esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 1 Introduction  
The most important challenge for 5G networks is to face the huge increase in mobile data traffic. One of the promising technologies to improve spectral efficiency (SE) is the use of non-orthogonal multiple access (NOMA) [1, 2]. NOMA techniques are able to support the incorporation of billions of Internet of Things (IOT) devices with a more efficient use of spectrum [3]. In power domain NOMA, users with poor channel gains are assigned more power than users with better channel gains [4]. Users with poor channels deal with interference as noise, whereas users with higher channels depend on SIC to eliminate the interference. One of the main challenges for applying SIC is the need to have perfect channel state information (CSI), which necessitates the use of downlink pilot signaling. Additionally, computational complexity is another obstacle in the execution of SIC [5]. Moreover, NOMA has multiple challenges, e.g., dynamic user pairing algorithms, receivers utilizing low complexity SIC, and optimal resource allocation schemes [6, 7, 8, 9, 10, 11]. One of the crucial NOMA schemes is the sparse code multiple access (SCMA) scheme, where the bits are mapped to sparse codewords by the SCMA encoder after multi-dimensional modulation and low density spreading [12]. The major advantage of the SCMA schemeis the provided shaping gain. Another important NOMA scheme is the non-orthogonal pattern division multiple access (PDMA) scheme proposed in [13]. Data symbols of users are guaranteed to be of adequate diversity disparity and power disparity by careful design of the patterns of different users. This improves the performance by allowing the use of low-complexity SIC-based multi-user detection. On a parallel theme, massive MIMO technology has been proposed to improve the SE of 5G communication networks [14] and enhance system capacity [15]. In massive MIMO, the base station (BS) communicates simultaneously with multiple mobile stations (MSs) on the same time-frequency resources. Massive MIMO systems can support hundreds or even thousands of antenna channels in the array [16]. The possibility of applying large antenna arrays faces many challenges, e.g., high complexity and power consumption [17; 18]. The integration of massive MIMO and NOMA offers a great opportunity to improve the SE in 5G and beyond. EE is now an important demand in designing wireless communication networks. Typically, EE is defined as the number of reliably transmitted bits per one joule [19]. It shows the cost paid in power consumption to afford a certain throughput as a service quality metric. The considered energy efficiency optimization problem is a joint user pairing and power allocation problem. The problem is decoupled into two subproblems for less complexity. The user pairing subproblem is solved by applying three different pairing methods: Hungarian, Gale-Shapley, and correlation. The second sub-problem of power allocation is formulated as a fair energy efficiency maximization problem and solved by applying sequential convex optimization.
## 3 Results and Discussion
### Quantitative Analysis of Biospeckle Activity  
Arithmetic means and medians of BA indices for an ROI of 51 \(\times\) 51 pixels, calculated for the THSP obtained from a single column, are presented in Table 1.  
Analysing means in F0, F24 and F72 groups, it may be noticed that IM and BAI had the greatest discrimination potential because their values differed the most between groups. The highest disproportions between groups were observed for means and medians of the IM indicator. The IM mean in the F24 group was 3.35 times higher than in the F0 group, whereas the mean calculated for F72 exceeded it more than seven times, and being higher than the mean in group F0 by about 25 times. Similar means proportions were observed for the BAI indicator which was more than 23 times lower in F0 and five times lower in the F24 group compared to the F72 group. AVD means, as well as NAD means, were not so varied, whereas mean value of RVD had the lowest variation between groups. Taking into account the mechanism of calculating IM as the squared distance from the NCOM diagonal, it was not a good measure of BA in the case of regions with heterogeneous activity, as in our experiment. It should be also noted that in F24 and F72 stages, high disproportions between means and medians of some parameters were observed, which is probably the effect of differentiation between biological samples. Nonetheless, this fact may definitely affect numerical analysis, especially evaluation of differences between groups. As may be noticed, differences between means and medians of IM proved to be considerable, indicating a strong skewness which was of 1.6, 3.36 and 2.78 in the F0, F24 and F72 groups, respectively. High skewness was also observed for the AVD indicator (2.93 and 1.7 in F24 and F72 group, respectively) as well as for the NAD variable within the F24 group. For this reason, homogeneity of variances in groups was tested, which in each case resulted in rejection of the hypothesis concerning their equality. Therefore, significance of differences between groups was tested using the non-parametric Kruskal-Wallis test for ranks. Results showed no significant differences between groups in terms of RVD values. As for AVD, IM and NAD, differences were significant only between F24-F72 and F0-F72 group pairs, as marked in Table 1. The only variable with values significantly different in all groups was BAI, based on correlation with THSP. Similar to the GD method with correlation of THSP, BAI considers non-consecutive frames in an image sequence, and is more sensitive to subtle and local changes of BA levels, being robust to incidental noise in the spatial domain at the same time. Taking into account sensitivity to heterogeneity of activity areas observed in the case of IM, we decided to check BA indicators in terms of their ability to discriminate between groups of fruits depending on ROI size. Basic statistics for tested BA indicators calculated from THSP obtained from a single column of different sizes are shown in Table 2.
