esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
A block diagram of the overall design of the proposed system is shown in Figure 1. It consists of three main modules: pre-processing, feature extraction, and disaster event classification. First, we processed each tweet through a pre-processing module to remove any noise. Afterwards, the noise-free tweets were tokenized and annotated using the part-of-speech (POS) tagger. The selected tagger can also perform the lemmatization, NER, and entity-relationship functions. We used the CoreNLP tools that label every tweet word to the corresponding part-of-speech (POS) tag and also recognize the relationship among the words within the sentence. The output was then critically analyzed to identify the grammar rules for all features shown in Table 1.  
The manually identified grammar rules were then converted into automatic functions and applied to the dataset to automatically extract feature words for each category. The next step was to identify the class of the tweets, such as \"eyewitness\", \"non-eyewitness\", or \"Unknown\" source, based on the extracted features. Machine learning and deep learning approaches were used for the classification task rather than manual rule-based classification. The following subsections briefly describe each process, from data set selection to results evaluation.
## 3 Results Analysis and Discussion
### Velocity Field and Pressure Loss  
Figure 9 shows the time-averaged dimensionless velocity distributions calculated by LES for various cooling holes in the mid-span section plane. The region of low momentum near the lee side (blue region in Figure 9) is caused by detachment induced by inlet effects and diffuses toward the expansion region of the hole. From Figure 9, it can be seen that Case 3 has a larger and shorter low-momentum region compared to the other cases, effectively reducing the blow-off phenomenon observed in Case 1. This is because a peak in Case 3 is located behind the cooling hole with a larger expansion angle. At the same time, due to two peaks upstream along the flow direction, there is an early interaction between the coolant jet and the hot main flow at the outlet, which is a very important reason for the improvement of cooling performance.  
Figure 9 shows that Case 1 (circular hole) has a higher jet velocity both at the outlet of the cooling hole and in the high momentum region within the cooling hole compared to the shaped cooling holes. This results in stronger blow-off phenomena and deeper penetration of the jet into the main flow. As a result, the cooling performance of Case 1 decreases. It can also be seen from the figure that in Cases 2 to 4, the angle between the high-velocity jet in the cooling hole and the wall is smaller, effectively preventing the blow-off phenomenon. In Cases 1 and 4 (the yellow striped area), the high-velocity jet in Case 1 exits the cooling hole in the upstream half with a larger angle of inclination, while the high-velocity jet in Case 4 exits almost parallel to the wall surface at the downstream end of the cooling hole with a smaller angle of inclination and almost sticks to the wall surface during ejection.  
In Figure 10, it can be seen that the pressure loss coefficients of shaped cooling holes are smaller than those of circular cooling holes, which proves that shaped cooling holes can reduce the total pressure loss along the cooling hole due to their geometric advantages. This shows that shaped cooling holes have a significant advantage in terms of pressure drop. Although Case 2 has the lowest total pressure drop, as mentioned earlier, its cooling  
Figure 9: Time-averaged non-dimensional velocity distributions calculated from LES for different cooling holes in the center of the section plane.  
performance is very poor. This indicates that although Case 2 has minimal pressure drop, it is not a good cooling solution.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 5 Conclusions  
This paper introduces a novel framework for the Zero-Shot Detection of Visual Food Safety Hazards that enables the identification of previously unseen contamination types without requiring explicit training examples. We present three main contributions: (1) a specialized FSKG that encodes domain-specific relationships between food categories and visual safety attributes, (2) an adapted Knowledge-Enhanced Feature Synthesizer with Multi-Source Graph Fusion and Region Feature Diffusion modules tailored for food safety detection, and (3) a comprehensive FSVH dataset with 18,326 images across 26 food categories annotated with 48 visual attributes.  
Extensive experiments demonstrate that our approach achieves 63.7% mAP in zero-shot detection, significantly outperforming state-of-the-art methods by 6.9 percentage points. In the more challenging generalized zero-shot setting, our framework maintains a robust performance with a 59.8% harmonic mean, effectively balancing the detection of both seen and unseen hazards. These results validate the effectiveness of leveraging structured domain knowledge for zero-shot food safety hazard detection, representing a significant step toward automated, adaptable food safety inspection systems capable of identifying emerging visual hazards without a costly retraining process.  
