esa-sceva/satcom-chunk-collection huggingface dataset:
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 3 SCAR-Phase of Spectrum Sensing  
Commonly, the energy detector has been widely studied to accomplish spectrum sensing in CR due to its low computational complexity and simplicity of implementation. However, its performance considerably degrades under noise power uncertainty. Besides, in the low SNR region, the required number of samples to achieve a target detection performance dramatically increases as the SNR decreases [19], which reduces the maximum SUs achievable throughput. In order to reduce the spectrum sensing phase without compromising the detection performance, the PU's ST sequence can be used to improve sensing in the low SNR by combining the energy detector along with an enhanced ST-based detector. Additionally, the ST information can be used to synchronize the SU with the PU's transmissions.  
The proposed SCAR-Phase of spectrum sensing block diagram is shown in Figure 2. The scheme considers that spectrum sensing is performed periodically followed by the SUs' transmissions if the frequency band is idle. The SCAR-Phase of spectrum sensing consists of a sample collection period and two sample processing periods. Hence, after the SU collects the samples, in the first sample processing period a synchronization process (SYNC block in Figure 2) with the transmitted PU's ST sequence is carried out at the same time that the energy of the received signal is calculated to make a first decision about the presence or absence of the PU's signal. If a PU's signal is detected (i.e., hypothesis \(\mathcal{H}_{1}\) is true), the SU waits until the next spectrum sensing phase. Otherwise (i.e., hypothesis \(\mathcal{H}_{0}\) is true), the second sample processing period is performed. During this period, the already synchronized ST sequence can be exploited through an enhanced ST-based detection that uses the set of collected samples. If an enhanced ST-based detector decides that the PU's signal is present, the SU waits until the next spectrum sensing phase, or else the SU can transmit data. The first and second sample processing periods are explained in more detail next.
# Advanced Default Risk Prediction in Small and Medium-Sized Enterprises Using Large Language Models
## 4 Experiments and Results
### Quantitative Analysis
#### 4.1.2 Baseline Regression Results  
A regression analysis on the normalized data was executed, and the results are displayed in Table 4. The coefficient represents the magnitude and direction of the impact that the independent variable has on the dependent variable. A positive value indicates a positive impact, while a negative value indicates a negative impact. T-value is used to determine the significance of the coefficient; the larger the T-value, the more significant the effect of the independent variable on the dependent variable. The regression results show that the key variables identified in this paper have a significant impact on corporate bill defaults. The age of a company has a significant positive effect on its default risk. Startups are riskier, but as companies enter the growth and maturity phases, the risk decreases. However, when companies reach old age or decline, the risk increases again. Most companies in the sample are over 10 years old, which may mean they have passed their peak, leading to an increase in default risk. The number of defendants and the ratio of negative news are significantly positively related to default risk. When a company is a defendant, it often faces more defaults. Negative news reflects the company's operating conditions and public opinion, indicating increased risk. Additionally, limiting high consumption is a serious measure that reveals possible dishonest behavior or financial crises, which is also significantly positively related to risk. The number of defaults last year serves as a temporal variable, reflecting the company's historical credit status. If a company has a record of defaults, the probability of defaulting again increases, similar to how insurance premiums rise after a claim.  
Overall, the core variables studied in this paper are significantly positively related to corporate default risk. Except for last year's default numbers, other indicators are easy to obtain, which is important for researching the default risk of small and medium-sized enterprises.
# A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse
## 4 The Utility-Based Ontology Recommendation System
### The Ontology Evaluation Component  
The ontology evaluation component receives information about the user's preferences from the User Preferences Model. Then, it evaluates each candidate ontology from the ontology repository according to four evaluation criteria: the preferred ontology domain, input coverage, ontology popularity, and ontology type. For each ontology \(O\), the ontology utility function (\(OUI(O)\)) is an aggregated sum function that aggregates the scores of the evaluation criteria to provide an ontology utility score for each candidate ontology. Now, we will explain how each evaluation criteria score is calculated.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Results and Discussion
#### 5.2.1 Time  
Time is required in order to make a path among different nodes dataset to reach at sink nodes. For the small set of nodes, maximum time is required to create a path from source to the destination node. As the number of nodes increases, the selection of next node becomes easy, therefore less time took by TBRL. For the selection of path from multiple path environment, TBRL only considers the path having less time required to forward the data packets. Figure 7 shows different paths with respect to the time taken.
# Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation
## 3 Component Modeling
### Fiber Bends  
In a similar way to connectors, fiber bends can be modeled via a matrix \(\mathbf{M_{bend}}\), or via an equivalent fit of the Gloge power-flow equation [32]. The matrix will depend on the bend radius and also on the bend angle (e.g., a complete turn of \(360^{\circ}\), will be different from a right-angle turn of \(90^{\circ}\)). Once again, the key assumption is that the output intensity at some angle \(\theta_{i}\) is a weighted linear summation of the input powers at all input angles. In other words  
\[p_{out}(\theta_{i},t)=\sum_{j=0}^{N}m_{bend}(\theta_{i},\theta_{j})\cdot p_{in }(\theta_{j},t) \tag{23}\]  
where \(m_{bend}\) (\(\theta_{i}\), \(\theta_{j}\)) is a coupling coefficient between the input at \(\theta_{j}\) and the output at \(\theta_{i}\). Representing the intensities as vectors \(\mathbf{P_{out}}\) and \(\mathbf{P_{in}}\), that can be functions of time or frequency, we can also express this relationship as:  
\[\mathbf{P_{out}}=\mathbf{M_{bend}}\cdot\mathbf{P_{in}} \tag{24}\]  
where \(\mathbf{M_{bend}}\) is a matrix with elements \(m_{bend}\) (\(\theta_{i}\), \(\theta_{j}\)). Furthermore, similar to the Injection and Connector models, and as described in [32], it is possible to fit this matrix to the Gloge power-flow equation [20; 23; 29; 30]. Similarly, the interpretation of the Gloge power-flow equation is the same as that of the Injection and Connector models above. The fiber bend matrix \(\mathbf{M_{bend}}\) can be derived from measurements, as described in detail in [32].  
Figure 5: Basic connector matrix with no lateral or longitudinal misalignment (center up) and examples of connector matrices with various combined misalignments. Values of the misalignment are given in mm.
## 3 Results and Discussion
### Corrosion Susceptibility Assessment Tests
#### 3.3.1 Electrochemical Impedance Spectroscopy (EIS)
The substantial increase in R\({}_{\text{coat}}\) between 720 and 1440 h, followed by a more moderate increase at 2160 h, suggests a gradual densification and stabilization of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Element Circuit**} & \multirow{2}{*}{**Uncoated Sample**} & \multicolumn{5}{c}{**Coated Sample**} \\ \cline{3-6}  & & & \multicolumn{3}{c}{**Oxidized Sample**} \\ \cline{3-6}  & & & **720 h** & **1440 h** & **2160 h** \\ \hline R\({}_{\text{ex}}\), \(\Omega\)-cm\({}^{2}\) & 331.4 & 325.6 & 259.8 & 300.3 & 242.7 \\ \hline R\({}_{\text{ox}}\), \(\Omega\)  
the oxide layer on the NiCrAJY coating. This sequential increase in resistance, mirrored by the rising Rox values over time, reflects the coating's enhanced ability to impede corrosion with prolonged exposure. Additionally, the high CPE\({}_{\text{ox}}\)-\(p\) values, which approach 1, reflect a strong capacitive behavior that is indicative of a more uniform and protective oxide layer after extended oxidation. Table 5 shows that, in the case of all tested samples, a value of Chi-square (\(\chi^{2}\)) \(\approx\) 10\({}^{-3}\) was found, and the fitting errors were quite small. An approach based on the Kramers-Kronig transforms was used to determine the polarization resistance values (Rp). The imaginary impedance component summed over the measured frequency domain is used in this method to estimate the polarization resistance [55]. Table 6 presents Rp and i0 values obtained for uncoated and coated samples with NiCrAJY, unoxidized and oxidized for 720 h, 1440 h, and 2160 h in water at 550 \({}^{\circ}\)C and 25 MPa. Comparing the Rp values estimated from the Nyquist diagrams (Figure 8a) and using a method derived from the Kramers-Kronig transforms (Table 6), it is observed that for the samples coated and oxidized in a supercritical environment for a longer period of time (2160 h), the highest polarization resistance value (0.514 M\(\Omega\)\(\times\) cm\({}^{2}\)) and the lowest corrosion current density value (24.6 nA \(\times\) cm\({}^{-2}\)) were obtained. According to the Buttler-Volmer equation (Equation (7)), polarization resistance is inversely proportional to the corrosion current density; therefore, a higher polarization resistance value indicates a lower corrosion rate value. The conclusion, which is congruent with the findings of the gravimetric study, would be that samples of oxidized steel that had been exposed to a supercritical environment over a longer period of time had lower corrosion rates than samples that were unoxidized or oxidized for a shorter period of time. \[R_{T}=\frac{\Delta\eta}{\Delta i}=\frac{RT}{zF}\frac{1}{i_{0}} \tag{7}\]  
where:  
i0: corrosion current density [A \(\times\) cm\({}^{-2}\)];  
R: gas constant [8.314] \(\times\) mol\({}^{-1}\)\(\times\) K\({}^{-1}\)];  
F: [PERSON]'s constant [9.65 \(\times\) 10\({}^{4}\) C \(\times\) mol\({}^{-1}\)];  
T: absolute temperature, [K];  
z: the valence of the ion;  
R: polarization resistance, [\(\Omega\)].
# Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil
###### Abstract  
Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO-ANN and SHO-ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO-ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO-ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.  
Keywords:swarm intelligence; metaheuristic optimization; nature-inspired; soil shear strength; artificial intelligence +
Footnote †: journal: Applied Sciences
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Overview of Domain Adaptation Techniques
However, Google Trends data can be influenced by various factors, including population, language, and user search habits. Additionally, the attribution of research articles to specific regions may have inherent complexities in cases of international collaboration or multi-region affiliations of authors. The methodology used for geographical attribution should be considered when interpreting the results. While the use of online data sources like Google Trends has faced criticism regarding reliability, it is essential to note that several reputable academic papers have been published in well-established and high-impact factors journals [24], as well as many others, emphasizing the credibility of these sources. The utilization of Google Trends data in these papers underpins its relevance and usefulness as a data source for rigorous research. This adoption within the academic community lends credence to the utility and acceptability of Google Trends as a viable data source for scholarly investigations. Based on data from Google Trends, Figure 2 shows the regional interest in domain adaptation, computer vision, and robotic vision. China emerges as the region with the most interest in these sectors, as seen by the heat map, which shows the differing levels of interest across various locations [25; 26]. The regional concentration of interest and the geographical distribution of interest provide important insights into the importance of certain issues on a worldwide scale. Collaborations between academics from various locations have the potential to create improvements and creative answers to pressing problems as the subject of domain adaptation in computer and robotic vision continues to develop [27; 28]. The combined knowledge and skill from these areas aid in the creation of effective domain adaptation approaches, enabling vision systems to succeed in a vast range of real-life tasks with different domain changes [29]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Region** & **Number of Papers** \\ \hline Asia & 180 \\ \hline Europe & 66 \\ \hline North America & 104 \\ \hline South America & 46 \\ \hline Oceania & 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Region-wise comparison of domain adaptation research papers.
## 3 Results and Discussions
### Sensory Analysis of the Mastic Gum Samples  
The sensory properties of the MGT and MGG samples were evaluated by a panel of seven panelists, who examined the aroma profiles based on 100 mm scales without marks. Both samples were evaluated based on eight criteria: resinous, fruity, floral, pine-like, woody/earthy, vinegary/sourish, citrus-like, and green. The result of the sensory analysis is shown in Figure 1 as a spider web diagram. No statistically significant difference was found between the two samples in terms of odor properties such as green, citrus-like, floral, and fruity (\(p>0.05\)). However, resinous, vinegar/sourish, woody/earthy, and pine-like attributes showed statistically significant differences (\(p<0.05\)) according to the t-test results. When the samples were compared, it was found that the MGG sample received higher scores in resinous, green, pine-like, and woody/earthy properties compared to the MGT sample. It is believed that these properties were due to the aroma compounds of \(\alpha\)-pinene, \(\beta\)-pinene, and \(\beta\)-myrcene found in the samples. Moreover, the (\(Z\))-verbenol and (\(E\))-verbenone, which were detected as AACs only in the MGG sample, and the perilene, which had a higher FD value (64), were thought to contribute to a more resinous, pine-like, and green odor in the MGG sample. On the other hand, the MGT sample was evaluated to have higher scores in terms of fruity, floral, vinegary/sour, and citrus properties compared to the MGG sample. The higher FD values of the limonene (FD: 32) and linalool (FD: 256), which were determined in the MGT sample through the olfactometric analysis, supported this observation.
## 3 Hydrogen Production Technologies
### Water Electrolysis Technologies  
While there are many different methods to produce hydrogen, currently the vast majority of the global hydrogen production comes from non-renewable fossil fuels--in particular, steam reforming of methane--mainly due to its low cost and high efficiency. However, these processes also tend to produce less-pure hydrogen, besides obviously releasing harmful greenhouse gasses to the atmosphere [17].  
So, today, new environmentally friendly energy strategies are sought to replace the current system, namely through water electrolysis; this method enables the production of eco-friendly, high-purity hydrogen, in addition to still releasing oxygen as a byproduct--as seen in Equation (5).  
\[\mathrm{H_{2}O}\xrightarrow{Electricity+Heat}\mathrm{H_{2}+\frac{1}{2}\,O_{2}} \tag{5}\]  
However, water electrolysis is still not economically competitive due to high energy consumption costs and low hydrogen yield rates [15]. In order to increase overall efficiency, many researchers have been looking to develop alternatives with low-cost electrocatalysts and less energy consumption.  
Equation (5) shows that, in electrolysis processes, water molecules are the reactant, which--under the influence of electricity and heat--dissociate into hydrogen and oxygen, in an oxidation-reduction process. This process can be then classified into three main types, based on its operating conditions, the electrolyte and electrolyzer used, and the ionic agent present (OH\({}^{-}\), H\({}^{+}\), O\({}^{2-}\)): alkaline electrolysis (AEL), proton-exchange membrane electrolysis (PEMEL), and solid oxide electrolysis (SOEL), respectively.
