esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Architecture
When the source node is in possession of the Container packets sent by each of the Announcement Packets, it broadcasts to the neighbors (AC). This particular kind of packet is utilized in the process of the concealed and ultimately fatal problem, which will be covered later in this section. The Announcement Packet follows this format for its contents: \(Announcement_{packet}=(id_{(highest_{\mathit{privity}})},x,y,z,\mathit{Type}_{packet})\), in which \(id_{(highest_{\mathit{privity}})}\),x, y, and z stand for the id and coordinates of the node with the highest priority; \(\mathit{Type}_{packet}\) represents the type of packet, i.e., the announcement packet, in the current case; and the final type of packet is called a Data Packet, and it contains the actual data or information that must be transmitted to the centralized station. Data Packet refers to the format that Data Packet uses--\(Data_{Packet}=(Header,\mathit{Payload},\mathit{Type}_{packet})\). The header of the packet includes information regarding the node that generated the packet, as well as the centralized station. The payload of the data packet is the most important component, as it contains the data that actually pertains to the environment, and \(\mathit{Type}_{packet}\) represents the packet type. In the event that the preceding packets of data are transmitted at frequent but brief intervals, then increased network overhead and power consumption can be anticipated. Because of this, in order to circumvent this issue, every node maintains its own corresponding neighbor table, for the purpose of keeping a record of their fellow community members: \(\mathit{Neighbor}_{table}=(id,\mathit{residual}_{\mathit{energy}},d, \mathit{Timer}_{\mathit{value}},\mathit{Update}_{time})\), where id is neighbor node unique number, \(\mathit{residual}_{\mathit{energy}}\) is the residual energy of the node, d represents the Depth information, \(\mathit{Timer}_{\mathit{value}}\) is the timer value of the node, and \(\mathit{Update}_{time}\) stands for the time required to update the neighbor entry. In the meantime, when there is sufficient time for the neighboring nodes to be updated, the source node will send out and receive the second packet. Then, the source will immediately broadcast the Data Packet, as well as the Announcement Packet that was contained in the prior table.
## 3 Drone Swarms as Networked Control Systems
### Interactive Deployment Strategy
#### 3.3.2 Major Technologies  
To model a drone swarm as NCS, it is necessary to address the communication requirements capable of guaranteeing maximum system performance over an airborne wireless network. From a networking point of view, challenges of transmission failures, delays, fading channels and message errors may lead to degradation of the overall system performance.  
It is essential to rely on wireless technology capable of connecting all drones together through effective mechanisms to allow for an effective interaction between drones [37]. Several technologies can be used to deploy an interactive strategy, including IEEE 802.11 [38], 3G/LTE [39], and satellite communications [40]. However, an alternative networking solution should be further investigated, since due to the mobility and power limitation of drones, the networking system needs to be able to operate even in the presence of irregular connection and device challenges, while still providing the needed delay and throughput requirements of several services. Moreover, wireless reliability and stability are key for the operation of swarms, but notably the communication protocols designed for close-loop control systems are challenged in providing the needed guarantees.  
Such an alternative networking system should rely on routing protocols capable of providing secure data transmission routes from source to destination. In this context, literature already provides several studies about the different families of routing protocols suitable for drone effective communications. These protocols include Swam Intelligence-based protocols, Position-based routing protocols and Topology-based routing protocols [6].  
In an interactive deployment strategy, computational resources are dispersed in different drones, as well as in the ground control station. This means that several computing tasks need to be designated to create a suitable decision-making process, namely to decide where and how the decisions are made. First, the coordination of computational tasks depends on the requirements of each one of them. For example, while simple flight control can be guaranteed by the controller on-board on each drone, more computing intensive tasks, such as image recognition may need to be transferred to the ground control, or to a set of drones that together may provide the needed resources. Moreover, the coordination of computational tasks may be done based on the exchanged information between drones, while avoiding links that may impact on the effective performance of the drone swarm.
