esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Artificial Neural Network Analysis
### Neural Network Prediction  
The trained artificial neural network, 7-6-1, was tested by applying it to the prediction of \(\tau_{fu}/\sigma^{{}^{\prime}}{{}_{v}}\), based on laboratory data from set \(A\). Independent variables \(X_{1}\), \(X_{2}\), \(X_{3}\), and \(X_{4}\) (Table 2) were introduced into the network, and \(\tau_{fu}/\sigma^{{}^{\prime}}{{}_{v}}\) values predicted by the network were obtained. A comparison between measured values of \(\tau_{fu}/\sigma^{{}^{\prime}}{{}_{v}}\) obtained from the TSHC tests and values of \(\tau_{fu}/\sigma^{{}^{\prime}}{{}_{v}}\) predicted using the proposed neural network 7-6-1 are shown in Table 4. The single maximum value of relative error was about 6%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Measured Values**} & **Predicted Values \(\tau_{fu}/\sigma^{{}^{\prime}}{{}_{v}}\) Based** & **Relative Errors of** \\  & \(\tau_{fu}/\sigma^{{}^{\prime}}{{}_{v}}\) **from Set A \(d_{i}\) (-)** & **on 7–6–1 Network \(y_{i}\) (-)** & **Individual Case \(RE_{i}\) (\%)** \\ \hline
1 & 0.737 & 0.775 & 5.16 \\
2 & 0.651 & 0.634 & 2.61 \\
3 & 0.576 & 0.59 & 2.43 \\
4 & 0.556 & 0.554 & 0.36 \\
5 & 0.516 & 0.512 & 0.77 \\
6 & 0.588 & 0.612 & 4.08 \\
7 & 0.572 & 0.558 & 2.45 \\
8 & 0.535 & 0.52 & 2.8 \\
9 & 0.485 & 0.491 & 1.24 \\
10 & 0.461 & 0.468 & 1.52 \\
11 & 0.454 & 0.455 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between measured values of \(\tau_{fu}/\sigma^{{}^{\prime}}{{}_{v}}\) obtained from the TSHC tests and values of \(\tau_{fu}/\sigma^{{}^{\prime}}{{}_{v}}\) predicted using the proposed neural network 7–6–1, in set \(A\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Measures of Errors**} & **Subset learning, \(L\)** & **Subset testing, \(J\)** & **Subset validation, \(V\)** \\ \hline RMS & 0.0057 & 0.0054 & 0.0052 \\ _MAE_ & 0.0109 & 0.0110 & 0.0105 \\ _R\({}^{2}\)_ & 0.998 & 0.998 & 0.998 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measures of network errors in the 7–6–1 network of subsets \(L\), \(T\), and \(V\) of set \(B\). MAE: mean absolute error; \(R^{2}\): coefficient of determination; _RMS_: root mean squared error.  
Figure 7: Comparison between measured values of independent variable \(Y\), \(\tau_{fu}/\sigma^{{}^{\prime}}{{}_{v}}\), and values of \(Y\) predicted by proposed neural network, 7–6–1, in set \(B\).
# A Dual-Band 8-Antenna Array Design for 5G/WiFi 5 Metal-Frame Smartphone Applications
## 1 Introduction
An antenna element encompassing C-shaped microstrip patches, two rectangular radiating microstrips on a metal frame, and a rectangular slot on the ground plane has been proposed, covering 5G N77 (3.3-4.2 GHz), N78 (3.3-3.8 GHz), N79 (4.4-5.0 GHz), and LTE46 (5.15-5.93 GHz) bands [13]. A metal-frame smartphone antenna design with dimensions of 150 mm \(\times\) 70 mm \(\times\) 6 mm incorporates an inverted-F antenna and an asymmetric T-slot on the circuit board edges, demonstrating significant engineering application value [16]. One of the primary challenges in contemporary mobile antenna design lies in ensuring optimal performance when the device is held by the user. With the escalating demand for broader frequency bands and the diminishing space allocated for antennas, achieving satisfactory performance, even in ideal free-space conditions, has become increasingly challenging [17]. In [18], an analysis of fundamental theories pertaining to models of objects (e.g., the human body) with high dielectric constants and significant losses offers a theoretical framework for subsequent hand model applications. Similarly, numerous case studies examining the interaction patterns between mobile phones and hands offer valuable insights into designing strategies aimed at enhancing the radiation characteristics of mobile phone antennas [19]. This paper introduces a 5G dual-band MIMO antenna operating at 3.36-4.2 GHz and 4.37-5.95 GHz frequencies. Eight elements are printed on two side-edge metal frames, and two types of decoupling structures (DS\({}_{1}\) and DS\({}_{2}\)) are employed to mitigate mutual coupling. The proposed MIMO antenna undergoes manufacturing and testing, and measurement results, including S-parameters, radiation efficiencies, radiation patterns, and calculated envelope correlation coefficient (ECC), are discussed. The proposed antenna effectively covers the 5G China bands N78 (3.4-3.6 GHz) and N79 (4.4-5.0 GHz), along with WiFi 5 (5.15-5.85 GHz).
