esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability-Aware SPICE Compatible Compact Modeling of IGZO Inverters on a Flexible Substrate
## 4 Conclusions  
In this study, both DC and AC bias-induced instability characteristics of flexible a-IGZO transistors and inverters were analyzed under electrical and mechanical stress. In particular, the electron trapping and hydrogen migration mechanisms that can occur in the IGZO transistors were analyzed and modeled under PBS/CS stress conditions. In this compact modeling, simulation parameters were extracted from both positive and negative \(\Delta V_{T}\) by applying the SEF model. When performing DC and AC simulation by loading the extracted parameter in HSPICE, real-time dependent \(\Delta V_{T}\) was successfully reflected, showing a high consistency of measurement values and simulation results.  
The proposed method enables a reliability-aware circuit simulation so that the operation of the flexible IGZO transistor and circuit can be predicted with high accuracy.  
Figure 7: Measured values and simulation results for flat inverters when (**a**) \(V_{OV}\) = 3 V and toggle number = 10, (**b**) \(V_{OV}\) = 3 V and toggle number = 10,000, (**c**) \(V_{OV}\) = 5 V and toggle number = 10,000. Similar results for bending inverters when (**e**) \(V_{OV}\) = 3 V and toggle number = 10, (**f**) \(V_{OV}\) = 3 V and toggle number = 10,000, (**g**) \(V_{OV}\) = 7 V and toggle number = 10, and (**h**) \(V_{OV}\) = 7 V and toggle number = 10,000.  
Future studies with various operating conditions, such as different duty, voltage, and toggle numbers, will bring more accurate predictions of the flexible IGZO circuit operation.  
Conceptualization, J.-H.K., Y.S., D.-W.P. and D.H.K.; methodology, J.-H.K., Y.S.; validation, J.-H.K., Y.S., J.T.J., S.P., D.K., J.P., M.H. and C.K.; writing--original draft preparation, J.-H.K., Y.S., D.-W.P. and D.H.K.; writing--review and editing, J.-H.K., Y.S., D.-W.P. and D.H.K.; supervision, D.-W.P. and D.H.K.; project administration, D.-W.P. and D.H.K.; funding acquisition, M.H., C.K., D.-W.P. and D.H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) of Korea under Grant (2016R1A5A1012966 and 2020R1A2B5B01001979). [PERSON] and [PERSON] acknowledge support from the Basic Study and Interdisciplinary R&D Foundation Fund of the University of Seoul (2019).  
Not applicable.  
The raw/processed data required to reproduce these findings will be shared upon request from the corresponding author.  
The CAD software was supported by SILVACO and IDEC.  
The authors declare no conflict of interest.
## 6 Case Studies
### A Case Study of Bridge Inspection
The prediction performance of the CACF depends on the GPS device class. In Figure 12, WASS-GPS and RTK-GPS denote Wide Area Augmentation System-capable GPS and Real Time Kinematic GPS, respectively. The highest precision rate of the CACF is 56%. The candidate elements of our algorithm include the next target element avergeely in 80% of cases, which is much higher than the precision rate of the CACF. In addition, on average, the first candidate element of our algorithm can be right 69% of the time, which is still 13 points higher than the highest precision rate of the CACF. It means that the inspector can directly choose the target element, and the prototype system can even treat the first candidate element as the next target element by default in most cases. From the above observations, it can be concluded that our algorithm is a more effective method in improving prediction performance for the target element. Moreover, the proposed algorithm does not rely on the specialist equipment. Therefore, compared with CACF, the proposed methodology is convenient and economical. Figure 12: Comparison of prediction results between our algorithm and the CACF.
