esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison Table Summarizing the Performance of Our Model  
We acknowledge the importance of providing more detailed information about the baseline models and related works to enhance the transparency and rigor of our study. To address this, we have included a comprehensive comparison of our proposed model with previous studies in sentiment analysis and ABSA for Indonesian texts. These prior studies serve as baseline models, and their methodologies and datasets are briefly described below for clarity. The comparison performance model is shown in Table 14.  
Figure 8: Model performance is evaluated using precision, recall, and F1-scores, comprehensively assessing the curriculum’s classification accuracy.  
Figure 7: Model performance is evaluated using precision, recall, and F1-scores, which comprehensively assess the lecturer’s classification accuracy.  
The first baseline, Study A, conducted by [35], utilized an SVM-based approach for sentiment classification. The model relied on manually extracted features, such as word frequencies and specific lexical cues, to perform classification. The dataset used in this study consisted of 5000 student reviews collected from Indonesian universities. While effective for smaller datasets, the reliance on manually engineered features limited the scalability and ability of the model to handle complex relationships in textual data.  
The second baseline, Study B, proposed by [14], employed the LSTM model with pre-trained Indonesian word embeddings. This model leveraged distributed representations of words to capture semantic relationships within the text. The dataset included 8000 reviews from diverse sources, including educational and business domains. Although the LSTM model improved performance over traditional methods like SVM, it struggled to manage intricate linguistic nuances, such as idiomatic expressions and long-range dependencies.  
The third baseline, Study C, presented by [12], utilized a fine-tuned multilingual BERT model mBERT for sentiment and aspect analysis. This study used a dataset of 10,000 student reviews collected from feedback from Indonesian universities. The multilingual nature of mBERT allowed it to handle a wide range of languages, including Indonesian. However, as mBERT is not specifically tailored to the Indonesian language, it often fails to capture subtle linguistic and cultural nuances in local expressions.  
In comparison, our proposed IndoBERT-based ABSA model leverages a pre-trained language model specifically designed for the Indonesian language. This allows it to address the limitations observed in these baseline models by effectively capturing linguistic and contextual nuances unique to Indonesian student reviews. The comparative results in Table 12 highlight our approach's substantial improvements in aspect extraction and sentiment classification tasks.
# Adhesion Promoters in Bituminous Road Materials: A Review
## 4 Effects of Adhesion Promoters on the Mechanical Performances of Modified Bitumen  
Bitumen is a viscoelastic liquid that exhibits Newtonian flow behavior at high temperatures (typically above 60 \({}^{\circ}\)C), viscoelastic behavior at intermediate temperatures (typically between \(-20\) and 60 \({}^{\circ}\)C), and glassy, brittle behavior at low temperatures (typically below \(-20\)\({}^{\circ}\)C). Specifically, according to [PERSON] et al. [55; 56], two regimes can be identified for the temperature dependence of bitumen rheology, namely, a Newtonian-viscoelastic flow transition occurring above room temperature attributed to the solid phase (asphaltene) and a viscoelastic-elastic glass transition showing up in the low temperature regime due to the vitrification of liquid phase (maltene). The addition of organic compounds able to stabilize/destabilize the dynamics of the complex bituminous colloidal microstructure would give rise to either a hardening or softening effect on the correspondent modified bitumen. For practical cases where it is desirable or required that the addition of APs does not significantly alter the mechanical properties of bitumen [36], OSAP has been made useful for fulfilling this requirement. Indeed, the transition temperature from a viscoelastic to a viscous regime was found to be independent of the addition of organosilane surfactant, which was able to act as a strong and efficient AP [39; 40]. An organic compound endowed with a dual-property, viz. adhesion promoter and rheological modifier, would be appealing in most of the applications of the bitumen industry. To the best of our knowledge, FGPs added at a few percentages to bitumen represent, at the moment, the unique class of organic additives that fulfill the double requirement. As it has recently been documented in [54], the Newtonian-viscoelastic flow transition determined by time cure rheological tests can be shifted to temperatures higher than 64 \({}^{\circ}\)C, correspondent to virgin bitumen, upon addition of FGPs provided either as solid powder or as liquid dispersion. In the former case, a maximum increment of \(\Delta t_{\text{max}}=17\)\({}^{\circ}\)C has been detected for the softening temperature measured for the correspondent modified bitumen, while, in the latter, an even higher \(\Delta t_{\text{max}}=25\)\({}^{\circ}\)C has been obtained in the presence of 6 wt. % of additives (see Figure 4).  
Figure 4: Softening temperature as a function of Food Grade Phospholipid (FGP) content in hot bitumen. FGP added as solid powder (circles) or as liquid dispersion (rhombus). Reproduced with permission from [54], Elsevier, 2017.  
