esa-sceva/satcom-chunk-collection huggingface dataset:
# Statistical Analysis and Modeling for Optical Networks
## 6 Discussion and Applications
Precisely understanding factors like fiber length, attenuation, dispersion, and nonlinearity is not just about measurement; it is about predicting their impact on signal integrity and network capacity through sophisticated models. For instance, statistical models of signal degradation over varying fiber lengths inform the strategic placement of optical amplifiers like EDFAs and Raman amplifiers, ensuring power compensation and extended reach without costly OEO conversions. Dispersion management, including chromatic and polarization mode dispersion, relies on statistical estimation to deploy compensation modules or advanced digital signal processing techniques effectively, preserving signal clarity across the network. Furthermore, modeling the effects of fiber nonlinearities, such as self-phase modulation and four-wave mixing, is crucial for maintaining spectral efficiency and network reliability, as these impairments are often stochastic in nature. Beyond physical impairments, intelligent link dimensioning leverages statistical analysis to optimize spectral efficiency and network scalability. This involves using statistical models to determine proper wavelength spacing in WDM and assess the benefits of coherent detection techniques, maximizing data rates while minimizing interference. Maintaining optical SNR within acceptable limits is a continuous estimation and control problem, solved through optimized amplifier placement and power control strategies derived from statistical network performance models. Emerging technologies like EONs and SDN enable dynamic link adaptation, driven by real-time traffic demands and intelligent algorithms that use statistical analysis to predict and respond to network changes. As optical networks evolve to support high-speed applications, the accurate estimation of link parameters through robust statistical analysis and modeling remains paramount for achieving high performance, reliability, and cost-effectiveness.
## 6 Experimental Results  
Aiming to check the validity of what presented so far, experimental measurements were carried out on the Buck converter introduced in Section 5. The switching waveforms were acquired for different values of source inductance, and frequency spectrum of the switching voltage \(v_{\text{sw}}\) was evaluated to assess the reduction in the peak corresponding to the turn-on oscillation. Then, a setup compliant to the CISPR-25 [31] standard was used, and the CM EMI was measured, assessing the optimal value of source inductance found in Section 5.2. Different operating conditions of the Buck converter were also tested to verify the effectiveness of the technique in a real-case scenario. Finally, the proposed solution was compared against the traditional one, i.e., the use of a low-side snubber, resulting in almost the same spectra reduction, but in a higher efficiency.
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 3 Literature Review
### Predictive Features for the Preferences
#### 3.2.1 Rhythms as Features  
The beta and gamma oscillations from consumers who watched movie trailers were utilized to predict the box office sales and recall [24]. These factors were also used as an indicator of the willingness-to-pay to evaluate consumer preference and choice [32].  
The alpha oscillations were used to compute the neural likeness and forecast recall and ticket sales. High-frequency EEG components were connected to both the individual preference (beta wave) and population preference (gamma wave) [24].  
In addition, alpha frontal asymmetry was linked to the consumers' unconscious reactions to the product attractiveness [29]. Similarly, [PERSON] et al. [33] linked the higher alpha frequencies to comfort food as well as foreign food products. Moreover, awarded campaigns (i.e., the campaigns that received prizes) in anti-smoking public service announcements were linked to higher alpha frequencies [30].  
Lower theta frequencies were associated with the negative results toward choosing products [3]. Moreover, these frequencies have been linked to effective anti-smoking public service announcements [30] and foreign products compared with local products [33].
## 4 Materials and Methods
### Model Evaluation Metrics  
To compare the performance of the MLP classifier with other methods, classical machine learning algorithms--random forest (RF) and support vector machine (SVM)--were employed to segment the plant images. RF is an ensemble learning method composed of a collection of multiple decision trees capable of modelling nonlinear variable interactions while avoiding overfitting. It evaluates an optimal set of decision trees that can analyze high-dimensional data. Each decision tree is developed using a set of data that does not depend on earlier decision trees. In this study, the probability distribution of each decision tree was aggregated and averaged to provide the final classification by labelling each pixel as 0 (background) or 1 (foreground). The SVM is a supervised learning method that is useful for the classification and regression of complex variables. In classification, an SVM model fits a hyperplane to a set of input data that separates one class from another. The best hyperplane is the one with maximum margin between a set of classes [60]. Generally, the SVM performs better and is more tolerant to irrelevant and interdependent features.  
