esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PLS-DA Analysis  
However, PLS-DA was also applied to classify the flours, as shown in Figure 7a-c indicate that some samples overlap with other samples of different types, and this behavior made it more difficult to identify which variables allow for differentiation among the samples. Figure 7d shows the score plot for Factor 1 and Factor 2, explaining 85% of the total variance, and it suggests that soybean flour is different from the other samples; this is explained in Figure 2, in which the soybean sample displays two bands in the 4500-5000 cm\({}^{-1}\) region. Meanwhile, the other samples show one band in this region. Oatmeal flour displays a remarkable internal variation between them; meanwhile, broad bean flour shows a slight internal variation. Broad bean and algarrobo flour are similar due to the fact that the broad bean and algarrobo clusters are closer; this situation is evidenced in Figure 2. Additionally, the value of the loading plot for LV1 in each PLS-DA model is low (as indicates Figure 8), which suggests that the variables are not relevant enough to explain the associated LV.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Color**} \\ \cline{3-6}
**Item** & **Sample** & **L** & **a** & **b** & **WI** \\ \hline
1 & Algarrobo (H2) & 68.92 (0.38) & 7.98 (0.17) & 33.87 (0.46) & 53.34 \\
2 & Broad bean (H3) & 82.26 (0.43) & 1.15 (0.09) & 20.06 (0.30) & 73.20 \\
3 & Yellow maca (H4) & 87.35 (0.31) & 1.93 (0.04) & 23.18 (0.23) & 73.52 \\
4 & Soybean (H1) & 86.99 (0.08) & 1.41 (0.08) & 23.56 (2.30) & 73.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of Hunter scale and WI for algarrobo, broad bean, maca, and soybean. Data taken and adapted from reference [55].  
Figure 6: Score plots of PCA model for pre-treatment with SNV + detrending + 1 st derivative with 19 smoothing points: (**a**) PC-2 vs. PC-3 score plots, and (**b**) PC-2 vs. PC-4 score plots.  
Figure 7: _Cont._  
Figure 8: _Cont._  
Figure 7: PLS-DA score plot (LV1 vs. LV2) in the analysis of the spectra (12,000–4000 cm\({}^{-1}\) range), for (**a**) raw data, (**b**) SNV, (**c**) SNV + detrending (2-order), and (**d**) SNV + detrending (2-order) + Staviský–Golay first-derivative with 19 smoothing points. LV: Latent Variable.  
The loading plot for Factor 1 in Figure 8c shows 10 relevant variables (wavenumbers) in the 10,400-4397 cm\({}^{-1}\) region that explain 63% of the data retained by Factor 1, and this indicates that 10 variables could explain the classification and differentiation between samples. Meanwhile, the loading plot for PC-1 in Figure 5 suggests that one relevant variable at 4644 cm\({}^{-1}\) describes the variation between the samples. As mentioned above, Figure 4c shows better separation of the groups than Figure 7d, and this allowed it to describe the variables that explain the differences between the samples most accurately.
## 2 Related Work
### Review-Based Recommendations  
Due to the sparsity of data, collaborative filtering algorithms have difficulty providing accurate recommendation services. Researchers have proposed some methods to alleviate the problem of data sparsity [14; 15; 16], and recommendation algorithms based on reviews are one of them. The early work [17; 18] typically incorporated review information using topic modeling. However, this method cannot effectively capture the deep features in review text.  
