esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Results  
### Simulation Scenarios  
The following section analyzes the behavior of an office building under fire scenarios, comparing the influence of two types of ventilation system--organized natural ventilation [10] and mechanical ventilation--on the evolution of the fire and indoor environmental parameters. The objective is to highlight how each system affects the distribution of temperature, smoke, pressure, HRR, carbon monoxide, and carbon dioxide under extreme conditions, in order to evaluate the performance of each ventilation solution in preventing fire spread and ensuring occupant safety.  
Three ventilation scenarios were considered to evaluate their influence on fire dynamics in the high-rise building:* Scenario S0 represents natural ventilation with organized openings: the window (1.40 m \(\times\) 1 m) and door (1 m \(\times\) 1 m) of the fire compartment are open, while on the top floor, smoke is evacuated through both a 1 m \(\times\) 1 m smoke exhaust hatch and an open exterior window (1 m \(\times\) 1 m) connected to the stairwell via a partially open door (0.5 m \(\times\) 2 m). This configuration enables vertical smoke movement driven by thermal buoyancy (Figure 5--S0).
* Under mechanical ventilation, scenario S1 includes a single air supply on the top floor, with the fire compartment window permanently open and the door opening at t = 120 s; the airflow rate is 2 m\({}^{3}\)/s (Figure 5--S1).
* Scenario S2 features dual air supply inlets located on the ground and top floors, with the fire compartment window also permanently open and the door opening at t = 60 s; each inlet provides an airflow rate of 1 m\({}^{3}\)/s. These defined conditions provide a comparative framework for analyzing the effects of natural versus mechanical ventilation on smoke control, airflow patterns, and overall fire behavior in high-rise structures (Figure 5--S2).  
The three fire scenarios were selected to reflect typical and critical fire conditions in office buildings, focusing on ground floor fires that challenge evacuation and smoke control. Fire growth was modeled using the standardized t-squared curve to realistically simulate fire development. Fuel load assumptions were based on a fire load density of 420 MJ/m\({}^{2}\), consistent with average office environments, resulting in a total heat release of approximately 6720 MJ [10]. Building characteristics, including geometry and ventilation openings, were defined to replicate realistic physical conditions. The numerical model was deliberately simplified to capture essential fire and ventilation dynamics without unnecessary complexity, ensuring computational efficiency while maintaining representativity. This balance allows a robust evaluation of ventilation strategies under realistic fire conditions.  
Figure 5: Fire simulation scenarios [10].
## 1 Introduction
### Contingencies  
In the scientific literature dealing with electrical power systems, the term \"contingency\" or \"contingency analysis\" is quite common. A contingency is a failure or loss of an element (e.g., generator transformer, transmission line, etc.) or a change in the state of a device (e.g., in a substation) in the power system. It is accepted that an electric power system is considered \"safe\" when it can withstand the failure of one or more elements and continue to operate without major disruptions/disconnections [10]. The first articles [11; 12], which presented performance indices, were published more than 40 years ago, and this research area still remains active. Information technology has changed significantly in various aspects over the past few decades, but there is still no unified solution for contingency assessment.  
Contingency analysis is a mathematical method to predict the failure of equipment or a specific line and take corrective action before the system enters an unstable state [13]. Contingency analysis can analyze the safety aspects of the power system during both planning and operation phases. Unforeseen circumstances can cause serious violations of the electrical network operational limits (safe operational limits of the electrical network). Therefore, contingency planning is an important aspect of safe operation, enabling smooth system operation through the protective aspects [14]. Contingency selection methods are shown in Figure 2.  
In real time, there are many possible events that can happen in electrical power systems. Impacts of various events on the power system varies, so contingency ranking and contingency screening help identify severe and less severe contingencies. The advantages and disadvantages of these methods are described below.
## 1 Introduction
They are employed to improve the accuracy and stability of the UWB positioning system. Finally, the improved CKF algorithm demonstrated a stronger dynamic stability performance and higher positioning accuracy via dynamic trajectory and static reference checkpoint experiments. The rest of this paper is organized as follows: The SRD model based on the TDOA algorithm is presented in Section 2. The improved adaptive CKF algorithm based on the Sigmoid function and SVD decomposition method is given in Section 3. The experimental testing and evaluation of the proposed algorithm are detailed in Section 4. Finally, conclusions and future research arrangements are drawn in Section 5.
## 3 Materials and Methods
### The APP Feedback Case  
It is seen in Section 2.2 that in the case of extrinsic feedback, there is nothing to be gained from the use of a weighted delay filter. However a large performance gains is observed when a weighted delay filter is used with APP feedback. The following questions are raised:  
* What are the significant differences between the extrinsic feedback case and the APP feedback case?