## 3 Engineering Background and Main Parameters Affecting Support Structure Design
### Major Parameters  
#### 3.2.1 High Ground Stress  
Deep drilling holes are arranged along the water conveyance route in the study area at intervals of 2 km to 3 km. Ground stress tests are conducted in each deep hole using the hydraulic fracturing method, with intervals of approximately 50 m. The relationship between the maximum horizontal stress coefficient (\(\alpha_{\rm H}\)) and the burial depth (\(D_{\rm e}\)) in the study area is illustrated in Figure 2. The value of \(\alpha_{\rm H}\) generally decreases with increasing burial depth \(D_{\rm e}\). When H \(<\) 600 m, the value of \(\alpha_{\rm H}\) ranges from 1.38 to 2.41, with an average value of 1.86. For depths in the range of 600 m \(\leq\) H \(\leq\) 1200 m, the value of \(\alpha_{\rm H}\) varies from 1.48 to 1.91, with an average value of 1.68.
## 2 Problem Formulation
### OFDM-Based Communication Systems  
The proposed system for OFDM-based image reconstruction is designed to operate within the conventional OFDM communication framework, where image data are transmitted as symbols modulated over multiple orthogonal subcarriers. This subsection provides an overview of the OFDM-based transmission process and the distortion of the received signal.
# Haze-Aware Attention Network for Single-Image Dehazing
## 3 Method  
### Image Dehazing  
As shown in Figure 1, our dehazing model employs a classic encoder-decoder architecture as its backbone. This framework performs a 4 \(\times\) downsampling operation, which greatly reduces memory usage during both training and inference, thus enhancing the model's operational efficiency. It is worth noting that our model uses three different types of activation functions. ReLU, validated in the gUnet [44] study for image dehazing, effectively learns complex patterns for image dehazing. Tanh, with its output range of \((-1,1)\), constrains the model's output to prevent extreme values, thus enhancing stability and output quality. Additionally, the model employs a dynamic fusion module to merge features from the downsampling and upsampling layers, which is a strategy that helps to retain more information from the image and strengthens the model's ability to capture details, thus resulting in a more compact and efficient dehazing model. Within this refined feature space, we have further enhanced feature extraction and optimization through the cascade of HAABs. Each HAAB has been meticulously designed and consists of two key components: a Haze-Aware Attention Module and a Multiscale Frequency Attention Module. The HAAM, inspired by physical priors, guides the network to progressively extract clear, fog-free features, which are crucial for the dehazing effect in images. Meanwhile, the MFEM enriches the features through multiscale modulation, thus intelligently identifying and emphasizing features that contain important information and then fusing these features through channelwise weighted fusion using learnable parameters, which further improves the model's performance. With this innovative structural design, our dehazing model can effectively handle a variety of complex haze images. It not only preserves the original details of the image but also significantly improves the clarity and quality of the image.
# Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification +
## 4 Results
The confusion matrix obtained after the execution of the Hybrid-QCNN model is shown in Figure 7.  
