esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Imputation Based on Retrieval-Augmented Generation
## 4 Experimental Verification
### Evaluation of Retriever Effectiveness
#### 4.2.4 Experimental Results and Analysis
Since the retriever is applied to data imputation, for an anchor tuple (\(t\)) with a missing \(t[j]\), it is natural to treat the augmented anchor with the \(j\)-th attribute removed as negative, i.e., a tuple very similar to the anchor but lacking information to assist in imputing the missing value. This type of negative sample is added to the training data. However, we observe that it leads to a significant decline in retrieval results, primarily because accurately capturing and distinguishing cell-level semantics when encoding each tuple as embeddings is challenging. Consequently, incorporating these negative samples adversely affected performance. Moreover, this change resulted in a substantial drop in performance compared to other discussed modifications. Based on the above research and discussion, we find that the data construction method for the retriever is highly effective and that the combination of anchor tuples, positive tuples, and negative tuples is crucial. Notably, the impact of the methods used to construct positive and negative samples on performance is more significant than changes in anchor tuple construction.
# Shear Capacity of Textile-Reinforced Concrete Slabs without Shear Reinforcement
## 4 Discussion
### Size Effect  
An influence of the effective depth on the shear resistance (size effect) can be derived from Figure 7b. A clear trend of diminishing shear resistance for increasing effective depths is visible. Once again, all results are presented regardless of their failure mechanism. Note that despite bending failure, the specimens with a \(d\) of \(\sim\)40 mm bear the highest shear stresses. The size effect has been described extensively in literature by various researchers, of whom [PERSON] is arguably the most renown (see the extensive compilation in [78]). Especially for shear, current design provisions consider the size effect either directly (by a reduction factor as Eurocode 2 [79]), incorporated in the strain (e.g., in the Modified Compression Field Theory [80] and the Critical Shear Crack Theory [22; 23]), or in a combined factor as in [27].  
[PERSON] and [PERSON] [81] describe the structural size effect according to Equation (1)  
\[\phi(d)=\frac{1}{\sqrt{1+\frac{d}{\lambda_{0}\cdot d_{a}}}} \tag{1}\]  
where \(d\) = effective depth, \(\lambda_{0}\) = empirical constant, and \(d_{a}\) = maximum aggregate size. For steel reinforced concrete beams, an empirical value of 25 was determined from a large set of experiments [81]. For the typical lower limit for the maximum aggregate size in normal concrete of 8 mm, the term \(\lambda_{0}\cdot d_{a}\) leads to a constant value of 200, which is often used in shear prediction models (e.g., [26]). Although the data set in this study is too small to properly adjust an empirical constant for TRC, the linear trend lines in Figure 8 indicate that the first approach with \(\lambda_{0}\) = 25 and \(d_{a}\) = 4 mm already led to satisfying results. It should be mentioned that in the present study, the concrete cracks passed through the aggregate grains rather than around them and thus the influence of aggregate size is debatable.  
Figure 8: Relative shear stress considering the size effect factor according to Bazant [81] with \(\lambda_{0}\) = 25 and \(d_{a}\) = 4 mm.  
In contrast to the size effect law in (1), Eurocode 2 [79] limits the influence of size effect to a lower boundary of \(d=200\) mm. This derives both from the typical slab or beam dimensions and the typical minimum aggregate sizes of 8 mm. For TRC, smaller aggregate sizes as well as reduced depth are typical and intended. Thus, a lower limit is considered critical and more general approaches should be used for TRC. For future research, it could be advisable to additionally test specimens with doubled effective depth in logarithmic scale (10 times the current depth) in order to validate the fit of chosen size effect factors. Alternatively, nonlinear finite element modeling can be used to validate the size effect laws (e.g., [82]).
## 4 Performance Evaluation and Analysis
### Efficiency Evaluation Result  
This section will discuss the method and results of the efficiency evaluation in the established test bed, which was set by the method explained in Section 3 and the cloud system. All nodes were created by the proposed mesh network system and the mesh network control module was shut down when all nodes were connected, while one of them was set as the master node for additional evaluation. Therefore, only five Raspberry Pis remained in the network and the evaluation was done while the network was set independently, separately from the Internet. The evaluation result for analyzing potential service availability according to the transfer of data between the master node and member nodes and the virtual network function recovery achieved by the monitoring module are explained below. In addition, the virtualized QR code identification program's database was used for evaluation to test the proposed network function orchestration system's network device information. In order to compare the improvement, we tested QR code identification using the image-matching method. Table 2 shows information on the detailed network device of the test bed.
# Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention
## 4 Interference-Aware Schedulability Analysis for Fixed Priorities
### Deadline Monotonic Schedulability Analysis  
Fixed-priority schedulers assign an initial priority to tasks that remains constant during all execution. Deadline Monotonic scheduling (DM) [30] considers task sets with deadlines shorter than periods and assigns the highest priority to the task with the shortest deadline. An exact schedulability test (necessary and sufficient condition) for fixed priorities is based on calculating the WCRT of each task.  
Figure 1: Example of task interference.  
The calculation of the WCRT of \(\tau_{i}\) in a monocore system is presented in Equation (2) [31]:  
\[WCRT_{i}=C_{i}+\sum_{\begin{subarray}{c}\forall\tau_{j}\in hp(i)\\ \forall\tau_{j}\in M\tau_{i}\end{subarray}}\left|\frac{WCRT_{i}}{T_{j}}\right|C_ {j} \tag{2}\]  
The second term of the preceding equation signifies the execution of higher-priority tasks in the worst case, that is, assuming the synchronous activation of tasks. Equation (2) is solved by an iterative method. The stop conditions are the violation of a deadline (\(WCRT_{i}>D_{i}\) for any task \(i\)) or convergence (\(WCRT(k+1)=WCRT(k)\)).  
Then, the task set is schedulable if and only if  
\[WCRT_{i}\leq D_{i}\quad\forall\tau_{i}\in\tau \tag{3}\]  
The outcome of this test not only determines whether the system is schedulable or not but also provides the worst-case response time (WCRT) of each task. Additionally, it identifies which tasks are implicated in deadline misses, if any occur.  
Without interference considerations, the WCRT of all tasks in the task set is obtained when all tasks are activated simultaneously. It corresponds to the first activation of the tasks, so the response time is not calculated for all the activations but only the first.
## 3 Mathematical Model of Gas-Liquid Separation
### Establishment of Multiphase Flow Simulation Model  
In Fluent, (Version 16.0) there are three kinds of Euler-Euler [19] multiphase flow models: VOF (volume of fluid), mixture models, and Euler models. In order to solve the problem of multiphase flow in this project, it is necessary to ensure the correct flow mode is selected. In this project, we chose the Euler-Euler model because the volume ratio was 1, that is, the volume ratio sum of each phase in the gas-water two-phase flow (or the oil-gas-water multiphase flow) was 1. In the two-phase flow and multiphase flow, the gas's volume ratio was less than 10%, and thus we adopted the discrete phase model. Since the fluid flow is a type of stratified/free surface flow, we selected a VOF model, a thus established a multiphase flow model. Finally, we performed numerical simulation according to the established model to obtain the required data.  
The multiphase flow's fluid flow can be divided into laminar flow and turbulence. Turbulence [20] models in the Fluent software include the single equation (Splaltrt-Allmaras) model, double equation model (Standard \(k-\varepsilon\) model, RNG \(k-\varepsilon\) model, Realizable \(k-\varepsilon\) model), Reynolds stress model, and large eddy model. In Fluent, the turbulent boundary conditions are set as follows:  
1. [label=()]
2. Turbulence intensity The ratio of the velocity fluctuation's root mean square to average velocity. Less than 1% is low turbulence intensity and at least 10% is high turbulence intensity. Calculation formula: \[I\cong 0.16\big{(}\text{Re}_{\text{D}_{\text{H}}}\big{)}^{-\frac{1}{8}}\] (23) where \(I\) is turbulence intensity, and Re is the Reynolds number.
3. Turbulence scale and hydraulic diameter \[I=0.07~{}L,\] (24) where \(L\) is the characteristic scale, which can be considered as the hydraulic diameter, and the factor of 0.07 is the maximum mixing length in fully developed turbulent pipe flow. \(l\) is the turbulence scale. We selected turbulence intensity and hydraulic diameter for fully developed internal flow.  
