esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art
## 4 Conclusions  
This study focuses on the analysis of public opinion on the Weibo topic of AI Art. Commencing with text sentiment analysis and text clustering analysis, a deep learning-driven framework was developed to examine users' nuanced opinions on the topic. For sentiment analysis, the LERT_BiSRU++ sentiment analysis model was developed for the binary classification of Weibo posts related to the topic. The experimental results showed that the proposed model achieved better results in terms of both classification metrics and computational efficiency. To delve deeper into the nuanced opinions expressed by users carrying different sentiments, a text clustering analysis based on an autoencoder network, K-means, and C-TF-IDF keyword extraction was conducted to produce keyword lists representing the detailed opinions expressed by users. The results of the clustering analysis offer valuable data on public opinion that can be used by relevant authorities in the establishment of laws and regulations, along with the healthy development of AI Art.  
The work presented in this paper offers an analysis that provides a framework for further research. Firstly, the LERT pre-training model was employed to enhance text representation within the intricate semantic context of Weibo. In topics like AI Art, where timeliness plays a crucial role, novel internet terms and expressions are consistently emerging, and are difficult for pre-trained models to capture. Collecting and integrating this external knowledge into pre-training models has the potential to further improve the performance of sentiment analysis models. Secondly, the K-means clustering algorithm was utilized for clustering analysis, and the results demonstrated the presence of hierarchical relationships among various topics. These hierarchical relationships can be more effectively extracted through clustering algorithms like DBSCAN and spectral clustering. Finally, the emergence of AI Art technology has ignited significant discussions worldwide. Conducting a multi-language analysis can enhance the comprehension of the public's attitudes towards AI Art, offering a more comprehensive basis for the sustainable development of AI Art technology.  
Conceptualization, W.W.; Software, W.W.; Data curation, R.H.; Writing--original draft, W.W.; Writing--review and editing, W.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Influence Analysis by Simulation
### In Situ Stress Simulation
#### 4.2.1 Vertical Stress  
The distribution characteristics of vertical stress obtained from the numerical simulation are presented in Figure 7. In Model 1, a distinct vertical stress concentration phenomenon emerged in the surrounding rock adjacent to the goaf and around the arch angle sides of the lower igneous layer. As the coal face advanced, both the peak value of the vertical stress and the advanced influence range grew steadily. Stress concentration was observed in the two igneous layers. Specifically, the maximum vertical stress reached 36.4 Mpa, and the advanced influence range extended up to 32.4 m.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lithology** & **Bulk Density KN/m3** & **Elastic Modulus** & **Poisson’s** & **Compressive Strength** & **Cohesion** & **Internal Friction Angle** \\  & **GPa** & **Ratio** & **MPa** & **Cohesion** & \(\circ\) \\ \hline mudstone & 25.7 & 1.8 & 0.30 & 26.8 & 0.8 & 20 \\ siltstone & 26.37 & 7.3 & 0.25 & 60.5 & 2.2 & 23 \\ diorite & 27.9 & 21.2 & 0.20 & 144.2 & 12.6 & 43 \\ sandstone & 26.6 & 8.5 & 0.25 & 75.9 & 3.0 & 25 \\ coal & 14.1 & 2.5 & 0.35 & 20.0 & 0.5 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of overburden.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Survey Line Number** & **Distance from Coal** & **Lithology** & \\  & **Seam** & **Model 1** & **Model 2** \\ \hline NO 1 & 20 m & fine sandstone & fine sandstone \\ NO 2 & 50 m & mudstone & mudstone \\ NO 3 & 100 m & siltstone & siltstone \\ NO 4 & 150 m & diorite (lower igneous layer) & mudstone \\ NO 5 & 200 m & diorite (upper igneous layer) & mudstone \\ \hline \hline \end{tabular}
\end{table}
Table 2: Horizons of survey line location.  
In Model 2, an evident vertical stress concentration was present in the surrounding rock close to the goal. As the coal face progressed, both the peak value of the vertical pressure and the range of its advanced influence kept increasing. In this case, the maximum vertical stress at the coal face reached 35.6 MPa, and the maximum advanced influence range measured 29.6 m. Therefore, compared to Model 2, in Model 1, apart from the goal, vertical stress concentration also took place in the igneous layer, and it exhibited a more extensive advanced influence range.  
