esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Practices of Railway Ballastless Track Design Methods in China
## 4 Results and Analysis
### Load Effect Analysis
#### 4.1.1 Train Load Effect  
For a unit track slab, considering the vertical and lateral train loads, which have great influence on the stress of track structure, and ignoring the traction and braking load, the structural mechanical response of track is analyzed.  
1. Vertical train load  
The vertical train load causes longitudinal and lateral bending of the track slab. Based on the finite element model, the load effects of the track slab under different working conditions are analyzed. Supposing that the moment is positive when the undersurface of the slab is under tension, the moment effect in different positions of the mid-span track slab under D1 is shown in Figure 10. The maximum longitudinal and lateral positive moment occurs at the loading position.  
In the longitudinal direction, the maximum moment is at the loading position, and the moment reduces to 0 at the end of track slab. In the lateral direction, the maximum moment also appears at the loading position. The calculation results of the two methods are compared in Figure 11.  
The train load effects under various working conditions are different. In D1, the moment is the largest due to the largest design of the axle load and design speed of the train, while in D2, it is the smallest. Therefore, the working conditions have a great influence on the train load effects. In addition, it can be seen from Figure 11 that under all three conditions, the train load effects calculated according to the ASM are greater than or equal to these calculated according to the LSM, and there is an obvious surplus. In D1 and D3, the dynamic load coefficients are different between the two methods, so their load effects also show a difference. The dynamic load coefficient of the LSM in D1 is smaller than that of the ASM by 20%, and the load effects of the two methods have a difference approaching  
Figure 10: Moment of track slab for the LSM (D1). (**a**) Longitudinal moment of the track slab. (**b**) Lateral moment of the track slab.  
20%, which means that the dynamic load coefficient is the major factor in influence the effect under train load.  
(2) Lateral train load  
Under the lateral train load, the lateral bending moment is the main effect. For the ASM and the LSM, the calculation method of the lateral moment is same:  
\[M=0.3Q\cdot h \tag{9}\]  
The distance from the bottom of the track bed to the track surface 'h' is considered as 0.2 m. The maximum moments of the track slab under three working conditions are as shown in Table 4.
# Development of Active Microvibration Isolation System for Precision Space Payload
## 3 Hybrid Control Design
### Feedback Control
The transfer function of the lead-lag phase compensation filter is as follows:  
\[H_{lead-leg}(s)=K\frac{\frac{s}{Z_{c}}+1}{\frac{s}{Z_{c}}+1}=K\frac{\frac{i \omega_{1}}{\omega_{2}}+1}{\frac{i\omega_{2}}{\omega_{2}}+1}, \tag{11}\]  
where \(K\) is the gain, \(Z_{c}\) and \(P_{c}\) are the zero and pole of this filter, respectively, \(\omega_{1}\) and \(\omega_{2}\) are the corresponding angular frequencies. With the Tustin transformation as follows  
\[s=\frac{2}{T}\frac{1-z^{-1}}{1+z^{-1}}, \tag{12}\]  
where \(s=i\omega\), \(T\) is the sampling time. The filter transfer function can be transformed into discrete \(z\)-plane  
\[H_{lead-lag}(z)=K\frac{\omega_{2}}{\omega_{1}}\frac{T\omega_{1}+2+(T\omega_{ 1}-2)z^{-1}}{T\omega_{2}+2+(T\omega_{2}-2)z^{-1}}, \tag{13}\]  
The lead phase filter, \(0<\omega_{1}<\omega_{2}\), is used in this study. After the lead phase filter is added, the open-loop Bode diagram of the system is shown as the real line in the figure, the crossover frequency has been changed from 127 to 437 rad/s, and the phase margin was increased to 30\({}^{\circ}\). In addition to the lack of phase margin, the modal frequencies of the system and the resonant frequencies from the base platform can cause large vibrations when driving the  