## 2 Materials and Methods
### Prediction Models Workflow
#### 2.2.5 HMM Design and Training  
A Hidden Markov Model (HMM) is a probabilistic graphical model used to represent systems that evolve over time offering flexibility and scalability compared to deterministic predictive models [60]. It comprises of five main elements [61] shown in Table 4. The first is the set of hidden states (N), which signifies the unobservable underlying conditions or states within the system. In our application, that is our three defined collaborative behaviors: _Engaged_, _Struggling_, and _Waiting_. Second, there are observations (M) associated with each state. In our application these observations are the seven binary features explained in Section 2.2.1. Figure 6: Flowchart for the rule-based prediction model. The model's dynamics are governed by state transition probabilities, represented as a state transition matrix (A). The matrix encodes the probabilities of transitioning from one state to another at each time step, reflecting how the system evolves over time. This matrix is generated when training the model. In addition to the transition matrix, there is an emission matrix (B) that defines the likelihood of generating a particular observation given the current state. Finally, the model requires an initial probability distribution (\(\pi\)) which specifies the initial likelihood of beginning the sequence in each hidden state. Mathematically, HMMs address two fundamental problems: the evaluation problem, solved using the Forward Algorithm [62], which quantifies the likelihood that the HMM generated a specific sequence of observations, and the decoding problem, solved using the Viterbi Algorithm [63] or the Baum-Welch Algorithm [64], which determines the most probable sequence of hidden states that generated a given sequence of observations. In our application, we train the HMM by calculating the maximum likelihood estimate of the transition (A) and emission (B) probabilities for a sequence of distinct observations (M) with known states (N). Using the estimated transition and emission probabilities, we use the Baum Welch Algorithm to determine the most probable sequence of hidden states for the remainder of our observations. An ergodic state transition model was designed for our model as we assumed that the collaboration state can change from one state to any of the other states. Figure 7 shows a possible diagram of the HMM. The HMM training was done in MATLAB [65] using the Statistics and Machine Learning Toolbox [66]. The MATLAB function hmmestimate was used to generate estimated transition and emission matrices for the model by calculating the maximum likelihood, and the MATLAB function hmmviterbi was used to predict the collaborative behaviors. We used the \(k\)-fold cross-validation method to enhance the training outcome of the HMM to achieve optimal performance. As illustrated in Figure 8, we split 70% of the hand-labeled data as the HMM training set, while the remaining 30% of the hand-labeled data was used as the hold-out test set to evaluate the selected HMM.
## 4 Discussion
### Limitation  
In the present study, only one intensity of electric field was employed. EF treatments with other intensities may produce different effects on autonomic activity. A treatment length of longer than 20 min can also induce different results. In addition, the number of subjects was small in the present study (n = 12). A larger sample size may reveal additional aspects of the effects of EFs on autonomic indices, as well as the relationships between autonomic indices. Ten of twelve subjects in the present study were female, and the mean age was 62.2 years. The gender and age distribution should also be balanced in future studies. Future studies with other EF intensities and different durations in a larger sample would be important for the utilization of ELF-EFs in the field of healthcare.  
The food content, amount of water intake, body weight, lifestyle, stress level, and sleep/awake schedule could also affect SC and HRV, but were not examined in the present small samples. Future studies are necessary to control these factors.  
The time course of the effects of EFs after the end of EF treatment should also be clarified in future studies. The present study only assessed the changes immediately after the end of the treatment. The persistence of the effects of EFs can lead to the clinical application of ELF-EFs.  
The present study did not examine the changes in subjective sensations following the EF treatment. Decreases in sympathetic activity can modify psychosomatic symptoms such as pain and fatigue. It is important to assess this issue in future research.
# Encoding the Intensity and Phase Gradient of Light Beams with Arbitrary Shapes
## 3 Results
### Testing the Intensity of the Designed Light Beams  
By inserting the estimated equations from Table 1 into Equation (2), the phase holograms for the SLM are generated. In Figure 5, we present the intensity of the designed light beams.  
Comparing the intensity images displayed in Figure 5 to the desired contour of the shapes shown in Figure 4, only minor differences are detected over the sharp edges in (c), (e), and (g). These differences are due the quantity of harmonics considered for reconstruction, that sum 95% of the energy for the shape in Figure 5e, and 92% for Figure 5c,g.
