esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Program
### Material Strength and Test Setup  
Table 4 shows the material strengths of the hollow steel sections, plates, and bolts. For the steel sections used as the chord and branch members, yield strength was \(F_{y}\) = 405 MPa for B-150 \(\times\) 150 \(\times\) 4.5 and B-150 \(\times\) 200 \(\times\) 4.5, and \(F_{y}\) = 395 MPa for B-100 \(\times\) 100 \(\times\) 4.0. For the plates used as the longitudinal plate and chord face stiffener, yield strength was \(F_{y}\) = 429 MPa for thickness 15 mm and \(F_{y}\) = 328 MPa for thickness 25 mm. For the concrete of composite branch members, the average compression strength of three concrete cylinders (100 mm diameter and 200 mm height) was \(f_{c}\) = 57.4 MPa. The yield and ultimate strengths of high-tension M24 bolts used for the bolted joint of branch members were \(F_{y}\) = 1040 MPa and \(F_{u}\) = 1102 MPa, respectively.  
Figure 5 shows the test setup for lateral loading. A hydraulic actuator of nominal loading capacity 1000 kN was attached to the left end of the chord member. The hydraulic actuator could exert its full loading capacity (i.e., 1000 kN) during positive loading (i.e., pushing to the right) whereas exerting only 65% of its loading capacity during negative loading (i.e., pulling to the left). As shown in Figure 5, four ball jigs were installed to the chord member as lateral support to minimize rotation of the planar truss. Lateral displacements were measured at both ends of the chord member, using the linear variable differential transducers (LVDTs) fixed to a vertical member standing independently.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Type** & & **Member** & **Yield Strength (MPa)** & **Ultimate Strength (MPa)** \\ \hline \multirow{2}{*}{RHS} & 4.5 mm & Chord & 405 & 503 \\ \cline{2-5}  & 4.0 mm & Branch & 395 & 508 \\ \hline \multirow{2}{*}{Plate} & 15 mm & Longitudinal plate and stiffener & 429 & 574 \\ \cline{2-5}  & 25 mm & Stiffener & 328 & 519 \\ \hline Bolt & M24 & Bolted joint of branch & 1040 & 1102 \\ \hline Bar & D10 & Branch & 511 & 665 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material strength.  
Figure 5: Test setup for cyclic lateral loading of RHS truss specimens.
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 4 Simulation Results and Discussion  
In this section, we conducted simulations to validate the convergence and effectiveness of the proposed methodology. The specific simulation parameters are presented in the following Table 2:
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 6 Conclusions  
This paper presents a very compact size tri-band antenna operating at mm-wave frequency bands of 28, 38, and 60 GHz. A CPW-fed radiating monopole is printed on a flexible Roger 5880 substrate. The dimensions of the design are as follows: 4 \(\times\) 3 \(\times\) 0.25 mm\({}^{3}\). The proposed design is simulated in free space and on the rubber material with a skin tissue for the use of a smart watch. It is observed that over different layers of phantom, the total radiation efficiency is found to be more than 85%, and the gain of the proposed antenna at 28 GHz, 38 GHz, and 60 GHz is 5.29 dB, 7.47 dB, and 9 dB, respectively. SAR values are found to be 1.49 W/kg, 1.17 W/kg, and 1.47 W/kg at the frequencies of 28 GHz, 38 GHz, and 60 GHz. Furthermore, a comparison of the proposed antenna to current work shows that the proposed design is a perfect candidate for future communication systems.  