The Hybrid-QCNN model had a significantly larger number of misclassified samples than the Standard-CNN model. Similar to what happened with the Standard-CNN model, the majority of misclassified samples belonged to the Airpush and Dowgwin malware families and were designated as Trusted by the Hybrid-QCNN model. A possible reason for this result is that, by reducing both sets of images, the Airpush and Dowgin malware looks similar to the Trusted applications. As we can see from the confusion matrix shown in Figure 2, the number of Airpush and Dowgwin malware applications labeled as Trusted is, respectively, 24 and 25 samples. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Output Classes** & **Models** & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **AUC** \\ \hline \multirow{2}{*}{Airpush} & _Standard-CNN_ & 0.979 & 0.906 & 0.952 & 0.929 & 0.968 \\ \cline{2-7}  & _Hybrid-QCNN_ & 0.951 & 0.837 & 0.811 & 0.824 & 0.893 \\ \hline \multirow{2}{*}{Dowgin} & _Standard-CNN_ & 0.978 & 0.902 & 0.848 & 0.874 & 0.919 \\ \cline{2-7}  & _Hybrid-QCNN_ & 0.945 & 0.694 & 0.703 & 0.699 & 0.836 \\ \hline \multirow{2}{*}{FakelInst} & _Standard-CNN_ & 0.997 & 0.990 & 1.0 & 0.995 & 0.998 \\ \cline{2-7}  & _Hybrid-QCNN_ & 0.991 & 0.985 & 0.978 & 0.982 & 0.987 \\ \hline \multirow{2}{*}{Fusob} & _Standard-CNN_ & 0.999 & 0.996 & 1.0 & 0.998 & 0.999 \\ \cline{2-7}  & _Hybrid-QCNN_ & 0.998 & 0.988 & 1.0 & 0.994 & 0.998 \\ \hline \multirow{2}{*}{Jisut} & _Standard-CNN_ & 0.996 & 0.956 & 0.990 & 0.973 & 0.993 \\ \cline{2-7}  & _Hybrid-QCNN_ & 0.994 & 0.971 & 0.936 & 0.953 & 0.967 \\ \hline \multirow{2}{*}{Mecor} & _Standard-CNN_ & 0.999 & 1.0 & 0.996 & 0.998 & 0.998 \\ \cline{2-7}  & _Hybrid-QCNN_ & 0.998 & 0.996 & 0.996 & 0.996 & 0.997 \\ \hline \multirow{2}{*}{Trusted} & _Standard-CNN_ & 0.976 & 0.930 & 0.882 & 0.905 & 0.936 \\ \cline{2-7}  & _Hybrid-QCNN_ & 0.930 & 0.714 & 0.745 & 0.729 & 0.851 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results obtained with the convolutional and the quantum model obtaining the best performances for the family classification task. Figure 6: Confusion matrix obtained with the Standard-CNN model for the family classification task. Figure 7: Confusion matrix obtained with the Hybrid-QCNN model for the family classification task.
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 2 Hybrid FSO/RF Link System
### Radio Frequency Channel  
The input signal \(x_{2}\) undergoes upconversion to transform it into an RF signal prior to transmission. At the receiver's end, the signal undergoes demodulation, which can be described as  
\[r_{2}=\sqrt{P_{2}}h_{RF}x_{2}+n_{2}, \tag{4}\]  
where \(n_{2}\) is the additive white Gaussian noise, \(P_{2}\) and \(h_{RF}\) indicate the average transmitted power and the channel fading coefficient of the RF link. Meanwhile, the RF link is modeled by the Rician distribution with the Rice parameter \(K\), where \(K\) represents the relative strength of the direct LOS path. The PDF of the RF fading is provided in [15]  
\[f_{\gamma_{RF}}(\gamma_{RF})=\frac{K+1}{\overline{\gamma}_{RF}}exp\bigg{(}-(K +1)\frac{\gamma_{RF}}{\overline{\gamma}_{RF}}-K\bigg{)}I_{0}\bigg{(}2\sqrt{K (K+1)\frac{\gamma_{RF}}{\overline{\gamma}_{RF}}}\bigg{)}, \tag{5}\]  
where \(I_{0}(\cdot)\) is a zero-order modified Bessel function of the first kind, and \(\gamma_{RF}\) represents the instantaneous SNR, which relates to \(h_{RF}\) and average SNR \(\overline{\gamma}_{RF}\) as \(\gamma_{RF}=\overline{\gamma}_{RF}h_{RF}^{2}\).  