Conceptualization, L.G. and Y.L.; methodology, L.G. and Y.L.; validation, L.G., X.H. and W.L.; formal analysis, X.H.; writing--original draft preparation, L.G.; writing--review and editing, X.H. and W.L.; supervision, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Superoxide Anion Radical Scavenging Assay  
Figure 6C indicated that the scavenging ability of six isolated APs (P1 to P6) on the superoxide anion radical enhanced gradually following the increase in peptide content within the range of 0.05-5.0 mg/mL. The EC\({}_{50}\) value of P4 was 0.38 mg/mL, which was significantly less than those of other five isolated APs (EC\({}_{50}\) values of 1.79, 0.56, 1.51, 3.04, and 0.71 mg/mL for P1, P2, P3, P5,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**EC\({}_{50}\) (mg/mL)**} \\ \cline{2-4}  & **DPPH Radical** & **Hydroxyl Radical** & **Superoxide Anion Radical** \\ \hline P1 & 3.76 \(\pm\) 0.16  & 1.79 \(\pm\) 0.11  \\ P2 & 0.31 \(\pm\) 0.02  & 0.30 \(\pm\) 0.03  \\ P3 & 1.33 \(\pm\) 0.09  & 1.71 \(\pm\) 0.06  & 1.51 \(\pm\) 0.11  \\ P4 & 0.33 \(\pm\) 0.02  & 0.38 \(\pm\) 0.03  \\ P5 & 0.93 \(\pm\) 0.04  & 0.81 \(\pm\) 0.04  & 3.04 \(\pm\) 0.18  \\ P6 & 0.46 \(\pm\) 0.04  & 0.52 \(\pm\) 0.04  & 0.71 \(\pm\) 0.04  \\ GSH & 0.22 \(\pm\) 0.01  & 0.12 \(\pm\) 0.01  & 0.09 \(\pm\) 0.01  \\ \hline \hline \end{tabular}
\end{table}
Table 2: EC\(and P6, respectively), but significantly greater than that of GSH (0.09 mg/mL) (\(p<0.05\)). What is more, the EC\({}_{50}\) value of P4 was lower than those of APs from hydrolyses of muscle of monkfish (EWPAQ: 0.62 mg/mL) [45], skate cartilage (FIMGPY: 1.61 mg/mL; GPAGDY: 1.66 mg/mL; IVAGPQ: 1.82 mg/mL) [3], hairtail muscle (EC\({}_{50}\) values of 1.34, 2.54, and 2.08 mg/mL for IYG, AKG, and KA, respectively) [44], swim bladders of miuiv croaker (EC\({}_{50}\) values of 3.35, 1.92, 0.87, 3.03, and 3.04 mg/mL for FSGLR, FYKWP, GFEPY, GFYAA, and FTGMD, respectively) [8], _Tergillarca granosa_ muscle (MDLFTE: 0.75 mg/mL; WPPD 0.46 mg/mL) [13], and croceine croaker muscle (MILMR: 0.993 mg/mL) [38]. However, the EC\({}_{50}\) values of P4 were higher than those of APs from protein hydrolyses of round scad (HEKVC: 0.235 mg/mL; HDHPVC: 0.265 mg/mL) [46], bluefin leatherjacket skin (FIGP: 0.311 mg/mL) [12], red stingraz cartilages (IEPH: 0.17 mg/mL; IEEEQ: 0.16 mg/mL) [37], and muscle of monkfish (FLHRP: 0.101 mg/mL) [45]. As a major product of the primary oxidase sources of ROS in organisms, superoxide anion radicals can damage the carbonyl compounds and initiate lipid peroxidation when they convert to the highly reactive hydroxyl radical. In addition, the potential damage of superoxide anion radicals can be scavenged by SOD. Therefore, six isolated APs (P1 to P6), especially P4, can act as the radical eliminators to eliminate the radical damage along with SOD.
# A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization
## 5 Conclusions  
This paper proposed an improved binary bat algorithm for antenna topology designs. The idea was to reduce the optimization time by introducing angle modulation technology to reduce the optimization dimensions. Compared with other optimization algorithms, it also has advantages in global search capability and convergence speed. The knapsack problem was used to verify the optimization ability of P-AMBA in high-dimensional problems. The results showed that the higher the dimension, the more obvious the advantage of this algorithm. Finally, through a topology design example of a compact dual-band planar monopole antenna, the superiority of the proposed P-AMBA in the optimization of high-dimensional discrete search space was verified.  