## 3 Applications of Game Theory in WSN
### Game Theory for Wireless Communication in WSN
#### 3.2.1 Wireless Communication  
[PERSON] and [PERSON] [135] analyzed effects of misbehavior based on game theoretic approaches to design a mechanism which would enable wireless terminals to select reliable partners in the presence of uncertainty. The reputation mechanism introduced would lead to a perfect Bayesian equilibrium. [PERSON] _et al_. [58] used a Bayesian game-theoretic approach to model transmission control in energy-harvesting WSN. In this problem, the energy state of an energy-harvesting sensor varies more dramatically with time as compared to traditional battery-powered sensors. Each sensor decides its transmission strategy according to its belief of its opponents' energy states. There exists a Bayesian Nash Equilibrium (BNE) where a sensor with energy higher than its energy threshold. Authors of literature [58] showed how each sensor determines its threshold to maximize its utility function. They demonstrated via simulations that the performance of the Bayesian game model is close to that of a perfect-information game where energy states are common information to all sensors. In addition, since the Bayesian game has the advantage of requiring less information exchange overhead, it seems to be more feasible to implement than the perfect-information game.  
[PERSON] and [PERSON] [45] proposed a game-theory-based cross-layer optimization scheme for wireless Direct Sequence Code Division Multiple Access (DS-CDMA) visual sensor networks. The scheme uses Nash Bargaining Solution (NBS) which assumes that nodes negotiate with the help of a centralized control unit on how to allocate resources. The NBS takes into account the video quality, and each node could achieve without making an agreement. The cross-layer optimization scheme determines source coding rate, channel coding rate, and transmission power for each node. A virtual multiple input multiple output wireless sensor network (VMIMO-WSN) communication architecture is considered and the power control of sensor nodes based on the approach of game theory was formulated in [120]. The game was categorized as an incomplete information game, in which the nodes do not have complete information about the strategies taken by other nodes. For virtual multiple input multiple output wireless sensor network architecture considered the NE is used to decide the optimal power level at which a node needs to transmit and to maximize its utility. [PERSON] _et al_. [121] proposed a power control solution for WSN considering MIDRS code in the analytical setting of a game theoretic approach. The game is formulated as a utility maximizing distributed power control game while considering the pricing function and the existence of NE is premeditated.
