esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Herbs are widely utilized in the Western Ghats region of India to treat liver diseases and viral-like infections. However, such practices lack scientific evidence at the molecular level and may often pose adverse drug reactions. This study intends to identify phytocompounds with druggability and non-toxic profiles with potential activity against hepatitis B virus-induced hepatocellular carcinoma. The details of phytocompounds in traditionally utilized herbs in the Western Ghats region were collated from chemical databases and publications. The druggability and toxicity of these compounds were predicted using MolSoft and ADVERpred, respectively. The probable targets of these phytocompounds were predicted using BindingDB. Moreover, compound-gene set pathways, cellular processes, and functional enrichment analyses were also performed using STRING and KEGG pathway databases. Subsequently, herb-compound-target-disease pathway networks were constructed using Cytoscape. The potential hub protein was virtually screened against the ligand dataset using the POAP pipeline. Finally, molecular dynamics (MD) simulations of the most potential protein-ligand complexes were performed in triplicate using Schrodinger Desmond. Amongst 274 compounds from 16 herbs studied, 36 showed drug-likeness with nontoxic properties and were also predicted to modulate 16 potential targets involved in the pathogenesis of HBV-induced HCC. Among all the molecules screened, flavonoids and diterpenoids from _Andrographis paniculata_ and _Thesepisa populnea_ scored the highest edge count via modulating multiple targets and pathways. Moreover, molecular docking and MD simulation (100 ns) also inferred that the top-ranking Andrographin and Gossypetin exhibit stable intermolecular interactions with EGFR protein, which was identified as a highly connected hub protein in the constructed network. All these findings are suggestive of identified moieties as potential therapeutics for targeting HBV-associated HCC sans adverse drug reactions.  
Andrographis paniculata; hepatitis B; hepatocellular carcinoma; molecular docking; molecular dynamics; network pharmacology; _Thepisa populnea_
## 4 Results and Discussion  
### Baseline Results  
For tractability, detailed documentation of the computations and all the matrices involved in the proposed computational framework is presented as Supplementary Materials. For brevity, only the key results are presented. The prominence and relation vectors are presented in Table 2. Results show that six, or 50%, of the barriers identified in the systematic review of [PERSON] et al. [10] are dispatchers (net cause), and six, or 50%, are receivers (net effect). The net cause group contains barriers with positive \(\left(D-R^{T}\right)\), implying that they had a lower influenced impact \(\left(R\right)\) than the influencer \(\left(D\right)\); otherwise, net effect. The barriers belonging to the net cause group (arranged from the most influential to least) are costly (B2), lack of training resources (B5), lack of collaboration (B6), apprehensive stakeholders (B4), cybersecurity threat (B1), and insufficient foundation of basic education (B12). These barriers impact the identified system of barriers in implementing EDUC4 from a developing economy perspective. On the other hand, the net effect group consists of the insufficient available technologies (B8), skills gap of human capital (B3), knowledge gap for the customization of curriculum design (B7), time constraints for material preparation (B10), health issues (B9), and complexity of learning platforms (B11). These barriers are the most easily influenced ones by other barriers. As to the prominence, the lack of training resources (B5), skills gap of the human capital (B3), insufficient available technologies (B8), the complexity of learning platforms (B11), and costly (B2) are considered the topmost prominent barriers. The prominence of the barriers is associated with the measure of \(\big{(}D+R^{T}\big{)}\). Identifying the most prominent barriers only highlights relative significance, not the most significant. The ranking of barriers based on \(\big{(}D+R^{T}\big{)}\) measures is as follows: (B5) \(\succ\) (B3) \(\succ\) (B8) \(\succ\) (B11) \(\succ\) (B2) \(\succ\) (B7) \(\succ\) (B4) \(\succ\) (B10) \(\succ\) (B6) \(\succ\) (B1) \(\succ\) (B12) \(\succ\) (B9). On the other hand, the results of the MMDE algorithm are presented in Table 3.  