Those differences have been ascribed to the different miscibility properties manifested by both liquid and solid forms of food grade additives, once mixed to the binder.
# Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance
## 3 Cyber Resilience: Fundamental Concepts
### Concepts of Resilience in Its Various Contexts
#### 3.1.3 Cyber Resilience  
Cyber resilience is part of the concern of organizational resilience, since cyber incidents that jeopardize information security have the potential to damage the operation of organizations, jeopardize the achievement of their strategic goals and cause financial losses. \"Organizational resilience refers to the ability of a system to adapt to change: a very contemporary concept that is finding increasing importance in our ever-changing society and is also taking on greater relevance in the cyber context. Therefore, the ability of organizations to react to cyber-attacks and to evolve to a new robustness after successful outbreaks recalls the concept of resilience and brings to the evolution of this concept that of cyber resilience\" [27].  
Cyber security can also be understood as \"the ability of a cyber physical production system to respond to and recover from operational disturbances and risks\" [28].  
The Glossary of Information Security prepared by the Office of Institutional Security of the Brazilian Presidency of Republic defines resilience as the \"ability of an organization or infrastructure to withstand the effects of an incident, attack or disaster and return to normal operations\" [29].  
The NIST Computer Security Resource Center Glossary defines cyber resilience as \"the ability to anticipate, withstand, recover from, and adapt to adverse conditions, stresses, attacks, or compromises in systems that use or are enabled by cyber resources. Cyber resilience is intended to enable mission or business objectives that rely on cyber resources to be achieved in a contested cyber environment\" [30].  
Another definition is presented by the World Economic Forum, where cyber resilience is understood as \"the ability of systems and organizations to withstand cyber events, measured by the combination of'mean time to failure' and'mean time to recovery\" [31].  
Other authors define cyber resilience as \"the ability to continuously deliver the expected outcome despite adverse cyber events\" [32] or as \"the ability of a system to protect itself from incidents of cyber-attacks and maintain an acceptable level of performance, maintaining critical functionality and the timely restoration of the quality of services to the level that existed before the incident\" [33].
## 4 Simulation Results
### Convergence Behavior  
An initial value was first assigned to the hovering radius \(r_{o}\), and then the optimal user scheduling under the current radius were obtained through the ACA. Then, user scheduling is substituted into the GA to obtain the optimal hovering radius of the UAV under the current user scheduling. In the following simulation figures, the objective function of the vertical axis is the fairness metric we defined in the previous section and smaller value means a better fair performance.  
Figure 2 shows the iterative process of the optimal solution when optimizing user scheduling with the ACA. Furthermore, the impact of the different initial radius on the ACA optimization process is shown in the figure. As shown, the initial radius with bigger value will be the smaller value of the objective function optimized by the ACA. This is because the optimal radius was obtained at a certain value in the radius of the target area. The objective function takes the minimum value at the optimal value. Therefore, the initial value with a large difference from the optimal value corresponds to a larger objective function value. Figure 3 shows the iterative process of the optimal solution when optimizing the hovering radius of the UAV with the GA. By comparing Figures 2 and 3, it can be seen that the number of iterations required by the GA to optimize hovering radius of the UAV was less than what was required by the ACA to optimize user scheduling. The problem of optimizing the hovering radius of the UAV is much more complicated than that of optimizing user scheduling, and, therefore, it requires a large number of iterations.
## 3 Results
### Analysis of Bank Correlation Network Model
#### 3.2.2 Community Division and Systemic Risk  
Now, we focus on studying the relationship between the community sub-network structure and systemic risk of the bank correlation networks. The systemic risk is measured by the proportion of bank failures in a network from 2008 to 2013. The previous community division was carried out under the default resolution parameter of 1.0. If the resolution parameter is smaller, the division level is deeper and the number of communities obtained is larger. In this section, we set the resolution parameter to a range of [0.6, 1.0] for the network and take the values at 0.05 intervals for community division. If we take IL, with the greatest risk of bankruptcy, as an example and calculate the proportion of bankruptcies in each community under different resolution parameters, we can obtain the results shown in Figure 7. For the bank correlation network of IL, when the resolution of community division is set to be 1.0 the systemic risk of the two communities is 0.084906 and 0.084071, which means that the rough community division does not significantly distinguish the number of failed banks; if the resolution parameter is set to be 0.85, the community with the highest systemic risk reaches 0.3333, while the average risk of other communities is only 0.0607. Thus, the community division with a resolution parameter of 0.85 is more effective in distinguishing communities with higher systemic risk than that with a resolution parameter of 1.0, and this uncovers some nodes with higher risk possibilities. Bringing attention to such communities will help us to effectively supervise and control the systemic risk of the financial system.  
