esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Impacts of \(\varepsilon_{\text{max}}\) on GPP Estimations  
The default \(\varepsilon_{\text{max}}\) value for grassland in the BPLUT was less than half of the optimized \(\varepsilon_{\text{max}}\) estimations from field observations in this specific biome (Table 1). Validations in parameters of LUE model in most recent studies revealed that the realistic \(\varepsilon_{\text{max}}\) values were undervalued in the MODIS default GPP algorithms [12; 25; 27; 34] with a few exceptions [26], which emphasized the urgent need to reconcile the optimized \(\varepsilon_{\text{max}}\) for more extensive biomes [17; 29; 35; 38; 69]. The global look-up table of \(\varepsilon_{\text{max}}\) in the MOD17A2 GPP algorithm is hard to satisfy all vegetation properties due to various biomes with complex climatic, soil types, and associated stand structures and ages [25; 28]. Actually, these uncertainties in \(\varepsilon_{\text{max}}\) estimations might also attribute to the practice that LUE is alterable against different cloudiness, generally highest on overcast days and decreases on clear sky days [17], whereas the overcast conditions are not considered in the MOD17 default \(\varepsilon_{\text{max}}\) list and they are assumed to change only with vegetation types [11]. Optimized \(\varepsilon_{\text{max}}\) inputs directly caused that no significant difference was existed between GPP_MODR3 and GPP_EC in daily and annual GPP estimations (Figure 5b and Table 2), which indicated that GPP_MODR3 was an alternative estimation method to evaluate seasonal and annual GPP on this alpine wetland on the Tibetan Plateau. Contrast to GPP_MOD, GPP_MODR3 improved approximately 60% of RPE in the agreements of the model for the insitu flux tower GPP with the lowest RMSE from \(-0.86\) to \(-1.3\) g C m\({}^{-2}\) (Table 3 and Figure 6). Therefore, refinements of MOD17 \(\varepsilon_{\text{max}}\) may be beneficial to have a more agreeable fit between GPP_EC and MODIS products because optimized \(\varepsilon_{\text{max}}\) inputs directly improve the LUE model for GPP estimation [11; 38; 39].
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 3 Preliminaries on Channel Estimation  
### LS/MMSE Method  
Using the \(N\times 1\) reference signal vector \(\mathbf{S}\) for the received signal \(\mathbf{Z}\), we perform the LS method and estimate the channel as follows  
\[\begin{split}\mathbf{\hat{G}}_{\text{LS}}&=\mathbf{S }^{-1}\mathbf{Z}\\ &=\mathbf{S}^{-1}(\mathbf{SG}+\bar{\mathbf{w}})\\ &=\mathbf{G}+\mathbf{S}^{-1}\bar{\mathbf{w}}\\ &=\mathbf{G}+\bar{\mathbf{w}}\end{split}, \tag{6}\]  
where \(\bar{\mathbf{w}}\) represents \(N\times 1\) i.i.d. complex Gaussian random vectors with vectors zero mean and variance \(\sigma_{n}^{2}/\mathbb{E}\{\mathbf{S}\mathbf{S}^{\ddagger}\}\). As shown in Equation (6), the LS method is one of the most common approaches because of the simple calculation. However, it is difficult to make precise channel estimation because \(\bar{\mathbf{w}}\) remains in the estimated channel, and it does not use the statistical information of the channel. Therefore, to overcome the limitations of the LS method and minimize mean square errors, the MMSE method is performed as follows  
\[\mathbf{\hat{G}}_{\text{MMSE}}=R_{\mathbf{GZ}}R_{\mathbf{ZZ}}^{-1}\mathbf{Z}, \tag{7}\]  
where \(R_{\mathbf{GZ}}=\mathbb{E}\{\mathbf{GZ}^{\ddagger}\}\) is an \(N\times N\) cross-correlation matrix between the channel and received signal, and \(R_{\mathbf{ZZ}}=\mathbb{E}\{\mathbf{Z}\mathbf{Z}^{\ddagger}\}\) is the \(N\times N\) auto-correlation matrix for the received signal. The MMSE method may improve channel estimation accuracy by using the statistical information of the channel. However, it has a higher computational complexity than the LS method and requires statistical information of the channel. Additionally, there is a limitation in using the MMSE method because it may be difficult to obtain channel information, and accurate information is not guaranteed.
