esa-sceva/satcom-chunk-collection huggingface dataset:
# Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
## 6 Experiments and Results
### Ablation Experiments  
#### 6.5.1 Validation of Restart Coefficient \(\alpha\)  
In order to select the best restart coefficient \(\alpha\), the validation experiments of the restart coefficients are carried out in this paper under different \(train\_ratio\) and different vector dimensions \(total\_dimension\), respectively. The \(train\_ratio\) takes values in the range of \(0.1,0.2,\ldots,0.8\), and \(total\_dimension\) takes values in the range of \(16,32,64,128,256,512\); \(\alpha\) takes values in the range of \(\alpha\in\{0.1,0.2,\ldots,0.8\}\).  
For each parameter pair \(train\_ratio,total\_dimension\), the \(\alpha\) is set as different values and the model run for 10 times; the maximum \(p@30\) value is recorded as \(p@30_{max}\) and the average \(p@30\) values as \(p@30_{ave}\). We can obtain nine \(p@30_{max}\) and nine \(p@30_{ave}\). We count the number of times the maximum optimal result and the average optimal result are obtained for each \(\alpha\).  
For example, when the dimension is 64 and the \(train\_ratio\) is 0.1, each \(\alpha\) runs 10 times as a set of results, and will recode the \(p@30_{max}\) and \(p@30_{ave}\) for it. For \(\alpha\) in \(\alpha\in\{0.1,0.2,\ldots,0.8\}\), we will obtain nine \(p@30_{max}\) and nine \(p@30_{ave}\). Then, we will compare the nine \(p@30_{max}\) and nine \(p@30_{ave}\), respectively, for different restart coefficients. If an \(\alpha\) like 0.4 obtains the max \(p@30_{max}\) of \(p@30_{ave}\), it receives 1 point.  
In a similar fashion, for \(6\times 8\) kinds of combinations of dimensions with \(train\_ratio\), \(total\_dimension\), we run 90 experiments for all the nine restart coefficients, and obtain 96 best results for \(p@30_{max}\) and \(p@30_{ave}\). Since there are cases where some restart coefficients obtain the optimal value at the same time, the total score is 111. The statistical results are shown in Table 4; the \(\alpha\) of 0.4 obtains the highest score, so we set the \(\alpha\) as 0.4 for Adap-UIL  
Figure 5: Changes in loss values per iteration or training epoch.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Accuracy** & **MAP** & **p@5** & **p@10** & **p@20** & **p@30** \\ \hline PALE & 0.0450 & 0.0881 & 0.1050 & 0.1500 & 0.2225 & 0.2750 \\ IONE & 0.0350 & 0.0712 & 0.1010 & 0.1375 & 0.1801 & 0.2075 \\ Deeplink & 0.0200 & 0.0230 & 0.0275 & 0.0550 & 0.1100 & 0.1475 \\ DSANE & 0.1386 & 0.1138 & 0.2079 & 0.3036 & 0.3597 & 0.4026 \\ ERW & 0.0594 & 0.0726 & 0.1122 & 0.2046 & 0.2772 & 0.3333 \\ Adap-UIL & **0.1401** & **0.1151** & **0.2214** & **0.3082** & **0.3993** & **0.4462** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results on Facebook and Twitter.
# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 4 Experiments and Results Analysis
### Multimodal Knowledge Graph Construction Experiments  
#### 4.2.1 Multimodal Feature Fusion Effectiveness  
To evaluate the impact of multimodal feature fusion, this section introduces different variables in the experiment to explore the effects of multimodal combinations and fusion strategies, as well as the relative importance of each type of feature. The experiment covers multiple scenarios, from single-modality and dual-modality combinations to full multimodal fusion, comparing various fusion strategies from simple feature concatenation to multimodal transformers. The dataset includes large-scale online course resources spanning text, video, and audio modalities. The evaluation metrics include accuracy, F1 score, and NDCG, which collectively reflect classification accuracy, a balance of precision and recall, and the ranking quality of recommendations.  
Figure 6 shows a comparison of modality fusion effects. The results indicate that multimodal fusion has significant advantages in knowledge graph construction. The accuracy of the single-text modality is 0.75, which provides basic semantic representation but cannot capture the rich information contained in video and audio. The text and video combination performs well, achieving an accuracy of 0.82, as video data enhances semantic expression through visual demonstration and dynamic content, offering additional support for understanding complex concepts. In contrast, the text and audio combination achieves an accuracy of 0.75, as audio only supplements information in terms of emotion and tone, making it slightly less effective than the text and video combination. Full multimodal fusion (text, video, and audio) yields the best performance, with an accuracy of 0.88, indicating the strong complementarity of multimodal information. By integrating all modalities, the system can more comprehensively capture complex semantics and relationships.