The obtained threshold value discriminates less critical/crucial total causal relationships from the crucial ones. Figure 3 categorizes all barriers into four distinct categories: minor key barriers (low prominence, high relation), key barriers (high prominence, high relation), indirect barriers (high prominence, low relation), and independent barriers (low prominence, low relation). It also presents the crucial barriers and their relationships. Based on Figure 3, the minor key barriers comprise cybersecurity threat (B1) and insufficient foundation of basic education (B12). The key barriers include costly (B2), skills gap of
# Mechanical Response Law and Parameter Influence Analysis of Karst Tunnel Dynamic Excavation
## 4 Numerical Model  
Using FLAC3D to build the model, the model size should be no less than four times the diameter of the tunnel [29], and also take into account the computational volume calculation time when the model is too large. In this model, half of the tunnel structure was taken according to the symmetrical nature. It is assumed that the interior of each stratum is homogeneously isotropic and that the different strata are parallel to each other.  
The model size is 42 m in the direction along the tunnel, the model width is 30 m, and the distance from the bottom of the tunnel to the bottom of the model is 27 m.  
The diameter of the tunnel is 6.4 m, the tube sheet uses C50 concrete, the thickness of the tube sheet is 0.2 m, only the displacement and stress changes of the surrounding rock of the initial tunnel support are considered, and the influence of the secondary support is not considered.  
As shown in Figure 2. The model uses the Mohr-Coulomb constitutive model, and the support part uses the elastic model. The model is simplified to a homogeneous medium. According to the site conditions, the cavern is simplified to a small rectangular cavern.  
Based on the above model, a 15 m deep tunnel is established, with a cycle step of 3 m. The lining segment lags behind the excavation step, and each excavation cycle step can obtain the stress and displacement process of the tunnel at different stages. Changes in stress and displacement at the arch crown, arch bottom, and arch waist of the tunnel at 21 m were monitored under three different conditions: no karst cave, critical safety distance of the upper karst cave, and critical safety distance of the lower karst cave. The size of the karst cave is 2 m in span and 0.5 m in thickness, as shown in Figures 3 and 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Elastic Modulus (MPa)** & **Poisson’s Ratio** & \
\begin{tabular}{c} **Angle of Internal** \\ **Friction (\({}^{\circ}\))** \\ \end{tabular} & **Cohesion (kPa)** &
\begin{tabular}{c} **Volumetric Weight** \\ **(kN/m\({}^{\circ}\))** \\ \end{tabular} & **Thickness (m)** \\ \hline Plain fill & 40.6 & 0.30 & 10.0 & 15.0 & 18.5 & 0.5 \\ Loess-like silty clay & 83.5 & 0.30 & 20.6 & 42.3 & 18.6 & 3.5 \\ Silty Clay & 124.5 & 0.33 & 21.4 & 40.5 & 19.2 & 2.0 \\ Strongly weathered & 5014 & 0.22 & 20.0 & 52.0 & 26.0 & — \\ Filting of karst & 62.5 & 0.30 & 20.0 & 42.0 & 19.5 & — \\ C50 concrete & 35,000 & 0.20 & — & — & 25.0 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of geotechnical and material.  
Figure 2: Schematic diagram of the geological model: 1 is plain fill, 2 is Loess-like silty clay, 3 is silty clay, 4 is strongly weathered limestone, and 5 is the tunnel.
## 2 Overview of Drone Technologies  
### Drone Anatomy  
The anatomy of a drone is a sophisticated integration of various technological components that enables it to perform a wide range of functions, from simple recreational flying to complex commercial tasks [8].  
Figure 1 illustrates the essential components and functionalities of drones, including the flight controller, propellers, brushless motors, and gimbal systems, and their roles in operation. Each part is vital for the drone's flight, navigation, and tasks like video recording or streaming [9].  
The flight controller, a minicomputer, processes sensor inputs and pilot commands to maintain stable flight, enabling both basic maneuvers and advanced navigation. Brushless motors, chosen for efficiency and reliability, adjust propeller speed and rotation to create lift and thrust, which are crucial for speed and stability [10]. Propellers are aerodynamically designed to optimize performance.  