## 5 Experimental Results and Analysis
### Experiment 1: Analysis of Ball Bearing Faults
The classification accuracy of ELM is 97.5% and the classification accuracy of SVM is 93.33%. The contrast results show that PSO-RF is more accurate compared to ELM and SVM. It should be noticed that the actual output value of ELM is based on the eigenvector and may not be an integer. To accomplish the classification, the output value of ELM needs to be rounded. So the final classification result of ELM may not necessarily be 0, 1, 2 and 3. Some other integer values may be obtained as shown in Figure 13a. Figure 11: Identification results of: (**a**) random forest (RF); (**b**) particle swarm optimization-random forest (PSO-RF). Figure 12: The recognition results of: (**a**) EMD-SVD; (**b**) MPE.
## Abbreviations  
EC Eddy covariance  
PPFD Photosynthetic photon flux density  
NEE Net ecosystem CO\({}_{2}\) exchange  
Re Ecosystem respiration  
GPP Gross primary productivity  
VPM Vegetation photosynthesis model  
MVPM Modified VPM model  
MODIS-GPP Moderate Resolution Imaging Spectroradiometer GPP algorithm  
TG Temperature and greenness model  
GR Greenness and radiation model  
VI Vegetation index model  
AVM Alpine vegetation model  
PCM Photosynthetic Capacity Model  
LUE Light use efficiency  
\(\varepsilon_{\text{max}}\) Potential LUE without environmental stress  
\(\varepsilon_{\text{g}}\) Actual LUE  
ft Effects of air temperature on \(\varepsilon_{\text{max}}\)  
fw Effects of water availability on \(\varepsilon_{\text{max}}\)  
m Slope between GPP and environmental scalars  
PAR Photosynthetically active radiation  
fPAR Fraction of PAR absorbed by the vegetation canopy  
Ta Air temperature  
VPD Vapor pressure deficit  
EVI Enhanced vegetation index  
LSWI Land surface water index  
LST Land surface temperature  
Last annual nighttime LST
## 4 Simulation Results
### Convergence Analysis  
Figure 2 shows the convergence rate of Algorithm 1 and a performance comparison of the proposed method with that of the exhaustive search method. It is observed that the global optimal solution calculated by the exhaustive search method is close to the solution obtained by the proposed algorithm. However, the exhaustive search method is highly intractable and cannot be used in practice. The proposed algorithm could achieve the near-optimal solution within a finite number of iterations. The computational complexity of Algorithm 1 is reduced a lot compared with the exhaustive search method, and the calculated results from the proposed algorithm are satisfactory.
# Detection of Reflected XSS Vulnerabilities Based on Paths-Attention Method
## 3 Vulnerability Detection Method Based on Paths-Attention
### Theoretical Analysis of Paths-Attention Vulnerability Detection Method
#### 3.1.2 Semantic Extraction and Vulnerability Detection Compatibility Analysis of Paths-Attention
This method labels similar vulnerability functions as 1 and dissimilar patch functions as 0. By training and learning the vulnerability matching degree between functions and function libraries, if it exceeds a certain threshold, it is judged to have vulnerabilities. The detection method proposed by [PERSON] in the article has a major drawback. If it is necessary to detect vulnerabilities by comparing the similarity between vulnerable functions and detected functions, then the sample coverage needs to be large enough and the feature quantity needs to be rich enough to achieve more comprehensive detection. However, the sample selection method mentioned in his method is too strict, which limits the range of samples that cannot be sufficiently abundant. Therefore, although this method avoids the problem of vulnerability detection between semantically similar codes, it has significant limitations. Paths-attention has made the following optimizations to avoid these drawbacks:  
1. Difficulty in distinguishing between code snippets with similar semantics: The paths-attention method starts from the syntax path and decomposes code snippets into groups of syntax paths. Similar semantics may have similar syntactic structures, but the importance of deeper syntactic paths is not necessarily the same. The semantic analysis process for two similar pieces of code is shown in Figure 4.  
From Figure 4, although the two code snippets have similar syntax and structure, after parsing the syntax paths, it is found that the semantic focuses of the two code snippets are different. The red lines in the figure represent the most prominent syntax features, with other lines decreasing in order. Similar code snippets can be well distinguished in the PATS model. 2. Issue of feature coverage range of the dataset: The model samples are selected from the National Institute of Standards and Technology (NIST) in the United States. After processing, a relatively rich set of samples can be used for training to allow the model to learn semantic features of vulnerability code snippets as fully as possible. This enables the model to have both high accuracy and fast detection capabilities. In summary, the semantic extraction method based on paths-attention is theoretically very suitable for detecting reflected XSS vulnerabilities.