# Application of EDEM Simulation for Calculating and Optimizing a Closed Coal Fly Ash Screw Conveyor
## 3 Results and Discussion  
### Created Model Screw Conveyor and Actual Test  
Experimental Method: Fly ash will be fed into the screw conveyor via a rotary valve with a capacity of approximately 30 kg/s. The screw conveyor will be operated at speeds of 300 rpm, 500 rpm, and 700 rpm by adjusting the frequency of the variable frequency drive through the speed sensor of the screw conveyor shaft. The actual testing system is illustrated in Figure 18. The experimental results are recorded and presented in Table 4. As a result of this operation, fly ash will be transported to the end of the screw conveyor at a specific rate. The output of fly ash will be directed into a silo with a total volume of 35 m\({}^{3}\). The time taken to completely fill the silo was recorded and is presented in the table below.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & **Speed** & **Mass** & **Time** & **Capacity of Screw** & **Capacity of Screw** \\  & **(rpm)** & **(ton)** & **(min)** & **Conveyor (m\({}^{3}\)/h)** & **Conveyor (ton/h)** \\ \hline
1.1 & 300 & 35 & 33 & 63.6 & 56.3 \\
1.2 & 300 & 35 & 33 & 63.6 & 56.3 \\
1.3 & 300 & 35 & 32 & 65.6 & 58.0 \\
1.4 & 300 & 35 & 34 & 61.8 & 54.6 \\
1.5 & 300 & 35 & 32 & 65.6 & 58.0 \\
2.1 & 500 & 35 & 28 This method allows for the practical evaluation of the screw conveyor's performance in handling and transporting fly ash. Based on the data provided in Table 4, the average capacity of the screw conveyor is calculated to be 68.3 ton/hour at the normal operating speed of 500 rpm. Similarly, at speeds of 300 rpm and 700 rpm, the corresponding average capacities of the screw conveyor are 56.7 ton/hour and 61.5 ton/hour, respectively. These figures provide a clear understanding of the performance of the screw conveyor at different speeds, which is crucial for optimizing its operation and efficiency.
## 2 Data and Methods
### CSES-01 Satellite Data
[14] discussed the reliability and application of PAP data in 2018 based on the distribution characteristics of ion parameters around the magnetic equator and the observations of ionospheric disturbance phenomena induced by natural sources (e.g., strong EQ and geomagnetic storms) and artificial sources (e.g., ground-based powerful VLF transmitters), which indicated the reliability of the relative variation of PAP data. [PERSON] et al. [21] reported significant ionospheric responses to the intense geomagnetic storm event on 25 August 2018, using observations from PAP and the other payloads onboard CSES-01. After the comprehensive analysis, we suggested that the oxygen ion density (\(No^{+}\)) and ion drift velocity in the vertical direction (\(V_{z}\)) of PAP in 2018 are reliable when studying the relative variations in a short time (not exceeding one month). Meanwhile, the LAP data are considered to have good performance based on comparison with the Swarm constellation and ground-based observations [18]. [PERSON] et al. [15] made a preliminary statistical analysis of the seismic information on ionospheric plasma parameters detected by CSES-01 from August 2018 to November 2019. The results show that the CSES-01 can effectively record strong EQ precursors, and \(No^{+}\) seems more sensitive to seismic activities than _Ne_. This viewpoint needs further investigation with more observation data. In this study, we mainly used \(No^{+}\), \(V_{z}\), _Ne_, and _Te_ to analyze strong EQ precursor anomalies.
## 3 Results  
### Butyrate Strongly Increases Insulin Gene Expression in a Dose- and Time-Dependent Manner  
To investigate the potential role of SCFAs to support beta-cell maturation, we cultured NPICCs from day three after isolation for one to eight days in our regular culture medium (B-IC medium), or in medium supplemented with 100-2000 uM acetate, butyrate and propionate, respectively. The NPICC count, determined as IEQ per g of pancreas (IEQ/g), was 15,880 \(\pm\) 3315 (_n_ = 9) at day three. Treatment with SCFAs did not affect NPICC yield compared to cells cultured in basal medium (day six: acetate 94.5 \(\pm\) 7.6%, butyrate 94.8 \(\pm\) 4.8%, propionate 87.7 \(\pm\) 6.3% of controls). SCFA treatment did not affect cell viability measured by a live/dead assay. As illustrated in Figure 1A,D, there was a significant dose- and time-dependent up-regulation of _INS_ gene expression after incubation with 500-2000 uM butyrate for six days (2.6-4.7-fold) and at 1000 uM butyrate for six to eight days (2.2-4.1-fold). Butyrate treatment also resulted in an increase of _GCG_ (1.5-2.2-fold) and _PDX1_ gene expression (1.8-3.2-fold). This was only significant at a butyrate concentration of 1000 uM for glucagon and at 500 and 1000 uM for Pdx1 for six days. In contrast, exposure to acetate had only a modest effect with an upregulation of insulin gene expression at 1000 uM for two days and propionate did not significantly affect mRNA expression of _INS_, _GCG_ and _PDX1_ (Supplemental Figure S1). Based on the results, butyrate at the concentration of 1000 uM was selected to examine mRNA expression of the endocrine progenitor cell marker NGN3, the beta cell maturation marker _NKX6.1_ and _ALDOB_, a marker of functionally immature beta cells. During butyrate exposure, mRNA expression of _NGN3_ and _ALDOB_ were decreased and _NKX6.1_ gene expression was significantly upregulated, confirming the assumption that butyrate favors maturation of NPICCs toward a beta cell phenotype.