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 3 Sensing Matrix Optimization of Compression Acquisition
### Sensing Matrix Optimization
#### 3.2.1 Conjugate Gradient Updating Sensing Matrix  
Denoting \(\mathbf{F}=\mathbf{G}_{\mathrm{t}}-\overline{\mathbf{G}}\), the objective function can be obtained by using the following equation  
\[\rho(\mathbf{\Theta})=\left\|\mathbf{F}\right\|_{\mathrm{F}}^{2}=\mathrm{tr} \Big{[}\mathbf{F}\mathbf{F}^{\mathrm{T}}\Big{]} \tag{17}\]  
The gradient of the objective function is written as  
\[\frac{\partial\rho}{\partial\theta_{ij}}=2\mathrm{tr}\Bigg{[}\frac{\partial \mathbf{F}}{\partial\theta_{ij}}\mathbf{F}^{\mathrm{T}}\Bigg{]} \tag{18}\]  
It can be simplified to [31]  
\[\frac{\partial\rho}{\partial\mathbf{\Theta}}=-2\mathbf{\Theta}\mathbf{\Theta} _{x}(\mathbf{F}+\mathbf{F}^{\mathrm{T}})\mathbf{\Theta}_{x}\mathbf{\Psi}^{ \mathrm{T}}+2\mathbf{\Theta}(\mathbf{\Psi}\mathbf{\Lambda}\mathbf{\Psi}) \tag{19}\]  
where \(\mathbf{\Lambda}\) denotes the diagonal matrix, whose diagonal element is the same as the one of the matrix \(\mathbf{D}\).  
\[\mathbf{D}=(\mathbf{F}+\mathbf{F}^{\mathrm{T}})\overline{\mathbf{G}}\mathbf{ \Theta}_{x}^{2} \tag{20}\]  
In the meantime, updating with the conjugate gradient [32] method reduces the number of iterations that have to be done.  
\[\mathbf{\Theta}^{k+1}=\mathbf{\Theta}^{k}+\mathbf{\alpha}\mathbf{d}^{k} \tag{21}\]  
where stepsize \(\mathbf{\alpha}\) is positive and can be computed by the Armijo-Goldstein condition [33], and \(\mathbf{d}^{k}\) is defined by  
\[\mathbf{d}^{k+1}=\left\{\begin{array}{cc}-\frac{\partial\rho}{\partial \mathbf{\Theta}}|\mathbf{\Theta},&\mathrm{if}\ \ \ k=0\\ -\frac{\partial\rho}{\partial\mathbf{\Theta}}|\mathbf{\Theta}=\mathbf{\Theta}^{ k+1}+\beta_{k}\mathbf{d}^{k},&\mathrm{if}\ \ \ k>0\end{array}\right. \tag{22}\]  
where \(\beta_{k}\) is a scalar.  
In this paper, we take a minor modification to the New-Polak-Ribiere-Polyak (NPRP) method [34], combine fundamental inequalities \(ab\leq\left(\frac{a+b}{2}\right)^{2}\), and construct the modified Fletcher-Reeves (FR) method as follows  
\[\beta_{k}^{\text{NFR}}=\frac{\left\|g_{k}\right\|^{2}-\frac{\left\|g_{k}\right\| }{\left\|g_{k-1}\right\|}\left(\frac{\left\|g_{k-1}\right\|+\left\|g_{k}\right\| }{2}\right)^{2}}{\left\|g_{k-1}\right\|^{2}} \tag{23}\]  
where \(g_{k}=-\frac{\partial\rho}{\partial\mathbf{\Theta}}|\mathbf{\Theta}=\mathbf{\Theta}^{k}\).
## 4 Emergency Data Handling  
In the Healthcare system, it is fundamentally required to provide minimized latency for immediate transmission of emergency data when the human body is suffering from severe emergency situation. However, many types of research have just focused on separated time slot assignment for emergency data transmission, and they could not fulfill the optimized solution due to holding contention-free approach. Hence, we have focused on finding the appropriate solution to guarantee prioritized QoS of emergency data transmission. In the proposed MAC protocol, the emergency data can be sent at any time.
## 4 Results
### Correlation Analysis  
The results of the correlation analysis between the representative dimensions derived from the factor analysis ('Refined' and 'Powerful') and acoustic parameters (Loudness, Sharpness, Roughness, Fluctuation, Tonality, and SPL) of each RPM are shown in Table 7 below. First, in the case of 'Refined', the highest value of the correlation coefficient was Sharpness\({}_{2000-5000}\) (\(-0.566\)). Sharpness in all RPM range and roughness\({}_{3500-5000}\) showed a negative correlation with 'Refined', and SPL\({}_{2000-5000}\) and SPL-A\({}_{2000-3500}\) showed a positive correlation. This indicates that as sharpness and roughness increased, perceived refined decreased.  
