esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling of Circular Quality Responses
At the same time, very low and very high arc current values cause an increase in the circularity error, Figure 10a. A high cutting speed brings instability and the dispersion of the plasma jet, and thus its deviation from a regular circular path motion. In this case, very low arc current values (45 A) additionally empower this instability effect of the plasma jet and result in a larger circularity error that is more noticeable at higher cutting speeds. On the other hand, a very high arc current (85 A) results in the more intense removal of the workpiece material, which results in the greater chipping of the cutting edge and the creation of unevenness. This finally brings an increase in the circularity error. At the very high cutting speed (6000 mm/min), this effect is combined with plasma jet instability and deviation that also enlarges the circularity error--top response. Figure 10c represents that at cutting speed 4000 mm/min, a higher cutting height contributes to an increase in the circularity error. This is especially visible at lower arc current values (45 A). As previously mentioned, very high cutting height values result in arc deflection that, as it is obvious, enhances the circularity error. Regarding the circularity error--bottom, the ANOVA results defined that the cutting speed has the most influential effect on this response in comparison to other parameters and their interactions. From Figure 11 it can be concluded that a higher cutting speed brings a higher circularity error. The reason for this is the same as previously mentioned. A higher cutting speed results in the instability and deviation of the plasma jet and the loss of its shape and form, which is particularly evident at the bottom side of the workpiece sheet, and this, consequently, results in a lower cut quality and more circularity errors. The analysis and discussion of the results presented in this study are strictly limited to the investigated experimental domain and are based on the structure of the defined experimental design. The reliability of the findings is reinforced by conducting multiple measurements for each response (five replicates), with the mean value adopted as the representative input for subsequent modeling, as outlined in Section 2. Furthermore, the quality and consistency of the analysis are substantiated by the strong predictive performance and robustness of the developed models, as illustrated in Figure 7a-d. The predictive capability of the models will be further demonstrated through validation experiments presented at the end of this study.
## 3 Simulation Results and Discussion
### Performance Analysis
#### 3.2.5 Navigation Solution Error  
The mean visibility of a GNSS satellite for 24 h according to the longitude of the geostationary orbit is shown in Figure 4. The result shows that if each of the GNSS systems is utilized individually, the number of satellites necessary to point positioning does not exceed the minimum number of satellites.  
The position estimation error is computed by the covariance propagation rule of least-squares. Figure 12 shows the position estimation error for 24 h in two geostationary orbits, where the black line is the result of multi-GNSS using main and side lobe signals together. Navigation is possible in almost all sections with the GPS alone system using main and side lobe signals, whereas the single-GNSS system using only the main lobe signal cannot achieve comparable results because it has low accuracy except in areas where non-MEO signal reception is possible.  
Figure 11: _URE_ for 24 h in Geostationary orbit.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Figure**} & \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Main Lobe Signal Only**} & \multicolumn{2}{c}{**(Main + Side) Lobe Signal**} \\ \cline{2-5}  & & **GPS** & **Multi** & **GPS** & **Multi** \\ \hline Above Korea & Mean (m) & 2.67 & 2.63 & 3.15 & 3.21 \\ (128\({}^{\circ}\)E) & Std (m) & 0.47 & 0.45 & 0.46 & 0.26 \\ \hline Opposite Korea & Mean (m) & 2.35 & 2.34 & 3.35 & 3.29 \\ (308\({}^{\circ}\)E) & Std (m) & 0.46 & 0.30 & 0.47 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _URE_ for 24 h in geostationary orbit.  
Table 6 summarizes the results in terms of mean value and standard deviation. The estimated position errors in case of multi-GNSS are 279 m (main lobe only) and 35 m (main and side lobe), approximately, above Korea, whereas the opposite side of Korea has the results of 92 m (main lobe only) and 23 m (main and side lobe), respectively.  
