esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Effective Length Factor of Frame-Core Wall Structure with Cross-Layer Columns
## 2 Effective Length Modification for Frame-Core Wall Structures with Cross-Layer Columns
### Buckling Load for Frame-Core Wall Structure  
It is assumed that the overall buckling load of the frame-core structure, which consists of the sum of the buckling loads of both the frame part and the shear wall part, can be expressed by Equation (9).  
\[P_{FW,cr}=P_{F,cr}\ +\ P_{W,cr}=S_{F}\ +\ \frac{3\pi^{2}E_{W}I_{W}}{4H^{2}} \tag{9}\]  
where \(S_{F}\) is the lateral stiffness of the frame; its value is equal to the buckling load of the frame \(P_{F,cr}\). \(P_{W,cr}\) is the buckling load of shear walls, as the shear walls under lateral load to bending deformation can be regarded as cantilever vertical members, and their buckling load can be calculated by the Euler formula approximation. \(E_{W}I_{W}\) is the equivalent lateral stiffness of the shear walls.  
The parameter \(\gamma\) is introduced as the buckling load correction factor for the frame section, as shown in Equations (10) and (11).  
\[\gamma=\frac{R_{F}}{R_{FW}}\cdot\frac{P_{FW,cr}}{P_{F,cr}}=\rho\cdot\frac{ \left(4\lambda^{2}\ +\ 3\pi^{2}\right)}{4\lambda^{2}} \tag{10}\]  
\[\rho=\frac{R_{F}}{R_{FW}} \tag{11}\]  
where the parameter \(\gamma\) is the frame buckling load correction factor. \(\rho\) is the design value of the gravity load at the bottom of the frame as a proportion of the overall structure of the structure.  
The modified buckling load of frame column \(j\) at floor \(i\) is shown in Equation (12).  
\[P_{ij,cr}=\frac{N_{ij}}{\sum\limits_{j=1}^{n}N_{ij}}\cdot\gamma\cdot P_{F,cr} \tag{12}\]  
As can be seen from Equation (10), the frame buckling load correction factor \(\gamma\) can be used to quantitatively characterize the extent to which the frame is subjected to the strength of the lateral support of the core barrel; when \(\gamma>1\), it indicates that the frame is helped by the core barrel stiffness, and its buckling load is higher than that of the frame with the lateral shift; when \(\gamma=1\), it indicates that the buckling factor of the frame is the same as that of the core barrel, and the buckling load of the frame is the buckling load of the frame with the lateral shift; when \(\gamma<1\), it indicates that the stability of the core barrel needs the help of the frame part, so the flexural load of the frame is lower than the flexural load of the frame with the lateral shift.  
The variation in the frame buckling load correction factor \(\gamma\) with the stiffness characteristic value \(\lambda\) is shown in Figure 3. It can be seen from the figure that as \(\lambda\) increases, the buckling load correction factor \(\gamma\) of the frame decreases; when the vertical load shared by the frame is larger, the buckling load correction factor \(\gamma\) also increases. For most of the dual lateral force resisting systems, the frame buckling load correction factor \(\gamma\) is greater than 1.0, and its buckling load is between the frame with the lateral shift and the frame without the lateral shift.
# Identification and Isolation Techniques for Plant Growth Inhibitors in Rice
## 3 Mechanism of Action of PGIs
### Photosynthesis, Respiration, and Transpiration  
PGIs can also inhibit photosynthesis, which is an indispensable process in plants. Caffeic, coumaric, ferulic, cinnamic, and vanillic acids found in rice can be mentioned as photosynthetic inhibitors [47; 48]. These allelochemicals can disrupt the photosynthesis process via the inhibition of or damage to the synthesis apparatus and the stimulation of the degradation of photosynthetic pigments. Accordingly, energy and electron transfer are prevented due to the reduced content of photosynthetic pigments. In addition, PGIs can reduce ATP synthase catalytic activities, ATP synthesis, and transpiratory conductivity, thereby inhibiting photosynthesis [58]. Allelichemicals affect photosynthesis primarily by affecting PS II function [59]. In addition, PGIs can affect respiration at different stages, which is related to the energy metabolism in plants. In particular, PGIs can suppress available oxygen, NADH oxidation, ATP synthase catalysis, and mitochondrial ATP formation. In addition, these compounds can inhibit the plant's oxidative phosphorylation process, and consequently disrupt the respiratory process [58]. Benzoic and cinnamic acids derived from rice were reported to suppress respiration, leading to plant growth inhibition [49]. Transpiration is another important process that can be impeded by PGIs, resulting in the loss of functions or even the death of plant leaves [60].
