esa-sceva/satcom-chunk-collection huggingface dataset:
# The Numerical Study for the Effect of Stiffness Matching on Wheel-Rail Curve Squeal Noise
## 4 Discussion
### Complex Modal Analysis of Wheel-Rail System
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modal Order** & **Modal Frequency** & **Main Vibration Position** & **Max. Amplitude** \\ \hline
52 & 1265.1 Hz & Rail foot & 1.02 \\
147 & 2378.4 Hz & Rail foot & 0.83 \\
272 & 3799.8 Hz & Wheel Rims & 1.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Unstable modal shape analysis of the wheel–rail system. Figure 10: Amplitudes of the modal shapes of the unstable modes of the wheel–rail system: (a) 52 nd-order mode; (b) 147 th-order mode; (c) 272 nd-order mode. wheel's amplitude increases, the transverse vibration at the top of the rail tends to weaken, while vertical vibration at the rail's foot becomes more prominent. At the wheel-rail contact surface, there is a persistent transverse vibration component from the wheel and a vertical vibration component from the rail. Drawing on the principles of modal coupling theory, it can be inferred that friction-induced self-excited vibrations lead to system instability. This assessment suggests that at least two modes are coupled, with at least one out-of-plane mode present within the coupled modes. The interplay of these vibrations and modes within the wheel-rail system contributes to the dynamic complexity that underlies the occurrence of curve squeal noise.
## 3 Results
### Comparison  
To verify the validity of the proposed model, two comparison studies were conducted. The first comparison is between optimizable phase sequence and fixed phase sequence schemes. The second comparison is between models with relaxation constraints and models without relaxation constraints under a fixed common cycle time.  
In the first comparative study, the optimizable phase sequence refers to those phase sequences that can be optimized at each intersection. That is, phase sequences are decision variables. Fixed phase sequence means that the phase sequence at each intersection is predetermined. We have designed three different fixed phase sequence schemes, and the specific phase sequences for each intersection are detailed in Table 10.  
When generating green wave schemes for different fixed phase sequence schemes, the \(0/1\) variable \(u_{(p,q);(r,s)}\), \(\
u_{(p,q);(r,s)}\), \(y_{(p,q);(r,s)}\), and \(z_{(p,q);(r,s)}\) in the proposed model should be set to the corresponding value. In this way, four kinds of bandwidth optimization models were obtained: the proposed model and the models corresponding to fixed phase sequence schemes 1, 2, and 3, respectively.  
In the second comparative study, green wave control schemes for the test road network were generated by the model with constraint relaxation (the proposed model) and the model without constraint relaxation. The objective function values of these two models when the common cycle time is fixed are presented in Table 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Intersections** & \begin{tabular}{c} **Fixed Phase** \\ **Sequence Scheme 1** \\ \end{tabular} & \
\begin{tabular}{c} **Fixed Phase** \\ **Sequence Scheme 2** \\ \end{tabular} &
\begin{tabular}{c} **Fixed Phase** \\ **Sequence Scheme 3** \\ \end{tabular} \\ \hline \((1,1)/(4,1)\) & sequences 2 or 5 & sequence 6 & sequence 1 \\ \((1,2)/(5,1)\) & sequence 1 & sequence 1 & sequence 1 \\ \((1,3)/(6,1)\) & sequences 3 or 4 & sequences 3 or 4 & sequences 2 or 5 \\ \((2,1)/(4,2)\) & sequence 6 & sequences 3 or 4 & sequences 3 or 4 \\ \((2,2)/(5,2)\) & sequences 3 or 4 & sequence 1 & sequence 6 \\ \((2,3)/(6,2)\) & sequences 2 or 5 & sequences 2 or 5 & sequences 3 or 4 \\ \((3,1)/(4,3)\) & sequence 6 & sequences 2 or 5 & sequences 2 or 5 \\ \((3,2)/(5,3)\) & sequence 1 & sequence 1 & sequence 6 \\ \((3,3)/(6,3)\) & sequences 2 or 5 & sequence 1 & sequence 1 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Three different fixed phase sequence schemes.  
