esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results  
A 30-bus factory power system, as shown in Figure 10, is used to illustrate the results of the simulation. The micro-turbine generators are at buses 3-6. Table 1 presents the cost coefficients and MW limits of these generators. Table 2 presents the other operational parameters of these generators. The solar PV generations from 7:00 a.m. to 6:00 p.m. at buses 7-10 are modeled using Gaussian distributions, as shown in Figure 11. The standard deviation is set to 3% of the mean value (see page 17 in [42]); that is, the range of PV generation can be estimated approximately to be (the mean value) \(\times\) (1 \(\pm\) 3 \(\times\) 3%), which covers 99.73% of possible PV generation cases. The maximum sizes of these four arrays are 450, 350, 400 and 500 kW. This factory purchases/sells power from/ to the utility in a manner determined by the time-of-use prices, as shown in Table 3. In addition to inelastic loads, this factory has five production lines with elastic loads at buses 6, 16, 18, 20 and 26, as shown in Table 4. For example, the total energy consumption of the production line at bus 6 must be 75 MWh (10 \(\times\) 7.5) daily to produce the required amount of products. Other bus data are given in Appendix A.  
A personal computer with Intel (R) Core (TM) i5-2500 CPU @ 3.3 GHZ and 8 GB RAM was used to develop a MATLAB (R2011a, The Mathworks Inc., Natick, MA, USA) code to study the problem. Many scenarios (experimental designs) were studied: PV consideration (see Sections 4.1 and 4.2), different electricity tariffs, \(M_{i}^{on}\) and \(M_{i}^{off}\), ramp rates, and must-run hours (see Section 4.3). For each scenario, the MATLAB code was run 20 times and the best fitness in these 20 results was considered as the optimal solution. It was found that the proposed novel GA always converges in a finite iterations. Section 4.5 gives the statistics of convergence performance of the proposed method.  
Figure 9: Flowchart of the proposed method.  
Figure 11: Mean values and standard deviations of PV power at different buses: (**a**) bus 7; (**b**) bus 8; (**c**) bus 9; and (**d**) bus 10.  
Figure 10: One-line diagram of studied power system.
## 5 Open Research Issues and Challenges
### Enhancing QoS  
QoS provision is also a major concern. QoS requirements vary according to application and operating environment. QoS support is a challenging issue because of the numerous resource constraints, such as limited power, bandwidth, memory, processing power, _etc._, in CRBANs. Another challenge in CRBANs is to protect the rights of PUs to access incumbent spectra. Primary users' communication should be free of interference from secondary users. Priority-based scheduling schemes can be applied to provide context-based QoS differentiation. A greater challenge is accurately predicting a PU's arrival at the channel. False alarms and missed detections of primary users pose further challenges in CRBANs.
## 5 Results and Analysis
### Key Performance Indicators
#### 5.5.1 Energy Indicator  
The household demands on the four chosen days were 9.86 kWh, 10.40 kWh, 14.65 kWh, and 11.75 kWh, respectively. The late autumn had the highest electricity need. In the study, the demand was met by the wind energy and the fuel cells. Taking the spring day for example, the wind energy was 9.99 kWh. The energy from fuel cells was 6.25 kWh and that for electrolysis was 6.36 kWh. The total energy required for the load was \(9.99+6.25-6.36=9.88\) kWh. However, the load consumed 9.86 kWh and the rest was a power loss in the process (0.02 kWh). The losses in the rest days were 0.99 kWh, 0.94 kWh, and 0.04 kWh, respectively.  
It can be seen that autumn and winter days consumed more energy than spring and summer. Electricity from fuel cells was used to make up the difference on these selected days. In addition to supplying the load, the wind power is partially used to generate hydrogen so as to store energy for later consumption. Extra wind power is fed into the grid when the winter day has more wind power than local need.
