esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Model and Solution Algorithm
### Model Solving Algorithm Design  
#### 3.3.1 Simulation-Based Genetic-Simulated Annealing Algorithm  
The integrated optimization model presented in this study is an NP-Hard problem, often addressed using heuristic algorithms to obtain an approximate optimal solution. Hence, we propose a genetic-simulated annealing algorithm. The objective function reveals that passenger waiting time at station \(s\) upon arrival of train \(i\) comprises waiting time of remaining passengers from train \(i-1\) and new arrivals. Since this is challenging to express mathematically, we embed passenger flow distribution simulation into the genetic-simulated annealing algorithm.
# The Impact of Hollow Wear on the Stability of High-Speed Railway Vehicles
## 2 The Influence of Tread Hollow Wear on the Wheel-Rail Contact Relationship  
### The Profile of Worn Wheels  
Characterizing hollow wear solely by its depth has certain limitations. In fact, hollow wear on the wheel tread is not only related to the depth and width of the wear, but also closely associated with the position of the hollow wear. For hollow wear of wheel tread (as in Figure 1), the wear depth H is defined as the radius difference between the standard and worn wheel profiles at a point 70 mm from the flange's back. However, this position is not the deepest point of hollow wear. Point A is defined as the highest point on the outer side of the wheel wear profile (away from the flange), Point B is the lowest point of the hollow wear, and the vertical distance h between A and B is the depth of hollow wear. The investigation showed that a hollow wear depth between 0.1 mm and 0.3 mm can easily lead to frame lateral acceleration exceeding the limit, triggering safety alarms and necessitating vehicle deceleration or halt [3].  
It is well understood that the hollow wear in wheel treads intensifies as mileage increases. To study the wheel-rail contact situation of worn wheel, a wheel profile measuring instrument was used to measure the tread profile of a selected wheelset of the high-speed train during one maintenance cycle, as shown in Figure 2.  
Figure 1: The wheel hollow wear.  
The standard wheel tread profile (which had been re-profiled) was defined as W1. Corresponding to different operating mileage of the vehicle (45,000 km, 95,000 km, 185,000 km, and 260,000 km), the profiles of worn wheel were labeled as W2-W5, respectively. The main parameters characterizing the hollow wear are listed in Table 1, as the calculation basis for subsequent analysis of wheel-rail contact point evolution and dynamic simulation. The data show that with the increase in mileage, the depth and width of wear on the wheel tread progressively rise, and the profile of wheel undergoes a dynamic development process.
# 4D Building Reconstruction with Machine Learning and Historical Maps
###### Abstract  
The increasing importance of three-dimensional (3D) city modelling is linked to these data's different applications and advantages in many domains. Images and Light Detection and Ranging (LiDAR) data availability are now an evident and unavoidable prerequisite, not always verified for past scenarios. Indeed, historical maps are often the only source of information when dealing with historical scenarios or multi-temporal (4D) digital representations. The paper presents a methodology to derive 4D building models in the level of detail 1 (LoD1), inferring missing height information through machine learning techniques. The aim is to realise 4D LoD1 buildings for geospatial analyses and visualisation, valorising historical data, and urban studies. Several machine learning regression techniques are analysed and employed for deriving missing height data from digitised multi-temporal maps. The implemented method relies on geometric, neighbours, and categorical attributes for height prediction. Derived elevation data are then used for 4D building reconstructions, offering multi-temporal versions of the considered urban scenarios. Various evaluation metrics are also presented for tackling the common issue of lack of ground-truth information within historical data.  
machine learning; 3D building modelling; historical maps; 4D city modelling +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Microstructural Characterization  
The microstructure of the as-received base copper alloy is shown in Figure 4. It can be observed from the micrograph that the dendritic structure of the silicon brass alloy is clearly visible. Similarly, the composition of the copper alloy can also be confirmed from the EDX images, which shows the zinc and silicon element in the alloy. The FESEM microstructure of the Cu-SiC-Gr and Cu-WC-Gr hybrid composites are shown in Figures 5 and 6, respectively. The homogenous distribution of the reinforcement particulates could be visible from the SEM micrographs, which was the result of the optimum stirring action throughout the casting process. Moreover, the vortex formation due to the mechanical stirring within the melt resulted in the uniform dispersion of the reinforcement phases during the stir-casting process [21]. When the FESEM images were examined, it could be said that there were no signs of agglomeration of the particles for both the hybrid composites. Pointed and irregularly formed SiC could be easily observed from the microstructures, whereas the spherical (round) shaped WC was observed for the respective composites. Moreover, there were no shrinkage porosity defects which also demonstrates an effective stir-casting process. Similarly, the intensity of the alloying elements as well as the reinforcing elements was also  
Figure 3: Process flow chat for the current research.  