Figure 4: Bode diagram of open-loop and close-loop. motor. Moreover, the output of the controller is not completely ideal, and some noise signals not coming from disturbance sources are found in the output voltage. These noises can also bring vibration to the system at the corresponding frequencies and affect the stability of the active control. Therefore, it is necessary to design notch filters for these noises to further improve the vibration isolation performance. To reduce the calculation in the program, IIR (infinite impulse response) notch filter is selected, and the transfer function is as follows:  
\[H_{notch}(s)=K\frac{s^{2}+2\gamma_{1}\omega_{0}s+\omega_{0}^{2}}{s^{2}+2\gamma _{2}\omega_{0}s+\omega_{0}^{2}}, \tag{14}\]  
where \(K\) is the gain, \(\omega_{0}\) is the cut-off frequency, \(\gamma_{1}\) and \(\gamma_{2}\) are the parameters of depth and bandwidth and \(0<\gamma_{1}<\gamma_{2}\). The transfer function of notch filter is given using the Tustin transformation function as follows:  
\[H_{notch}(z)=K\frac{(4+4\gamma_{1}\omega_{0}T+\omega_{0}^{2}T^{2})+(2\omega_{ 0}^{2}T^{2}-8)z^{-1}+(4-4\gamma_{1}\omega_{0}T+\omega_{0}^{2}T^{2})z^{-2}}{(4 +4\gamma_{2}\omega_{0}T+\omega_{0}^{2}T^{2})+(2\omega_{0}^{2}T^{2}-8)z^{-1}+(4 -4\gamma_{2}\omega_{0}T+\omega_{0}^{2}T^{2})z^{-2}} \tag{15}\]  
The parameters for all the filters are listed in Table 1.
## 2 Materials and Methods
### Apparatus and Experimental Steps
#### 2.2.4 Single Fluidized Bed Reactor  
The CLC tests were conducted in a single fluidized bed, as shown in Figure 5. The single fluidized bed 32 mm ID and 1340 mm high was electrically heated by two semi-cylindrical electric furnaces. A 2-mm-thick perforated plate with 100 holes located 450 mm from the bottom was used as a gas distributor and bed materials placement. In CLC tests, hematite was selected as the OC, and its mass was fixed at 30 g, when the bed height reached about 70 mm. In the blank tests, the mass of quartz sand was also 30 g, and a batch sample of 30 g hematite or quartz sand was added into the reactor under N\({}_{2}\) (1.5 L/min) atmosphere. Meanwhile, steam (0.8 g/min) preheated at 180 \({}^{\circ}\)C was introduced to the bottom of the fluidized bed as a gasification agent. The fuel chute was opened, and briquette cyanobacteria of 0.5 g were introduced into the reaction zone when reaching the experimental temperature. The gaseous products passed through a cooler (15 \({}^{\circ}\)C), drier and filter, and the compositions were analyzed by a gas analyzer. In the cycle experiments, air was used in the oxidation process for 10 min after reduction. The specific redox cycle experimental steps are as follows:  
1. Thirty grams of hematite particles are placed on the porous plate of the single fluidized bed, and air flow of 1.5 L/min is introduced to ensure that the hematite oxygen carrier is in the oxidation state;
2. When the single fluidized bed reactor is heated to the experimental temperature, steam as gasification agent is introduced and air flow is switched to nitrogen (1.5 L/min);
3. When the temperature of the reactor is stable, a batch of 0.5 g briquette cyanobacteria fuel is put into the reactor through the storage bin, and the reaction starts;
4. The total reaction time is set at 30 min, and the gas composition and concentration in the flue gas are monitored online;
5. When there is no carbonaceous gas detected, steam flow is cut off and nitrogen flow is switched to air again (1.5 L/min), reducted hematite starts to be oxidized and completes regeneration. Then, the air flow is switched to nitrogen (1.5 L/min), and briquette cyanobacteria of 0.5 g are put into the reactor again.
