esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Routing Protocols Based on Metaheuristic Algorithm  
Ant Colony Optimization (ACO) which is inspired by ants foraging has been adopted by several literatures for routing path selection. In [28], the authors proposed an algorithm combined the fuzzy logical system with ACO. The fuzzy logical system is used for the CHs selection according to the input features such as distance to BS, residual energy, and node degree. The ACO is used for optimal routing path discovery for each node by using the probability function. In [31], the authors use the type-2 fuzzy logical system combined with ACO to further improve the network's performance, and the Sugeno fuzzy complement operator is used to update the pheromone concentration. In [30], the author defines the CHs routing problem in WSN as the traveling salesman problem (TSP) and ACO is utilized to search for an optimal trajectory with shortest distance.  
Particle swarm optimization (PSO) is another metaheuristic algorithm widely applied among many optimization problems. In [31], [PERSON] et al. proposed an energy efficient routing schema. Their work's contribution is to establish the mapping relation between particles and sensors. The network is constructed by two different types of sensors, ordinary nodes and gateways, and each particle represents the whole solution for the routing path. The dimension of the particle is the number of gateways and each dimension represents the index of the candidate relay node. The fitness function mainly contains two parts, Maxdis and Maxhop, and the ultimate goal is to minimize the fitness function. In [32], the authors proposed an energy efficient and energy balanced algorithm based on [PERSON] et al. and they pay more attention to the energy balance. In [33], a novel clustering method is created by using region partition line represented by a tuple \(L=(x,y,\theta_{x},\theta_{y})\). PSO algorithm is used to adjust the region partition line in order to achieve better network performance. In [34], an algorithm called VD-PSO is presented by [PERSON] et al. In VD-PSO, the dimension of each particle is double the number of rendezvous points and it stores the coordinate of CHs. Due to the uncertainty of the number of the rendezvous points, the dimension of particles is different, therefore, the authors proposed a special way to update the speed and location of the particles. In [37], the author proposed a coverage hole patching scheme using PSO to search coverage holes and fix it with a mobile agent.
## 4 Simulation Analysis Results and Discussion
### Temperature Distribution Pattern during Braking
#### 4.1.2 Analysis of Internal Gap Structure of Ventilated Brake Discs
In the node near the middle of the gap structure, in the initial stage of braking, due to the high-speed rotation of the brake disc, the gap structure of the ventilated disc introduces a large amount of air, forming turbulence under the action of high-speed fluid, and the heat dissipated by convection heat dissipation on its surface is higher than the heat transferred from the friction contact surface to the node due to heat conduction, so the temperature of the node near the centre of the gap structure drops first. As the braking time increases, the heat generated in the friction contact area of the ventilated disc is continuously transferred from the friction disc surface to the gap structure using heat conduction. At the same time, the speed of the disc decreases, and the airflow rate introduced into its gap structure  
Figure 12: Three-dimensional time-temperature cloud along the axial direction of the ventilated brake disc gap structure. gradually decreases, making the convective heat exchange between its surface and the air less efficient. As a result, the convective effect between the gap structure and the air is weakened, so the temperature of the gap structure is on the rise, as the energy transferred by heat conduction is greater than the energy dissipated in the form of convective heat dissipation after about 10 s.
# Numerical Simulation of Non-Isothermal Mixing Flow Characteristics with ELES Method
## 2 Turbulence Model and CFD Methodology
### Meshing Requirement
#### 2.5.2 Meshing Requirement in the Mixing Zone Downstream in the LES Portion  
To meet the mesh resolution of the separating shear layer in the mixing zone downstream, [PERSON] et al. [32] recommended a minimal LES mesh resolution with which the mesh size \(\Delta\) was taken as max (\(\lambda_{R}\), \(L_{t}{}^{R}/10\)), where the Taylor microscale \(\lambda_{R}\) and the turbulent energy length \(L_{t}{}^{R}\) were both obtained from RANS estimations, as seen in Equations (18) and (19).  
