esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of Flight Operations on the Risk of Runway Excursions
## 3 Classification of Operating Styles
### K-Means ++ Cluster Analysis  
The K-means ++ clustering method represents an optimization of the standard K-means clustering algorithm. The K-means ++ clustering method is an unsupervised clustering algorithm that is preferred due to the absence of labeled data. This method is considered more reasonable compared to other unsupervised clustering algorithms for its initial cluster center selection approach, which is designed to converge faster. Additionally, the K-means ++ algorithm selects initial cluster centers that are more uniformly distributed across the data space, thereby better capturing the overall characteristics of the dataset. During the selection of the (n + 1)th cluster center, points that are farther away from the current n cluster centers are assigned a higher probability of being chosen as the (n + 1)th cluster center. This approach has found application in the clustering of driving styles [37] within the transportation domain.  
Based on the results of operations related to runway excursions, the flight data are clustered and labeled using landing distance, taxiing distance, and magnetic heading change rate when dividing the operational styles into three categories, as shown in Figure 1 and Table 1. The clustering center of ground distance for Style 1 is 641.86 m, making it the shortest among the styles. It is 155.35 m shorter than Style 2, indicating that Style 2 has a moderate touchdown distance. Furthermore, it is 327.03 m shorter than Style 3. Style 3 has the shortest taxiing distance, measuring 1023.89 m. Style 1 is 320.73 m longer than Style 3, indicating that its distance is moderate. Lastly, Style 2 is 847.58 m longer than Style 1. Style 1 exhibits a short touchdown distance, moderate taxiing distance, and minimal change in magnetic heading. Style 2 demonstrates a moderate landing distance, extensive rollout distance, and stable heading change. Style 3 displays a long landing distance, moderate taxiing distance, and unstable heading control. This categorization enables the classification of flights into three risk levels: low risk of longitudinal runway excursion, high risk of longitudinal runway excursion, and high risk of lateral runway excursion. Therefore, the data are categorized into three classes for subsequent comparative analysis. The increased touchdown distance and magnetic heading variation in Style 3 markedly elevate the risk of runway excursions during the touchdown phase. Examining the operational outcomes during the taxiing phase of Style 2, it is evident that the distance covered is significantly greater than the other two styles, indicating operational issues during this phase and an increased risk of runway excursions. To address the issue of excessive taxiing distance, particular attention can be given to the timing of reverse thrust activation and speed brake deployment. Regarding the problems of excessive touchdown distance and significant changes in magnetic heading, emphasis can be placed on directional control during landing, flare operations, and speed control.
# Train-Induced Vibration Analysis and Isolation Trench Measures in Metro Depot Structures
## 4 Study on Vibration Isolation Trench Measures for Depot
### The Effect of Open Trench Position on Vibration Isolation  
Similar to the constraints on trench width, the position of open trenches is also restricted by the distance between the track and adjacent structural columns. Therefore, in this study, to investigate the effect of trench position on vibration isolation performance, the trench distances were set to 1.9 m, 2.4 m, and 2.9 m. Under all three scenarios, the trench width and depth were uniformly set to 0.5 m and 6 m, respectively. Figure 23 shows the vibration acceleration attenuation coefficients of slab 1 on the 1 st, 4 th, 6 th, and 9 th floors under the three distances. It can be observed that, in the 1-5 Hz frequency range, the acceleration attenuation coefficients of the slabs are nearly identical under all three conditions. In the 5-31.5 Hz frequency range, the acceleration attenuation effect shows an overall increasing trend as the distance of the trench increases. In the 40-200 Hz frequency range, the acceleration attenuation coefficients exhibit fluctuations under all three conditions, but the overall attenuation trend is approximately similar.  
Figure 23: Influence of open trench position on floor acceleration: (**a**) 1 st floor; (**b**) 4 th floor; (**c**) 6 th floor; (**d**) 9 th floor.  
Figure 22: Influence of open trench width on floor Z-vibration level.  