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 3 Computational Concept
### Propagation Models  
The relations between service centres and customer locations are based on the final signal strength. Using this approach we will get the relation between each gNodeB node and each selected area (e.g., each user location). Here, we assume that each gNodeB node is configured at its best for the given position to cover as many locations as possible [72; 73]. Further, this prediction has to be done depending on the deployment scenario (urban, suburban, rural area). For example, for urban/suburban areas the suitable path loss prediction models are (i) Okumura-Hata Model; (ii) Stanford University Interim (SUI) Model; and (iii) Cost 231 Hata Model. From that list, for 5 Gand beyond deployments (in urban and suburban areas) the SUI Model seems promising [74] for frequencies ranging from 2 to 11 GHz. This model is expressed by the following formula:  
\[PL=A+10\gamma\log_{10}\left(\frac{d}{d_{0}}\right)+X_{f}+X_{h}+s, \tag{42}\]  
where \(d\) is the distance between the gNodeB node and the receiving antenna, \(d_{0}=100\)[74; 75], \(\gamma\) is the path-loss exponent, \(X_{f}\) is the correction for frequency above 2 GHz, \(X_{h}\) is the correction for receiving antenna height, \(s\) is the correction for shadow fading due to trees and other clutter and \(\lambda\) is the wavelength [74]. The other parameters are defined as:  
\[A=20\log_{10}\left(\frac{4\pi d_{0}}{\lambda}\right) \tag{43}\] \[\gamma=a-bh_{b}-c/h_{b}, \tag{44}\]  
where \(h_{b}\) is the gNodeB node height above ground in meters, and \(a,b,c\) are constants that vary with terrain.
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## 4 Conclusions  
In this study, the goal of classifying five varieties of wheat seeds was implemented and completed while using hyperspectral imaging technology combined with chemometric methods. During the process, two methods about exploratory classification analysis were studied: PCA and LDA. The latter one displayed the higher distinguishable degree specially, which also manifests the feasibility of identifying wheat grains precisely. Some research regarding the influence of three pretreatment methods on discriminant models was investigated. The results showed that the performance of three models based on different pretreatment had no obvious improvement when compared with that based on raw spectra data and indicated that using preprocessing methods could not always get good classification recognition. Besides, based on full wavelengths and feature wavelengths, the LDA, SVM, and ELM models were established. It can be found that accuracy of discrimination model based on the former was also higher than that of the latter. However, the classification result of RF feature extraction algorithm is also acceptable, which indicated feasibility of feature variable selection. In view of operational speed and economical cost, the model' performance on feature wavelengths also indicates that it can be likely to be of more potential for developing a portable multispectral instrument to discriminate wheat seeds non-destructively and rapidly. However, the classification performance of wheat grains still needs to be improved. It is worthy of consideration to explore more kinds of spectral preprocessing and feature extraction methods even develop higher accuracy' deep learning model. Besides, the pseudo-color visualization map was generated to observe the specific variety of each sample clearly and intuitively, which also contributes to developing an online and large-scale detecting system to screen the purity of different wheat varieties. All of the relative results show that with the characteristic of both spectral and spatial information and high throughout, HSI with chemometrics is potential to detect and identify large-scale grain seeds in the modern seed industry.  
[PERSON] and [PERSON] provided the conceptualization; [PERSON] performed the experiments, processed and summarized the spectral data; [PERSON], [PERSON], [PERSON] and [PERSON] designed and wrote the article; [PERSON] is a corresponding author and [PERSON] is a principle investigator for the project.  
The research was funded by National Key R&D Program of China (No. 2018 YFD0101002), and the Fundamental Research Funds for the Central Universities (No. 2019 FZA5007).  
We want to express thanks to Anhui Longping High-Tech Seed Industry Co., Ltd. for providing precious materials.  
The authors declare no conflict of interest.  
Figure 6: Visualization maps of five varieties of wheat seeds: (**a**) Grayscale image; (**b**) Pseudo-color classification map; (from left to right: Annong1124, Longpingmia6, Shannon102, Weilong169, Zhenmi9).
