esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 5 Research Challenges of IRS-Assisted UAV Communications
### Reflection Efficiency  
Securing the high reflection efficiency of IRSs is another important issue for practical implementation [106; 107]. The location and orientation of the IRS are main factors that determine the reflection efficiency, and thus, they should be carefully designed [108]. On the other hand, to compensate for long-distance path loss, it is necessary to improve the reception performance by using large IRSs. However, the use of large IRSs will increase the cost and cause several difficulties, such as size and weight [109]. Therefore, it is necessary to study an appropriate tradeoff between the number of elements in the IRS and the reception performance.
# Calculation Method of Loose Pressure in Surrounding Rock Mass
## 1 Introduction
[18] established explicit expressions to calculate the loose pressure on a shallow, buried tunnel based on the upper bound theorem of limit analysis and the Terzaghi failure mode. Based on the spatial discretization model, [PERSON] et al. [19; 20; 21] studied the impact mechanism of a heterogeneous stratum, pore water pressure, and a non-circular excavation face on the tunnel face stability. [PERSON] et al. [22] proposed an analytical method for calculating the ultimate support force of an excavation face in the clay layer and analyzed the local and whole stability of the excavation face for a large-diameter shield tunnel in practical engineering. According to the tunnel face failure mode in purely cohesive soil proposed by [PERSON] [23], [PERSON] et al. [24] established an analytical model considering the shear failure boundary which further improved the calculation accuracy of the upper bound solution for tunnel face stability in non-homogeneous and anisotropic undrained clay. To assess the stability of a tunnel face in inclined strata, [PERSON] et al. [25] optimized the 3D rotating rigid body collapse mechanism by improving the spatial discretization technique. However, the failure mechanism constructed in the current research is highly subjective and lacks numerical simulation and field test verification. Such flaws can be solved by combining a numerical analysis and an upper bound limit analysis. With its lower cost, shorter period, and better repeatability, a numerical simulation is helpful for predicting the failure position [26; 27]. [PERSON] et al. [28; 29] verified the rationality of the analytical solution for the collapse curve via a vertical displacement cloud picture obtained from a simulation. In view of the shortcomings of the existing research, this paper discussed the impact of rock conditions and cavern size on the range of the loose pressure zone by combining a numerical analysis and an orthogonal test. The investigation is based on the Bifurcation Tunnel. The limit analysis model of the loose pressure in the surrounding rock is established using the fitted boundary function of the loose zone and the upper bound limit analysis. Based on this model, this paper deduces the upper bound solution for a deep tunnel's loose pressure, which is verified by pressure-monitoring results obtained for the different sections. The results of this research can provide new ideas for the selection of a supporting structure for similar super-section tunnel projects.
###### Abstract  
Volatile organic compounds determine the aroma properties of durian, and it is an important factor in durian acceptance by consumers. However, limited information is devoted to volatile organic compounds and aroma in wild edible durians. Therefore, the present study aims to characterize and compare the volatile organic compounds and aroma properties of the indigenous wild edible durians from Sarawak, Borneo. Seven genotypes, namely _Durio dulcis_, _Durio gravolens_ (yellow-fleshed), _Durio gravolens_ (orange-fleshed), _Durio gravolens_ (red-fleshed), _Durio kutejensis_, _Durio oxlegans_, and _Durio zibethinus_ were characterized in this study. Solid phase microextraction combined with gas chromatography-mass spectrometry (SPME GC-MS) was used to detect the volatile organic compounds, while the quantitative descriptive analysis (QDA) method was used to characterize the aroma properties of wild edible durians. A total of 119 volatile organic compounds comprising alcohol, aldehyde, amine, ether, ster, ketone, nitrogen-containing, and sulfur-containing compounds were detected. Ester and alcohol compounds are the most predominant in the composition, especially _D. gravolens_ (yellow- and orange-fleshed), and _D. dulcis_ possessed three to nine times higher relative amount of ester compounds. PCA clearly classified the wild durians into different groups. Based on the QDA analysis, _D. kutejensis_ has the mildest aroma among wild edible durians, while _D. dulcis_ perceived a stronger sweet and grassy aroma. Partially Least Square (PLS) regression model analysis indicated a strong relationship between the volatile organic compounds and the aroma intensity perceived by the panelists. These findings could be the major component in the durian industry, paving the way for breeding efforts to create new cultivars that can improve consumers' satisfaction.  