Figure 1: ML-based soft-switching model for Hybrid FSO/RF links.
## 4 Results and Analysis
### Response Surface Model  
The first four modal frequencies of the flexible PV support structure were selected as the response features. The corresponding frequencies obtained from the FE model and field-measured results are presented in Table 5. The smaller error in the third-order modal frequency should not be interpreted as the FE model being inherently accurate for this mode. It is possible that certain inaccuracies or simplifications in the FE modeling assumptions coincidentally led to the third-order modal frequency being closer to the actual value. However, for other modes, these same assumptions resulted in larger deviations, indicating that the uncorrected FE model does not fully capture the true dynamic characteristics of the structure.  
Figure 16: Full-wave vertical bending mode shape.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Response Features**} & \multicolumn{2}{c}{**Modal Frequencies (Hz)**} & \multirow{2}{*}{**Relative Error (\%)**} \\  & **FE** & & **Field-Measured** \\ \hline \(f_{1}\) & 1.693 & 1.488 & 13.78 \\ \(f_{2}\) & 2.205 & 1.775 & 24.22 \\ \(f_{3}\) & 3.081 & 2.980 & 3.38 \\ \(f_{4}\) & 4.242 & 3.526 & 20.31 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of field-measured and FE analysis modal frequencies.  
Figure 15: First-order torsional spatial mode shape.
# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 6 Discussion
### Analytical Review of Experimental Outcomes  
This work sought to create an AI-driven stroke detection system utilizing multi-modal data from the eICU database and selected imaging sources. The experimental findings provide numerous critical insights:  
* The effectiveness of Gradient-Boosted Models: Both XGBoost and CatBoost attained elevated overall accuracy (?92%) on structured clinical data. Nevertheless, the modest stroke recall (-45-47%) underscores a persistent issue in imbalanced datasets--despite a seemingly strong overall performance, the minority stroke class continues to be challenging to accurately record. This result highlights the importance of meticulous threshold calibration and ensemble techniques.
* Robustness of CNN-Driven Image Analysis: The single-stage MobileNet CNN achieved an accuracy of 96%, with a stroke recall of 91% and a precision of 97%. The unique, bimodal probability distribution of the CNN indicates that deep learning is exceptionally proficient at extracting discriminative features from the picture input. These findings align with the extensive literature regarding the effectiveness of CNNs in medical imaging tasks.
* Trade-Offs of Ensemble Methods: Ensemble techniques, especially the stacking ensemble, significantly improved the overall accuracy (up to 94.4%) but resulted in a considerable decline in stroke recall (26%). Conversely, the soft-voting ensemble preserved a balance more akin to the base models. The results suggest that whereas ensemble approaches can reduce variance and enhance precision, they may unintentionally compromise sensitivity, a crucial characteristic in urgent medical diagnoses.
* The significance of the calibration and threshold optimization: Our sensitivity and calibration analyses demonstrate that even slight modifications in decision thresholds can significantly affect the performance measures, particularly stroke recall. This discovery underscores the essential requirement for meticulous threshold adjustment to synchronize model outputs with clinical necessities, as the omission of a stroke diagnosis may have grave repercussions.  
Our comprehensive methodology, grounded in the diverse data from the eICU database and augmented with imaging information, exhibits promise efficacy for stroke diagnosis. Nevertheless, the findings underscore the fundamental trade-offs between the overall accuracy and the sensitivity necessary for effective clinical screening.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 4 Deployment Cycle
### Site Survey and Calibration Phase  
One of the drawbacks of any fingerprinting-based ILS is the offline stage. This stage requires time and manpower. In addition, this process must be repeated for every scenario where the system is set up or if the infrastructure suffers major modifications. For this reason, some authors are looking  
Figure 2: System deployment cycle.  
for research lines to deal with the reduction of the radio map generation efforts [18] or trying to develop systems that omit this step totally or partially as much as possible [53].
