esa-sceva/satcom-chunk-collection huggingface dataset:
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 2 Agricultural Concrete and Metallic Structures
### Steel Structures  
Steel is regarded as an alternative or complementary material to concrete. The factors that inform the selection of the material include the fact that it is less susceptible to corrosion and attack by acid, alkali, and other chemicals, as noted in the preceding section [2; 7]. Steel is also resistant to attack by bacteria [8], and structures made of steel can be alloyed to form, nickel, chromium, and other forms of steel. Steel is also the material of choice in the construction of roofing materials for agricultural buildings [9]. Steel roofs are preferred because they are waterproof--a necessary requirement for long-lasting structures. The durability of steel structures can also be reinforced by hot-dipping or galvanizing. The treatment process for steel facilitates the formation of a robust bond between zinc atoms (on the surface) and the steel material. In addition, the treatment of steel ensures that the structure is covered with a uniform layer of base material to offer protection against the elements. According to the data in Figure 2, galvanized steel sheets have a probable lifetime of up to 70 years. However, only 10% of the material would achieve such high rates of survival. On average, galvanized steel roofs have a survival rate of 25-30 years [9].  
Beyond galvanization and hot dipping, the durability of the steel structures is influenced by the frequency of exposure to the natural elements. [PERSON] notes that the risk of corrosion was exacerbated in steel (carbon steel) reinforcements in concrete that comes into contact with acids. In particular, exposure to acids in the presence of nitrate ions leads to the reduction of nitrate and the formation of ammonium ions. The reaction is optimized at a pH between 11 and 12. Therefore, the treatment of steel does not offer absolute protection against degradation.
## 3 Performance Analysis
### Channel Fading
#### 3.4.4 Rician Fading Channel  
When there is an LoS between S and D or when one transmission line is more dominant than the others, fading coefficients seem to be distributed according to the Rician distribution. The means of the Gaussian processes used to develop this channel model may differ from zero while also having the same variances. The Rician fading channel system's SNR PDF may be expressed as [40]  
\[f(\gamma)=\frac{K+1}{\overline{\gamma}}e^{(-K-\frac{(K+1)\gamma}{\overline{\gamma} })}\,I_{0}\left(2\sqrt{\frac{K(K+1)\gamma}{\overline{\gamma}}}\right), \tag{21}\]  
where \(K\) stands for power distribution between the scattered multipath components and the LoS path. \(I_{0}(.)\) signifies a Bessel function of zero order. \(I_{0}(.)\) is expressed in its broad form as  
\[I_{\alpha}(x)=\sum\limits_{n=0}^{\infty}\frac{K+1}{n!\Gamma(n+1+\alpha)}\Big{(} \frac{x}{2}\Big{)}^{(2n+\alpha)}. \tag{22}\]  
Outage probability can be provided by  
\[\begin{array}{ll}P_{out}&=P(\gamma<\gamma_{th})=\int_{0}^{\gamma_{th}}f_{ \gamma}(\gamma)d\gamma=\int_{0}^{\gamma_{th}}\frac{K+1}{\overline{\gamma}}e^{ \left(-K-\frac{(K+1)\gamma}{\overline{\gamma}}\right)}\,I_{0}\left(2\sqrt{ \frac{K(K+1)\gamma}{\overline{\gamma}}}\right)d\gamma\\ &\cong 1-Q_{1}\left(\sqrt{2K},\sqrt{\frac{2(K+1)\gamma_{th}N_{0}(4\pi)^{2}d}{G_{ \mathrm{R}}C\gamma^{\mathrm{A}}P_{\mathrm{T}}}}\right).\end{array} \tag{23}\]  
The Marcum-Q function can be written as  
\[Q_{Z}(x,y)=e^{(-\frac{x^{2}+y^{2}}{2})}\sum\limits_{k=1-Z}^{\infty}\left(\frac {x}{y}\right)^{k}I_{k}(xy). \tag{24}\]
## 3 Specimens and Experimental Techniques
### Electronic Speckle Pattern Interferometry (ESPI)
#### 3.3.2 In-Plane Vibration  
As shown in Figure 3, a beam splitter creates two laser beams which reach the target object from different sides but at identical angles of incidence with equal optical path lengths. Spatial filters then scatter the laser beams, and a CCD camera records the optimal interference images which undergo computer processing and produce vibration interference data.