# UAV Formation Shape Control via Decentralized Markov Decision Processes
#### Key Contributions  
* We formulate the UAV swarm formation control problem as a decentralized Markov decision process (Dec-MDP).
* We extend an approximate dynamic programming method called _nominal belief-state optimization_ (NBO) to solve the formation control problem.
* We perform numerical studies in MATLAB to validate the swarm formation control algorithms developed here.
* One of the key contributions of this paper is to induce cooperative behavior among the UAVs in the swarm via the following novel decentralized control optimization strategy:
* Each UAV \(i\) optimizes the control vector \([a_{k}^{i},a_{k}^{un}]\) at time \(k\), where \(a_{k}^{i}\) is the control vector for UAV \(i\), and \(a_{k}^{un}\) is the control vector for its nearest neighbor.
* Next, UAV \(i\) discards the optimized controls for its neighbor and implements just its own controls \(a_{k}^{i}\).
* Each UAV in the system implements the above approach.  
The rest of the paper is organized as follows. Section 2 provides the problem specification and objectives. We also formulate the problem using decentralized Markov decision process in Section 2 followed by the discussion on the NBO approach in Section 3. UAV motion model and dynamics are provided in Section 4. In Section 5, we discuss simulation results to evaluate the performance of our method.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Aggregating/Dispatching Modules  
The CMS is designed as a general scheduling framework, enabling its operation in heterogeneous environments. This adaptability is achieved through its modular design, which includes aggregation modules for collecting input data and dispatching modules for distributing operational information to relevant system agents (e.g., SNOs and GSNs). Additionally, all proposed architectures support this flexibility by providing the necessary data aggregation and dissemination interfaces. From the infrastructure and architecture challenges perspective, this design supports highly heterogeneous network architectures, such as integrated NTN-TN networks. Moreover, it allows for a hybrid approach that combines centralized and decentralized components, enhancing both the robustness and efficiency of the overall system. Regarding integrated management challenges, these modules improve adaptive scheduling by centralizing feedback from various system agents and dynamically adjusting schedules accordingly. Furthermore, they enable comprehensive end-to-end scheduling by directly delivering operational plans to each segment, including the network, ground segment, and satellites. Finally, in addressing technology maturation and deployment challenges, these modules allow for prototyping and testing in real heterogeneous environments, such as NTNs. This is achieved by leveraging existing interfaces for aggregating and distributing operational data, ensuring compatibility with current systems, and easing integration into operational deployments.
# Mutation Rate Model Used in the DNA VIEW Program
## 7 Conclusions  
1. The mutation model assumed in the DNA VIEW program is not correct. After a long-time simulation (\(\sim\)8000 generations), this model results in almost uniformly distributed frequencies (the value of entropy in the D3S1358 locus case is over 93% its maximal value, much more than the value observed in human population, and the trend is still growing, however much slower than in earlier generations) of alleles but in reality no such uniform distribution at any locus can be observed. The uniformly distributed allele frequencies give the maximal entropy, but in reality, the entropy at different loci is smaller than the maximal one (between 62.9% of the maximal entropy in the case of LPL and 87.9% in the case of Penta E).
2. The speed of convergence \(H_{k}^{(3)}\) is directly proportional to the number of allele \(N\) and inversely proportional to the difference between the uniform distribution and the distribution of \(v^{(o)}\) (expressed by \(H_{1}\)). A statistical analysis gives the formula \(H_{\infty}-H_{k}^{(3)}=0.33923+0.013062\times N-0.138466\times H_{1}\) (\(p=0.0146\) for \(N\) and \(p=0.00229\) for \(H_{1}\)). In the case of the Kolmogorov distance and indices of similarity, this dependence is not statistically significant.
3. Among the considered models: Models 1, 2, and 3, Model 3 is the best. However, from a viewpoint of the Hardy-Weinberg hypothesis, Model 4 is the best. On the other hand, Models 1 and 4 \"change\" the observed mutation rate; however, this change is very small. Model 3 is preferred from the viewpoint of genetic observation.