## 4 Results
### Evaluation of Load-Displacement Behavior  
Figure 9 illustrates the tensile load-displacement results of the one-open-hole tensile tests and the PFA. Composite specimens that were fractured at the end demonstrated a sudden decrease in the tensile load. The maximum load of type-1 is lower than type-2; this is because a type-1 specimen contains 90-degree plies in the middle of the laminate. The PFA results indicate the stiffness of the composite material well. The maximum load difference between the experiment and the PFA results was only 2.30% and 0.59% for the type-1 and type-2 specimens, respectively. The stiffness and strength were predicted for all of the specimen cases. This indicates that the suggested PFA model can be applied regardless of the specimen stacking sequence. Figure 10 presents the compressive load-displacement results of the one-open-hole compressive tests and the PFA. The compressive tests displayed a greater variation in the stiffness than the tensile tests. The variation can be induced from the test set-up when assembling the parts of the test fixtures. The predicted stiffness and strength using the PFA was in good agreement with the experimental results. The maximum load difference between the PFA and the experiment results was 1.30%. The small difference shows that the suggested PFA model predicts the compressive strength of the open-hole composite laminate accurately. To evaluate the PFA model with a notch effect, two-open-hole composite laminates were tested. Figure 11 shows the test and analysis results for the two-open-hole composite specimens under the tensile and compressive loading conditions. Similar to the one-open-hole cases, the suggested PFA model accurately predicted the stiffness and strength regardless of the loading conditions. The PFA model predicted the strength accurately in the range of 2.58% and 0.79%, respectively, under tensile and compressive loading conditions. The stiffness values were also accurately predicted in the range of 3.47% for the tensile test condition and 3.15% for compressive test condition. These results show that the developed PFA model can predict the strength and stiffness of the composite laminate accurately regardless of the stacking sequence, load direction condition, and notch effect.  
Figure 8: Maximum Load value according to total number of elements.  
Figure 9: Tensile load-displacement curves for one-open-hole tensile tests and the PFA.
## 3 Results and Discussion
### Stage Evolution of Overlying Rock Fracture  
During the advancement process of the working face, the fracture process of the overlying rock can be generally summarized into three stages in total.  
In the first stage, as shown in Figure 10a, fractures begin to occur, which affect the migration and change in the structural layers of the overlying rock. The basic roof fractures, and the first weighting of the working face occurs. The key stratum is not affected by the mining activities. The overall fracture pattern of the overlying rock in the goaf presents a shape of \"rectangle-isosceles trapezoid\". The lower part of the goaf is a trapezoidal cavity area, and above the caving area is a \"V\"-shaped hinged structure. As the working face continues to advance, the cracks continue to expand in the advancing and vertical directions, and an obvious delamination phenomenon appears below the key stratum.  
In the second stage, as shown in Figure 10b, the key stratum fractures, and the overall fracture pattern of the overlying rock evolves into a trapezoidal shape. The delamination cracks obviously develop upward, and the affected area of the upper rock stratum caused by the mining activities presents a shape of \"inverted trapezoid-trapezoid\". At this moment, the migration pattern of the entire overlying rock is similar to a hyperbola. After the key stratum fractures, the displacement field changes quite violently, and the mining influence begins to affect the surface relatively early, indicating that a compac  
Figure 10: Evolution diagram of the overburden rock fracture stage: (**a**) the initial stage; (**b**) the intermediate stage; (**c**) the final stage.  
in the middle of the overlying rock, and the affected height of the caving area decreases to some extent.  