Conceptualization, S.A., A.G. and S.N.; methodology, H.B. and Y.A.S.; software, S.A. and M.E.A.; validation, A.G., J.A.A.-G. and N.O.P.; formal analysis, N.O.P. and Y.A.S.; investigation, A.G.; resources, Y.A.S.; data curation, A.J.A.-A.-G.; writing--original draft preparation, S.A. and H.B.; writing--review and editing, S.A., N.O.P. and A.G.; visualization, S.A. and Y.A.S.; supervision, A.G., N.O.P. and S.N.; project administration, A.J.A.-G. and Y.A.S.; funding acquisition, N.O.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Security Aspects Regarding Wireless Body Area Framework
### Special Consideration
#### 3.4.1 Data Collection  
The communication within the WBAN relies on efficient communication protocols to ensure seamless data transmission while minimizing unnecessary traffic. This is achieved by employing cyclical data transmission methods. In case this is conducted by merely concatenating previously collected encrypted information, the final ciphertext may carry patterns from the same plaintext (from different nodes), and the same repetitive part-blocks of ciphertext can emerge, making the encryption not semantically secure and vulnerable to Chosen Plaintext Attack (CPA).  
To prevent these attacks, the data that are concatenated with the previous data must be encrypted using a different initialization vector each time. Another approach to preventing such vulnerability is the encryption of the entire message each time data are collected from a node.
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 4 Map Construction and Test
### Construction of Grid Map and Test
#### 4.2.2 Mapping Test Based on Laser SLAM  
Laser SLAM is divided into filtering-based and optimization-based, the Gmapping algorithm [35] in Table 1 belongs to the former, and the algorithms, Cartographer [36], Karto [37], and Hector [38], belong to the latter. In this paper, the four abovementioned laser SLAM algorithms are used to carry out mapping tests of the simulated roadway along the long corridor shown in Figure 7.  
The test results of the four algorithms are shown in Figure 10. Figure 9(a) shows a map of simulated roadway, built based on the Gmapping algorithm, and the Gmapping algorithm relies on the Rao-Blackwellized particle filter, which takes laser data and odometer data as inputs; the map constructed based on the Cartographer algorithm is shown in Figure 9(b). The Cartographer algorithm consists of two phases of local and global SLAM, with the first phase establishing and maintaining grid submaps, and the second phase eliminating cumulative errors and carrying out global optimization. Figure 9(c) shows a map built based on the Karto algorithm, which is based on the graph optimization theory, and uses the graph mean to represent the map, and the front-end and back-end are carried out by a single thread. The map constructed based on the Hector algorithm is shown in Figure 9(d); the Hector algorithm only creates a grid map by matching laser data without the need for odometer data.  
The Gmapping algorithm based on the filtering framework has a lower computational complexity, but it relies too heavily on odometer data. Although the Hector algorithm is not limited to the odometer, it requires a high laser frequency. In addition, the Cartographer algorithm is relatively resource intensive. Consequently, based on the 2D occupy grid map constructed using the Karto algorithm, the cost map for the navigation of the roadheader is constructed by introducing parameters, such as the expansion radius of obstacles and the updating frequency of map. The simulation test results are shown in Figure 11, the red arrow represents the body pose.  
Figure 10: Grid map of simulated roadway based on laser SLAM.
## 2 System Model
### Transfer Learning and Meta-Learning  
In conventional learning, the number of required training slots \(L^{\mathrm{tr}}\) can be reduced by selecting hyperparameters in the learning problem that reflect prior knowledge about the prediction problem at hand. In the next sections, we will explore solutions that optimize such hyperparameters based on data received from multiple previous frames. To this end, as illustrated in Figure 2, we assume the availability of channel data collected from \(F\) frames received in the past. In each frame, the channel follows the model described in Section 2.2. Accordingly, data from previous frames consists of \(L+N+\delta-1\) channels \(\{h_{1,f},\dots,h_{L+N+\delta-1,f}\}\) for some integer \(L\).  
By using these channels, the dataset  
\[\mathcal{Z}_{f}=\{(x_{i,f},y_{i,f})\}_{i=1}^{L}\equiv\{(\mathrm{vec}(H_{i,f}^{ N}),h_{l+\delta,f})\}_{l=N}^{L+N-1} \tag{9}\]  
can be obtained as explained in Section 2.1, where \(L\) is typically larger than \(L^{\mathrm{tr}}\), although this will not be assumed in the analysis. Correspondingly, we also define the \(L\times N\) input matrix \(X_{f}\) and the \(L\times 1\) target vector \(y_{f}\). We will propose methods that leverage the historical knowledge available from dataset \(\mathcal{Z}_{f}\) for \(f=1,\dots,F\) via transfer learning and meta-learning with the goal of reducing number of pilots, \(L^{\mathrm{tr}}\), needed for channel prediction in a new frame (i.e., frame \(F+1\) in Figure 2).