Figure 2: Schematic of the tensile test specimen.  
observed in the regional EDX analysis. Several elements like Zn, Si, C, O, and W existed along with the copper matrix that established the development of the composites. The micrographs also revealed that the concentration of the elements in the base alloy was different from that of the composites, which corresponds to the different content of the reinforcements. Further, the very low oxygen content, as shown in the EDX spectrum, revealed that substantially minimum oxidation happened during the fabrication as the whole process was carried out under the argon atmosphere. This restricted any possible contamination with the outside oxide environment, which could have deteriorated the quality of the casting.  
Figure 4: FESEM micrograph and EDX analysis of Cu alloy matrix.  
Figure 5: FESEM micrograph and EDX analysis of Cu-SiC-Gr hybrid composites with SiC variation of (**a**) 3, (**b**) 6, and (**c**) 9 wt.%.
# Potential Role of Plant Extracts and Phytochemicals Against Foodborne Pathogens
## 3 Plant Extracts and Phytochemicals against Bacteria Causing Foodborne Diseases
### Clostridium spp.  
_Clostridium botulinum_ (_C. botulinum_) is a Gram-positive, rod-shaped, motile, obligate anaerbe [164]. It is a spore-forming bacterium and produces a neurotoxin known as botulinum [165]. It causes a foodborne disease known as foodborne botulism. This happens after ingestion of the preformed toxin produced by _C. botulinum_. The major virulence factor is toxin production [166]. Seven types of toxins have been identified. These are named as A-G. Among these, A, B and E toxin types are associated with foodborne illness. The symptoms of foodborne botulinum are blurred vision, dry mouth, nausea, vomiting, abdominal cramps and difficulty in swallowing [167; 168]. Toosendanin, a natural compound, prevents botulinum in animals [169].  
_C. perfringens_, a Gram-positive, spore-forming bacteria has a widespread anaerobic environmental distribution including soil, foodstuff and is part of human flora [170]. Extracts from traditionally important medicinal plants including _Psidium guajava_ L., _Haematoxylum brasiletto_ H.Karst. and _Euphoria prostrata_ [PERSON] were found highly effective against _C. perfringens_ type A [171]. Likewise, essential oils from _Satureja montana_ L. tested against _C. perfringens_ type A at a concentration of 1.56%, showed inhibitory activity causing structural damage and cell lysis. Moreover, a synergistic effect between NaNO\({}_{2}\) and _Satureja montana_ EOs was observed and the findings suggest the potential combined use of savory essential oil and minimal amounts of the synthetic additive, NaNO\({}_{2}\) to control _C. perfringens_[172]. The natural product, berberine demonstrated efficacy towards the _C. perfringens_ disease based on significantly decreased mortality and lesion scores at 1.0 mL/L, in vitro [173]. Extracts from several plants _Pueraria thunbergiana_ ([PERSON] & [PERSON].) [PERSON], _Astragalus membranacetus_ ([PERSON].) Bunge, _Eucommia ulmoides_ Oliv., _Coptis japonica_ ([PERSON].) Makino, _Akebia quinata_ ([PERSON].) Decne. and _Rhus chinensis_ Mill. exhibited considerable activity against _C. perfringens_[174].
# Combined Optimization of Maintenance Works and Crews in Railway Networks
###### Abstract  
This study develops optimal maintenance schedules for train lines, a critical endeavor ensuring the safety, efficiency, and reliability of railway networks. The study addresses the combined scheduling problem of maintenance works and crews on the railway networks. The baseline scheduling model is initially established with the primary objective of allocating maintenance tasks efficiently while adhering to pertinent constraints, encompassing task grouping and cost minimization. Subsequently, this baseline model is enhanced through the integration of crew scheduling, wherein work crews are strategically assigned to execute predefined tasks, thereby facilitating effective workload distribution. The combined maintenance work and crew scheduling problem is mathematically formulated as a binary linear programming model, enabling the attainment of globally optimal solutions. Comparing the outcomes of our enhanced model, which incorporates both maintenance works and crew schedules, with the baseline model that solely addresses maintenance works, we reveal that task grouping in accordance with predefined conditions leads to reduced overall costs by minimizing maintenance duration during various periods. Additionally, the judicious distribution of workload among the crews ensures comprehensive coverage of all essential tasks. These findings underscore the significance of our proposed approach in enhancing the operational efficacy and economic viability of railway maintenance scheduling, thereby offering valuable insights for practical implementation and future research endeavors.  