Figure 24 presents the Z-vibration levels of slab 1 on each floor under three different distances of the trench. When the distance is 1.9 m, the Z-vibration levels of the slabs decrease by 8.2-12.2 dB. When the distance is 2.4 m, the Z-vibration levels decrease by 11.0-17.2 dB. When the distance is 2.9 m, the Z-vibration levels decrease by 10.2-15.6 dB. Based on the Z-vibration levels attenuation values under the three conditions, it can be concluded that the attenuation effect is relatively better when the distance is 2.4 m, while the attenuation is relatively weaker when the distance is 1.9 m. Due to the close proximity between the tracks and the building structures within the depot, the active and passive vibration isolation characteristics of the trench are not clearly defined. However, considering the attenuation of both acceleration and Z-vibration levels under the three conditions, it can be observed that the vibration isolation effect is weakened to some extent when the trench is located too close to the track structure. It is recommended that the trench distance should be greater than 0.4 times the Rayleigh wavelength of the surface soil.
## 9 Conclusions
** We shall only prove the first statement; the others are straightforward to conclude. (1) Suppose \(\tau\underline{\subseteq}\bar{\ell}\) and \(\bar{\ell}\underline{\subseteq}\bar{\psi}\). Therefore, \(\theta_{\bar{\tau}}^{+}\leq\theta_{\bar{\ell}}^{+}\), \(\eta_{\bar{\tau}}^{+}\leq\eta_{\bar{\ell}}^{+}\), \(\lambda_{\bar{\tau}}^{+}\geq\lambda_{\bar{\ell}}^{+}\), \(\theta_{\bar{\tau}}^{-}\geq\theta_{\bar{\ell}}^{-}\), \(\eta_{\bar{\tau}}^{-}\geq\eta_{\bar{\ell}}^{-}\), \(\lambda_{\bar{\tau}}^{-}\leq\lambda_{\bar{\ell}}^{-}\), and \(\theta_{\bar{\ell}}^{+}\leq\theta_{\bar{\psi}}^{+}\), \(\eta_{\bar{\ell}}^{+}\leq\eta_{\bar{\psi}}^{+}\), \(\lambda_{\bar{\ell}}^{+}\geq\lambda_{\bar{\psi}}^{+}\), \(\theta_{\bar{\ell}}^{-}\geq\theta_{\bar{\rho}}^{-}\), \(\eta_{\bar{\ell}}^{-}\geq\eta_{\bar{\psi}}^{-}\), and \(\lambda_{\bar{\ell}}^{-}\leq\lambda_{\bar{\psi}}^{-}\). Now, we have the following:  
\[\theta_{\bar{\tau}}^{+}\leq\theta_{\bar{\ell}}^{+}\text{ and }\theta_{\bar{ \ell}}^{+}\leq\theta_{\bar{\psi}}^{+}\implies\theta_{\bar{\tau}}^{+}\leq \theta_{\bar{\psi}}^{+},\] \[\eta_{\bar{\tau}}^{+}\leq\eta_{\bar{\ell}}^{+}\text{ and }\eta_{\bar{ \ell}}^{+}\leq\eta_{\bar{\psi}}^{+}\implies\eta_{\bar{\tau}}^{+}\leq\eta_{\bar {\psi}}^{+},\] \[\lambda_{\bar{\tau}}^{+}\geq\lambda_{\bar{\ell}}^{+}\text{ and }\lambda_{\bar{\ell}}^{+}\geq\lambda_{\bar{\psi}}^{+}\implies\lambda_{\bar{ \tau}}^{+}\geq\lambda_{\bar{\psi}}^{+},\] \[\theta_{\bar{\tau}}^{-}\geq\theta_{\bar{\ell}}^{-}\text{ and }\theta_{\bar{\tau}}^{-}\geq\theta_{\bar{\rho}}^{-}\implies\theta_{\bar{\tau}}^{-} \geq\theta_{\bar{\psi}}^{-},\] \[\eta_{\bar{\tau}}^{-}\geq\eta_{\bar{\ell}}^{-}\text{ and }\eta_{\bar{\ell}}^{-}\geq\eta_{\bar{\psi}}^{-}\implies\eta_{\bar{\tau}}^{+} \geq\eta_{\bar{\rho}}^{-},\] \[\lambda_{\bar{\tau}}^{-}\leq\lambda_{\bar{\ell}}^{-}\text{ and }\lambda_{\bar{\ell}}^{-}\leq\lambda_{\bar{\psi}}^{-}\implies\lambda_{\bar{ \tau}}^{-}\leq\lambda_{\bar{\psi}}^{-}.\]  
Summing up all of the implications above yields \(\bar{\tau}\underline{\subseteq}\bar{\psi}\). \(\square\)  