## 4 Measured Results
Figure 11: Simulated and measured results of the proposed antenna: (**a**) S-parameters \((S_{11},S_{21})\) and (**b**) axial ratio (AR) of the proposed antenna when port 1 is excited. Figure 12: Simulated and measured realized gain of the proposed antenna as a function of frequency. Figure 13: Simulated and measured normalized co- and cross- polarized radiation patterns at f = 3.7 GHz of the proposed antenna for port 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Antenna Dimensions at \(\lambda_{o}\)** & **Feeding Structure** & \begin{tabular}{c} **-10 dB BW** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **3 dB ARBW** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Single** \\ **Element** \\ **Gain (dB)** \\ \end{tabular} \\ \hline
[12] & \(0.36\lambda_{o}\times 0.36\lambda_{o}\times 0.04\lambda_{o}\) at 3.7 GHz &
\begin{tabular}{c} One MS-line + two pairs of \\ unequal slots \\ \end{tabular} & 10.9 & 4.12 & 2.3 \\
[17] & \(0.78\lambda_{o}\times 0.78\lambda_{o}\times 0.128\lambda_{o}\) at 2.425 GHz & Two MS-lines + 1 square ring slot & 7 & 8.1 & 5.1 \\
[18] & \(1.456\lambda_{o}\times 1.456\lambda_{o}\times 0.013\lambda_{o}\) at 2.4 GHz & Four port beam-switching & 2.4 & 1.84 & 3.3 \\
[19] & \(2.1\lambda_{o}\times 2.1\lambda_{o}\times 0.026\lambda_{o}\) at 10 GHz & Two port arrays & 5.5 & 10 & 2.4 \\
[35] & \(0.33\lambda_{o}\times 0.33\lambda_{o}\times 0.019\lambda_{o}\) at 3.6 GHz & One MS-line + two pairs of slits & 13.8 & 4.65 & 3 \\  & & and chip resistors & & & \\
[36] & \(0.83\lambda_{o}\times 1.25\lambda_{o}\times 0.006\lambda_{o}\) at 2.5 GHz & Two MS-lines + two patches & 3.2 & 2.2 & 6 \\ This work & \(0.81\lambda_{o}\times 0.43\lambda_{o}\times 0.019\lambda_{o}\) at 3.7 GHz & One hybrid coupler + four equal slots & 10.6 & 8.2 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the proposed design with some important designs related to some other important designs.
# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 5 Comparison and Analysis of Experimental Results
### Impact of Low Carbon Constraints
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Example Set** & **Reduction in** & **Reduction in Total** & **Reduction in Carbon** \\  & **Transportation Cost (\%)** & **Cost (\%)** & **Emissions Cost (\%)** \\ \hline \hline \multirow{6}{*}{Average} & C101 & 0.8323 & \(-\)0.4088 & \(-\)7.9097 \\  & C201 & 0.4662 & \(-\)0.5230 & \(-\)5.8763 \\  & R106 & 0.1869 & \(-\)0.2729 & \(-\)2.6833 \\  & R206 & 0.0083 & \(-\)0.6535 & \(-\)4.9924 \\  & RC108 & 1.7616 & \(-\)0.4373 & \(-\)7.7308 \\  & RC208 & \(-\)0.1791 & 0.1489 & 1.0440 \\  & & 0.5127 & \(-\)0.3578 & \(-\)4.6914 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The reduction in cost for two models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Example Set** & **Number of Vehicles** & **Transportation Distance Cost** & **Total Cost** & **Carbon Emissions Cost** \\ \hline \multirow{6}{*}{X} & C101 & 8 & 600.0125 & 4917.2758 & 317.2633 \\  & C201 & 5 & 536.1564 & 3379.4549 & 343.2985 \\  & R106 & 6 & 1090.2500 & 4637.8854 & 547.6354 \\  & R206 & 5 & 941.6520 & 3961.8207 & 520.1687 \\  & RC108 & 6 & 576.5320 & 3930.2307 & 353.6987 \\  & RC208 & 6 & 851.2321 & 4321.4887 & 470.256 protecting the environment (companies can better meet future challenges and lay a solid foundation for long-term development) but also enhances brand image and reduces carbon emission costs by showing that companies are actively fulfilling their responsibility to protect the environment, which helps to build a good corporate image and attract more environmentally conscious customers. 2. Comparison of distribution paths  