In the third stage, as shown in Figure 10c, the entire working face is fully mined, and the influence on the surface has reached the maximum. There is an obvious subsidence on the ground, and a large-scale basin appears. Compared with the previous stage, the ground influence angle reaches the minimum value. Compaction overload occurs in the middle of the overlying rock in the goal, and the delamination cracks disappear in a large range. However, due to the relatively soft rock stratum, there are still many longitudinal cracks. As the working face continues to advance, the horizontal range of the fracture field of the overlying rock increases, and the pattern evolves into a trapezoid.  
Through the establishment of a particle flow model for overlying strata fracture evolution during working face advancement using PFC, this study complements previous research by quantitatively characterizing the three-stage development pattern of displacement fields in shallow-buried soft coal conditions: \"rectangle-trapezoid \(\rightarrow\) hyperbola-like \(\rightarrow\) trapezoid\". It addresses the missing details in simulating particle bonding failure mechanisms during the modeling of low-strength, high-plasticity rock strata in prior studies.
## 4 Application Trends
### Intelligent Spectrum Management Based on Spectrum Knowledge Graph  
The spectrum knowledge graph offers new perspective on how human language can be comprehended by machines. Combined with natural language processing techniques, the spectrum knowledge graph can support many downstream applications of collaborative human-computer interaction. With the spectrum knowledge graph, managers can directly perform intelligent knowledge retrieval and human-machine Q&A related to spectrum or LEO satellite knowledge in natural human language. The labor costs associated with spectrum management and the reliance on spectrum experts can be effectively decreased with this strategy.  
1. Integrated Management System for LEO Spectrum Sensing Information: The LEO satellite spectrum sensing information is stored in the spectrum knowledge graph in a unified knowledge representation form. Users can explicitly observe and invoke the knowledge. For a specific observation time under a beam, important data like center frequency, bandwidth, modulation type of the real-time sensing signal, etc. can be shown in real time. This makes it easier for users to track and identify specific signal. Additionally, knowledge retrieval can be performed in the form of a graph, where entities and relationships associated with the queried entity can be presented together. This approach allows for more comprehensive and relevant knowledge for user decision-making.
2. Intelligent Q&A System for LEO Spectrum Sensing Information: This system supports the use of natural language input for factual, right-and-wrong types of questions about spectrum field or LEO satellite field. Questions such as \"the beam coverage of a particular satellite\" and \"what is the center frequency of the sensing signal at a particular time\", etc., are permitted. The intelligent Q&A system can respond to questions instantly after natural language comprehension, spectrum knowledge graph querying, and reasoning.  
By constructing an intelligent spectrum management system based on a spectrum knowledge graph, changes in the spectrum can be captured in a timely manner. The optimization of spectrum scheduling strategy can be achieved by utilizing the capability of data  
Figure 9: The principle of TransR model. It should be noted that circles represent correct entities or attributes, while triangles represent incorrect entities or attributes. The blue graphics stand for entities and the green graphics for attributes.  
analysis and prediction of frequency-usage behavior of the spectrum knowledge graph. Spectrum congestion and conflicts can be effectively avoided, improving the utilization efficiency of spectrum resources. In addition, the knowledge graph can help analyze whether spectrum usage is in compliance with regulations and policies to ensure standardized spectrum usage.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 2 Materials
### Ground Data  
The land cover of 832 reference plots were collected during a field survey performed in July 2017 ([PERSON] et al., 2017). The observed crop types are: Alfalfa, Clover, Grassland, Lawn, Melon, Rice, Sunflower, Swamps, Tomato, Vineyards, and Wheat (Figure 1). Table 1 summarizes the number of plots for each type. The crop type classification of 2017 performed by [PERSON] et al. (2017) revealed that the agricultural area of the Camargue region is mostly occupied by rice, covering 29.3% of the total agricultural area. The second most important crop observed is winter wheat with 20.5%. Additionally, both lawn and grassland occupy 20% of the total crop area.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Crop Type** & **Number of Plots** & **Surface Area (ha)** \\ \hline Alfalfa & 45 & 139 \\ Clover & 26 & 76 \\ Grassland & 49 & 157 \\ Lawn & 27 & 201 \\ Melon & 23 & 76 \\ Rice & 319 & 1072 \\ Sunflower & 78 & 230 \\ Swamps & 19 & 101 \\ Tomato & 14 & 48 \\ Vineyards & 28 & 119 \\ Wheat & 204 & 650 \\ Total & 832 & 2869 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of the reference plots per class of land cover.  
