esa-sceva/satcom-chunk-collection huggingface dataset:
# The Virial Effect--Applications for SF\({}_{6}\) and CH\({}_{4}\) Thermal Plasmas
###### Abstract  
A tool based on the mass action law was developed to calculate plasma compositions and thermodynamic properties for pure gases and mixtures, assuming a local thermodynamic equilibrium for pressures of up to 300 bar. The collection of the data that was necessary for tool calculation was automated by another tool that was written using Python, and the formats for the model were adapted directly from the NIST and JANAF websites. In order to calculate the plasma compositions for high pressures, virial correction was introduced. The influences of the parameters that were chosen to calculate the Lennard-Jones (12-6) potential were studied. The results at high pressure show the importance of virial correction for low temperatures and the dependence of the dataset used. Experimental data are necessary to determine a good dataset, and to obtain interaction potential. However, the data available in the literature were not always provided, so they are not well-adapted to a large pressure range. Due to this lack, the formulation provided by [PERSON] and [PERSON] (Journal of Chemical and Engineering Data, vol. 7, 1962, p. 234-236) is a good alternative when the considered pressure is not close to the critical point. The results may depend strongly on the system studied: examples using SF\({}_{6}\) and CH\({}_{4}\) plasma compositions are given at high pressure.  
plasma composition; high pressure; virial coefficient; SF\({}_{6}\); CH\({}_{4}\)
## 2 Related Work
The angle is captured from a scanning sweep from the BS at a specific time. S-WEB [23] is a spectrum structure based clustering algorithm. The first step in S-WEB is similar to CCS. All sensors are partitioned into layers based on their distance (measured but signal strength) to the BS. Then the BS does a 360-degree scanning sweep by sending out signal at one angel at a specific time. Sensors are clustered into cells based on the layer number and scanning angle. In each cluster cell, the sensor with the highest residual energy will be elected as the CH. All the sensors are responsible for forwarding packets. The cluster structure of the network is fixed after performing S-WEB. It is not adaptive to the dynamic changes in the network, like node failure. The evaluation of S-WEB is only based on the comparison with a non-cluster routing solution--Direct Routing. In Table 1, we summarise the characteristics of 6 existing clustering algorithms. Few of the clustering algorithms consider network coverage. Furthermore, we have reviewed most of the existing survey papers for clustering in WSNs for the last decade. There are many overlapping studies and investigations. Many of them lack deep analysis and comprehensive introduction. These four selected survey papers shown in Table 2 ([24; 25; 26; 27]) are good to start with when researchers are about to explore in this area. The selected survey papers in Table 2 include a large number of clustering algorithms. There are also some new studies in recently two years (2015 and 2016) on clustering that are not covered by the existing reviewing papers, such as RINtraR [28], SenCar [29], FL-LEACH [30], BEEM [31] and PathQuality [32]. The common analysis topics include: convergence time, node mobility, cluster overlapping, location awareness, energy efficiency, failure recovery, balanced cluster/cluster size, cluster stability, cluster count, load balancing, deliver delay, intra/inter routing schemes, objectives and complexity, etc. However, besides energy efficiency, other discussed metrics are mostly about network topology. As we can see, most existing studies are for transitional WSNs and limited work has shown interests in highly diverse IoT systems. Even fewer clustering algorithms consider the impact from 5G communication, especially the MIMO communication features. Communication abilities of different sensors or IoT devices vary greatly in IoT networks. The heterogeneous networks we are talking nowadays are no longer limited to the scenarios with just several super nodes. Supporting context aware clustering is missing in such a context.
## 2 Principle  
### UOWC System Model  
The schematic diagram of the typical UOWC system model is presented in Figure 2. At the transmitter side (Tx), the input binary bits are first modulated to drive the optical source via a driver circuit for emitting visible light signals. At the receiver side (Rx), after passing through the underwater channel, the optical signal is captured by a photodetector (PD), converted to photocurrent and amplified by a trans-impedance amplifier (TIA) to voltage values. Finally, the data is retrieved after analog-digital conversion (ADC) and the data detection process.  