3. Kinetic energy The most common physical quantity '\(k\)' in the turbulence model. Turbulent kinetic energy is estimated by turbulence intensity: \[k=\frac{3}{2}(uI)^{2}\] (25) where \(u\) is the average velocity, and \(I\) is the turbulence intensity. 4. Turbulent dissipation rate The turbulent dissipation rate is \(\varepsilon\), which is usually estimated by \(k\) and turbulence scale \(l\). \[\varepsilon=C_{\mu}^{3/4}\frac{k^{3/2}}{l}\] (26) where \(C_{\mu}\) is usually 0.09, \(k\) is kinetic energy, and \(l\) is the turbulence scale.  
Through the derivation of the above formula, we can get the standard \(k-\varepsilon\) model turbulence model settings in Fluent.
# Discussions on the Processing of the Multi-Component Seismic Vector Field
## 2 Multi-Component De-Noising Methods with Respect to the Vector Seismic Field
### The Vector Separating Method of the P-wave and S-wave
Therefore, the P-waves and S-waves are separated naturally during the upward extrapolation, and their phase and amplitude are the same as before separation. The P- and S-wave-separated elastic equation used by these two vector separation methods is the equivalent form of the conventional coupled elastic wave equation proposed by [PERSON] and [PERSON] [81], and it is only applicable to an isotropic medium. Using the vector decomposition method for an elastic wavefield developed by [PERSON] and [PERSON] [19], the vector separation method for P-waves and S-waves could be extended to an anisotropic medium. The abovementioned vector wavefield separation methods all imply that the P-waves and S-waves are orthogonally polarized, and this assumption is generally not valid for an anisotropic medium [82]. For the wavefield separation problem of non-orthogonal P-waves and S-waves in an anisotropic medium, [PERSON] [33] and [PERSON] et al. [34] discussed this problem from the perspective of natural earthquakes and exploration seismology and provided a solution. The basic idea of the solution is to apply a rotational transform to transform the vertical and horizontal components based on the Cartesian coordinate system to the affine coordinate system composed of the wave vector directions of realistic P-waves and S-waves, thereby achieving separation of the P-wave and S-wave. Although the separated P-waves and S-waves given in their paper are scalar fields, the wave vector directions of the different waves in the Cartesian coordinate system have been calculated in the separation process, and according to the directions of the wave vectors, the scalar field could be restored to the vector field. Because this method can calculate the actual wave vector direction at every sample point, it provides a new approach for the de-noising technique based on the characteristics of wave vectors.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme  
### Concept  
We propose a drone-based sensor network system using OCC. The conceptual system architecture is illustrated in Figure 1, while the block diagram of the system is detailed in Figure 2. Many sensor nodes are deployed in a target area. A sensor node is equipped with sensors and a light source. The light source can be an LED light, an LED panel, or a display. The sensor data are encoded and modulated as optical signals to be transmitted from the light source. A receiver drone equipped with a camera moves around the target area to film the optical signals from the sensor nodes. The received signals are demodulated at either the drone or a cloud/edge server in real time.  
We propose a trajectory control algorithm for the receiver drone to efficiently collect the sensor data. The proposed algorithm calculates the flight route and speed based on the locations and sizes of the light sources and the specifications of the receiver camera. The proposed system can accommodate a large number of sensor nodes without time and frequency-domain interference thanks to OCC. It can be deployed in radio quiet or infrastructure-underdeveloped areas since it does not use any radio waves.
## 6 Discussion, Limitations, and Future Work
### Future Research Directions  
Future research should aim to overcome these limitations and enhance the model's impact. Most importantly, the model requires extensive real-world validation beyond simulations to confirm its operational effectiveness and build credibility. Promising avenues include the following:  
* Broad-Spectrum Validation: Future research is needed to validate the model across different sectors and architectures and against comparative baselines. We plan on partnering with industry stakeholders for pilot programs in operational settings (e.g., transportation, healthcare) to generate empirical performance data and refine predictive accuracy.
* Automated Parameter Calibration: To address parameter uncertainty, future work should focus on employing machine learning techniques, such as reinforcement learning, to dynamically adjust coefficients based on historical data and system feedback, improving precision and adaptability.