## 3 Results  
### QC Assessment and Global Metabolite Overview  
To ensure data quality and reproducibility, we first conducted quality control analyses across all samples. The empirical cumulative distribution function (ECDF) of the coefficient of variation (CV) values showed that over 85% of the detected peaks had CVs below 0.5 across all groups (Figure 1A), demonstrating the high technical consistency of the GC-MS measurements and sample preparation. A PCA was performed based on the mass spectrometry profiles of all samples and QC samples. In the PCA score plot (Figure 1B), the QC samples clustered tightly together, further demonstrating the technical reproducibility. Clear separations among different groups were observed along PC1 (39.95%) and PC2 (38.75), suggesting distinct volatile metabolite profiles among the different tissues and species. A total of 1666 volatile metabolites were detected and classified into different chemical categories (Supplementary Table S1). The circular composition plot (Figure 1C) revealed that terpenoids, alcohols, aldehydes, ketones, and aromatic compounds were the predominant classes. An HCA was subsequently performed to visualize the overall differences in metabolite accumulation patterns (Figure 1D). Using all_heatmap_row_cluster, clustering was performed only on metabolites, highlighting the metabolites with similar abundance patterns across samples. These results confirmed the high quality and consistency of the data and provided a robust basis for subsequent differential metabolite analysis.
## 3 Results and Discussion  
### Shear Stress-Strain Analysis  
To evaluate the effect of different fiber orientations on the mechanical reinforcement, a series of numerical direct shear tests were performed on FRS using PFC3D. As shown in Figure 7, the angular orientations of fibers in the simulations were set to \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\) against the shearing surface.  
Figure 8 shows the shear stress-horizontal displacement curves obtained for samples with different fiber orientations under the applied normal stresses of 200 kPa in DEM simulations. The shear resistance of FRS was not significantly affected by fiber orientation at small horizontal displacements (less than 2 mm). However, a preferential orientation of fibers induced significant shear strength anisotropy, in particular at large horizontal displacements. The highest shear stress is observed at a fiber orientation  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Density of sand particle/(kg/m\({}^{3}\)) & 2690 \\ Effective modulus of sand particle/Pa & 5e7 \\ Normal-to-shear stiffness ratio of sand particle & 1 \\ Friction coefficient of sand particle & 0.2 \\ Rolling resistance coefficient of sand particle & 0.25 \\ Density of fiber particle/(kg/m\({}^{3}\)) & 910 \\ Normal stiffness of fiber particle/(N/m) & 5e4 \\ Normal-to-shear stiffness ratio of fiber particle & 1 \\ Parallel bond normal stiffness of fiber particle/(N/m) & 1e11 \\ Parallel bond stiffness ratio of fiber particle & 1 \\ Tensile strength of fiber particle/Pa & 1e12 \\ Cohesion of fiber particle/Pa & 1e12 \\ Friction coefficient of sand–fiber contacts & 0.90 \\ Friction coefficient of fiber–fiber contacts & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Microscopic parameters for DEM simulation.  
Figure 7: DEM model of FRS with different fiber orientations.  
angle of 60\({}^{\circ}\), and then gradually decreasing values are observed for fiber orientation angles of 90\({}^{\circ}\) and 30\({}^{\circ}\). The specimen with a fiber orientation angle of 0\({}^{\circ}\) produced the weakest reinforcement effect. This result is consistent with that of [PERSON] and [PERSON] [11], who also found that an angle of 60\({}^{\circ}\) produced the greatest strength increase.  