To assess the impact of feature selection on the performance of the models, six different models were developed using the AF and SF as inputs. The performance of the models was evaluated using the average accuracy score, F-score, and recall score metrics, as given in Equations (6)-(8):  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{6}\]  
Figure 10: Examples of images obtained from the glasshouse and field, segmented as reference images. (**a**) Original image, (**b**) manually segmented image, and (**c**) binary image. Reference images were randomly selected from the dataset with different nutrient content and illumination and at variable growth stages.  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
\[\text{F-Score}=2\ \frac{\text{P}\ \times\ \text{R}}{\text{P}+\text{R}} \tag{8}\]  
where P = \(\frac{\text{TP}}{\text{TP}+\text{FP}}\); P is precision, R is recall, TP is true positive, TN is true negative, FN is false negative, and FP is false negative.  
Each model used the entire dataset, which was divided into a 3:1:1 ratio for training, validation, and testing subsets, respectively. The validation subset was used to test the model during training, while the testing subset (independent data) was used to test the model after training. Supplementary Table S1 shows the amount of data used for each feature dataset for training, validating, and testing the models. To ensure a robust and stable model, the training and testing procedures were repeated 100 times (with a random seed set to 0-99), and the results represent the average classification metrics for each set of data. Table 6 outlines the optimal hyperparameters used to train each model.
## 3 Results
### Effect of Gas Amount under Different Loading Rates and System Temperatures
#### 3.2.2 Comparison of \(P\)-\(\Delta V\) Curves in Comparative Working Condition  
Figure 4a,c show the 10 cycles of \(P\)-\(\Delta V\) curves of the undegassed glycerol/zeolite system at 0.01 mm/s and 0.10 mm/s, respectively. At both loading rates, the glycerol/zeolite system without degassing pretreatment acts as a disposable system. After the 1 st intrusion, a large amount of accessible nanopore space is permanently occupied by the invaded glycerol molecules, thus little infiltration is allowed in subsequent cycles. In Figure 4c, although a higher loading rate leads a better outflow after the first intrusion, the hysteresis loop of the undegassed system shrinks afterwards.  
Figure 4: \(P\)-\(\Delta V\) curves of undegassed and degassed glycerol/zeolite system under ten cyclic loadings (loading rate 0.01 mm/s and 0.10 mm/s; system temperature 303 K and 348 K).  
Figure 4b and d show the 10 cycles of \(P\)-\(\Delta V\) curves of the degassed glycerol/zeolite system at 0.01 mm/s and 0.10 mm/s separately. When comparing with the undegassed condition, eliminating excessive air bubbles during preparing glycerol/zeolite mixture promotes the reversibility of the system dramatically. In the degassed system, although part of the glycerol molecules trapped in the nanopores and could not flow out of the channels freely after the first cycle, the system reaches its thorough balance, and performs a pressure plateau after the first two or three cycles.  
Furthermore, the \(P\)-\(\Delta V\) curves of the degassed and the undegassed systems at a lifted system temperature of 348 K are obtained. The results are plotted in Figure 4e,f. As shown in Figure 4f, the stability of the degassed system is still good at higher system temperature, and the reversibility of the degassed system is improved as well. The system achieves its throughput balance after three cycles. While for an undegassed system in Figure 4e, the \(P\)-\(\Delta V\) curves during loading processes fluctuate, indicating an unstable intrusion process. This is caused by the enhanced solubility of gaseous phase under higher system temperature. The detailed liquid-gas interaction will be analyzed in Section 4, the discussion section.
## 3 Results  
### Effect of Axial Pressure on Permeability  
Variations in axial pressure induce modifications to the internal porosity of coal specimens, thereby directly governing of permeability. Thus, elucidating the interdependence between the axial stress and permeability in broken coal is a research priority. According to [PERSON]'s law, the permeability in the test is calculated as follows [16]:  
\[-\frac{dp_{k}}{dx}=\frac{\mu}{k}v \tag{2}\]  
Figure 4: Flow chart of triaxial percolation test.  
where \(p_{k}\) is the pore pressure, MPa; \(\mu\) is the hydrodynamic viscosity, Pa-s; \(k\) is the permeability of coal sample, m\({}^{2}\); \(v\) is the fluid seepage velocity m/s.  