Obviously, the system performance is highly affected by two aspects, (i) spatial and temporal dispersion caused by the underwater medium multipath scattering phenomenon, (ii) electrical noises resulting from sunlight and hardware components such as PD and TIA (Figure 2). Moreover, in the shallow underwater environment, the water quality tends to be more turbid and ambient light is much stronger, which can exacerbate these problems. Typical attenuation coefficients for different oceanic types are summarized in Table 2. The attenuation coefficient \(c\) describes the amount of energy loss of light propagating through the water medium and is defined as the sum of absorption \(a\) and scattering coefficients \(b\). These coefficients are associated with the concentration of dissolved particles, planktonic matter, detritus, and change with different water types. The characteristics of the four major water types are as follows:  
* Pure sea water--absorption effect is the major limiting factor. The light propagation trajectory is nearly a straight line due to the low scattering coefficient.
* Clear ocean water--Concentration of dissolved particles is higher than that in pure sea water, leading to an emerging scattering effect.
* Coastal water--Concentration of suspended matter and detritus becomes much higher in that it affects both absorption and scattering.
* Turbid harbor water--Concentration of planktonic matter, dissolved particles and mineral components is highest among the four water types, which can cause severe light scattering effect and attenuation.
# Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell +
## 2 Four-Phase IBC and PEM Fuel Cell Modeling
### Four-Phase Interleaved Boost Converter Modeling  
As evidenced in various studies, particularly in [11,24], increasing the number of phases in the converter reduces current ripple in the fuel cell, thereby enhancing its energy efficiency. However, considering the cost implications and energy losses associated with a higher number of phases, four phases appear most suitable for our study. Figure 2 illustrates the topology of the four-phase IBC.  
The interleaved converter structure comprises inductances \(L_{j}\)\((j=1,\ldots,4)\), diodes \(D_{k}\)\((k=1,\ldots,4)\), power switches \(Q_{i}\)\((i=1,\ldots,4)\), a capacitor, and resistance that represents the load. \(v_{in}\), \(i_{in}\), and \(V_{out}\) denote the stack voltage, stack current, and the output voltage of the four-phase interleaved boost converter, respectively.  
The IBC converter proposed in this paper operates in continuous conduction mode (CCM) using interleaved drive signals \(Q_{i}\)\((i=1,\ldots,4)\). The interval between two adjacent drive waveforms is \(\frac{1}{4J}\). The currents denoted as \(i_{k}\)\((k=1,\ldots,4)\) represent the inductor currents.  
The sum of all inductor currents is the PEMFC current \(i_{in}=\sum_{k=1}^{4}i_{k}\). To simplify the analysis, we make the following assumptions: \(L_{i}=L\), and the duty cycle \(u_{k}(t)=u(t)\) is consistent for all switches \(Q_{k}\). However, each switch has two states, open and closed, resulting in a total of sixteen operational states.  
By taking into account the activation times and the equations associated with each operating state of the four-phase interleaved boost converter, illustrated in Figure 2, under various scenarios, the average state-space model of this converter, based on physical laws, is given by the following equation:  
\[\left\{\begin{array}{l}\frac{d_{i}}{dt}=-\frac{1-u(t)}{L}v_{out}+\frac{1}{L} v_{in}\\ \frac{dv_{out}}{dt}=\frac{1-u(t)}{\mathbb{C}}\sum_{i=1}^{4}i_{i}-\frac{1}{ \mathbb{R}\mathbb{C}}v_{out}\end{array}\right. \tag{10}\]  
Figure 2: Schematic diagram of a four–phase interleaved boost converter with control architecture: (**a**) DL-STSM controller, (**b**) proposed controller, (**c**) BS + STSM controller, (**d**) PI controller.
# Spatial Performance Indicators for Traffic Flow Prediction
## 3 Materials and Methods
### Spatial KPIs
#### 3.1.1 Spatial Autocorrelation (Global Moran's I)
The Global Moran's I provides valuable insights into this spatial autocorrelation, allowing us to quantify whether prediction errors are clustered, dispersed, or randomly distributed across the network.
# An Approach to the State Explosion Problem: SOPC Case Study
## 7 Experiment  
In order to verify the state space reduction method proposed in our paper, we have conducted verification in the SOPC software. We compare the number of states, running time, and memory in the original design and the design after state reduction. The experimental results show that the algorithm proposed in this paper can be effectively applied to state space reduction in the SOPC software, and the algorithm does not require significant additional costs.  
The algorithm proposed in Section 6.4 is verified in the experiment. Firstly, we applied the method in Section 5.1 to model the SOPC system and selected nine properties to verify the software and hardware interaction area of the SOPC model. Secondly, we applied the methods proposed in Sections 6.2 and 6.3 to compress space states. Finally, model checking was applied to the SOPC model, and we compared the results of the original SOPC model with the compressed SOPC model.  
