esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The utilization of low-cost AGVs in the industry is increasing every day, but the efficiency of these systems is low due to the lack of a central management system. Low-cost AGVs' main characteristic is navigation via magnetic sensors, which they follow via magnetic tape on the ground with a low-level automation system. The disadvantages of these systems are mainly due to only one circuit assignment and the lack of system intelligence. Therefore, in this study, AGV pools were employed to determine the required AGV number. This study begins by calculating the required AGV number for each AGV circuit combination assigned to every parking station by the time window approach. Mathematical-solution-based mixed integer programming was developed to find the optimum solution. Computational difficulties were handled with the development of a genetic-algorithm-based approach to find the solutions for complex cases. If production requirements change, system parameters can be changed to adapt to the production requirements and there is a need to determine the number of AGVs. It was demonstrated that AGVs and pool combinations did not lead to any loss in production due to the lack of available AGVs. It was shown that the proposed approach provides a fleet size which requires five fewer AGVs, with a 29% reduction in the number of AGVs. The effects of system parameter changes were also investigated with artificial neural networks (ANNs) to estimate the required AGVs in the case of production requirement changes. It is necessary to determine the effect of the change in system parameters on the number of AGVs without compromising on computational cost and time, especially for complex systems. Thus, in this study, an artificial neural network (ANN), the response surface method (RSM), and multiple linear regression (MLR) techniques were used to examine the effects of the system parameter changes on the AGV number. In the present case, the ANN obtained the solution at a good rate with reduced computational costs, time, and correction errors compared to the GA, at 0.4% (ANN), 7% (RSM), and 24% (MLR). The results show that the ANN provides solutions which can be used in workshops to determine the number of AGVs and also to predict the effect of changes in system parameters.  
automated guided vehicles (AGVs); genetic algorithm (GA); artificial neural network (ANN); AGV pools +
Footnote †: journal: Applied Sciences  
1
## 2 Preliminary
### VPLS Description  
VPLS has emerged as the preferred networking option for industrial businesses to connect aging Supervisory Control and Data Acquisition (SCADA) and process control equipment over a common network. It is a VPN technology that securely connects provider networks and remote client locations using a tunnel called PWs to transport ethernet packets. VPLS networks operate at high speeds, are cost-effective compared to traditional VPN networks, and they are protocol-independent. As a result, VPLS has gained significant popularity in various application areas, ranging from industrial networks to mobile backhaul networks. In this model, PWs are used to provide seamless multipoint-to-multipoint connectivity while maintaining the privacy and security of the data transmitted over the network. Our proposed VPLS service model, as illustrated in Figure 1, comprises several components.  
* Customer Site
* Customer Edge device or simply CE: Customer network which is directly connected to the ISP.  
Figure 1: Our proposed VPLS service model exhibiting PW significance.  
* Provider Edge device or simply PE: This connects one or multiple CEs to the ISP. PE is responsible for converting IP to MPLS packets.
* Virtual Switch Instances or simply VSI: This is an ethernet bridge function entity of a VPLS instance on a PE. It forwards Layer 2 frames based on MAC addresses and VLAN tags.
* Network Provider device or simply P: ISP's devices inside the core network which runs MPLS.
* PW signaling or simply PW: This is a bidirectional virtual connection between VSIs on two PEs. It consists of two unidirectional MPLS virtual circuits.
* Attachment Circuit or simply AC: The connection between CE and PE.
* The tunnel is a direct channel between PEs for data transmission. In this work, we use an MPLS tunnel.  
A Packet Switched Network (PSN) tunnel must first be created between the two edge routers, and then a PW will be created inside the tunnel. The common physical link is called the Attachment Circuit [35]. As shown in Figure 2, the router can take the frame from two sides. When the router shares a frame from a shared physical link, it performs a MAC learning procedure just like a Bridge module.  
When the router takes the frame from the network, it performs MAC Learning via PW (Virtual Forwarding Instance (VFI) operation). Regardless of whether the PE router carries out the MAC Learning from a shared physical link, it puts the MACs on a shared MAC table and performs the layer 2 Forwarding using the layer 2 Forwarding Table accordingly. So, when the router takes a frame from the shared physical link (Attachment Circuit), the MAC takes a MAP from the shared physical link. And when it takes a frame from the network, it writes the MAC Source to the frame to PW Label. As a result, when the frame reaches the router, it determines which PW package should be sent to the destination [12].