In the antenna optimization example, since much time was spent on the antenna electromagnetic simulation process, the advantages of the algorithm in terms of optimization time were not obvious. Subsequent research will aim at neural networks-based antenna designs by introducing surrogate models instead of electromagnetic simulation tools to achieve fast antenna topology optimization. Moreover, a multi-objective antenna design will also be the task of follow-up research.  
Conceptualization, J.D. and Z.W.; methodology, J.D. and Z.W.; software, Z.W. and J.M.; validation, [PERSON] and [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and J.M.; visualization, [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] and J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China, grant number 61801521, 61471368, and 61971450, in part by the Natural Science Foundation of Hunan Province, grant number 2018 JJ2533, and in part by the Fundamental Research Funds for the Central Universities, grant number 2018 gczd014 and 20190038020050.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.  
Figure 10: Final geometry (**a**) and S\({}_{11}\) curve (**b**) of the final antenna.
# Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique
## 3 Proposed Wind Power Curve Model
### Fuzzy C-Means Clustering  
FCM was first proposed by [PERSON] in 1981 [39]. In FCM, each sample is not strictly divided into a certain class. It has a degree of membership for each class. Generally, the objective function for FCM can be expressed as [40]  
\[J_{m}(\mathbf{U},v_{1},\cdots,v_{k})=\sum_{i=1}^{k}J_{i}=\sum_{j=1}^{n}\sum_{i =1}^{k}u_{ij}^{m}d_{ij}^{2} \tag{2}\]  
where the membership matrix \(\mathbf{U}=\left\{u_{ij}\right\}_{k\in\mathcal{U}^{n}}\), \(1\leq i\leq k\), \(1\leq j\leq n\), \(\{v_{1},\ldots,v_{k}\}\) are \(k\) cluster centers, \(n\) is the number of samples, \(m\) is the weighting exponent, and its value is usually selected as 2, \(u_{ij}\) denotes the degree that the \(j\)th sample belongs to the \(i\)th cluster, \(0<u_{ij}<1\), \(\sum_{i=1}^{k}u_{ij}=1\), \(d_{ij}=\left\|x_{j}-v_{i}\right\|\) is the Euclidean distance between the sample \(x_{j}\) and the cluster center \(v_{i}\).  
Considering the constraint on \(u_{ij}\), the objective function can be rewritten as the following function by introducing a Lagrange multiplier \(\lambda\),  
\[F=\sum_{j=1}^{n}\sum_{i=1}^{k}u_{ij}^{m}d_{ij}^{2}+\lambda\Bigg{(}1-\sum_{i=1} ^{k}u_{ij}\Bigg{)} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Expression** & **Parameters** \\ \hline
3-PLF & \(P=\frac{k_{W}\sigma^{W}}{k+\gamma_{0}(\sigma^{W}-1)}\) & \(k\): the capacity of the system; \(r\): the rate of increase; \(y_{0}\): no special mean. \\
4-PLF & \(P=\alpha+\frac{\beta-\alpha}{1+\left(\frac{r}{r}\right)^{3}}\) & \(\alpha\): the maximum asymptote; \(\beta\): the minimum asymptote; \(\gamma\): the inflection point, \(\delta\): means the hill slope. \\
5-PLF & \(P=\alpha+\frac{(\beta-\alpha)}{\left(1+\left(\frac{r}{r}\right)^{3}\right)}\) & \(\epsilon\): the asymmetric factor; \(\alpha,\beta,\gamma,\delta\) are the same with the parameters in \\
6-PLF & \(P=\delta+\frac{\alpha-\delta}{\left(\epsilon+\exp(-\beta(V-V_{0}))\right)^{1/ \gamma}}\) & \(\delta\): the lower asymptote; \(\alpha\): the upper asymptote; \(\beta\): the growth rate; \(\gamma\): the closet asymptote to the maximum growth part of the curve; \(V_{0}\): the value \(P(0)\); \(\epsilon\): the value around 1. \\ MHTan & \(P=\frac{\alpha_{1}\epsilon^{2}V-\alpha_{W}\sigma^{W}-\alpha_{W}\sigma^{W}}{ \alpha_{W}\sigma^{W}-\alpha_{W}\sigma^{W}-\alpha_{W}\sigma^{W}}+\alpha_{9}\) & \(\alpha_{1},a_{2},\cdots,a_{9}\): nine model parameters. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parametric wind turbine power curve models.  