Results for the single-GPS system and multi-GNSS eventually differ significantly in the GDOP due to the number of visible satellites. This affects the estimated position error. As previously described, in addition to the multi-GNSS, the use of a side-lobe signal also increases the number of visible satellites. Additionally, non-MEO BDS satellites provide additional signals. On the other hand, the magnitude of \(C/N_{0}\) is larger when using only the main lobe. Thus, the UERE error is better than using side lobe signals together. However, it can be seen that navigation errors could be more effected by the visibility of increasing numbers of satellites, as indicated from the results.
## 2 Refined Numerical Modelling Approach
### Material Models  
According to previous studies [17; 18; 19; 20; 21], the stress experienced by concrete materials during the service stage of a bridge is typically below 0.4 times its strength and is primarily in an elastic state. Therefore, numerical analysis often employs an elastic constitutive model to enhance the calculation efficiency. Given this fact, material nonlinearity was disregarded and the concrete was modeled using a linear elastic constitutive model. The elastic modulus and Poisson's ratio of concrete are \(3.45\times 10^{4}\) MPa and 0.2, respectively. The rate of superstructure pouring concrete volume deviation was taken as 4% for a continuous concrete rigid-frame bridge, which was based on the statistical results achieved by [PERSON] et al. [20], and the density could be determined as 2649 kg/m\({}^{3}\). For prestress tendons, the modulus of elasticity, yield stress, Poisson's ratio and density were \(E=1.95\times 10^{5}\) MPa, \(f_{y}=1860\) MPa, \(\mu=0.3\) and \(\rho=7850\) kg/m\({}^{3}\), separately, based on the design data, and  
Figure 1: Schematic diagram of Bridge A: (**a**) size picture; (**b**) description of numerical segmental model.  
the ideal elastic-plastic model was chosen to simulate the stress-strain relationship of prestressed tendons. Based on the above description, the constitutive models of concrete and prestress are diagrammed in Figure 2. The cooling method was employed to induce prestress in the steel bar. For further information, refer to Section 2.5.4.
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 5 Experimental Result
### Result
#### 5.5.1 Result of Our Approach
This represents a performance improvement of approximately 10.49 points, highlighting the contribution of top-K code similarity learning in enhancing bug localization accuracy. The observed performance difference indicates that incorporating top-K code similarity learning allows the model to retrieve more relevant source code files before processing them with the LSTM-Attention mechanism. By identifying highly similar source code files in the initial retrieval stage, the model benefits from a more refined input set, leading to improved prediction of buggy files. In contrast, when this feature is not applied, the model relies solely on deep learning-based feature extraction, which may result in reduced accuracy because of less relevant input data. Based on this analysis, top-K code similarity learning was integrated into the final experimental setup to optimize performance. The results confirm that this approach effectively enhances the accuracy and reliability of the proposed bug localization method. A comparative analysis was conducted to evaluate the performance of different model configurations, including LSTM-Attention, standalone LSTM, and standalone Attention mechanisms, as shown in Figure 10. The x-axis represents the performance values, while the y-axis denotes the respective models. The results indicate that the LSTM-Attention model achieved an average performance of 79.09%, significantly outperforming both the LSTM-only model (70.12%) and the Attention-only model (68.05%). These findings suggest that the combination of LSTM and Attention mechanisms enhances bug localization accuracy more effectively than using either model individually. The performance improvement can be attributed to the complementary strengths of LSTM and Attention. While LSTM effectively captures long-term dependencies within  
Figure 9: Top-K Code Similarity Learning and Non-Learning. sequential data, it may struggle to focus on the most relevant sections of the input. The Attention mechanism mitigates this limitation by assigning importance scores to key features, allowing the model to emphasize the most relevant patterns in bug reports and source code. When integrated, LSTM and Attention work synergistically to improve both sequence modeling and feature selection, leading to superior predictive performance. Based on these results, the LSTM-Attention model was selected as the final approach for the experiment. The findings confirm that combining these two mechanisms provides a more effective solution for bug localization compared with using them separately.
## Appendix A T-H-O-Risk Methodology
### Step 3b--Bayesian Network  
When unknown elements are given, Bayesian networks are generally used as the decision-making criteria [49] because they help incorporate the following:  
* Multi-state variables;
* Dependent failures;
* Expert opinions that cannot be performed using standard FTA.  