\[\lambda_{R}=\sqrt{10k\
u/\epsilon} \tag{18}\]  
\[L_{t}^{R}=\frac{k^{3/2}}{\epsilon}=\frac{k^{1/2}}{0.09\omega} \tag{19}\]  
[PERSON] et al. [33] concluded that the mesh size of \(\Delta\approx\lambda/3\), where \(\lambda\) was the Taylor microscale obtained from a posteriori LES analysis, can give sufficiently accurate results, and the value of \(\lambda/3\) was found to be equivalent to the RANS Taylor microscale \(\lambda_{R}\). [PERSON] et al. [31] recommended a mesh size of \(\Delta/\eta_{R}<\) (50-100) for LES mixing layers, where the Kolmogorov scale \(\eta_{R}\) can be calculated as Equation (20):  
\[\eta_{R}=L_{t}^{R}(k^{2}/\
u)^{-3/4} \tag{20}\]  
Here, when non-equilateral cells are used, the mesh scale \(\Delta\) refers to max (\(\Delta x\), \(\Delta y\), \(\Delta z\)); in addition, \(k\), \(\epsilon\), and \(\omega\) are the turbulent kinetic energy, the turbulent dissipation energy, and the specific dissipation rate, respectively. Eventually, the mesh scale of the free shear portion was set as Equation (21):  
\[\Delta=min\ (\lambda_{R},L_{t}^{R}/10,\ 50\eta_{R}) \tag{21}\]  
As precursor RANS simulations were performed, a typical mesh size was obtained by the above estimator, that is \(\Delta\) = 2.5 mm in the free shear portion. Then, hexahedral structure grid elements were constructed in the current work.
## 3 Control Strategies
### DC-AC Four-Leg Converter Control
#### 3.2.1 Control Assumptions  
Classical AC droop control operation principles are obtained from the steady-state equations that describe the power flow between two AC voltage sources connected by an inductive line, as widely detailed in [6, 28]. A predominant resistive behaviour is adopted by using the virtual impedance concept [29, 30] to provide a reliable relationship between the sets active-reactive power and voltage-frequency, being as independent as possible from the grid impedance. The virtual resistance concept is described in Figure 4. In Figure 4a, \(u_{C}\) designs the controlled AC voltage, \(u_{PCC}\) is the voltage of Point of Common Coupling (PCC), \(R_{2}\) the physical equivalent series resistance of \(L_{2}\), and \(R_{v}\) is the value of the virtual resistance (see Figure 2).  
The adaptive virtual resistance concept is currently used for hot-swapping (soft-start) [6] and to smooth the effect of grid fluctuations. In this case, it is also applied to enhance the transference between operation modes, as is later shown in Section 4.2. It should be noted that the virtual resistance should be disabled progressively to not affect the operation of a pure V/f strategy in the grid-disconnected mode. Figure 4b illustrates the proposed behaviour of the \(R_{v}\) module during and between the operation modes. Furthermore, as it has been aforementioned, this fact makes it possible to improve the time response performance in grid-disconnected mode thanks to the master voltage role change between the mains and the converter.  
It is possible to deduce the dynamic AC control droop schemes depicted in Figure 5 under a resistive behaviour assumption and considering dynamic phasors [31]. This control schemes are the basis control schemes adopted for the grid-connected operation in the paper. In Figure 5, \(G_{ctrl}\) represents the transfer function of the AC droop control law between the node \(A\) that is the AC controlled capacitor and a node \(B\) corresponding to the PCC. For the \(G_{ctrl}\), it is assumed that a \(\tau_{f}\) time constant for emulating mechanical inertias of synchronous generators [28]. \(P^{*}/Q^{*}\)-\(P/Q\) are the active/reactive powers set-points and measured values, and \(U_{A}\) and \(U_{B}\) represent the voltage at the mentioned nodes \(A\) and \(B\). The \(\omega_{UB}\) is the angular frequency at the PCC, \(\theta_{UA}\) and \(\theta_{U2}\) are again the phases at nodes \(A\) and \(B\), respectively. Finally, \(R\) and \(L\) are the total resistive and inductive part between \(A\) and \(B\). Note that \(R\) is the addition of the real equivalent series resistance of the wiring, the output transformer involved and the forced virtual part \(R_{v}\).