optimization; maintenance works; crew scheduling; railway networks; integer linear programming +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Research on SWIM Services Dynamic Migration Method +
## 1 Introduction
[PERSON] proposed a technical solution to improve the security of the SWIM, which can enhance information sharing operability and data security [7]. In the process of a civil aviation department as a SWIM service provider, SWIM service consumers and SWIM service providers use SWIM core services to achieve data transmission and information sharing. Although a series of security measures have been deployed in the SWIM core services, there is still no way to ensure that the system is completely protected against security threats. Suppose a malicious attacker successfully bypasses the SWIM firewall and multiple security measures to enter the SWIM disguised as a legitimate user. The malicious attacker first obtains the SWIM advanced access right by using the security service in the SWIM core service, and then obtains the description information of the SWIM service provider through the registration center in the SWIM interface management service. According to the description information of the SWIM service provider, a malicious attacker can launch a malicious attack after long-term detection of the SWIM service provider, which may cause the SWIM service provider to interrupt the service or even crash the system, or the malicious attacker successfully obtains the control of the SWIM service provider to provide untrustworthy service data to the outside world, thus affecting the analysis and decision-making of SWIM service consumers, and even affecting the safe and stable operation of the civil aviation industry. SWIM service providers will also face security threats such as natural disasters and system failures, which seriously affect the security and reliability of SWIM services. Therefore, in order to improve the fault tolerance capability of the system, this paper transforms the traditional passive defense into active defense from the perspective of protecting the SWIM service data source, and proposes a dynamic migration method of SWIM service to enhance the survivability of the system. The main contributions of this paper as follows:  
1. According to the characteristics of the pseudo-random sequence, a new service migration trigger mechanism is designed, so that the SWIM service migration frequency can be regulated by the SWIM region. 2. On this basis, according to the different service migration trigger mechanism, A destination node selection strategy based on SWIM core service and randomness is proposed in this paper, which guarantees seamless migration of SWIM services. The remaining chapters of this paper are organized as follows: Section 2 firstly introduces the related concepts of SWIM and the current state of research on service migration. Section 3 introduces the SWIM security threats scenario. Section 4 designs a SWIM services dynamic migration method. Section 5 analyzes the validity of the method from both theory and simulation. Section 6 discusses and summarizes the full text.
## 1 Introduction
### Related Work  
In 1973, methodologies for modeling the S2A channel for stationary and quasi-stationary scenarios were presented in [2]. As of the writing of this paper, non-stationary characteristics of S2A channel have not been discussed although the channel can be described more precisely with the considerations on non-stationary features. As surveyed in [9; 10], A2G channel models have been presented especially for low-altitude platform. Time-variant measurements of A2G channel were reported in [11]. A non-stationary A2G GBSM for airplane to ground station at regional airport was presented in [6]. A non-stationary GBSM for unmanned aerial vehicle (UAV) to ground vehicle communication was developed in [12]. Non-stationary characteristics have also been widely discussed in V2V channel modeling [13; 14] as well as for A2A communications [15; 16; 17]. On the modeling of the geometrical structure of the scatterers and the deriving of distributions of the channel coefficients, in [7], as a kind of RS-GBSM, three-dimensional (3D) ellipsoidal model was proposed for A2G communication, and the distributions of the received power and Doppler shift were derived. The 3D ellipsoidal model for A2A communication was expanded in [15], and the distributions of the azimuth and elevation angels of arrival (AOAs) based on based on uniform distribution (UD) of the scatterers was simulated. The ground scattering and the Doppler effect were modeled and simulated in details by [6; 16; 18]. And a simpler closed form expression of Doppler shift was obtained in [6] through utilizing the prolate spheroidal coordinate system. Further developments can be found in [19; 20; 21]. The distributions of path attenuation and path delay based on the distribution of the scatterers have not been analyzed, although the distribution of path power have been mentioned [15]. In [1], a multi-path channel model including the line-of-sight (LOS) and two reflected paths based on the measurements with the satellite as the emitter and the aircraft as the receiver was presented. This work enhanced the S2A channel model with considerations on the effects of the body of the aircrafts. These above-mentioned articles provided worthy ideals for us on developing non-stationary RS-GBSM based S2A channel model.
## 1 Introduction
The developed numerical methods are applied for the benchmark problem of the HSM [7]. The main contributions of the present study are fivefold. Firstly, a highly accurate LES simulation with high-resolution grids is carried out with a focus on the accurate computation of external flow, including both incompressible and compressible flow fields. The predicted surface pressure spectra show excellent agreement with the measured ones. Secondly, a detailed signal processing algorithm based on the wavenumber-frequency transform is developed to accurately separate incompressible and compressible surface pressure fields on the windows of the HSM as input for the pressure loading of the vibroacoustic model. The successful separation of these two fields enables the assessment of the relative contribution of the incompressible and compressible pressure fields to the interior noise of the vehicle and, thus, an effective measure for suppression of interior wind noise. Thirdly, a vibroacoustic solver based on the combination of the FE and SEA methods is developed for efficient computation of the interior acoustic field due to the vibration of windows. Specifically, the FE and SEA methods are applied in low- and high-frequency bands, respectively. The modal overlap factor is used to determine the low- and high-frequency bands. Fourthly, the injected power formula that reflects the vibration characteristics of the structure is defined, and a method that can be applied to practical problems is presented, showing excellent agreement between the predicted and measured sound pressure spectral levels. Lastly, the physical mechanism leading to more contribution of the compressible surface pressure field to the interior sound in high-frequency bands is made clear.