## 6 Pesticide Degradation Pathways
### Insecticides  
Insecticides have a variety of structures and degradation pathways. The degradation of organophosphorus insecticides by chlorine dioxide and ozone involves desulfurization of the thiophosphoryl group, dealkylation, and hydroxyl dehydration (Figure 9(1-3)) [36; 112; 113]. The degradation of carbamate insecticides involves the breaking of the carbamate group (Figure 9(4)) [113; 114]. In addition, the thioether bond in the carbamate insecticide methio-carb reacts with chlorine dioxide to form intermediate adducts, which gradually oxidize into sulfonyl and sulfonyl groups (Figure 10(1)) [35]. The organophosphorus insecticide pirimiphos-methyl reacts with ozone in two pathways that result in dealkylated products, acetaldehyde, hydrogen peroxide, and carbonyl products (Figure 10(2)) [115]. The reaction of the organochlorine pesticides lindane and endosulfan with ozone under alkaline conditions involves dechlorination, ring opening, and demethylation (Figure 10(3,4)) [116].  
Figure 8: (1) Degradation pathway of chlorine dioxide on triazine herbicide ametryn. (2) Degradation pathway of chlorine dioxide on sulfonylurea herbicide. (3) Degradation pathway of ozone on triazine herbicide metribuzin.  
Figure 7: (1) Degradation pathways of triazine herbicides by chlorine dioxide and ozone. (2) Degradation pathways of phenylurea herbicides by chlorine dioxide and ozone.  
Figure 9: (1–3) Degradation pathways of organophosphorus insecticides by chlorine dioxide and ozone. (4) Degradation pathways of carbamate insecticides by chlorine dioxide and ozone.
## 2 Description of the Vibrating System
\begin{array}{l}Q_{Z}=F_{1}\cos(\Omega_{1}t)-C_{1}Z^{\prime},\\ Q_{\Phi}=M_{0}\cos(\Omega_{2}t)-C_{2}{\Phi^{\prime}},\\ Q_{\xi}=-C_{3}\tilde{\varsigma}^{\prime}.\end{array}  \tag{3}\]  
Substituting from (1) and (3) into (2), the dimensionless form of the controlling system of EOM has the following form  
\[ \begin{array}{ll}\tilde{z}+c_{1}\dot{z}&+z+z_{r}+\alpha(z+z_{r})^{2}+(1-\cos \[PERSON])w_{1}^{2}-(1+z)\dot{\[PERSON]}^{2}+\beta[(\tilde{\eta}-(r+\eta)\dot{\[PERSON]}^{2}) \\ &\times\sin(2\[PERSON])+((r+\eta)\dot{\[PERSON]}+2\dot{\[PERSON]}\eta)\cos(2\[PERSON])]-\frac{ \tilde{\varsigma}^{2}}{T}[b\sin(\alpha\tau)\sin\[PERSON]\\ &+a\cos(\alpha\tau)\cos\[PERSON]]=f_{1}cos(p_{1}\tau),\\ \Big{\{}(1+z)^{2}&+\beta[(r+\eta)^{2}-2(1+z)(r+\eta)\sin(2\Phi)]\Big{\}}\ddot{ \Phi}+c_{2}\dot{\Phi}+2[1+z\\ &-2\beta(r+\eta)\sin(2\Phi)]\dot{z}\dot{\Phi}+2[(r+\eta)-(1+z)\sin(2\Phi)]\dot{ \Phi}\,\dot{\eta}\\ &+(1+z)w_{1}^{2}\sin\Phi+\beta(r+\eta)w_{1}^{2}\cos\Phi+\beta[(r+\eta)\ddot{z}+ (1+z)\ddot{\eta}\\ &-2(r+\eta)(1+z)\dot{\Phi}^{2}]\cos(2\Phi)-\frac{\tilde{\varsigma}^{2}}{T}(1+ z)[b\sin(\alpha\tau)\cos\Phi-a\cos(\alpha\tau)\\ &\times\sin\Phi]+\frac{\tilde{\varsigma}^{2}}{T}\beta(r+\eta)[b\sin(\alpha\tau) \sin\Phi-a\cos(\alpha\tau)\cos\Phi]=m_{0}\cos(p_{2}\tau),\\ \ddot{\eta}+\ddot{z}\sin(2\Phi)&+c_{3}\dot{\eta}-\frac{\tilde{\varsigma}^{2}}{ T}[b\sin(\alpha\tau)\cos\Phi+a\cos(\alpha\tau)\sin\Phi]-[(1+z)\sin(2\Phi)\\ &+r+\eta]\dot{\[PERSON]}^{2}+[2\dot{z}\dot{\[PERSON]}+(1+z)\dot{\[PERSON]}]\cos(2\Phi)+w_{1}^{2 }\sin\Phi+w_{2}^{2}(\eta+\eta_{r})=0,\end{array}  \tag{4}\]  
