esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Excitation Approach for Power Transformer Simulation Based on Finite Element Analysis
## 2 2D Finite Element Analysis Model
### Core Loss Model  
The core loss model consists of hysteresis loss, eddy current loss, and excessive loss. Its equation can be written as:  
\[P_{\rm{e}}=P_{h}+P_{\rm{c}}+P_{\rm{e}} \tag{5}\]  
The symbols in Equation (5) are defined as:  
\[\text{Hysteresis loss}:\ P_{h}=k_{h}f_{0}B_{m}{}^{2} \tag{6}\]  
\[\text{Eddy current loss}:\ P_{c}=k_{c}(f_{0}B_{m}){}^{2} \tag{7}\]  
\[\text{Excessive loss}:\ P_{\rm{e}}=k_{e}(f_{0}B_{m}){}^{1.5} \tag{8}\]  
In Equations (6)-(8):  
\(B_{m}\): magnetic flux density of the iron core;  
\(f_{0}\): operating frequency of the transformer;  
\(k_{h}\): coefficient of the hysteresis loss;\(k_{c}\): coefficient of the eddy current loss coefficient;  
\(k_{\epsilon}\): coefficient of the excessive loss.  
The three loss coefficients are determined according to the selected material of the iron core. Therefore, the Equations (5)-(8) are combined to get Equation (9):  
\[P_{v}={B_{m}}^{2}(k_{h}f_{0}+k_{c}{f_{0}}^{2})+{B_{m}}^{1.5}k_{c}{f_{0}}^{1.5}= k_{1}B_{m}^{2}+k_{2}B_{m}^{1.5} \tag{9}\]  
The symbols in Equation (9) are defined as:  
\[k_{1}\text{ coefficient}:\ k_{1}=k_{h}f_{0}+k_{c}{f_{0}}^{2} \tag{10}\]  
\[k_{2}\text{ coefficient}:\ k_{2}=k_{\epsilon}{f_{0}}^{1.5} \tag{11}\]  
The equation of the classic eddy current loss model can be written as:  
\[k_{c}=\pi^{2}\sigma\frac{d^{2}}{6} \tag{12}\]  
The symbol \(\sigma\) is the conductivity of the iron core and the symbol \(d\) is the thickness of the iron core in Equation (12). The least square of the method can be used to solve the \(k_{1}\) coefficient and \(k_{2}\) coefficient. Equation (13) can be written as:  
\[err(k_{1},k_{2})=\sum_{i}\left[P_{vi}-\left(k_{1}{B_{m}}^{2}+k_{2}{B_{m}}^{1. 5}\right)\right]^{2}=\text{min} \tag{13}\]  
In Equation (13):  
\(P_{vi}\): \(i\)-th iron loss of the \(P\)-\(B\) loss curve in the iron core;  
\(B_{vi}\): \(i\)-th magnetic flux density of the \(P\)-\(B\) loss curve in the iron core.  
Equations (10), (12) and (13) can be solved for the hysteresis loss coefficient \(k_{h}\). Therefore, the hysteresis loss coefficient \(k_{h}\) and the excessive loss coefficient \(k_{\epsilon}\) can be written as:  
\[k_{h}=\frac{k_{1}-k_{c}{f_{0}}^{2}}{f_{0}} \tag{14}\]  
\[k_{\epsilon}=\frac{k_{2}}{{f_{0}}^{1.5}} \tag{15}\]
# Graphene Quantum Dots Doped PVDF(TBT)/PVP(TBT) Fiber Film with Enhanced Photocatalytic Performance
###### Abstract  
We report the fabrication of polyvinylidene fluoride (tetrabutyl titanate)/polyvinyl pyrrolidone ((tetrabutyl titanate))-graphene quantum dots [PVDF(TBT)/PVP(TBT)-GQDs] film photocatalyst with enhanced photocatalytic performance. The polyvinylidene fluoride (tetrabutyl titanate)/polyvinyl pyrrolidone ((tetrabutyl titanate)) [PVDF(TBT)/PVP(TBT)] film was first prepared with a dual-electrospinning method and then followed by attaching graphene quantum dots (GQDs) to the surface of the composite film through a hydrothermal method. Later, part of the PVP in the composite film was dissolved by a hydrothermal method. As a result, a PVDF(TBT)/PVP(TBT)-GQDs film photocatalyst with a larger specific surface area was achieved. The photocatalytic degradation behavior of the PVDF(TBT)/PVP(TBT)-GQDs film photocatalyst was examined by using Rhodamine B as the target contaminant. The PVDF(TBT)/PVP(TBT)-GQDs photocatalyst showed a higher photocatalytic efficiency than PVDF(TBT)-H\({}_{2}\)O, PVDF(TBT)/PVP(TBT)-H\({}_{2}\)O, and PVDF(TBT)-GQDs, respectively. The enhanced photocatalytic efficiency can be attributed to the broader optical response range of the PVDF(TBT)/PVP(TBT)-GQDs photocatalyst, which makes it useful as an effective photocatalyst under white light irradiation.  