BNs allow for the combination of previous probability assignments with the newly available statistical data. In this study, [PERSON] theorem is applied to derive a scenario probability that depends on uncertain factors. The key features of the method are:  
* For the incorporation of HOEs, ET is mapped into a BN.
* In the first instance, the BN inserts observations in the nodes that are observable and then utilizes the rules of probabilistic calculations forward and backward from the nodes that are observable to the target node via an intermediate node, if exists.
* The extended BN model incorporating HOEs, determines a more precise estimate for the probability of occurrence of the top event if a specific configuration of critical HOEs is given.
* The critical parameters are revised based on prior probability, posterior probability, and mutual information (i.e., entropy reduction) computed for each given HOEs.
* The BN scheme is essential when the system state depends on more than one event. Since ETs are only capable of representing single input in a node, multiple inputs are ensured by adopting a Bayesian approach [50]. This is the case when human errors are considered.  
By writing a conditional probability table, an ET can transform into a BN that provides the probability of an outcome given the probability of its causative events using the method suggested by [PERSON] et al. [51]. Netica which is a BN tool from Norsys was used for the BN modeling due to its ability to:  
* To incorporate case files;
* To provide sensitivity analysis;
* To operate in batch mode.  
Netica computes standard belief updating which solves the network by finding the marginal posterior probability for each node. The Netica BN scripts are given as supplementary material. In Figure A3, the aforementioned scenarios are depicted in the BN structure. It should be noted that in Figure A3, the symbol 0+ indicates values that are very small and negligible. The exact calculation of those values is computed by Netica and exported to an Excel spreadsheet.  
Notations for the BN structure below:  
* FY: fire ignition.
* FN: no fire ignition.
* DY: detection ON.
* DN: detection OFF.
* SuY: Suppression ON.
* SuN: Suppression OFF.
* SpY: fire and smoke spreads outside AOF
* SpN: fire and smoke does not spread outside AOF.
* NY: alarm/notification ON.
* NN: alarm/notification OFF.
* EY: egress protection ON.
* EN: egress protection OFF.
## 2 Materials and Methods
### Block Coordinate Descent  
The starting point is the block coordinate descent (BCD) algorithm introduced by [4]. It can be used to train a generalized linear model in a federated setting. In the standard approach, all parties know the label \(\mathbf{y}\). The first party tries to create a linear model to predict as much from \(\mathbf{y}\) as possible from its own data. It hands its prediction to the next player, who tries to improve the prediction as much as possible. This continues until the stopping criterion is met.  
A simple modification of the original algorithm communicates the missing parts rather than their own predictions. This has the advantages that only a single party needs to know the true label. This is a common situation in many joint learning problems. For this reason the _single label owner_ variant Algorithm 1 of BCD will be used here.  
```
1:[PERSON] and [PERSON] initiate \(\mathbf{\beta}_{a}^{(0)}\leftarrow\mathbf{0}\) and \(\mathbf{\beta}_{b}^{(0)}\leftarrow\mathbf{0}\), respectively
2:[PERSON] initiates \(v_{b}\leftarrow\mathbf{y}\)
3:\(i\gets 0\)
4:while stopping criterion is not met do
5:player [PERSON] do
6:\(\mathbf{\beta}_{a}\leftarrow(\mathbf{X}_{a}^{T}\mathbf{X}_{a})^{-1}\mathbf{X}_{a}^{T}\mathbf{v}_{b}\)
7:\(\mathbf{\beta}_{a}\leftarrow\mathbf{\beta}_{a}+\mathbf{\beta}_{a}\)
8:\(\mathbf{v}_{a}\leftarrow\mathbf{v}_{b}-\mathbf{X}_{a}\mathbf{\beta}_{a}\)
9:send \(\mathbf{v}_{a}\) to [PERSON]
10:end player
11:player [PERSON] do
12:\(\mathbf{\beta}_{b}\leftarrow(\mathbf{X}_{b}^{T}\mathbf{X}_{b})^{-1}\mathbf{X}_{b}^{T}\mathbf{v}_{a}\)
13:\(\mathbf{\beta}_{b}\leftarrow\mathbf{\beta}_{b}+\mathbf{\beta}_{b}\)
14:\(\mathbf{v}_{b}\leftarrow\mathbf{v}_{a}-\mathbf{X}_{b}\mathbf{\beta}_{b}\)
15:send \(\mathbf{v}_{b}\) to [PERSON]
16:end player
17:\(i\gets i+1\)
18:endwhile
```  
**Algorithm 1** Incremental 2-party block coordinate descent algorithm. The subscript \(a\) is for [PERSON] and \(b\) for [PERSON]
## 4 Discussion
#### Threats to Validity and Limitations  
While conducting this research, we encountered various constraints and limitations that require recognition. A significant limitation involved the utilization of a single dataset, potentially overlooking the diverse traits and features of the entire population. This reliance could potentially bias our models and restrict the applicability of our conclusions. Thus, future additional validation of our findings using broader and more diverse datasets is desirable to ensure the robustness and verifiability of our outcomes.  