where  
\[\begin{array}{ll}\tau=\omega_{1}t,\ \ z=\frac{Z}{T},\ \ \tau_{r}=\frac{Z_{r}}{T},\ \ \eta=\frac{\tilde{\varsigma}}{T},\ \ \eta_{r}=\frac{\tilde{ \varsigma}}{T},\ \ \omega_{1}=\sqrt{\frac{K_{1}}{m_{1}+m_{2}}},\ \ \omega_{2}=\sqrt{\frac{\tilde{\varsigma}  
Denote the parameters that are dimensionless, and the dots represent the differentiation regarding to \(\tau\), in which the generalized coordinates and their corresponding first derivatives have the following initial conditions  
\[ \begin{array}{llll}z(0)=z_{0},&\Phi(0)=\Phi_{0},&\eta(0)=\eta_{0},\\ \dot{z}(0)=0,&\dot{\Phi}(0)=0,&\dot{\eta}(0)=0.\end{array} \]
## 3 Results
### Staged Deployment of LEO Constellation for MEO Navigation Satellite Failure
#### 3.2.2 Analysis of Three-Satellite Failure Based on Monte Carlo Method  
Given the \(C_{24}^{3}\) (2024) combinations of three-satellite failures, which represent a multitude of failure modes, making enumeration impractical, the Monte Carlo method was employed. The health status of constellation performance after a three-satellites failure can be categorized into four levels:  
When \(CV\geq 96\%\), the constellation health status is excellent.  
When \(95\%\geq CV\geq 91\%\), the constellation health status is good.  
When \(90\%\geq CV\geq 86\%\), the constellation health status is moderate.  
When \(85\%\geq CV\geq 81\%\), the constellation health status is poor.  
The three-satellite failure simulation parameters are configured identically to those of single and double satellites. In this section, the Monte Carlo method is employed to conduct 1000 random simulations of the three-satellite failure model within the constellation, with the simulation results presented in Figure 11.  
In Figure 10(a), the minimum CV for the constellation falls in the ranges 96-100%, 91-95%, 86-90%, and 81-85%, representing proportions of 22.30%, 60.30%, 15.00%, and 2.40%, respectively. As depicted in Figure 10(b), the mean CV of the constellations is calculated as 94.83% (dash line). The majority of CV fluctuates between 89-98%. There is a small probability that three-satellite failure would degrade the constellation's health status from \"excellent\" to \"poor\"(red circle). As the number of failed satellites increases, the CV decreases, and its global navigation service performance also deteriorates.  
The correlation between the failure mode probability of various satellites and the minimum CV is illustrated in Figure 12, where \(m-q\) denotes the failure of \(q\) satellites in the \(m_{\text{th}}\) orbital plane.  