## 2 Materials and Methods
### Binning Method of NmF2/hmF2 Observed by FY3C/GNOS and COSMIC  
To validate the feasibility of FY3C/GNOS IRO products in climatology research, the consistency of the ionospheric climatological characteristics of NmF2/hmF2 between FY3C/GNOS and COSMIC need to be presented. Thus, we divided observations of FY3C/GNOS and COSMIC into four seasons [28], ME-month (\(\pm\)45 days to March equinox of 2016), JS-month (\(\pm\)45 days to June solstice of 2016), SE-month (\(\pm\)45 days to September equinox of 2016), and DS-month (\(\pm\)45 days to December solstice of 2016), and the IRO data were binned into grids in each season to observe the season pattern of the ionosphere morphology. To ensure the number of data grids, in which the number of NmF2/hmF2 is greater than 5, occupies at least 60% of the whole grid within \(\pm\)60\({}^{\circ}\) latitude regions, a 5\({}^{\circ}\times 10^{\circ}\) grid was adopted in our work. The season median value of NmF2/hmF2 in one grid represents its NmF2/hmF2 level [1], which can effectively reduce the deviations of NmF2/hmF2 caused by contingency factors that are not considered in Section 2.1. In this way the ionospheric climatological features of NmF2/hmF2 observed by FY3C/GNOS and COSMIC are depicted intuitively.
## 4 Model Training and Evaluation Methods
### Hyperparameter Optimization with GA
GA-based hyperparameter optimization in the K-NNs model is structured to cover four basic parameter sets. The n_neighbors is defined as containing ten different neighbor counts, namely, 3, 5, 7, 9, 11, 13, 15, 17, 19, and 21; weights is defined as containing two weighting strategies, uniform and distance; metric is defined as containing four distance metrics, namely, Minkowski, Euclidean, Manhattan, and Chebyshev; and leaf_size is defined as containing ten different values, starting from 10 and increasing by increments of 10 up to 100. This configuration yielded a total of 10 \(\times\) 2 \(\times\) 4 = 800 theoretical combinations, as shown in Table 4. However, the GA efficiently explored the search space by setting the population size to 20 individuals and the number of generations to 10, performing only 200 evaluations. The best performance was obtained with [n_neighbors = 15, weights = 'distance', metric ='manhattan', leaf_size = 10]. In the AdaBoost model, GA-based hyperparameter optimization is structured to examine three basic parameter sets simultaneously. The n_estimators value is defined as starting from 100, increasing by increments of 100, and ranging up to 2000 (20 different values), while learning_rate, which controls the learning rate, is defined as ten equal increments from 0.01 to 1.0 (10 different values). Furthermore, the loss function, which determines the error weighting strategy of weak learners, was associated with three possible values, namely, linear, square, and exponential. This arrangement theoretically creates 600 different combinations, whereas GA only creates 150 different combinations within the framework of a population size of 15 individuals and 10 generations; 150 evaluations were performed to ensure an effective evolutionary filtering of the search space. Similar to the above four ML models for which hyperparameter optimization was performed with the GA, the selected hyperparameters and their values for the six ML models are given in detail in Table 5.  
The evaluation metrics results of the models after hyperparameter optimization with GA are given in Table 6.  
In this comparative analysis, the XGBoost and GBM models achieved the highest scores for coefficient of determination, with R\({}^{2}\) = 0.9449 and R\({}^{2}\) = 0.9440, respectively, indicating that the two models each have a strong generalization ability. These models also excelled at the mean squared error level, achieving the lowest MSE (0.0455 and 0.0462) and RMSE (0.2134 and 0.2151). SVR achieved the lowest mean absolute error, with MAE = 0.1827, closely followed by the XGBoost model, with MAE = 0.1660, and the GBM model, with MAE = 0.1680. In contrast, the BR model showed the lowest accuracy value, with R\({}^{2}\) = 0.1082, well behind the
## 1 Introduction
In direct systems, the heat transfer fluid is also the storage element of the system, while in indirect systems, the fluid serves as the transfer medium and another material is used as the storage element [34]. Active systems have been studied [35] and applied to suspended ceilings [36; 37], ventilation systems [38; 39], external double-skin facades [40], solar collectors [41; 42], heat storage water tanks [43; 44], integrated photovoltaics [45; 46], and building cores enhanced with PCMs activated through the use of ducts or pipes [47]. When considering passive systems, there is no forced convection, and, in this case, these systems can be classified according to the way the PCMs are embedded in the building element: inside the material, as a new layer, and in