## 4 The Dual-Radio Interface Routing Protocol for LLNs
### Theoretical Comparison with POOF  
While the idea behind POOF and DRiPLOF is similar, the execution is very different. This stems from the side-effects introduced by wireless interfaces. For example, consider a likely scenario wherein each node has one sub-GHz interface, which is typically reliable and longer range, but at a reduced data rate, and one 2.4 GHz interface, typically with a higher data rate, but shorter range and less reliable. As shown in Figure 6a, POOF tends to make long paths. Besides, preferred interface selection typically favors low-rate links. Long paths and slow links mean relatively high end-to-end latency.  
Now, consider the same network for DRiPLOF (see Figure 6b) and say that \(I_{node}=2\), \(IL_{max}=1\), \(IL_{div}=4\), and \(S=8\) (which is also the ETX metric threshold). As preferred interface selection is identical for POOF and DRiPLOF, we still favor low-rate links. However, sometimes, a shorter path is more sensible, even if a virtual link provides less redundancy (because one of its physical links is down). After all, the longer a path, the less reliable it becomes, and in Figure 6, it is clear that the direct physical link between #2 and the root offers the best inferred metric anyway. The paths resulting from DRiPLOF (Figure 6b) reflect this trade-off between path length and link redundancy with the goal of providing similar reliability and reduced end-to-end latency.  
Figure 5: Operation example of the Dual-Radio interface RPL Objective Function (DRiPLOF). This figure should be viewed left to right and top to bottom. All nodes have a green and a blue interface. Inferred metrics are displayed in the same color as their corresponding interface; normalized metrics are written in black; the arrows indicate which interface is preferred. Note that \(I_{node}=2\), \(IL_{max}=1\), \(IL_{div}=4\), and \(S=8\).  
To be clear, there is a trade-off here. Encouraging interface redundancy remains a valid idea, and so, if the physical link between #2 and the root has an ETX \(\geq 2\), the normalized metric of the corresponding virtual link becomes \(\geq 3.5\), and #2 shall take the path through #1 instead, even if the combined ETX (\(1+2=3\)) of that path suggests that it is less reliable. That is, it takes this path unless the root was already #2's preferred parent prior to the ETX increase, in which case, hysteresis might prevent #2 from changing preferred parent. This addresses the network stability concerns expressed by [PERSON] et al. [9,10] in a different way. Their PO proposal was based on OF0 [16], and thus requires a custom stability mechanism that is difficult to enforce consistently. More specifically, in certain situations, they defer metric averaging for the currently preferred parent by one OF call. However, when the OF is called, it varies between operating systems. Since DRiPLOF is based on MRHOF, which has a built-in stability mechanism that is not defined with respect to implementation-dependent events, we do not introduce such inconsistencies.
## 2 Modeling of the DC Sub-Microgrid
### Modeling of Energy Storage System (ESS)  
Figure 4 represents the circuit diagram of the battery connected with the DC-link bus via a DC-DC buck-boost converter. The circuit of the buck-boost converter is comprised of an inductor \(L_{bat}\), the filter capacitor \(C_{dc}\), and two IGBT switches, \(S_{3}\) and \(S_{4}\). Both the switches are controlled through PWM applied at their gate terminals. The buck-boost converter is connected with the battery because it can operate for charging and discharging  
Figure 3: PV energy system configuration.  
