esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimal Solution
### Solution Procedure  
The bi-level optimization for cooperation between the DSO and MGO is implemented in the following sequential manner:  
Step 1. Set the input parameters as well as the lower and upper limits of each power system variable for the bi-level optimization process.  
Step 2. Choose the population size \(M\), crossover and mutation probability, crossover and mutation index, and maximum number of generations.  
Step 3. Solve the upper-level without the lower-level and obtain the initial base-case solution.  
Step 4. After obtaining the base-case solution by solving the upper-level without any energy band constraints, solve the lower-level to determine the new base case. Here, the tie-line flow from the energy band between the DSO and MGO is taken as a decision variable, and this variable is returned to the upper-level.  
Step 5. When violations are detected in the lower-level, solve the upper-level with all of the feedback information included, thereby creating a new base-case.  
Step 6. Solve the lower-level in parallel with the new base-case.  
Step 7. Repeat the bi-level process until the new base-case is established.  
Step 8. If the multi-objective functions are to obtain the converged Pareto solution, the process terminates; otherwise, it is repeated from Step 3.  
A flowchart of the detailed approach is presented in Figure 3.  
Figure 3: Bi-level optimization process.
## 4 Case Studies
### Tunnel Beneth the Taiyuan Railway Station  
Figure 23 presents the Yingze Street tunnel beneath the Taiyuan Railway Station. The tunnel's north and south lines measure 102.5 m and 105 m in length, respectively. It spans 18.2 m beneath the station, with a height of 10.5 m and a depth of 3.5 m. The maximum allowable settlement for both the railway track surface and the ground surface is 12.0 mm. An integrated support structure is employed to control settlement, as illustrated in Figure 24.  
The Taiyuan Railway Station tunnel is shallowly buried at a depth of 3.5 m. It is located beneath a railway where heavy haul trains and high-speed trains frequently pass, imposing substantial dynamic loads on the structure. Millimeter-level control of surface settlement is required for this project. Therefore, a high-stiffness integrated support structure is adopted. The pipe roof is composed of steel pipes with a diameter of 2000 mm and a wall thickness of 950 mm. The mechanical calculation model is illustrated in Figure 25. The  
Figure 23: Large cross-section tunnel beneath the Taiyuan Railway Station.  
Figure 22: Time history diagram of surface deformation.  
strate in the project area consist of loose to dense plain fill and soft to hard plastic new loess. Since the groundwater level lies below the tunnel invert, the surface settlement caused by groundwater level drawdown does not need to be considered.  
The elastic resistance coefficient of the soil is 10,000 kN/m, the elastic modulus of the concrete is 230 GPa, the elastic modulus of the pipe curtain is 240.5 GPa, the weight is \(\gamma\) = 19 kN/m\({}^{3}\), the lateral pressure coefficient is taken as 0.5, the distance from the palm face to the initial support closure ring is 2 m, the wall thickness is taken as 950 mm, and the grouting pressure is 0.16 MPa. According to Equation (10), the total surface settlement resulting from tunnel excavation is calculated as \(u_{1}\) = 0.79 mm.  
The net spacing between steel pipes in the pipe roof was designed to be 165 mm. During single-pipe jacking, the grouting pressure around the pipe was set at 0.38 MPa to maintain pressure and reduce resistance. An alternate pipe jacking method was employed to minimize the settlement superimposition effect. Based on Equation (25), the surface settlement resulting from pipe roof construction is calculated to be \(u_{2}\) = 7.3 mm.  
The total surface settlement induced by tunnel construction is calculated as u = 8.09 mm. Monitoring data revealed that the surface settlement caused by pipe roof construction ranged from 5.5 mm to 6.4 mm, while the total settlement resulting from the entire construction process ranged from 6.3 mm to 7.7 mm. The calculated results closely match the measured data, and the settlement control satisfies the requirements for ensuring the uninterrupted operation of Taiyuan Railway Station.  
Figure 24: Cross-section of the tunnel beneath the Taiyuan Railway Station. (Unit: mm).  
Figure 25: Cross-section of the Taiyuan Railway Station tunnel.