\begin{table}
\begin{tabular}{c c} \hline
**Intersections** & **Optimum Phase Sequences** \\ \hline \((1,1)/(4,1)\) & sequences 3 or 4 \\ \((1,2)/(5,1)\) & sequence 6 \\ \((1,3)/(6,1)\) & sequences 3 or 4 \\ \((2,1)/(4,2)\) & sequences 3 or 4 \\ \((2,2)/(5,2)\) & sequence 1 \\ \((2,3)/(6,2)\) & sequences 3 or 4 \\ \((3,1)/(4,3)\) & sequence 6 \\ \((3,2)/(5,3)\) & sequences 2 or 5 \\ \((3,3)/(6,3)\) & sequence 1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimum phase sequences at each intersection in the test road network.
# Optimization of Damping in a Semi-Active Car Suspension System with Various Locations of Masses
## 3 Results and Discussion
Meanwhile, the sum of the RMS of  
Figure 4: Case of all masses \(m_{1}=m_{2}=m_{3}=m_{4}=80\) kg (**a**) difference between the sum of the RMS of the vertical accelerations when optimized for the driver and when non-optimized; (**b**) difference between the sum of the RMS of the vertical accelerations when optimized for the driver with front-right passenger and when optimized for the driver only; (**c**) difference between the sum of the RMS of the vertical accelerations when optimized for the whole crew and when optimized for the driver only. Black dotted line shows values of major ticks. Black solid line is zero. the vertical accelerations is up to 9% smaller when optimizing for the whole crew than for only the driver. Simultaneously, the RMS of the vertical acceleration for the driver or front-right passenger increases up to 11% and 9%, respectively, and it decreases to 24% for rear passengers. [PERSON] et al. [75] summarized the results of a simulation of an unmanned vehicle driving a measured road profile at 60 km/h: (1) when optimizing the RMS vertical acceleration of the driver, lower RMS vertical acceleration values for the driver are obtained; however, the RMS vertical acceleration of the rear-left passenger is 14% greater than that obtained by optimizing the combined driver and passenger RMS as an optimization objective; (2) when optimizing for combined RMS vertical acceleration, the lowest RMS vertical acceleration values for the passenger are obtained, but the driver RMS vertical acceleration is 13% higher than when optimizing for the driver only. One can state that these tendencies are true for all our cases when optimizing for the driver or the driver with a front passenger and when optimizing for the whole crew. So, further investigation will be required to discover how optimization is influenced if it will be used with different damping values for the front and rear wheels or for all wheels and various optimization objective functions.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 6 Conclusions  
In this paper, a mathematical model of a constellation design was established by considering various factors, and was optimized by using a genetic algorithm to obtain the optimal orbital height of 609.782 km, the optimal orbital inclination angle of 96.142\({}^{\circ}\), and a high-quality constellation configuration, which was mainly used to observe space objects with an orbital altitude below 1000 km. A greedy algorithm based on a multi-dimensional list was designed to optimize the scheduling of the constellation observation tasks and realize the optimization of the single-satellite observation tasks and constellation observation tasks. Through the simulation experiments, the mathematical model and the optimization algorithm established above were verified.  
It was found that the constellation composed of 15 satellites had a high cost performance, achieved a high observation efficiency with relatively few satellites, and tracked 91% of the objects with an orbital altitude of less than 1000 km every 24 h. The constellation composed of 24 satellites distributed on three orbital planes had the best observation efficiency. It tracked approx. 93% of LEO orbit objects in 24 h. Through long-term continuous observation, these constellations could maintain an orbit catalogue of most low orbit objects, identify dangerous rendezvous times, and prevent the loss of space assets.  
Figure 14: The number of objects that could be tracked by each satellite in the 15_3 constellation in 24 h.  