4. The choice of model taken for paternity testing has an essential influence in the case of maternal mutation. The only cause why it is hard to detect is that maternal mutations occur rarely. However, if they do, the differences can be enormous, as it was shown in Table 3 (\(P[H_{1}|V]\) value 0.0003674 in Model 3 suggests that the man almost sure is not a father while values over 0.9967 in other models contradict this statement). In this case, we may get a different conclusion dependent on the taken mutation model (the defendant is accepted or excluded as a father).
5. By choosing a model different than Model 4, the researcher can omit the difference between two theoretically identical equations (see (30) and (31)). From a theoretical viewpoint, this is a significant lack of cohesion.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The author would like to express his gratitude to the referees for their constructive comments which led to an improved presentation of the paper.  
**Conflicts of Interest:** The author declares no conflicts of interest.
# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
## 3 The Proposed Method
### Parameter Optimization of Long Short-Term Memory Neural Network  
In the process of model training, the optimal set of hyperparameters can be selected to improve the model diagnosis accuracy and performance. The hyperparameters include the number of hidden layers, the learning rate, and the number of hidden layer neurons in the Bi-LSTM prediction model. The hyper-parameter selection of the Bi-LSTM prediction model depends on manual training and is adjusted multiple times according to experience. However, the training of the Bi-LSTM prediction model requires a certain amount of time, which will inevitably decline the work efficiency. Moreover, the hyper-parameter combination of the prediction model selected by this method is not optimal, which will affect the accuracy and effectiveness of the prediction model. To address this issue, the whale algorithm is employed to optimize the hidden layer neural network and learning rate of the Bi-LSTM fault model to enhance the generalization capability and estimation accuracy of the model.  
The whale algorithm is a mathematical model developed by based imitating the behaviors of humpback whales to surround, dabble, and search for prey. It has the advantages  
Figure 5: The time-frequency characteristic method at a scale of 3.  
of less parameter adjustment, simple operation, and a strong ability to jump out of local optima.  
The first stage is:  
\[D=|C\cdot X^{*}(t)-X(t)| \tag{40}\]  
\[X(t+1)=X^{*}(t)-A\cdot D \tag{41}\]  
where \(D\) denotes the distance vector between the current optimal solution and the search body; \(t\) denotes the current iteration times; \(X^{*}\) denotes the location vector of the current optimal solution; \(X\) denotes the location vector of the search volume.  
The second stage is:  
\[X(t+1)=D^{\prime}.e^{bl}.cos(2\Pi l)+X^{*}(t) \tag{42}\]  
where \(D^{\prime}=|X^{*}(t)-X(t)|\) denotes the distance vector between the searching individual and the target prey; \(b\) denotes the constant coefficient of logarithmic spiral shape; \(l\) denotes a random number in the interval \([-1,\ 1]\).  
The third stage is:  
\[D=|C\cdot X_{rand}-X(t)| \tag{43}\]  
\[X(t+1)=X_{rand}-A\cdot D \tag{44}\]  
where \(X_{rand}\) denotes the location of a random search body in the current population.  
The flowchart of using the whale algorithm to optimize the hyperparameters of the Bi-LSTM diagnostic model is shown in Figure 6.