Then, we carried out community division for the bank correlation networks of TX, IL, MN, and MO states, which have a larger number of banks. The resolution parameter range was also [0.6, 1.0], and its value was taken at intervals of 0.05. The results are shown in Figure 8, where the horizontal coordinate is the resolution value and the vertical coordinate  
Figure 7: The results of the community division of the IL network with a different resolution.  
is the failure ratio of the community--namely, the systemic risk of the community. The horizontal dotted line is the entire failure ratio of the state. It can be seen that the overall systemic risks of different states vary significantly. In addition, for the same resolution, the systemic risks of different communities are quite different. Taking MN state as an example, only one of the three communities obtained under the resolution parameter of 1.0 has a higher systemic risk than the overall systemic risk value. After the resolution parameter gradually decreases, the number of communities whose systemic risk is higher than the overall systemic risk value gradually increases, and the systemic risks of different communities become more different.  
Therefore, a more detailed community division may lead to significant differences in systemic risk between communities. These conclusions show that deep community divisions can effectively divide high-risk and low-risk communities.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 3 Adsorption Mechanism  
The adsorption process is the most efficient and admirable process for the treatment of toxic metals present in wastewater. In this process, toxic material is shifted by physical or chemical means onto the available surface of the adsorbent (Figure 2) [83]. The adsorption process is a cheap method and has a very low operating cost, and causes less contamination during the extraction process of toxic metal in comparison to conventional methods. In the adsorption methods, sorbents can be regenerated as well as reused several times for effective removal, and hence are considered to be an environmentally friendly method [84]. The major characteristics required for the choice of adsorbents are price effectiveness, large surface area, pore size distribution, presence of functional moiety, and polar characteristics of the sorbent which determines the efficiency of adsorption methods [85]. It is, therefore essential to understand the adsorption process. Adsorption is a mass transport method of solute present in the solution and accumulated onto the surface of the adsorbent which is generally a solid substance [86]. There are two kinds of force i.e., physical and chemical interactions that exist between adsorbent and adsorbate. Physical forces are weak and adsorbed molecules can be attached to adsorbents anywhere, meaning physical forces are non-specific in nature. Chemical adsorption is specific in nature and adsorbate binds to adsorbents through the covalent or electrostatic bonds. In the case of physical adsorption, forces are Van der Waals, dispersion interactions, and hydrogen bonding.
## 4 Experimental Results and Discussion  
For our experiments, we scanned the stele \"Musul-ojakbi\" to obtain a 3D mesh with size of 672.9 \(\times\) 987.1 \(\times\) 32.4 mm. The mesh contains over five million vertices, with an average resolution and accuracy of 250 \(\upmu\)m and 30 \(\upmu\)m, respectively. Musul-ojakbi monument is recorded to have been created in AD 578 during the Silla Dynasty. The monument has a rough surface because it was not ground before engraving and has undergone severe weathering. As illustrated in Figure 5, the characters inscribed on the surface are very difficult to interpret by visual inspection, especially at the center and the lower right portions of the stele. For further information on the Musul-ojakbi stele, we refer the reader to the previous study [24].  
Figure 4: Relative vertices localization. For \(\mathbf{v}_{i}\), a 3D point located at a distance of \(d_{CSI}\) in \(\mathbf{t}_{max}\) direction is identified. The 3D point is projected on the mesh and the closest vertex is obtained as the relative vertex \(\mathbf{v}_{i}^{1}\). Similarly, \(\mathbf{v}_{i}^{2}\) is identified using the opposite direction of \(\mathbf{t}_{max}\).  
To reduce the presence of the degraded region in the ground truth dataset before extracting important features, we carefully cropped fine partitions of the 3D scanned data using the MeshLab 3D processing tool [34]. The ancient stele has 169 partitions containing different characters, but we selected 70 partitions because the reliefs pattern on their surfaces can be traced and labeled as relief, background, or degraded region. The SI and CSI information was extracted per vertex for all the surface points of the 70 partitions. From the information obtained from these 70 partitions, 56 mesh partitions were used for training while 14 partitions for testing. The experiments are presented in the following subsections.
## Nonlinear Detection for a High Rate Extended Binary Phase Shift Keying System  
**[PERSON] * and [PERSON]**  
School of Information Science and Engineering, University of Southeast, Nanjing 210096, China;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-139-1596-9125; Fax: +86-025-8379-2443.  