The additional overload reduces the lifespan of these critical nodes and leads to network partitioning [2], usually known as hot spot problem. For wide area phenomena monitoring, path elimination (aka the hot spot problem) not only diminishes the reliability of the network but also reduces the phenomena monitoring which becomes in a unacceptable behaviour. Therefore, this paper presents our advances to customize routing and reliability of deploy and forget WSN considering the trade-off between reliability and energy consumption. On the one hand, at Network layer a generalization of the Collection Tree Protocol (CTP)[3] is presented which enables probabilistic load balancing of routes to the sink. Due to its randomized nature, the proposed protocol is autonomous and decentralized only requiring each node to probabilistically decide the path to the sink. On the other hand, at the Link layer, retransmission for reliability is also introduced. The paper evaluates the combination of both techniques in a real deployment.
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 4 Performance Evaluation  
To verify the validity of the proposed CARA algorithm, MATLAB was used to simulate the experiment. The comparison algorithms included CCOR and TER algorithms. The final results are presented as the average of multiple experiments. During the simulation process, the simulation area is \(100\times 100\) square meters. Then, a certain number of wireless sensor nodes are randomly deployed in the area. Finally, a node transfers self-collected data to the sink located in the center of the simulation region. To simplify the simulation, we assumed that all nodes produced data at the same rate. The specific simulation parameters are shown in Table 1. During the simulation, we modified some parameter values according to our different needs.  
In the performance analysis, we used the following three route equation metrics:  
(1) **The Packet Loss Rate**. This metric represents the ratio of the number of lost packets to the total number of packets sent by the source node. In the WSN, the sink node is the final destination of all packets. Therefore, this metric indicates the data delivery reliability.  
(2) **The Average Hops**. This metric represents the average number of nodes that the source node experiences transmitting packets to the sink, which can to some extent indicate the real-time performance of the network.  
(3) **The Average Energy Consumption**. This metric represents the average energy consumption per round in the whole network. In our simulation, a round means sensor nodes send data packet to the next hop. This metric reflects the energy efficiency of the data transfer.
# Augmented Reality in Precision Farming: Concepts and Applications
## 2 Augmented Reality in Agriculture  
In this section, the focus is on AR applications currently deployed for agricultural use and documented in research articles. Only specific use cases are included, as many papers mention AR as a keyword within the investigation, or part of a wider discussion on emerging technologies (such as Digital Twins for agriculture [19]) to support precision farming, but do not cover specific applications of the AR technologies for a direct use. For example, [PERSON] et al. discuss AR as part of an introduction to the different forms of potential digitalisation within agriculture to produce novel spatial dimension effects, but no specific applications of the technology are discussed [20]. As is the case in [21], where AR is reflected on in a high-level context as a potential solution for merging graphical depictions with real-world objects for assisted decision support. Similarly, [PERSON] et al. outline how the technology could be part of an elevated user interface innovation for digital twin settings [19], but in this case the focus is on wider digital twin technologies rather than applied examples.  
Also, [PERSON] et al., discuss how AR could be integrated into a learning framework to support the digital learning process of students in the agricultural technology domain [22], however education falls outside the scope of this research.  