Communication with the operator is managed through a radio receiver and transmitter, which handle commands and data such as live video feeds or telemetry [11]. The gimbal controller and motors stabilize mounted devices like cameras, ensuring steady  
Figure 1: Anatomy of drones.  
footage. Electronic speed controllers (ESCs) regulate motor power from the battery to adjust propeller speed for smooth flight dynamics [12].  
Each drone component enhances its flight capabilities and broadens its applications, making drones versatile tools for civilian and commercial use for usages such as aerial photography or complex surveillance [13].  
Drone operations involve multiple layers to ensure optimal performance across tasks, with usages ranging from aerial photography to complex data collection and analysis [14; 15; 16; 17]. The architecture of a drone system is divided into specific functional layers:  
* **Planning layer**: Defines missions or services and develops executable plans; this is essential for disaster management.
* **Flight management layer**: Executes the planned route, managing dynamic flight, obstacle avoidance, and real-time path modifications to ensure secure mission completion.
* **Control layer**: Interfaces directly with drone hardware, sending commands to sensors and actuators and handling real-time adjustments to maintain the stability and trajectory.  
The drone's hardware includes the frame, motors, propellers, and electronic speed controllers (ESCs) and enables physical operation. Sophisticated equipment like GPS and antenna systems ensure communication with ground control and navigational accuracy via GPS and RTK networks. In scenarios with multiple drones, robust data link systems facilitate drone-to-drone communication for coordinated task execution [18; 19]. Figure 2 illustrates the multi-layered approach to drone operations and highlights key components and communication frameworks that enable autonomous and coordinated functions.
## 6 Numerical Results
### Considered Scenarios  
We focus on evaluating the performance of two specific services, namely, web browsing and bulk data transfer. The numerical parameters for each service can be found in Table 6. The recommended delay time (\(t_{rk}\)) and allowable delay time (\(t_{ak}\)) for data transfer are utilized to estimate the minimum-bit-rate guarantee (\(b\)) and the abandonment rate due to impatience of users in buffers (\(\varepsilon_{k}\)). The total bit rate for all network slice resources is calculated based on the chosen bandwidth, modulation and coding scheme (MCS), and MIMO scheme.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Description** & **Value (Case 1)** & **Value (Case 2)** \\ \hline \(B\) & Bandwidth & 5 MHz & 5 MHz \\ \(-\) & MCS & QPSK & QPSK \\ \(-\) & MIMO scheme & \(2\times 2\) & \(2\times 2\) \\ \(C\) & Total bit rate for two network slices & 10 Mbps & 8.016 Mbps \\ \(\delta\) & Arrival rate of signals from the controller & 0.000001 1/s & 0.000001 1/s \\ \((w_{1},w_{2},w_{3})\) & Weights for the reallocation principles & \((1,1,1)\) & \((1,1,1)\) \\ \(t_{r1},t_{r2}\) & Recommended delay time & 15, 2 s & 15, 2 s \\ \(t_{a1},t_{a2}\) & Allowable delay time & 60, 4 s & 60, 4 s \\ \(b\) & Minimum-bit-rate guarantee & 1.067 Mbps & 1.067 Mbps \\ \(R_{1}\), \(R_{2}\) & Size of 1-buffer and 2-buffer & 5, 5 & 2, 2 \\ \(\lambda_{1},\lambda_{2}\) & Arrival rate of requests from 1-users and 2-users & 0.03, 0.6 1/s & 2–10, 26–50 1/s \\ \(\mu_{1},\mu_{2}\) & Average volume of traffic transmitted by 1-users and 2-users & 0.125, 0.937 Mb & 0.125, 0.937 Mb \\ \(\varepsilon_{1},\varepsilon_{2}\) & Abandonment rate due to impatience of 1-users in 1-buffer and of 2-users in 2-buffer & 0.01, 0.25 1/s & 0.01, 0.25 1/s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters for numerical example.
# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 2 PCA-LSTM Based Fault Prediction Model
### Long Short-Term Memory's Introduction to Neural Networks (LSTM)
The candidate cell status is represented as follows:  
\[\widetilde{C}_{t}=tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c}) \tag{13}\]  
In the equation, \(\widetilde{C}_{t}\) represents the candidate cell state, \(W_{xc}\) is the weight matrix between the input and the cell state, \(W_{hc}\) is the weight matrix between the historical output and the cell state, and \(b_{c}\) is the bias term for the cell state. The tanh function is used to scale the values. After obtaining the outputs of the forget gate and the input gate, the current cell state, \(C_{t}\), is composed of two parts. One part is the information retention determined by multiplying the output of the forget gate with the cell state of the previous moment. The other part is the information added by multiplying the output of the input gate with the current candidate state. The cell state is represented as follows:  
\[C_{t}=f_{t}C_{t-1}+i_{t}\widetilde{C}_{t} \tag{14}\]  
The output gate controls the final output of the cell state. The output of the output gate is multiplied by the current cell state information and then passed through the tanh function to obtain the cell output. The output of the output gate is represented as follows:  
\[o_{t}=\sigma(W_{xo}x_{t}+W_{ho}h_{t-1}+b_{o}) \tag{15}\]  
\[h_{t}=o_{t}\text{tanh}(C_{t}) \tag{16}\]  
where \(o_{t}\) is the output of the output gate, \(W_{xo}\) is the weight matrix between the input and the output gate, \(W_{ho}\) is the weight matrix between the historical output and the output gate, \(b_{o}\) is the bias term for the output gate, \(h_{t}\) is the cell output, and \(C_{t}\) is the current cell state.
## 3 Results
### Truncations of the Other TSs  
As demonstrated in the GC-MS analysis (Figure 3A), _E. coli_ strain BS1101 expressing TS 1411(44) exhibited the production of various monoterpenes, including tricyclene, \(\alpha\)-pinene, \(\beta\)-pinene, sabinene, camphene, and limonene. To assess byproducts and titers of the remaining 15 TSs investigated in this study, truncations of the transit peptide were performed. Due to the high sequence similarities of these TSs and 1411, all of them were truncated by 44 amino acids and subsequently subjected to shake-flask fermentation under identical conditions. The results of the shake-flask fermentation are shown in Figure 3B, including titers of tricyclene and the other monoterpene byproducts. The strains containing 4091(44), 4926(44), 5066(44), 8653(44), 9309(44), 5307(44), and 8401(44) were capable of synthesizing detectable tricyclene, with 8653(44) producing 4.329 mg/L of tricyclene, which was the second highest titer after 1411(44). These TSs were not able to produce tricyclene before being truncated, indicating that the truncation of the transit peptide had a significant enhancement in the synthesis of tricyclene. The rest of the TSs in Table S3 could not produce detectable tricyclene after being truncated. In addition, these truncated TSs also produced other monoterpene by-products including \(\alpha\)-pinene, \(\beta\)-pinene, camphene, sabinene, and limonene in _E. coli_ strain BS1101. The proportions of tricyclene among all monoterpenes varied across different TSs, as presented in Table S4. Among these, 1411(44) exhibited the highest proportion of tricyclene at 60.8%. The proportion of by-products derived from different sources of TPSs varied significantly. For instance, \(\beta\)-pinene and limonene were the predominant by-products of 1411(44) and 8653(44), whereas camphene, \(\beta\)-pinene, and  
Figure 2: Tricyclene titers of truncated 1411 at different sites. (**A**) Protein structure prediction of 1411 using Uni-Fold. (**B**) Schematic of the N-terminal truncation of 1411. Blue bar, truncated part. Orange bar, resting part. The arrows indicated corresponding cutting sites. (**C**) Corresponding tricyclene titers at the truncation sites of P19, P24, H37, 149, and W73. (**D**) Tricyclene titers of the 13 truncation sites from H37 to I49. The significances of wild-type 1411 compared to other datasets were tested using a two-tailed _t_-test. The error bars represented one standard deviation for three replicates. Significance (_p_-value) was evaluated using a two-tailed _t_-test (**_p_ \(<\) 0.01; ***_p_ \(<\) 0.001, ***_p_ \(<\) 0.0001).  
limonene were the main by-products of 5307(44) and 8401(44). In conclusion, these findings suggest that truncation at the N-terminal of TSs effectively enhances tricyclene production in _E. coli_.