_Received: 5 March 2013; in revised form: 16 March 2013 / Accepted: 28 March 2013 / Published: 28 March 2013_  
**Abstract:** The algorithm and the results of a nonlinear detector using a machine learning technique called support vector machine (SVM) on an efficient modulation system with high data rate and low energy consumption is presented in this paper. Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector. The two detectors detect the received signals together with the special impacting filter (SIF) that can improve the energy utilization efficiency. However, unlike the TD detector, the SVM detector concentrates not only on reducing the BER of the detector, but also on providing accurate posterior probability estimates (PPEs), which can be used as soft-inputs of the LDPC decoder. The complexity of this detector is considered in this paper by using four features and simplifying the decision function. In addition, a bandwidth efficient transmission is analyzed with both SVM and TD detector. The SVM detector is more robust to sampling rate than TD detector. We find that the SVM is suitable for extended binary phase shift keying (EBPSK) signal detection and can provide accurate posterior probability for LDPC decoding.  
**Keywords:** support vector machine; low density parity check codes; extended binary phase shift keying; posterior probability; intermediate frequency demodulator
## 2 NOMA System Model
### Uplink NOMA  
The uplink NOMA requires signal aggregation. In order to successfully implement the aforementioned architecture, we experimentally demonstrated a \(2\times 1\) MISO system enabled by the QD concentrator. Since the signals come from different directions, the photodetector suffers from relatively low energy efficiency and a narrow FoV, and the introduction of a QD concentrator can improve the energy conversion efficiency as well as the FoV. In our experimental demonstration of the uplink NOMA, we used two different QDs and compared their BER performance for two users. Both QDs varied in terms of characteristics and optical performance. For instance, the full widths at half maximum  
Figure 1: Indoor optical communication scene diagram.  
of QD Material-1 (CdSe/ZnS) and QD Material-2 (CsPbBr\({}_{3}\)-50) were 26 nm and 18 nm with central emission wavelengths of 526 nm and 514 nm, respectively. In order to further calculate the frequency response in QD-converted exciting light, first we needed to measure the photoluminescence (PL) lifetime of each material. The photoluminescence (PL) lifetime of CdSe/ZnS and CsPbBr3-50 were determined to be 32 ns and 29 ns, respectively. We collected the time-resolved PL lifetime of QDs at 526 nm and 514 nm, respectively, which was comparable to the lifetime of other inorganic color converters, ensuring potential use for hundreds of Mbps VLC [17]. The photoluminescence quantum yield was 86% at 450 nm for Material-1 and 80% at 450 nm for Material-2, which was high enough to support optical gain. The different characteristics and optical performance of the two quantum dots used in our experiments are provided in Tables 2 and 3, respectively.  
The absorption and emission spectra of QDs (Material-1, CdSe/ZnS) are shown in Figure 2a,b. The transmission electron microscopy (TEM) images of the QDs at different scales are provided in Figure 2c,d, which clearly indicate uniform core-shell QD particles with an average size of 7.2 nm.  
Similarly, the absorption and emission spectra of QDs (Material-2, CsPbBr\({}_{3}\)-50) are shown in Figure 3a,b. The TEM images of the QDs at different scales are provided in Figure 3c,d, which clearly indicate uniform core-shell QD particles with an average size of 9.8 nm.
## 2 Multiphysics-Coupled Analysis-Based Induction Hardening Process
### Fundamental Theories of Electromagnetic-Thermal Interaction
#### 2.1.3 Skin Effect Theory  
The skin effect refers to the tendency of the electrical current to concentrate near the surface of a conductor, leading to a decrease in current density toward the interior. The penetration depth (\(\delta\)), where the current density drops to 1/e (approximately 36.8%) of its surface value, is a function of the angular frequency (\(\omega\)), permeability (\(\mu\)), and resistivity (\(\rho\)) of the conductor, expressed in Equation (10). As resistivity (\(\rho\)) is inversely proportional to conductivity (\(\sigma\)), it can be rewritten as Equation (11). Angular frequency (\(\omega\)) is a function of the frequency (\(f\)), as expressed in Equation (12). Substituting Equations (12) and (13) into Equation (10), the final expression for penetration depth is derived as in Equation (14) [13,14].  
\[\delta=\sqrt{\frac{2\cdot\rho}{\omega\cdot\cdot\cdot_{H}\cdot\mu_{0}}} \tag{11}\]  
\[\rho=\frac{1}{\sigma} \tag{12}\]  
\[\omega=2\cdot\tau\cdot f \tag{13}\]  
\[\delta=\frac{1}{\sqrt{\pi\cdot f\cdot\mu_{r}\cdot\mu_{0}\cdot\sigma}} \tag{14}\]  
The skin effect's importance increases with frequency, as higher frequencies lead to smaller penetration depths, making the surface current concentration more significant. This relationship is crucial for optimizing induction heating systems.  
Figure 1: Direction and shape of surrounding magnetic field due to heated object and magnetic field.