esa-sceva/satcom-chunk-collection huggingface dataset:
# Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions
## 3 Technology Enablers for Future Control Architecture
### Tsn
Based on the two scenarios described above, one can identify the need for a technology migration path in order to achieve the desired market penetration to justify technology investments. In fact, the transition from the de-facto hierarchical system architecture is essential for business and technology success. Standardization of communication protocols used in hierarchical industrial systems is a critical issue. Today's control systems widely use Profinet and OPC Unified Architecture (OPC UA) to meet the communication requirements of different levels. OPC UA has its strengths in vertical communication between devices of different levels and controller-to-controller communication at the control level. At the same time, Profinet meets all the communication requirements in the field network. Today's network only allows Profinet as the real-time capable protocol (besides TCP/IP-based traffic). TSN will make it possible to converge different communication means by simultaneously running multiple real-time-capable protocols in a single convergent network. The IEC/IEEE 60802 TSN Industry Automation profile proposes flattening of networks so that Profinet and OPC UA to operate on the same network physical layer along with IT data communication such as cloud and video. With TSN integration, the field-level device data and diagnostic information can be collected by controllers from the field network using Profinet over TSN. OPC UA over TSN can deliver the aggregated information to higher-level systems such as ERP, MES and cloud to make informed decisions. To achieve a flattened network architecture, the network infrastructure needs to cope with the aggregated traffic while preserving the requirement of each traffic flow. As the primary goal of process industries is to produce goods, all the functions are required to operate flawlessly with high availability. If one function fails, the most common scenario is that the process cannot continue to operate. This implies that the requirements and characteristics of different types of traffic flows needs to be studied in relation to other traffic. Furthermore, a high-level classification of various traffic flows can be done by grouping them into different traffic types. The traffic types can then be mapped to network functionality that enables the desired characteristics.
## 4 FE Analysis and Result
### Load, Boundary Conditions, Interactions, and Constraint Conditions  
As depicted in Figure 16, the analysis was performed using 1/4 model of the actual push-out specimens. Therefore, axisymmetric boundary conditions were used on the Y- and Z-axes, as shown in Figure 16a. For the boundary conditions, multi-point constraint conditions were applied to the entire bottom surface of the concrete, which is the supporting surface of the specimen, separately from the axisymmetric boundary conditions, and all of the degrees of freedom of the standard node were confined. For the load, to minimize the strain rate caused by the loading rate, a rigid body was modeled at the top of the steel beam, and displacement control (U1) was performed in parallel to the X-axis, where the shear connector was located. The displacement control was increased using a smooth amplitude function. Based on the actual experimental results, different values were used for the maximum analysis displacement of each analysis model.  
Because the push-out specimens contained connections between heterogenous materials, it was necessary to perform contact analysis on the contact surfaces where the members formed the connections. Therefore, contact interactions were used for all the slip surfaces between members. The contact surfaces were defined as the steel beam and concrete slab, stud bolt and nut (or embedded nut), steel beam top surface, tapering surface of the Y-shaped stud bolt, stud (head and column) surface, concrete internal stud embedded surface, etc., and each contact surface was set as a surface-to-surface interface. Table 6 summarizes the contact properties of the contact surface interactions of each analysis model. A friction coefficient of 0.3 was used for the contact surfaces (H beam-nut and bolt-nut thread) caused by the bolt-nut fastening in all models, and that of 0.14 was used for the other contact surfaces. However, in the case of the S1 (welded stud) model, when a friction coefficient of 0.14 was used on the stud and concrete contact surface, the strength was greatly decreased  
Figure 16: Constrain, interaction surfaces, and boundary conditions (BC): (**a**) boundary conditions and constraints; (**b**) surfaces in contact interaction.  
compared to the experimental results in the plastic region after yielding; therefore, a friction coefficient of 0.3 was used. For the other models, using 0.14 produced results that were similar to the actual experimental results. The reason for this result is thought to be because threads were machined on the stud bolts used in models S2-S4, and the reduction in strength after yielding was large compared to that in model S1, which involved without any thread machining; therefore, using a friction coefficient of 0.14 produced similar results.  
Regarding the interaction, the penalty contact method was used for the mechanical constraint formulation in all cases, and finite sliding was utilized for the sliding formulation. Table 6 summarizes the tangent and normal values of the contact properties.