## 6 Intelligent Evaluation of Bolt Preload Using SVR
### Arrangement for SVR Modeling and Prediction  
The data set of each group was a 6 \(\times\) 10 matrix, whose 10 columns corresponded to the preload levels (i.e., \(y_{i}\)\(\in\)\(\{1,2,3,4,5,6,7,9,11,13\}\)) and the 6 rows consisted of the amplitudes of linear and nonlinear features (i.e., \(x_{i}=\{A_{LF}\), \(A_{SOH}\), \(A_{TOH}\), \(A_{LS}\), \(A_{HF}\), \(A_{RS}\)\(\}\)\(\}\)\(\}\)\). The data in each row (i.e., the amplitudes of each feature for different preloads) were normalized within a range of 0\(\sim\)1 before being input to the model.  
Figure 9 shows the flow of the tests and the source of data. Data groups 1\(\sim\)3 and 4 were all collected from the experiment with joints of the same size and material parameters. The difference was that groups 1\(\sim\)3 were the repeated VAM results from specimen 1,  
while group 4 was collected from a new specimen, which may have possessed some slight manufacture deviations (e.g., interface friction or clamping boundary) compared to specimen 1. Groups 5 and 6 were collected from the same numerical model with different friction coefficients \(\tau\). The friction coefficients \(\tau\) of group 5 were 0.1 for the Al-Al joints and 0.2 for the C-C joints, respectively. The coefficients of group 6 were 0 for the Al-Al joints and 0.4 for the C-C joints.  
A total of eight tests were conducted using the SVR method for the Al-Al joints (four tests) and C-C joints (four tests), the test flows for the two materials were the same. In test 1, groups 1 and 2 were input to the SVR model as training samples, and group 3 was used to validate the predicted results. The evaluation of test 1 was carried out under ideal conditions without deviation between the specimens. In test 2, the SVR model was trained by groups 1-3 and validated by group 4 in order to reveal the variation int he evaluation accuracy due to manufacturing deviations between the different specimens. In tests 3 and 4, FEM samples 5 and 6 were adding to the training data to improve the applicability and accuracy of the SVR prediction by simulating the deviation of the frictional conditions.  
The parameter \(v\) for the \(v\)-SVR model was 0.5 and the optimization based on grid search (GS) was used to choose the penalty parameter \(c\) and the kernel parameter \(\sigma\). The parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Test 1** & **Test 2** & **Test 3** & **Test 4** \\ \hline \multirow{3}{*}{Al–Al} & \(c\) & 11.3137 & 181 & 256 & 1.414 \\ \cline{2-6}  & \(\sigma\) & 0.0884 & 0.0221 & 0.02 & 0.25 \\ \hline \multirow{3}{*}{C–C} & \(c\) & 181 & 5.657 & 2.828 & 4 \\ \cline{2-6}  & \(\sigma\) & 0.1768 & 0.125 & 0.5 & 0.354 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SVR parameters \(c\) and \(\sigma\).  
Figure 9: Flow of SVR modeling and evaluation.