# Social Network Sentiment Analysis Using Hybrid Deep Learning Models
## 2 Related Works
### Sentiment Analysis Based on Lexical Resources
Furthermore, the authors in [25] implemented a naive-Bayes classifier to detect the polarity of Spanish tweets, identifying different levels of polarity along with unigrams of lemmas and multiwords based on PoS tag patterns to detect the scope of negation. The accuracy of the system was 66% for four polarity levels and 55% for six polarity levels. Finally, the authors of [26] describe a Transformer-based approach to detect negation in a corpus of Spanish product reviews, achieving accuracies between 80% and 90%, although this system has not been applied in social network contexts or on Twitter. However, other negation strategies are based on annotated corpora with negation in contrast to negation cues [20]. Thus, we can find several corpora related to clinical records, such as the lxaMed-GS corpus, the UHU-HVVR, and the IULA Spanish Clinical Record. Furthermore, the UAM Spanish Treebank corpus was extracted from syntactically annotated newspaper articles. Finally, the most recent Spanish negation corpus, the SFU ReviewSP-NEG18, also considers discontinuous negation markers and is related to products and services reviews. Therefore, it can be concluded that lexical approaches require the use of sentiment word dictionaries combined with strong negation detection strategies. However, in addition, much of these works have not been applied in social network contexts such as Twitter, where language features are more complex (sparsity, lack of context, little concern for grammar usage, informal linguistic style, etc.). Moreover, these approaches also need to integrate strategies to detect other language features, such as sarcasm or irony, which are very typical in social networks.
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 3 Proposed RBF-GAT Framework
### Unsupervised Training  
Since it is difficult for the IRS-assisted LEO satellite system to obtain data labels, we cannot train it by a classic deep learning algorithm with supervised learning techniques. Thus, unsupervised training is adopted for training the network. We define the loss function as:  
\[Loss=-\frac{1}{T}\sum_{i=1}^{T}\sum_{k=1}^{K}w_{k}R_{k} \tag{16}\]where \(T\) represents the total number of training samples in a batch. To generate a training dataset, first, we generate the channel data according to the channel model discussed in Section 2. Then, all users transmit orthogonal pilot signals and additional information (i.e., location, priority, etc.) to the satellite. The LEO satellite can recover the pilots of all users from the received pilots and use it as part of the input of the neural network. The details of the training procedure are summarized in Algorithm 1.  
Note that our model is trained offline, thus, the training process does not increase its computational complexity. During training, we use the stochastic gradient descent method to update the neural network parameters to minimize the loss function, which is equivalent to maximizing the objective function P1.  
```
Input: learning rate \(\alpha\), maximal epoch \(E_{p}\), batch size \(N_{p}\), training samples \(T\),\(I_{ite}\). Output: Optimal network weights parameter \(\mathbf{\phi}\)
1: Randomly initialize network parameter \(\mathbf{\phi}\).
2: Calculate loss \(L_{0}\) according to Equation (16) and initialize \(s=0\).
3:for\(i=1,\cdots,E_{p}\)do
4:for\(j_{ite}=1,\cdots,I_{ite}\)do
5: Initialize the phase shift matrix \(\mathbf{\sigma}\) and generate received pilot according to [19]
6: Randomly select \(T\) samples to compose a batch task.
7: Update the network weights parameter as \(\mathbf{\phi}^{\prime}\) by Adam optimizer.
8:endfor
9: Calculate loss \(L_{i}\) according to Equation (16).
10:if\(L_{i}>L_{i-1}\)do
11: Set \(s=0\), \(\mathbf{\Phi}\leftarrow\mathbf{\phi}^{\prime}\) and save network weights.
12:else
13: Update \(s\gets s+1\) and judge whether the learning rate \(\alpha\) needs to be updated.
14:endif
15:endfor
```  
**Algorithm 1** Training procedure for RBF-GAT.
## Appendix B Calculation Procedure  
It is obvious that the contact radial stress is greater than zero and smaller than \(p_{0}\); i.e., \(0<\sigma_{r(R+lb)}\)\(<p_{0}\). The rigid displacement along the whole bolt is greater than zero and smaller than a large value (such as 10 m), i.e., \(0<u_{b}^{rig}<10\). The reasonable values of \(\sigma_{r(R+lb)}\) and \(u_{b}^{rig}\) can be obtained using two boundary conditions in combination with the bisection method, and the details are described below and plotted in Figure 12.  