Using C201 as an example, we explore the variations in delivery routes for two objective models, as shown in Tables 7 and 8. A comparison of the delivery routes for the two objective models in Tables 7 and 8 reveals that the number of vehicles remains unchanged, and the majority of delivery routes are the same. Despite the high similarity in delivery routes between the two models, they exhibit significant differences in terms of carbon emission costs and total costs. In the objective model that considers carbon emissions, the average transportation cost increased by 0.5127%, while the carbon emission cost decreased by 4.6914%, resulting in an average reduction of 0.3578% in total costs, as shown in Table 6. This reduction may be attributed to the prioritization of serving customers with high demand and distant locations in the model considering carbon emission costs. Despite the increase in transportation costs, the decrease in carbon emission costs led to an overall cost reduction. The comparison of the models indicates that the vehicle routing model considering carbon emissions can effectively reduce both carbon emission costs and total costs. Furthermore, the improved genetic algorithm proposed in this study significantly impacts the resolution of vehicle routing problems related to carbon emissions.
## 2 Methodology
### Identification of Mode Shapes  
To obtain the mode shape of a specific mode, it is necessary to consider the single-mode responses of the structure at all sensor locations. The modal vector is constructed from the single-mode response values at all sensor positions at an identical time instant tm, which is typically selected at a local minimum or maximum. Accordingly, the modal vector of the \(k\)-th mode for the structure can be expressed as follows [47]:  
\[\Phi_{k}=\Bigl{\{}S_{k,1}(t_{m}),S_{k,2}(t_{m}),\cdots,S_{k,j}(t_{m}),\cdots,S _{k,N}(t_{m})\Bigr{\}}^{T}, \tag{4}\]where \(S_{k,j}\) represents the modal response of the \(k\)-th mode at the \(j\)-th sensor location, \(t_{m}\) is the time instant when a local minimum or maximum of the modal response occurs, and \(N\) is the total number of sensors. Figure 2 illustrates the process of identifying the \(k\)-th mode shape using signals from three sensors.  
The modal assurance criterion (MAC) [48] serves as a quantitative measure of the consistency (or degree of linear correlation) between estimates of a modal vector. The MAC enables the comparison of modal vectors obtained from different sources, thereby allowing for an assessment of the consistency between FE analysis results and field-measured results. The MAC is defined as follows [48]:  
\[\text{MAC}_{cdr}=\frac{\left|\{\psi_{cr}\}^{T}\{\psi_{dr}^{*}\}\right|^{2}}{\{ \psi_{cr}\}^{T}\{\psi_{cr}^{*}\}\{\psi_{dr}^{*}\}^{T}\{\psi_{dr}^{*}\}}, \tag{5}\]  
In this equation, \(\{\psi_{cr}\}\) is a modal vector for reference \(c\) mode \(r\), and \(\{\psi_{dr}\}\) is a modal vector for reference \(d\) mode \(r\). MAC is between 0 and 1, with 0 representing no consistent correspondence and 1 representing consistent correspondence.
# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 3 Materials and Methods
### The Proposal of BA-SVR Hybrid Model  
The main model constructed in this paper is a hybrid model of BA and SVR, and the main principle lies in the optimal estimation of the three parameters \(C\), \(\epsilon\), and \(\sigma\) in the SVR model using the bat algorithm to achieve the initialization of the three parameters in the SVR model under the optimal prediction accuracy.  
The steps of the hybrid BA-SVR prediction algorithm model are as follows.  