mode depending upon the load demand and state of charge (SoC). When \(S_{3}\) is ON and \(S_{4}\) is OFF, the converter will be in boost mode, and discharging (\(I_{batter}>0\)) happens to provide the required energy to the DC link bus. When \(S_{4}\) is ON and \(S_{3}\) is OFF, then buck (\(i_{batter}<0\)) operation will occur, and the battery will be charging. Mathematical expression for buck-boost operation can be represented as:  
\[K=\left\{\begin{array}{cc}1,&if\ (i_{batter}>0)\\ 0,&if\ (i_{batter}<0)\end{array}\right. \tag{13}\]  
In Equation (13), \(i_{batter}\) is the reference value of the battery current, generated according to the power demand and SoC controlled through the high-level controller. For the discharging mode of the battery, the converter can be demonstrated through the following equation:  
\[\frac{di_{bat}}{dt}=\frac{V_{bat}}{L_{bat}}-\frac{R_{bat}}{L_{bat}}i_{bat}-(\ 1-\mu_{3})\frac{V_{dc}}{L_{bat}} \tag{14}\]  
\[\frac{dV_{dc}}{dt}=\ \ (1-\mu_{3})\frac{i_{bat}}{C_{dc}}-\frac{i_{bat}^{\prime}}{C _{dc}} \tag{15}\]  
where \(i_{bat}^{\prime}\) and \(i_{bat}\) are converter and battery current, respectively, and \(V_{dc}\) and \(V_{bat}\) are DC-link bus and battery voltages. For the charging mode of the battery, the converter can be determined by the following equations:  
\[\frac{di_{bat}}{dt}=\frac{V_{bat}}{L_{bat}}-\frac{R_{bat}}{L_{bat}}i_{bat}-(\ \mu_{4})\frac{V_{dc}}{L_{bat}} \tag{16}\]  
\[\frac{dV_{dc}}{dt}=\ \ (\mu_{4})\frac{i_{bat}}{C_{dc}}-\frac{i_{bat}^{\prime}}{C _{dc}} \tag{17}\]  
A virtual control represents the simplification of the system by the following equation:  
\[\mu_{34}=[K\ (1-\mu_{3})+(1-K)\mu_{4}] \tag{18}\]  
where, \(\mu_{34}\) is a virtual control law for battery system. By using Equation (18), buck-boost converter model for the battery can be simplified by the following two equations:  
\[\frac{di_{bat}}{dt}=\frac{V_{bat}}{L_{bat}}-\frac{R_{bat}}{L_{bat}}\ i_{bat}-\mu _{34}\frac{V_{dc}}{L_{bat}} \tag{19}\]  
\[\frac{dV_{dc}}{dt}=(\mu_{34})\frac{i_{bat}}{C_{dc}}-\frac{i_{bat}^{\prime}}{C _{dc}} \tag{20}\]  
Figure 4: Battery bank configuration.
## 4 Establishment of Vehicle-Turnout Coupled Dynamic Model  
### Vehicle Model  
The vehicle with the most severe acceleration exceeding the standard was chosen based on survey results from Section 2, and a corresponding vehicle system dynamic model was created (see Figure 10). One carbody, two bogie frames, eight axle boxes, and four wheelsets make up the dynamic model's 50 degrees of freedom. Each of these components has six degrees of freedom, with the exception of the axle boxes, which have one rotational degree of freedom with respect to the wheelsets. The primary suspension links the wheelsets to the bogie frames, while the secondary suspension links the carbody to the bogie frames. Nonlinear characteristics like the suspension force element and lateral stopping block are fully taken into account during the modeling process. The pivot joint is simulated by a special force element, and the secondary air spring is simulated by a spring-damping force element. The nonlinear wheel-rail creep characteristics and the nonlinear wheel-rail contact geometric relationship are fully taken into account by the vehicle model.  
The vehicle's dynamic equilibrium equations may be written in the following general form:  
\[\mathbf{M}_{v}\mathbf{\ddot{X}}_{v}+\mathbf{C}_{v}\mathbf{\dot{X}}_{v}+\mathbf{K}_{v}\mathbf{X}_{v}= \mathbf{F}_{uv} \tag{1}\]  
where \(\mathbf{X}_{v},~{}\mathbf{\dot{X}}_{v},~{}\mathbf{\ddot{X}}_{v}\) represent the displacement, velocity, and acceleration vectors of the vehicle, respectively. \(\mathbf{M}_{v}\) is the mass matrix of the vehicle; \(\mathbf{C}_{v}\) and \(\mathbf{K}_{v}\) are the damping and stiffness matrices, respectively; \(\mathbf{X}_{v}\) is the displacement vector of train components; and \(\mathbf{F}_{uv}\) is the vector of the nonlinear wheel-rail forces.  