electrospinning; nanofiber film; tetrabutyl titanate composites; graphene quantum dots; visible light photocatalysis +
Footnote †: journal: Applied Physics Letters  
Article
## 3 Results
\begin{tabular}{c c c c c c c} \hline \hline
**Time Point** & **Group** & **VE (L/min)** & **petCO2 (mmHg)** & **pHv (1)** & **Lacv (mM)** & **Gluv (mM)** \\ \hline \multirow{2}{*}{Rest} & N & 14.7 \(\pm\) 5.0 & 35.9 \(\pm\) 1.9 & 7.36 \(\pm\) 0.02 & 1.04 \(\pm\) 0.25 & 4.55 \(\pm\) 0.71 \\  & P & 13.1 \(\pm\) 3.1 & 32.6 \(\pm\) 3.1 & 7.34 \(\pm\) 0.03 & 1.25 \(\pm\) 0.48 & 4.36 \(\pm\) 0.62 \\ \hline \multirow{2}{*}{Warm-up} & N & 66.1 \(\pm\) 10.3 & 43.2 \(\pm\) 4.5 & 7.33 \(\pm\) 0.03 & 2.31 \(\pm\) 0.49 & 3.93 \(\pm\) 0.59 \\  & P & 68.8 \(\pm\) 14.8 & 39.8 \(\pm\) 2.9 & 7.32 \(\pm\) 0.03 & 2.43 \(\pm\) 0.58 & 3.83 \(\pm\) 0.66 \\ \hline \multirow{2}{*}{5 th interval} & N & 74.2 \(\pm\) 12.1 & 41.2 \(\pm\) 5.8 & 7.33 \(\pm\) 0.03 & 4.03 \(\pm\) 0.60 & 3.94 \(\pm\) 0.61 \\  & P & 76.8 \(\pm\) 17.9 & 38.0 \(\pm\) 3.7 & 7.33 \(\pm\) 0.03 & 3.89 \(\pm\) 0.89 & 3.89 \(\pm\) 0.78 \\ \hline \multirow{2}{*}{20 th interval} & N & 79.4 \(\pm\) 13.6 & 37.8 \(\pm\) 6.2 & 7.35 \(\pm\) 0.05 & 5.08 \(\pm\) 0.90 & 4.23 \(\pm\) 0.64 \\  & P & 83.3 \(\pm\) 21.6 & 35.2 \(\pm\) 4.0 & 7.34 \(\pm\) 0.03 & 5.10 \(\pm\) 1.29 & 4.67 \(\pm\) 0.44 \\ \hline \multirow{2}{*}{35 th interval} & N & 84.8 \(\pm\) 17.8 & 36.7 \(\pm\) 6.3 & 7.35 \(\pm\) 0.04 & 5.46 \(\pm\) 0.95 & 4.40 \(\pm\) 0.64 \\  & P & 88.7 \(\pm\) 21.9 & 32.8 \(\pm\) 3.7 & 7.35 \(\pm\) 0.04 & 5.57 \(\pm\) 1.34 & 4.90 \(\pm\) 0.45 \\ \hline \multirow{2}{*}{45 th interval} & N & 85.0 \(\pm\) 14.4 & 35.3 \(\pm\) 7.1 & 7.36 \(\pm\) 0.04 & 5.76 \(\pm\) 0.98 & 4.40 \(\pm\) 0.57 \\  & P & 91.8 \(\pm\) 23.4 & 32.4 \(\pm\) 4.7 & 7.36 \(\pm\) 0.04 & 5.85 \(\pm\) 1.49 & 4.93 \(\pm\) 0.63 \\ \hline \multirow{2}{*}{Cool-down} & N & 78.9 \(\pm\) 17.8 & 37.1 \(\pm\) 5.7 & 7.39 \(\pm\) 0.05 & 3.86 \(\pm\) 0.95 & 4.23 \(\pm\) 0.60 \\  & P & 86.3 \(\pm\) 20.0 & 34.4 \(\pm\) 3.9 & 7.36 \(\pm\) 0.08 & 4.34 \(\pm\) 1.55 & 4.73 \(\pm\) 0.81 \\ \hline \hline \end{tabular} 
\begin{tabular}{c c c c c c} \hline \hline
**Time Point** & **Group** & **VE (L/min)** & **petCO2 (mmHg)** & **pHv (1)** & **Lacv (mM)** & **Gluv (mM)** \\ \hline \multirow{2}{*}{8 th interval} & N & 14.7 \(\pm\) 5.0 & 35.9 \(\pm\) 1.9 & 7.36 \(\pm\) 0.02 & 1.04 \(\pm\) 0.25 & 4.55 \(\pm\) 0.71 \\  & P & 13.1 \(\pm\) 3.1 & 32.6 \(\pm\) 3.1 & 7.34 \(\pm\) 0.03 & 1.25 \(\pm\) 0.48 & 4.36 \(\pm\) 0.62 \\ \hline \multirow{2}{*}{Warm-up} & N & 66.1 \(\pm\) 10.3 & 43.2 \(\pm\) 4.5 & 7.33 \(\pm\) 0.03 & 2.31 \(\pm\  
Figure 4: Concentration of metabolites in plasma (left, \(y\)-axis) and power (right, \(y\)-axis) during the 5 th session of high-intensity, high-volume intermittent training: (**a**) creatinine (CRN); (**b**) xanthine (XA); (**c**) group \(\times\)time interaction for ammonia (NH\({}_{3}\)); (**d**) total protein concentration. * = significantly different between the groups.