## 2 RadCom System Model and Processing
### RadCom System Model  
We consider a system model as depicted in Figure 2. A data source generates random bits, which are then modulated using QAM mapping. Afterwards, the QAM symbols are rearranged in a time-frequency grid. For communication purposes, \(N_{p}\) pilots are inserted to be used in channel estimation at the communication receiver side. This grid is then shaped in multicarrier symbols using either UFMC or OFDM waveform before passing through the wireless channel following the model given in (5).  
At the receiver side, multicarrier symbols are demodulated using the corresponding waveform. Channel estimation is performed for both the communication and the radar system, but in different ways and for different purposes. For the communication system, the received frequency-domain pilots are first extracted to perform channel estimation using least-squares (LS) or minimum mean squared error (MMSE) estimation. Depending on the pilot arrangement, interpolation in the frequency or time domain (or both) is needed to estimate the channel at the nonpilot subcarriers of each symbol. Once the channel matrix is constructed, a simple zero-forcing equalizer is applied before QAM demodulation.  
For radar, however, the entire transmitted signal grid is used to perform channel estimation. Below, we focus on explaining how this processing works, which is presented in the radar receiver section in Figure 2.  
Blocks represented in yellow are only needed in the case that MC-CDMA is deployed. User symbols are first spread over the frequency domain using unique uncorrelated codes before grid mapping, and despreading is only triggered by the communication receiver in order to decode the user signals.  
In the receiver side, the MC-CDMA system suffers from huge performance degradation due to multiaccess interference (MAI) and ISI. Thus, many receivers implementing equalization and multiuser detection were studied for better interference mitigation. The first studied multiuser detector is the maximum likelihood (ML) detector, offering close to optimal results, but it is highly complex. Other blind joint equalizers, such as MMSE, were considered, but for a vehicular environment, training sequences are mandatory to perform multiuser detection, degrading the data rate on the other hand. In [19], a two-stage receiver was proposed and proved to surmount many state-of-the-art receivers. In our case, this receiver could not be implemented due to its unsustainable implementation complexity in uplink scenarios. Regardless of all the above-mentioned receivers and their performance, we chose to implement a decorrelator receiver that was a single user detector that offered a great trade-off between performance and complexity.  
Lastly, the \"filter equalization\" block, presented in pink, is used only for UFMC-modulated symbols.
# Deformation Characteristics of Mechanical Seal Face under Alternating Loads
## 3 Results
### Stable Working Conditions
#### 3.1.2 Rotational Speed  
Figure 5 presents the axial displacement of the end face of the rotating ring along the radial direction under different rotational speeds. The deformation of the seal face increases significantly as the rotational speed increases. This is attributed to the increased stress and friction heat on the seal face based on the increased rotational speed. The higher the rotational speed, the larger the taper of the end face caused by the thermal deformation and coupling deformation.  
In addition, the thermal deformation of the rotating ring is greater than that of the coupling deformation. When the rotational speed is 5040 rpm, the maximum displacement of the end face during thermal deformation is about 1.1 \(\upmu\)m, as shown in Figure 5a, and larger than the maximum thermal-mechanical coupling displacement which is about 0.85 \(\upmu\)m, as shown in Figure 5b. This indicates that the gap generated at the outer diameter of the end face under thermal deformation is greater than that of the coupling deformation.  
For the minimum displacement, the thermal deformation of the rotating ring is closer to the inner diameter of the end face, as shown in Figure 5a, and the minimum thermal-mechanical coupling deformation shown in Figure 5b also gradually approaches the inner diameter with the increase in the rotational speed. The main reason is that the temperature difference between the inner and outer diameter of the end face gradually increases with the increase in the rotational speed due to the contact friction at the inner diameter and the strong convective heat transfer at the outer diameter. Thus, the degree of convergence at the inner diameter and the degree of divergence at the outer diameter of the end face are increasing. The inner diameter side of the end face is more prone to extrusion and  
Figure 4: Axial displacement of the rotating ring along the radial direction under different medium pressures: (**a**) Thermal deformation; (**b**) Thermal–mechanical coupling deformation.  
localized wear based on the increased rotational speed. Therefore, as much as possible, the mechanical seal should not be kept in high-speed operation for a long time. In addition, the damage to the mechanical seal caused by thermal stress and thermal deformation can be reduced by reducing the heat generated by friction and improving the heat transfer effect of the system.
