esa-sceva/satcom-chunk-collection huggingface dataset:
# A Link Prediction Method Based on Neural Networks
## 3 Problem Description and Formula
### Link Prediction Models  
There exist many methods applied in the link prediction field. Some of the well-known techniques [38], including local link prediction methods and global link prediction methods, are selected as the necessary link prediction models to compare with the neural network methods.  
Common Neighbors (CN) [39] takes the number of common neighbors as a score to determine whether or not a link can be generated between two nodes. The more common neighbors two nodes share, the greater the probably they are related with a link. For nodes \(x\), \(y\), the sets of their neighbors are \(\Gamma(x)\), \(\Gamma(y)\); their common neighbors' set is \(\Gamma(x)\cap\Gamma(y)\). CN is defined as:  
\[S_{xy}^{CN}=|\Gamma(x)\cap\Gamma(y)| \tag{1}\]  
Resource Allocation (RA) [40] measures the possibility of a link occurs by common neighbors' degree. Here, \(z\) represents the common neighbor node of node \(x\) and \(y\), \(k_{z}\) is the degree of node \(z\). Then, the expression of RA is:  
\[S_{xy}^{RA}=\sum_{z\in\Gamma(x)\cap\Gamma(y)}\frac{1}{k_{z}} \tag{2}\]  
The Jaccard Coefficient (JC) [41] is proposed by [PERSON], which considers the neighbor nodes and common neighbors of two nodes. It is defined as:  
\[S_{xy}^{IC}=\frac{|\Gamma(x)\cap\Gamma(y)|}{|\Gamma(x)\cup\Gamma(y)|} \tag{3}\]  
Hub Promoted (HP) [42] is proposed for quantifying the topological overlap of pairs of substrates in metabolic networks, and defined as:  
\[S_{xy}^{HP}=\frac{|\Gamma(x)\cap\Gamma(y)|}{\min\{k_{x},k_{y}\}} \tag{4}\]  
Leicht-Holme-Newman (LHN) [43] considers the number of common neighbors and degree of two nodes. The expression of LNH is:  
\[S_{xy}^{LHN}=\frac{|\Gamma(x)\cap\Gamma(y)|}{k_{x}\times k_{y}} \tag{5}\]Average Commute Time (ACT) [44] thinks that the shorter the time, the more similar the two nodes, then the possibility of link between the nodes \(x\) and \(y\) can be defined as:  
\[S_{xy}^{ACT}=\frac{1}{l_{xx}^{+}{+}l_{yy}^{+}{-}2l_{xy}^{+}} \tag{6}\]  
where \(l_{xy}^{+}\) denotes the corresponding entry in terms of the Laplacian matrix's pseudoinverse, \(L+\) (\(L=D-A\)) [45].  
Cosine Time (CT) [45] is an inner-product-based measure, which is based on \(L+\) by calculating the possibility of two vectors. The cosine time is defined as the cosine of the node vectors:  
\[S_{xy}^{CT}=\cos(x,y)^{+}=\frac{l_{xy}^{+}}{\sqrt{l_{xx}^{+}{-}l_{yy}^{+}}} \tag{7}\]  
Matrix Forest (MF) [46] thinks the similarity between \(x\) and \(y\) can be understood as the ratio of the number of spanning rooted forests, such that nodes \(x\) and \(y\) belong to the same tree rooted at \(x\) to all spanning rooted forests of the network. MF is defined as:  
\[S^{MF}=(I+L)^{-1} \tag{8}\]
## 4 Confirmation of Flow Data According to Dynamic Characteristics
That the velocity near the pin was large can be considered advantageous because it could facilitate uniform mixing of the base material. Therefore, the velocity distribution in Figure 22 and the data in Figure 24 confirm that high RPM and low progress velocity are effective for FSW. \begin{table}
\begin{tabular}{c c} \hline
**Case** & **Value Range** \\ \hline Rotation Velocity (RPM) & 1100–2000 \\ Progress Velocity (mm/s) & 10–25 \\ \hline \end{tabular}
\end{table} 
Table 11: Analysis case. Figure 21: FVM modeling. Figure 25 shows the velocity distribution around the pin in the Y direction, which is the moving direction of the welding tool. The welding tool moves from left to right, and the velocity distribution diminishes in the right peripheral part of the pin because the progress velocity is higher. In addition, the higher the RPM for the same moving velocity, the broader is the range of the velocity distribution along the periphery of the pin. These findings are summarized in data form in Figure 26, and the front data and rear data of the pin are compared with respect to the progress direction. Figure 23: X direction velocity data: (**a**) progress velocity of 10 mm/s; (**b**) progress velocity of 25 mm/s. Figure 22: Velocity distribution along X direction in the constant velocity state: (**a**) velocity distribution; (**b**) area with a velocity of 0.1 m/s or higher. Figure 24: Correlations between progress velocity and rotation velocity of welding tool in the X direction. Figure 25: Y direction velocity distribution in constant velocity state: (**a**) velocity distribution; (**b**) area with a velocity of 0.1 m/s or higher. At the same velocity, the higher the RPM, the wider the gap between the front and rear data of the pin. It has the same tendency even when the progress velocity changes. These data are comprehensively organized in Figure 27. According to this figure, the higher the moving velocity of the welding tool, the smaller the velocity difference around the pin along the moving direction. The lower the rotational speed, the smaller the velocity difference. When welding was performed at a uniform velocity around the pin, mixing of the base material could proceed uniformly. However, considering that the overall velocity distribution around the pin was inadequate at a low RPM and high progress velocity, welding at a high RPM and a low progress velocity can be considered effective for FSW, even in the presence of a velocity gap.