## 4 Evaluation
### Evaluation Metrics  
In our study, we used two main metrics to evaluate the performance and subjective task load. Here are the detailed descriptions:  
Task Completion Time (TCT): In order to accurately measure how long it took each participant to complete a given task, the experiment recorded the moment a target first appeared within the task scenario, as well as the instance when the participant successfully executed the required input action. This method ensured a comprehensive assessment of the participants' task completion times.  
NASA-TLX Questionnaire: The experiment utilized the NASA-TLX (Task Load Index) questionnaire [13] to evaluate the subjective task load of participants following each interaction technique. It encompasses six questions on mental, physical, and temporal demands, as well as perceived success, effort, and frustration, rated on a 7-point Likert scale from very low to very high.  
In summary, this section meticulously outlined the experimental design, participant demographics, task procedures, and evaluation metrics, aiming to rigorously investigate the effectiveness of various interaction methods within MR environments. This study sought to assess the impact of these technologies on user experience and task performance efficiency, ultimately contributing to the optimization and innovation of data input and interaction techniques in MR environments.  
Figure 5: An illustration of the experiment process.
## 2 Materials and Methods
### Breakthrough Modelling  
Ion breakthrough was analysed using multiple breakthrough models commonly applied to ion exchange data; the modified dose-response (MDR), Bohart-Adams, Thomas, and Yoon-Nelson models. It is important to note is that the models were not necessarily intended to describe a solid-liquid ion exchange extraction process at the time they were developed, and as such the calculated values may not accurately describe experimental reality [19]. However, given the widespread use of such models in the field, this remains the only way to consistently compare new experimental data with existing literature, and so this approach is justified. Model fitting was performed for individual metal breakthrough in OriginPro 2020b software using non-linear regression analysis.  
The MDR model is given in Equation (1) [20], where \(F_{t}\) is the cumulative flow-through (mL) at a given time, and \(a\) and \(b\) are model constants. From evaluating the MDR model, the maximum column loading capacity for each metal (\(Q_{o}\)) can also be derived using Equation (2), where \(m\) is the mass of resin used (g).  