The relationship between the axial pressure and permeability \(k\) is shown in Figure 5. The results show a gradual decrease in permeability \(k\) of the broken coal samples with increasing the axial pressure. Under constant confining pressure, the rate of permeability reduction decreases progressively with the axial pressure escalation, suggesting attenuated stress-induced permeability suppression. In comparison, samples with \(n=1.0\) show a 40% decrease in permeability under the axial loading, while those with \(n=0.4\) show a 25% decrease followed by stabilization. Under these conditions, the coal samples attain structural stability and incompressibility [31], while the permeability decreases slowly, tending to a stable state.  
A stable value emerges as permeability asymptotically approaches a steady state. This behavior is primarily determined by the progressive stabilization of the pore structure of the coal samples induced by the axial pressure. These observations are consistent with the compaction-dependent permeability reported by [PERSON] et al. [37]. This implies the existence of a critical permeability threshold during triaxial loading cycles. The threshold value is contingent upon three interdependent factors: internal structural stability of the coal samples, progressive closure of interparticle flow pathways, and intrinsic permeability of constituent particles.
# Study on Stress-Strain Relationship of Coir Fiber-Reinforced Red Clay Based on Duncan-Chang Model
## 4 Duncan-Chang Model and Parameter Analysis of Coir Fiber-Reinforced Red Clay
### Parameter Analysis  
The above triaxial shear test data are organized by \(a\)-\(b\), and the results obtained are shown in Figure 10. According to the test analysis, the \(\frac{\varepsilon_{1}}{\sigma_{1}-\sigma_{3}}\sim\varepsilon_{1}\) of coir fiber-reinforced cohesive soil is approximately a first-order linear relationship, indicating that the \(\left(\sigma_{1}\text{-}\sigma_{3}\right)\sim\varepsilon_{1}\) hyperbolic relationship can be well established. The strength and tangent elastic modulus of coir fiber-reinforced cohesive soil can be reasonably determined according to the Duncan-Chang model, which conforms to the strain-hardening relationship. Parameters \(a\) and \(b\) are the intercept and slope of the curve \(\frac{\varepsilon_{1}}{\sigma_{1}-\sigma_{3}}\sim\varepsilon_{1}\), respectively. By fitting and calculating the data of unreinforced fiber soil and coir fiber-reinforced soil, this paper gives the Duncan-Chang model parameters of the specimens with different fiber content under different confining pressures, as shown in Table 3. It can be seen from the table that under the same fiber content, the parameters \(a\) and \(b\) gradually decrease with the increase in confining pressure. Contrarily, the initial deformation modulus \(E_{\text{i}}\) and the ultimate deviatoric stress \(\left(\sigma_{1}\text{-}\sigma_{3}\right)_{\text{ult}}\) of the sample increase with the increase in confining pressure, while the failure ratio \(R_{\text{f}}\) is in a fluctuating state. In 1963, [PERSON] proposed an empirical formula reflecting the relationship between initial tangent modulus \(E_{\text{i}}\) and confining pressure:  
\[E_{\text{i}}=Kp_{a}\left(\frac{\sigma_{3}}{p_{a}}\right)^{n} \tag{8}\]  
In the formula, \(K\) and \(n\) are test constants, and \(P_{\text{a}}\) is atmospheric pressure, generally taken as 101.33 kPa. It can be seen from Equation (8) that \(\log_{10}\frac{E_{i}}{p_{a}}=\log_{10}K+n\log_{10}\frac{\sigma_{1}}{p_{a}}\), that is, \(\log_{10}\frac{E_{i}}{p_{a}}\sim\log_{10}\frac{\sigma_{1}}{p_{a}}\) is a linear relationship. The \(\log_{10}\frac{E_{i}}{p_{a}}\sim\log_{10}\frac{\sigma_{1}}{p_{a}}\) relationship curves of samples with different levels of fiber content were drawn in the coordinate system, and linear fitting was carried out. The fitting results are shown in Figure 11. From the diagram, it can be found that on the one hand, the initial tangent modulus increases with the increase in confining pressure; on the other hand, the initial tangent modulus increases first and then decreases with the increase in fiber content, but both are higher than the initial tangent modulus of unreinforced fiber soil. This shows that adding coir fiber to the soil can improve the initial tangent modulus of the soil, and when the fiber content is 0.3%, the initial tangent modulus reaches its highest value.