windows or as sun protection [48]. Considering the PCMs embedded inside the material, encapsulated PCMs can be easily added to other construction materials such as concrete [49], plaster [50], cellulose, or glass fiber [51]. Another diffuse solution to applying PCMs in buildings is to add a new layer to increase the thermal inertia of lightweight constructions. The most common application of PCMs as a new layer is the PCM-enhanced gypsum plasterboard; many products, such as the Alba Balance (Rigips-Saint Gobain), are already commercially available. Other applications of PCMs as new layers are PCM sandwich panels [52] and macroencapsulated PCMs in plates or bags, such as the Delta-Cool24 (Dorken) or the Energain (Dupont). Lastly, PCMs can be used for both sun protection (for example, in internal blinds [53]) and inside windows (for example, with an extra air gap, behind the inner glass [54]). Considering all these available PCMs, particular attention should be paid to their melting point because, due to the broad variability of this parameter, each application has its own more suitable range. Different studies concerning building energy performance [28; 55; 56; 57] have identified that the most suitable melting points for cooling are up to 21 \({}^{\circ}\)C, while for heating, they are 22 \({}^{\circ}\)C or, in general, 2 \({}^{\circ}\)C higher than the heating setpoint temperature. Moreover, suitable melting point ranges for thermal comfort are between 22 and 28 \({}^{\circ}\)C; for hot water applications, they are between 29 and 60 \({}^{\circ}\)C. Finally, higher melting points--between 61 and 120 \({}^{\circ}\)C--are suitable for waste heat recovery applications. For the current study, a PCM layer was considered the inner layer of external walls, roofs, and floors, implementing a paraffinic microencapsulated organic PCM (Micronal) embedded in gypsum plasterboard with a melting point of 23 \({}^{\circ}\)C, the characteristics of which are described in the following sections. Considering that the models were run for active and passive strategies and for heating and cooling systems, the choice of this transition temperature allows us, as described before, to take advantage of heating, cooling, and thermal comfort applications.
## 6 Evaluation
### Evaluation Based on the Questionnaire Survey
#### 6.1.3 Evaluation Concerning the Originality of the System  
Figure 11 shows the evaluation results of the originality of the system. Regarding the comprehensibility of the display using pictograms, 95% answered \"I think so\" or \"I somewhat think so\". Additionally, regarding the operability of functions using pictograms, 92% answered \"I think so\" or \"I somewhat think so\". Therefore, it was evident that the system made it easy for users to understand information concerning sightseeing spots, tourism-related facilities, and disaster support facilities using pictograms. Additionally, it was also evident that the system made the information easy to intuitively understand when they used each function with the use of pictograms.  
Regarding the operability of the navigation function using AR, 90% answered \"I think so\" or \"I somewhat think so\". As mentioned in Section 6.1.1, though many users were not familiar with applications using AR, the operability of the function was highly rated. The system shows the locations and routes to sightseeing spots and tourism-related facilities using AR. Therefore, by means of AR, the system can effectively navigate users from their present locations to their destinations.  
Regarding the easiness of obtaining information during disasters, 90% answered \"I think so\" or \"I somewhat think so\". Therefore, it can be said that the system was developed in a way that enabled users to efficiently obtain information concerning disaster support facilities. Additionally, as mentioned in Section 6.1.1, though 78% of the users did not know much about disaster support facilities within Chofu City, these users could obtain sufficient information concerning disaster support facilities by using the system.
## 5 Simulation
### Simulation Results and Analysis
#### 5.2.2 Tool Face Angle Simulation with Friction  
The input signal of the system was set as \(\theta_{r}=\sin\ (\pi t)\), the external disturbance signal was set as \(F_{n}=\sin\ (t+\pi)\), and the friction disturbance was modeled as the LuGre friction. Figure 9 shows the comparative experimental results of PID, PID_DOB, DDPG, and DDPG_DOB control methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control Method** & **PID** & **PID\_DOB** & **DDPG** & **DDPG\_DOB** \\ \hline \hline Overshoot (\%) & 0 & 0 & 4.762 & 7.143 \\ Settling time (\(\pm\)5\%, s) & 3.181 & 2.454 & 0.727 & 0.636 \\ Steady-state error (\(\circ\)) & \(-\)0.047 & 0.036 & \(-\)0.038 & 0.024 \\ Rise time (s) & 0.545 & 0.509 & 0.295 & 0.236 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of system performance indicators.  