Figure 13: Proportion of objects that can be tracked within 24 h in the different constellation configurations.  
**Author Contributions:** Conceptualization, G.L.; methodology, G.L., J.L. and H.J.; software, G.L.; validation, G.L., J.L. and H.J.; writing--original draft preparation, G.L.; writing--review and editing, G.L., J.L., H.J. and C.L.; supervision, J.L., H.J. and C.L.; project administration, J.L.; funding acquisition, J.L. and H.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Space Debris Research Project of China (No. KJSP2020020201, No. KJSP2020020202), the China National Key R&D Program during the 14 th Five-year Plan Period (No. 2022 YFC2807304), and the Foundation Laboratory of Pinghu (No. 2020 AB01001).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Comparison of Single Control Loop Performance Monitoring Methods
## 2 Materials and Methods
### Simulated Process  
A supercritical fluid extraction (SFE) process utilizes properties of a supercritical fluid to extract product from a raw material. Carbon dioxide (CO\({}_{2}\)) is commonly used as the supercritical fluid due to the properties of CO\({}_{2}\), with it being sufficiently easy to achieve pressure (73.8 bar) and temperature (32.1 \({}^{\circ}\)C) for the critical point. The process consists of six parts, namely, the extraction reactor, extract separator, condenser, CO\({}_{2}\) storage, CO\({}_{2}\) flow pre-heater, and pump, as shown in Figure 2. A set of central composite design experimental test runs were performed and state-space models for the process components were identified in [23; 24]. Thus, the simulator used represents a validated model of the physical process.  
One control loop from the previously identified simulator was isolated for this work. The selected discrete-time, state-space model (see Equations (8) and (9)), namely, the CO\({}_{2}\) flow, was identified from open-loop measurements of the original work [17; 18], while other portions of the simulator were identified with the existing PID control in the process. The CO\({}_{2}\) flow is the controlled variable, \(y(n)\), and valve position is the manipulated variable, \(u(n)\). The second input is the external variable, \(\Delta P\). In this study, the external variable is utilized as a disturbance, with a value of 0 in normal operation.  
\[\begin{array}{c}x(n+1)=\left[\begin{array}{ccc}0.9895&0.03677\\ -0.01237&0.9649\end{array}\right]x(n)+\\ \left[\begin{array}{ccc}-2.520e-05&-1.673e-06\\ -2.199e-04&-5.346e-06\end{array}\right]u(n),\end{array} \tag{8}\]  
\[y(n)=\left[\begin{array}{ccc}-10.22\\ -0.1235\end{array}\right]x(n)\left(+\left[\begin{array}{ccc}0\\ 0\end{array}\right]u(n)\right), \tag{9}\]  
The state-space model for the supercritical fluid extraction process was implemented to MATLAB(r) and Simulink(r) software (Version R2020b Update 2). PI control, with parameters 8 for proportional gain and 0.2 for integral gain, was added to the simulator, and the parameters were kept constant for the simulations. As the simulator models the CO\({}_{2}\) flow into the reactor, the lower limit for the output was limited to 0. The closed-loop process settling time after a step change is approximately 400 s. Therefore, the simulation scenario involved setpoint changes every 1200 s. The setpoint values were selected randomly from an even distribution between 0 and 0.8, with an interval of 0.1. With these chosen step changes, a representative dataset for the process was obtained for the whole area of operation of the process. Additionally, having frequent step changes in the process, the overall size of the dataset could be reduced, decreasing computing time in the later stages of the demonstration. The obtained measurement data were then sampled every 10 s to further reduce the size of the data matrix.  
Figure 2: Flow chart of the supercritical CO\({}_{2}\) fluid extraction process [23].
## 5 Results and Analysis
### Comparison with Conventional MIMO Methods  
This section analyzes the performance of the DNN-based MIMO detection model, focusing on the impact of activation functions and training algorithms on SER. The results are then compared to those achieved using conventional MIMO methods (MLD and MMSE).  
