esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Channel Model
### Energy Consumption Model  
The nodes consume different amounts of energy during transmission and reception of the data. When a node transmits \(n\) bits of data at a distance \(d\), the energy \(E_{tx}\) is used by the sender, which is [33]:\[E_{tx}=n\times E_{elec}+n\times T_{b} \tag{7}\]  
where \(E_{elec}\) represents the dissipated energy and \(T_{b}\) is the time duration for a single bit. When a node receives data, it consumes some amount of energy \(E_{rx}\), which is determined as [33]:  
\[E_{rx}=n\times E_{elec} \tag{8}\]  
The initial energy of every node \(E_{in}\) is dissipated after processing of the data, and the residual energy \(E_{re}\) is given as:  
\[E_{re}=E_{in}-(E_{tx}+E_{rx}) \tag{9}\]  
While the MS residual energy is:  
\[E_{re}=E_{in}-(E_{tx}+E_{rx}-E_{m}) \tag{10}\]  
where \(E_{m}\) is the consumed energy due to the movement of the MS.
# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
###### Abstract  
Adaptive control of traffic engineering (TE) based on 5G network function virtualization (NFV) authorizes the efficient and dynamic network resource allocation, whose utilization is increasingly wide and will become more widespread. In this paper, we first devise an adaptive control scheme for data-driven traffic migration engineering (TME) on the 5G virtual network. The proposed TME technology focuses on a 5G enhancing mobile broadband (eMBB) network application scenario and takes the network operating expenditure (OPEX) as the main research target. Firstly, we predict the network traffic of the virtual network through the constructed traffic predicted mathematical model. Then, based on the triangle inequality violation (TIV) theorem, some local network traffic is adaptively migrated when the predicted link traffic exceeds the peak rate. Consequently, the migrations of logical links in the virtual network layer are completed. Finally, our experiments show that the proposed protocol can effectively improve the key performance indicators (KPIs) of the re-configured network, such as throughput, delay and energy consumption. Furthermore, the Fridman and Holm statistical hypothesis tests are also used to analyze the simulation data, which proves that the proposed approximate TME algorithm has statistical significance.  
traffic engineering; traffic prediction; traffic migration; key performance indicators; statistical hypothesis tests +
Footnote †: journal:  
00 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 1 Introduction
### IoT in Cast Manufacturing
The specialized literature studies implement security measures that include end-to-end encryption and secure communication protocols for protecting sensitive data and maintaining operational integrity [22]. In addition, integrating blockchain technology has been proposed to enhance security and facilitate trust in IoT communications, especially in machine-to-machine scenarios [23]. Implementing IoT in production also requires a careful approach to the underlying infrastructure, which includes cloud and fog computing systems. These systems enable data processing and analysis as close to the source as possible, reducing latency and improving response times [24]. The use of hybrid communication protocols, for example, those based on NarrowBand IoT, can optimize data transmission in environments with limited bandwidth. Additionally, implementing Software-Defined Networking (SDN) can provide dynamic management capabilities [22]. This subsection reviewed the integration of IoT technologies in manufacturing, particularly casting. The complexity of IoT systems, with multiple layers such as Application, Network, and Perception, requires specialized communication protocols like MQTT and CoAP, as mentioned above. Security and confidentiality issues, such as cyber threats and data integrity, are marked as principal concerns. The infrastructure must accommodate cloud and fog computing systems, particularly in resource-constrained environments.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 5 Toward Fog-Based Smart City Data Management and Analytics  
Although relatively new, fog computing holds great promise for effectively solving data analysis and management problems in smart cities, using one of the following fog node deployment models, as prescribed by NIST [12].  
* Private fog cluster (or node): A private fog cluster is typically designed to be used exclusively by a single organization with multiple consumers. The organization could own the cluster, operate it, and manage it or delegate its management fully (or partially) to a commercial third party.
* Community fog cluster (or node): A community fog cluster is generally designed for exclusive use and exploitation by a specific community of consumers belonging to many organizations that share common concerns. One or more community organizations can own, operate, and manage it or delegate its management to a third party.
* Public fog cluster (or node): A public fog cluster is designed to be open to the general public. A business, government agency, university, or a combination of these three entities can own, operate, and manage the cluster.