* Set the parameters of the BA-SVR model; The parameters that need to be set include the maximum number of iterations of the bat algorithm \(maxgen\), the size of the bat population (that is, the number of bats) \(N\), the minimum loudness \(A_{min}\), the loudness attenuation coefficient \(\alpha\), the rate enhancement coefficient \(\gamma\) for controlling the pulse rate, and the maximum frequency \(f_{max}\) and minimum value \(f_{min}\), random movement step length (that is, the maximum value \(v_{max}\) and minimum value \(v_{min}\) of moving speed \(v_{i}\)), the number of parameters to be optimized, and the range of parameters to be optimized. Among them, if the linear kernel function is selected, the free parameters that SVR needs to optimize are the penalty factor \(C\) and the insensitive loss factor \(\epsilon\). If the RBF Gaussian radial basis kernel function is selected, the free parameters to be optimized for SVR also include the width parameter \(\sigma\) of the Gaussian radial kernel function.
* Initialize the bat population;This step includes pulse emission rate initialization, pulse loudness initialization, population initialization, flight speed initialization and pulse frequency initialization. Generally speaking, when initializing the bat population, it is often considered that the bat population is randomly distributed in a search space of a certain dimension, and the search dimension is the number of parameters that need to be optimized.
* According to the setting of the model iteration of the bat algorithm, the bat is constantly looking for the optimal position in the search space, that is, constantly generating new solutions;
* Calculate the fitness function; As the basis for selecting the best position (optimum solution) in the BA-SVR model, the adaptability functions generally include MSE, RMSE, MAPE and other functions. This paper refers to the literature [20], and uses the mean square error (MSE) as the fitness function of the model to verify the model, \(\text{MSE}=\frac{1}{n}\sum_{m}^{i=1}w_{i}(y_{i}-\hat{y}_{i})^{2}\)
* Get the optimal solution; According to the preset maximum number of iterations, when the number of iterations reaches the maximum number of iterations, the iteration is stopped, and the optimal solution is obtained. At this point the algorithm ends.
## 3 Mathematical Model and Steady State Solution
### Mathematical Model
#### 3.2.2 Computation of the Stationary Distribution  
If \((b_{b}\lambda+\
u)P_{B}<\
u-\frac{\delta}{\gamma+\delta}\frac{b_{r}\lambda+\
u }{\sum_{i=0}^{\delta}a_{i}}\), let the limiting probabilities for the stationary distribution be denoted as  
\[\pi_{i,j,k}(n)=\lim_{t\rightarrow\infty}Pr\{n_{1}(t)=i,\ n_{2}(t)=j,\ L(t)=n, \ X(t)=k\},\ (i,\ j,n,\ k)\in\Omega.\]  
Denote the stationary probability vector of the matrix \(\mathbf{Q}\) as \(\boldsymbol{\pi}\). We also partitioned the vector \(\boldsymbol{\pi}\) as \(\boldsymbol{\pi}=[\boldsymbol{\pi}(0),\boldsymbol{\pi}(1),\boldsymbol{\pi}(2 ),\ldots]\), where \(\boldsymbol{\pi}(n)=[\boldsymbol{\pi}_{\psi}(n),\boldsymbol{\pi}_{0}(n),\ \ldots,\ \boldsymbol{\pi}_{c}(n),\ \boldsymbol{\pi}_{r}(n)]\) and  
\[\boldsymbol{\pi}_{i}(n)=\begin{cases}[\pi_{0,0,0}(n),\ \pi_{1,0,0}(n),\ldots,\ \tau_{c,0,0}(n)],i=v,\\ [\pi_{0,j,1}(n),\ \pi_{1,i,1}(n),\ldots,\ \pi_{c-i,j,1}(n)],i=0,1,\ldots,c _{,}\\ [\pi_{0,0,2}(n),\ \pi_{1,0,2}(n),\ldots,\ \pi_{c,0,2}(n)],i=r.\end{cases}\]  
Using matrix geometric method, we have \(\boldsymbol{\pi}(n)=\boldsymbol{\pi}(0)\mathbf{R}^{n}\) for \(n\geq 0\), and \(\boldsymbol{\pi}(0)\) meets the following equations  
\[\boldsymbol{\pi}(0)\Big{(}\mathbf{A}^{0}+\mathbf{R}\mathbf{A}^{-}\Big{)}=0, \tag{5}\]  
\[\boldsymbol{\pi}(0)\Big{(}\mathbf{I}_{(c+2)(c+3)/2}-\mathbf{R}\Big{)}^{-1} \mathbf{e}_{(c+2)(c+3)/2}=1. \tag{6}\]  
Therefore, once \(\boldsymbol{\pi}(0)\) is obtained by solving Equations (5) and (6), all other stationary probabilities are easily obtained by using \(\boldsymbol{\pi}(n)=\boldsymbol{\pi}(0)\mathbf{R}^{n}\) for \(n\geq 0\).