The performance of a DNN-based MIMO detection model was evaluated and compared to conventional methods (MLD, MMSE, and ZF) using SER as a metric. While the DNN explored various training functions, the conventional methods consistently outperformed the DNN in terms of SER. This discrepancy can be attributed to the DNN model's sensitivity to training data and its inability to generalize well across different signal conditions. Additionally, the high complexity and inter-antenna interference in MIMO systems pose significant challenges for the DNN. As shown in Figure 13, the MIMO-based DNN functions were consistently outperformed by the conventional techniques, particularly at higher SNR levels. This highlights the continued effectiveness and stability of conventional methods for signal detection tasks in complex MIMO communication systems.
# Joint Entity-Relation Extraction for Knowledge Graph Construction in Marine Ranching Equipment
## 4 Results and Analysis
### Experimental Analysis  
#### 4.4.1 Comparison with Models  
As illustrated in Table 8, experimental results demonstrated superior performance over the BiLSTM-CRF and BERT-BiLSTM-CRF models, achieving 86.58% precision, 77.82% recall, and 81.97% F1 score. Specifically, compared with the BiLSTM-CRF model, the precision, recall, and F1 score have increased by 9.38%, 9.37%, and 9.45%, due to BERT providing deep semantic representations and solving the problems of polysemy and data sparsity. Compared with the BERT-BiLSTM-CRF model, the precision, recall, and F1 score have increased by 1.21%, 2.50%, and 1.94%, respectively. It indicates that, in contrast to the BiLSTM model, the BiGRU model boasts a reduced parameter count, which not only enhances model performance but also accelerates the training process. The utilization of BiGRU as the encoding layer is deemed more appropriate for the text-based entity recognition task specific to marine ranching equipment.
# Plasma-Activated Tap Water Production and Its Application in Atomization Disinfection
## 2 Experimental Setup
### Bactericidal Effect Analysis  
3.1 Comparison of Bactericidal Effect between Plasma-Activated Tap Water (PATW) and Plasma-Activated Distilled Water (PADW)  
We used _E. coli_ (A TCC25922) provided by the School of Life Science and Technology of Huazhong University of Science and Technology to determine the bactericidal effect of PAW. To do this, 1 L of tap water and distilled water were treated by plasma for 1 h, respectively, and the obtained PATW and PADW were immediately used to cultivate E. coli with a concentration of 10\({}^{8}\) CFU/mL. 5 mL of normal saline to the centrifuged colony as the control group and 5 mL of PATW and PAW as the experimental group, mixed well and reacted with _E. coli_ for 15 min and 30 min. After oscillating evenly, 100 \(\upmu\)L of the corresponding mixed solution was used to coat the petri dish (90 mm CITOCIST) and was incubated in a constant temperature incubator at 37 \({}^{\circ}\)C for 18 h. We used a CFU counting method to measure the number of surviving colonies after 18 h of cultivation. Each experiment was repeated 3 times. By comparing the number of surviving colonies in the control group and the experimental group, the bactericidal ability of PATW and PADW were determined.
# Optimization Design of the Lower Rocker Arm of a Vertical Roller Mill Based on ANSYS Workbench
## 1 Introduction  
The Vertical Roller Mill (VRM) is a large piece of equipment for grinding materials and is usually used in the production fields of milling slag, cement raw material, clinker, and coal. With the development of social productivity, the grinding industry is making increasing demands on the production capacity and efficiency of equipment, and the size of VRMs is also increasing. Relatively conservative methods are usually adopted for calculations in the design process of VRMs. These methods are adopted in order to improve the structural strength of VRMs, but lead to a waste of materials resulting from the VRM's excessively large structure [1, 2].  