# EEG-Based Attention Classification for Enhanced Learning Experience
## 3 Results
### Training the k-NN Classifier
Moreover, HA networks demonstrate denser connectivity across all frequency bands compared to LA networks, suggesting improved network robustness and functional integration during tasks that demand sustained attention. Figure 12 depicts topological differences between high and low attention states, defining brain region properties. Alpha and Beta bands exhibit complex patterns involving temporal, parietal, occipital, and frontal regions. In contrast, the Theta band shows predominant activity in the right hemisphere, while the Gamma band displays increased connections in the left hemisphere. Overall, these findings support the hypothesis that attention states influence neural synchronization patterns and network properties, shaping the functional organization of the brain under different cognitive conditions. Figure 11: Bar graph depicting global efficiency (GE), small-worldness (SWN), and network density (ND) for high attention (HA) and low attention (LA) tasks across the Theta, Alpha, Beta, and Gamma frequency bands. Error bars emphasize significant differences (\(p<0.05\)) between conditions. Figure 12: Graph of binary matrices from Figure 10, showing a top-down view of 30 electrodes arranged by the 10-20 system. Red lines indicate channel pairs with higher synchronization in high attention (HA) states, highlighting enhanced functional connectivity. Note: Ensure that node labels are clearly visible and not overlapped by the red lines in the final image. Table 3 presents a comparison of classification performance between NA + KNN and NA + PCA + KNN across all frequency bands.We compared the performance of NA + KNN and NA + PCA + KNN across EEG frequency bands (Theta, Alpha, Beta, and Gamma) and network metrics (global efficiency [GE], small-worldness [SWN], and network density [ND]) for high attention (HA) and low attention (LA) tasks. Paired t-tests (Bonferroni) were conducted to compare the methods, while repeated measures ANOVA was used to assess differences in network metrics between the HA and LA conditions. In the Beta band, however, both methods performed similarly, with no significant difference (\(p\)-value = 0.150). Overall, PCA enhanced classification performance, particularly in the Theta, Alpha, and Gamma bands.
## 3 Results
### Functional Properties of Powdered Egg Products W, M1:3, and WE5%  
In order to evaluate the functional properties of the W, M1:3, and WE5% dried egg products, WHC at 25 \({}^{\circ}\)C and WSI, dispersibility in water, and wettability at 25 \({}^{\circ}\)C and 50 \({}^{\circ}\)C were determined. The W egg product had the highest WHC value, 3.03 \(\pm\) 0.00 mL water/g of powder, and the lowest ISA value, at 25 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, which agrees with the observation  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Model**} & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{1}}\)*\(\mathbf{X_{2}}\) & **Error** & **Total** \\ \hline Variable & DF & 8 & 2 & 2 & 4 & 9 & 17 \\ MC [\%] & SS & 6.21 & 2.47 & 1.73 & 2.02 & 2.17 & 8.38 \\  & \(p\)-value & 0.05 A & 0.03 A & 0.07 & 0.17 & & \\  & SS & 0.01 A & 0.00 A & 0.01 A & 0.00 A & 0.00 A & 0.02 A \\ Aw & \(p\)-value & 0.01 A & 0.41 & 0.00 A & 0.10 & & \\  & SS & 119.45 & 56.04 & 12.04 & 51.38 & 8.47 & 127.92 \\  & \(p\)-value & 0.00 A & 0.00 A & 0.02 A & 0.00 A & & \\ pH & SS & 3.34 & 3.00 & 0.18 & 0.17 & 0.16 & 3.51 \\  & \(p\)-value & 0.00 & 0.00 & 0.04 & 0.14 & & \\ Lightness (L) & SS & 193.43 & 182.54 & 6.10 & 4.78 & 7.02 & 200.45 \\  & \(p\)-value & 0.00 & 0.00 & 0.06 & 0.27 & & \\ Chromaticity & SS & 176.39 & 171.08 & 2.31 & 3.01 & 4.37 & 180.76 \\ coordinate a* & \(p\)-value & 0.00 & 0.00 & 0.15 & 0.27 & & \\ Chromaticity & SS & 930.07 & 882.44 & 37.01 & 10.62 & 46.20 & 976.27 \\ coordinate b* & \(p\)-value & 0.00 & 0.00 & 0.07 & 0.73 & & \\ \hline \end{tabular} MC = moisture content, Aw = water activity, WSI = water solubility index, DF = degree of freedom, SS = type III sum of squares, X\({}_{1}\) = liquid egg component mixture, X\({}_{2}\) = sucrose content (%). A Statistically significant differences at \(p\)-value < 0.05.  