* Hybrid fog cluster (or node): A hybrid fog cluster consists of at least two different fog nodes (private, community, or public), which operate independently. The portability of data and applications between nodes (e.g., fog bursting for load balancing between fog nodes) is ensured using proprietary or standardized technology.
## 4 Proposed Architecture and Implementation
### System Architecture Overview  
The architecture implemented in this research consists of the following core layers:  
1. Vehicles layer: vehicles equipped with IoT devices and sensors on board generate real-time data.
2. Edge/fog layer: roadside units (RSUs) serve as edge nodes to facilitate local processing and reduce latency.
3. Blockchain layer: a decentralized ledger where all transactions (vehicle interactions and data exchanges) are securely recorded.
4. Cloud layer: for high-level processing and analysis, data that do not occur in real time can be outsourced to the cloud.
5. Smart contracts: to automate the enforcement of policies for secure communication, authentication, and data exchange.
# A Framework for 3D Plant Simulation of Meal-Kit-Packaging Robot Automation System
## 1 Introduction
[11] reported that the kinematic analysis of the KUKA KR5 Arc robot was conducted using Denavit-Hartenberg parameters, with its motion validated through RoboAnalyzer simulations, while MATLAB-based path planning optimized its trajectory for industrial applications. [PERSON] et al. [12] used the simulation software RobotSudio to carry out motion simulation on the design scheme in the Stacking Robot Workstation and optimize the mechanical structure for small-paper-packaging boxes. Many studies on robot-based packaging processes so far have focused on the optimization of the packaging motion of the robot. However, there is an increasing demand for contemporary advancements in integrating various automation technologies to enhance the efficiency and precision of primary food processing [13]. An integrated simulation with surrounding production equipment is essential in order to enhance industrial practicality. Recently, plant simulation tools have facilitated evidence-based decision-making, allowing companies to optimize production capacity effectively [14]. By addressing identified bottlenecks, these tools improve a company's ability to meet customer demands efficiently. This highlights the critical role of simulation in streamlining production processes and maximizing operational effectiveness. In investment decision-making processes for production equipment, software tools designed for production process modeling and simulation are invaluable. Through simulations, it is possible to assess production capacities in a virtual environment, evaluate their utilization, and consequently improve the quality of investment decisions [15]. [PERSON] et al. [16] reported that plant simulation enables the creation of complex, full-scale microsimulation models to analyze the impact of broader factors on efficiency, extending beyond production logistics to include processes' effects on industrial and planning. In addition, [PERSON] et al. [17] reported that plant simulation models are powerful tools that help the food industry to navigate its complex landscape and overcome the low feasibility of actual implementation. In this study, a framework to develop a 3D simulation for a meal kit packaging process was suggested because it involves handling multiple components, making it highly complex and labor-intensive, and the most challenging process. In detail, 3D Lidar scanning was implemented to design a robot automation layout, and a robot kinematic was designed in 3D simulation to introduce a robot automation system to replace workers in a meal kit packaging line in consideration of the surrounding equipment. The plant simulation setting was determined based on analysis of the workflow, and the environment of the manual process was designed. Two plant simulation models for robot automation processes using one and two delta robots were developed. The performance evaluation, such as productivity and utilization rate for improvement effect, was investigated by comparing the manual process and robot automation packaging process.
# Simulation of Wave Propagation Using Finite Differences in Oil Exploration
## 4 Conclusions  
According to the wave obtained in the seismogram, the experts determined whether there was oil in the subsoil. The algorithm presented in this paper showed a high efficiency when propagating the acoustic wave equation in a geological profile using a firing line. This algorithm can be used in real seismic exploration studies to refine the acquisition parameters in prospective studies.  
The results allow us to conclude that, in a reasonable time, a set of experiments helps decision making regarding recording time, the separation of receivers, the separation of sources, and the maximum frequency range of the source for reliable oil exploration studies.  
Figure 7: The operational structure of the developed system.  
This algorithm can be used in real seismic exploration surveys to fine-tune acquisition parameters in the prospective surveys.  