wild durians; aroma; indigenous; Sarawak; volatile organic compounds 2023
## 3 Twin Support Vector Machine  
### The Model of TWSVM  
Unlike the standard SVM, TWSVM constructs two hyperplanes \(xw_{1}+b_{1}=\ 0\) and \(xw_{2}+b_{2}=0\), and then makes the sample points with interferences as close as possible to the hyperplane \(xw_{1}+b_{1}=0\), and as far as possible from the hyperplane \(xw_{2}+b_{2}=0\). Likewise, the sample points without interferences are made as close as possible to the hyperplane \(xw_{2}+b_{2}=0\) and as far as possible from the hyperplane \(xw_{1}+b_{1}=0\).  
Supposing that a n-dimension sample set \(T=\{(x_{1},y_{1}),...,(x_{l},y_{l})\}\in(\chi\times\gamma)^{l}\) with \(m_{1}\) training points with interferences, that is, \(+1\) class of sample, and \(m_{2}\) training points without interferences, that is, \(-1\) class of sample, the \(+1\) class of training points are denoted by the matrix \(A\in\Re^{m_{1}\times n}\), and the \(-1\) class of training points are denoted by the matrix \(B\in\Re^{m_{2}\times n}\). The corresponding optimization problems of TWSVM are:  
\[\begin{array}{ll}\min_{w_{1},b_{1}}&\frac{1}{2}(Aw_{1}+e_{1}b_{1})^{T}(Aw_{ 1}+e_{1}b_{1})+c_{1}e_{2}^{T}\xi\\ s.t&-(Bw_{1}+e_{2}b_{1})+\xi\geq e_{2},\quad\xi\geq 0\\ \min_{w_{2},b_{2}}&\frac{1}{2}(Bw_{2}+e_{2}b_{2})^{T}(Bw_{2}+e_{2}b_{2})+c_{2} e_{1}^{T}\xi\\ s.t&(Aw_{2}+e_{1}b_{2})+\xi\geq e_{1},\quad\xi\geq 0\end{array} \tag{6}\]  
where \(c_{1},c_{2}>0\) are the penalty parameters which can be selected by the heuristic algorithm proposed in [13], and \(e_{1},e_{2}\) are the column vectors with all of elements being one.  
The computational complexity of the standard SVM is \((n^{3})\), and each complexity of the two optimization problems Equations (6) and (7) solved by TWSVM is nearly \((n/2)\), therefore, the computational speed of TWSVM is about four times faster than that of the standard SVM.