Recently, with the rapid development of deep learning, an increasing number of researchers are introducing deep learning to process review information and provide personalized recommendations to users [19; 20; 21]. For example, [PERSON] et al. proposed the DeepCoNN model, which first uses two CNNs to extract the user and item review features from the user and item review documents, respectively. The user and item review features are stitched into a vector, and finally, the rating prediction is performed using FM. [PERSON] et al. pointed out that obtaining the target reviews in practice is difficult, and the DeepCoNN model cannot solve this problem. Therefore, they proposed the TransNets model. The TransNets model consists of a source network and a target network. The source network learns to predict the features from the user and item review documents. The target network takes the target reviews as input, first learning the representation of predictive features, and then guiding the source network to learn predictive features. [PERSON] et al. proposed the D-Attn model based on the DeepCoNN, which introduced local and global attention mechanisms to find more reviews and words related to the rating in user (item) reviews and increase their corresponding weights, thereby, improving the recommendation performance. [PERSON] et al. subsequently proposed the NARRE model based on the DeepCoNN, which introduced a review attention mechanism to remove reviews unrelated to the predicted rating and improve the recommendation performance. Since attention mechanisms are implemented differently, [PERSON] et al. proposed the CARL [22] model, which learns the critical information of a review in the form of an attention matrix and fuses the interaction features by linear interpolation. [PERSON] et al. proposed the MPCN model, which selected several relevant reviews first through a multi-pointer scheme and a joint attention mechanism, and then constructed a user-item matrix. They retained the most relevant part of the rating through a hierarchical filtering approach, thereby, improving the recommendation performance. [PERSON] et al. proposed the MPCAR model based on MPCN to suppress the selection of irrelevant reviews for the target item by introducing the ID information to enrich the feature representation. However, the aforementioned studies focused on one of the two issues in Figure 2 and did not comprehensively investigate both issues.
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 3 Experimental Confirmation of the Modelling Method  
Credibility of this modelling method was confirmed with experimental tests. Specifically, [PERSON] [25] tested the axial performance of rockbolts with a diameter of 28 mm. The rockbolt was installed in the borehole with an installed length of 6.5 m. The mortar with a type of M30 was used to bond rockbolts with surrounding rock mass. Load cells and displacement transducers were installed to monitor the tensile force and deformation. Then, the monitored tensile force and deformation were used to reflect the axial performance of rockbolts.  
To simulate this loading process, the analytical modelling method was used. For the b/g face, the strength, the slippage at the ultimate load, and the residual strength were 1.65 MPa, 2.8 mm, and 0.3 MPa. There was an agreement between the experimental data and modelling results, which can be referred to Figure 11 in [PERSON] et al. [24]. The analytical modelling method was robust in depicting the axial performance of rockbolts.
# Enhancing Real-Time Video Streaming Quality via MPT-GRE Multipath Network
## 5 Waterloo Streaming QoE Database  
The Waterloo Streaming Quality of Experience (QoE) Database III (SQoE-III) [21], comprising 20 pristine, high-quality videos with a resolution of 1920 \(\times\) 1080, was selected to encompass a wide range of content, including humans, plants, natural scenes, architecture, screen content, and computer-generated scenes. Among these, RushHour, TallBuildings, and TrafficAndBuilding were sourced from the SJTU 4K video dataset [22], with each video lasting 10 s. Detailed specifications for these videos are provided in Table 1. Additionally, spatial information (SI) and temporal information (TI) offer an approximate measure of video complexity. The video sequences exhibit significant variation in spatio-temporal complexity and span a broad range within the SI-TI space. Each video was encoded with an x264 encoder into eleven different representations and bitrates (235, 275, 560, 750, 1050, 1750, 2350, 3000, 4300, 5800, and 7000 kbps). The videos were processed using a set of algorithms and different conversions, considering 13 different network conditions to evaluate video streaming performance.  
Additionally, subjective assessments conducted by 34 individuals provide valuable insights into user perceptions of streaming quality. SQoE-III is notable for its large size, variety, and comprehensive evaluation of the streaming experience, making it a valuable resource for researchers and practitioners. Figure 3 shows screenshots of the original video sources. It is important to note that the SQoE-III data were created using a range of source resolutions and distortion methods to ensure a natural representation of real-world strategies. We also selected some videos from external 4K sources outside the database [23].
## 3 Results and Discussion
### Removal of Basic Blue Properties
#### 3.2.4 Effect of the pH of the Basic Blue-41 Solution  
The pH value plays an important factor during the removal process. It does not alter the adsorbent site dissociation, however, it alters the chemistry and the structure of the dye. The latter could be achieved by adding HCl or NaOH solutions to the dye solution [67; 68].  