Figure 1: Location of the study site, Camargue, France. The red border represents the study area and the colored polygons represent the reference parcels. The background is a SAR) composite of three bands (Red: VV, Green: VH, and Blue: VV/VH).  
Figure 2 shows the temporal variation of the Normalized Differential Vegetation index (NDVI) for each crop type between March and October 2017. These NDVI values are calculated using Sentinel-2 images corrected for atmospheric effects and averaged for each crop type. Among the observed crop types, wheat is the only winter crop in the region. Additionally, it can be noticed that Camargue has a single cropping season of rice. According to the performed field surveys, the sowing of the rice crop in 2017 occurred in the first 15 days of May and the harvesting occurred from the end of September to the beginning of October.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 5 Experimental Results
### Experimental Setup for Performance Evaluation  
To simulate the proposed research method, Ns-3.35 with Python-3.9.6 (64-bit) version was used. This tool is efficient and provides all specifications for the proposed technique. Table 4 indicates the system specifications.
## 2 Materials and Methods
### Data Analysis  
For statistical analysis, SPSS (version 25.0, IBM SPSS Statistics, Chicago, IL, USA) was used with a significance level of <0.05. Paired samples _t_-tests were used to analyse changes in health, performance and metabolic factors in stages 0 and 1. Descriptive statistics was used to present anthropological factors, age, energy parameters, blood values, performance and psychological evaluation in all stages. Pearson's correlation coefficient was used to find associations between EA and other measured parameters. Minimal sample size needed for this study (N = 12) was calculated with the G*power 3.1.9.2 software (Heinrich-Heine University, 40213 Dusseldorf, Germany), assuming expected effect size 0.47 as reported in previous studies [10], with type I(a) error = 0.05 and 30% dropout of participants.
## 3 Results

Table 1: The results of Corneometer and Cutometer measurements performed before and after a series of five treatments. kles, and lightening of hyperpigmentation. These results highlight the versatility of carboxytherapy combined with active acids in addressing various cosmetic concerns in the delicate periorbital region. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Treatment Efficacy [\% vs. Baseline]**} & \multicolumn{1}{c}{\(p\)} \\ \cline{2-4}  & **Right** & **Left** & **\(p\)** \\ \hline Corneometer t [L] & \(0.8\) (\(-4.1\); \(9.0\)), n = 39 & \(4.4\) (\(0.6\); \(11.9\)), n = 25 & NS (\(p\) = 0.299) \\ \hline Improvement [L] & \(23\) [\(59.0\%\)] & \(20\) [\(80.0\%\)] & NS (\(p\) = 0.080) \\ \hline Cutometer R6 [L] & \(-18.3\) (\(-43.2\); \(-2.1\)) & \(-26.0\) (\(-49.7\); \(-7.7\)) & NS (\(p\) = 0.367) \\ \hline Improvement R6 [L] & \(32\) [\(82.1\%\)] & \(21\) [\(84.0\%\)] & NS (\(p\) > 0.999) \\ \hline Corneometer [F + A] & \(0.8\) (\(-4.1\); \(9.0\)), n = 39 & \(1.7\) (\(-6.7\); \(5.2\)), n = 14 & NS (\(p\) = 0.672) \\ \hline Improvement [F + A] & \(23\) [\(59.0\%\)] & \(9\) [\(64.3\%\)] & NS (\(p\) = 0.728) \\ \hline Cutometer R6 [F + A] & \(-18.3\) (\(-43.2\); \(-2.1\)) & \(-41.1\) (\(-56.2\); \(-16.0\)) & NS (\(p\) = 0.069) \\ \hline Improvement [F + A] & \(32\) [\(82.1\%\)] & \(13\) [\(92.9\%\)] & NS (\(p\) = 0.665) \\ \hline Corneometer [L + (F + A)] & \(0.8\) (\(-4.1\); \(9.0\)), n = 39 & \(2.5\) (\(-3.4\); \(9.4\)), n = 39 & NS (\(p\) = 0.586) \\ \hline Improvement [L + (F + A)] & \(23\) [\(59.0\%\)] & \(29\) [\(74.4\%\)] & NS (\(p\) = 0.229) \\ \hline Cutometer R6 [L + (F + A)] & \(-18.3\) (\(-43.2\); \(-2.1\)) & \(-32.0\) (\(-50.9\); \(10.0\)) & NS (\(p\) = 0.120) \\ \hline Improvement [L + (F + A)] & \(32\) [\(82.1\%\)] & \(34\) [\(87.2\%\)] & NS (\(p\) = 0.755) \\ \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Age**} \\ \cline{2-4}
**Right Eye** & **25–40 Years.** & **41–55 Years.** **[n = 24]** & **\(p\)** \\ \hline Corneometer measurement [\% vs. baseline] & \(0.7\) (\(-4.1\); \(7.0\)) & \(1.5\) (\(-8.2\); \(16.6\)) & NS (\(p\) = 0.743) \\ \hline Improvement [\%] & \(14\) [\(58.3\)] & \(9\) [\(60.0\)] & NS (\(p\) = 0.916) \\ \hline Cutometer R6 [\% vs. baseline] & \(-26.9\) (\(-49.6\); \(-9.6\)) & \(-7.9\) (\(-40.3\); \(-0.4\)) & NS (\(p\) = 0.146) \\ \hline Improvement R6 [\%] & \(20\) [\(83.3\)] & \(12\) [\(80.0\)] & NS (\(p\) > 0.999) \\ \hline Left Eye & \(25\)–40 Years & 41–55 Years [n = 24] & \(p\) \\ \hline Corneometer measurement [\% vs. baseline] & \(2.3\) (\(0.4\); \(10.1\)) & \(2.9\) (\(-6.6\); \(9.4\)) & NS (\(p\) = 0.679) \\ \hline Improvement [\%] & \(19\) [\(79.2\)] & \(10\) [\(66.7\)] & NS (\(p\) = 0.463) \\ \hline Cutometer R6 [\% vs. baseline] & \(-31.6\) (\(-53.5\); \(-11.3\)) & \(-32.0\) (\(-50.6\); \(-1.9\)) & NS (\(p\) = 0.765) \\ \hline Improvement R6 [\%] & \(21\) [\(87.5\)] & \(13\) [\(86.7\)] & NS (\(p\) > 0.999) \\ \hline \end{tabular}
\end{table} 
Table 2: The efficacy of applied treatment.
## 4 Methodology to Define the Haptic Scales
### Creating Haptic Frequency Scales  
With the lower and upper-frequency values obtained in experiment 2 (Section 4.2), it is possible to determine each of the notes that compose each haptic scale. Based on the JND, we have designed three haptic frequency scales obeying Weber's Law. The equation used to generate the frequency scale is:  
\[freq_{m}=freq_{min}*inc^{(m-1)}, \tag{1}\]  
where \(freq_{min}\) is the minimum frequency obtained in Experiment 2, \(m\) is a counter indicating the position of a note within the scale, and \(inc\) is calculated as:  
\[inc=(freq_{max}/freq_{min})^{1/(sampleNum-1)}, \tag{2}\]  
where \(freq_{max}\) is the maximum frequency defined in Experiment 2, and \(sampleNum\) is the number of values that are in the sorted list. Using (1), we ensure that our haptic scale follows a logarithmic scale. Besides, we must ensure that the increment between two consecutive notes is higher than \(\frac{1}{5}\) of the current value to follow Weber's Law. This condition will be satisfied if the inc value computed in (2) is above \(\frac{6}{5}\).  