Figure 6: Geological models for numerical simulation.
## 1 Introduction
The latter is gaining researchers' attention due to the possibility of increasing the reuse of SS as an alternative, environmentally friendly application [11]. The number of publications is still scarce, and thus, it is difficult to establish concrete conclusions about the real influence of SS and activation parameters on the final properties of AAMs. However, some significant findings were reported, as follows. [PERSON] et al. [45] found that the calcium silicates (C\({}_{2}\)S and C\({}_{3}\)S) present in the SS composition dissolve under alkaline conditions to form reaction products. [PERSON] et al. [42], [PERSON] et al. [43] and [PERSON] et al. [44] concluded in their studies that significant amounts of C-S-H-type gel and CaCO\({}_{3}\) are formed as hydration products, and the best SiO\({}_{2}\)/Na\({}_{2}\)O molar ratio in the activator was between 2.0-2.3. [PERSON] et al. [43] only evaluated the effect of the silica modulus variation using the same Na\({}_{2}\)O concentration (only 4%) and [PERSON] et al. [44] evaluated the carbon storage capacity of BOF slag using CO\({}_{2}\) curing by assessing mortars and not pastes. They also used different activation parameters (molar ratio, activator type) and a different curing regime, and the focus was not on the influence of the activation parameters. Additionally, contradictory results were published, confirming the need for a more thorough investigation of the alkaline activation of SS [11]. This study aimed to tackle some of the issues stated in the last paragraphs, i.e., the need for alternative binders, the necessity for further reuse of steel slag and the scarce number of investigations on steel slag activation. It investigated the influence of activation parameters on the mechanical and microstructural properties to validate the use of BOF slag as a single precursor in AAM. Six different AAM pastes were produced by changing the silica modulus and the sodium concentration. The fresh properties were evaluated using the heat of hydration and the mechanical properties were evaluated using compressive strength. The pastes were also submitted to XRD, TGA and SEM to assess the microstructure changes, i.e., the mineral composition, crystalline and solid phase variation, pore structure and variation in the reaction products.
# _Photonics_
## 4 Experimental Results  
For the experimental studies, the allegedly proposed polarization converter was manufactured by the use of a PCB prototyping machine as illustrated in Figure 8a. It consisted of a 25 \(\times\) 25 unit cell on a 160 mm \(\times\) 160 mm substrate. The reflected field components were measured by a vector network analyzer (VNA) which can perform between 10 MHz and 43.5 GHz. During the measurement process, two types of horn antenna were employed considering the working frequency band of this study. One of them had an operating frequency band between 3 GHz and 18 GHz and the other one had between 15 GHz and 40 GHz. As evident from the experimental setup given in Figure 8b, for the orderly measurement of the co-polarized reflected field parameter, the horn antennas are to be placed in the same direction respective to the E-field. Then, one of the antennas was rotated 90\({}^{\circ}\) to obtain the cross-polar reflected field parameter. The obtained parameter results were normalized concerning free space measurements. Finally, these reflected field parameters and calculated PCR values were analyzed and compared to the simulated ones as demonstrated in Figure 9.  
Figure 8: (**a**) Prototype of the polarization converter and (**b**) the experimental setup.  
Figure 7: Frequency-dependent PCR plots at different sizes of the unit cell.  
Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors thank the Department of Electrical and Computer Engineering COMSATS University Islamabad for providing facilities to conduct this research.  
The authors declare no conflicts of interest.
## 2 Evaluation Methodology
### Intrareheicular Protocols  
One of the most relevant intrareheicular communication protocols in the automotive and industrial sectors is related to the controller area network (CAN) [27]. The protocol defines four different types of messages or frames: data, remote, error, and overload frames [28; 29]. That protocol is based on data bus configuration to transmit messages over distributed environments with a velocity of 500 kbits/s, connecting the different ECU's present in the vehicle (chassis and powertrain) [30]. The Local Interconnect Network (LIN)  
Figure 2: Coverage map in Mexico territory: (a) 2G coverage map (green color), (b) 5G coverage map (green color).  