Figure 12 examines the failure modes of satellites, analyzing 10 different scenarios. The left bar chart (blue) illustrates the proportion of each case in the simulations, while the right bar chart (red) depicts the minimum CV for each scenario. Notably, a single failure within each orbital plane significantly impacts the overall constellation performance, with a simulation probability of 25.30% and a minimum CV of 84.11. Conversely, the failure of three satellites within the same orbital plane has minimal impact on the constellation, occurring with probabilities of 2.50%, 3.30%, and 2.40%, respectively, and resulting in a minimum CV of approximately 92.57. Additionally, failures involving two satellites or one satellite from different orbital planes rank second in terms of their impact on the constellation after the failure of three satellites within the same orbital; these occur with probabilities in the range of 9.60%-12%, yielding respective minimum  
Figure 11: Simulation results of three-satellite failure: (**a**) proportion of different failure modes and (**b**) simulation times and CV.
## 2 Materials and Methods  
### Nano-Nacre Particles Preparation  
The shells of adult _H. diversicolor_ abalone (50-55 mm in length) were provided by Phuket Abalone Farm (Phuket, Thailand). To prepare the nano-nacre particles, the outer surface of the shells was cleaned using a metal brush, and then the prismatic layer was removed with sandpaper. The shells were then fragmented into small pieces (2-10 mm) and ground into a powder. The powder was suspended in a nano-grinding facility for further grinding, resulting in particles ranging in size from 10 to 1000 nm, which were then freeze-dried at \(-50\)\({}^{\circ}\)C for 48 h (FreeZone, Labonco, Kansas City, MO, USA). For structural analysis, the generated nano-nacre particles were placed on a sample plate for scanning electron microscopy (SEM). The samples were diluted in distilled water, and a drop of the suspension was applied to copper tape and allowed to dry at room temperature overnight. The dried samples were then sputter-coated with gold, and SEM images were obtained under high vacuum conditions with an operation voltage of 15 kV using a JSM-5910 SEM (JEOL USA, Peabody, MA, USA). Similar methods have been used in other studies to prepare and analyze nano-particles from natural sources for biomedical applications [26].
# Hazard Model: Epidemic-Type Aftershock Sequence (ETAS) Model for Hungary
## 6 Parameter Downscaling Results
### Test of Event Accumulation by Zone, 2000-2021  
The comparison of modelled versus observed cumulative event numbers (cf. Figure 6) is performed in this test for each zone separately. The results are shown in Figure 13. The model shows a better fit to the observations in the central Zones (01-02-03 and 04-10) than on the periphery; this can also reflect variations of underlying data quality. The biggest deviation is observed in Zone 13 where the smallest events were excluded from the downscaling to obtain a better fit to long-term event counts in higher magnitude classes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Zone**} & \(\mu\) & \multirow{2}{*}{\(K\)} & \multirow{2}{*}{\(\eta\)} & \multirow{2}{*}{\(q\)} & \multirow{2}{*}{\(\alpha=\beta\)} & \(D\) & \(\varepsilon\) & \multirow{2}{*}{\(N\)} & \multirow{2}{*}{\(\varrho\)} \\  & & & & & & & & &  
Figure 12: _Cont._  
Figure 12: Comparison of long-term event counts by zone, modelled and observed. The modelled distributions were obtained by generating 500 stochastic catalogues using the downscaled parameters and another 500 stochastic catalogues with the original parameters, each one spanning 300 years. The observation periods reflecting completeness differ by magnitude class; therefore, the event numbers shown are not always decreasing with the magnitude threshold. (**a,c,e,g,i**) Zone-by-zone downscaled model results. (**b,d,f,h,j**) Results for the same zones with parameters before downscaling, for comparison. (**k,l**) Results for Zones 05–06 and 99 where the downscaling approach is not used. (**m,n**) Results for the total modelled area, with and without downscaling.  
Figure 13: Modelled versus observed event accumulation by zone in the study period 2000–2021 for \(M\geq 2.5\). (**a**–**e**) Results of the test shown for each zone. Zones 05–06 and 99 with no downscaling are omitted.