To evaluate haptic scales with different numbers of notes, we have created scales of three (2, 8.6, 36.97 Hz), five (2, 4.14, 8.6, 17.83, 36.97 Hz) and seven (2, 3.25, 5.28, 8.6, 13.98, 22.73, 36.9 Hz) haptic notes. In all three cases, we have defined haptic scales of the frequency with an uneven number of notes, so that, afterwards, we can situate the haptic tuning fork in the middle note.  
Setting the Value and Duration of the Haptic Tuning Fork  
Following the musical analogy and knowing that the tuning fork in music corresponds to a note in the centre of the musical scale of the human voice, we hypothesised that a central value of our haptic tuning fork may serve as a reference to improve the discrimination of the different haptic icons. For this reason, our haptic tuning fork has a value equal to 8.6 Hz, which is the central value in all the defined scales. In addition, the haptic tuning fork lasts two seconds to complete a full cycle in the waveform, so that the tuning fork finishes at the neutral point of the wave. Thus, when the haptic tuning fork ends at zero, the corresponding haptic note can begin at that point, avoiding discontinuities between the haptic tuning fork and the haptic note that could confuse users.
## 5 About the Rapini-Papoular Form of the Anchoring Energy  
Numerous experiments suggest that the actual anchoring potential shifts from the Rapini-Papoular form at a large tilt angle [2; 3; 15; 40; 41; 42; 43]. Another possibility to improve the quality of the fits is to modify the form of the anchoring potential. Several solutions have been proposed in the literature. One of them consists of replacing the sinus in the Rapini-Papoular potential by an elliptic sinus [42]. This function is complicated to work with and I did not use it. Another much simpler and very classical solution is to add a term in \(\sin^{4}\theta\) to the potential [15; 40; 41; 44; 45]. In that case, the anchoring potential can be written in the form  
\[W(\theta)=\frac{1}{2}W\ \frac{\sin^{2}\theta+A\sin^{4}\theta}{1+A}, \tag{8}\]  
Figure 11: (**a**) Values of \(L\) obtained from the fit of the capacitance curves shown in Figure 7 by taking into account the electric field variation of \(\varepsilon_{\parallel}\). (**b**) Corresponding values of \(W\). Solid lines are just guides for the eye.  
and the torque Equation (5) becomes  
\[\frac{1+2A\sin^{2}\theta}{1+A}\,\sin\theta=\sqrt{\frac{1+\kappa\sin^{2}\theta}{1+ \gamma\sin^{2}\theta}}\frac{\pi}{\theta}\frac{\pi}{\sqrt{1+\gamma}}\frac{L}{d} \frac{V}{V_{c}}\frac{C}{C_{\perp}}. \tag{9}\]  
To test the pertinency of this correction, I fitted the capacitance curve measured at 40 \({}^{\circ}\)C with this new potential by using the value of \(\kappa\) previously found and I minimized the root-mean-square deviation (RMSD) between the experimental curve and the theoretical curve obtained by solving numerically with Mathematica Equations (4) and (9). In practice, the RMSD was minimized as a function of \(L=K_{1}/W\) for different values of \(A\). The result is shown in Figure 12. This graph shows that the RMSD passes through a minimum for \(A=-0.188\) and \(L=33.8\) nm.  
This calculation shows that the actual potential is different from the Rapini-Papoular potential and flattens when \(\theta\rightarrow\pi/2\). Such a tendency was already observed experimentally [14; 15]. The difference between this potential and the one of the Rapini-Papoular form is nonetheless very small in the range of angles \(\theta\) probed in this experiment (\(\theta<0.4\) rad according to Figure 9c as the reader can see in Figure 13). This explains why the RMSD does not change much as a function of \(A\) in Figure 12a. In practice, it would be interesting to use larger field to test the relevance of this correction to the Rapini-Papoular potential. I also mention that this value of \(A\) is compatible with the second order character of the Freedericksz transition observed here as shown by [PERSON] et al. [46].  
Figure 12: (**a**) Root-mean-square deviation (RMSD) between the experimental curve measured at \(T=40\) \({}^{\circ}\)C and the theoretical curve as a function of \(A\). (**b**) Value of \(L\) that minimizes the RMSD as a function of \(A\).