## 4 Discussion
### Normalized Time-Averaged Gain
Adding more BS antenna elements while using either the CB or MRT scheme, does not decrease \(G_{m}^{N,K}\) significantly. Their lowest 95 th percentile values were observed for \(T=1\) s: \(G_{MRT}^{100,5}\simeq G_{MRT}^{100,10}\simeq 0.14\) and \(G_{CB}^{100,1}\simeq G_{CB}^{100,2}\simeq 0.13\). The 95 th percentiles of \(G_{ZF}^{N,K}\) were lower than the respective \(G_{MRT}^{N,K}\) and \(G_{CB}^{N,K}\) values for all \(K\geq 2\). This difference was larger with for larger \(K\) and shorter connection time \(T\). In the realistic scenario with \(T=10\) s, the 95 th percentiles of \(G_{ZF}^{64,K}\) were equal to around 0.19 (7.2 dB), 0.13 (8.9 dB), and 0.1 (10 dB) for \(K=2,\ 5,\ \text{and}\ 10\), respectively. These values are nearly two times lower that the MRT and CB schemes demonstrated in the same parameter configurations. The lowest \(p^{95}\) was found with \(G_{ZF}^{100,10}\simeq 0.06\) (12.2 dB), which is around one third of the corresponding CB value, and more than two times lower than the minimum for the MRT or CB scheme. As the time-averaged gain is directly related the average \(E_{RMS}\) measured at some location in the cell. The far-field instantaneous E-field magnitude is proportional to the square root of the antenna gain. Therefore, the time-averaged \(E_{RMS}\) is reduced at least in proportion to the square root of the time-averaged gain \(G_{m}^{N,K}\), relative to the \(E_{RMS}\) estimate based on the maximum achievable gain \(G_{max}^{N}\). In a scenario with \(N=64\), \(K=5\), and \(T=10\) s, this leads to the E-field reduction in 95% of the observations by at least a factor of around 2.1 (3.2 dB), 2.2 (3.4 dB), and 2.8 (4.5 dB) for the CB, MRT, and ZF schemes, respectively, compared to the theoretical maximum. The theoretical maximum gain value was never reached in samples with \(K\geq 5\).
# \(\alpha\)-Fluctuating Nakagami-\(m\) Fading Model for Wireless Communications
## 5 Simulation and Results' Analysis
### \(\alpha\)-Fluctuating Nakagami-m Model Performance
Figure 7: ABER for coherent and non-coherent modulations for various \(\alpha\) with \(m=\frac{1}{2}\), \(m_{s}=2\): solid lines—the derived analytic solutions (24) and (25), dashed blue lines—the derived asymptotic expression (29) and (30), circle markers—numeric integration (22) and (23), black cross markers—Monte Carlo simulation. A cross-comparison between the results validates the statistical accuracy of the model in capturing real-world fading dynamics, reinforcing the reliability of the theoretical performance metrics, and reveals a strong correlation between outage probability and ABER, as both metrics improve significantly with increasing \(\alpha\). This consistency indicates that optimizing \(\alpha\) leads to systematic improvements in all key performance indicators (enhancing both link reliability and quality), demonstrating the importance of parameter selection in adaptive wireless communication systems.
# Electrocardiographic Fragmented Activity (II):
###### Abstract  
Hypertrophic cardiomyopathyopathy, according to its prevalence, is a comparatively common disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This illness is characterized by the excessive deposition of collagen among healthy myocardium cells. This situation, which is medically known as fibrosis, constitutes effective conduction obstacles in the myocardium electrical path, and when severe enough, it can be outlined as additional peaks or notches in the QRS, clinically entitled as fragmentation. Nowadays, the fragmentation detection is performed by visual inspection, but the fragmented QRS can be confused with the noise present in the electrocardiogram (ECG). On the other hand, fibrosis detection is performed by magnetic resonance imaging with late gadolinium enhancement, the main drawback of this technique being its cost in terms of time and money. In this work, we propose two automatic algorithms, one for fragmented QRS detection and another for fibrosis detection. For this purpose, we used four different databases, including the subrogated database described in the companion paper and incorporating three additional ones, one compounded by more accurate abrogated ECG signals and two compounded by real and affected subjects as labeled by expert clinicians. The first real-world database contains QRS fragmented records and the second one contains records with fibrosis and both were recorded in Hospital Clinico Universitario Virgen de la Arrixaca (Spain). To deeply analyze the scope of these datasets, we benchmarked several classifiers such as Neural Networks, Support Vector Machines (SVM), Decision Trees and Gaussian Naive Bayes (NB). For the fragmentation dataset, the best results were 0.94 sensitivity, 0.88 specificity, 0.89 positive predictive value, 0.93 negative predictive value and 0.91 accuracy when using SVM with Gaussian kernel. For the fibrosis databases, more limited accuracy was reached, with 0.47 sensitivity, 0.91 specificity, 0.82 predictive positive value, 0.66 negative predictive value and 0.70 accuracy when using Gaussian NB. Nevertheless, this is the first time that fibrosis detection is attempted automatically from ECG postprocessing, paving the way towards improved algorithms and methods for it. Therefore, we can conclude that the proposed techniques could offer a valuable tool to clinicians for both fragmentation and fibrosis diagnoses support.  