\end{table}
Table 4: Results of the ANOVA variance analysis for the physicochemical properties of liquid egg components that are processed into dried powders.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Analysis** & **W** & **M1:3** & **WE5\%** \\ \hline Moisture and volatile materials (g/100 g) & 4.51 & 3.22 & 4.79 \\ Total protein (g/100 g) & 80.48 & 58.55 & 39.92 \\ Total fat (g/100 g) & 1.21 & 29.61 & 13.18 \\ Total carbohydrates(g/100 g) & 8.19 & 4.45 & 39.42 \\ Ash (g/100 g) & 5.61 & 4.17 & 2.69 \\ Cholesterol (mg/100 g) & 5.62 & 553.41 & 518.36 \\ Total calories (kCa1/100 g) & 365.57 & 518.49 & 435.98 \\ \hline \end{tabular} \(W\) = egg white, M1:3 = a mixture of 1 part egg yolk and 3 parts egg white, and WE5% = whole egg with the addition of 5% sucrose. The results are reported on a wet basis.  
\end{table}
Table 5: Mean values for the nutritional compositions of egg powders W, M1:3, and WE5%.  
made by [PERSON] and [PERSON] [21] that whenever a product has a high WHC, it also exhibits a low WSI (see Table 6).
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### All Features Together  
When all extracted features were combined, including color histograms, shape features, distance transformation features, and texture features, optimal close recognition results were achieved. As demonstrated in Table 13, a training set of only five images could potentially yield a 60% recognition rate. This rate increased to 82% with 20 training images and reached an impressive 93.49% with 160 training images (Table 13).
## 8 Discussion and Conclusions
In this case, between 10 and 30% of the original data can be considered as missing data and a comparison between the real data and the imputations can be made to choose the optimal rank. This approximation is used in the R _imputation_ package ([[https://github.com/jeffwong/imputation](https://github.com/jeffwong/imputation)]([https://github.com/jeffwong/imputation](https://github.com/jeffwong/imputation))) (accessed on 1 April 2022). In the literature, there is also the option to perform leave-group-out elimination in the EK82 method [34]; this alternative computationally reduces calculation times, but thecomputational gain is accompanied by a decrease in accuracy in determining the optimal rank of a matrix. Finally, future research could be conducted to continue testing the methods proposed here. For example, more types of contamination could be considered. In the works of [PERSON] and [PERSON] [29] and [PERSON] et al. [35], there are more alternatives to produce outliers. However, there is still no robust cross-validation procedure in the literature to choose the best additive main effects model with a robust multiplicative interaction or robust AMMI model [32], so the EK84 (or EK01) scheme could be tested for the choice of that model. The methodologies presented here can also be compared with those that detect outliers [36] and assume the outliers as missing values for later imputation by EM algorithms or with multiple imputations [37]. Finally, the EK84 and EK01 schemes are based on the EK method that could be replaced by [PERSON]'s method [4] or by the Eigenvector method [9], which can be computationally faster for large matrices. Conceptualization, S.A.-A., M.G.-P. and W.J.K. ; methodology, S.A.-A., M.G.-P. and W.J.K. ; software, S.A.-A., M.G.-P. and W.J.K. ; validation, S.A.-A., M.G.-P. and W.J.K. ; formal analysis, S.A.-A., M.G.-P. and W.J.K. ; investigation, S.A.-A., M.G.-P. and W.J.K. ; resources, S.A.-A., M.G.-P. and W.J.K. ; data curation, S.A.-A., M.G.-P. and W.J.K. ; writing--original draft preparation, S.A.-A., M.G.-P. and W.J.K. ; writing--review and editing, S.A.-A., M.G.-P. and W.J.K. All authors have read and agreed to the published version of the manuscript. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Not applicable. The authors of this paper acknowledge the High-Performance Computing Center-ZINE of Pontificia Universidad Javeriana for assistance during the simulation study. The authors declare no conflict of interest.