## Abbreviations  
The following abbreviations are used in this manuscript:  
SOFC Solid oxide fuel cell PEMFC Proton exchange membrane fuel cell EMF Electromotive force  
**Nomenclature**  
\(E_{Nernst}\) Nernst potential (open-circuit EMF) of a single cell, V  
\(E_{0}\) Standard (reference) EMF of a single cell, V  
\(E_{0}^{0}\) Standard (reference) EMF of a single cell at temperature \(T_{0}\), V  
\(V\) Output terminal voltage of a single cell, V  
\(T\) Temperature, K  
\(n\) Number of electrons transferred  
\(a\) Activity  
\(a_{H_{2}}\) Activity of hydrogen  
\(a_{O_{2}}\) Activity of oxygen  
\(a_{H_{2}O}\) Activity of water vapor (steam)  
\(\Delta s\) Change in entropy, J/(mol K)  
\(p\) Pressure or partial pressure, atm  
\(p^{0}\) Standard-state pressure, atm  
\(p_{H_{2}}\) Partial pressure of hydrogen, atm  
\(p_{O_{2}}\) Partial pressure of oxygen, atm  
\(p_{H_{2}O}\) Partial pressure of water vapor, atm  
\(I_{FC}\) Fuel cell current, A  
\(u\) Fuel utilization ratio  
\(r_{HO}\) Ratio of hydrogen-to-oxygen input flow rates  
\(K_{H_{2}}\) Value molar constant for hydrogen, mol/(s atm)  
\(K_{O_{2}}\) Value molar constant for oxygen, mol/(s atm)  
\(K_{H_{2}O}\) Value molar constant for water vapor, mol/(s atm)  
\(K_{r}\) Modeling constant, mol/(s A)  
\(q_{H_{2}}^{in}\) Hydrogen input flow rate, mol/s  
\(q_{H_{2}}^{out}\) Hydrogen output flow rate, mol/s  
\(q_{H_{2}}^{\text{recot}}\) Hydrogen flow rate that takes part in the reaction, mol/s  
\(q_{O_{2}}^{in}\) Oxygen input flow rate, mol/s  
\(q_{O_{2}}^{out}\) Oxygen output flow rate, mol/s  
\(q_{O_{2}}^{\text{recot}}\) Oxygen reacting flow rate, mol/s\(\begin{array}{ll}a_{H_{2}O}^{out}&\text{Water vapor output flow rate, mol/s}\\ \par a_{H_{2}O}^{react}&\text{Water vapor flow rate produced in the reaction, mol/s}\\ r&\text{Ohmic resistance of a single cell, Ohm}\\ R&\text{Universal gas constant, J/(mol K)}\\ F&\text{Faraday's constant, Coulombs/mol}\end{array}\)
## 2 Materials and Methods
### Statistics  
Kolmogorov-Smirnov tests confirmed that each parameter analyzed in the study (EMG\({}_{\text{T1}}\), EMG\({}_{\text{T2}}\), VT\({}_{1}\), and VT\({}_{2}\)) was normally distributed. For the validity study (16 subjects), one-way repeated-measures ANOVA was used to determine whether there were significant differences between, on the one hand, EMG\({}_{\text{T1}}\) and VT\({}_{1}\), and on the other hand, between EMG\({}_{\text{T2}}\) and VT\({}_{2}\), when these variables were expressed in W, VO\({}_{2}\) (ml/kg/min), and % VO\({}_{\text{MAX}}\). Pearson correlation (r) was used to determine the possible relation between EMG\({}_{\text{T1}}\) and VT\({}_{1}\), as well as between EMG\({}_{\text{T2}}\) and VT\({}_{2}\). To further analyze the validity of the EMG method, the Bland-Altman method was followed [22]. For this analysis, the mean differences (bias) and SD of the differences between the mean values (W) obtained with the two methods (EMG\({}_{\text{T1}}\) vs. VT\({}_{1}\), and EMG\({}_{\text{T2}}\) vs. VT\({}_{2}\)) were calculated. The mean difference plus or minus two SD is shown in Figure 3. In this way, the bias and precision of the EMG technique could be calculated [6]. For the group of six subjects that performed the test on two different days (reliability study), a paired Student's t test was used to compare mean values of EMGT1 and EMGT2 obtained with both tests. Intraclass Pearson's correlation coefficients were also calculated to determine the degree of correlation between mean values of EMGT1 and EMGT2 reported with repeated tests. Significance was set at \(p\) < 0.05.  
Figure 1: Multi-regression method step by step, S represents the residual sum of squares (RSS). (**A**) Original data, workload (gray line), and EMG amplitude for each epoch. (**B**) Best single-segment regression. (**C**) Better two-segment regression. (**D**) Better 3-segment regression.  
Figure 2: A representative example of a surface electromyographic (EMG) recording of the vastus lateralis obtained during the incremental test of one cyclist. Each data point represents the root mean square of the EMG (RMS EMG), obtained at two-second intervals. The RMS EMG data were fitted with three regression lines using the multi-segment linear regression method. The intersections between the regression lines mark the breakpoints in the linearity of the curve, thus yielding the first (EMGT1) and second (EMGT2) thresholds.