# A Review: Factors Affecting Internal Two-Phase Flow-Induced Vibrations
## 1 Introduction
### Two-Phase External Flow
#### 1.1.3 Two-Phase Internal Flow  
Two-phase internal flow is the most common type of flow in oil and gas, chemical industries, and numerous engineering systems. In this flow, fluid flows inside the pipe as shown in Figure 3c. FIV in two-phase internal flow has attracted considerable attention due to high instabilities and complex interactions at interface. Flow turning elements causing a sudden momentum change and pressure fields change are the main reason for FIV in internal two-phase flow [45; 46; 47; 48; 49; 50; 51; 52]. Different factors affecting FIV in internal two-phase flow, such as geometry, flow pattern, velocity, and void fraction, are discussed in detail in the next section. Various correlations are developed over time to calculate flow-induced vibrations in different geometries, which are further explained in Section 4. These correlations mainly focus on determining dimensionless forces and dominant frequency. Computational fluid dynamics methods are instrumental in FIV research and many scholars [53; 54; 55; 56] have clearly verified the use of VOF model to handle multiphase flow patterns and turbulence inside the pipe. With the development of computational methods, many researchers have implemented one-way and two-way FSI models to study fluid-structure interactions in pipes with bends [57; 58; 59; 60; 61]. Moreover, different studies have been conducted to study the displacement due to vibration.  
[PERSON] et al. [62] published a review paper which explains FIV in internal two-phase flow. However, the authors were more focused on developing a correlation to determine the forces on bend. In contrast, the present review paper discusses various parameters affecting the magnitude and frequency of the forces causing these vibrations, in addition to the inclusion of the most recent literature. Moreover, the relationship between different influencing parameters and dominant frequency of forces is discussed. A detailed comparison of dimensionless forces against Weber number is performed, which is helpful in comparing the major works performed in this field. These data can be used to easily validate the accuracy of CFD results with previous experimental results. Different correlation models developed in the past are collected and discussed, along with up-to-date models presented in the literature. An overview of the work performed on two-phase internal flow-induced vibrations is listed in Section 4.
# Optimization of Cellular Automata Model for Moving Bottlenecks in Urban Roads
## 3 Simulation Setup and Result Analysis
### Analysis of the Impact of Heavy-Duty Vehicles on Traffic Flow
#### 3.2.3 Impact of the Proportion of Heavy-Duty Vehicles on Vehicle Speed  
From Figure 8, it can be observed that the average vehicle speed is maximized when only small cars are present in the traffic flow, while it is minimized when the proportion of heavy-duty trucks is 0.4. As the input traffic density increases, the average speed of the road network's traffic flow gradually decreases, but the rate of decline gradually flattens out. When the traffic flow density (k) of the road network is less than 80 vehicles per kilometer (veh/km), the proportion of heavy-duty vehicles has a significant impact on the average speed of the traffic flow. With a constant traffic flow density, the average speed decreases as the proportion of heavy-duty trucks increases. However, when the  
Figure 6: **(a,b)** Spatial-temporal operation diagram when the proportion of heavy-duty vehicles is 0.3.  
traffic flow density (k) exceeds 80 vehicles per kilometer (veh/km), the influence of the proportion of heavy-duty vehicles on the average speed becomes less pronounced. These results indicate that when the traffic density is low, the introduction of heavy-duty vehicles restricts the speed of following vehicles in the same lane, forcing them to decelerate, follow at a distance, or change lanes to maintain a safe distance between vehicles. At this point, the moving bottleneck effect is evident. However, as the traffic density increases and road traffic approaches saturation, the impact of heavy-duty vehicles on traffic flow diminishes, and the moving bottleneck effect weakens.  