###### Abstract  
We proposed a novel approach based on a complementary split-ring resonator metamaterial in a two-port MIMO antenna, giving high gain, multiband results with miniature size. We have also analyzed a circular disk metasurface design. The designs are also defected using ground structure by reducing the width of the ground plane to 8 mm and etching all other parts of the ground plane. The electric length of the proposed design is 0.53 \(\times\) 0.35\(\lambda\) \(\times\) 0.02\(\lambda\). The design results are also investigated for a different variation of complementary split-ring resonator ring sizes. The inner and outer ring diameters are varied to find the optimized solution for enhanced output performance parameters. Good isolation is also achieved for both bands. The gain and directivity results are also presented. The results are compared for isolation, gain, structure size, and the number of ports. The compact, multiband, high gain and high isolation design can apply to WiMAX, WLAN, and satellite communication applications.  
antenna; MIMO; directivity; gain; compact; wireless communication systems Article Design and Fabrication of Compact, Multiband, High Gain, High Isolation, Metamaterial-Based MIMO Antennas for Wireless Communication Systems.
## 3 Evaluation of Cyclic Behavior of OWT Foundation under Cyclic Loads
### Evaluation of Cyclic Behavior of OWT Foundation under Different Load Amplitudes
Figure 12 shows a plot of the cyclic stiffness as a function of the temporary rotation per cycle with different loading amplitudes. It can be seen that the foundation stiffness exhibits a tendency opposite to that of the damping ratio. As the load level increases, the stiffness decreases, and soil densification occurs as the number of cycles increases. Further, the change in foundation stiffness in the initial cycle occurs rather prominently during the cycles, similar to the change in damping. Consequently, the displacement of the OWT foundation is affected by both stiffness and damping ratio, and these have various cyclic behaviors depending on the load level and cycles. Therefore, to accurately analyze the long-term behavior of the OWT, it is necessary to consider both the stiffness and the damping ratio.
## 3 Results

Table 8: Differences in coefficient of variation scores of the CSAI-2 over the average of 16 matches between non-injured (n = 15) and injured (n = 6) players. Figure 6: CSAI variability scores based on the average of the 16 matches between non-injured players (n = 15) and injured players (n = 6). Regarding the CSAI-2 questionnaire, its three subscales (cognitive anxiety, somatic anxiety, and self-confidence) appear to be largely independent as no significant correlations are observed among them. However, the total CSAI-2 score shows a strong positive correlation with cognitive anxiety (rho = 0.649; \(p\)! 0.001) and a strong negative correlation with self-confidence (rho = -0.677; \(p\)! 0.001), with no significant association being found with somatic anxiety. In terms of the relationship between the POMS and CSAI-2 factors, moderate correlations are observed between somatic anxiety and tension (rho = 0.642; \(p\)! 0.001), and between somatic anxiety and anger (rho = 0.523; \(p\)! 0.05). A correlation approaching statistical significance is also detected between somatic anxiety and depression (rho = 0.373; \(p\)! 0.10). Self-confidence shows a strong positive correlation with vigor (rho = 0.835; \(p\)! 0.001) and a moderate negative correlation with TMD (rho = -0.475; \(p\)! 0.05). Finally, a moderate positive correlation is found between TMD and the total CSAI-2 score (rho = 0.481; \(p\)! 0.05). Altogether, these results suggest that players with higher self-confidence and vigor may present a more adaptive psychological profile, while elevated mood disturbance and anxiety (especially cognitive) could reflect maladaptive patterns associated with a higher injury risk.