* Advanced Threat Modeling: To overcome the simplification of threat entropy, we suggest developing more sophisticated threat models, such as using attack graph-based approaches or Bayesian networks to better account for complex attack scenarios and allow for disaggregation of \(\sigma\) into subsystem-specific metrics.
* Scalability and Optimization: Research into optimizing the real-time calculation of RC(t) is needed to address the potential implementation burden and improve scalability for large-scale systems.
* Interdependency Analysis: Extending the model to include interdependencies between critical infrastructures (e.g., using network theory or system dynamics) to provide a more holistic, systemic resilience metric.
* Economic Dimension: Developing cost-benefit analyses for resilience investments to guide resource allocation and make the framework more actionable for budget-conscious operators.  
In summary, the RC model provides a strong foundation for assessing cybersecurity resilience. Pursuing these directions can evolve the framework into a cornerstone of next-generation resilience engineering.
## 7 Detailed Analysis of Parameters and Nodes
### Branch Node  
Numerous derivatives of branch nodes can be designed. However, we have designed branch nodes manually. Automatic generation of branch nodes has not yet been achieved. For this experiment, we tested the property that the number of derivatives is limited. We added four branched nodes, N21-N24, to the existing branch nodes as shown in Table 9. The definitions of the two formulas are presented below:  
\[d_{2}(a)=a(t+2)+a(t+1)-4a(t)+a(t-1)+a(t-2), \tag{18}\]  
\[d_{3}(a)=a(t+2)-2a(t)+a(t-2). \tag{19}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(O_{M}\) & **Dataset 4** & **Dataset 5** & **Dataset 6** \\ \hline
10 & 50.43 & 45.50 & 17.94 \\
15 & 50.53 & 45.50 & 48.01 \\
20 & 50.53 & 45.50 & 47.39 \\
25 & 50.53 & 45.50 & 48.07 \\
30 & 50.53 & 46.17 & 48.46 \\
35 & 50.53 & 45.44 & 48.13 \\
40 & 50.79 & 46.80 & 49.97 \\
45 & 51.09 & 46.31 & 46.87 \\
50 & 50.53 & 46.59 & 47.97 \\ \hline Mean & 50.61 & 45.92 & 48.05 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimization experimentally obtained results for \(O_{M}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(O_{C}\) & **Accuracy [\%]** & **Improvement [\%]** & **Computational time [h]** \\ \hline
100 & 47.59 & 17.94 & 40.75 \\
200 & 49.56 & 22.78 & 78.90 \\
300 & 49.21 & 21.96 & 114.85 \\
400 & 50.52 & 25.20 & 151.58 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimization experimentally obtained results for \(O_{C}\).  
Table 10 denotes the top five recognition accuracies of the added branch nodes for Dataset 6. The maximum recognition accuracy of 50.00% was obtained in the additional case of N22 and N24. The recognition accuracy was 4.05% higher than that of 48.05% before the addition.  
Table 11 presents the combination of the respective branch nodes and the number of branch nodes used for the application experiment in the previous section. As a utilization tendency, N1 and N2, which compare closed neighbor sensor signals, are used up to one time. By contrast, N3, N4, and N5, which compare wide sensor signals, are used two or three times.  
Table 12 shows the top five recognition accuracies after changing N1 and N2 to the new branch nodes, as denoted in Table 10. The maximum recognition accuracy of 50.50% was obtained in the case which exchanged N2 and N24. The recognition accuracy was 5.10% higher than that before the exchange.