Second, as a result of the correlation analysis for the 'Powerful', the all psychoacoustic parameters in 2000 to 5000 RPM, except fluctuation, were found to be significantly related. Loudness is the parameter with the highest correlation coefficient with 'Powerful'. In addition, unlike 'Refined', the psychoacoustic parameters in the 2000 to 3500 RPM also showed a significant correlation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **Affective Dimension** & **Sum of Squares** & **df** & **Mean Square** & **F** & \(p\) \\ \hline \multirow{3}{*}{Cylinder} & \multirow{3}{*}{Refined} & Between & 0.011 & 2 & 0.006 & 0.042 & 0.959 \\  & & Within & 4.244 & 32 & 0.133 & & \\  & & Total & 4.255 & 34 & & & \\  & & Between & 0.305 & 2 & 0.153 & 0.443 & 0.646 \\  & & Within & 11.031 & 32 & 0.345 & & \\  & & Total & 11.336 & 34 & & & \\ \hline \multirow{2}{*}{Size} & \multirow{2}{*}{Refined} & Between & 1.177 & 2 & 0.588 & 1.853 & 0.173 \\  & & Within & 10.159 & 32 & 0.317 & & \\  & & Total & 11.336 & 34 & & & \\ \multirow{2}{*}{Powerful} & \multirow{2}{*}{Between} & 0.373 & 2 & 0.186 & 1.536 & 0.231 \\  & & Within & 3.882 & 32 & 0.121 & & \\ \multirow{2}{*}{} & \multirow{2}{*}{Total} & 4.255 & 34 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of ANOVA for extracting representative affective dimensions in engine sound.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Affective Dimension**} & \multirow{2}{*}{**Category**} & \multicolumn{4}{c}{**Shapiro-Wilk**} \\ \cline{3-6}  & & & **Statistic** & **df** & \(p\) \\ \hline \multirow{4}{*}{Refined} & & V4 & 0.947 & 14 & 0.517 \\  & & V6 & 0.961 & 11 & 0.790 \\  & & V8 & 0.931 & 10 & 0.453 \\  & & V4 & 0.938 & 14 & 0.388 \\  & & V6 & 0.930 & 11 & 0.409 \\  & & V8 & 0.920 & 10 & 0.356 \\ \hline \multirow{2}{*}{Refined} & \multirow{2}{*}{Type} & Compact & 0.941 & 14 & 0.437 \\  & & Luxury & 0.954 & 11 & 0.694 \\  & & Sports & 0.861 & 10 & 0.078 \\ \multirow{2}{*}{Powerful} & & Compact & 0.973 & 14 & 0.909 \\  & & Luxury & 0.931 & 11 & 0.422 \\ \multirow{2}{*}{} & \multirow{2}{*}{Sports} & 0.936 & 10 & 0.505 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for tests of normality.
# Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
## 2 Materials and Methods
### Remote Sensing Data  
Aerosol optical depths (AOD) for 550-nm wavelengths are available from the Office of Global Modeling and Assimilation (GMAO) of NASA (Available online: [[https://disc.gsfc.nasa.gov/datasets/](https://disc.gsfc.nasa.gov/datasets/)]([https://disc.gsfc.nasa.gov/datasets/](https://disc.gsfc.nasa.gov/datasets/)), accessed on 14 July 2021). The data is part of the NASA atmospheric reanalysis dataset (MERRA-2) for the satellite era [39]. In the MERRA project, historical climate analyses are conducted at a range of time scales covering weather and climate, and the NASA EOS observations are placed in a climate context.  
Figure 1: Geographical distribution of the eddy covariance (EC) flux sites used in this study. Green circles stand for sites used for calibration, while blue triangles indicate sites used for validation.  