Figure 7 indicates that the removal efficiency of Med-WB was improved when the pH of the solution was increased from 2 to 3.05. Further increase of pH did not significantly change the removal percentage and it was about 99%. In the case of Jed-WB material, the pH of the BB-41 solution modified the removal percentage, however, in a certain extent, it was raised from 13% to 37% at a pH value of 9.3. When the pH value was greater than 10, the BB-41 was unstable and, hence, no further investigation was conducted [33; 68]. The high percentage obtained at higher pH values indicated the increase of the negative charge density of the waste brick, and thus, the electrostatic interaction between these charges and the positively charged dyes (see paragraph 3.2.5). Another explanation could be associated with the dimerization of basic blue at higher concentrations [69] or to the reaction of BB-41 with the NaOH solution. To confirm that, a solution of NaOH was added to a solution of BB-41 at a pH higher than 9, without adding waste brick. The color of the solution was monitored over time and showed a change in its intensity over time until it became clear after two days. Similar observations were reported for  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \(\Delta\)H\({}^{\circ}\) & \(\Delta\)S\({}^{\circ}\) & \multicolumn{3}{c}{\(\Delta\)G\({}^{\circ}\) (kJ\(\cdot\)mol\({}^{-1}\))} \\  & **(kJ\(\cdot\)mol\({}^{-1}\))** & **(kJ\(\cdot\)mol\({}^{-1}\)-K)** & \multicolumn{3}{c}{\(\Delta\)G\({}^{\circ}\) (kJ\(\cdot\)mol\({}^{-1}\))} \\ \hline \multirow{2}{*}{Med-WB} & \multirow{2}{*}{135.12} & \multirow{2}{*}{0.45} & 303 K & 323 K & 333 K \\ \cline{3-5}  & & & \(-\)1.49 & \(-\)3.59 & \(-\)17.05 \\ \cline{3-5}  & & & \(-\)0.74 & \(-\)1.36 & \(-\)4.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermodynamics of BB-41 removal using Med-WB and Jed-WB, respectively.  
the methylene blue dye solution [39]. The lower removal efficiency at acidic values could be associated with the excess of proton ions in the solution and, as a consequence, could compete with the basic dye cations on the removal sites of waste brick materials [30]. This assumption seemed to be valid in the case of Jed-WB. However, in the case of Med-WB, the highest removal of BB-41 was achieved at a pH value of 3, indicating other effects could contribute to this behavior.
# Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
## 2 Materials and Methods
### Mix Design
#### 2.3.2 Selection of Response Variables  
Three critical performance indicators were selected as response variables to comprehensively evaluate pavement concrete performance.  
R\({}_{\ell}\)/R\({}_{\mathrm{c}}\) was selected as a primary indicator of concrete toughness and crack resistance capability. Unlike individual strength measurements, this ratio provides a more comprehensive assessment of the material's structural behavior under the complex loading conditions experienced in pavement applications. As demonstrated by [PERSON] (2016), the R\({}_{\text{f}}\)/R\({}_{\text{c}}\) is particularly significant for predicting long-term performance in fiber-reinforced concrete pavements subjected to flexural fatigue loading.  
Dry shrinkage was identified as a critical durability parameter, particularly relevant in the Gobi Desert environment where extreme temperature variations and low humidity significantly increase cracking potential. [PERSON] (2016) identified shrinkage as one of the most decisive factors affecting long-term pavement durability in arid regions. This parameter directly measures the concrete's volumetric stability and, consequently, its resistance to environmentally induced cracking.  
Porosity was selected to evaluate the microstructural properties that fundamentally govern transport mechanisms and long-term durability. [PERSON] (2016) established that pore structure characteristics, particularly pore size distribution and connectivity, directly influence both the mechanical performance and durability aspects of pavement concrete. This parameter provides essential insights into the material's resistance to aggressive environmental factors prevalent in arid regions.  
A Box-Behnken experimental design under the RSM framework was employed to systematically analyze both the individual effects and interactions among these variables. A total of seventeen experimental groups (including five centerpoint replicates) were constructed to ensure statistical robustness and model reliability. The comprehensive experimental design parameters are presented in Table 3.