ECG; fragmentation detection; multivariate techniques; fibrosis detection; machine learning 2019 3565; doi:10.3390/app9173565
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 4 Results and Discussion
### Model Evaluation
#### 4.1.3 Model Performance-to-Size Ratios
The marginal difference in performance, despite being only around 58.9% the size of MobileViT_V2_175, highlights a key benefit of using RepViT_M11. It is worth noting that no previous work was conducted on Kvasir-Capsule specifically for on-edge classification; therefore, the comparisons are not on fair grounds. Nonetheless, Table 10 attempts to compare our best-performing quantized model, MobileViT_V2_175, with the literature available on the Kvasir-Capsule dataset. Previous works have not reported model size, but we can estimate sizes based on the number of learnable parameters [38]. The authors in [16] and [18] both used ViTs in their work. The ViT base model amounts to 86 M parameters [5], whilst our base model only has 14.3 M parameters [33]. This allows us to assume model sizes that are at least 6 times larger than our model before quantization. The TNCB model, however, reported 12.2 M parameters, which is less than our MobileViT_V2_175 model. We cannot conclude that the TNCB model is larger than our model before quantization, but we can safely assume that it is larger than our F16 quantized model, which is almost half the size of our original model before quantization, as reported in Table 8.
## 3 Results and Discussion
### Methylene Blue Adsorption
#### 3.2.4 Adsorption Thermodynamics  
Table 5 presents the thermodynamic parameters of the methylene blue adsorption by H-180, obtained by linearizing Equation (7) (Figure 4). The value of Gibbs free energy change (\(\Delta G\)) tends to become more negative as the temperature increases from 30 to 60 \({}^{\circ}\)C. This suggests that increasing the temperature favors the spontaneity of the process. Also, the positive value of enthalpy change (\(\Delta H\)) indicates that the process is endothermic. In  
\begin{table}
\begin{tabular}{c c c} \hline
**Kinetic Model** & **Parameter** & **H-180** \\ \hline \multirow{3}{*}{Pseudo-First-Order} & \(K_{1}\) & 0.011 \(\pm\) 0.001 \\  & \(q_{e}\) (mg:g\({}^{-1}\)) & 22.85 \(\pm\) 0.85 \\  & \(R^{2}\) & 0.9124 \\  & \(MRD\) (\%) & 5.57 \\ \hline \multirow{3}{*}{Pseudo-Second-Order} & \(K_{2}\) & 0.001 \(\pm\) 0.0002 \\  & \(q_{e}\) (mg:g\({}^{-1}\)) & 24.54 \(\pm\) 1.11 \\  & \(R^{2}\) & 0.8909 \\  & \(MRD\) (\%) & 7.09 \\ \hline \multirow{3}{*}{Elovich} & \(\beta\) & 0.225 \(\pm\) 0.012 \\  & \(\alpha\) & 1.08 \(\pm\) 0.19 \\ \cline{1-1}  & \(R^{2}\) & 0.9779 \\ \cline{1-1}  & \(MRD\) (\%) & 2.81 \\ \hline \end{tabular}
\end{table}
Table 4: Values of parameters related to pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich kinetic models.  
Figure 3: Removal percentage (**a**) and kinetic models (**b**) of methylene blue adsorption by hydrochar (H-180) obtained with an initial dye concentration of 100 mg:L\({}^{-1}\) and a hydrochar load of 3 g:L\({}^{-1}\) at pH 10, 30 \({}^{\circ}\)C, and 100 rpm.  
this instance, the positive value of entropy change (\(\Delta\)S) is advantageous for the spontaneity of adsorption, as it makes \(\Delta\)G negative by compensating for \(\Delta\)H, thereby facilitating the occurrence of adsorption. This corroborates with previous studies using other hydrocarbons for adsorbing methylene blue [30; 42].
# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 2 Methods
### Model Evaluation  
In this study, we used the MSE, RMSE, MAE, and R\({}^{2}\) to verify the performance of the regression algorithm. The definitions of the four indicators are as follows:  
\[\text{R}^{2}=1-(\text{SSE}/\text{SST}) \tag{1}\]  
where SSE is the residual sum of squares and SST is the total sum of squares.  
\[\text{SSE}=\sum(\mathbf{y}_{i}-\hat{\mathbf{y}}_{i})^{2} \tag{2}\]  
\[\text{SST}=\sum(\mathbf{y}_{i}-\hat{\mathbf{y}})^{2} \tag{3}\]  
Here, \(\mathbf{y}_{i}\) denotes actual observations, \(\hat{\mathbf{y}}_{i}\) denotes model predictions, and \(\hat{\mathbf{y}}\) denotes the average of the observations.  
\[MSE=\frac{1}{m}\sum_{i=1}^{m}(\mathbf{y}_{i}-\hat{\mathbf{y}}_{i})^{2} \tag{4}\]  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}\left(\mathbf{y}_{i}-\hat{\mathbf{y}}_{i} \right)^{2}} \tag{5}\]  
\[MAE=\frac{1}{m}\sum_{i=1}^{m}|(\mathbf{y}_{i}-\hat{\mathbf{y}}_{i})| \tag{6}\]  
Here, m is the number of samples, while \(\mathbf{y}_{i}\) is the actual value, and \(\hat{\mathbf{y}}_{i}\) is the predicted value of \(i^{th}\) sample.  
Generally speaking, the lower the MSE, RMSE, and MAE and the higher the R\({}^{2}\), the better.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 2 Knowledge-Based Approaches
### Categories of Knowledge Representation and Reasoning Approaches
#### 2.1.6 Simulation-Based Formalisms  
Simulation-based formalisms involve modeling a system or phenomenon to study its behavior over time. They allow for the representation of dynamic processes, enabling the examination of different scenarios and the prediction of outcomes based on the defined rules and constraints. Reasoning is performed based on emulating the behavior of networks' components to simulate scenario outcomes and make predictions on taken actions. Simulators, frames, and digital twins [18] are examples of such formalism realizations.
## 3 Results and Discussion
### Droplet Impact on Horizontally Moving Micro-Pillar Surfaces
#### 3.2.1 Droplet Impact Behavior
This is because the top region of the micro-pillars transitions abruptly from a wide open space to a narrow micro-pillar space, causing a sharp change in the fluid direction and generating vortices, which in turn create pressure peaks at this location. Furthermore, it can be observed that the pressure peak is smaller when the droplet impacts a surface with horizontal motion compared to a stationary surface. This is because the impact energy of the droplet is more concentrated in a localized area when the surface is static, whereas on a horizontally moving surface, part of the energy is dispersed due to the motion of the surface, thereby reducing the magnitude of the pressure peak.
## 2 Materials and Methods  
Epoxy oligomer based on ED-20 dian resin (GOST 10587-84) was produced by AO REACHIM, Moscow, Russia; the content of epoxy groups was 22.6 wt. %. 4/4'-diaminoliphenylmethane (DDM) manufactured by Sigma-Aldrich, which was used as a hardener without additional purification. The structural formulae are shown in Figure 1.  
Two different types of TiO\({}_{2}\) nanopowders obtained by the plasma-chemical method [48] have the following characteristics: (1) \(d_{\mathrm{av}}\) = 46 nm, specific surface area (SSA) = 42.5 m\({}^{2}\)/g, \(\rho\) = 4.2495 g/cm\({}^{3}\), and phase composition is anatase--75% and rutile--25%; and (2) \(d_{\mathrm{av}}\) = 100 nm, SSA = 13.3 m\({}^{2}\)/g, \(\rho\) = 4.2700 g/cm\({}^{3}\), and phase composition is anatase--60% and rutile--40%.  