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
## 2 Materials and Methods
### Simulation Model
#### 2.1.4 Vehicle Lateral Tracking Deviation Model  
To determine the lateral control deviation of the vehicle during autonomous driving, the established linear two-degrees-of-freedom vehicle dynamics model was transformed into a lateral tracking deviation model using coordinate transformation. The vehicle lateral tracking deviation model is illustrated in Figure 4.  
Figure 4: Vehicle lateral tracking deviation model.  
First, the heading angle deviation, \(e_{\varphi}\), is obtained as:  
\[e_{\varphi}=\varphi_{h}-\varphi_{r}, \tag{7}\]  
where \(\varphi_{h}\) and \(\varphi_{r}\) are the actual and ideal vehicle heading angles, respectively.  
Next, based on Figure 4, the deviation of the vehicle's lateral acceleration, \(\dot{e}_{d}\), can be derived as:  
\[\dot{e}_{d}=v_{x}e_{\varphi}+v_{y}, \tag{8}\]  
Finally, by combining Equations (7) and (8) with the linear two-degrees-of-freedom vehicle model, the vehicle lateral deviation model is obtained as:  
\[\ddot{e}_{d}=\bigg{(}\frac{C_{f}+C_{r}}{mv_{x}}\bigg{)}\dot{e}_{d}+\bigg{(}- \frac{C_{f}+C_{r}}{m}\bigg{)}e_{\varphi}+\bigg{(}\frac{aC_{f}-bC_{r}}{mv_{x}} \bigg{)}\dot{e}_{\varphi}+\bigg{(}\frac{aC_{f}-bC_{r}}{mv_{x}}-v_{x}\bigg{)} \dot{\varphi}_{r}+\bigg{(}-\frac{C_{f}}{m}\bigg{)}\delta_{f}, \tag{9}\]  
\[\ddot{e}_{\varphi}=\bigg{(}\frac{aC_{f}-bC_{r}}{lv_{x}}\bigg{)}\dot{e}_{d}+ \bigg{(}-\frac{aC_{f}-bC_{r}}{I}\bigg{)}e_{\varphi}+\bigg{(}\frac{a^{2}C_{f}+ b^{2}C_{r}}{lv_{x}}\bigg{)}\dot{e}_{\varphi}+\bigg{(}\frac{a^{2}C_{f}+b^{2}C_{r}}{lv_{ x}}\bigg{)}\dot{\varphi}_{r}+\bigg{(}-\frac{aC_{r}}{I}\bigg{)}\delta_{f}. \tag{10}\]  
Subsequently, the state-space equation is derived as:  
\[\dot{X}=AX+BU+C\dot{\varphi}_{r}, \tag{11}\]  
\[\text{where }A=\begin{bmatrix}0&1&0&0\\ 0&\frac{C_{f}+C_{r}}{mv_{x}}&-\frac{C_{f}+C_{r}}{m}&\frac{aC_{f}-bC_{r}}{mv_{x }}\\ 0&0&0&\frac{aC_{f}-bC_{r}}{lv_{x}}&-\frac{aC_{f}-bC_{r}}{I}&\frac{a^{2}C_{f}+b^ {2}C_{r}}{lv_{x}}\end{bmatrix},\,X=\begin{bmatrix}e_{d}\\ \dot{e}_{d}\\ e_{\varphi}\\ \end{bmatrix},\,B=\begin{bmatrix}0\\ -\frac{C_{f}}{m}\\ 0\\ -\frac{aC_{r}}{I}\end{bmatrix},\,U=\begin{bmatrix}\delta_{f}\\ 0\\ -\frac{aC_{r}}{I}\end{  
Based on the geometric relationship in Figure 5, the following equation can be derived:  
\[y_{h}=\frac{L_{m}}{\cos\left(\arcsin\left(\frac{L_{h}}{L_{m}}\right)\right)}, \tag{12}\]  
where \(y_{h}\) is the lateral deviation, \(L_{m}\) is the preview distance, and \(L_{h}\) is the deviation between the path trajectory and the driver's prediction point. Considering the issue of insufficient vehicle steering, a PID (proportional integral derivative) controller was employed to optimize the lateral error and address this problem.