# Application Characteristics of Bioethanol as an Oxygened Fuel Additive in Diesel Engines
## 3 Results and Discussion
### Emissions  
#### 3.3.1 Co  
Figure 7 displays the CO emissions for all the tested fuels under different engine loads. CO emissions from diesel engines are mainly caused by incomplete combustion, which largely depends on the cylinder temperature. The lower the temperature, the more CO is generated [21]. In Figure 7, it can be clearly seen that the CO emissions of all the tested fuels decrease with the increase in the engine load. The specific result is that the CO emissions of all fuels at 60 Nm and 80 Nm were reduced by 35.83% and 39.72% on average compared with that at 40 Nm, respectively. This finding is due to the increase in the combustion temperature in the cylinder with the increase in engine load, which is beneficial to the oxidation reaction of CO. On the other hand, at the low load of 40 Nm, CO emissions for blended fuels show a decreasing trend with the addition of ethanol. This may be related to the high evaporation latent heat of ethanol. However, with the increase in the engine load, the temperature in the cylinder is increased, which counteracts some negative impacts of the high evaporation latent heat of ethanol. Therefore, under the high load of 80 Nm, the CO emissions of blended fuels were lower than that of diesel. Specifically, the CO emissions of E5, E10 and E15 were decreased by 16.13%, 8.07% and 25.81% compared with E0, respectively. Moreover, the reduction in CO emissions caused by the addition of ethanol is also related to the oxygen content of ethanol, which improves the shortage of air in the rich oil area and the post-flame oxidation of CO in the late combustion process. This is similar to the result from [21].
## 3 Implementation of the Model-Based Algorithm and the Experimental Setup
### Simulation Setup
The standard deviation of the mean is given as the measurement uncertainty. simulations, the systematic and the random errors of the wedge position \(x_{i}\), the wedge size \((h_{i},w_{i})\) and the wedge area \(A_{i}\) are investigated. For the investigation of the dependency of the algorithm on the CNR, the CNR of a single turbulence wedge is changed in a range of 2 to 20 by changing the pixel intensity of the turbulence wedge. The choice of the CNR range places the mean value of \(\langle\text{CNR}\rangle=10\) found in real thermograms in the middle of the range, but also covers more extreme CNR values. Varying the intensity of the turbulence wedges instead of changing the amount of noise in the image is more realistic, as the noise in real thermograms stays approximately constant throughout the image. To investigate the dependency of the algorithm on the size of the turbulence wedges, the height of the turbulence wedge in the thermogram is changed systematically in a range of \(0.1\leq h_{i}/h_{\text{max}}\leq 1.0\). Due to the identified relationship \(\langle h_{i}/w_{i}\rangle=3\), the wedge width is changed accordingly. The CNR value of the turbulence wedge, however, remains constant at \(\text{CNR}=11\) throughout this analysis.
## 4 Abstract Concept Modeling with RANs
### Assumptions and Boundaries  
The necessary boundary related to input data is, data value should be between \"0' and \"1\" (both inclusive), this limitation has its inspiration from biological neurons. A value \"0\" indicates neuron (or node) is inactive, whereas \"1\" shows the neuron is highly active. The model is, by design, applicable only to multidimensional data sets where each feature takes A real value between 0 and 1--It works as well for discrete data sets where the variables take either 0 or 1 values. If the user data is in a different format, the user must define the transformation and inverse transformation of the data. The following are a few possibilities of such alterations for some of the most common kinds of data:  
* If a variable in the input data is categorical, e.g., _blue; green; red_, transform the data using One Hot Coding technique.