## 4 Experimental Studies
### Simulation of the DWTA Problem  
#### 4.2.1 Decision-Making Process of DWTA Problem  
Figure 3 shows the decision-making process of the DWTA problem. We can see from the figure that DWTA decision-making needs to determine the targets to be attacked and available weapons at the current stage on the basis of the results of battlefield situation analysis. Then, the determined targets and weapons are input into the IMOPSO algorithm to perform optimization iteration to obtain WTA decision. DWTA decision-making process also needs to evaluate and monitor the allocation results and battlefield situation, and determine whether iteration needs to be continued in accordance with the effect of the allocation scheme and battlefield situation.
# SPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation
## 3 Methods
### Overview of SPDepth  
We introduce a self-propagation module to spread the high-quality depth from the well-supervised regions to those lacking supervision. The pipeline of SPDepth is presented in Figure 2.  
Figure 2: The pipeline of SPDepth. Features with a resolution half the size of the training images are encoded from the target image. The features are then up-sampled to the same size as the image. The depths from DepthCNN and up-sampled features \(F\) are together input to the self-propagation module. Based on self-attention, the depths are weighted with the self-similarity scores to perform propagation.  
It can be noticed that the depth map and the target image share high structural similarities. To improve the performance in areas not supervised, we exploit the self-similarity of features by introducing a self-attention mechanism. The general self-attention is formulated by the following:  
\[\widetilde{D}=\text{softmax}\left(\frac{F_{Q}F_{K}^{T}}{\sqrt{C}}\right)D \tag{1}\]  
where \(C\) denotes the number of feature channels, \(D\) represents the depth produced from the DepthCNN and \(\widetilde{D}\) denotes the propagated depth results. The softmax function normalizes self-attention weights. \(F_{Q}\) and \(F_{K}\) stand for the projected query feature and key feature and the projection can be written as follows:  
\[F_{Q} =W_{qry}F \tag{2}\] \[F_{K} =W_{key}F \tag{3}\]  
where \(F\in\mathbb{R}^{H\times W\times C}\) are the up-sampled features, \(H\) and \(W\) are the height and width of the image, \(W_{qry}\in\mathbb{R}^{C\times C}\) and \(W_{key}\in\mathbb{R}^{C\times C}\) are linear transformation matrices. The propagated depth \(\widetilde{D}\) is integrated with depth information from other pixels.  
In the previous optical flow estimation works [5; 46], global propagation was mostly adopted because optical flow has high self-similarities in the global range. However, since the depth values vary in different regions of the image, it is improper to perform propagation globally. The global depth self-propagation leads to too much computational cost. Instead, we use local window self-propagation to weight the depths within a neighborhood.
# Code Similarity and Location-Awareness Automatic Program Repair
## 2 Background  
Automatic program repair involves automatically finding a solution to bugs without manual operation and using a certain program specification to automatically convert the buggy program into a program variant that satisfies the specification. Automatic program repair can be transformed into a search problem of finding the correct patch in a potential search space. Genetic programming [12] is a heuristic search method that can effectively represent the evolution process of programs. Automatic program repair typically uses genetic programming to search for the correct program patch. Many studies have shown that the more similar a program is to a buggy program, the more likely it is to contain the correct code. Therefore, code similarity is gradually used as an important heuristic to guide the search process for program repair.