Different simulations were performed using different parameters, boundary conditions, and meshes. There are solutions better than the finite forward and backward differences for thick meshes with spatial discretizations. When the passage of time is considerably long, the answers are similar and deviate from the analytical solution. For depths greater than 17,400 ft, the code becomes unstable. More accurate results can be obtained using a cluster with more processors.  
In the future research conducted in this field, improvements in computational efficiency should be implemented to explore the techniques to improve the computational efficiency of the finite difference simulation method. This study could investigate alternative numbering schemes, optimize code implementations, and use parallel computing techniques to reduce simulation times. In addition, a sensitivity analysis should be performed to assess the impact of various parameters on the simulation results. This research could involve studying the influence of factors, such as mesh size, time step, source frequency, and geological properties, on the accuracy and stability of the simulation.  
Conceptualization, F.S.-C. and L.R.-R; methodology, F.S.-C.; software, L.R.-R.; validation, F.S.-C., L.R.-R. and J.S.; formal analysis, H.-F.M.-C.; investigation, H.E.V.-L. and O.F.-U.; writing--original draft preparation, F.S.-C. and P.A.-V.; writing--review and editing, F.S.-C. and P.A.-V. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Universidad de las Americas, Quito, Ecuador.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Modeling of the Grid-Connected Systems of Doubly Fed Wind Turbines Through Series Compensation
### Frequency Domain Impedance Model  
According to Figure 2, the impedance function of the system is constructed as follows:  
\[Z=\frac{Z_{\text{GSC}}*Z_{\text{RSC}}}{Z_{\text{GSC}}+Z_{\text{RSC}}}+Z_{l}+Z_{ s}+Z_{r} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Meaning** & **Parameter** & **Meaning** \\ \hline \(v_{gl}\) & \begin{tabular}{c} GSC three-phase AC \\ modulation voltage \\ \end{tabular} & \(K_{m}\) & PWM gain \\ \hline \(v_{rl}\) & \begin{tabular}{c} RSC three-phase AC \\ modulation voltage \\ \end{tabular} & \(K_{e}\) & Stator and rotor turns ratio \\ \hline \(v_{\text{dc}}\) & DC voltage & \(m_{gi}\) & \
\begin{tabular}{c} GSC three-phase AC \\ modulation signal \\ \end{tabular} \\ \hline \(v_{l}\) & Three-phase AC voltage & \(i_{gl}\) &
\begin{tabular}{c} GSC three-phase \\ AC current \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Meaning of each physical quantity in Equations (1) and (2).  
where \(Z\) is the total impedance of the system, \(Z_{1}\) is the line impedance, \(Z_{s}\) is the grid-side impedance, \(Z_{r}\) is the rotor-side impedance, \(Z_{s}\) is the rotor-side impedance, and \(Z_{GSC}\) and \(Z_{RSC}\) are the impedances of GSC and RSC in the control system, respectively. Each component is given by Equation (4):  
\[\left\{\begin{array}{c}Z_{l}=R+sL-\frac{1}{sL}\\ Z_{s}=R_{s}+sL_{ls}\\ Z_{r}=R_{r}\frac{s}{s-\omega_{m}}+sL_{lr}\\ Z_{GSC}=\frac{s}{s-\omega_{m}}\Big{(}K_{pg}+\frac{K_{lg}}{s-\omega}\Big{)}\\ Z_{RSC}=\frac{s}{s-\omega_{m}}\Big{(}K_{pr}+\frac{K_{lr}}{s-\omega}\Big{)} \end{array}\right. \tag{4}\]  
where \(R\), \(R_{s}\), and \(R_{l}\) are the line, stator-side, and rotor-side resistances, respectively. L, \(L_{ls}\), \(L_{lr}\), and \(L_{M}\) are the line inductance, grid-side inductance, rotor-side inductance, and grid and rotor mutual inductance, respectively; \(K_{pg}\) and \(K_{ig}\) are the proportional and integral coefficients, respectively, in the GSC. \(K_{pr}\) and \(K_{ir}\) are the proportional and integral coefficients of RSC, respectively. \(\omega\) is the nominal angular velocity, taken as 377 rad/s; \(\omega_{m}\) is the angular velocity of the rotor; C is the series compensation capacitance; j is the imaginary number unit; and the resonant frequency can be obtained from \(s=j\omega_{0}\). The electrical power from the rotor side is converted to the grid side.  