## 5 Design and Implementation
### Graph-Based Vulnerability Search  
Upon completion of progressive taint analysis, the details of taint propagation between distinct snippets were elucidated. However, vulnerability mining requires finding a complete path from the input to the sink point. Therefore, FLuaScan proposes a graph-based vulnerability search method. Drawing on the previously mentioned concept of analysis nodes, vulnerability detection is redefined as a graph search problem. Specifically, our initial step involves utilizing the Neo4j database to store all nodes that have been analyzed in prior stages. Then, _FLuaScan_ creates three labels (INode, MNode, PNode, and SNode) in the Neo4j database corresponding to three different types of nodes. _FLuaScan_ inserts INode and MNode into the Neo4j database, creating the internal taint relationships, based on the result of the handler analysis process. Subsequently, based on the results shown in (b) and (c), the relationships between PNode and MNode, as well as between PNode and SNode, are processed in a similar manner. The necessary condition for the existence of a vulnerability is to find a path from INode to SNode. Currently, finding such a path is not possible due to our limitation in obtaining only the relationships of direct contamination within different code snippets. The analysis of subfunctions is essential to connect these distinct code snippets into a whole path. During the progressive taint analysis, in the (a) process, the invocation of subfunctions acts as the sink point and is represented as MNodes in the Neo4j database. In contrast, in the (b) and (c) processes, the parameters of subfunctions function as taint sources and are represented as PNodes in the Neo4j database. _FLuaScan_ examines the _pos_ attributes of all MNodes and PNodes. A relationship between the MNode and the PNode is established if they pertain to the same subfunction. Finally, _FLuaScan_ employs a Cypher query to search for paths that start from the INode, traverse multiple MNodes, and end at the SNode. Detail of the Cypher query are demonstrated in Listing 8.  
```
1 MATCHp=(a:INode)-[*]->(b:SNode)returnp
```  
**Listing 8** Cypher query to detect vulnerability.  
To assist users in manual analysis, FLuaScan provides an interactive web page displaying all identified suspicious vulnerability points. Figure 3 (raw figure at [[https://sm.ms/image/L8](https://sm.ms/image/L8) VRorXCgnUSefk]([https://sm.ms/image/L8](https://sm.ms/image/L8) VRorXCgnUSefk) accessed on 20 August 2023) shows a screenshot of the web page, which represents the paths that meet the vulnerability requirements.  
The purple, red, blue, and yellow nodes, respectively, represent INodes, MNodes, PNodes, and SNodes, respectively. In addition to this web page, FLuaScan also includes a  
Figure 3: The visual result of a graph-based vulnerability search in FLuaScan.  
VSCode plugin, assisting in manual analysis. With the assistance of this plugin, comments are automatically added at the positions of analysis nodes and displayed with different highlight colors based on their type.
# Repeater Insertion for Multi-Walled Carbon Nanotube Interconnects
## 3 Repeater Insertion
Therefore, an MWCNT interconnect exhibits superior performance over its Cu counterpart, as shown in Figure 5. However, considering the contact resistance induced by the inserted repeaters, an MWCNT interconnect may lose its performance advantage, which will be discussed in the next section. It is evident that that the time delay is a function of \(a_{1}\), \(a_{2}\), and \(a_{3}\), that is, \(T_{seg}=T_{t}\cdot F(a_{1},a_{2},a_{3})\). The total propagation delay of the MWCNT interconnects can be written as  
\[T_{\text{total}}=kT_{t}\cdot F(a_{1},\ a_{2},\ a_{3})=\sqrt{lc}L\cdot F(a_{1}, \ a_{2},\ a_{3}). \tag{14}\]  
According to [29], the repeater size and number of segments can be expressed as  
\[h=\sqrt{\frac{R_{tr0}cL}{C_{g0}(rL+R_{c})}}h^{\prime} \tag{15}\]  
\[k=\sqrt{\frac{4}{7}\frac{(rL+R_{c})cL}{R_{tr0}C_{g0}}}k^{\prime} \tag{16}\]  