# Adding Edges for Maximizing Weighted Reachability +
## 5 Polynomial-Time Algorithm for DAG with a Single Source or a Single Sink
We analyse the following cases recalling that \(e=(t^{\prime},s)\):  
* If there exists an edge \((t,s)\in S\) such that \(t\in R(v)\), then \(p(R(v,S\cup\{e\}))=p(R(v,S))=p(R(s))\). * If \(t^{\prime}\in R(v)\), then \(p(R(v,S\cup\{e\}))=p(R(s))\) and \(p(R(v,S))=p(R(v))\). * If \(t^{\prime}\
ot\in R(v)\), then \(p(R(v,S\cup\{e\}))=p(R(v,S))=p(R(v))\). It follows that \(p(R(v,S\cup\{e\}))\geq p(R(v,S))\). To prove that \(f(S)\) is submodular, we prove that, for any node \(v\in V\), any two solutions \(S,T\) of MCI such that \(S\subseteq T\), and any edge \(e=(t^{\prime},s)\
ot\in T\), where \(t^{\prime}\) is a sink node, it holds:  
\[p(R(v,S\cup\{e\}))-p(R(v,S))\geq p(R(v,T\cup\{e\}))-p(R(v,T)). \tag{2}\]  
We analyse the following cases:  
* If there exists an edge \((t,s)\in S\) such that \(t\in R(v)\), then, \(p(R(v,S\cup\{e\}))=p(R(v,T\cup\{e\}))=p(R(v,T))=p(R(s))=p(R(v,T))=p(R(s))\). * If there exists \((t^{\prime\prime},s)\in T\) such that \(t^{\prime\prime}\in R(v)\), then \(p(R(v,S\cup\{e\}))-p(R(v,S))\geq 0=p(R(v,T\cup\{e\}))-p(R(v,T))\) because \(p(R(v,T\cup\{e\}))=p(R(v,T))=p(R(s))\). * If for each \((t^{\prime\prime},s)\in T\), \(t^{\prime\prime}\
ot\in R(v)\), then \(p(R(v,S\cup\{e\}))=p(R(v,T\cup\{e\}))\) and \(p(R(v,S))=p(R(v,T))\). In all the cases the inequality in Equation (2) holds. **Theorem 5**: _Function \(f(S)\) is monotone and submodular with respect to any feasible solution for MCI on DAGs with a single sink._  
Proof: To prove that \(f(S)\) is monotone, we show that for each \(v\in V\), \(S\subseteq E^{\prime}\), and \(e=(d,s^{\prime})\in E^{\prime}\setminus S\), we have \(p(R(v,S\cup\{e\}))\geq p(R(v,S))\). We observe that  
\[R(v,S) = R(v)\cup\bigcup_{(d,s_{i})\in S}R(s_{i})\] \[R(v,S\cup\{e\}) = R(v)\cup\bigcup_{(d,s_{i})\in S}R(s_{i})\cup R(s^{\prime})=R(v,S )\cup R(s^{\prime}) \tag{3}\]  
Thus, \(p(R(v,S))\leq\sum_{u\in R(v,S)\cup R(s^{\prime})}p_{u}\). To prove that \(f(S)\) is submodular, we prove that for any node \(v\in V\), any two solutions \(S,T\) of MCI such that \(S\subseteq T\), and any edge \(e=(d,s^{\prime})\
ot\in T\), where \(s^{\prime}\) is a source node:  
\[p(R(v,S\cup\{e\}))-p(R(v,S))\geq p(R(v,T\cup\{e\}))-p(R(v,T)). \tag{4}\]  
We first make the following observations based on Equation (3):  
* \(R(v,S\cup\{e\})=R(v,S)\cup R(s^{\prime})=R(v,S)\cup(R(s^{\prime})\setminus R( v,S))\) and
* \(R(v,T\cup\{e\})=R(v,T)\cup R(s^{\prime})=R(v,T)\cup(R(s^{\prime})\setminus R( v,T))\)  
Then, \(p(R(v,S\cup\{e\}))-p(R(v,S))=p(R(s^{\prime})\setminus R(v,S))\) and \(p(R(v,T\cup\{e\}))-p(R(v,T))=p(R(s^{\prime})\setminus R(v,T))\). The inequality in Equation (4) follows by observing that \(R(v,S)\subseteq R(v,T)\). **Corollary 1**: _Algorithm 2 provides a \(\left(1-\frac{1}{e}\right)\)-approximation for the MCI problem either on DAG with a single source or with a single sink._