## 2 Predictions of the Stability of Headings in the LGCB Mines  
The decrease or the loss of stability in headings located at different depths (1000 m b.g.l. and 1300 m b.g.l.) in one of the LGCB mines were modelled with the use of numerical simulations. The numerical calculations were performed in the RS2 software, which is based on the finite element method (FEM), i.e., one of the most popular numerical methods. \begin{table}
\begin{tabular}{c c c} \hline
**Roof Class** & **Maximum Excavation Width [m]** & **Bolting Mesh [m]** \\ \hline I & 6.0 & 1.0 \(\times\) 1.0 \\ II & 7.0 & 1.0 \(\times\) 1.0 \\ III & 7.0 & 1.5 \(\times\) 1.5 \\ IV & 7.0 & 2.0 \(\times\) 2.0 \\ V & 8.0 & 2.0 \(\times\) 2.0 \\ \hline \end{tabular}
\end{table} 
Table 1: Selection of a roof bolting system for headings in the LGCB mines [3]. Table 2 lists rock parameters used in the numerical simulations of the stability of headings in the conditions of one of the LGCB mines. The parameters were determined from geomechanical tests of rock samples. The rock samples for the laboratory tests were obtained from two boreholes: Jm-15/H-173 (rock layers in the roof and in the floor) and Jm-15-460 (rock layers in the walls). The rock parameters obtained from the Jm-15/H-173 and Jm-15-460 boreholes show the typical geological structure of the Fore Sudetic Monocline. In this structure, the access and preparatory excavations of the LGCB mines are driven. The immediate roof layers contain very strong carbonate formations (calcareous dolomite), as opposed to the rock layers which form the mined deposit and the floor layers. Subsequently, the Hoek-Brown failure criterion, which is broadly used in geomechanical analyses of rock mass deformations and effort, was assumed for the rock mass. The generalised Hoek-Brown failure criterion for a fractured rock mass may be described with the following equation [39]:  
\[\sigma_{1}=\sigma_{3}+\sigma_{ci}\cdot\left(m_{b}.\frac{\sigma_{3}}{\sigma_{ci }}+s\right)^{a} \tag{1}\]  
where:  
\(\sigma_{1}\)--value of the maximum principal effective stress at failure,  
\(\sigma_{3}\)--value of the minimum principal effective stress at failure,  
\(m_{b}\)--the Hoek-Brown constant for the rock mass,  
\(s\) and \(a\)--constants depending on the rock mass properties,  
\(\sigma_{ci}\)--uniaxial compressive strength of the rock sample. When rock mass tensile strength \(\sigma_{tm}\) is exceeded, the equation for \(a=0.5\) can be formulated as follows:  
\[\sigma_{tm}=\frac{\sigma_{ci}}{2}\cdot\left(m_{b}-\sqrt{{m_{b}}^{2}+4s}\right) \tag{2}\]  
After the failure criterion had been assumed, the RocLab 1.0 software and Hoek-Brown classification [39; 40; 41; 42] were used to determine the rock mass parameters (strength
###### Abstract  
This paper proposes a radiating pattern reconfigurable antenna by employing RF Micro-electromechanical Systems (RF MEMS) switches. The antenna has a low profile and small size of 4 mm \(\times\) 5 mm \(\times\) 0.4 mm, and mainly consists of one main patch, two assistant patches, and two RF MEMS switches. By changing the RF MEMS switches operating modes, the proposed antenna can switch among three radiating patterns (with main lobe directions of approximately \(-\)17.0\({}^{\circ}\), 0\({}^{\circ}\) and +17.0\({}^{\circ}\)) at 35 GHz. The far-field vector addition model is applied to analyse the pattern. Comparing the measured results with analytical and simulated results, good agreements are obtained.  
Keywords:pattern reconfigurable antenna; RF MEMS switch; far-field addition model; Ka-band +
Footnote †: journal: Accepted: 3 August 2016; Published: 17 August 2016
# Recovery of Polyphenols from Agri-Food By-Products: The Olive Oil and Winery Industries Cases
## 4 Polyphenol Analysis: From Quantification to Antioxidant Capacity
### Spectrophotometric Methods
#### 4.2.3 2,2\({}^{\prime}\)-Azino-bis(3-ethylbenzothazoline-6-Sulfonic Acid) Assay  
In this method, the 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulfonic) acid (ABTS) is used. Antioxidants reduce the cation radical (ABTS\({}^{+}\)), throughout mixed-mode HAT/SET mechanisms, and a change of color occurs from a bluish-green solution to a decolorized one. The absorbance diminution at 743 nm depends on the concentration of antioxidants in the sample. Trolox is commonly used as a standard in calibration, and the ABTS results are expressed in terms of Trolox equivalent concentration [72; 73; 74; 75; 77]. [PERSON] et al. [70] applied the ABTS assay to the analysis of winery by-products, such as grape seeds, pomace, and stems. The highest antioxidant capacity values were found in grape seeds extracts (0.061-0.065 mmol Trolox g\({}^{-1}\)). Similarly, [PERSON] et al. [47] applied the ABTS assay to evaluate the antioxidant activity of olive oil production by-products (olive pomace, olive leaves, and pomace olive oil). The highest antioxidant capacity was observed in olive pomace samples.