protocol is considered a sub-bus of the Controller Area Network (CAN) and is typically used to connect low-speed systems, with a maximum speed of 20 kbits/s [31]. Another intravehicular protocol, FlexRay, provides several advantages over CAN, including greater flexibility and faster data rates of up to 10 Mb/s. This enhanced performance is achieved through temporal factors, redundancy, security, and error tolerance, making FlexRay particularly suitable for X-by-Wire applications, where electronic and communication systems replace traditional mechanical and hydraulic components. However, FlexRay nodes tend to be more expensive than CAN nodes [32; 33; 34]. Finally, the Media Oriented System Transport (MOST) protocol is a standard developed for multimedia and infotainment networks. It allows for the transmission of audio, video, and even Internet Protocol (IP)-based data packets within a vehicle's infrastructure, achieving a maximum speed of 150 Mb/s [35]. These technologies: CAN, MOST, FlexRay are mainly used for internal communications in the vehicles which are mentioned, however, the technologies mentioned in Table 2 are used in infrastructure vehicles, making a comparison of them.
## 3 Results and Discussion
### Effect of Reuse of Pretreatment Liquid on Removal of Metal Ions in Corncob  
The concentration of metal ions as well as the increment of metal ions in the solution after pretreatment of corncobs are presented in Table 2. It can be seen that the concentrations of K\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\), and Na\({}^{+}\) in solution increase gradually as the reuse round of the pretreatment liquid increases. Meanwhile, the increment of K\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\), and Na\({}^{+}\) in the solution after repeated use of the pretreatment liquid exhibits slight difference comparing to the increment in the solution after pretreatment of the corncob with the new added pretreatment liquid. In Figure 3, the increment of total amount of metal ions in the solution drops only a bit after the pretreatment liquid being reused for three rounds. This negligible drops of the amount of total metal ions found from the pretreatment liquid being reused for three rounds (\(n=3\)) as compared to those in the pretreatment liquid only after first use (\(n=0\)) can ensure the reuse of the pretreatment liquid for effectively removing metal ions from the corncob.  
Note that as long as the increment of metal ions in the pretreatment liquid was significantly reduced and/or the concentration of metal ions reached saturation, the pretreatment liquid were considered no longer usable. At this point, we believe that the pretreatment liquid has attained a threshold processing round with respect to effective removal of metal ions in the corncob.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reuse Times of the Pretreatment Liquid/(n)**} & \multicolumn{6}{c}{**Concentration of Metal Ions after Pretreatment/(mg\(\cdot\)L\({}^{-1}\))**} & \multicolumn{6}{c}{**Increment of Metal Ions after Pretreatment/(mg)**} \\ \cline{2-10}  & \multirow{2}{*}{**K\({}^{+}\)**} & \multirow{2}{*}{**Ca\({}^{2+}\)**} & \multirow{2}{*}{**Mg\({}^{2+}\)**} & \multirow{2}{*}{**Na\({}^{+}\)**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**A\({}^{2+}\)**} & \multirow{2}{*}{**Mg\({}^{2+}\)**} & \multirow{2}{*}{**Na\({}^{+}\)**} & \multirow{2}{*}{**Total**} \\  & & & & & & & & & & **Amount** \\ \hline
0 & 638.3 & 59.3 & 16.9 & 3.2 & 717.7 & 191.5 & 17.8 & 5.1 & 1.0 & 215.4 \\
1 & 1107.6 & 102.2 & 29.1 & 6.0 & 1244.9 & 188.7 & 17.3 & 4.9 & 1.1 & 212.0 \\
2 & 1477.1 & 136.7 & 39.1 & 7.7 & 1660.6 & 190.6 & 17.7 & 5.1 & 1.0 & 214.4 \\
3 & 1743.6 & 161.2 & 46.2 & 9.1 & 1960.1 & 189.2 & 17.5 & 5.0 & 1.0 & 212.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Concentration of metal ions and the increment of metal ions in pretreatment solution after corncobs pretreatment.  