* How does the analysis in Section 2.2 differ when APP feedback is used?  
In the case where APP feedback is used, we have \(\mathcal{E}[x_{k}\hat{x}_{j}^{(n-1)}]\
eq 0\); as correlations have been allowed to build. As a result, the expressions for the mean and variance at the PIC output, no longer hold. The analysis provided is therefore invalid for APP data. To investigate this further, it is necessary to first determine the statistics at the PIC output.
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 2 Analysis of Key Technologies for the Transition from Open-Pit to Underground Mining
### Selection of Mining Methods and Technical Adaptability Analysis
#### 2.2.2. Caving Mining Method  
The caving mining method leverages the self-weight of the ore or rock mass to induce natural collapse for recovery [26]. It is suitable for mining scenarios where the ore is loose, the orebody is inclined, the roof is unstable, or surface subsidence constraints are minimal. This method is characterized by high efficiency and a high recovery rate, making it widely applicable in large-scale deep metal mines. For instance, when the Shilu Iron Mine transitioned to underground mining in 2018, it employed the sublevel caving method without crown pillars [27]. This approach effectively utilized the orebody's dip angle and joint surface distribution characteristics to enhance unit production capacity. However, the method involves considerable uncertainties and irreversibility, particularly in mines with surface structures, water-sensitive zones, or strict slope control requirements [28]. The large-scale ground pressure disturbances, subsidence, or instability risks induced by the method are significant and cannot be overlooked [29]. Improper control of the caving range may lead to the expansion of large-scale voids and structural disasters, placing high demands on engineering controllability and risk management capabilities.
# The Current Development Status of Agricultural Machinery
## 5 Summary and Future Perspectives
### Cooperative Control Algorithm Optimization
#### 5.2.1 Energy-Drive Cooperative Control  
Hilly and mountainous regions are characterized by steep terrain gradients, variable soil adhesion, and high operational loads. Traditional agricultural machinery, which typically employs a single-power allocation mechanism and energy source, often experiences wheel slip or power interruptions during operation. To address these challenges, it is essential to integrate power distribution optimization, hybrid energy management systems, and hydraulic-electric hybrid drive systems into cooperative control strategies for energy drives. This integration enhances dynamic response and operational stability while balancing endurance with energy efficiency; it also achieves high precision and strong adaptability.  
Currently, significant progress has been made domestically in the application of model predictive control (MPC) algorithms and the development of hybrid powertrains. However, challenges remain, including reliance on imported high-precision sensors and hydraulic components as well as insufficient generalization of algorithms. Looking ahead, breakthroughs in digital twin technology, lightweight edge computing solutions, and domestically developed core components--coupled with policy support and collaboration across the industrial chain--are expected to propel energy-drive cooperative control forward. This advancement will facilitate the transition of agricultural machinery in hilly regions toward full autonomy while enhancing energy efficiency. Ultimately, this evolution aims to improve both agricultural productivity and sustainability.
## 5 Intervalley Scattering and Franck-Condon Transition
### Non-Condon Effect  
In the elastic e-ph scattering, the phonons are frozen at the starting ground state configuration, exactly following the Franck-Condon principle of the photo-excitation. In this section, let us consider the situation that a small oscillation is allowed for the phonons. This is known as the non-Condon effect, which is taken into account by considering the higher order terms in the time evolution of the phonon operator in Equation (20). In this case, the transition rate can be written as:  
\[\Gamma_{\rm ep,\mathbf{k},\sigma}=\Gamma_{\rm ep,\mathbf{k},\sigma}^{(0)}+\Gamma_{\rm ep,\mathbf{k},\sigma}^{(1)}+\Gamma_{\rm ep,\mathbf{k},\sigma}^{(2)}+\cdots, \tag{25}\]  
where:  
\[\Gamma_{\rm ep,\mathbf{k},\sigma}^{(0)}=\left[CN(T_{\rm p})\sum_{\mathbf{q}}F(\mathbf{q}, \Delta)\right]_{\Delta=0}, \tag{26}\]  
\[\Gamma_{\rm ep,\mathbf{k},\sigma}^{(1)}=\left[-C\frac{\partial}{\partial\Delta} \sum_{\mathbf{q}}\omega_{\mathbf{q}}F(\mathbf{q},\Delta)\right]_{\Delta=0}, \tag{27}\]  
and:  
\[\Gamma_{\rm ep,\mathbf{k},\sigma}^{(2)}=\left[CN(T_{\rm p})\frac{\partial^{2}}{ \partial\Delta^{2}}\sum_{\mathbf{q}}\frac{\omega_{\mathbf{q}}^{2}}{2}F(\mathbf{q},\Delta) \right]_{\Delta=0}, \tag{28}\]  
with \(C=\frac{2\pi}{N}S_{\pi}^{2}\), \(N(T_{\rm p})=\langle\langle n_{\pi}(T_{\rm p})\rangle\rangle+\frac{1}{2}\), and \(F(\mathbf{k},\mathbf{q},\Delta)=\delta(E_{L}(\mathbf{k}+\mathbf{q})+\Delta-E_{\Gamma}(\mathbf{k}))\). \(\Delta\) is a hypothetical variable only for the formulation and is finally set to zero. The zeroth order term  
Figure 11: The adiabatic energy of three-level localized electronic system distorted by the thermal randomness of the phonon is given. For more details, see the text.  