## 3 Hydro-Mechanical Coupled Model for Fractured Rock Mass
### Fracture Network Generation for Rock Mass  
In order to understand the flow behavior of a jointed rock mass, the fractured network should be built first, as it relates to the statistic distribution of fractures. In general, the characteristic fracture geometry parameters, including the inclination angle, spacing distribution, and trace length, follow certain statistical distributions in space. The process of generating the network and numerical mesh can be stated as follows:  
(1) Assuming that the crack dip angle and length follow a normal distribution law, \(\theta\) represents the geometric parameters of the fracture angle and length, and the probability density function \(\text{f}(\theta)\) has the following form:  
\[\text{f}(\theta)=\frac{1}{\sqrt{2\pi\sigma^{2}}}\text{exp}\left[-\frac{1}{2} \left(\frac{\theta-\upmu}{\sigma^{2}}\right)^{2}\right],(-\infty<\theta<+\infty) \tag{18}\]  
where \(\upmu\) and \(\sigma\) are the expected value and standard variance of the parameters of the fracture network geometry, respectively.  
(2) The geometric parameters of a single fracture applied in the DFN, such as the trace length \(\text{L}(\text{i})\), the dip angle \(\theta(\text{i})\), and the coordinates of the start point \((\text{x}_{0}(\text{i}),\text{y}_{0}(\text{i}))\) are elected as the most significant input data. The coordinates of the end points are calculated with \(\text{L}(\text{i})\), \(\theta(\text{i})\), and \((\text{x}_{0}(\text{i}),\text{y}_{0}(\text{i}))\).  
Figure 8: (**a**) Coupled process of the model; (**b**) geometric model.  
(3) By copying a single fracture that has been established and shifting it by the distance of the desired fracture width, a fracture with thickness can be obtained, and the fracture aperture is the shifted distance.  
(4) Several fractures are interwoven into the fracture network, but there are some isolated fractures and dead ends in the fracture network. Therefore, the isolated fractures and dead ends in the fracture network are removed to form a fully interconnected fracture network. According to the linear equation for a fracture element, all fracture elements can be selected and set up.  
For generating the mesh of the random discrete fracture network model through ABAQUS, a triangular element is more suitable for regional division when multiple fractures are being considered. After mesh generation, matrix elements and fracture elements are distinguished by the geometric relationship between the linear equation, where the fractures are located, and the element nodes.
## 4 Experiments and Result Analyses
### Experimental Results
#### 4.4.4 Comparison of Different Sample Sizes  
The original automobile wheel hub defect dataset contains 348 image samples, and convolutional neural network often requires a large sample size. To explore the influence of different sample sizes on the performance of the defect detection network, this section increases sample size by data enhancement, such as rotating, flipping and cropping. After data enhancement, sample sizes are expanded to 3 times, 6 times and 9 times. Then, experiments were conducted on the wheel defect datasets with sample sizes of 348, 1044, 2088, and 3132.  
As shown in Figure 17, when sample size increased from 348 to 3132, F1 score increased by 2.54% and IoU increased by 3.9%, indicating that the increase in sample size can improve the detection performance of the defect detection network. Furthermore, when sample size is small, the defect detection network still has a high F1 score and IoU, indicating that the defect detection network still has accurate detection performance in the small sample size.  
When sample size increased from 2088 to 3132, the improvement of detection performance was small, but the training time was greatly increased because sample size was increased. When sample size was 2088, a balance between detection performance and training time was achieved.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\{\lambda_{final}\), \(\lambda_{body}\), \(\lambda_{edge}\}\) & **F1** & **IoU (\%)** \\ \hline \(\{1,1,1\}\) & 0.833 & 71.5 \\ \(\{1,1,0.5\}\) & 0.841 & 72.6 \\ \(\{1,1,0.1\}\) & **0.845** & **73.2** \\ \(\{1,1,0.05\}\) & 0.826 & 70.5 \\ \(\{1,1,0.01\}\) & 0.823 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influence of different loss weight settings on F1 score and IoU on the wheel defect dataset.