where \(h^{\prime}\) and \(k^{\prime}\) are the correction factors. Substituting (15) and (16) into (14), the total propagation delay can be written as \(T_{\text{total}}=\sqrt{lc}L\cdot F\left(m+\sqrt{7}/\left(\sqrt{4}k^{\prime}pq \right)\), \(q/(h^{\prime}p)\), \(\sqrt{4}/\left(\sqrt{7}h^{\prime}k^{\prime}\right)\right)\), where \(p=\sqrt{1/\left(rR_{tr0}C_{g0}\right)}\), \(q=\sqrt{1+R_{c}/(rL)}\), and \(m=R_{c}/Z_{0}\). In real-world applications, \(R_{c}\) must be smaller than the interconnect resistance \(rL\). Otherwise, the fabrication process should be optimized first [22]. Therefore, \(q\) is limited to [1, 1.4], and \(p\) is in the range of [0, 10][29]. After careful examination of the characteristic impedance of the MWCNT interconnects according to the geometrical and physical  
Figure 4: Distributed circuit model of a driver-interconnect-load (DIL) system. Figure 5: Propagation delays versus length in the intermediate-level (**a**) Cu and (**b**) MWCNT interconnects. parameters adopted from the ITRS projection, \(m\) is defined in the range of [0, 10]. In order to minimize the total propagation delay, \(\partial F/\partial t^{\prime}=0\) and \(\partial F/\partial k^{\prime}=0\) need to be solved simultaneously. By numerically solving these equations, \(h^{\prime}\) and \(k^{\prime}\) can be obtained. Using the multivariable curve fitting technique, the closed-form expressions of \(h^{\prime}_{opt}\) and \(k^{\prime}_{opt}\) are obtained and given as  
\[h^{\prime}_{opt}=\cos(q-1)\frac{0.839+0.269m-0.021m^{2}-0.917\ln p+0.345(\ln p )^{2}-0.037(\ln p)^{3}}{1+0.209m+0.045m^{2}-0.005m^{3}-0.854\ln p+0.361(\ln p)^{ 2}} \tag{17}\]  
\[k^{\prime}_{opt}=\cos(q-1)\frac{0.835+0.307m-0.047m^{2}+0.002m^{3}-0.606\ln p+ 0.116(\ln p)^{2}}{1+0.739m-0.073m^{2}+0.002m^{3}-0.474\ln p+0.161(\ln p)^{2}}. \tag{18}\]  
The optimized repeater size \(h_{opt}\) and number of segments \(k_{opt}\) can be finally obtained by substituting (17) into (15) and (18) into (16), respectively. Then, the minimum propagation delay \(T_{\text{min}}\) can be obtained by (14). Here, the optimal number of repeaters is denoted as \(n_{opt}\), which is calculated as \(k_{opt}-1\).
## 5 Simulation Results  
Aiming to further investigate the effect of the source inductance on the switching waveforms during the transients and to provide a method to obtain the optimal \(L_{\text{s}}\) value, time-domain simulations were carried out on the circuit shown in Figure 7. It models an asynchronous Buck converter, which was lately prototyped and exploited as EUT in Section 6. Such a converter was designed to step down an input voltage (\(V_{\text{PS}}\)) of 48 V to a 12 V output voltage, and it is able to provide the load with a maximum current of 5 A. The high-side transistor (\(T_{\text{HS}}\)) is periodically turned on and turned off with a 0.25 fixed duty cycle and 150 kHz fixed switching frequency by the switches \(S_{\text{on}}\), \(S_{\text{off}}\), that model the gate driver output stage. The converter is provided with a set of input (\(C_{1},C_{2}\)) capacitors, which lower the voltage ripple affecting the input voltage, and a freewheeling diode \(D_{\text{LS}}\), which allows the output current to flow when \(T_{\text{HS}}\) is off. The simulated schematic, which is shown in Figure 7, comprise the active and the passive devices, as well as the parasitics, which are encircled in dashed boxed to be found at a glance. The parasitics of the PCB traces (\(L_{\text{t1}},L_{\text{t2}},L_{\text{t3}},L_{\text{t4}},C_{\text{t1}},L_{ \text{s,0}}\)) were evaluated from the extraction of the scattering parameters of the PCB by means of a 3D electro-magnetic simulator, \(C_{\text{ind}}\) models the parasitic capacitance of the power inductor, which are not shown in Figure 7, and it was evaluated from its resonant frequency provided by the manufacturer. The internal gate resistance \(R_{\text{g,int}}\) of \(T_{\text{HS}}\) is equal to its nominal value, and the driver resistances \(R_{\text{g,on}}\), \(R_{\text{g,off}}\) are discrete level components which can be modified. The values of the adopted components are listed in Table 1.  
Figure 7: Power stage of the designed step-down Buck converter, including the active (\(T_{\text{HS}},D_{\text{LS}}\)) and the passive (\(C_{1},C_{2}\)) devices. Parasities due to printed circuit board (PCB) traces, packages and non-idealities of the passive components are enclosed in dashed boxes.
## 4 Results of Experiments and Discussion
### The Application of a Preload Ball Screw System with Feedforward Control and PID Control Strategy  
In practical applications, it is easily understood that the settling time of the control strategy has a significant effect on the feed drive system with respect to stability control, leading directly to better machining accuracy.  
