ Range of transmission & 50 m \\ Running rounds & 3500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters Used for CTP-SEEC Performance Evaluation.
# Study on Stress-Strain Relationship of Coir Fiber-Reinforced Red Clay Based on Duncan-Chang Model
## 3 Experimental Results and Analyses
### Shear Strength Parameters  
The Mohr-Coulomb strength theory was used for the analysis based on the results of triaxial tests on the soil, and the Mohr stress circle at the time of sample damage and the shear strength envelope of the sample could be plotted as shown in Figure 7. According to the Mohr-Coulomb strength envelope of each specimen, the cohesive force \(c\) and the internal friction angle \(\varphi\) of each specimen were obtained by fitting as shown in Table 2.  
Figure 7: Mohr–Coulomb strength envelope of soil samples. (**a**) 0% coir fiber; (**b**) 0.2% coir fiber; (**c**) 0.25% coir fiber; (**d**) 0.3% coir fiber; (**e**) 0.35% coir fiber; (**f**) 0.4% coir fiber.  
Figure 8 shows the influence of different levels of coir fiber content on the shear strength index of soil. For the consolidated undrained test of normally consolidated saturated soil, the cohesion is 0 in theory. Because the maximum dry density method was used in this test, partial cohesion also existed in the plain soil sample. From the diagram, it can be seen that with the increase in fiber content, the cohesion of coir fiber-reinforced soil showed a trend of increasing first and then decreasing. When the fiber content was 0.3%, the cohesion of the soil increased most significantly. Therefore, 0.3% fiber content was the optimal coir fiber content. With the increase in fiber content, the change in internal friction angle was not large, indicating that the friction characteristics of the fracture surface were basically unchanged after adding coir fiber to clay.
# An Emotion Speech Synthesis Method Based on VITS
## 4 Experiment and Result
### Subjective Evaluation  
We evaluated the naturalness and emotional similarity of our system through a subjective evaluation test. Twenty groups, consisting of text and reference speech, were randomly selected from the test set, and a total of 40 pieces of audio were synthesized by the two emotion systems. The reference audio and synthetic speech were grouped, and the 13 workers who had never touched speech synthesis rated the naturalness and emotional similarity of the speech on a scale of 5, respectively. The naturalness was from 1 (bad) to 5 (excellent), and the emotional similarity was from 1 (not similar at all) to 5 (highly similar). Each audio was rated by different workers, and we reported an average opinion score (MOS) and a 95% confidence interval. In the table, Emo-VITS (Global) represents that only global vectors are used as the result of emotion embedding, while Emo-VITS (Local) represents that only local vectors are used as the result of emotion embedding. The results are shown in Table 3.  
From this table, we can see that our model is obviously better than LST-A in terms of naturalness, although there is still a big gap with the ground truth. Through experiments, we found that our advantage over LST-A was reflected in long statement modeling. When the text information is too long, LST-A often has the phenomenon of repetition and missing words. The reason this happens is that the alignment of the TransformerTTS depends on the attention mechanism, and it is not stable enough. This kind of problem can greatly affect the naturalness of speech. In contrast, Emo-VITS is more stable. Even if there is a problem, it will only appear in the pronunciation of a certain word and will not cause a greater impact on the overall structure of the sentence, so the negative effect is limited.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Angry & Happy & Fear & Sad & Surprise \\ \hline
100\% & 72.7\% & 46.9\% & 28.2\% & 50\% \\ \hline \hline \multicolumn{5}{c}{Total Accuracy (_WA_) 61\%} \\ \hline \multicolumn{5}{c}{Total Accuracy (_LA_) 59.6\%} \\ \hline \multicolumn{5}{c}{Emotion objective evaluation of LSA-A} \\ \hline Angry & Happy & Fear & Sad & Surprise \\ \hline
67\% & 34.6\% & 39.1\% & 75.0\% & 30\% \\ \hline \multicolumn{5}{c}{Total Accuracy (_WA_) 48.5\%} \\ \hline \multicolumn{5}{c}{Total Accuracy (_LA_) 49.1\%} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Emotion objective evaluation of Emo-VITS.  
However, under the same acoustic model of Emo-VITS and VITS + GST, Emo-VITS's emotional similarity is significantly better than VITS + GST's, which can show that the Emo-VITS embedding method is better. By comparing the separate embedding results of the global and local vectors of Emo-VITS, it can be found that the global emotion vector of Emo-VITS focuses on maintaining the naturalness of the whole statement, while the local emotion focuses on the expression of emotion.