## 3 Materials and Methods
### Technology Level--Network Segmentation (VLANs)  
In OT network, IEEE 802.1Q VLANs were leveraged to divide the environment into separate security zones to isolate the most critical assets. Dedicated VLANs were established for each production line, as well as for sensitive systems like controllers and historians. This micro-segmentation contained threats by preventing lateral movement between areas if one was compromised.  
Figure 4: Illustration of Long Short-Term Memory model; the gates equations illustrated on right-hand side.  
Inter-VLAN routing was carefully controlled via firewalls and gateways to allow only required connections. For example, the control center VLAN could access the historian VLAN but not directly interact with production floor endpoints. Cryptography was implemented to further secure necessary inter-VLAN communications, using IPsec VPNs between gateways.  
By partitioning the OT network into isolated segments with restricted visibility, the attack surface was greatly reduced. VLANs provided the layer 2 separation that was needed, while additional controls at layers 3+ enabled secure routing between zones. This defense-in-depth approach limited potential infection blast radius while still permitting essential data flows. VLAN segmentation was found to be a key best practice, alongside proper network-level controls, for improving security posture in the SCADA environment.
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 3 Dataset and Methodology
### Spatial-Temporal Self-Attention Graph Convolution Networks (STA-GCN)
#### 3.3.2 Temporal Gated Convolution  
Although the RNN model is widely used for time-series prediction, the computational complexity of this recurrent neural network structure is relatively high. In contrast, CNNs have the advantages of fast training speed, simple structure, and no dependency constraints. Therefore, we introduced gated temporal convolution [35], and used an appropriate dilation rate for dilated convolution to obtain richer temporal dynamic correlations along the time axis. The structure of the gated temporal convolution is shown in Figure 4. After \(F^{\mathcal{T}}\in\mathbb{R}^{N\times S\times d}\) is passed through a 1D convolution layer, it is divided into two parts, and \(F^{c}\in\mathbb{R}^{N\times S\times d^{c}}\) is obtained through the GLU activation function, as shown in Equation (4).  
\[F^{c}=\psi\Big{(}\Phi_{1}*F^{\mathcal{T}}+F^{\mathcal{T}}\Big{)}\odot\sigma \Big{(}\Phi_{2}*F^{\mathcal{T}}+b_{2}\Big{)} \tag{4}\]  
where \(\Phi_{1}\) and \(\Phi_{2}\) are two one-dimensional convolution operations. The \(\psi\) and \(\sigma\) represent the tanh and sigmoid functions, respectively. The symbol \(\odot\) represents the element-wise multiplication.
# Mapping of the Upper Limb Work-Space: Benchmarking Four Wrist Smoothness Metrics
## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Smoothness Metrics  
Four smoothness metrics based on wrist kinematics were computed. The normalized jerk (NJ) [11] was computed as follows:  
\[\text{NJ}=\sqrt{\frac{1}{2}\cdot\frac{t_{tot}^{5}}{L^{2}}\cdot\int\ j^{2}\ dt}\]  
where \(t_{tot}\) is the task execution time, \(j\) is the third derivative of the wrist 3D trajectory, and \(L\) is the length of the wrist trajectory during the execution of the task. Low NJ indicates high smoothness.  
The speed metric (SM) [20] is defined as the average velocity divided by the maximum velocity:  
\[\text{SM}=\frac{\overline{v}}{\text{max}(v)}\]  
where \(v\) is the velocity profile of the wrist and \(\overline{v}\) is the average velocity. SM increases with the smoothness of the movement.  
The spectral arc length (SAL) is computed from the arc length of the power spectrum of the Fourier transform of the velocity [21] or of the acceleration [23] profile, as follows:  
\[\text{SAL}=-\int_{0}^{\omega_{c}}\sqrt{\left(\frac{1}{\omega_{c}}\right)^{2}+ \left(\frac{d\hat{A}(\omega)}{d\omega}\right)^{2}}d\omega,\ \hat{A}(\omega)=\frac{A(\omega)}{A(0)}\]  
where \([0,\ \omega_{c}]\) is the frequency band, \(A(\omega)\) the Fourier magnitude spectrum of the acceleration signal \(a(t)\), and \(\hat{A}(\omega)\) is the normalized magnitude spectrum. High SAL corresponds to high smoothness.  
Last, the number of speed peaks (NP) [18] metric is defined as the number of local maxima of the speed profile \(v\):  
\[\text{NP}=\#\{v_{max}\}\]  
NP increases with the smoothness decrease and is near \(1\) in smooth reaching movements.  
The smoothness metrics were computed for each repetition in each movement phase.