In this section, the P-I model-based feedforward control strategy is introduced, aiming at improving the feed drive stability response. The proposed block diagram of the P-I model feedforward control system is shown in Figure 12. The same working conditions and desired force output are set in the experiment in order to compare the feedforward control system with a classical PID controller. The force response of the system is shown in Figure 13. The results in Figure 13 reveal that the rise time, peak time and settling time are greatly shortened, to 0.08 s, 0.13 s, and 1.43 s, respectively. Compared to the former results without P-I model-based feedforward control, the rise time decreased by approximately 95%, the peak time by approximately 96%, and the settling time by approximately 58%. This demonstrates that the performance of the machining tool with P-I model feedforward control is significantly improved. Similarly, the corresponding tracking performance with a step-like trajectory is also tested, with results shown in Figure 14, further verifying the effectiveness of the proposed feed drive stabilizing system.  
On the basis of the aforementioned results of comparative experiments, it can be seen that the proposed control strategy has a higher efficiency. Therefore, the preload force is able to be adjusted accordingly, and the increase in stiffness and the enhancement of stability of the drive system can be achieved. All in all, this method has a bright future for use in cutting-edge equipment in the ultra-precision manufacturing industry.  
Figure 11: Tracking performance of PID control with a step-like trajectory.  
Figure 12: Block diagram of feedforward control and PID control.  
In future, the optimization of the whole system will be discussed, and the criteria for designing preload feed drive systems will be discussed and analyzed in future studies. It is also believed that with the implementation of displacement measurement, the efficiency of the preload control system could also be improved.
## 3 Bibliometric Review
### Spatial Location-Based Analysis  
The information on academic articles from various nations is depicted in Figure 2. This interactive map was made with Google Sheets in a collaborative environment [33]. A Google spreadsheet receives data from an excel document with two columns for the nation and number of publications, then uses this information to create a map. The publication count information for each country on this global map can be read using a scale. Smaller contributions to the research area correspond to smaller dots on the map. At the left-hand bottom of the page is a scale representing the number of publications. The United States is indicated by a dark green dot on the map, indicating that it has the most articles published (26 total) on the use of IoT to identify plant diseases.  
Figure 3 illustrates the top ten countries with publications in the field of smart agriculture. The bar graph reveals that the United States has the largest share of publicizing documents in the smart agriculture research sector, with 26 contributions, followed by India with 19 contributions. France, Malaysia, and Pakistan are the countries that contribute the least to research in the field of smart agriculture.  
Figure 1: Yearly publishing trends of IoT in crop disease detection.
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 5 Experiment and Performance Verification
### Parameter Analysis  
Figure 4 illustrates the convergence performance of the EMC-PPO method under different learning rate conditions. From Figure 4, it can be observed that when the learning rate was set to 0.001, 0.01, and 0.1, due to the learning rate being too high or too low, the adjustment steps of the model parameters were either too large or too small, making it impossible to find the optimal solution. On the other hand, when the learning rate was 0.001, the model achieved the best convergence effect and reward outcome. Therefore, 0.001 was determined to be the optimal learning rate hyperparameter.  
Figure 4: MEC-PPO convergence graph under different learning rates.  
Figure 5 illustrates the convergence performance of the EMC-PPO method under different discount rates. From Figure 5, it can be observed that when the discount rate was set to 0.93, the model could achieve convergence in less than 4000 iterations. However, under discount rates of 0.99 and 0.90, even after 6000 iterations, the model failed to converge. Additionally, when the discount rate was 0.96, the convergence time of the model was relatively late. This paper adopted a discount rate of 0.93 as the optimal discount rate.
## 2 Methods
### Statistical Analysis  
Propensity scores (PSs) were calculated using a logistic regression model to determine the effects of vaccination by accounting for covariates that predict receiving the intervention (vaccine) in observational studies to reduce selection bias [25; 26]. The endpoint was the incidence of stroke (ICD-9-CM 430-437) in the vaccinated or unvaccinated patients with a subsequent outpatient visit, emergency department visit or inpatient hospitalization for stroke within 12 months; the unvaccinated patients served as the reference arm. The protective effects of the vaccination are specific to the influenza season; therefore, evaluating patients in the non-influenza season can indicate the possible contribution of bias to estimates obtained during the influenza season. Thus, we analyzed the relationship between the seasonal effects of the vaccination and risk of stroke.  