3.2.4. Impact of Heavy-Duty Vehicle Proportion on Lane-Changing Rate  
From Figure 9, it can be observed that when the density \(\rho\) is less than or equal to 45 vehicles per kilometer (veh/km), under the condition of constant traffic density, the lane-changing rate of small cars increases as the proportion of heavy-duty vehicles increases. This is due to the fact that a higher proportion of heavy-duty vehicles creates a more significant moving bottleneck effect, which subsequently increases the lane-changing demand for small cars. Additionally, in low-density conditions, the road space does not undergo significant changes with the increase in the number of heavy-duty vehicles, allowing small cars, even after being affected by heavy-duty vehicles, to still have sufficient space for lane changing. When \(\rho\) is greater than 45 vehicles per kilometer (veh/km), under the condition of constant traffic density, the lane-changing rate of small cars decreases as the proportion of heavy-duty vehicles increases. This is because when the traffic density is sufficiently high, as the proportion of heavy-duty vehicles increases, the space they occupy becomes larger and larger, causing the gaps between vehicles on the road to shrink, which in turn makes it increasingly difficult for small cars to change lanes.
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 2 Collaborative Channel Perception Algorithm
### Problem Description  
In a data link network, nodes employ a distributed and decentralized network topology to count the number of traffic pulses. The aim is to address the shortcomings of the local channel perception, thus overcoming the dependence on the channel perception capability of single-node devices and enhancing the accuracy and robustness of channel state perception. In the collaborative channel perception, each node perceives the channel state independently to obtain perceived data. These data are then fused and calculated into a quantitative value for the channel state, either through equal gain combining or weighted combining. This enhances the accuracy of channel state perception and provides a basis  
Figure 1: Network architecture for channel state perception of data link.  
for decision making when data link messages access the channel. However, the existing data fusion weight allocation in collaborative channel perception is unreasonable as it sets weight factors solely based on distance factors. This mismatch between weight allocation and the actual channel perception capability of node devices can easily result in significant deviations between the channel perception results and the actual channel state.  
When perceiving channel state in a data link network, the channel occupancy statistics are typically calculated based on the number of traffic pulses in the channel counted by node devices. The capability of a node device to count the number of these pulses is not only influenced by distance factors, but also closely related to other factors such as the device's receiving and transmitting performance, component aging, and operational reliability. Any changes in these factors can significantly impact the node device's capability to accurately count channel traffic pulses, thereby affecting the perception results of the channel state.  
Therefore, in the collaborative channel perception, it is essential to scientifically evaluate the perception capability of each node device and assign node weights based on these capabilities. Thus, the data fusion center can enhance the credibility of channel state perception during the fusion and calculation of channel perception results. The results can ensure a more accurate reflection of the true channel state of the current data link network, providing a reliable basis for decision making in data link message transmission.
# Simulation and Optimization of Available Local Feed Resources for Dairy Cattle in Burkina Faso
## 3 Results  
### Feed Ingredient Resources Used by Peri-Urban Farms of Ouagadougou  
The screening of local feed used in the dairy cattle feeding system showed two types of ingredients: the forage as the feed base, in which there are ligneous species (_P. reticulatum_ pod), _grass species_ (_A. gayanus, P. pedicellatum, B. ruiziziensis, P. maximum, Maralfalfa_ sp., _M. sativa_), and crop residue products (maize and sorghum silages, rice, maize, sorghum straw, and soy bean hay). Among those species, sorghum stem and _P. pedicellatum_ straw are the most dominant forages for dairy cattle with percentages of 84.85% and 90.91%, respectively. The second group of ingredients includes cottonseed cake, soya bean cake, maize bran,maize flour, sugar molasses, wheat bran, sorghum bran, maize, and sorghum brewer grain, SOFAB (livestock feed production company in French, Ouagadougou, Burkina Faso) concentrates. Maize bran, cottonseed cake, and maize flour were the most dominant concentrates used in the dairy cattle feeding system at percentages of 32%, 26%, and 22%, respectively.
## 6 Testing and Measuring Performance: Analysis of PSO, GWO, and Hybrid PSO-GWO Methods
### Results
#### 6.4.6 Best Objective Function Value  
The bar graph below compares the best objective function values obtained by the PSO, GWO, and hybrid PSO-GWO methods.  
Results: The best objective function values are 0.99888 for PSO, 0.99733 for GWO, and 0.999145 for the hybrid PSO-GWO method. The hybrid method returns the highest objective function value, making it the optimal solution among the three methods for enhancing our control of traffic lights. Figure 8 shows the best objective function results of the three methods.  
Figure 8: Best objective function results of the three proposed methods.  
Figure 7: Throughput improvement for the three proposed methods.