Let the partial derivatives of function \(F\) with respect to \(u_{ij}\) and \(v_{i}\), respectively, equal to 0, the updated formulas for \(u_{ij}\) and \(v_{i}\) can be expressed as [40]  
\[u_{ij}=\left[\sum_{g=1}^{k}\left(\frac{d_{ij}}{d_{gj}}\right)^{\frac{2}{m-1}} \right]^{-1} \tag{4}\]  
\[v_{i}=\frac{\sum_{j=1}^{n}u_{ij}^{m}x_{j}}{\sum_{j=1}^{n}u_{ij}^{m}} \tag{5}\]  
FCM employs the above two functions to update the membership matrix \(\mathbf{U}\) and all cluster centers so as to make the objective function reach its minimum.
## 3 Pavement Dynamics Theory and Finite Element Model Verification  
### Pavement Dynamics Theory  
According to the layered dynamic theory of asphalt pavements, the finite element dynamic equation for the layered pavement structure is given by:  
\[M\{\ddot{u}\}+C\{\dot{u}\}+K\{u\}=P(t) \tag{3}\]  
In the equation, \(\ddot{u}\), \(\dot{u}\), and \(u\) represent the acceleration, velocity, and displacement vectors, respectively. \(P\) (t) is the vehicle load matrix, and \(M\), \(C\), and \(K\) are the mass, damping, and stiffness matrices, respectively.  
The mass matrix \(M\), damping matrix \(C\), and stiffness matrix \(K\) are defined as follows:  
\[M_{ij}=\int\limits_{V}\rho N_{i}N_{j}dV \tag{4}\]  
\[C=\alpha M+\beta K \tag{5}\]  
\[K_{ij}=\int\limits_{V}B_{i}^{T}\rho DB_{j}dV \tag{6}\]  
Figure 2: The method of fitting deflection from the deflection slope.  
In the equation, \(\rho\) represents the material density, \(N_{\rm i}\) and \(N_{j}\) are the shape functions used to describe the relationship between the displacement at any point within the element and the nodal displacements, and \(V\) is the element volume. \(\alpha\) and \(\beta\) are the damping coefficients, while \(B_{i}\) and \(B_{j}\) are the strain-displacement matrices that describe the relationship between the element strain and nodal displacements. \(D\) is the elastic matrix.
## 3 Numerical and Experimental Studies
### Numerical Study
#### 3.1.2 Numerical Setup  
In order to investigate the effect of pipe flow on the obscuration in aspirating smoke detection systems, different numerical case studies have been investigated. Firstly, the pipe with a length of 20 m and ten holes was considered. Then, the pipe with 20 m and twenty holes pipe was also investigated. The detailed information of case studies was shown as follows.  
1. ASD system with one branch pipe with ten holes.  
Based on the Pipe Hole Network Program, the parameter of fluid flow in the pipe can be calculated. As the first step, a straight pipe with a length of 20 m was considered to investigate the flow in the ASD system. The pipe had 10 holes. The diameter of the hole was 5 mm. The specifications of the pipe are shown in Table 1.  
2. ASD system with one branch pipe with twenty holes.  
In this case, the pipe with a length of 20 m was considered to investigate the flow in the ASD system. The pipe had 20 holes. The parameters of the are shown in the Table 2.
## 2 Materials and Methods
### Gas Chromatography Coupled to Mass Spectrometry Method  
The RGEO was analyzed using a sensitive and qualitative gas chromatographic technique performed on an HP6890 gas chromatograph coupled with an HP5973 mass spectrometer. The sample, diluted 1:1000 in hexane, was injected in a splitless mode in a heated inlet at 230\({}^{\circ}\)C, and run through a Bruker Br-5 MS column (30 m \(\times\) 0.25 mm; film thickness 0.25 \(\upmu\)m) (Agilent Technologies, Santa Clara, CA, USA), carrier gas: helium, flow rate 1.0 mL/min. The gas chromatograph oven temperature was set up to 50 \({}^{\circ}\)C for 5 min, raised to 300 \({}^{\circ}\)C at a temperature rate of 6 \({}^{\circ}\)C/min, and kept there for 5 min. The HP5973 mass spectrometer operating parameters were as follows: ionization potential, 70 eV; mass analyzer quadrupole 150 \({}^{\circ}\)C; solvent delay 3.0 min; mass range 50 to 550 amu. The NIST0.2 spectral library (USA National Institute of Science and Technology software) was employed to identify the compounds (similarity indexes \(>\) 90 %), followed by a comparison of the retention index (RI), calculated based on the n-alkanes C\({}_{8}\)-C\({}_{20}\) homologous series, with the values reported in the literature [21].