Also, it is important to address the practical challenges associated with the inclusion of 95% confidence intervals (CIs) in evaluating the statistical significance of metrics, particularly in the context of imbalanced datasets. Our analysis employed five different sampling techniques across seven distinct models, necessitating extensive bootstrap sampling to calculate CIs for each combination. However, conducting 1000 bootstraps for example for each of the five sampling techniques across seven models would result in a total of 35,000 cycles, rendering it impractical within the scope of our study. Therefore, we focused on cross-validation, prediction on unseen data, and comparison of these sets as our primary validation strategies. While traditional CIs were not incorporated, these validation methods are recognized as efficient and reasonable alternatives, providing robust evidence for the validity of our findings. We encourage future studies to explore methods for incorporating CIs in similarly complex analyses, balancing statistical rigor with practical considerations.  
Figure 7: Models’ recall performance with optimal combinations per resampling technique.
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 3 Hydrophilic Phytochemicalemicals
### Apigenin
#### 3.4.2 Biological Activity  
Apigenin demonstrates cytotoxic effects against cancer cells, primarily through the induction of apoptosis and inhibition of angiogenesis, as confirmed in studies on skin melanoma [111; 114; 118]. Its hydroxyl groups allow it to act as both an antioxidant and a metal ion chelator, protecting cells against oxidative stress [133]. Thanks to moderate hydrophilicity and a small number of functional groups, apigenin easily penetrates biological membranes, enabling intracellular action and interaction with key molecular pathways [116; 129]. Apigenin improves skin barrier function by increasing filaggrin expression, lipid synthesis, and lamellar body formation, contributing to improved skin hydration and integrity [134]. It also exhibits significant anticancer effects by inhibiting mTOR, MAPK, PI3K/Akt, and Wnt/\(\beta\)-catenin pathways and inducing apoptosis [135]. Formulations with carriers such as PLGA and transfersomes significantly enhance its bioavailability and skin penetration [136].  
In summary, although quercetin, luteolin, and apigenin differ structurally and vary in polarity and molecular weight, a consistent trend emerges in their delivery outcomes: encapsulation in lipid-based nanocarriers improves dermal targeting by enhancing compound stability, modulating release kinetics, and facilitating efficient skin penetration. Notably, each flavonoid responds differently depending on the carrier type, suggesting the importance of customized formulation strategies to optimize therapeutic performance.