Figure 9: Turnout wheel–rail contact points distribution before and after grinding: (**a**) front panel; (**b**) switch panel; (**c**) closure panel; (**d**) crossing panel; (**e**) rear panel.
###### Abstract  
The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures.  
structural health monitoring; acoustic emission; empirical wavelet transform +
Footnote †: journal: Physics Letters A
# Modular Spiral Heat Exchanger Thermal Modelling
## 3 Results and Discussion  
### One-Module SPHE  
The variation in the SPHE effectiveness, as predicted by the present model, on NTU was compared to results published for other studies, as shown in Figure 4. The SPHE geometry used in the work of [PERSON] et al. [9], with \(Nt=8.75\), was used for comparison.  
The presented effectiveness variation was in good agreement (a difference less than 0.6%) with results obtained by the model of [PERSON] and [PERSON] [5] across the whole range of NTU values studied. There was visibly lower effectiveness observed in this investigation compared to the study of [PERSON] et al. [9], probably due to a difference in the geometry configuration.  
It should be noted that there was a difference in \(\pi\) in the angular offset of the stream inlets and outlets in the two geometries. However, a noticeable shift in both the optimal NTU value and the maximal effectiveness was evident by comparison to the model of [PERSON] [12].  
The maximal value of effectiveness, as well as the optimal NTU value, were predicted very closely in the present investigation and using the model of [PERSON], [PERSON] [5]. There was, however, change in the slope of the effectiveness curve between the two models in the upper range of the studied NTU. There was another local minimum in the \(NTU-\epsilon\) curve, especially for small numbers of \(N_{t}\). It is inferred that the effectiveness curve asymptotically approaches a value of 1 at increased NTU values, as is obvious for smaller NT values in Figure 5. The model shows that a two-module SPHE with \(N_{t}=2.375\) shifts the maximum effectiveness, as well as the \(NTU_{o}\), to a higher level compared to a one-module SPHE with \(N_{t}=3.625\), having the same total area.  
This behavior was due to the increasing effect of the purely counterflow heat exchange manifest in the central core and the periphery of the SPHE model investigated. The same explanation for the behavior was assumed by [PERSON] [12].  
The present model was able to represent the features for maximal effectiveness for finite, optimal values of NTU, characteristic of plate-type heat exchangers, including SPHEs. These features have been overlooked by SPHE designers for a long time, with [PERSON] et al. [9] being among the first researchers to notice them. However, a correlation-based model proposed by [PERSON] et al. [15] and [PERSON] [16] was not able to capture the behavior. The effectiveness parameter continuously increased with increasing NTU in their model.  
Figure 4: Prediction of the effectiveness parameter for a one-module (IM) SPHE in comparison to results from the literature.  
The maximal effectiveness of an SPHE is linked to the relative temperature level of the cold stream and the temperature in the two hot streams surrounding the cold stream. The temperature variation for the proposed model with positions along the channel are given by the turn number, as presented in Figure 6 for three values of NTU. The predicted variation was in agreement with the explanation provided by [PERSON] et al. [9].
# MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey
## 1 Introduction
In this article, a detailed survey about WMSNs for underground coalmine is conduct to discuss the main research challenges and the state of the art supporting technologies for the development of WMSNs for coalmine. The main purposes of our paper are twofold: to make a summary of the application status of WSNs and WMSNs in the underground coalmine, and to present a framework and related open issues for designing underground coalmine WMSNs based on MB-OFDM-UWB scheme. Since the MB-OFDM-UWB scheme has great potentials to promote the performance of the existing monitoring and communication system, we believe that our work can serve as a point of inspiration for readers wishing to start researching into this area or researchers who try to design innovative emergency communication and monitoring system. The rest of this article is organized as follows. Section 2 introduces the overall design of WMSNs for underground coalmine. Section 3 discusses the key supporting technologies for WMSNs. After that, other research issues of WMSNs in coalmine scene are presented in Section 4. At last, Section 5 concludes this work.