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 7 Implementation and Real-World Performance Analysis
### Model Deployment in Mixed Reality  
Once the model achieved satisfactory accuracy, it was converted to the ONNX format for lightweight inference on the HoloLens 2 headset. This step is critical as real-time applications demand low-latency performance to ensure smooth user interactions.  
The deployment process followed these steps: 1. The trained YOLOv4-Tiny model was exported to ONNX format. 2. ONNX Runtime was integrated into Unity to enable real-time inference on HoloLens. 3. The model was optimized for mobile GPU execution to maintain at least 15 FPS on the headset. 4. The MR environment was developed using Unity and MRTK (Mixed Reality Toolkit), allowing for the real-time visualization of detected vials.
## 6 Conclusions  
In this paper, the detailed design analysis and manufacturing of the printed circuit board (PCB) of two topologies of the telecom PFC converters with high-power density, high conversion efficiency, high input power factor, and lower THD are performed. The proposed small signal modeling is used to design the parameters of the PI controllers for the current and voltage control loops for both topologies. Simulation and experimental tests are used to test the performance of the designed converters with different loading conditions. The simulation and experimental results show that the conventional PFC topology with the proposed design offers higher efficiency and higher power factor at lower loading condition until 800 W as compared with the interleaved PFC converter, but when the loading condition is increased more than 800 W, the thermal heating in the conventional boost inductor, the boost diode, and Mosfet is increased leading to higher conduction losses and higher current distortion factor which reduced the conversion efficiency and, also, reduced the input power factor.  
The experimental results show that, at the higher loading condition, the efficiency of the designed interleaved boost PFC converter is higher as compared with the conventional boost PFC converter since the power is channeled through two paths which reduced the boost inductor size and weight. Furthermore, the interleaving technique reduces the voltage and current stresses across the Mosfet switches and improves the component's thermal performance, improvement of the converter thermal performance decreases the conduction losses of the inductor, the boost diode, and the Mosfet, increased the conversion efficiency and input power factor with higher loading condition.  
A.H.O. created, designed and applied the proposed controlling technique. The literature review and manuscript preparation, as well as the simulations, were carried out by A.H.O. Experimental verification was carried out by A.H.O. and J.B. Final review of manuscript corrections was conducted by J.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grant (2018R1D1A3B0704376414) from National Research Foundation of Korea (NRF), grant (21 RTRP-B146046-04) from Railroad Technology Research Program (RTRP) funded by Ministry of Land, Infrastructure and Transport of the Korean government and research grant from Hyundai Motor Group.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Numerical Experiments and Results
### Example and Parameter Setting
#### 4.2.3 Effectiveness Analysis of the Acceptance Criteria of the Solution  
The optimal acceptance criterion (Strategy 3), the simulated annealing (SA) acceptance criterion (Strategy 4), the acceptance criterion of the stage decline tolerance coefficient strategy (Strategy 1) and the acceptance criterion of the exponential decreasing tolerance coefficient strategy (Strategy 2) are compared. The optimal value of the objective function value of Strategy 1 is better than that of the other strategies, and the solution quality of Strategy 1 is higher. There is little difference in the stability of the solutions for all the acceptance criteria. In addition, the algorithm based on Strategy 4 has the longest running time. Its average running time is 2.08 times, 1.93 times and 1.87 times that of Strategy 3, Strategy 2 and Strategy 1, respectively. This is because Strategy 4 judges and accepts solutions with random probability, which substantially increases the number of invalid searches. In particular, contrast Strategy 1 with Strategy 2. The algorithm based on Strategy 2 has a slightly shorter running time, a slightly faster convergence speed and a poor solution quality. The algorithm based on Strategy 1 has a slightly longer running time and a higher solution quality. The algorithm based on Strategy 1 performs better. This is because Strategy 1 avoids the problems of too-fast convergence and premature maturity, accounts for the solution quality and solution timeliness and jumps out of the local optimal solution. It can be observed that the design of the stage decline tolerance coefficient of Strategy 1 is more effective (Figure 5).