# Performance Analysis of Ambient Backscatter NOMA Systems
## 3 OP Analysis
### OP Expressions for the \(d\)-th User  
An outage event happens if the \(d\)-th user is unable to detect the previous \(q\)-th data signal. As a result, the OP expression of the \(d\)-th user in the case of ipSIC in the AmBC-NOMA systems can be represented as  
\[P_{d}=1-\Pr\{\gamma_{d\to 1}>\gamma_{th1},\cdots,\gamma_{d\to d}>\gamma_{thd}\}, \tag{8}\]  
where \(R_{q}\) is the threshold rate of the \(d\)-th user, with \(\gamma_{thq}=2^{R_{q}}-1\), and there is no ipSIC case (i.e., no self-interference) in (8), because the first user does not operate the SIC operations.  
Theorem 3.1: _The closed-form OP expression of the \(d\)-th user in the case of ipSIC for AmBC-NOMA systems is given by_  
\[P_{d}^{ipSIC}=\phi_{d}\sum_{l=0}^{D-d}\binom{D-d}{l}\frac{(-1)^{l}}{d+l}\left[ \sum_{r=1}^{d+1}C_{d+l}^{r}(-1)^{r+1}\times e^{-\frac{\Psi_{d}^{2}r^{\prime} }{\gamma_{bd}}}Q_{d}e^{Q_{d}}\mathrm{Ei}(-Q_{d})\frac{1}{\lambda_{hl}\xi_{1}} +1\right], \tag{9}\]  
_where \(\phi_{d}=\frac{D^{\mathrm{tr}}}{(D-d)!(D-1)!}\), \(\psi_{d}^{*}=\max\{\psi_{1},\cdots,\psi_{d}\}\) and \(\psi_{d}=\frac{r_{thd}}{\rho(a_{d}-r_{thd}\sum_{l=0+1}^{D}a_{i}+\kappa_{sd}^{2})}\), \(\psi_{D}=\frac{r_{thd}}{\rho(a_{d}+\kappa_{D}^{2})}\), with \(a_{d}>r_{thd}(\sum_{i=q+1}^{D}a_{i}+\kappa_{sd}^{2})\), \(r_{thd}=2^{R_{d}}-1\) with \(R_{d}\) to detect the goal rate at the d-th user. \(\zeta_{1}=\frac{\psi_{d}^{2}\mu_{0}\lambda_{kl}r+\lambda_{kl}}{\lambda_{kl}\lambda _{kl}\lambda_{kl}}\), \(Q_{d}=\frac{\lambda_{kl}(a_{d}-rt_{kl}\sum_{l=q+1}^{D}a_{l}+a_{sd}^{2})}{\lambda_{ jl}\lambda_{jl}\beta[\beta^{2}\left(1+x_{sd}^{2}\right)r_{th}r}\). \(\mathrm{Ei}(x)\) is a function of exponential integral and \(\mathrm{Ei}(x)=\int_{-\infty}^{x}\frac{e^{t}}{t}dt\)[39], where \(t\) denotes the variable._  
Proof.: Refer to Appendix A.  
Corollary 1: _For \(\varpi=0\), the closed-form OP expression of the d-th user in the case of pSIC for AmBC-NOMA systems is written as_  
\[P_{d}^{pSIC}=\phi_{d}\sum_{l=0}^{D-d}\binom{D-d}{l}\frac{(-1)^{l}}{d+l}\sum_{ r=1}^{d+l}C_{d+l}^{r}\times(-1)^{r+1}e^{-\frac{\psi_{d}^{2}r}{\lambda_{kl}}}Q_{d}e^{Q_ {d}}\mathrm{Ei}(-Q_{d})+1\Bigg{]}. \tag{10}\]  
For AmBC-OMA systems, the outage event appears when the instantaneous SNR is below the threshold SNR. The OP of the \(d\)-th user is defined as  
\[P_{d}^{OMA}=\mathrm{Pr}\Big{\{}\gamma_{d}^{OMA}<\gamma_{th}^{OMA}\Big{\}}, \tag{11}\]  
where \(rth_{d}^{OMA}=2^{R_{OMA}}-1\), with \(R_{OMA}\) being the threshold rate of the user. Based on the following inference, by removing the order operation, we can get the closed-form OP expression in the AmBC-OMA systems.  