## 4 Discussion  
### The Importance of Evapotranspiration  
Before conducting the formal experiment in this study, a preliminary experiment was conducted using the SPI to define drought. However, the experimental results indicated that using the SPI based solely on precipitation failed to effectively capture the sensitivity changes in GPP in both the peak rainy season and the early dry season (Figure S1). In the peak rainy season, the vulnerability of GPP to short-duration drought scenarios was also not ideal (Figure S2). The average annual precipitation of the research area is 1607 mm, mainly in the rainy season from July to September, accounting for more than 70% of the precipitation for the year [32]. However, droughs still occur, mainly due to the significant evapotranspiration caused by strong radiation, which may be why the SPI cannot capture droughs.  
Although precipitation is one of the important factors affecting the initiation season duration and intensity of droughs [49; 50], global warming has become an indisputable fact. Given that the temperature may continue to rise in the future, the influence of temperature on drought events cannot be ignored [51].  
The temperature acts as a determining factor for potential evapotranspiration. It has been proven that strong evapotranspiration caused by high temperatures greatly reduces precipitation, and its drying efficiency is equivalent to that caused by a lack of precipitation [52]. Extreme temperatures cause obvious damage to both natural and artificial ecosystems and significantly increase evapotranspiration rates and water stress [53].  
The SPI does not capture droughs well during the rainy season, a fact corroborated by present research [54]. This may be due to the drought occurring in the rainy season, and the GPP loss is not obvious. Whether in Xishuangbanna, which serves as the study area in the present research, or at the Danzhou site in this study, precipitation during the rainy season significantly exceeds the water demand of the vegetation. When precipitation decreases abruptly during the rainy season, vegetation can still utilize the remaining precipitation andsoil moisture to sustain its growth without experiencing significant water stress. However, high temperatures directly influence potential evapotranspiration. Intense evaporation and transpiration leads to a significant loss of soil moisture and water stored within vegetation. The insufficiency of available water resources for vegetation consequently affects vegetation productivity.
# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Model Feature Variables
#### 3.1.1 Airport and Aircraft Operational Features  
Table 1 lists several common aircraft and airport operational features, including categorical features such as runway configuration mode, airline category, aircraft type, and operating hours; numerical features such as aircraft taxiing distance, turning angle/count of turns; and binary variables such as arrival or departure.
## 4 Intraoral Molar Distalization Methods
### Distalization with Clear Aligner Systems  
In recent years, aesthetics has become a significant requirement in orthodontic treatments due to the increasing number of adult orthodontic patients. Clear aligner technology was developed to meet this demand and has become highly popular. Today, there are numerous clear aligner manufacturers. Effective molar distalization can be achieved with clear aligner systems. [PERSON] et al. [67] reported an 87% success rate for maxillary molar distalization with clear aligners, while [PERSON] et al. [68] also noted that maxillary molar distalization with clear aligners is among the most reliable movements, with an 88% success rate. When greater distalization is needed beyond the movement capacity of aligners alone, auxiliary elements should be added, as aligners may not suffice independently. Attachments, Class II elastics, and TAD support are examples of such auxiliaries.  
[PERSON] et al. [69] conducted a retrospective study evaluating the effects of bilateral maxillary molar distalization with Class II elastics and attachment support in 20 adult patients. The average treatment duration was found to be 24.3 \(\pm\) 4.2 months, with the first molars distalized by an average of 2.25 mm without tipping or vertical movement.  
Figure 12 illustrates the use of Class II elastics with skeletal anchorage support for distalization with clear aligners.
# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 2 Materials and Methods
### Data Processing Methodology
#### 2.3.1 Required Input Data  
The AquaCrop model requires climatic parameters, crop information, soil characteristics, field management, and irrigation [22]. It is essential to emphasize that this model comprises a comprehensive set of input parameters, allowing the adjustment of relevant factors to the soil type in the JECAM area and the type of cultivation, particularly concerning meteorological conditions. Weather, crop, soil, and test area data were prepared in accordance with AquaCrop input data requirements.  
Figure 2: Schematic representation of AquaCrop model workflow and data interactions in the study.