**Proof of proposition 2. ** (1) Starting from LHS, we have the following:  
\[(\tau\bar{\cap}\bar{\ell})^{c} =\left( \begin{array}{c}\min(\theta_{\bar{\tau}}^{+},\theta_{ \bar{\ell}}^{+}),\min(\eta_{\bar{\tau}}^{+},\eta_{\bar{\ell}}^{+}),\max( \lambda_{\bar{\tau}}^{+},\lambda_{\bar{\ell}}^{+}),\\ \max(\theta_{\bar{\tau}}^{-},\theta_{\bar{\ell}}^{-}),\max(\eta_{\bar{\tau}}^{- },\eta_{\bar{\ell}}^{-}),\min(\lambda_{\bar{\tau}}^{-},\lambda_{\bar{\ell}}^{- })\end{array} \right)\] \[=\left( \begin{array}{c}\max(\lambda_{\bar{\tau}}^{+},\lambda_{ \bar{\ell}}^{+}),\min(\eta_{\bar{\tau}}^{+},\eta_{\bar{\ell}}^{+}),\min( \theta_{\bar{\tau}}^{+},\theta_{\bar{\ell}}^{+}),\\ \min(\lambda_{\bar{\tau}}^{-},\lambda_{\bar{\ell}}^{-}),\max(\eta_{\bar{\tau}}^{ -},\eta_{\bar{\ell}}^{-}),\max(\theta_{\bar{\tau}}^{-},\theta_{\bar{\ell}}^{- })\end{array} \right)\] \[=\bar{\tau}^{c}\bar{\cup}\bar{\ell}^{c}.\]  
The other parts are similar. Proof of Theorem 2.: We prove that 1 and 3, and the proofs of 2 and 4 are similar, respectively.
# Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems
## 1 Introduction
* Proposing a sequential controller design technique for the IBDGs within a subnetwork which takes into account the strong interconnection dynamics among the inverters and takes the weak cross-subnetwork dynamics as disturbances. * Using the \(\mu\)-synthesis technique to make the control system robust against LC filter parameter variations. * Conducting simulations to compare the superior performance of the proposed technique versus independently-designed benchmark controllers. * Conducting simulations to illustrate the performance of the proposed sequential control scheme in terms of transient overload and nonlinear load analyses. The rest of this study is organized as follows. In Section 2, the directed graph of the network of IBDGs is introduced, the dynamical equations of each of the IBDGs are formulated, and the overlapping decomposition framework is presented as a tool to expand the original state space model and to cluster the network of IBDGs into subnetworks. The \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Ref.** & **Voltage Control Scheme** & **R/X** & \begin{tabular}{c} **Robustness** \\ **Parameters** \\ \end{tabular} & \
\begin{tabular}{c} **Overload** \\ **Analysis** \\ \end{tabular} &
\begin{tabular}{c} **Nonlinear** \\ **Load** \\ \end{tabular} \\ \hline [PERSON] et al. [20] & Multivariable Adaptive Robust & 0.76 & — & \(\Delta V<\pm 10\%\) & \(HVD\leq 4\%\) \\ \hline [PERSON] et al. [19] & MPC & — & — & \(\Delta V<\pm 10\%\) & — \\ \hline [PERSON] et al. [27] & Adaptive PID & — & — & \(\Delta V<\pm 10\%\) & — \\ \hline [PERSON] et al. [28] & Adaptive & — & — & \(\Delta V<\pm 10\%\) & — \\ \hline [PERSON] et al. [8] & Impedance Estimator + Optimal & 0.32 & LC filter & \(\Delta V<\pm 10\%\) & — \\ \hline [PERSON] et al. [9] & PI + Washout Filter & 1 & — & \(\Delta V<\pm 10\%\) & — \\ \hline [PERSON] et al. [10] & Robust LMI-based & 5.3 & LC filter & — & — \\ \hline [PERSON] et al. [11] & Robust LMI-based & 0.01 & LC filter & — & — \\ \hline [PERSON] et al. [17] & Sliding Mode & 3.33 & LC filter & — & \(HVD\leq 4\%\) \\ \hline This paper & Sequential Robust & 7.74 & LC filter & \(\Delta V<\pm 10\%\) & \(HVD\leq 4\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of previous studies and reported performance compared to the results of this paper. R/X: Resistance to Reactance Ratio, \(HVD\): Harmonic Voltage Distortion. sequential design of decentralized robust controllers using \(\mu\)-synthesis is formulated in Section 3. In Section 4, the simulation results are presented in terms of time response, transient overload, nonlinear load, and robustness analyses. Finally, our conclusions are presented in Section 5.