The cumulative incidence of stroke in the vaccinated and unvaccinated patients with hypertension was calculated using the Kaplan-Meier method. To examine the effects of the total number of vaccinations on the cumulative incidence of stroke, we categorized the patients into 4 groups according to their vaccination status: unvaccinated and 1, 2 or 3 and \(\geq\)4 total vaccinations. A time-dependent Cox proportional hazard model was used to calculate the hazard ratios (HRs) for hypertension in both vaccinated and unvaccinated patients [27]. A stratified analysis was conducted to evaluate the effects of vaccination according to age and sex (Table 2). All analyses were performed using SAS (Version 9.4, SAS, Cary, NC, USA); 2-tailed P b 0.05 was considered significant. In the sensitivity analyses, external adjustments clarify the effects of drugs and other covariates in epidemiological database studies [28]. Most of the statistically different variables between groups were adjusted in our analyses.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Unvaccinated**} & \multicolumn{3}{c}{**Vaccinated**} & \multicolumn{3}{c}{**Vaccinated**} \\ \cline{2-9}
**All Group** & \multicolumn{3}{c}{**(Total Follow-Up 177,838.0 Person Years)**} & \multicolumn{3}{c}{**(Total Follow-Up 201,118.5 Person Years)**} & \multicolumn{3}{c}{**Adjusted HR \({}^{\dagger}\)**} \\ \cline{2-9}
**(\(\mathbf{n=59\),S21)**} & \multicolumn{1}{c}{**No. of**} & \multicolumn{1}{c}{**Incidence Rate**} & \multicolumn{1}{c}{**No. of**} & \multicolumn{1}{c}{**Incidence Rate**} & \multicolumn{1}{c}{**(95\% CD)**} \\ \cline{2-9}
**Patients** & \multicolumn{1}{c}{**(per 10\({}^{\dagger}\) Person Years)**} & \multicolumn{1}{c}{**Patients**} & \multicolumn{1}{c}{**(per 10\({}^{\dagger}\) Person Years)**} & \multicolumn{1}{c}{**(95\% CD)**} & \multicolumn{1}{c}{**(95\% CD)**} \\ \cline{2-9}
**White cohort** & & & & & & & & \\
**Induenza season** & 2579 & 1450.2 & (1394.2, 1506.2) & 2809 & 1396.7 & (1345.0
# A Study on the Creep Characteristics of Gassy Clay Mixed with Silt
## 3 Results
### Analysis of Creep Characteristics  
Based on the test loading schemes outlined in Tables 3 and 4, triaxial creep tests were conducted on the prepared silt-containing gassy clay. Subsequent to the creep tests, full-process creep curves were plotted for the silt-containing gassy clay under identical gas content with varying confining pressures and under identical confining pressure with varying gas contents, as illustrated in Figures 4 and 5.  
Figures 4 and 5 reveal that with consistent gas content and varying confining pressures, or with consistent confining pressure and varying gas contents, the axial strain of the specimen exhibits a consistent pattern of change over time following the application of each deviatoric stress level. The axial strain gradually increases with time, ultimately reaching stabilization, indicating a decaying development of creep. Subsequent applications of deviatoric stress consistently result in greater axial strain compared to the preceding levels. Figure 4 further demonstrates that when the applied deviatoric stress is below the yield stress observed in conventional triaxial shear tests with the same confining pressure, the  
axial strain of the specimen increases relatively slowly over time, with a smaller magnitude of increase and a smaller stable-state axial strain. Conversely, when the applied deviatoric stress approaches the yield stress observed in conventional triaxial shear tests under the same confining pressure, the increase in axial strain becomes more pronounced over time, with a larger magnitude of increase and a larger stable-state axial strain.  
Analyzing the phenomena depicted in Figures 4 and 5, when the applied deviatoric stress is below the yield stress observed in conventional triaxial shear tests under the same confining pressure, the structure of the gassy soil remains intact. Consequently, the shear deformation exhibited by the specimen primarily entails elastic deformation. Conversely, when the applied deviatoric stress approaches the yield stress resulting from the same confining pressure as in conventional triaxial shear tests, the structure of the gassy soil experiences partial disruption. As a result, the shear deformation undergone by the specimen includes both elastic and plastic deformation. However, owing to the presence of confining pressure, the axial strain of the specimen can stabilize. Upon surpassing the yield stress observed in conventional triaxial shear tests with the same confining pressure, the structure of the gassy soil undergoes complete breakdown. At this juncture, the shear deformation displayed by the specimen primarily involves plastic deformation. Nevertheless, as the test's confining pressure remains at a level higher than the deviatoric stress, the specimen continues to exhibit a state of decaying creep by the end of the test.