Finally, consideration is also not given to agricultural articles that are unrelated to actual deployment within an agricultural setting but may have an association with farming as a keyword; for example, within the food supply chain as in [23]. Therefore, in this section, only specific use-cases of the technology are considered. After the snowballing approach was completed, it was evident that all articles found could be classed into two application domains, either (i) crop applications or (ii) livestock; both with unique technological challenges. The section also reflects on technologies that are coupled with AR for the benefit of precision farming, concerning papers published from 2016 onwards, with the search string as follows for selecting the papers for review.  
_(\"Abstract\":Augmented reality) AND ((\"Abstract\":farm\") OR (\"Abstract\":agri\") OR (\"Abstract\":crops) OR (\"Abstract\":livestock) OR (\"Abstract\":cultivation) OR (\"Abstract\":plantation) OR (\"Abstract\":cattle))._
# A Path Recommendation Method Considering Individual Driving Preferences
## 2 Factors Influencing Individual Preferences
### Traveling Properties  
Traveling attributes [24], including the travel purpose, expected duration, and frequency, play a significant role in shaping a driver's travel behavior. These attributes, either independently or in combination, have an impact on path selection and the likelihood of path changes.  
1. Purpose of trip and expected length of trip  
The arrival time of commuting trips is typically fixed and, so, the desired travel time for these drivers is determined by the time difference between their departure time and the desired arrival time. When the expected travel time is lengthy, drivers usually opt for paths based on their regular habits. Conversely, when the expected travel time is short, they prioritize the travel time factor of the path, sometimes overlooking other relevant factors. Furthermore, when selecting paths for medical and business trips, drivers tend to plan ahead and avoid paths with high levels of uncertainty. On the other hand, drivers embarking on leisure and recreation trips do not have strict expectations regarding travel time and display a degree of spontaneity when choosing their paths.  
2. Frequency of trips  
The frequency of trips is indicative of a driver's travel experience and their familiarity with the road network. Drivers who travel more frequently tend to select travel paths based on their established driving habits. Conversely, drivers who travel less frequently tend to rely on external path recommendations, as they may have limited knowledge of the road network and traffic conditions.
## 2 Numerical Model and Experimental Verification  
### Fiber Element Model  
Reinforced concrete is composed of two kinds of materials, concrete and steel bars. The nonlinearity of materials will greatly affect the performance of reinforced concrete. Therefore, the appropriate material constitutive model, hysteretic energy dissipation and element model must be selected for nonlinear dynamic analysis of structures.  
The finite element stiffness method is generally used to solve the elastoplastic problem; it is accurate and efficient. The displacement shape function used by the fiber beam-column element is a typical Hermite cubic interpolation function. The flexibility method is based on the force function corresponding to the stiffness method. When the material possesses a nonlinear curvature distribution, the flexibility method can accurately simulate the mechanical properties and material properties of reinforced concrete members. The fiber element model in the LSDYNA software used in this paper is based on the finite element flexibility method.  
The basic assumptions of LSDYNA [16] fiber element: (1) based on geometric linear small deformation; (2) satisfying the assumption of flat section; (Based on the assumption of flat section, the fiber model divides the member into many fiber bundles in the direction of cross section. Each fiber is uniaxial stressed. According to the assumption of flat section and the stress-strain relationship, the strain of each fiber can be obtained. The unit stiffness matrix is formed by iteration calculation within the unit); (3) the beam unit is divided into multiple finite element sections. Each section maintains the assumption of flat section and maintains a consistent constitutive relationship; (4) shear slip and bond slip are not considered; (5) torque, bending moment, and shear are not coupled with each other.