## 2 Materials and Methods
### System Model
#### 2.1.2 5G NR Duplex Pattern  
Time Division Long Term Evaluation (TD-LTE) fixes each radio frame to 10 slots, and the time length of each slot is 1 millisecond. In addition, uplink and downlink duplex  
Figure 1: Wireless network.  
pattern of TD-LTE is fixed. This means that the number of uplink and downlink time slots cannot be adjusted according to the service buffer situation. In order to improve the quality of service for UEs, 5G NR provides a variety of radio frame structure settings, and the uplink and downlink duplex pattern is more flexible.  
The 5G defines a numerology parameter \(\mu\in\{0,1,2,3,4\}\). The 5G NR specifies five physical layer numerologies, which result in different sub-carrier spacing (SCS) and slot durations. For the sake of paper readability, we summarized the 5G NR supported transmission numerology and relevant information in Table 1. For the details, the SCS and slot duration are calculated by \(15*2^{\mu}\) and \(1/2^{\mu}\), respectively. The 5G NR still fixes the number of sub-frames in each radio frame and the number of symbols in each slot with 10 and 14. However, we can change the number of slots in a sub-frame which is calculated by \(2^{\mu}\).  
In the 5G NR protocol, BS informs UE of uplink/downlink pattern and time slot configuration through broadcast or RRC configuration message. The key parameter of uplink/downlink pattern is UL/DL transmission periodicity, denoted as \(\delta\). \(\delta\) indicates the repeated period of UL/DL pattern. According to different Numerology parameter configuration \(\mu\), \(\delta\) value has different options (see Table 2). For example, when \(\mu=2\), \(\delta\) can only be set to 1.25 ms or 2.5 ms. Moreover, the 5G NR implements symbol-level time division duplex and sets time slots configuration through several parameters:  
* \(d_{slots}\): the number of downlink time slots after the start of transmission period.
* \(u_{slots}\): the number of uplink time slots before the end of transmission period.
* \(d_{sym}\): the number of downlink symbols in the time slot after the last complete downlink time slot.
* \(u_{sym}\): the number of UL symbols in the end of a slot preceding the first full UL slot.  
The remaining symbols are regarded as flexible symbols which can be allocated to uplink or downlink by dedicated configuration. So, we can obtain the transmission direction \(\tau\) of BS \(b\) at time \(t\). When \(\tau_{b}(t)=1\), UE receives downlink data transmitted by BS, and when \(\tau_{b}(t)=0\), UE sends uplink data to BS.
# Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty
## 2 Problem Formulation  
### Relief Items' Distribution under Uncertainty  
Information about flood victims is vital in optimizing relief efforts, particularly when planning delivery routes for essential supplies. Knowing the precise locations of the victims, understanding their specific needs, and having accurate details about the road conditions in their area are all critical factors determining the effectiveness of the response operation [29]. However, the uncertainty inherent in flood situations adds significant complexity to this task. Flooding can lead to rapidly changing conditions, where roads that are passable one moment can become obstructed or entirely submerged the next due to sudden increases in water levels. This unpredictability makes maintaining accurate, up-to-date information on road conditions difficult.  