Our experiment was implemented using the AveMC model checking tool, which is an industrial-grade model checker. The description language of the design is SVA, Verilog, Very-High-Speed integrated circuit hardware description language (VHDL), or SystemVerilog, and the property language is Verilog and SVA.
## 3 Results and Discussions
### Noise Analysis
The above three corresponding values for GNSS with GRACE corrections are 90/186, \(-\)12.5% and 10.5%. Although the GGFC loading can lead to more stations with noise decrease, noise amplitudes of stations with noise increase are much bigger than that of the solutions from GRACE corrections. The average amplitudes of total amplitudes of the PL+WN for GNSS, GNSS-GGFC and GNSS-GRACE are 4.69, 4.92 and 4.46 mm, while for the FN+WN they are 4.61, 4.67 and 4.49 mm, respectively. The results indicate that for both types of optimal noise model, after subtracting the loading effects, GNSS corrected by GRACE solutions have the lowest noise level, demonstrating that  
Figure 8: Noise amplitudes variations for the GNSS vs.GNSS corrected by GGFC solutions (up panel) and GNSS vs. GNSS corrected by GRACE solutions (bottom panel) solutions. the GNSS corrected by GRACE solutions present more favorable results than that of GNSS corrected by GGFC solutions. The GRACE deformations can occupy 5% of the noise behavior presenting in the global GNSS stations, while GGFC results could bring more than 5% of the noise into the global stations. Therefore, for the analysis of the surface loading effects on the global stations noise, GRACE can provide better results for these investigations.
## 5 Conclusions  
In conclusion, the proposed intelligent weighing scale system for small-scale fruits and vegetables shops in Pakistan holds great potential to revolutionize the retail process and improve efficiency in this sector. By incorporating deep learning algorithms, and weighing sensors, the system automates the detection, recognition, weighing, and pricing of various fruits and vegetables. The use of deep learning algorithms, such as YOLOv5n and YOLOv7, enables real-time and accurate object detection and recognition, eliminating the need for manual entry of prices for each item. This not only saves time but also reduces labor requirements, allowing shopkeepers to serve multiple customers simultaneously. The graphical user interface (GUI) provides a user-friendly platform for adding items to the bill and displaying their prices based on their weights. By modernizing small-scale retail operations, especially in low-income segments of the population, this system addresses the challenges of labor-intensive and time-consuming pricing methods. It empowers shopkeepers to serve customers faster, improves accuracy in weighing and pricing, and reduces human error. However, further research and real-world implementation are necessary to evaluate the system's performance, scalability, and impact on the industry. Additionally, continuous refinement and updates to the deep learning models and algorithms can enhance the system's accuracy and adaptability to different fruits and vegetables. Overall, the intelligent weighing scale system has the potential to transform the small-scale retail sector in Pakistan, providing a more efficient, automated, and customer-friendly shopping experience while benefiting both shopkeepers and customers alike.  
Conceptualization, A.Z. and I.U.H.; methodology, T.A., G.H., S.R., M.A. and H.G.M.; software, A.Z. and I.U.H.; validation, T.A. and M.A.; formal analysis, A.Z., G.H. and Y.Y.G.; investigation, A.Z., I.U.H., T.A., G.H. and S.R.; resources, T.A. and M.A.; data curation, G.H. and Y.Y.G.; writing--original draft, A.Z., I.U.H., T.A. and S.R.; writing--review and editing, G.H., M.A., Y.Y.G. and H.G.M., visualization, S.R. and I.U.H.; supervision, G.H. and H.G.M.; project administration, Y.Y.G. and H.G.M.; funding acquisition, H.G.M. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023 TR140), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors declare no conflict of interest.
## 4 The Architecture of IoT-Enabled Smart City
### Weightless Protocol  
The weightless system is controlled by the Weightless Special Interest Group (SIG) [98], a nonprofit organization for implementing the standard architecture. The reference architecture of the weightless system is illustrated in Figure 6. It includes the uplink and downlink, which are part of the physical layer. The uplink transmission adopts FDMA to enable concurrent transmissions and prevent interference. The base station also uses a time scheduling method to notify the devices of their time allocation.  
The data link layer comprises three sub-layers: the baseband, lower link layer, and upper link layer. The management of radio resources at the Medium Access Control (MAC) layer and configuration of network arrangements are handled by the Radio Resource Manager (RRM). Additionally, the system supports transmission acknowledgement, fragmentation, and multi-cast and can interrupt the base station.  