Equation (1). Modified dose response model.  
\[\frac{C_{t}}{C_{o}}=1-\frac{1}{1+\left(\frac{F_{t}}{b}\right)^{a}} \tag{1}\]  
Equation (2). Calculation of \(Q_{o}\) from MDR constant \(b\).  
\[Q_{o}=\frac{b\,C_{o}}{m} \tag{2}\]  
The Bohart-Adams model is given in Equation (3), where it is assumed that the rate of adsorption is dependent on both the concentration of sorbing species in solution and on the remaining capacity of the adsorbent. While originally developed for describing a gas-charcoal adsorption system [21, 22] the model can also be applied to a solid phase extraction system from a solution phase. In Equation (3), \(K_{a}\) is the Bohart-Adams adsorption rate constant (L mg\({}^{-1}\) min\({}^{-1}\)), \(W\) is the column adsorption capacity (mg/g), and \(F\) represents the volumetric flow rate (L/hour).  
Equation (3). Bohart-Adams model.  
\[\frac{C_{t}}{C_{o}}=\left(\frac{e^{K_{a}C_{t}t}}{e^{K_{a}C_{t}}+e^{K_{a}(\frac {W}{b})}-1}\right) \tag{3}\]  
The [PERSON] model (Equation (4), where \(K_{t}\) is the model constant (L min\({}^{-1}\) mg\({}^{-1}\)) is also commonly applied to ion exchange breakthrough data and was originally developed to describe adsorption to a zeolite bed [23]. A high goodness-of-fit to this model would suggest that uptake is governed by mass transfer at the resin-solution interface [24].  
Equation (4). Thomas model.  
\[\frac{C_{t}}{C_{o}}=\frac{1}{1+e^{(\frac{K_{0}C_{0}m}{P}-K_{t}C_{t})}} \tag{4}\]  
The final model fitted to breakthrough data is the Yoon-Nelson model, originally developed for describing the adsorption of gases to solid adsorbents, is presented in Equation (5) where \(K_{yn}\) is the model constant (min\({}^{-1}\)), and \(t_{50}\) is the predicted time for 50% breakthrough to be-a useful parameter for understanding column operating times.  
Equation (5). Yoon-Nelson model.  
\[\frac{C_{t}}{C_{o}}=\frac{1}{1+e^{K_{yn}(t_{50}-t)}} \tag{5}\]
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 5 Conclusions and Future Work  
We propose a digitally fed wideband transmit multibeam beamformer using low-complexity 2-D sparse FIR trapezoidal filters with multiple trapezoidal passbands. The 2-D sparse FIR trapezoidal filter transforms a given wideband temporal input signal into a 2-D spatiotemporal signal with appropriate delays so that directional transmission can be achieved with a ULA of wideband antennas. We adapt a two-step sparse filter design method to design the 2-D sparse FIR trapezoidal filter optimal in the minimax sense. Full-wave electromagnetic simulations verify accurate wideband transmit beam patterns with more than 70% fractional bandwidth. More importantly, our 2-D sparse FIR trapezoidal filter provides significantly lower computational complexity compared with nonsparse counterparts. Furthermore, the proposed beamformer provides significantly better beam patterns for single-beam beamforming compared with the previously proposed sparse beamformer designed using the windowing method. Future work may include an extension of the proposed transmit beamformer to planar and other array geometries, implementations using analog-domain multidimensional filters, and experimental validation.  
Conceptualization, N.D., C.U.S.E., C.W. and A.M.; methodology, N.D. and C.U.S.E.; software, N.D.; validation, N.D., C.U.S.E., C.W. and A.M.; formal analysis, N.D., C.U.S.E., C.W. and A.M.; investigation, N.D., C.U.S.E., C.W. and A.M.; resources, N.D. and C.U.S.E.; data curation, N.D.; writing--original draft preparation, N.D., C.U.S.E. and A.M.; writing--review and editing, N.D., C.U.S.E., C.W. and A.M.; visualization, N.D.; supervision, C.U.S.E., C.W. and A.M.; project administration, C.U.S.E. and A.M.; funding acquisition, C.U.S.E. and A.M. All authors have read and agreed to the published version of the manuscript. This research was partly supported by National Science Foundation (NSF), USA under the grant NSF SWIFT 229471 and Senate Research Committee, University of Mortuwa under the grant SRC/LT/2020/08. The original contributions presented in the study are included in the article, futher inquiries can be directed to the corresponding authors. The authors declare no conflict of interest.