Figure 2: Increment of the soluble proteins in pretreatment liquid for different reuse round of the pretreatment liquid. The error bars show the standard error of the three mean values from soluble protein measurements.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 3 Taxonomy  
The network architecture of UAWSNs is quite different from that of traditional WSNs. Therefore, the existing routing protocols for stationary or low-mobility WSNs are impractical to apply. Routing protocols for UAWSNs must adapt to networks' high mobility, sudden changes in network topology, and sporadic communication links. In general, WSNs require different quality of service (such as delay, packet loss, and reliability) on the underlying networks. In addition, WSNs have limited energy resources, low node computation power, and limited network resources. To meet the requirements while respecting the limitations of WSNs, appropriate routing metrics are curial parameters to design an efficient routing protocol. In Reference [33], the authors analyzed the routing metrics, which guarantee convergence, optimality, and loop freeness of the routing protocol, resulting in a low-power and lossy network. In Reference [34], the authors provided guidelines for designing efficient composite routing metrics to be applied to the routing protocol for low-power and lossy networks. In Reference [34], the authors discussed and defined the different routing metrics such as basic metric, derived metric, composite metric, composite function, optimal path, sub-path, path weight, metric order relation, and metric operation. As explained in Reference [34], the compositionof several basic or derived routing metrics into a composite routing metric is a challenging problem. Moreover, the authors provided guidelines for the proper selection and composition of basic metrics for applicability to the routing protocol.  
The routing protocols can be classified into two categories: network structure-based routing [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38] and protocol operation-based routing [39; 40; 41], as shown in Figure 1. The network structure-based routing is further classified into flat routing [35; 36; 37; 38], hierarchical routing [39; 50; 51], and location-based routing [52; 53]. The protocol operation-based routing is also classified into three categories: swarm intelligence routing [54], multi-path routing [55], and shortest-path routing [56]. The routing protocols are technically examined and qualitatively compared with each other in the next two sections.
# GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization
## 4 Graph Condensation with Adversarial Regularization  
In this section, we introduce our proposed method, namely GCARe, which is used to regularize the graph condensation process for a more fair distillation of all node subgroups.  
A rich body of literature focuses on GNN fairness from different perspectives, including data augmentation [15], graph contrastive learning [16], pseudo label prediction [17], adversarial training [24], and fairness constraints [26]. While one cannot directly apply these methods to GNN training on condensed graphs owing to the absence of explicit group IDs after condensation, we chose to regularize the condensation GNNs during condensation. The intuition behind this choice is that, the condensation GNN plays the role of information bottleneck between the original graph and the GNNs trained on condensed graphs, and the latter will inherit the bias and unfairness from the former.  
The framework of our proposed method GCARe is depicted in Figure 2. At its core, GCARe adopts a Generative Adversarial Network (GAN) [24; 31; 32] to debias condensation GNNs. We treat condensation GNNs as the generator, and it generates hidden embeddings for the node \(v\) from the original graph:  
\[h_{v}=\text{GNN}_{\theta}(\mathcal{G}_{v},x_{v})\]  
where \(\mathcal{G}_{v}\) is the adjacency associated with \(v\), and \(x_{v}\) is the input node feature. On the other hand, a linear classification layer \(f\) was adopted as the discriminator to predict which subgroup \(v\) comes from. The generator plays a minimax game against the discriminator to prevent correct group prediction. The game can be formulated as follows:  
\[\max_{\theta}\min_{f}\ L_{adv}:=-\sum_{v\in X}\sum_{k=1}^{K}\mathbb{1}_{\{s_{ v}=k\}}\log f(\text{GNN}_{\theta}(\mathcal{G}_{v},x_{v}))_{k}\]  
where \(\mathbb{1}\) is the indicator function, \(f(\cdot)\) is a vector containing the predictive probabilities for each subgroup, \(f(\cdot)_{k}\) is the probability specifically for the k-th subgroup, and \(K\) is the number of all of the subgroups. We add this loss term to the original condensation loss, weighted by a hyperparameter \(\lambda\), as a regularization of the condensation GNN. Thus, the total loss at the t-th iteration is as follows:  
\[L_{t}:=D(g_{t}^{S},g_{t}^{T})+\lambda L_{adv}\]  
In practice, we adopt an alternative optimization scheme. Specifically, in each iteration, we first freeze the condensation GNN, and minimize \(D(g_{t}^{S},g_{t}^{T})\) with respect to the condensed graph. Then, we minimize the adversarial loss with respect to \(f\). Lastly, we unfreeze the GNN and maximize the adversarial loss with respect to it.