As shown, the fiber orientation not only affects the ultimate shear strength level, but it also changes the shapes of the shear stress-total displacement curves. Specimens with fibers having orientations of 0\({}^{\circ}\) and 30\({}^{\circ}\) showed peak shear strength followed by softening behavior. However, those with fibers having orientations of 90\({}^{\circ}\) and 60\({}^{\circ}\) showed continuous hardening behavior, indicating greater ductility.
# Application of Microgel as a Sorbent for Bisphenol Analysis in Liquid Food Samples
## 2 Materials and Methods
### Comparison of Microgel with Commercial SPE Cartridges  
Two different commercial SPE cartridges--SupelcleanTM ENVI-18 (500 mg) and AffinimipR (100 mg)--were chosen to compare their sorption performance with the synthesized microgel. The ENVI-18 cartridge has a polymerically bonded octadecyl sorbent, which is dedicated for extracting and concentrating pollutants from aqueous environmental samples. The AffinimipR cartridge has a molecularly imprinted polymer sorbent that is dedicated for the extraction of bisphenol A or closely related structures and has been already used for bisphenol's preconcentration from food samples [29; 30].
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Uncertainty Quantification System Performance
Blue dots represent individual data points, and the red dashed horizontal line indicates zero residual. The random scatter around zero with no apparent patterns confirms that the model adequately captures the underlying relationships without systematic prediction bias across the range of UCS values. (**d**) Relative importance of input variables. The uncertainty model achieved an R\({}^{2}\) of 0.5632 in predicting error magnitudes--a substantial accomplishment considering that predicting prediction errors is fundamentally more challenging than predicting the original target values. The uncertainty model must identify subtle patterns in how prediction accuracy varies across different input conditions, and capturing over half of the variance in prediction uncertainty provides substantial information about confidence levels that would otherwise remain unknown. Figure 9: Performance analysis of uncertainty Random Forest model for error magnitudes prediction: (**a**) Actual vs. predicted absolute error. (**b**) Distribution of prediction errors (actual absolute error minus predicted absolute error). The red dashed vertical line indicates zero error (perfect uncertainty prediction), while the orange solid curve represents the fitted probability distribution overlaid on the histogram. The near-zero-centered distribution confirms that the uncertainty model provides unbiased estimates of prediction reliability. (**c**) Residual plot showing the relationship between predicted absolute errors and residuals (actual minus predicted absolute error). Orange dots represent individual data points, and the red dashed horizontal line indicates zero residual. The random distribution around zero demonstrates that the uncertainty model accurately captures the magnitude of prediction errors without systematic bias. (**d**) Relative importance of input variables. The system's ability to generate meaningful uncertainty estimates was validated through analysis of the relationship between predicted uncertainties and actual prediction errors. The uncertainty model demonstrated clear heteroscedastic behavior, correctly identifying that prediction uncertainty varies systematically across the input space, with higher uncertainty estimates corresponding to regions where the primary model exhibited larger prediction errors.
# Triple-Attention Mixed-Link Network for Single-Image Super-Resolution
## 6 Conclusions  
In this work, we propose a novel single-image super-resolution method which used mixed-link connections and three different attentions, including channel attention, fuse kernel attention, and global spatial attention, and we name the model triple-attention mixed-link network (TAN). The mixed-link structure helps the network gain stronger representation ability and proved to be more powerful than residual or dense networks. Moreover, the attention mechanisms give an impressive improvement on performance. The channel attention (CA) could recalibrate the information among channels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Set5** & **Set14** & **BSD100** \\
**Blocks** & **Scale \(\times\) 2** & **Scale \(\times\) 2** & **Scale \(\times\) 2** \\ \hline
1 & 37.97 & 33.53 & 32.15 \\
2 & 38.12 & 33.69 & 32.26 \\
3 & 38.23 & 33.79 & 32.32 \\
4 & 38.28 & 33.87 & 32.34 \\
5 & 38.29 & 33.96 & 32.34 \\
6 & 38.30 & 33.98 & 32.37 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of network with different numbers of AE-MLBs.  