Step 1. Preliminary calculations  
1. Input parameters: \(R\), \(p_{0}\), \(\beta\), \(p\), \(E\), \(\mu\), \(\psi\), \(E_{b}\), \(l_{b}\), \(d_{s}\), \(A_{b}\), \(l_{z}\), \(\omega\), \(K_{s}\), \(K_{ep}\), \(c_{s}\), \(\varphi_{s}\), \(\Delta r\), \(c_{p}\), \(c_{r}\), \(\varphi_{p}\), \(\varphi_{r}\), \(\sigma_{c,ip}\), \(\sigma_{c,i,r}\), \(m_{b,pr}\), \(m_{b,r}\), \(sp_{s}\), \(\varphi_{r}\), \(a_{p}\), and \(a_{r}\).
2. Calculate \(u_{r}^{ini}\) by replacing \(p\) in the equations in Appendix A with \(\beta p_{0}\).
3. Assume an initial value of \(\sigma_{r(R+lb)}=(\sigma_{r1(R+lb)}+\sigma_{r2(R+lb)})/2\), where \(\sigma_{r(R+lb)}=0\) and \(\sigma_{r2(R+lb)}=p_{0}\).
4. Calculate \(u_{r(R+lb)}\) and \(\sigma_{\theta(R+lb)}\) by replacing \(p\) and \(R\) in the equations in Appendix A with \(\sigma_{r(R+lb)}\) and \(R+l_{b}\), respectively.
5. Assume an initial value of \(u_{b}^{rig}\)= (\(u_{b1}^{rig}+u_{b2}^{rig}\))/2, where \(u_{b1}^{rig}=0\) and\(u_{b2}^{rig}\)= 10.
6. Calculate \(\tau_{s(0)}\) and \(F_{n(0)}\) using Equations (32) and (33), respectively.  
Step 2. The sequence of calculation for each annulus in a bolted region  
1. Verify the elastoplastic state of the rock mass in the \(i\)th annulus using Equation (4) (M-C rock mass) or Equation (5) (H-B rock mass).
2. Calculate \(\sigma_{r(i)}\) using Equation (24) (elastic state), or Equation (27) (plastic state, M-C rock mass) or Equation (29) (plastic state, H-B rock mass).
3. Calculate \(\sigma_{\theta(i)}\) using Equation (25) (elastic state), or Equation (28) (plastic state, M-C rock mass) or Equation (30) (plastic state, H-B rock mass).
4. Calculate \(u_{r(i)}\) using Equation (26) (elastic state) or Equation (31) (plastic state).
5. Calculate \(\tau_{b(i)}\) and \(F_{n(i)}\) using Equations (32) and (33), respectively.  
Repeat (1)-(5) until \(i=n\).  
Step 3. Verify the boundary conditions  
(1) Verify the boundary condition at the near end of the bolt (Equation (18)). If this condition is satisfied, verify the next boundary condition; otherwise, the bisection method is employed to determine another \(u_{b}^{rig}\) until Equation (18) is satisfied:  
\[F_{n(n)}\ =\ K_{ep}\Delta u_{s(n)}. \tag{19}\]  
(2) Verify the boundary condition at the tunnel periphery (Equation (19)). If this condition is satisfied, the values of \(u_{b}^{rig}\) and \(\sigma_{r(R+lb)}\) are reasonable; otherwise, the bisection method is employed to determine another \(\sigma_{r(R+lb)}\) until Equation (19) is satisfied:  
\[\sigma_{r(n)}\ =\ p_{ep}+p\ =\ \frac{K_{ep}\Delta u_{s(n)}}{l_{z}R\omega}+p. \tag{20}\]
# Construction of a Numerical Model for Flow Flash Evaporation with Non-Condensable Gas
## 3 Results and Discussion  
### Model Verification  
The results obtained from the numerical model constructed in this study were compared with experimental data [39], and the release rates (\(c_{\mathrm{s}}\) and \(c_{\mathrm{id}}\)) were determined based on the experimental data. The experimental test shown in Figure 4a illustrates a cylindrical vessel with an inner diameter of 0.045 m, a height of 0.2 m, and a water height of 0.15 m. The experimental operation used dissolved saturated CO2 water from 5.4 bar decreasing to three pressure cases (5.31, 5.23, and 5.08 bar). The experimental release rate at 100 s was selected and compared with the numerical results, as shown in Figure 4b, and the maximum error was less than \(\pm\)10%. The release coefficients of supersaturation CO2 (\(c_{\mathrm{s}}\)) and interface diffusion CO2 (\(c_{\mathrm{id}}\)) were 8.8 \(\times\) 10\({}^{-6}\) (1/s) and 0.01 (1/m\({}^{2}\cdot\)s), respectively.  