From Equations (3) and (4), it can be seen that the changes in the proportional coefficients of series capacitors C, GSC, and RSC, \(K_{pg}\) and \(K_{pr}\), directly affect the size of the system impedance, while the number of fans affects the system torque, and the wind speed affects the rotor speed and indirectly changes the size of the system impedance. When the real part of the impedance is negative, the system presents a negative damping state, and there is a risk of SSO.
# Effect of Working Atmospheres on the Detection of Diacetyl by Resistive SnO2 Sensor
## 2 Materials and Methods
### Sensor Preparation and Testing  
The sensing film was realized using a tin oxide/ethanol mixture sonicated for 15 min, in order to obtain a homogeneous paste, and deposited on an alumina planar substrate (6 mm \(\times\) 3 mm) supplied with interdigitated Pt electrodes on the front side and a heating element on the back side [33]. The paste was dried at room temperature and finally annealed at 80 \({}^{\circ}\)C for 1 h. Before measurements, in order to improve the deposited film stability, the sensor was conditioned in air at 400 \({}^{\circ}\)C for 2 h. Tests were performed by positioning the sensor in a testing cell and flowing a total gas stream of 100 sccm. To assess the different working conditions' effects on the performance of the SnO\({}_{2}\) sensor, different pure gas atmospheres, namely Air, N\({}_{2}\) and CO\({}_{2}\) were evaluated. Diacetyl was chosen as the target analyte, both in aqueous and in alcoholic (5% ethanol) solutions. Analyte vapor was obtained by bubbling carrier gases in the solution that was maintained at 20 \({}^{\circ}\)C during the tests. All gas fluxes were measured by computer-controlled mass flow meters, and humidity was monitored and controlled to oscillate between 5% and 10%. The sensors' resistance data were collected in four-point mode by an Agilent 34970A (Santa Clara, CA, USA) multimeter, while sensor temperature was controlled by using a dual-channel power supplier instrument (Agilent E3632A, Santa Clara, CA, USA).  
The sensor working temperature was fixed at 200 \({}^{\circ}\)C. Sensor response was defined as R/R\({}_{0}\) when the sensor showed an n-type response and the sensor resistance R measured in the presence of the reducing analyte gas decreased in respect to the sensor resistance R\({}_{0}\) of the sensor exposed to the carrier flow. Differently, when the sensor showed a p-type response and the sensor resistance R in the presence of the same analyte gas increased, the response was defined as R\({}_{0}\)/R [34]. The response and recovery time of the sensor were defined as the time needed for the sensor to reach 90% of its saturation limit after the exposure to the analyte gas and as the time needed for the sensor to reach the 10% of its original resistance value once the target gas was switched off and the sensor was exposed to the carrier gas only, respectively [14].
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
## 3 The Proposed Safety Message Dissemination Scheme
### V2I Communication  
The proposed scheme performs message dissemination using the RSU as a supplementary tool when a vehicle is connected to an RSU [23; 24; 25; 26; 27]. V2I communication refers to communication between vehicles and RSUs within the RSU's communication range. RSUs have several advantages, such as larger storage space, a wider communication range, and fast collaboration between RSUs. However, since vehicle density is high in urban environments, direct communication between all vehicles and the RSU can create a heavy load on the RSU [27]. Therefore, in the proposed scheme, V2I communication operates similarly to cluster-based V2V communication. V2I communication in the proposed scheme is divided into a cluster-based V2I communication technique and an RSU scheduling technique.
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 4 Tests and Discussion of Results
### Performance Analysis
#### 4.3.1 Comparison of ILP Solvers  
Previous works show that Gurobi generally offers better resolution times and performance than GLPK, especially for large and complex problems [35]. Indeed, in agreement with the literature, Figure 14 shows that the performance of Gurobi is considerably superior to that of GLPK. In the figure, the colored bar is the average value, and the segment in each bar denotes the minimum and maximum values.  
Figure 13: Topology used in the tests.  