# Analysis of Non-Minimum Phase System for AC/DC Battery
## 6 Simulation Results
Figure 19: Steady state waveforms for boost mode of input voltage \(v_{g}(t)\), input current \(i_{L1}(t)\), output voltage \(v_{o}(t)\) and the output current \(i_{o}(t)\) with: (**a**) Full power. (**b**) Half power. Figure 18: Steady state waveforms for buck mode of input voltage \(v_{g}(t)\), input current \(i_{L1}(t)\), output voltage \(v_{o}(t)\) and the output current \(i_{o}(t)\) with: (**a**) Full power. (**b**) Half power. Figure 20a shows the boost mode transients waveforms for input voltage \(v_{g}(t)\), input current \(i_{g}(t)\), output voltage \(v_{o}(t)\) and the output current \(i_{o}(t)\) at two different output current values. The transient happens at 0.3 s where the current output change from full current load to half current load. Examining the output voltage and the input current, it is shown in the figure that they reach the steady state after 200 ms, which represents 10 line cycles. The other transient happens at 0.6 s where the current load change from the half current load to full current load reaching the steady state after 100 ms, which represents 5 line cycles. It should be noted here that in the first transient the time response was slower than the second transient due to the slow dynamic of the bulky output capacitor. The other buck mode transients waveforms for input voltage \(v_{g}(t)\), input current \(i_{g}(t)\), output voltage \(v_{o}(t)\) and the output current \(i_{o}(t)\) at two different output current values are shown in Figure 20b. The same as the boost, the first transient happens at 0.3 s, where the current output changes form full current load to half current load. Examining the output voltage and the input current, it is shown in the figure that steady state is reached after 60 ms, which represents three line cycles. The other transient happens at 0.6 s, where the current load changes from the half current load to full current load and the waveforms reached steady state after 40 ms, which represents two line cycles. it should be noted that the snubber circuit will decrease the total converter efficiency, due to the power loss in the resistance Rd. Therefore, a correct choice of Rd and Cd values must be done in order to achieve minimum-phase characteristic minimizing the effects on the efficiency.
## 1 Introduction  
Triterpenoid saponins are a class of widespread secondary metabolites in the plant kingdom. Chemical composition of triterpen saponins includes a triterpene moiety as the sapogenin and one or more attached sugar moieties such as glycosyl, glucuronyl or xylosyl. Triterpenoid saponins have drawn the attention of researchers because of their diverse bioactivities, including anti-inflammatory [1], anti-cancer [2], anti-microbial [3], insecticidal and anti-herbivore [4,5] activities. Owing to their significant pharmacological activities, plants rich in triterpenoid saponins are usually exploited as drug sources. However, the availability of triterpen saponins is hampered due to their low yield of crude drug extraction and difficulties in purification. Understanding the biosynthesis of triterpen saponins could help solve this problem. _Ilex asprella_, a traditional herbal drug widely used in _Lingnan_ area of China, is a major component of some popular cooling beverages and anti-influenza remedies, and with an annual consumption of over 10,000 tons, has great economic value. The most characteristic constituents of _I. asprella_ are triterpen saponins, which show anti-cancer [6] and anti-virus activity [7]. To date, over 30 triterpen saponins have been isolated from the _I. asprella_ leaves and roots (see Figure 1 and Table 1) [7-11]. These compounds can be classified into two main types: \(\alpha\)-amyrin and \(\beta\)-amyrin. \(\beta\)-Amyrin, which is an oleanane, is a major configuration of pentacyclic triterpenoids, whereas \(\alpha\)-amyrin, which is an usane, is the isomer of \(\beta\)-amyrin but with a different location for C29 [12]. Interestingly, most of the triterpen saponins that were isolated from _I. asprella_ roots were of the \(\alpha\)-amyrin type (summarised in Table 1), except for one, which was of the \(\beta\)-amyrin type. Figure 1: Putative triterpenoid saponins biosynthetic pathway downstream of 2,3-oxidosqualene in _I. asprella_. Over the past few years, the biosynthesis of triterpenapenoid saponins in some economically important plants, such as _Glycine max_[13] and _Panax ginseng_[14], has been studied. A biosynthetic pathway starting with the cyclisation of 2,3-oxidosqualene was suggested and involves three main steps: (i) cyclisation of 2,3-oxidosqualene catalysed by oxidosqualene cyclase (OSCs, EC 5.4.99.x); (ii) oxidative modification at various positions of the skeleton mediated by cytochromes P450 (P450s, EC 1.14.x.x); and (iii) glycosylation of the decorated skeleton catalysed by family 1 uridine diphosphate glycosyltransferases (UGTs, EC 2.4.1.x). Accordingly, a hypothetical biosynthetic pathway of triterpenapenoid saponins in _I. asprella_ is described in Figure 1. The biosynthetic pathway upstream of 2,3-oxidosqualene is believed to be the mevalonic acid (MVA) pathway in the cytosol, although evidence exists for crosstalk between the MVA and the methylerythritol phosphate (MEP)