Figure 1 shows an example of delivering relief items to 14 flood victims using three vehicles. Each vehicle journeys from the warehouse to different nodes according to the planned route. In route 1, when the vehicle departs from node 3, the road between node 3 and node 4 is flooded. In route 2, the vehicle departs from node 8 but encounters flooding. In both cases, vehicles can continue their journey but must reduce speed for safety, resulting in delays in delivering the aid. Additionally, road conditions may change multiple times on  
the same road due to the uncertainty of flooding or other environmental factors. Therefore, vehicle speed is adjusted accordingly based on road conditions.  
From Figure 2, the road between node 2 and node 3, position U2, indicates a flooded section where the vehicle slows down until it reaches position U3, where the water level decreases, and the vehicle resumes normal speed. The orange line is average speed, and the green line is low speed.
###### Abstract  
The plant _Ophiorrhiza pumila_ produces camptothecin (CPT), a kind of terpene indole alkaloid (TIAs) that has been widely used in treatment of cancer. Tryptophan-arginine-lysine-tyrosine (WRKY) transcription factors have been reported to play important roles in plant metabolism and development. In this study, a novel WRKY transcription factor named _OpWKKY3_ was isolated from _O. pumila_, with full-length open reading frame (ORF) of 1128 bp, encoding 375 amino acids. Phylogenetic tree analysis revealed that OpWKKY3 shared the highest homology with VvWRKY30, and it is a significant feature belonging to group III. _OpWKKY3_ was responsive to various treatments, including gibberell (GA3), methyl jasmonate (MJ), acetylsalicylic acid (ASA), salicylic acid (SA), and abscisic acid (ABA). Besides, _OpWKKY3_ is expressed predominantly in stems. Subcellular localization analysis showed that OpWKKY3 localized in the nucleus. The biomass of _OpWKKY3-SRDX_ transgenic hairy roots (S line) was visibly suppressed, while there were slight changes between overexpression of the _OpWKKY3_ line (OE line) and the control. In addition, the concentration and total production of camptothecin precursors including loganin and secologanin were significantly changed in both OE and S lines while total production of CPT was significantly changed in most transgenic lines. Thus, the present work revealed that _OpWKKY3_ may act as a regulator in the growth and development of _O. pumila_, and in production of camptothecin and its precursors.  
_Ophiorrhiza pumila_; camptothecin; WRKY transcriptional factor; metabolic regulation; hairy roots +
Footnote †: journal: Molecular Sciences
# ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies
## 3 The Semantic Generation Algorithm
### DL-BiLSTM Layer  
Double-layer BiLSTM (DL-BiLSTM) consists of two layers of BiLSTM, which we utilize for context feature extraction. The given input \(X=[x_{1},x_{2}\ldots x_{n}]\) is a set of inputs that can embody long sequential information. Inputting it into the first BiLSTM procures a context vector for each time step \(t\). The hidden state of the first BiLSTM layer is \(h_{1,t}\), which performs forward and backward operations on the input \(X\), denoted as the contextual feature vector for the \(t^{\text{th}}\) tag, i.e., at the \(t^{\text{th}}\) time point, the procured feature data contains both past and future information, as in Equation (5). The output \(c_{t}\) of the first BiLSTM layer is used as an input to the second BiLSTM layer.  
\[h_{1,t}=[\overset{\rightarrow}{h_{1,t}}:\overset{\leftarrow}{h_{1,t}}] \tag{5}\]  
The second BiLSTM is recursively executed to perform label probability prediction, and then the prediction vectors are input to the dense layer to procure the score matrix. The state of the hidden layer of the second BiLSTM layer at moment \(t\) is \(h_{2,t}\), which is used as the output of the probabilistic prediction vector \(p_{n}\), \(p_{n}\) composition of the predicted score matrix as an input to the next layer. The process is shown in Figure 2.  