## 3 Results
### As-Built LPBF Micrographs
Although there are few gas pores in these cubes, no cracks are observed with scanning speed between 550 mm/s and 850 mm/s. Lower hatch distances tend to reduce the building rate, and they are typically characterized by increased melt pool overlap, as well as an increase in local melt pool temperature (vice versa for increased hatch distance) [25]. However, for complete fusion and good metallurgical bonding, the amount of laser and material interaction time should be enough to generate the right volumetric energy density available for complete melting [26]. As previously explained, in spite of the higher hatch distance of 140 \(\upmu\)m, as seen in Figure 4, cracks which were not observed in this print set can be attributed to the lower scanning speed between 550 and 850 mm/s. This may have led to a reduction in the build-up of high residual stresses leading to fracture or cracks as seen in Figure 3e,f. In both hatch distance print sets (80 \(\upmu\)m and 140 \(\upmu\)m), however, well-defined melt pool boundaries with orientation along the building direction and a lot of epitaxial growth were observed. Scanning electron micrographs of the as-built cubes shown in Figure 5 highlight discontinuous cellular dendritic sub-structures with high levels of epitaxial growth at different portions of the microstructure. Epitaxial growth is seen to be interrupted at some point at the top of the melt pool as shown in Figure 5a and grows again only to be terminated at the melt pool boundary. In Figure 5b, epitaxial growth is seen stretching across the melt pool boundary. In Figure 5d, it is interlaced between dendritic cellular substructures. The microstructural features, such as the growth of epitaxy and the formation of fine dendritic structures, as seen in Figure 5, are consistent with typical as-built LPBF microstructures reported in several nickel-based alloys such as IN718, IN625, and Haynes(r) 230(r) [27; 28]. The unique microstructure observed in the LPBF built nickel-based alloys, which is also seen in Figure 5, is influenced not only by the process parameters, but also by the extremely fast cooling rate associated with the LPBF process, which makes it different from the cast [29]. Figure 3: (**a–c**) Dense 80 \(\upmu\)m hatch distance cube (etched and unetched) built at 1000 mm/s; (**d–f**) 80 \(\upmu\)m hatch distance cube (etched and unetched) built at 1500 mm/s showing a high number of cracks.
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 3 Results
### Distribution of Operating Cost Components
#### 3.1.2 Blasting  
Figure 3 illustrates the distribution of costs among the various components contributing to the total cost associated with blasting.  
Figure 3: Distribution of operating cost components associated with blasting in percent fraction.  
Figure 2: Distribution of operating cost components associated with drilling in percent fraction.  
Explosives and accessories contribute 97.9% of the total blasting cost, making them the primary cost driver. In comparison, fuel, labor, permits, and regulatory costs collectively account for 1.71% of the total cost, representing the least significant cost components.
## 4 Discussion  
Previous studies have shown that an external magnetic field could help to mitigate the signal attenuation in hypersonic plasma sheaths. Nevertheless, most of previous work assumed that the external magnetic field is uniform. It should be realized that this is an idealized assumption. In realistic scenarios, strong magnetic fields are generated by the coil or a permanent magnet. On the other hand, only rarely could permanent magnets work stably in high-temperature environments. The temperature of the hypersonic plasma sheath could be up to thousands of Kelvins, which is not an appropriate working environment for a permanent magnet. Therefore, the magnetic field generated by the coil may be the only choice for hypersonic vehicles.  
On the other hand, the present study has shown that, although the efficiency for the dipole magnetic field to weaken the attenuation is lower than that of a uniform magnetic field, it is still effective. Therefore, weakening the signal attenuation with a dipole magnetic field generated by the coil is a reasonable and possible approach to mitigate the communication blackout problem.  
In addition, the present study has shown that a new dissipation yielded by the dipole magnetic field and the magnetic field component of an electromagnetic wave for high-frequency waves is ignorable, compared to the dissipation yielded by the collision between free electrons and neutral particles. Nevertheless, high-frequency communication is not the only possible approach to mitigate the \"blackout\". Low-frequency waves, which could penetrate the plasma sheath via the skin depth effect, is also considered a potential solution to the blackout problem. Once the carrier wave is in the low-frequency (LF) band, the new dissipation could be significant. Moreover, the present study assumed that the wave vector is always parallel to the normal direction of the coil, which is not always true in realistic scenarios. Once the wave is propagating obliquely, the new dissipation may need to be taken into account.