## 4 Conclusions  
In this article, a comparative study highlights the difference between the two techniques of controlling a low-power PV system. One technique used the fuzzy logic MPPT and fuzzy PI controllers in the control loop and SOGI-PLL in the grid synchronization. The other was based on incremental conductance MPPT, classic PI controllers, and classic PLL grid synchronization. Theoretically and through simulations in the Matlab/Simulink software, the perspectives were established and contrasted based on the strengths and disadvantages of the two techniques. Based on theoretical analyses and results of the simulations, we can draw the following conclusions:  
* Intelligent techniques based on FLC and ANN are very sensitive and consume significant computational resources.
* The AI method based on fuzzy logic presents advantages over the InC method. These advantages are mainly due to the fuzzy MPPT controller, which offers a better extraction of the maximum power from PV modules.
* The fuzzy PI regulators help improve the control loop of the single-phase inverter and reduce the transient response comparatively with classic PI.
* The transient regime represents 1.5% from simulation time, allowing the system to reach fast (0.03 s) in tracking the maximum power. Moreover, the proposed model converts solar energy with a performance of 99.8%. On other existing research [29; 30], several energy conversion performance results are presented, where the best fuzzy logic technique reported a 99.19% conversion efficiency with a 1.7 % transient regime of the simulation time.
* The SOGI-PLL grid synchronization mechanism with the utility grid is more powerful with a higher stability margin than the classic PLL.  
Consequently, the FLC technique proves to be fast, flexible, and robust. These benefits represent key features for spontaneous change situations in environmental conditions. Moreover, fuzzy logic helps the inverter convert over 99% of the power generated by the photovoltaic panels, while the incremental conductance algorithm converts around 80%. In conclusion, energy losses in the proposed PV system are more significant for InC approaches when compared with the FLC method.  
Figure 21: Comparison between the output powers of the PV system to the irradiance variation (InC vs. FLC).  