Our security policy, as long sequence data, requires the model to fully understand its context. Therefore, we chose the BiLSTM structure, which consists of two LSTMs that are responsible for capturing the before and after information of the sequence, respectively, and improves the model's expressive ability.  
To further enhance the model's ability to capture temporal information, we adopted a double-layer BiLSTM structure; i.e., stacking another layer on top of the standard BiLSTM, which enables the upper BiLSTM to process the output of the lower layer, thus extracting and integrating temporal sequence information in a deeper way. This structure not only extracts information from past and future directions, but also enhances the feature extraction ability of the model for complex data by double stacking. In addition, the double-layer BiLSTM has good generalization ability to capture rich timing information, strong expressive power, and stable and reliable performance in processing new data, which is crucial for dealing with new data or scenarios in real tasks.  
However, due to its depth and complexity, it may face issues such as gradient vanishing or exploding during training, which requires adjusting parameters to enable the model to converge better.
# Aerospace
## 5 Single Small Satellite to Venus Orbit as Secondary Payload
### Orbiter Deflection Maneuver  
The small satellite approach is defined by \(r_{p}=(6051.8+103.85)\) km and \(\psi=\pi\). The orbiter's trajectory for insertion into a 400 \(\times\) 150,000 km polar orbit is defined by \(r_{p}=(6051.8+400)\) km, \(\psi=\pi\). The aim points on the Venus B-plane are shown in Figure 23. After SmallSat release, the host performs a deflection maneuver to target its aim point.  
Based on navigation studies for aerocapture at Venus [42], the SmallSat is released 2 days prior to entry to keep the 3\(\sigma\) EFPA error below \(\pm\)0.1 deg. The deflection maneuver \(\Delta V\) is 1.28 m/s. Note that the magnitude of the deflection maneuver is much less than that for Mars (see Section 5.8) because the host is also targeting a polar orbit. Figure 24 shows the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Achieved Apoapsis Altitude Bin** & **Percentage** \\ \hline \(\pm\)400 km of target & 91.4\% \\ \(\pm\)600 km of target & 96.2\% \\ \(\pm\)800 km of target & 98.4\% \\ \(\pm\)1000 km of target & 99.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Statistics for Venus aerocapture orbit apoapsis altitude.  
Figure 23: Small satellite and host orbiter aim points on the Venus B-plane. The inset shows a magnified view of the two aim points, both of which have \(\psi=\pi\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Min.** & **5\%-ile** & **Avg.** & **95\%-ile** & **Max.** \\ \hline Apoapsis alt., km & 1399 & 1865 & 2107 & 2510 & 5418 \\ Periaps alt., km & 97.5 & 99.3 & 101.2 & 102.6 & 103.4 \\ Deceleration, g & 4.13 & 5.47 & 7.11 & 8.81 & 9.51 \\ Heat rate, W/cm\({}^{2}\) & 129 & 144 & 157 & 168 & 191 \\ Heat load, kJ/cm\({}^{2}\) & 10.4 & 12.3 & 16.1 & 19.7 & 22.2 \\ PRM \(\Delta V\), m/s & 22.9 & 26.1 & 26.5 & 27.1 & 28.2 \\ ACM \(\Delta V\), m/s & \(-\)552.7 & \(-\)101.5 & \(-\)21.0 & 28.2 & 131.4 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Statistics from Venus aerocapture Monte Carlo simulations.  
approach directory of the small satellite until the entry interface and the approach trajectory of the host spacecraft until periapsis, where it performs propulsive orbit insertion.  
The SmallSat performs aerocapture and executes a 28 m/s periapsis raise maneuver to complete its orbit insertion to a 200 \(\times\) 2000 km polar orbit. The host orbiter performs a 784 m/s burn to capture into its 250 \(\times\) 150,000 km polar orbit. These results illustrate how a very small deflection maneuver can be used to send a small satellite into a low-circular orbit as a secondary payload on a Venus orbiter mission. Such an orbit configuration, for example, offers the possibility of collecting simultaneous multi-point measurements of the magnetosphere. Figure 25 shows the two spacecraft in their captured orbits.