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
## 3 Analysis of Stability Factors for GER with Paste Backfill
### Load-Bearing Characteristics of Paste Backfill Materials  
The load-bearing characteristics of paste backfill materials are critical indicators influencing the effectiveness of filling operations [44]. To analyze the effects of mass concentration, fly ash proportion, and cementitious material proportion on the load-bearing properties of paste backfill materials, standard specimens with varying compositions were prepared and subjected to uniaxial compressive strength (UCS) tests.  
From Figure 5, it is evident that as mass concentration, fly ash proportion, and cementitious material content increase, the UCS of the paste backfill material consistently improves. When the mass concentration increases from 76% to 82%, the UCS of paste specimens increases from 2.9 MPa to 5.1 MPa. As the fly ash proportion rises from 10% to 16%, the UCS grows from 2.7 MPa to 3.8 MPa. With the cementitious material proportion increasing from 14% to 20%, the UCS improves significantly from 3.6 MPa to 7.1 MPa.  
The load-bearing performance of the paste material exhibits distinct age-dependent characteristics. During the first 1-7 days, the UCS of the specimens increases rapidly, marking the primary hardening phase of the backfill. In this phase, intense hydration reactions occur, ensuring full contact between the coal gauge and cementitious materials. This process redistributes and rearranges the particles within the coal gauge, expels water, and reduces the water content in pores, leading to a significant increase in compressive strength. Between 7-14 days, the hydration reaction gradually weakens, and the strength growth slows down. During this phase, the strength improvement is primarily attributed to the compaction of the backfill under load, further reducing porosity and enhancing strength. From 14-28 days, the UCS of the paste backfill stabilizes, with no significant fluctuations observed. The structure and performance of the backfill reach a relatively stable state.  
Considering factors such as material strength and flowability, the final ratio of gauge to cementitious material is 11:1, the ratio of cementitious material to fly ash is 0.9, and the mass concentration is 79%, and the UCS of the paste material at 28 days reaches 3.8 MPa.  
Figure 4: Characteristics of overburden rock movement in slopes with different backfill ratios: (**a**) low backfill ratio; (**b**) Medium backfill ratio; (**c**) high backfill ratio.
## 3 Materials and Methods
### Assessment Methods
#### 3.2.3 Random Forest  
A random forest is a method that creates many decision trees during training and combines their outputs for classification or regression. This method reduces the risk of overlearning and increases the accuracy of prediction. It is part of the category of ensemble learning, where multiple models (in this case, decision trees) are combined to achieve greater accuracy and robustness [65]. Unlike a single decision tree, random forest is more stable and robust because randomly combining trees reduces errors caused by variability in training data. The disadvantages include lower interpretability of the resulting model and higher computational demands.
## 5 Case Studies
### Hysteresis Control with PID Correction for Temperature Control  
In the first example, the heating system is considered. It was selected as an example of a non-linear plant. Moreover, the requirement of such a control structure can be simply determined: minimize the oscillations in a quasi-steady state and minimize the switching frequency. The considered heating system, described in the form of the transfer function, is as follows:  
\[G_{\text{plant}}(s)=\frac{k_{o}}{(T_{o1}s+1)(T_{o2}s+1)} \tag{36}\]  
where \(k\) is the static gain of the plant, and \(T_{o1}\) and \(T_{o2}\) are time constants. The output signal is the temperature, while the control signal is the heater state. Due to the consideration of a discontinuous control signal, i.e., on/off output state, the hysteresis controller is used. To obtain higher system performance, the PID correction method can be used.The hysteresis controller is covered by two additional close loops with the following transmutations:  
\[G_{c1}(s)=\frac{k_{c}}{T_{c1}s+1} \tag{37}\]  
\[G_{c2}(s)=\frac{k_{c}}{T_{c2}s+1} \tag{38}\]  
where \(k_{c}\), \(T_{c1}\) and \(T_{c2}\) are PID correction parameters. The general block diagram of the system is presented in Figure 4. The parameters of the control system are listed in Table 2.  
Figure 4: Control scheme of hysteresis control with PID correction for temperature control.  
Note that measurement noise was added to the plant's output to increase the impact of PID correction on system performance.