### Related Studies
RISs can be used to adjust the transmission characteristics of signals to adapt to different communication environments and requirements. Causal reinforcement learning can utilize historical data and environmental feedback to provide intelligent decision support for an RIS, enabling it to adjust its operating mode and parameter settings according to real-time demands and network conditions. By learning based on causal relationships, the system can better understand the impact of RISs and make decisions based on these causal relationships, thereby improving the performance of the communication system.
## 5 Results and Discussion
### Performance Comparison of the Different Forward Predictive Horizons  
Our research scenario was a mixed traffic scenario where the HDVs and CAVs coexisted. In this paper, the CAVs made decisions based on the predicted trajectories of all the vehicles over time. To validate the impact of the different time horizon for predicting the trajectories of all the vehicles, three settings were used for the training. The results are shown in Figure 9 and the performance metrics are presented in Table 3. The results indicated that a larger prediction time horizon did not imply a better performance. Although a larger predictive time horizon could reduce the collision rate, it would significantly reduce the metrics of the average speed. The performance depended on the design of the headway evaluation in the reward function and the desired headway time, as introduced in Section 4.1. The desired headway time \(t_{d}\) was set to 2 s during the training. When \(T_{h}=1\) s, even though the current headway time was less than \(t_{d}\), the vehicle agent still received a positive reward because no collision was detected within the predictive horizon. This could easily result in the vehicle anticipating a collision when the brakes would not be able to prevent a collision. Compared to \(T_{h}=3\) s, the prediction horizon of \(T_{h}=5\) s was conservative for the decision-making of the vehicle. As long as the collision was not detected within 3 s, the vehicle received a reward of one with a 3 s prediction horizon. In  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{3}{c}{**Reward Assignment Scheme**} \\ \cline{2-4}  & **Global** & **Local** & **Proposed** \\ \hline Collision rate & 0. 07 & 0.03 & 0 \\ \hline Average Speed (m/s) & 9.06 & 9.04 & 9.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison using the different reward assignment schemes.  
Figure 8: Comparison of the episode reward in the training using the different reward assignment schemes.  
the case of \(T_{h}=5\) s, the vehicle would be rewarded with the value of \(\ln(5/2)<1\), even though the collision was predicted at 5 s. This indicated that a suitable prediction time horizon could achieve a better performance. \(T_{h}=3\) s achieved a good trade-off between the collision rate and the prediction efficiency.
## 3 Experimental Methods
### Measurement of Focal Length and Beam Size  
Figure 4 shows the scheme used to measure the focal length and size of the beam emitting from the fabricated lensed optical fiber. Here, we used a laser light source with the operating wavelength of the SMF, i.e., 1550 nm. Because the tip of the circulator shown in Figure 4 was the APC-type, an APC-type ferrule was connected to the lensed optical fiber. In this setup, the laser beam with a wavelength of 1550 nm passes through the circulator, is made incident on the lensed optical fiber, and is reflected from the Au-coated mirror with good reflectance across all wavelength bands. The reflected beam again converges at the lensed optical fiber, and the beam travels to the laser power meter through the circulating device. In the study, the gold-coated mirror was moved in steps of 1 um, and the change in the output power with respect to the mirror distance was measured using a laser-power measuring device. The mirror position at which the strongest output power was obtained as per the power meter was determined as the focal point of the lensed optical fiber.  
Figure 4: \(Cont\).  
The mirror was also moved laterally by 1 \(\upmu\)m, and the output of the beam was measured. Figure 4c shows the method for measuring the Gaussian-beam profile using the knife-edge method. The method measures only a part of the laser power, except that of the Gaussian beam blocked by the knife edge, as a function of lateral mirror-position variation [18]. Thus, the Gaussian beam profile can be obtained from the laser-power gradient. The 1/e\({}^{2}\) points of the obtained line profile can be obtained using these beam profiles, and consequently, the beam size can be determined.