Low Power, Wide Area Networking (LoRaWAN)  
The LoRa Alliance [1] has developed a standard for connecting low-power IoT devices covering extensive areas. The standard uses different ISM bands according to the regulations in specific regions. Various ISM bands comply with specific regional regulations in the standard. The physical layer incorporates Chirp Spread Spectrum (CSS) modulation, facilitating two-way communication between base stations/gateways and end devices [41]. Three modes are available in the Medium Access Control, enabling access control while balancing the uplink and downlink capabilities.  
Figure 7 illustrates the reference architecture of the LoRaWAN. The Class A device requires low power and limited downlink channels for communication from the base station, and Class B shares the same characteristics. However, they have extra time windows not available on Class A devices. Class C device has continuous receiving time windows. It can support multicasts, such as Class A and Class B. During communication between the base station and the end devices, all classes of devices in LoRaWAN utilize the ALOHA access protocol.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & **Spatform** & **Channel Width** & **T. Rate Uplink** & \
\begin{tabular}{l} **T. Rate** \\ **Downlink** \\ \end{tabular} &
\begin{tabular}{l} **Packet Size** \\ **Packet Size** \\ \end{tabular} & **Max Range Gain** & **T. Power** & **Standard (6 days)** \\ \hline Sigfox & 866-902 MHz & 100 Hz & \(\leq\)100 bytes & 250 bytes/ & \(\leq\)12 bytes & 10-50 & 10 mV-100 mV & Available \\ \hline LoRaWAN & EU & 863-902 MHz & 252-200 MHz & 250-200 bytes & 250 bytes/ & \(\leq\)222 bytes & 2-15 & 14-dB & Available \\ \hline \multirow{2}{*}{WiFigure 6: The reference architecture for the weightless system.  
Figure 7: The reference architecture of LoRaWAN.
# Numerical Simulation of Polymer Solutions in a Single-Screw Extruder
## 2 Simulation Methodology
### Mesh Parameters  
For numerical simulation, it is important to determine the mesh convergence of the obtained results by studying the convergence according to the computational mesh. Hence, we performed additional numerical studies and determined the optimal mesh sizes. The studies were performed by the example of an isothermal flow of 0.5% PAA in the channel with a screw. It was previously noted that we are considering one of the areas bounded by a screw (Figures 1b and 2). Figure 4 presents the computational area. The geometric parameters of the computational area: \(L/D_{2}=2\), \(D_{2}=0.039\) m, \(D_{1}=0.026\) m, \(L_{0}=1.5\cdot D_{2}\), \(L_{1}=5.5\cdot D_{2}\), \(h=0.004\) m.  
It is known that the axial component of the velocity vector is less sensitive to the mesh parameters than the radial or tangential one. Therefore, the influence of the mesh parameters on the numerical results was estimated by comparing the profiles of the radial component of the velocity vector around the circumference in cross-sections \(A_{45}\) and \(A_{360}\) (dashed line in Figure 4b). Here \(A_{i}\) (\(i=45\dots\) 360) is the cross-section spaced apart from \(A_{0}\) at a distance corresponding to the rotation of the screw by \(i\) degrees, \(r_{3}=(r_{1}+r_{2})/2\), \(\beta\) is the current angle value, \(\alpha\) is the sector size. The problem was numerically solved by using a server with two Intel Xeon Gold 6126 processors (24 cores in total, 512 Gb RAM).  
Figure 5 presents the profiles of the dimensionless radial component of the velocity vector for the three partitions of the computational area. The partition of the computational area into 132,413 (mesh #1), 543,657 (mesh #2), 1,051,349 (mesh #3) elements was considered. It can be seen that the profiles of the radial component of the velocity vector coincide when using mesh #2 and mesh #3. Even though the profile \(v_{r}/v_{a}\) for mesh #1 practically coincides with the results obtained on a finer mesh, there is a small region in the vicinity of the right fin in which \(v_{r}/v_{a}\) is nonzero, which contradicts the no-slip boundary conditions of the fluid.  
Figure 4: Computational area (**a**) and area of the working fluid flow in cross-section \(A_{0}\) (**b**).  
Figure 3: The dependence of viscosity on shear rate.  