Figure 8: (**a**) Tool face step response of control system with frictional disturbances and (**b**) tool face step error of control system with frictional disturbances.  
From Figure 9, it is evident that the DDPG_DOB method outperforms both the PID algorithm and the DDPG algorithm in terms of tracking accuracy and tracking error, maintaining the tracking error within 8.7%. The stabilizing platform significantly enhances control performance, and the DDPG_DOB control method is effective at suppressing disturbances.
## 5 The Simulation Validation and Results Analysis
### Performance of the Algorithm in a Complex Scenario
#### 5.3.2 Three UAVs Localizing One Static Target  
In Scenario 4, a single moving target \(T\) is localized by three UAVs \(A_{1}\), \(A_{2}\) and \(A_{3}\). The detailed initial settings of the UAVs are listed in Table 8. The position of the static target is \((-300,\,600)\) m, and the position of the base station \(B\) is \((0,\,-200)\) m. The safety distance for collision avoidance \(d_{\text{safe}}=100\) m.  
The snapshots, Figures 33 and 34 show the trajectories and communication topology of the whole UAVs at 10 s and 100 s, respectively. From these snapshots, we can see that, the UAVs automatically establish a multi-hops chain \(\{A_{3}\to A_{2}\to A_{1}\to B\}\). Due to \(A_{1}\) being close enough to the base station, \(A_{1}\) continue to orbit around the optimal position \(\mathbf{O}_{1}=(-21.8,\,44.3)\) m to re-transmit the measurement packets back to the base station. \(A_{2}\) takes advantage of the position of \(A_{1}\) to acts as the relay for \(A_{3}\). The optimal position of \(A_{2}\) is \(\mathbf{O}_{2}=(-96.1,\,306.8)\) m. \(A_{3}\) gets close to the target from its starting position and keeps orbiting around the target \(T\) for  
The communication performances of \(A_{1}\), \(A_{2}\) and \(A_{3}\) are shown in Figure 35. The average transmission probability of \(A_{3}\) is 0.8894. In contrast, if \(A_{3}\) directly communicates with the base station without any relay, its average transmission probability is only 0.5215. The communication performance of \(A_{3}\) is vastly improved with \(A_{1}\) and \(A_{2}\) acting as the relays.  
The trace of the FIM of \(A_{1}\), \(A_{2}\)\(A_{3}\) and base station is shown in Figure 36 during the whole simulation process. \(A_{2}\) gets close to the target to get better measurements and send its measurements back to the base station by the multi-hops chain \(\{A_{3}\to A_{2}\to A_{1}\to B\}\). Hence, the base station has good estimation performance.  
In Scenario 4, the average estimate error of the target position, the average transmission probabilities, the average trace of the FIM are shown in Table 9.  
It can be seen from Table 9 that, our algorithm can make UAVs more focus on maintaining good communication with base station or other UAVs in the mission process, so as to improve the performance of the target position estimation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Item** & **Base Station** & \(A_{1}\) & \(A_{2}\) & \(A_{3}\) \\ \hline Average estimation error for position (m) & 0.4535 & 1.2332 & 0.8105 & 0.4610 \\ Average transmission probability & - & 0.9818 & 0.9460 & 0.8894 \\ Average trace of the FIM & 40.7112 & 13.0033 & 13.1245 & 14.9834 \\ \hline \end{tabular}
\end{table}
Table 9: The average estimate errors, the average transmission probability, and the average trace of the FIM in Scenario 4.  
Figure 35: The communication performances of \(A_{1}\), \(A_{2}\) and \(A_{3}\) (Scenario 4).  
Figure 36: The trace of FIM of \(A_{1}\), \(A_{2}\), \(A_{3}\) and base station (Scenario 4).