# Phoneme Recognition in Korean Singing Voices Using Self-Supervised English Speech Representations
## 4 Experiments
### Data Preparation and Performance Evaluation  
#### 4.1.1 Datasets  
The HuBERT encoder that we used to obtain feature representations was pre-trained by the LibriSpeech corpus [33], which is a corpus of 960 h of 16 kHz read English speech, derived from read audiobooks from the LibriVox project.  
The dataset used for English-Korean adaptation was sourced from the National Institute of Korean Language, and comprises the Reading Aloud Korean Short Stories by Seoul People 2.0 (NIKLSEOUL) corpus [34]. This Corpus is composed of audio recordings and transcriptions of 930 sentences from 19 books. These sentences were read by 77 individuals from Seoul, ranging in age from 20 to 60, encompassing both male and female participants. The audio data is organized at the sentence level for a total of 87,035 recordings of around 180 h in duration. Each individual audio file varies in length (from 3 to 10 s), with an average duration of approximately 5 s. Additionally, all audio files share a common sampling rate of 16 kHz.  
For speech-singing adaptation, we utilized the Children's Song Dataset (CSD) corpus [35]. This corpus includes 50 songs sung by children in both Korean and English.  
Figure 4: Sample annotation. The different colors are used to distinguish the boundaries of syllables or pitches.  
These songs were professionally recorded by a female singer. Each song was recorded in two different keys, with variations from 3 to 5 semitones. This dataset is complemented by MIDI transcription files that include precise information about the onset and offset times of various elements in the songs. These MIDI files have been manually adjusted to accurately align with the corresponding syllable-level and phoneme-level lyrics annotations for training the melody predictor (Figure 2). The Korean part of this corpus consists of 100 recordings in total, collectively amounting to approximately 2.5 h of audio content. The duration of each individual audio file ranges from 17 to 190 s, with an average duration of approximately 90 s. All audio files in this corpus share a common sampling rate of 44.1 kHz.
# Similarity Calculation via Passage-Level Event Connection Graph
###### Abstract  
Recently, many information processing applications appear on the web on the demand of user requirement. Since text is one of the most popular data formats across the web, how to measure text similarity becomes the key challenge to many web applications. Web text is often used to record events, especially for news. One text often mentions multiple events, while only the core event decides its main topic. This core event should take the important position when measuring text similarity. For this reason, this paper constructs a passage-level event connection graph to model the relations among events mentioned in one text. This graph is composed of many subgraphs formed by triggers and arguments extracted sentence by sentence. The subgraphs are connected via the overlapping arguments. In term of centrality measurement, the core event can be revealed from the graph and utilized to measure text similarity. Moreover, two improvements based on vector tunning are provided to better model the relations among events. One is to find the triggers which are semantically similar. By linking them in the event connection graph, the graph can cover the relations among events more comprehensively. The other is to apply graph embedding to integrate the global information carried by the entire event connection graph into the core event to let text similarity be partially guided by the full-text content. As shown by experimental results, after measuring text similarity from a passage-level event representation perspective, our calculation acquires superior results than unsupervised methods and even comparable results with some supervised neuron-based methods. In addition, our calculation is unsupervised and can be applied in many domains free from the preparation of training data.  
Keywords:text similarity calculation; passage-level event connection graph; vector tuning; graph embedding +
Footnote †: journal: Applied
## 5 Environmental Aspects and Health Risks  
### Fuming  
The incorporation of recycled plastics into asphalt, although ecologically beneficial, presents considerable issues related to the emission of VOCs and PAHs. These compounds are predominantly emitted during the production or application of plastic-modified asphalt at elevated temperatures (generally 140-200 \({}^{\circ}\)C), when bitumen and polymeric additives experience thermal degradation and volatilization [20].  
Research indicates that raising the mixing temperature can significantly increase emissions. For example, VOC emissions rose by as much as 50.3%, and PAH emissions by up to 50.4% when the operating temperature was elevated from 160 \({}^{\circ}\)C to 180 \({}^{\circ}\)C [20]. Increased emissions result from the degradation of long-chain hydrocarbons in bitumen and plastic additives, releasing chemicals such benzene, toluene, styrene, and benzo[a]pyrene, some of which are recognized carcinogens [66].  