# Optimization of Technological Parameters of Straw Fiber-Based Plant Fiber Seeding Pot Raw Materials
###### Abstract  
Straw fiber seedling pots are a promising substitute for plastic seedling pots. The mixing mode of straw fiber affects the mechanical properties of the raw material membrane of the seedling pot. To explore the processing technology of making the raw material membrane of the seedling pot with two kinds of plant fibers in a layered manner, the optimal combination of the process parameters of the raw material membrane of the seedling pot without additives in the production process was studied experimentally. Response surface methodology (RSM) was used to analyze the parameters (beating degree of unbleached softwood kraft pulp fiber, beating degree of wheat straw fiber, wheat straw fiber quality percentage content, and film grammage) with regard to the dry tensile index and Z-direction tensile index of the seedling pot body. The optimal process parameter combination with a certain dry tensile index and Z-direction tensile index of seedling pot raw material was obtained by using four factors and five levels of a quadratic regression orthogonal rotation center combination design parameter optimization experiment. The optimal technical parameters were optimized as wheat straw fiber quality percentage content 70%, film grammage 70 g/m\({}^{2}\), unbleached softwood kraft pulp fiber beating degree 47-48 \({}^{\circ}\)SR, and wheat straw fiber beating degree 65-75 \({}^{\circ}\)SR. With the optimal conditions, the dry tensile index of the seedling bowl raw material film was between 21 and 22 N-(m:g\({}^{-1}\)), and the Z-direction tensile index was greater than 2.1 N-(m:g\({}^{-1}\)). Using wheat straw fibers and unbleached sulfite wood pulp fibers as raw materials for seedling pots, the raw material membrane of the seedling pots was made in a layered manner. The experimental study proved this feasibility. With this mixing process of raw materials, the straw fiber-based plant fiber seedling pot would meet the demands of a crop nursery after adding chemical additives. The research results provide a theoretical basis and technical support for the manufacture of the raw material membrane of the seedling pot body.  
central composite design; seedling pot; wheat straw; completely degradable; optimization +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Cause of Cracking at Anchorage Zone  
Cracking of concrete was observed at the anchorage zone of the top and bottom slabs. The first cracking was formed at the concrete of the top surface of Section A-A at tension section when the top slab was loaded until prestressing force of 70% of the equivalent force. With the increase of the load, the cracking width gradually increased. When the load was equal to the equivalent force, crack was observed at the top surface of Section B-B. Finally, the cracking was formed at the top surface of the tooth plate of the top slab between Section A-A and Section C-C. The total length of cracking was 78 cm. The average width was 0.35 mm, as shown in Figure 14a. A crack was observed at the top surface of Section I-I when the load was increased to the prestressing force of 90%. The top surface of Section H-H started to crack when the load was equal to the prestressing force of 100%. The crack had  
Figure 13: The relationship between load and stress of the concrete at Section D-D on the bottom slab specimen: (**a**) the top surface of the concrete; and (**b**) the bottom surface of the concrete. MP, measuring point.  
a length of 62 cm and an average width of 0.1 mm. The cracking was also formed at the top surface of the anchorage block.  
The two-longitudinal cracks were observed at the top surface of the triangular anchorage block when the bottom slab was loaded until the prestressing force of 80%. They were located at the top surface of Sections B-B and C-C and further extended when the load was equal to the prestressing force of 100%. The crack at Section B-B had a length of 59 cm and an average width of 0.2 mm. The crack at Section C-C extended to the end of the anchorage block. The final crack was 62 cm long and 0.1 mm wide, as illustrated in Figure 14b.  
Through the analysis above, we can see that the concrete in the anchorage zone bear a great compressive stress under the large prestressed force. Accordingly, the transverse deformation of compressive concrete (extrusion and expansion) produced. However, influenced by the restraining effect of surrounding concrete, which is similar to the effect of stirrup, free deformation of the compressive concrete cannot be fully produced. Then, transverse tensile stress was formed in the surrounding concrete. With the increase of the load, the deformation and stress gradually increased. When the surrounding tensile stress exceeded the tensile ultimate strength, the local longitudinal crack was formed. Thus, the formation of crack is due to the fact that the deformation of compressed concrete in the anchorage zone is not fully restrained.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### Silver-Based NPs
#### 4.1.4 Attenuation of Abiotic Stress  
AgNPs enhanced the tolerance to drought stress in germination and early growth stages of _Thymus daenensis_ [PERSON] and _Thymus vulgaris_ L.; they improved germination characteristics and root length up to 200 mM salinity, although they exhibited positive impact on vigor and shoot length only up to 100 mM salinity as compared to the control [195].