**Author Contributions:** Conceptualization, methodology, validation, supervision, project administration, funding acquisition, review and editing was done by L.A.S.; Conceptualization, methodology, software, investigation, data curation, writing--original draft preparation, visualization was done by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Romanian Ministry of Education and Research, grant number PN-III-P2-2.1-PED-2019-2601, \"REGRENPOS\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 3 Interference Management
### Recent Advances in Interference Management
#### 3.1.3 Interference Cancellation  
Interference cancellation techniques use advanced decoding and coding schemes for the cancellation of interference signals at the DUE or CUE. The techniques used can enhance the cellular network's capacity. In [44], [PERSON] et al. proposed an interference mitigation algorithm that is based on the guard zone by allowing the capabilities of successive interference cancellation at the BSs. The D2D users are obligated to use the original cellular mode when they are in the range of a specific geographical area inside a cell. The result shows the improvement in the average throughput of a user equipment (UE) and the successful transmission probability achieved using the proposed scheme. [PERSON] et al. in [45] investigated the performance analysis of interference cancellation and beamforming with multiple antennas equipped at the BS. However, the authors equipped the users with a single antenna. For the system analysis, the ergodic process is used with some appropriate approximations. The result is quite similar to the Monte-Carlo results. In [46], [PERSON] et al. proposed a peer-to-peer (P2P) system called \"Lunius\" that is based on a harvesting data approach in a multihop D2D communications community. The system improves the user experience, optimizes P2P local-file-sharing in the D2D communications, and offloads traffic from the BSs. The system features cross-layer integration of the application layer P2P protocols based on interference cancellation scheme, Bittorrent protocol, and also an enhanced multihop D2D routing scheme called the Greedy Parameter Stateless Routing Protocol (GPSR), in order to maintain Radio Resource Management (RRM) mechanism and multihop communications to increase the total throughput while, at the same time, assuring the QoS requirements of user equipment. [PERSON] et al. [47] investigate nonorthogonal multiple access (NOMA) in a D2D 5G environment. The authors proposed an improved NOMA-based approach that uses multiple interference cancellation (MIC) techniques instead of the successive interference cancellation (SIC). The results show that MIC is better than SIC in terms of providing better performance and low complexity. The proposed MIC scheme demonstrates its effectiveness by optimizing the power consumption of the overall circuit. However, the authors did not highlight the reduction in the latency of the network. In [48], [PERSON] et al. study the D2D communication using a two-way decode-and-forward relay node. Beamforming and Interference Cancellation (IC) strategies were analyzed for asymmetric and symmetric outage probabilities. The result shows that there are some factors that correlate with the system performance, such as the installation of a large number of antennas in the BS, which will lead to outage probability of the cellular link prone to be zero.
## 1 Introduction
Figure 3: (**a**) Schematic illustration representing preparation of the assembly film composed of azotolane crosslinked liquid crystalline polymers (CLCP) film (**b**) Photographs of the as-prepared assembly film bending toward the light source along the alignment direction of the mesogens in response to the 635 nm laser with the power density of 200 mW cm\({}^{-2}\) (Thickness of each layer in the assembly film: 15 μm of upconverting film and 27 μm of CLCP) (**c**) Schematic illustration demonstrating plausible mechanism for the photoinduced deformation of the as-prepared assembly film, Adapted with permission from [50], ACS, 2013. (**d**) _Trans–cis_ isomerization of azobenzene molecule. Since many scientific efforts have been put forward to improve the photo-mechanical effect, the research works carried out by [PERSON] et al. [53; 54] interestingly studied the effect of different lengths of photo-active crosslink spacer (4,4'-dialkoxyazobenzene) on opto-mechanics of light-controlled actuators. The authors also proposed the incorporation of 4-alkoxy-4'-cyanoazobenzene chromophores as side chain monomers to nematic LCEs and obtained characteristic kinetic parameters for the thermal isomerization of this cyano-substituted azo chromophore and the final photo-actuator behavior was explained in detail [54]. The recent passionate growth in soft robotic designs offers new opportunities to construct simpler structures using soft polymeric materials like LCEs and dielectric elastomers. These can be utilized depending on the design objectivity and mechano-compatibility in soft robotics. The diversified approaches show that these stimuli-responsive soft materials have several applicational advantages in the development of artificial muscle-like actuators. Because of the combination of orientational order of liquid crystals and the elasticity of conventional rubbers, these materials can easily implementable in a wide range of technological fields from micromechanical systems to soft robotic haptic interfaces [55; 56; 57; 58; 59; 60]. Figure 4: Chemical structures of azobenzene chromophores (monomer and cross-linker) used to prepare liquid-crystalline gels (LCGs) by [PERSON] et al. [51]. Figure 5: Sketch and bending snapshots of azobenzene-doped liquid-crystal polymer (azo-LCP) at 445 nm (**Fast-Top**) and 365 nm (**Slow-Bottom**), respectively, representing trans-cis-trans reorientation (TCTR) and photoisomerization mechanisms. Adapted with permission from Reference [52], ACS, 2018.