* If a variable in the input data is numerical, bounded within a minimum and a maximum value it can be normalized into \([0,1]\), e.g., via \(\frac{\mathit{value}-\mathit{min}}{\mathit{max}-\mathit{min}}\);  
The user must implement these and the inverse transformation functions to interpret the results obtained from our model. Since our technique is designed to work with multi-variate data-sets, where each data value is a point in conceptual space, we assume that the data being used is compatible with the requirements. Though images are a form of multivariate data, pictures are not ideal candidates to be interpreted as points in conceptual spaces, (discussed in Section 3.2). For this reason, our approach will, most probably, underperform on image processing tasks against other models that are, individually, designed for these kinds of data, such as deep representations built with Convolutional Networks [47; 54; 55]; our technique is preferably suitable for understanding and simulating cognitive processes like abstract concept Identification. The version of RAN in this article can model data that consists of convex groups of data points, therefore, the model does not perform well well with the complex data having non-convex groups of data points. Modeling non-convex concepts is one of the ongoing research in RAN's modeling and out of the scope of this article but readers who are interested in knowing more can refer to the published research work [56].  
To use the RANs approach provide the data to the model with an additional header stacked over the data. The size of the header is the same as the dimension of the input data vector, and each header element holds the largest value of their corresponding input data attribute. See Appendix A.1 for elaboration.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 2 Advantages and Challenges of mmWave Industry 4.0 and beyond Communication
### Advantages of mmWave Communication
#### 2.1.3 Efficient Spectrum Reuse  
Since mmWave beamforming with large arrays produces highly directional beams, thus the effect of co-channel interference is greatly reduced. To benefit from this inherent feature, MIMO or multibeam antenna techniques can be employed to reuse the spectrum resource and produce redundance radiation beams. This will provide diversity gain and multiplexing gain, in addition to array gain [69]. This way, spatial redundancy, and diversity can be efficiently used to reuse the spectrum in industrial communication.
## 2 Overview of Blockchain Technology
### Blockchain Framework
#### 2.3.3 Application Layer  
The application tier has a variety of purposes for which blockchain can be utilized. Cryptocurrency, smart contracts, supply chains, networking, healthcare, insurance, voting,  
Figure 6: The structure of the blockchain framework.  
and intellectual property stand out as eight of the most dominant blockchain applications that are worth discussing briefly.  
Cryptocurrency  
The two most widely used cryptocurrencies built on blockchains are Ether and Bitcoin. Without the help of reputable centralized institutions like banks, financial transactions may be carried out securely and reliably using cryptocurrency [115]. The blockchain for Bitcoin offers a public ledger for every transaction, guaranteeing the transactions' immutability [2]. Contrarily, self-executing contracts may be created on Ethereum's blockchain, which pave the way for decentralized finance bound by contractual terms and conditions [81].  
A smart/self-executing contract is a blockchain application tier implementation that specifies contractual statements of an agreement that are automatically executed when certain events occur [116]. It can be deployed on a platform based on blockchains. Once deployed, the contract is immutable due to the immutable nature of the blockchain, so its integrity can be trusted. They eliminate the requirement for an intermediary to enforce the terms and conditions of contracts, so that business contracts can be automated using smart contracts. Self-executing contracts could be deployed for restricting access to ensure that only individuals with proper authorization can use particular assets [117]. The stipulations of the smart contract are able to be employed to establish the requirements for permitting access to certain assets, such that control over the resources will be provided upon meeting the access-granting conditions. However, all transactions that are executed upon reaching some condition of the contract are written to the blockchain. Smart contracts help automate transactions strictly following contractual conditions and thus result in reduced operational costs compared to conventional contract operations. Formal methods that are mathematical approaches to modeling and testing software to make sure that it operates in the desired manner can be deployed to verify the functioning of smart contracts in order to prevent costs and security breaches that occur in the case of smart contract errors or vulnerabilities [118]. These formal models can capture user behaviors and blockchain properties in the process of smart contract verification during execution, unlike traditional approaches for smart contract verification [119].