In addition, the flash evaporation process without the presence of non-condensable gases was also compared with the experimental results [40]. The flash chamber for the flow flash evaporation used in the experimental articles compared had an internal flash space of 480 mm in height, 100 mm in length, and 100 mm in width. Five sets of numerical simulation experiments with different inlet temperatures were carried out under the conditions of an initial water-level height of 120 mm and flow rate of 800 L/h. The comparison results are shown in Figure 5 below, and the steam generation rate obtained from the numerical simulation shows a good accuracy.  
Figure 4: Validation of the numerical model with experimental CO2 release rate data: (**a**) schematic diagram of the experimental model Re-printed with permission from Ref. [39]. 2014, Elsevier Inc.; (**b**) comparison of the experimental and numerical results.
## 4 Simulation Verification and Result Analysis
### Simulation Experiment Platform and Key Parameters Setting  
Based on the overall structure of PMSM sensorless drive system in Section 2, the structure diagram is built as shown in Figure 11.  
Moreover, the simulation model of sensorless drive system is built on the MATLAB/Simulink simulation platform. Meanwhile, the VPDPI regulator and the conventional PI regulator are tested under the same circuit parameters configuration, respectively. Table 3 present the parameters configuration of controller. The parameters configuration of PMSM are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Proportionality Coefficient** & **Magnitude Margin** & **Phase Margin** & **Stability** \\ \(K_{i}\) & \(G_{m}\) & \(P_{m}\) & **(Yes/No)** \\ \hline
0 & 6.94(at 379 rad/s) & 87.1(at 85.7 rad/s) & No \\
0.2 & 6.93(at 379 rad/s) & 86.5(at 85.7 rad/s) & Yes \\
0.4 & 6.92(at 379 rad/s) & 85.8(at 85.7 rad/s) & Yes \\
0.6 & 6.91(at 378 rad/s) & 85.1(at 85.7 rad/s) & Yes \\
0.8 & 6.9(at 378 rad/s) & 84.5(at 85.8 rad/s) & Yes \\
1 & 6.89(at 378 rad/s) & 83.8(at 85.5 rad/s) & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: The characteristics with different integral coefficients.  
Figure 11: The structure diagram of sensorless PMSM drive system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Regulator Type** & **Parameter** & **Value** \\ \hline PI & Proportionality coefficient \(K_{p}\) & 0.2 \\  & Integral coefficient \(K_{i}\) & 1 \\ \hline VPDPI & Basic proportionality coefficient \(K_{p1}\) & 0.2 \\  & Changing proportionality coefficient \(K_{p2}\) & 0.4 \\  & Integral coefficient \(K_{i1}\) & 1 \\  & Proportional switching threshold \(c\) & 50 \\  & Compensate switching threshold \(\varphi\) & 500 \\  & Compensation coefficient \(\gamma\) & \(-14\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The setting of regulators parameters.
## 3 Materials and Methods
### PLS-ECOC Model Optimization  
ECOC matrix, \(\mathbf{M}\), has a random origin, as explained in Section 3.3, and is not related to \(\mathbf{X}_{TR}\), but with \(\mathbf{M}\), the response \(\mathbf{Y}_{TR}\) is built. Therefore, the PLS model that relates \(\mathbf{X}_{TR}\) and \(\mathbf{Y}_{TR}\) depends on \(\mathbf{M}\). To select the best \(\mathbf{M}\) matrix, the steps from Section 3.3 to Section 3.6 were repeated 280 times, aiming to obtain the best \(\mathbf{S}\) matrix. The PLS model associated with it is called \(PLS_{OP}\) and is the one that will be used in Section 3.8 for the evaluation with the test subset, \(\mathbf{X}_{TS}\). This process is illustrated in Figure 1 as the left grey rectangle.