The kernel attention (KA) can fuse the features from convolution layers with \(3\times 3\) and \(5\times 5\) kernels, which helps the model learn information from different receptive fields. The spatial attention (SA) mixes the information from local parts and global parts among channels which greatly enhances the reconstruction network. With these attention modules, the proposed attention-enhanced mixed-link block (AE-MLB) is used as the basic part to build the whole network. According to the experiments, the mixed-link structure can decrease up to 27.06% model parameters and increase up to 0.12 dB in PSNR. The three attention modules help the network gain 0.49 dB, 0.58 dB, and 0.32 dB in Set5 [23], Set14 [24] and BSD100 [25]. Thanks to the sophisticated network structure and effective attention mechanisms, our proposed TAN could not only achieve better performance but also contain fewer parameters, which is of great practical value.  
Conceptualization, X.C. and X.L.; Methodology, X.C.; Software, X.C.; Validation, X.C.; Formal analysis, X.C.; Investigation, X.C.; Resources, X.C.; Data curation, X.C.; Writing original draft, X.C.; Writing review and editing, X.L.; Visualization, X.C.; Supervision, J.Y.; Project administration, J.Y.; Funding acquisition, J.Y.  
This work was supported by the National Science Fund of China under Grant No. U1713208, Program for Changjiang Scholars.  
The authors would like to thank the editor and the anonymous reviewers for their critical and constructive comments and suggestions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TAN & Triple-attention mixed-link network \\ CA & Channel attention \\ KA & Kernel attention \\ SA & Spatial attention \\ AE-MLB & Attention-enhanced mixed-link block \\ \end{tabular}
## 3 Results
### Summary  
From observing the analysis results, it is confirmed that two different image processing methods have a significant difference on the aperture boundary identification. Two kinds of experimental CT images are used, i.e., the CT images at 0 MPa and at 3.0 MPa. The aperture value generates a bit difference at 0 MPa. In contrast, the aperture difference presents itself as more significant at 3.0 MPa when the aperture becomes much narrower under an external confining stress. Moreover, the RG method concentrates on the voxel connectivity of the same region. However, this will result in several misidentifications of the fracture  
Figure 20: CT image comparison in different positions of Slice A and Slice E at 3.0 MPa. (**a1,a2**) are the comparative results of different positions in \(x\)-direction (i.e., positions of Slice A with the pixel number of 100–325 and Slice E is 80–220, respectively). (**b1,b2**) are the profile line comparative results of different CT images (i.e., the profile line of Slice A is picked up from the pixel number equal to 275 and Slice E is 142, respectively). (**c1,c2**) Comparative results of the 275 and 142 profile line value.  
aperture, and the voxel that existed around the discontinuity positions cannot be well recognized. The aperture boundary is omitted especially when applying the confining pressure. However, the ED segmentation method has a good property of recognizing the fracture aperture boundary at fine scales. The fracture undulation trend under a high stress and the aperture closure also can be extracted successfully from the original CT images. It is successful in avoiding the noise of the neighboring voxels and achieving more accurate image processing results than those of the RG segmentation method.