## 3 Results and Discussion
### Effects of Fiber Ratios on Tensile Strength
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample** & **Solutions** & \(T\) (\({}^{\circ}\)C) & **\(P\)** & **CK** & **NS** & **AV (N)** & **SD (N)** & **Scheff’s Test** (Kevlar®) \\ \hline \multirow{4}{*}{-} & & - & - & 0 & 5 & 105.82 & 9.51 & \multirow{4}{*}{20, 15 \textgreater{} 0} \\  & without & - & - & 5 & 5 & 113.70 & 6.07 & \\  & immersion & - & - & 10 & 5 & 116.32 & 11.53 & 20, 15 \textgreater{} 0 \\  & - & - & 15 & 5 & 117.72 & 11.80 & \\  & & - & - & 20 & 5 & 119.14 & 9.95 & \\ \hline \multirow{4}{*}{\(T_{1}P_{1}\)} & & & & 0 & 0 & 0 & - & \multirow{4}{*}{20, 15, 10, 5 \textgreater{} 0} \\  & & & & 5 & 5 & 0.57 & 0.06 & \\  & NaOH & 23 & 2 & 10 & 5 & 0.93 & 0.15 & \\  & & & & 15 & 5 & 1.40 & 0.20 & \\  & & & & 20 & 5 & 1.20 & 0.26 & \\ \hline \multirow{4}{*}{\(T_{1}P_{3}\)} & & & & 0 & 0 & 0 & - & \multirow{4}{*}{20, 15 \textgreater{} 0} \\  & & & & 5 & 0 & 0 & - & \\  & & & & 5 & 5 & 0.57 & 0.06 & \\  & & & & 10 & 5 & 0.93 & 0.15 & \\  & NaOH & 23 & 2 & 15 & 5 & 1.40 & 0.20 & \\  & & & & 20 & 5 & 1.20 & 0.26 & \\ \hline \multirow{4}{*}{\(T_{2}P_{1}\)} & & & & 0 & 0 & 0 & - & \multirow{4}{*}{20, 15, 10 \textgreater{} 5 \textgreater{} 0} \\  & & & & 5 & 0 & 0 & - & \\ \cline{1-1}  & NaOH & 23 & 3 & 10 & 3 & 0.73 & 0.06 & \\ \cline{1-1}  & & & & 15 & 3 & 1.10 & 0.10 & \\ \cline{1-1}  & & & & 20 & 3 & 1.10 & 0.20 & \\ \hline \multirow{4}{*}{\(T_{2}P_{1}\)} & & & & 0 & 0 & 0 & - & \multirow{4}{*}{20, 15 \textgreater{} 0} \\  & & & & 5 & 3 & 0.95 & 0.26 & \\ \cline{1-1}  & NaOH & 50 & 1 & 10 & 3 & 1.05 & 0.19 & \\ \cline{1-1}  & & & & 15 & 3 & 1.73 & 0.21 & \\ \cline{1-1}  & & & & 20 & 3 & 1.80 & 0.22 & \\ \hline \end{tabular}  
* \(T\) is the solution temperature, \(P\) is the immersion period, CK is the contents of Kevlar® fibers, \(NS\) is the number of specimens tested per sample, AV is the average values, and SD is the standard deviation. Samples are taken along the CD.  
\end{table} 
Table 2: Tensile strength and statistical analyses of recycled Kevlar®/PET/LPET nonwoven geotextiles that are immersed in NaOH, in relation to different immersion periods.
# Application of Mastic Asphalt Waterproofing Layer in High-Speed Railway Track in Cold Regions
###### Abstract  
Freeze-thaw damage is a typical distress incurred in road and railway engineering in cold regions. Concrete waterproofing layer is commonly used in high-speed railway tracks to prevent the penetration of surface water, however, it cracks easily under thermal stress, especially in cold regions. Recently solutions have been proposed to increase the waterproofing layer's cracking resistance by using asphalt layers. Nonetheless, the use of emulsified asphalt as well as dense-graded asphalt mixture were not effective enough. To improve the effectiveness, in this study, mastic asphalt was designed for application as the waterproofing layer on the subgrade surface of high-speed railway tracks in cold regions. The overall performance of mastic asphalt was preliminarily evaluated by laboratory tests, then a 200-m test section was constructed for field validation in northeastern China as part of a new high-speed railway line, and water content sensors were placed inside the subgrade to monitor the performance of the mastic asphalt waterproofing layer (MAWL). The subsequent field investigation and monitoring data during the two years operation showed that MAWL dramatically outperformed the conventional concrete waterproofing layer in terms of waterproof performance. Plenty of serious cracks were found in the conventional concrete waterproofing layer, but only a limited number of local cracks were observed in MAWL. As a result, MAWL keeps the water content of subgrade at a stable level. In addition, MAWL showed relatively high stability during the two years investigation period, and no obvious deterioration was observed in the test section.  
high-speed railway; cold region; waterproofing layer; mastic asphalt 2018 8 667 403990/app8050667