Nonetheless, not all forms of plastic exhibit the same behaviour. Certain polymers, such as recycled low-density polyethylene (LDPE) and commingled polyethylene (cPE), when integrated into bitumen via the wet method, showed decreased total VOC and PAH emissions compared to conventional bitumen. For instance, incorporating 6% cPE at 140 \({}^{\circ}\)C resulted in an 11.2% decrease in VOC emissions [20]. The decreases are partially attributable to the adsorption properties of plastics, which physically or chemically retain VOCs and PAHs on their surfaces during mixing [20].  
Conversely, some plastics, such as rABS, particularly at elevated temperatures of 180 \({}^{\circ}\)C, may lead to heightened emissions due to the degradation of polymer chains. The emission of benzene and other PAHs markedly increases with elevated rABS concentration, as observed with increases up to 19.5% for PAHs [20].  
The inhalation of these chemicals poses health concerns. Although VOC carcinogens such as benzene and styrene exhibited minimal non-carcinogenic and carcinogenic risks at occupational exposure levels, PAHs such benzo[a]pyrene and dibenz[a,h]anthracene presented negligible health risks [38]. Environmental risk studies indicate that asphalt workers, particularly under high-temperature conditions or when inappropriate plastic types are utilized, may be subjected to potentially harmful pollutants. Mitigation techniques have been investigated. Warm mix asphalt (WMA) technologies, which decrease operating temperatures, have demonstrated a substantial reduction in emissions [20]. Furthermore, additions such activated carbon, zeolite, geopolymer binders, and latex might diminish the release of VOCs and PAHs through improved adsorption and physical trapping processes [67; 68; 69; 70].
## 3 Results and Discussion
### Calibration of Critical Stresses and Critical Distances
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{\begin{tabular}{c} **Fracture** \\ **Criterion** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Notch** \\ **Radius, mm** \\ \end{tabular} } & \multicolumn{2}{c}{**Fracture**} & \multirow{2}{*}{\
\begin{tabular}{c} **Discrepancy,** \\ **N** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **\%** \\ \end{tabular} } \\  & & 0.5 & 6678 & 6678 & 0.0 & \\  & & 1.0 & 6864 & 7298 & \(-5.9\) & \\  & & 2.0 & 8433 & 8681 & \(-3.5\) & \\  & & 4.0 & 9266 & 9266 & 0.0 & \\ PMMA & & 0.5 & 6678 & 6678 & 0 & \\  & MS & 1.0 & 7036 & 7298 & \(-3.5\) & \\  & & 2.0 & 7797 & 8681 & \(-10.1\) & \(-6.8\) \\  & & 4.0 & 9266 & 9266 & 0 & \\  & & 2.0 & 7797 & 8681 & \(-10.1\) & \\  & & 4.0 & 9266 & 9266 & 0 & \\  & & 2.0 & 5048 & 5173 & \(-1.7\) & \(-1.4\) \\ GPPS & & 4.0 & 6125 & 6125 & 0.0 & \\  & & 0.5 & 3996 & 3996 & 0.0 & \\  & MS & 1.0 & 4320 & 4365 & \(-1.0\) & \\  & & 2.0 & 5061 & 5173 & \(-2.1\) & \\  & & 4.0 & 6125 & 6125 & 0.0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between experimental (exp.) fracture loads and fracture prediction (pred.) derived from MTS and MS criteria (experimental calibration of critical parameters). From the results shown in Table 5, it is evident that, unlike in Table 4, for the notch tip radii of 0.5 mm and 4 mm the discrepancies are not equal to zero, given that the values of the critical stress were not derived from the experimental calibration on such radii. Moreover, it is interesting to note that for all notch tip radii, the discrepancies were very low, proving that: (i) both EMC-MTS and EMC-MS combined criteria were successful in predicting the fracture load of the U-notched polymeric specimens under compression, and (ii) the EMC was quite efficient in estimating the compressive critical stress of the two polymers. In this case, all the predictions were slightly non-conservative (i.e., the predicted critical loads are a bit higher than the experimental ones). In this sense, the critical stress values obtained through the EMC were slightly larger (in absolute value) than those obtained from the experimental calibration, and this generates higher predictions of the critical loads. In the case of PMMA, the accuracy jumped from underestimations of \(-4.7\%\) and \(-6.8\%\) for MTS and MS criteria, respectively, to overestimations of \(+1.0\) % and \(+4.6\%\) for EMC-MTS and EMC-MS criteria; for GPPS (MTS), the accuracy changed from \(-1.4\%\) and \(-1.5\%\) (for MTS and MS) to \(+4.6\%\) and \(+5.4\%\) (EMC-MTS and EMC-MS, respectively).