\(\Gamma_{\text{ep},\mathbf{k},\sigma}^{(0)}\) corresponds to the elastic e-ph process, Equation (19). The evaluation of more higher order terms is straightforward, but their effects are very small.  
The evaluation of the non-Condon effect is performed in the same condition as Section 5.4, except the temperature is fixed to \(T_{\text{P}}=0\) K. The result is given in Figure 12. The effects of the first and second order terms are basically small. Thus, even if the non-Condon effect is taken into account, the characteristic time constant is not altered so much. However, the inclusion of the non-Condon effect makes the time constant larger compared with that without the non-Condon effect. This is an intrinsic point to understand the relationship between the elastic e-ph process and the inelastic e-ph one. As seen in Equations (27) and (28), the higher order terms explicitly depend on the phonon frequency \(\omega_{\mathbf{q}}\). Since the phonon oscillation period is long compared with the present time scale, the process including the phonon motion inevitably becomes slower than that without phonons. Although the argument of the non-Condon effect is valid only at around \(t=0\), this has a close connection with why the inelastic e-ph process is not suitable for the explanation of the present ultrafast intervalley transition.
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 1 Introduction  
Modern production cannot do without electric motors, which are used in various fields. Various types of electric motors are employed for diverse purposes, allowing for increased productivity, energy efficiency, and cost-effectiveness in any manufacturing process [1, 2, 3, 4]. Thanks to motors, the possibilities of production are constantly expanding, enabling the use of more complex and advanced mechanisms, and creating new development opportunities. Any of advancing production is the use of robotic systems, where servomotors are directly employed [5, 6, 7].  
Servomotors in robots are used to enhance the precision or smoothness of a mechanism's operation, depending on the task it performs. Therefore, the control of drives must have a similar nature, enabling the achievement of the required goals. However, the more complex the drive control method, the more intricate the control system will be, leading to additional costs but at the same time enhancing production characteristics [7, 8, 9, 10].  
The control of servomotors has not only changed in recent years but continues to evolve, revolutionizing various industries, particularly robotics and automation. Initially, these motors were controlled using analog methods, but with the advancement of digital technologies, control has become more precise and versatile [11, 12, 13, 14].  
Various technologies for servomotor control are currently employed, including digital signal processing systems, feedback systems, field-oriented control, and control systems integrated with Industry 4.0. All these types of servomotor control are used to achieve different results, each having its own merits and drawbacks, which are discussed in these works [15, 16, 17, 18, 19, 20, 21].  
This work is a general overview of the topic of servomotors, their devices and principles of operation, popular methods of control, and ways to expand existing and new control systems. It is worth noting that motor control is constantly evolving, allowing for the exploration of new control methods, such as the transition from analog to digitalsystems, from open-loop systems to feedback systems, and so on. The development of control systems enables the improvement of the efficiency, precision, and adaptability of motors in various industries, leading to the conclusion that new technologies will have more complex yet integrable solutions.  
The second chapter of this article provides a description of the structure and operating principles of servomotors and discusses the main objectives of their use. The third and fourth chapters introduce the control principles mentioned above and outline some methods of controlling servomotors that are suitable for a specific type of control. The fifth chapter is a comparison of the presented types of control. The chapter does not provide experimental data but rather offers a comparative characterization based on previously conducted research.