The performed analysis of mesh convergence showed that mesh #2 parameters provide sufficient numerical results that are independent of further partitioning of the computational area. Table 2 shows the parameters of the used mesh. Since we considered laminar flows of a viscous fluid at low Reynolds numbers (Re \(<11\)) then the channel length \(L_{1}=5.5\cdot D_{2}\) with \(L/D_{2}=2\) pitch of twist of screw is sufficient to exclude output effects from the channel.
# The Quality Analysis and Deterioration Mechanism of Liquid Egg White during Storage
## 3 Results and Discussion  
### Effect of Different Packaging Materials on LEW  
#### 3.1.1 Total Bacterial Count and TVB-N Analysis  
As shown in Figure 1a, there was no significant difference in total bacterial count for the LEW among the three different packaging materials when stored at room temperature for 0-3 days. These results might be attributed to the inhibition of anti-bacterial substances, such as lysozyme, ovotransserrin, etc. With an increase in storage time, the total bacterial count of LEW-PG (6 th day), LEW-PP (3 rd day), and LEW-PT (4 th day) began to increase sharply, which may be related to the decrease of antibacterial substance and thick white as well as the increase in thin white and dominant spoilage organisms (_Salmonella enterica_ and _Carnobacteriaceae_ family [33]. Additionally, the total bacterial count of LEW-PG (9 th day), LEW-PP (6 th day), and LEW-PT (8 th day) were greater than 1*10\({}^{6}\) CFU/mL, which exceeded the limit of pathogenic bacteria in food [24]. Hence, the shelf life of LEW-PG, LEW-PP, and LEW-PT was preliminarily determined to be 8, 5, and 7 days, respectively. Follow-up studies of egg white liquid were carried out within the shelf life.  
TVB-N is commonly used to measure the freshness index of protein-rich foods [9]. It was reported that a TVB-N content of 30 mg/100 mL was regarded as the upper acceptable limit for human intake [34]. As shown in Figure 1b, the TVB-N content of LEW-PP, LEW-PT, and LEW-PG showed an increasing trend with the increase in storage time, and the TVB-N values on the 5 th, 7 th and 8 th days were 19.15, 19.18, 18.64 mg/100 mL, respectively. A similar phenomenon was also found in previous work [9]. It could be inferred that these results may be due to ammonia production in LEW during storage. In addition, the growthrate of TVB-N content in LEW-PG was lower than that of LEW-PP and LEW-PT, presumably because of the slow microbial growth, to some extent.
## 3 Model Description
### The Fracture Intersection Point  
There are many fracture intersection points in Figure 4. During hydraulic fracturing, when fracture fluid flows through a fracture intersection, fracture fluid will enter into two fractures. Hence, the new vertices generated from fracture intersections have a significant influence on DFN connection, and these new vertices must be found out. Many mathematic methods to detect the fracture intersection have been studied, such as parameter equation and vector method [22]. Vector method is a great approach to this problem. \(e_{1}=(u_{1},v_{1})\) and \(e_{2}=(u_{2},v_{2})\) represent two fractures, respectively. Defining the two-dimensional vector cross product: \(e_{1}\times e_{2}=u_{1}v_{2}-u_{2}v_{1}\). Vector quantities \(r=(u_{3},v_{3})\) and \(s=(u_{4},v_{4})\) represent the other two fractures, respectively. Supposing the two line segments run from \(e_{1}\) to \(e_{1}+r\) and from \(e_{2}\) to \(e_{2}+s\). The main steps are as follows [23]:  
1. If \(r\times s=0\) and \((e_{2}-e_{1})\times r=0\), then the two lines are collinear. If either \(0\leq(e_{2}-e_{1})\bullet r\leq r\bullet r\) or \(0\leq(e_{2}-e_{1})\bullet s\leq s\bullet s\), then the two lines are overlapping.
2. If \(r\times s=0\) and \((e_{2}-e_{1})\times r=0\), but neither \(0\leq(e_{2}-e_{1})\bullet r\leq r\bullet r\) nor \(0\leq(e_{2}-e_{1})\bullet s\leq s\bullet s\), then the two lines are collinear but disjoint.  
Figure 4: An example of discrete fracture network.  
3. If \(r\times s=0\) and \((e_{2}-e_{1})\times r\
eq 0\), then the two lines are parallel and non-intersecting.
4. If \(r\times s=0\) and \(0\leq t,u\leq 1\), the two line segments meet at the point \(e_{1}+tr=e_{2}+us\).
5. Otherwise, the two line segments are not parallel but intersect.  
Using this method, all of the fracture intersection points can be found in DFN, which are shown in Figure 5, and marked by the red points.