## 2 Error Analysis of Overlapping Absorption Lines  
High-resolution transmission molecular absorption database (HITRAN) is the worldwide standard for calculating molecular transmission and radiation [21]. HITRAN absorption spectra of 100 parts per million by volume (ppmv) CH\({}_{4}\) at a 40 Torr gas pressure, a 298 K gas temperature, and a 41.5 m effective optical path length is presented in Figure 1. The overlapping absorption lines--including 4294.570 cm\({}^{-1}\), 4294.556 cm\({}^{-1}\), 4294.591 cm\({}^{-1}\), and 4294.595 cm\({}^{-1}\)--were selected as the research object. The absorption line at 4294.570 cm\({}^{-1}\) was selected as the target absorption line, which is minimally affected by the other absorption lines due to the line strength and the distance between the absorption lines. A HIRTAN simulation experiment was carried out based on the overlapping absorption lines at 4294.570 cm\({}^{-1}\) to analyze the effect of gas temperature on the concentration measurement. The interference of other absorption lines on the target absorption line at 297.5 K and 298 K are summarized in Table 1.  
The absorption lines at 4294.591 cm\({}^{-1}\) and 4294.595 cm\({}^{-1}\) hardly interfere with the target absorption line, and the absorption line at 4294.556 cm\({}^{-1}\) is considered the only interference absorption line. There was a gas temperature control error in the actual gas sensor system, which had a great influence on the measurement accuracy based on overlapping absorption lines. The relationship between the gas absorption and gas temperature was simulated using HITRAN at 297.5 K and 298 K. The measurement error was 2.91% for the selected overlapping absorption lines when the temperature was changed by 0.5 K. The error is about a few thousandths for the well-isolated absorption line under the same conditions. The overlapping absorption lines are affected by temperature an order of magnitude more than a single absorption line under the same conditions is. Therefore, gas temperature must be corrected to accurately measure gas concentration. At the same time, the output linewidth is influenced by laser parameters, including linear and nonlinear modulation parameters.  
In summary, measurements with overlapping absorption lines are strongly influenced by gas temperature and laser characteristics. These parameters must be considered to eliminate the interference of overlapping absorption lines. The principles of 2f-SR are introduced in the next section.
## 1 Introduction
Unnecessarily high specifications and various functions of the sensors definitely result in high cost, making it difficult to satisfy industries that require simple and rough torque measurements only. Complex usage and limited measurement capacity also make it difficult to use them with limited time and cost. Some studies have suggested methods for measuring torque at relatively low cost [22; 23; 24]. Especially for medical purposes, various studies on disposable torque sensors that can be discarded are being actively pursued to avoid the risk of infection [25; 26]. Unlike the medical systems, which are developed long-term, manufacturing fields are directly related to the practical use of the torque sensors. [PERSON] et al. developed a high performance torque sensor that can be applied to a milling system using microelectromechanical systems (MEMS) strain gages [27]. For a similar purpose, [PERSON] et al. are developing a multi-axial sensor that can be applied to a drilling bit [28]. However, in most of these torque measurements it is impossible to adjust the measurement range and sensitivity of the sensor, since the measuring sensor is tightly assembled to a specific part of a system. As can be seen from the history of the dynamometer, measuring the reaction torque using lever arms and load cells is one of the most typical torque measurement methods [29]. However, the ratio of the measured force to the converted torque is still not adjustable due to the fixed length of the lever arm. In general, the lever arm length acts as a moment arm that generates torque, so that the length and the accuracy can be easily changed by adjusting the moment  
way is presented. Then, it is extended to a generalized oblique model in which the arrangement of the load cell and lever/base arms is tilted to further adjust the length of the moment arm. By using the oblique model, an extremely short moment arm length can be achieved, which can guarantee very high sensitivity for torque measurement. In addition, the proposed torque sensor is combined with a dc motor, so that it can continuously measure the applied torque while changing the angle of the joint or other object. In order to verify the performance of the proposed torque sensor, a sinusoidal output torque is measured by rotating weights on a weight arm. Experiments are carried out by changing the applied torque and the moment arm length of the installed load cell. Furthermore, the sensor is applied to evaluate the performance of a commercial product to show a practical application example. Finally, discussion and conclusion regarding this study are presented in the last section.