To improve the dispersion of nanoparticles in the matrix, Pluronic L61 (L61) surfactant was used. L61 is a nonionic surfactant belonging to the Pluronic family of block copolymers. It is composed of a hydrophilic polyethylene oxide (PEO) block and a hydrophobic polypropylene oxide (PPO) block, arranged in a PEO-PPO-PEO structure. The molar mass of L61 is 2000 g/mol, and it has a PEO/PPO ratio of 1:6.  
Figure 1: Structural formulae: (**a**) ED-20 epoxy oligomer and (**b**) 4/4’-diaminoliphenylmethane.
## Materials and Methods
### Conversion of the SMAP/SMOS-Derived Brightness Temperature  
The brightness temperature \(\mathrm{T_{b}}\) observed by microwave radiometers has been demonstrated to have strong differential responses to water and land [16], with lower \(\mathrm{T_{b}}\) representing better land and vice versa. To compare this with CYGNSS data, it is better to use SMAP and SMOS data than other microwave radiometer data, since these two types of data have similar frequencies as that of the GPS L1 band (\(\sim\)1.58 GHz), therefore giving them similar sensitivities to surface conditions. The GNSS signals are circular polarization with a certain degree of ellipticity, while SMAP/SMOS measures the \(\mathrm{T_{b}}\) at the two linear polarizations (H and V) [12]. To correlate the CYGNSS-derived SR with the SMAP/SMOS-derived \(\mathrm{T_{b}}\), the SMAP/SMOS-derived \(\mathrm{T_{b}}\) was converted to total \(\mathrm{T_{b}}\) at circular polarization using the following equation [12]:  
\[\mathrm{T_{b_{C}}}=\left(\mathrm{T_{b_{H}}}+\mathrm{T_{b_{V}}}\right)/2 \tag{2}\]  
where \(\mathrm{T_{b_{C}}}\) is the total \(\mathrm{T_{b}}\) at circular polarization and \(\mathrm{T_{b_{H}}}\) and \(\mathrm{T_{b_{V}}}\) are the \(\mathrm{T_{b}}\) at H and V linear polarizations, respectively.  
Figure 1: Precipitation (Global Precipitation Measurement (GPM)-derived) and surface reflectivity (SR) (Cyclone Global Navigation Satellite System (CYGNSS)-derived) changes of southeast China during the 2017 typhoon season. **(a1)** Typhoon tracks; the background map is the NASA Shuttle Radar Topographic Mission (SRTM) 90 m digital elevation models (DEM), and the black line represents the 500 km influencing range of the typhoon [18]; **(a2)** Land cover/use classification; **(b1–b3)** Precipitation of non-typhoon season and typhoon season and their differences (Delta rainfall); **(c1–c3)** SR of non-typhoon season and typhoon season and their differences (Delta SR). Blue and black boxes **(b3,c3)** represent regions with positive and negative delta SRs, respectively. SR: Surface reflectivity.
# Anisotropic Shear Strength Behavior of Soil-Geogrid Interfaces
## 2 Experimental Program
### Testing Apparatus  
An interface shear test apparatus that can apply shear loading in different directions was developed to investigate the anisotropic shear strength behavior of soil-geogrid interfaces in this study. As shown in Figure 2, the anisotropic shear test apparatus consists of a mechanical system, pneumatic system, and electrical control system. The lower shear box has a diameter of 300 mm and a height of 150 mm, and the upper shear box has a diameter of 200 mm and a height of 100 mm. The lower box, which is of larger size, can ensure a constant interface contact area during shearing. Shear displacement would develop along the interface during shearing of the soil-geogrid interface. The gasket between the upper and lower shear boxes ensures that there is no friction between the geogrid specimen and the upper shear box. This apparatus can accommodate circular specimens to ensure consistent shear stress distribution for loading in different directions.