## 5 Reinforcement Learning for the CEVRP  
Reinforcement learning (RL) involves an agent interacting with its environment to learn an optimal policy through trial and error for sequential decision-making problems across various fields. An RL agent engages with an environment over a period. At each time step \(t\), the agent receives a state \(\textbf{{s}}_{t}\) from a state space \(\textbf{{S}}\) and chooses an action \(\textbf{{a}}_{t}\) from an action space \(\textbf{{A}}\). The agent receives a reward \(\textbf{{r}}_{t}\) and transitions to the next \(\textbf{{s}}_{t+1}\). The environment provides a reward to the agent based on the effectiveness of its actions. The agent experiments with various actions by assessing the total rewards received. Throughnumerous trials, the algorithm learns which actions yield higher rewards and develop a behavioral pattern. Over time, the agent aims to maximize the cumulative reward [37].  
RL algorithms are based on the Markov Decision Process (MDP). In this study, the route generation process to solve the CEVRP is considered a sequential decision-making problem. This problem is formulated using reinforcement learning (RL) and addressed accordingly. The route generation process is modeled as a Markov Decision Process (MDP). The elements of MDP are defined as state, action, transition rule, and reward function. In the study, states are nodes (depot, customers, charging stations), and actions are moving from one node to another node. RL aims to capture essential features of the environment with which an agent interacts to achieve its goal. The agent should be able to perceive the state of the environment and take actions that affect the state. RL aims to capture complex structures and go further than traditional machine learning by using more adaptable algorithms. RL algorithms have more dynamic behavior than conventional machine learning algorithms.  
RL algorithms can be divided into two groups: model-based and model-independent. Model-based algorithms enhance learning by using precise knowledge of the environment. On the other hand, model-free algorithms can be applied without knowledge of the environment and can theoretically be adapted to any problem. These algorithms model the environment with MDP and learn strategies through interaction. Model-free algorithms can be divided into value-based, policy-based, and actor-critic. In this study, the Q-learning algorithm, one of the value-based algorithms, was used.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 8 Experiments and Discussions  
The robot (Figure 16) performs experiments in the lab with the given pre-built map (Figure 17). The robot will be placed in two different scenarios are shown in Table 2. The robot starts at its station and moves to the workpiece-collection point. In the second scenario, the robot leaves the post-processing point to move to the storage station. The following picture shows a detailed description of the lab's recorded map:  
Figure 14: The WSM flow chart.  
Figure 15: Visualization of re-pathed trajectory.  
According to our distributions, we divide our experiments into three parts: path-planning performance, tracking trajectory by re-path algorithm performance and moving obstacle-avoidance performance.
## 3 Methodology
### System Model  
The optimization framework proposed is for heterogeneous UAV networks that consist of fixed-wing and multirotor drones operating in a specified mission area. The first process of the system is to set mission parameters, which include the geographical task boundary (as a polygon), the setup of the UAV fleet, and operational constraints such as the maximum communication range, energy constraints, and minimum coverage requirement.  
Every UAV is configured with its own performance limits--fixed-wing vehicles are capable of extensive travel and allocated to boundary areas, while multirotor vehicles function in high-resolution surveillance applications within significant inner areas. Both utilize line-of-sight communication links within a set range, creating a decentralized mesh network to enable ad hoc connectivity and real-time coordination.  
The optimization process starts by having the GA generate an initial population of candidate drone positions. The solutions are progressively optimized by a Q-learning agent that shifts positions based on feedback from environmental states. The feedback includes coverage values, energy consumption, and connectivity. The learning is progressively updated in the mutation phase of the GA.  
Following optimization convergence, the emerged drone deployment satisfies multiple conditions: it achieves maximum spatial coverage, high mesh-based connectivity, and minimum energy consumption.  
This architecture supports scalable, fault-tolerant, and adaptive UAV deployments for disaster response, urban surveillance, rural coverage, and other time-varying mission applications. A description of this architecture is shown in Figure 1, showing the data flow from mission initiation to the optimization pipeline and dynamic UAV deployment.