## 2 Materials and Methods
### Data collection and Processing
#### 2.2.2 Meteorology Data  
We calculated monthly minimum temperature (\({}^{\circ}\)C), maximum temperature (\({}^{\circ}\)C), average temperatures (\({}^{\circ}\)C), and precipitation (mm) based on the hourly recorded data from 2008-2021 that were acquired from the meteorological station nearby the study area. Considering some FSC have an older forest age, the temperature and precipitation data were extended to 1997-2007 using the data provided by National Aeronautics and Space Administration (NASA). Solar radiation data from 1997 to 2021 were also acquired from the website ([[https://power.larc.nasa.gov/data-access-viewer/](https://power.larc.nasa.gov/data-access-viewer/)]([https://power.larc.nasa.gov/data-access-viewer/](https://power.larc.nasa.gov/data-access-viewer/)), accessed on 10 January 2022) due to a lack of local observations. These data have been proven to be accurate enough to provide reliable meteorological and solar radiation data in areas where site observations are sparse [38; 39]. Compared with the data of the same year from the meteorological station, the two source data products have high consistency and can be used together for the model simulation.
## 3 Proposed Methodology
### Time Complexity Analysis  
In this section, we analyse the time complexity of the proposed distributed genetic algorithm for community detection along with the parallel fuzzy cognitive map. The time complexity of the algorithm can be analysed by examining each step and considering the dominant factors that contribute to the overall complexity. In the given distributed genetic algorithm for community detection, modularity was used as the fitness function. To compute its time complexity, we analysed each step of the algorithm. The algorithm involved mainly two steps, the focal node identification using a parallel fuzzy cognitive map and the community detection using the distributed genetic algorithm.  
We let G be a network, where \(n\) was the number of nodes, and \(m\) was the number of edges in the network. Firstly, the state vector was computed using the betweenness centrality measure for the focal node identification part, which took O(\(n\times(n+m)\)) for serial execution. Since a distributed approach was adopted, and the data were partitioned across PR physical nodes, the complexity was O(\((n\times(n+m))/PR\)). Also, the weight matrix was distributed to the PR nodes of the network; hence, it took the complexity O(\(PR\)). Subsequently, the FCM iterations were performed, which involved updating the state vector based on the weights and biases, which took O(\((max\_iterations\times n^{2})/PR\)). Therefore, the total time complexity of the FCM algorithm was expressed as O(\(PR\)) + O(\((n\times(n+m))/PR\)) + O(\((max\_iterations\times n^{2})/PR\)), and simplifying this expression, the total complexity of the FCM was O(\((max\_iterations\times n^{2})/PR\)).  
The next step was to calculate the time complexity of the distributed genetic algorithm. For the GA, the main operations that contributed to the time complexity were the evaluation of the fitness, selection, crossover, and the mutation. The first step was initialising the population, which took O(\(p\)), where \(p\) was the size of the population. The fitness was evaluated for the population using modularity as the fitness function. The time complexity for the fitness function was O(\(p\times n\times m\)). The selection operation had a complexity of O(\(plogp\)). The crossover and mutation operations had a complexity of O(\(p\)). Hence, combining these, the total complexity of the distributed GA was O(\((max\_generations\times(p\times n\times m+p\times\log(p)))/PR\)), which was further simplified to O(\((n\times m\times log(p))/PR\)).
## 2 Setup of Experiment
### Experiment Cases  
The parameters considered in this paper are the materials for the risers, velocities of flow, end conditions and top-tension forces. The values for each parameter are summarized in Table 2. As can be seen in Table 2, Aluminum, Poly(methyl methacrylate) (PMMA) and Unplasticized Polyvinyl Chloride (UPVC) are selected as the materials of the riser model; 5 N and 65 N are employed for the top tension force; simple support at top-simple support at bottom (S-S) and simple support at top-fix support at bottom (S-F) are chosen for the end conditions; the flow velocities considered are 0.2 m/s, 0.6 m/s and 1.0 m/s.  
According to the values in Table 2, there are 36 experiment cases in total, as presented in Table 3. Using \(\mathrm{Re}=\frac{\mathrm{UD}}{\upsilon}\), where Re is Reynolds number, D is the outside diameter of the riser model, and \(\upsilon\) is the kinematic viscosity coefficient (1.01 \(\times\) 10\({}^{-6}\) for 20 \({}^{\circ}\)C water), it is found that Re for the experiment cases with 0.2 m/s (cases 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31 and 34) is 3960; for experiment cases with 0.6 m/s (cases 2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32 and 35), it is 11,881; and for experiment cases with 1.0 m/s (cases 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33 and 36), it is 19,802. Therefore, turbulence intensities are 5.68%, 4.95% and 4.64%, respectively, according to \(\mathrm{I}=0.16\times\left(\mathrm{Re}\right)^{-1/8}\), where I is turbulence intensity, Re is Reynolds number.