## 1 Introduction  
Drought is the dryness condition of the environment that creates ecological stress due to lack of precipitation and shortages in water supply for plant growth. The Palmer Drought Severity Index (PDS) [1] is one of the most effective, well-acknowledged, and widely used drought severity index that particularly determines the long-term drought conditions over the forest and other terrestrial ecosystems.  
The PDSI is based on the demand and supply concept of the water balance model, taking consideration not only precipitation deficit but also includes local temperature and soil moisture anomalies to assess relative dryness [2]. Several studies were already conducted on the application of the PDSI; however, the sensitivity of the PDSI has not been explored yet based on productivity (i.e., Gross Primary Productivity or GPP), biophysical parameters (i.e., biomass\(-\)Leaf Area Index or LAI and Enhanced Vegetation Index or EVI and greenness content\(-\)Normalized Difference Vegetation Index or NDVI), and absorbed solar radiation by the plants (i.e., fraction of Absorbed Solar Radiation or fAPAR) over a humid-subtropical forest ecosystem. It was also observed that most of the existing literatures on drought severity preferred to use productivity, net photosynthesis (Gross Primary Productivity and Net Primary Productivity) [3; 4; 5; 6; 7; 8; 9; 10; 11; 12], or NDVI or near-infrared (NIR) based vegetation indices [13; 14; 15; 16; 17; 18; 19] for long-term drought condition assessments. Hence, a true sensitivity analysis of all important vegetation parameters like GPP, NDVI, EVI, LAI, and fAPAR was necessary to find out the most complimentary and effective PDSI indicator that shows the actual water deficit-induced stress conditions over the vegetative areas of a subtropical humid ecosystem. In this study, the sensitivity of PDSI was analyzed through uncertainty and error estimation modeling from long-term (2015-2019) MODIS GPP and other reflectance products (NDVL, EVI, and LAI) and fAPAR using the open-source cloud-computing platform Google Earth Engine (GEE).
# Synthetic Data and Computer-Vision-Based Automated Quality Inspection System for Reused Scaffolding
## 3 Materials and Methods  
In Korea, scaffolding quality certification is regulated by KOSHA, which determined three quality classes: A class--for parts without deformation that can be used without repairing, B class--for parts that can be used after repairing, and C class--for parts that should be recycled. For each quality class, KOSHA Guide has acceptable defect sizes and recommended test methods. In addition, the Korea Temporary Association has established procedures and methods stipulated in Article 27 of the Industrial Standardization Act to prevent industrial accidents the defective temporary equipment reusing case.  
Most of these methods are applicable only for visual inspection by a human.  
The current study proposes a method based on combining 2D image segmentation results and 3D point cloud comparison results to determine the final quality class. Figure 1 illustrates the main steps of the proposed approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Research** & **Data Type** & **Rust** & **Deformation** & **Crack** & **Limitation** \\ \hline
1 & [PERSON] (2014) [17] & Point cloud & X & O & X & Does not detect rust and crack \\
2 & [PERSON] (2017) [15] & Point cloud & X & O & O & Does not detect rust \\
3 & [PERSON] (2018) [18] & Point cloud & X & O & O & Does not detect rust \\
4 & [PERSON] (2017) [19] & Point cloud & X & O & O & Does not detect rust \\
5 & [PERSON] (2018) [20] & Image & O & X & O & Does not detect deformation \\  & & & & & & and defect size \\
6 & [PERSON] (2021) [21] & Image & X & X & O & Does not detect deformation \\  & & & & & & and rust \\
7 & [PERSON] (2020) [22] & Video & X & O & O & Does not detect defects size \\  & & & & & & and rust \\
8 & [PERSON] (2020) [23] & Image & X & O & O & Does not detect rust and \\
9 & The proposed & Image +Point & & & & & defect size \\  & methods & cloud & O & O & O & \\ \hline \end{tabular}
\end{table}
Table 1: A comparison between previous studies and the current study.  
Figure 1: Quality classification flow chart.  
Firstly, a tested part is scanned. The scanned data are analyzed using artificial intelligence, the sizes and types of deformations are calculated, and a quality class is determined based on size. Finally, all results are visualized in a web interface. The quality class B is divided into three new classes, depending on the repairing works, which is useful for the end-user when calculating the number of works. Furthermore, users can weight quality classes, shifting boundaries in one direction or another depending on the organization's requirements so that each user can customize a classification process.  
The deep learning part of the proposed method includes three main steps: data generation, data preparation, and model building. The model includes model tuning, training, and testing parts. The main objectives of the segmentation part are the detection of trusted metal areas, surface cracks, and deformation.