## 3 DR-integrated Framework for Optimized Design
### Generation of Multivariate Data Random Thickness  
The design framework in Figure 3 starts with the generation of random multivariate data including a strong correlation in Step 1. For example, in the design space of the antenna application in this study, the correlation will be considered for the substrate thickness. A Gaussian copula is proposed to be used in this study to meticulously create multi-dimensional random behavior. In comparison with the conventional methods that require a linear correlation coefficient [44], the copula takes two clear benefits: (1) it creates multi-dimensional data, although the DVs contain each distinct marginal distribution; and (2) it analyzes the correlation between the random variables that have non-linear behavior. Hence, in Step 1, the most realistic properties in the multi-dimensional space will be  
Figure 3: Proposed optimum design framework with the dimension reduction (DR). ECC, entropy-based correlation coefficient.  
demonstrated. A joint distribution, \(J_{XY}\), is denoted by the two distinct marginal distributions, \(F_{1}\) and \(F_{2}\),  
\[J_{XY}(x,y)=\mathit{CP}(F_{1}(x),F_{2}(y)). \tag{13}\]  
Here, \(\mathit{CP}\) represents the copula function (\(\mathit{CP}\): [0,1]\({}^{2}\rightarrow\) [0,1]),  
\[\begin{array}{c}\mathit{CP}_{\rho}(x,y)=\frac{1}{2\pi\sqrt{(1-\rho^{2})}} \int\limits_{-\infty}^{\Phi_{1}-1}(x)\int\limits_{-\infty}^{\Phi_{1}-1}(y) \exp\left(-\frac{h^{2}-2\mathit{b}\mathit{hk}+k^{2}}{2(1-\rho^{2})}\right) \mathit{H}\mathit{h}\mathit{dk},\\ \Phi(x)=\frac{1}{2\pi}\int\limits_{-\infty}^{x}\mathrm{e}\left(-0.5s^{2}\right) \mathit{d}\mathit{h},\end{array} \tag{14}\]  
where, \(\rho\), \(x\), and \(y\) stand for the linear correlation coefficient, and the marginal distributions of both \(x\) and \(y\). Moreover two different copula parameters are represented by \(h\) and \(k\), and the standard univariate Gaussian distribution is denoted by \(\Phi\).  
Therefore, the copula function builds the sample dataset by exploiting the parametric multivariate distribution. The samples can then be generated from the copula and can be utilized for stochastic analysis to model and simulate a complex engineering system.
# Improving the Performance of Vietnamese-Korean Neural Machine Translation with Contextual Embedding
## 4 Experimentations and Results
### Results  
We used BLEU, METEOR, and TER scores to evaluate the accuracy of the systems. BLEU is the most common method for assessing the accuracy of MT systems. It indicates how similar the candidate text is to the reference text. METER is another method of evaluating the similarity between candidate text and reference text. In addition, TER is used to measure the amount of correction the translator needs to produce a perfect translation. The ranges of BLEU and TER are from 0 to 100; the range of METEOR is from 0 to 1. A high BLEU score or METEOR score means that the MT system has higher accuracy, but a high TER score usually indicates lower accuracy.  
The result of the experiment is shown in Table 6.  
Table 6 shows that annotated WA and WSD for Korean sentences increased the quality of MT, as shown in the BLEU, METEOR, and TER scores. The quality of machine translation for Vietnamese also increased with tagging of POS. With WA and WSD tagging for Korean, POS tagging for Vietnamese, and the use of Vietnamese BERT to create representations for input sentences, Vietnamese-Korean MT produced the best results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Systems** & **BLEU** & **METEOR** & **TER** \\ \hline Baseline & 13.52 & 0.115 & 71.46 \\ KrUTagger & 25.10 & 0.189 & 64.03 \\ VnPOS & 26.81 & 0.197 & 61.07 \\
**VietBERT+NMT** & **28.22** & **0.209** & **58.53** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The Vietnamese–Korean translation results.  
Figure 7: The BERT-fused model.