###### Abstract  
The occurrence of cross-beam interference in the received signal is one of the main problems that limit the possibilities of massive multiple-input-multiple-output technology (massive-MIMO) in fifth-generation (5G) systems. Thus, the evaluation of the level of this interference is one of the most important procedures in the spatial planning of currently wireless networks. We propose a novel modification of simple antenna pattern models, which is based only on changing the directivity of real antenna system patterns. This approach is independent of the antenna system's type, structure, and analytical description. Based on the developed modification, the original methodology for assessing the signal-to-interference ratio (SIR) from adjacent beams of a common antenna system is presented. The change in the radiation direction and the accompanying change in the complex shape and parameters of the real antenna beam pattern is one of the problems that significantly hinders the evaluation of the analyzed interference. Hence, in the presented methodology, we propose using our modification. In this case, the modification is reduced to a proportional change in the directivity concerning the real antenna system, which results from a change in the beam direction. The simulation studies used a multi-ellipsoidal propagation model and a real massive MIMO antenna pattern description from 3 GPP. For the SIR error analysis, the 3 GPP pattern is used as a reference. The simulation results show that modifying simple antenna pattern models allows us to obtain an SIR error of no more than 3 dB and 0.1 dB under line-of-sight (LOS) and non-LOS conditions, respectively.  
wireless communications; 5G; massive MIMO; antenna beam pattern; inter-beam interference; signal-to-interference ratio (SIR); multi-ellipsoidal propagation model (MPM) +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 Results
### Estimating the Proportion of Vegetation Cover  
Based on the RVI and NDVI, the proportion of vegetation cover was estimated using the Equations (3)-(6). The results from this application are shown in Figure 7. As mentioned earlier, the proportion of vegetation was estimated using two different models per vegetation index. Especially for the application of Equations (3) and (4), vegetated areas and non-vegetated areas need to be identified. The areas were selected using vector polygons from the OpenStreetMap service, and elsewhere (Google Earth environment). Green-colour polygons in Figure 7 show areas that were selected as \"vegetated\", while yellow-colour polygons correspond to the \"non-vegetated\" areas. Figure 7a-c display the OpenStreetMap vegetated areas in the Google Earth environment, while Figure 7d-f show the non-vegetated areas. For estimating the proportion of vegetation cover from Equations (5) and (6), image image histogram statistics were used (max and min values). Based on the statistics, the RVI values for the vegetated and non-vegetated areas were 0.81 and 0.16, respectively, while the NDVI values for the vegetated and non-vegetated areas were estimated to be 0.80 and 0.27, respectively.  
Figure 6: Monthly Radar Vegetation Index—RVI—(left) and Normalized Difference Vegetation Index—NDVI—results (right) (**a**: May 2019, **b**: June 2019, **c**: July 2019, **d**: Aug. 2019, **e**: Sept. 2019, **f**: Oct. 2019, **g**: Nov. 2019, **h**: Dec. 2019, **i**: Jan. 2020, **j**: Feb. 2020, **k**: March 2020 and I: April 2020).  
Figure 8a,b show the proportion of vegetation cover after the RVI (Equations (3) and (5) respectively), while Figure 8c,d show the results generated from the NDVI using Equation (4) and Equation (6), respectively. It is evident from Figure 8 that the proportion of vegetation per index using image statistics or OpenStreetMap data is quite similar. However, the results vary when we use optical and radar datasets. The general deviation of the results was expected, as we also saw this in the previous findings (Figures 4-6). The RVI generates a noisier outcome (Figure 8a-b) compared to the results obtained from the NDVI (Figure 8c-d). Further elaboration of these results is discussed in the following section.  
Figure 7: Ground truth data as polygons extracted from the OpenStreetMap service used as “vegetated” and “non-vegetated” areas (top). (**a–c**) shows a high-resolution optical preview of the vegetated areas at Google Earth, while (**d–f**) the non-vegetated areas.