Corollary 2: _The closed-form OP expression for AmBC-OMA systems is given by_  
\[P_{d}^{OMA}=1+e^{-\frac{m_{d}^{OMA}}{\rho_{hd}^{2}}}Q_{d}^{OMA}e^{Q_{d}^{OMA}} \mathrm{Ei}\left(-Q_{d}^{OMA}\right), \tag{12}\]  
_where \(Q_{d}^{OMA}=\frac{\lambda_{kl}}{\lambda_{jl}\lambda_{jl}\mu_{d}^{OMA}|\beta[ \beta^{2}]}\)._
## 4 Characteristics of Different Time Distribution Systems
### Network Time Protocol
#### 4.1.1 Time Distribution Mechanism  
At the network level, NTP operates within a hierarchical structure of participating servers [75]. The client, acting as the initial sender, periodically exchanges messages with the NTP server using a poll interval that gradually increases. Each NTP message includes the three most recent timestamps from exchanges between the two nodes. Once the message returns to the sender, a fourth timestamp is added, enabling the calculation of the round trip time and clock offset [19]. The synchronization algorithm is employed regularly and multiple times in quick succession for enhanced accuracy and reliability.  
The measured offset is utilized to smoothly adjust the client clock through a control loop. Additionally, the client verifies the first timestamp in each incoming response, which should correspond to the transmission timestamp from the previous request, allowing it to detect packet loss or out-of-order delivery [19].  
Each NTP daemon can be configured to utilize multiple independent reference time sources. It periodically polls these sources to evaluate and classify them as reliable or unreliable. This process enables the daemon to select a new system peer if the current peer becomes unavailable [18].
## 2 State of the Art
### Methods for Optimal Layout Problems
#### 2.1.3 Metaheuristics  
Metaheuristics are problem-independent techniques that aim at exploring the search space more than heuristic methods and, thus, often provide more interesting solutions. Consequently, during the past 20 years, mostly metaheuristic algorithms have been used in order to solve optimal layout problems.  
In [75], [PERSON] et al. reviewed methods that have been applied to facility optimal layout problems, which are mainly metaheuristic techniques. Among them, Simulated Annealing (SA) [37; 38; 39; 40], Tabu Search (TS) [41; 42; 43], Variable Neighborhood Search (VNS) [76; 77; 78], Genetic Algorithms (GA) [15; 18; 79; 80], Particle Swarm Optimization (PSO) [48; 49], Differential Evolution [11; 81], Covariance Matrix Adaptation-Evolution Strategy [47; 50], Ant Colony Optimization [82; 83], Scatter Search [84; 85] and Quality-Diversity algorithms [86; 87; 88] have been successfully applied to various optimal layout problems.
## 5 Experiments and Discussion
### Experimental Datasets and Settings
#### 5.1.3 Experimental Setup  
The environmental configurations of all experiments in this study were as follows: Intel Core i9 CPU, 64 GB of RAM, GTX-3090 GPU.  
The experimental environments and parameters of all compared methods in this study were set according to the original reports. We used the PyTorch framework to implement our models. The model parameters were optimized and updated using the Adam optimizer. In our model, we used pre-trained BERT models (bert-base-uncased for English and bert-base-Chinese for Chinese) to initialize the vector representations of text information. We employed accuracy (A), precision (P), recall (R), and F1 as assessment indicators. Model hyperparameter details are provided in Table 15 in Appendix C.
## 5 Existing Security Mechanisms
### Secure Routing  
In home care or disaster scenarios sensor devices might require sending their data to other devices outside their immediate radio range [44]. Therefore, routing and message forwarding is a crucial service for end-to-end communication. So far, numerous of routing protocols have been proposed for sensor networks, but none of them have been designed with strong security as a goal [59,86]. [PERSON] [61] discussed the fact that routing protocols suffer from many security vulnerabilities, such as an attacker might launch denial-of-service attacks on the routing protocol. An attacker could also inject malicious routing information into the network, resulting in inconsistencies in the routing. Further, most of current proposals are designed for static wireless sensor networks but mobility has not been taken under consideration, whereas healthcare applications require mobility supported routing protocols. In addition, designing secure routing protocols for mobile networks is a complex task and current WMSNs healthcare security requirements will make it more complex when they become real-time applications.