###### Abstract  
When natural disasters strike, users in the disaster area may be isolated and unable to transmit disaster information to the outside due to the damage of communication facilities. Unmanned aerial vehicles can be exploited as mobile edge servers to provide emergency service for ground users due to its mobility and flexibility. In this paper, a robust UAV-aided wireless-powered mobile edge computing (MEC) system in post disaster areas is proposed, where the UAV provides charging and computing service for users in the disaster area. Considering the estimation error of users' locations, our target is to maximize the energy acquisition of each user by jointly optimizing the computing offloading process and the UAV trajectory. Due to the strongly coupled connectionbetween optimization variables and the non-convex nature for trajectory optimization, the problem is difficult to solve. Furthermore, the semi-infinity of the users' possible location makes the problem even more intractable. To tackle these difficulties, we ignore the estimation error of users' location firstly, and propose an iterative algorithm by using Lagrange dual method and successive convex approximation (SCA) technology. Then, we propose a cutting-set method to deal with the uncertainty of users' location. In this method, we degrade the influence of location uncertainty by alternating between optimization step and pessimization step. Finally, simulation results show that the proposed robust algorithm can effectively improve the user energy acquisition.  
unmanned aerial vehicle; mobile edge computing; wireless power transfer; trajectory planning; robust design +
Footnote †: journal: Applied sciences
## 5 System Constraints
### Design of False Target Delay Interval  
To ensure optimal jamming intensity, excessively large delay spacing for false targets should be avoided. Elevated false target density presents challenges in generating jamming signals, leading to heightened difficulty. Additionally, the impact of superposition introduces consequential reductions in the power and coherence of individual false target signals, thereby affecting jamming performance.  
Figure 8: Graph showing the required \(J_{SNR\text{min}}\) and \(\alpha_{\text{imin}}\) at different positions.  
From Equation (45), it can be observed that as the delay increases, the jamming matching filter gain-loss ratio \(\alpha_{i}\) also increases. The delays corresponding to positive and negative shifts of the target can be expressed as:  
\[\tau_{p}=\frac{(1-\alpha_{1})T_{r}}{n^{2}} \tag{46}\]  
\[\tau_{n}=\frac{(1-\alpha_{2})T_{r}}{n(n-1)} \tag{47}\]  
Upon comparing Equations (46) and (47), it becomes apparent that the matching loss incurred beyond false targets surpasses the corresponding loss behind false targets. Denoting the maximum delays for positive and negative shifts of the target as \(\tau_{\text{pmax}}\) and \(\tau_{\text{rmax}}\), the results of employing positive and negative frequency shifts at various positions to reduce the detection probability of the jammed radar to 0.1, as it progressively approaches the protected target, are shown in Figure 9.  
Figure 9 shows that as the distance decreases, the maximum delay also decreases; however, the delay associated with a positive frequency shift remains lower than that with a negative frequency shift. This discrepancy can be attributed to the greater matching loss beyond false targets, resulting in a diminished detection capability of the jammed radar, which is more favorable for protecting the target under detection.  
To enhance jamming suppression, the interval of false targets can be set to the length of the equivalent CFAR reference cell. From a communication standpoint, the delay interval \(\Delta\tau\) is considered as the symbol width, influencing the maximum communication rate. The expression for communication rate can be written as:  
\[R_{b}=\frac{1}{\Delta\tau\cdot\beta}\cdot n_{bit} \tag{48}\]  
where \(\beta\) represents the pulse coherent accumulation number captured by the intercepted radar, and \(n_{bit}\) denotes the communication data carried by a single UFMC symbol using QAM modulation. Analysis of Equation (48) suggests that, when \(\beta\) and \(n_{bit}\) are constant, a smaller delay interval \(\Delta\tau\) leads to a higher communication rate.