## 4 Differentiation of the Wright Functions of the Second Kind with Respect to Parameters  
We now consider, among the Wright functions of the second kind, functions, \(F_{\alpha}(t)\) and \(M_{\alpha}(t)\) introduced by [PERSON]:  
\[\begin{array}{l}F_{\alpha}(t)=W_{-\,\alpha,0}(t)\quad;\quad 0<\alpha<1\\ M_{\alpha}(t)=W_{-\,\alpha,1-\,\alpha}(t)\quad;\quad 0<\alpha<1\\ F_{\alpha}(t)=\alpha\,t\,M_{\alpha}(t)\end{array} \tag{12}\]  
Their series expansions explicitly read as follows:  
\[F_{\alpha}(t)=\sum_{k=1}^{\infty}\frac{(-t)^{k}}{k!\,\Gamma(-\,\alpha\,k)}=- \frac{1}{\pi}\sum_{k=1}^{\infty}\frac{(-t)^{k}}{k!}\,\Gamma(\alpha\,k+1)\,\sin (\pi\,\alpha\,k) \tag{13}\]  
and  
\[M_{\alpha}(t)=\sum_{k=0}^{\infty}\frac{(-t)^{k}}{k!\,\Gamma\,(-\,\alpha\,(k+1 )+1)}=\frac{1}{\pi}\sum_{k=1}^{\infty}\frac{(-t)^{k\,-\,1}}{(k-1)!}\,\Gamma( \alpha\,k)\,\sin(\pi\,\alpha\,k) \tag{14}\]  
The direct differentiation of (13) and (14) gives  
\[\begin{array}{l}\frac{\partial F_{\alpha}(t)}{\partial\alpha}=\frac{1}{\pi }\sum_{k=1}^{\infty}\frac{k(-t)^{k-1}}{k!}\,\Gamma(\alpha\,k+1)\left[\psi( \alpha\,k+1)\sin(\pi\,\alpha\,k)+\pi\cos(\pi\,\alpha\,k)\right]\\ \frac{\partial M_{\alpha}(t)}{\partial\alpha}=\frac{1}{\pi}\sum_{k=1}^{\infty }\frac{k(-t)^{k-1}}{(k-1)!}\,\Gamma(\alpha\,k)\left[\psi(\alpha\,k)\sin(\pi\, \alpha\,k)+\pi\cos(\pi\,\alpha\,k)\right]\end{array} \tag{15}\]  
Using the last equation in (12), we have  
\[\frac{\partial F_{\alpha}(t)}{\partial\alpha}=t\,M_{\alpha}(t)+\alpha\,t\, \frac{\partial\,M_{\alpha}(t)}{\partial\alpha} \tag{16}\]  
The second derivatives of these functions are  
\[\begin{array}{l}\frac{\partial^{2}F_{\alpha}(t)}{\partial\alpha^{2}}=\frac{ 1}{\pi}\sum_{k=1}^{\infty}\frac{k(-t)^{k-1}}{(k-1)!}\,\Gamma(\alpha\,k+1)\{ \psi^{\prime}(\alpha\,k+1)\!+\psi^{2}(\alpha\,k+1)]\sin(\pi\,\alpha\,k)\!+\\ 2\,\pi\cos(\pi\,\alpha\,k)\,\psi(\alpha\,k+1)-\,\pi^{2}\sin(\pi\,\alpha\,k) \}\end{array} \tag{17}\]  
and  
\[\begin{array}{l}\frac{\partial^{2}M_{\alpha}(t)}{\partial\alpha^{2}}=\frac{ 1}{\pi}\sum_{k=1}^{\infty}\frac{k^{2}(-t)^{k-1}}{(k-1)!}\,\Gamma(\alpha\,k)\{ \psi^{\prime}(\alpha\,k)\!+\psi^{2}(\alpha\,k)]\sin(\pi\,\alpha\,k)\!+\\ 2\,\pi\cos(\pi\,\alpha\,k)\,\psi(\alpha\,k)-\,\pi^{2}\sin(\pi\,\alpha\,k)\} \end{array} \tag{18}\]  
They are interrelated by  
\[\frac{\partial^{2}F_{\alpha}(t)}{\partial\alpha^{2}}=2\,t\,\frac{\partial\,M_ {\alpha}(t)}{\partial\alpha}+\alpha\,t\frac{\partial^{2}\,M_{\alpha}(t)}{ \partial\alpha^{2}} \tag{19}\]