The SYN1 deg Version 4.1 Cloud and Earth Radiant Energy System (CERES) product [40] provided the direct PAR and diffuse PAR data for clear skies for the period from 2000 to 2014. The PAR\({}_{\text{total}}\) was calculated as the sum of PAR\({}_{\text{dir}}\) and PAR\({}_{\text{diff}}\). The SYN1 deg Ed4A product is designed to provide top of the atmosphere (TOA) and surface flux data with the highest temporal resolution by incorporating hourly GEO imager data. The CERES PAR data are available online (Available online: [[https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1](https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1) degEd41 Selection.jsp]([https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1](https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1) degEd41 Selection.jsp), accessed on 3 May 2021).
# Effective Management System for Solar PV Using Real-Time Data with Hybrid Energy Storage System
## 2 Objective Function
### Interleaved Boost Converter  
The IBC is shown in Figure 5. The IBC is interleaved by two structures and it performs two operations in tandem at a time. Hence, it is named as an interleaved converter. The IBC reduces the conduction losses and increases the conversion ratio. Thus, the efficacy of the system can be increased by saving energy [24].  
The relation among output to input voltage is given by,  
\[\frac{V_{o}}{V_{in}}=\frac{1}{(1-D)} \tag{2}\]  
where \(V_{o}\) and \(V_{in}\) are the output and input voltages and \(D\) is the duty ratio. The inductor current is taken by the following relation,  
\[I_{L}=\left(\frac{V_{o}}{V_{in}}\right)I_{o} \tag{3}\]  
where \(I_{o}\) is the output current and \(I_{L}\) is the inductor current. The peak-peak inductor ripple is given by,  
\[\Delta I_{L1}=\Delta I_{L2}=\left(\frac{V_{in}D}{F_{sw}L}\right) \tag{4}\]  
where \(F_{SW}\) is the switching frequency and, \(L\) is the inductor.  
The minimum ripple current occurs at 50% of the duty ratio [9].  
If D is \(\leq 0.5\),  
\[\Delta I_{in}=\left(\frac{V_{o}-2V_{in}}{F_{sw}L}\right)D \tag{5}\]  
Figure 4: Charge and discharge characteristics of a battery using ramp-limit control.  
Figure 5: Interleaved boost converter (IBC) circuit diagram.  
If D is \(>0.5\),  
\[\Delta I_{in}=\left(\frac{V_{o}-2V_{in}}{F_{sw}L}\right)(1-D) \tag{6}\]  
The value of inductor and capacitor is taken by the following equations,  
\[L=\left(\frac{V_{in}D}{\Delta I_{L}F_{sw}}\right) \tag{7}\]  
\[C=\left(\frac{V_{o}DF_{sw}}{R\Delta V_{o}}\right) \tag{8}\]  
where \(C\) is the capacitance, \(R\) is the load resistance, and \(\Delta V_{o}\) is the change in output voltage.  
A traditional boost converter has several merits, such as simple design, continual input current, and provides one polarity output voltage. However, the high efficacy and voltage gain are difficult to achieve by the boost converter. Thus, the IBC control helps to enhance the voltage gain, reduces the current ripples in the system, and switch voltage stress. However, the current sharing amongst the parallel paths can be taken into account by the IBC. Therefore, high voltage and high efficacy of the system can be achieved by the IBC strategy [26]. Thus, the IBC can serve as conquering input and output current ripples with smaller duty ratio and conduction loss.
# Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach
## 5 Intelligent Servo Tuning Results
### Experiment Results  
The corresponding experimental results are introduced in Figure 14, Figure 14a,c,e are results before optimization and Figure 14b,d,f are results after optimization. Due to the limitations of the mechanism, the maximum value of angular frequency was about 3-4 Hz. Here, the reference command was 3 Hz. As shown in the Table 9, we observed our approach results had smaller contour errors, especially since the reference 3 Hz was within the bandwidth, and the reference frequency was at a distance from the bandwidth. In other words, the reference was located at a flat interval, which meant that the frequency had slight variations in the range. As Figure 14 shows, the experimental results still showed that our intelligent servo tuning method was effective.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Simulation Result of Circular Motion**} \\ \hline \multirow{2}{*}{**Axes\(\backslash\)Errors**} & \multicolumn{2}{c}{**Contouring Error**} & \multicolumn{2}{c}{**Tracking Error**} \\ \cline{3-6}  & **RMSE (mm)** & **Max. (mm)** & **RMSE (mm)** & **Max. (mm)** \\ \hline \multirow{3}{*}{X-Y Axes} & Default & 0.2566 & 0.2637 & 1.1497 & 1.1567 \\  & Our approach & 0.2079 & 0.2136 & 1.1362 & 1.1418 \\  & **Improvement** & 18.98\% & 19.00\% & 1.17\% & 1.29\% \\ \hline \multirow{3}{*}{X-Z Axes} & Default & 0.2253 & 0.2617 & 1.1402 & 1.1772 \\  & Our approach & 0.1984 & 0.2061 & 1.1330 & 1.1407 \\  & **Improvement** & 11.94\% & 21.25\% & 0.63\% & 3.10\% \\ \hline \multirow{3}{*}{Y-Z Axes} & Default & 0.2315 & 0.2743 & 1.1411 & 1.1852 \\  & Our approach & 0.2027 & 0.2159 & 1.1342 & 1.1476 \\ \cline{1-1}  & **Improvement** & 12.44\% & 21.29\% & 0.60\% & 3.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Contour error and tracking error RMSE of circular motion.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Experiment Result of Circular Motion**} \\ \hline \multirow{3}{*}{\(X\)-Y Axes} & \multicolumn{2}{c}{**Contouring Error**} & \multicolumn{2}{c}{**Tracking Error**} \\ \cline{3-6}  & **RMSE (mm)** & **Max. (mm)** & **RMSE (mm)** & **Max. (mm)** \\ \hline \multirow{3}{*}{X-Z Axes} & Default & 0.0087 & 0.0326 & 0.2483 & 0.7863 \\  & Our approach & 0.0076 & 0.0284 & 0.2478 & 0.7808 \\  & **Improvement** & **12.64\%** & **12.88\%** & **0.201\%** & **0.700\%** \\ \hline \multirow{3}{*}{X-Z Axes} & Default & 0.004 & 0.0142 & 0.2526 & 0.7753 \\  & Our approach & 0.0036 & 0.0132 & 0.2525 & 0.7745 \\  & **Improvement** & **10.00\%** & **7.04\%** & **0.04\%** & **0.103\%** \\ \hline \multirow{3}{*}{Y-Z Axes} & Default & 0.0065 & 0.0196 & 0.2475 & 0.7790 \\  & Our approach & 0.0060 & 0.0181 & 0.2474 & 0.7748 \\ \cline{1-1}  & **Improvement** & **7.69\%** & **7.65\%** & **0.04\%** & **0.54\%** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Contour error and tracking error RMSE of circular test.
## 4 Experiments
### Experimental Parameter Settings  
The framework was implemented in Python with the PyTorch library. For a fair comparison, in all experiments, the hyperparameter \(\text{J}\) was set to 0.1. A conventional SGD optimizer was used with a momentum of 0.9 and a weight decay rate of \(10^{-4}\). During the training process, the learning rate was set to 3 \(\times\)\(10^{-3}\), and the batch size in the experiments was set to 128. Further, 'WRN-28-2' [44] was used as our backbone network. It included leaky ReLU nonlinearity [45] and batch normalization [46].
## 5 Conclusions  
The performance of MIMO SM orientations was analyzed, in terms of achievable BERs. In this context, desired user signal and total interference were expressed in terms of summation of identically distributed random variables, which reduces computational complexity. As the results indicate, the calculation error is minimized for high diversity multi-user MIMO orientations. Knowledge of uncoded BER is particularly important for the design of next generation broadband wireless networks, since it allows capacity estimations when advanced encoding techniques are employed.  
Future work includes, among others, the extension of the presented results in multicellular orientations, as well as BER calculations in Non Orthogonal Multiple Access (NOMA) networks.  
Conceptualization, P.K.G. and D.I.K.; methodology, P.K.G. and D.I.K.; software, P.K.G.; validation, P.K.G. and D.I.K.; formal analysis, P.K.G. and D.I.K.; writing--original draft preparation, P.K.G. and D.I.K.; writing--review and editing, P.K.G. and D.I.K.  
This research received no external funding.  
The authors declare no conflict of interest.