## 4 Experiments and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Patient** & **Method** & **Domain** & **Embedding Parameters** & **Amplitude Similarity** & **SNR** \\
**ID** & & & & & \\ \hline \multirow{8}{*}{1} & \multirow{4}{*}{Reference [9]} & \multirow{4}{*}{Time domain} & \(Q=500\) & 0.98 & 41.73 \\  & & & \(Q=2000\) & 0.99 & 41.71 \\  & & & \(Q=5000\) & 1 & 41.72 \\  & & & \(Q=5000\) & 0.99 & 39.82 \\  & & & \(Q=2000\) & 1 & 36.37 \\  & & & \(Q=5000\) & 1 & 29.15 \\  & & & \(Q=5000\) & 1 & 40.26 \\  & & & \(Q=2000\) & 0.99 & 35.74 \\  & & & \(Q=5000\) & 0.99 & 29.75 \\  & & & \(Q=5000\) & 0.99 & 29.75 \\  & & & \(Q=5000\) & 0.99 & 27.05 \\  & & & \(Q=2000\) & 0.99 & 14.69 \\  & & & \(Q=5000\) & 1 & 8.73 \\  & & & \(\epsilon=20000\), \(n=2\) & 0.99 & 38.46 \\  & & & \(\epsilon=3000\), \(n=3\) & 0.99 & 38.44 \\  & & & & \(\epsilon=4000\), \(n=4\) & 0.99 & 38.55 \\ \hline \multirow{8}{*}{2} & \multirow{4}{*}{Reference [9]} & \multirow{4}{*}{Time domain} & \(Q=500\) & 0.98 & 38.93 \\  & & & \(Q=2000\) & 0.99 & 38.31 \\  & & & \(Q=5000\) & 1 & 39.24 \\  & & & \(Q=5000\) & 1 & 40.78 \\  & & & \(Q=2000\) & 1 & 34.67 \\  & & & \(Q=5000\) & 1 & 29.31 \\  & & & \(Q=5000\) & 1 & 40.26 \\  & & & \(Q=2000\) & 1 & 35.74 \\  & & & \(Q=5000\) & 1 & 29.37 \\  & & & \(Q=5000\) & 0.99 & 26.79 \\  & & & \(Q=2000\) & 0.99 & 13.79 \\  & & & \(Q=5000\) & 0.99 & 7.60 \\  & & & \(\epsilon=2000\), \(n=2\) & 1 & 32.59 \\  & & & \(\epsilon=3000\), \(n=3\) & 1 & 32.71 \\  & & & \(\epsilon=4000\), \(n=4\) & 0.99 & 32.29 \\ \hline \multirow{8}{*}{3} & \multirow{4}{*}{Reference [9]} & \multirow{4}{*}{Time domain} & \(Q=500\) & 0.98 & 39.24 \\  & & & \(Q=2000\) & 0.98 & 39.72 \\  & & & \(Q=5000\) & 0.99 & 39.70 \\  & & & \(Q=5000\) & 0.99 & 41.10 \\  & & & \(Q=2000\) & 0.99 & 37.05 \\  & & & \(Q=5000\) & 0.99 & 29.46 \\  & & & \(Q=5000\) & 1 & 40.26 \\  & & & \(Q=2000\) & 1 & 35.74 \\  & & & \(Q=5000\) & 1 & 28.15 \\  & & & \(Q=5000\) & 0.98 & 28.54 \\  & & & \(Q=2000\) & 0.99 & 17.93 \\  & & & \(Q=5000\) & 0.99 & 12.58 \\  & & & \(\epsilon=2000\), \(n=2\) & 1 & 34.25 \\  & & & \(\epsilon=3000\), \(n=3\) & 1 & 34.23 \\  & & & \(\epsilon=4000\), \(n=4\) & 1 & 32.83 \\ \hline \multirow{8}{*}{4} & \multirow{4}{*}{Reference [9]} & \multirow{4}{*}{Time domain} & \(Q=500\) & 0.99 & 29.51 \\  & & & \(Q=2000\) & 0.99 & 29.37 \\  & & & \(Q=5000\) & 1 & 29.25 \\  & & & \(Q=5000\) & 1 & 40.56 \\  & & & \(Q=2000\) & 1 & 37.65 \\  & & & \(Q=5000\) & 1 & 26.05 \\  & & & \(Q=500\) & 1 & 40.26 \\  & & & \(Q=2000\) & 1 & 35.74 \\  & & & \(Q=5000\) & 1 & 25.45 \\  & & & \(Q=500\) & 0.99 & 28.05 \\  & & & \(Q=2000\) & 0.99 & 15.72 \\  & & & \(Q=5000\) & 0.99 & 9.78 \\  & & & \(\epsilon=2000\), \(n=2\) & 1 & 26.21 \\  & & & \(\epsilon=3000\), \(n=3\) & 1 & 26.95 \\  & & & \(\epsilon=4000\), \(n=4\) & 1 & 26.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results of testing amplitude similarity and SNR.