While the time to find a solution using GLPK increases considerably, reaching up to 27 s maximum and 4.76 s on average in the first test with 15 streams, and up to a maximum of 80.63 s in the test with 25 streams, Gurobi times were never higher than 1.5 s, with average values of less than 1 s for all tests. An aspect that greatly differentiates the two solvers is the variance. Table 2 depicts the average time to solution value and the maximum and minimum values, together with the standard deviation. Considering this last value, Gurobi not only offers a higher performance, but also a much greater consistency than GLPK for our ILP model.
# Antimicrobial Effect of Submicron Complex Oxide Particles CsTeMoO\({}_{6}\) under Visible Light
## 2 Materials and Methods  
### Materials Synthesis and Characterization  
Complex metal oxide CsTeMoO\({}_{6}\) was synthesized at the Research Institute of Chemistry, Lobachevsky State University, Nizhny Novgorod, Russia. Average particle size of 670 nm.  
The CsTeMoO\({}_{6}\) compound was obtained by the solid-state reaction. Chemically pure cesium nitrate (CsNO\({}_{3}\)), tellurium oxide (TeO\({}_{2}\)) and molybdenum oxide (MoO\({}_{3}\)) with a molar ratio of Cs:Te:Mo = 1:1:1 were ground and calcined in a platinum crucible for 2 h in air at 400 \({}^{\circ}\)C to remove nitrogen oxides, after which the resulting sample was dispersed again and kept for 10 h at 700 \({}^{\circ}\)C. Then, the sample was abruptly cooled to room temperature. Dispersion in a planetary mill for 18 h in ethanol was used to obtain a polycrystalline sample of CsTeMoO\({}_{6}\) with a submicron particle size.  
The phase composition of the tested materials was determined on the basis of X-ray diffraction analysis. The Shimadzu XRD-6100 diffractometer (Shimadzu, Kyoto, Japan) equipped with a Ni-filter (CuK\(\alpha\), \(\lambda\) = 1.5418 A) was used in tests. Continuous scan mode of 2\(\Theta\) was recorded in the range of 10\({}^{\circ}\) and 60\({}^{\circ}\) at a rate of 1\({}^{\circ}\)/min.  
Scanning electron microscopy (SEM) imagery of metal oxide particles was performed with a JSM-IT300 LV microscope (JEOL, Tokyo, Japan) with an electron-probe diameter of up to 5 nm (operating voltage 20 kV). The study of topographic surfaces was carried out using low-energy secondary and reflective electrons.  
The elemental composition of the sample was studied using X-ray microprobe analysis (XMA) with an X-MaxN20 detector (Oxford Instruments, Abingdon, UK) according to K\(\alpha\) (O) and L\(\alpha\) (Cs, Te, Mo) lines.  
The volumetric and quantitative particle size distribution of the resulting powder sample was determined by laser-light diffraction using a SALD-2300 analyzer (SHIMADZU, Kyoto, Japan).
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 2 Circuit Structure and Operation Analysis
### Description of Operating Range
#### 2.3.4 Stage IV [\(t_{4}<t\leq t_{5}\)]  
As shown in Figure 6d, \(Q_{1}\) and \(Q_{4}\) remain conducting while \(Q_{2}\) and \(Q_{3}\) remain at the cut-off state. As \(i_{L71}\) equals \(i_{Lm}\) at \(t=t_{4}\), the transformer is decoupled, and electromagnetic induction takes a break. The secondary side bridge is temporarily disabled without current flow. The \(D_{B5}\) and \(D_{B8}\) is naturally cut off and gets ready for ZCS. In this mode, the transformer does not transfer energy to the load, and the magnetizing inductor \(L_{m}\) is not clamped by the output voltage \(V_{Bat}\) and the secondary resonant capacitor voltage \(V_{C72}\). The magnetizing inductor \(L_{m}\), \(L_{71}\), and \(C_{r1}\) constitute a resonant circuit while \(L_{r2}\) and \(C_{r2}\) are idle. As the gating signals \(v_{GSI}\) and \(v_{GS4}\) turn from high to low at \(t=t_{5}\), \(Q_{1}\) and \(Q_{4}\) cut off, and this stage ends.