The lower rocker arm is an important pressure-applying part of VRMs. Its tail is connected to a hydraulic device, its middle is fixed on the shaft of the rocker arm, and its top is connected with the upper rocker arm. The stability of the structural strength of the lower rocker arm plays a decisive role in ensuring that the vertical grinding is in a working state, thereby guaranteeing the grinding quality of the material [3, 4]. In light of experience, the product size parameters and the structure designed are relatively conservative; it is important to optimize the structure by means of computer-aided design and finite element analysis [5, 6, 7, 8]. In this study, a 3D model of the lower rocker arm was established by SolidWorks and a structural optimization design was carried out. ANSYS Workbench was used to carry out a finite element analysis on the strength and deformation of thelower rocker arm before optimization. By analyzing the strength and deformation before optimization, the strength margin was obtained, so as to determine the optimal design position, and the multi-objective genetic algorithm was used to optimize the position. In order to meet the requirements of strength and deformation, the quality of the lower rocker arm was reduced through optimal design to achieve the lightweight goal of the vertical mill, so as to save materials, reduce costs and improve the market competitiveness of the product.
# Numerical Simulation Study on the Performance of Buried Pipelines under the Action of Faults
## 3 Analysis of Pipeline Response under the Action of Strike-Slip Reverse Fault
### Analysis of Axial Tensile Strain of Pipeline  
In this section, the axial tensile strain of the pipe is analyzed to determine whether the damage to the pipe is tensile damage or not. This study refers to the reference value of the ultimate tensile strain specified in [24,31], and takes the ultimate tensile strain of the pipe under the action of the fault as 2.0%.  
The maximum tensile strain \(\varepsilon_{\text{tmax}}\) of the pipeline with the increasing displacement of the fault is shown in Figure 6. The tensile strain rises gradually as the displacement of the fault increases. When the fault displacement is 2.72 m, the maximum axial tensile strain reaches the critical tensile strain 2.0%. In accordance with the judgment standard selected as mentioned earlier, the pipeline has already begun to undergo tensile failure.  
The displacement of faults is constantly increasing. When the fault displacement is 1.096 m and 1.76 m, the growth rate of \(\varepsilon_{\text{tmax}}\) is slightly increased, and this combined with when the fault displacement is 0.82 m and 1.76 m, it happens to be at the critical point of different stages of shell buckling failure. By comparing the critical damage strain, it can be concluded that the pipe shell buckling damage occurs before the tensile damage. As can be seen in Figure 7, when the fault displacement is up to 1.76 m, it is in the direction of the fault displacement on both sides of the wall, assuming the relative symmetry of \(\varepsilon_{\text{tmax}}\) and \(\varepsilon_{\text{tmax}}\) positions. Therefore, it can be inferred that, when the pipeline undergoes shell buckling failure, it will have a moderate impact on the occurrence of tensile failure.  
Figure 5: Different stages of local buckling failure of the pipeline: (**a**) Before buckling occurs; (**b**) buckling is expanding; (**c**) tube wall protrusion caused by buckling.  
In summary, the buried pipeline in this study will not be subject to beam-type buckling damage, while tensile damage and shell buckling damage have occurred in the simulation process, and may cause more serious consequences once they have occurred. Therefore, this study will focus on these two failure modes as the key indicators for pipeline performance evaluation.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 3 Genes and Molecules Involved in Mycobacterial Biofilm Development
### Stringent Response-Related Molecules  
The stringent response is a kind of response to nutrient starvation that exists ubiquitously in bacteria [46]. This process stimulates bacteria to divert nutrients from growth to survival and, ultimately, to form biofilm. Therefore, the genes and molecules relative to stringent responses are responsible for regulating biofilm formation. In Mtb, the stringent response is mainly regulated by the second message signals, including (p)ppGpp [47].
# Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets
## Appendix C
Moreover, \(d_{i\mathcal{K}\left(l\right)}\left(x\right)\) is the lower bound on the distance of the \(i\)th tier, which can be obtained by using Equation (2) as:  
\[\begin{split}& if\ l=M,in:d_{1\mathcal{K}\left(l\right)}\left(x \right)=x,d_{2\mathcal{K}\left(l\right)}\left(x\right)=D\\ & if\ l=M,out:d_{1\mathcal{K}\left(l\right)}\left(x\right)=x,d_{2 \mathcal{K}\left(l\right)}\left(x\right)=\left(\  
where \(\rho\left(x,\alpha_{i}\right)=x^{2}/\alpha_{i}\int_{x^{-2}/\alpha_{i}}^{\infty}\frac {du}{1+u^{\alpha_{i}}/2}\).  
For a randomly chosen CoMP user, that is, \(\mathcal{B}=\{MBS_{0},FBS_{0}\}\), the coverage probability is given by:  
\[\mathcal{P}_{\text{C}}\left(\tau\right) =\int_{\Omega_{\text{C}}}\mathbb{P}\left(\frac{\left|\sqrt{P_{1}} h_{x_{1}}x_{1}^{-\alpha_{1}/2}+\sqrt{P_{2}}h_{x_{2}}x_{2}^{-\alpha_{2}/2} \right|^{2}}{\sum_{i=1}^{2}I_{i}+\sigma_{z}^{2}}>\tau\right)\!\!f_{\text{X}_{ \text{C}}}\left(x_{1},x_{2}\right)d\mathbf{X}_{\text{C}}\] \[\overset{(a)}{=}\int_{D}^{\infty}\int_{\left(\frac{P_{2}}{P_{1}} \right)^{1/\alpha_{2}}x_{1}^{\alpha_{1}/\alpha_{2}}}^{\left(\frac{P_{2}}{P_{1} }\right)^{1/\alpha_{2}}x_{1}^{\alpha_{1}/\alpha_{2}}}\exp\left(-\frac{\tau \left\{\sum_{i=1}^{2}I_{i}+\sigma_{z}^{2}\right\}}{\sum_{k=1}^{2}P_{k}x_{k}^{- \alpha_{k}}}\right)f_{\text{X}_{\text{C}}}\left(x_{1},x_{2}\right)dx_{2}dx_{1}\] \[=\int_{D}^{\infty}\int_{\left(\frac{P_{2}}{P_{1}}\right)^{1/ \alpha_{2}}x_{1}^{\alpha_{1}/\alpha_{2}}}^{\left(\frac{P_{2}}{P_{1}}\right)^{ 1/\alpha_{2}}x_{1}^{\alpha_{1}/\alpha_{2}}}\exp\left(-\frac{\tau\sigma_{z}^{2} }{\sum_{k=1}^{2}P_{k}x_{k}^{-\alpha_{k}}}\right)\prod_{i=1}^{2}L_{I_{i}}\left( \frac{\tau}{\sum_{k=1}^{2}P_{k}x_{k}^{-\alpha_{k}}}\right)f_{\text{X}_{\text{ C}}}\left(x_{1},x_{2}\right)dx_{2}dx_{1}\]  
which is obtained from the independence assumption [19; 23; 31]:  
\[\left|\theta_{1}h_{x_{1}}+\theta_{2}h_{x_{2}}\right|^{2}\approx\left|\theta_ {1}h_{x_{1}}\right|^{2}+\left|\theta_{2}h_{x_{2}}\right|^{2}\sim Exp\left( \frac{1}{\sum_{k=1}^{2}\theta_{k}^{2}}\right)\]  
where \(\theta_{k}=\sqrt{P_{k}}x_{k}^{-\alpha_{k}/2}\). Thus the received power from two cooperating BSs is approximated by the sum of two independent exponentially distributed random variables with mean \(\sum_{k=1}^{2}\theta_{k}^{2}\) because of the Rayleigh fading assumption and the independence of \(\left|h_{x}\right|^{2}\). This assumption gives a lower but tight bound on the Laplace transform of the interference power [19; 23; 31]. By following the same steps as for deriving Equation (A4), we obtain Equation (19) and complete the proof.