# A New Effective Narrowband Active Noise Control System for Accommodating Frequency Mismatch
## 1 Introduction
In [25], from the view of the complex domain, the ANC system dealing with the FM is experimentally and theoretically analyzed, and a new adaptive algorithm, named the variable step-size FXLMS algorithm, was used to improve the system performance. However, this is constrained by the fact that, in real life, there is no manipulation of complex signals, and the components of the primary noise are very complex. Another frequency correction method based on the iterative minimum variance distortionless responses (MVDR) spectrum estimator was proposed by [PERSON], in [26], for nullifying the ill effect of the FM. A momentum least mean square (MLMS) algorithm based on the ANC algorithm, in which a momentum correction term is artificially added between the weight vector correction and the gradient estimation, has been proposed by [PERSON] to develop the NANC algorithm in [27]; this momentum term will quickly increase at the beginning of the iteration, thus accelerating the gradient decline, making the weight of the coefficient more stable. There was also an adaptive algorithm with similar functions proposed by [PERSON] et al. ; the phase of the reference signal is changed by introducing a delay module in the \(i\)-th frequency channel, and then adjusted by the adaptive linear combination, and the correctness of the results was simulated by Simulink [28; 29]. In [30], the authors present the steady state analysis of the p-power algorithm for the sinusoidal signal. The presence of FM at the reference signal needs a large step size to maintain the noise-canceling performance in the NANC system. However, the large step size poses a bad steady state. Therefore, another LMP algorithm concept is applied to the NANC algorithm with a frequency correction feature. In light of the issues mentioned above, a new NANC system is developed in this paper, which attempts to address the convergence rate and the robustness of conventional NANC systems. The main contributions of this paper are summarized as follows: Firstly, we have dramatically reduced the time it takes for the system to converge with the FM. Secondly, we consider that a small step size can obtain a larger steady-state error with the FM,as the conventional variable step size algorithms are not useful. With the LMP, a large step size could achieve a better performance. Therefore, we use the LMP to optimize the dual frequency compensation so that the system could accommodate for a large FM and improve the tracking ability. Thirdly, the closed-form convergence analysis of the mean of the proposed system for discrete Fourier coefficients (DFC) are derived, along with the stability condition on the step size. The remainder of this paper is organized as follows. Section 2 describes the structure of the NANC system and the FM. The proposed method and stability analysis with a delay unit in the mean sense are described in detail in Section 3. Section 4 presents the representative simulations to clarify the validity of the proposed method. The conclusions are presented in Section 5.
## 2 Materials and Methods
### Statistics  
Statistical evaluation of experimental results is an essential part of the validation of new ML models. This is to infer if the new ML algorithm, feature selection, or vocal test, can provide a statistically significant improved performance with respect to others. Due to the nature of ML algorithms and datasets, we adopted non-parametric statistical methods [32; 33].  
In order to compare the ML algorithms, vocal tests, and feature selection methods against each other, we adopted the approaches of Iman and Davenport, [PERSON], and [PERSON] [32; 34; 35; 36].  
For comparing multiple classifiers, vocal tests, and feature selection methods, the Iman and Davenport test was used, based on [PERSON]'s approach that is a non-parametric equivalent of ANOVA. It ranks by performance and tests the significance of multiple results. The hypothesis to test is that classifiers, vocal tests, or feature selection methods perform unequally. The null hypothesis that we are trying to reject is that these methods are equally performing. The [PERSON]'s statistic is defined as follows:  
\[\chi_{F}^{2}=\frac{12*N}{k*(k+1)}*\left[\sum_{j}R_{j}^{2}-\frac{k*(k+1)^{2}}{ 4}\right] \tag{3}\]  
distributed according to \(\chi_{F}^{2}\), with \(k-1\) degrees of freedom, \(r_{j}^{i}\) being the rank of the \(j\)-th \(k\) algorithm on the \(i\)-th \(N\) dataset [32].  
[PERSON] and [PERSON] improved the conservatism of [PERSON]'s statistic and developed a better one, defined as:  
\[F_{F}=\frac{(N-1)*\chi_{F}^{2}}{N*(N-1)-\chi_{F}^{2}} \tag{4}\]which is distributed according to the \(F\)-distribution with \(k-1\) and \((k-1)(N-1)\) degrees of freedom [32, 35].  
For dual testing, the Wilcoxon Signed Rank Test, the Paired t-test, and Nemenyi's Test were used. Nemenyi's Test was the post hoc test following the [PERSON] and [PERSON] test if the null hypothesis is rejected on comparing classifiers against each other. [PERSON]'s test is defined as a critical difference as follows:  
\[CD=q_{alpha}*\sqrt{\frac{k(k+1)}{6N}} \tag{5}\]  
where alpha is the confidence level, \(k\) is the number of models, and \(N\) is the number of measurements. The obtained values of \(q\) (that change according to alpha) and \(k\) and are reported in Table 1. When the performances of the two classifiers, two vocal tests, or two feature selection methods under [PERSON]'s statistic is larger than this CD, the performances are significantly different and unequal.