esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Contributions
### Monitoring and Information Display System
#### 5.1.2 Mobile/Web System  
This system is similar to the one described in the previous section; however, in this system, the immersion of the patient is sacrificed to carry out VR, which allows greater accessibility to the system through the use of the web tool or mobile application, being accessible from any device with an operating system, a visual interface, and Internet access for everyday use, such as mobile phones and personal computers. These platforms can achieve the telerehabilitation that this work seeks to achieve. The use of Unity3D is key for the objectives mentioned in the previous section. Furthermore, this development environment allows for the export of the 3D scenario to different platforms, in this case, web and mobile. Mobile development is more oriented towards patient use, allowing them to carry out their rehabilitation exercises and obtain information on their progress during the rehabilitation process. One of the objectives to be achieved with the development of this mobile application is to make the application intuitive and understandable for the user so that it is easy to use. Figure 6(a) shows the screen of access to the application. Figure 6(b) shows a screenshot of a list of exercises to be performed by the patient.  
The web application is more oriented towards health professionals that can monitor, in a more precise way and with more information. The progress of a patient in their rehabilitation process is represented in the Figure 8. By means of this platform and this information, the doctor assigned to the patient can modify the exercises to be performed by the patient according to this progress. In addition, on this platform, patients are allowed to carry out consultations with their doctor through the use of a chat so as to increase communication between patient and doctor.  
Figure 7: Mobile’s interface examples.  
The aim of monitoring the patient through the platform is to provide the health specialist with important information, so as to go deeper into the rehabilitation process and improve it by adapting it to the progress of the patient. To achieve this, different visual representations of the information are shown, as can be seen in Figure 9.  
These representations show information to the user so that they know in detail what they need to check more precisely. The doctor assigned to the patient can replay the exercises in the virtual reality embedded player, as shown in Figure 9. In addition, the doctor is allowed to send a note to the patient about the exercise performed.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 6 Numerical Investigation
### Robustness of Methods to Exceeding the Signal Rank
#### 6.4.2 Case of Lack of Separability  
Let us first consider the case of time series of finite rank. In this case, we will discuss the robustness to rank overestimation. The most stable method for exceeding the rank of the signal, with the chosen parameters, is autoSSA. However, this method is suitable for trend extraction only when there is strong separability of the components. Such a case is considered in Section 6.4.1.  
Consider Example (7), where we fix \(\omega_{0}=\frac{1}{40}\). The graph of MSE in dependence on the rank estimate \(t\) is depicted in Figure 7.  
Figure 6: MSE vs number of components \(t\). Example (6).  
The ordering of the methods by the error growth rate is approximately the same (except for bad accuracy of autoFOSSA) as in the case of exact trend separability. The most stable methods are the autoSSA and autoFOSSA, and the fastest error growth is with the autoEOSSA method. However, due to the lack of separability, the autoSSA and autoFOSSA methods inadequately estimate the trend, so for quite large rank excess the autoEOSSA method is the most accurate.  
Case of Infinite-Rank Time Series  
Example (8) differs from Examples (6) and (7) in two points. First, the logarithmic trend is of infinite rank. Then, the periodic component has a not large period and has good weak separability from the trend. However, there is no strong separability due to close component contributions. The latter means that the autoFOSSA method should improve the separability.  
Indeed, Figure 8 confirms that autoFOSSA is the best, while the autoSSA method is the most robust, but it produces large errors.  
Figure 8: MSE vs number of components \(t\). Example (8).  
Figure 7: MSE vs number of components \(t\). Example (7).
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Sample (_n = 28_)**} & \multicolumn{4}{c}{**Group**} & \multirow{2}{*}{**Evening Training**} & \multirow{2}{*}{_p_} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} \\ \multicolumn{1}{c}{**(Mean, SD)**} & & & & & & & \\ \multicolumn{1}{c}{} & & & & & & & \\ \multicolumn{1}{c}{} & & & & & & & \\ \hline \multirow{2}{*}{TEE (Kcal/day)} & 4440.70 & 5385.86 (1734.97) & 3771.21 (2667.80) & 0.030 & 3082.54 & 8659.98 \\ El (Kcal/day) & 3107.90 (535.05) & 3024.87 (550.24) & 3203.71 (521.87) & 0.953 & 1983.33 & 3926.33 \\ Protein, g & 152.23 (32.20) & 149.92 (32.01) & 154.90 (33.50) & 0.882 & 92.40 & 205.00 \\ Proteins, \% & 19.81 (4.29) & 20.29 (5.45) & 19.25 (2.47) & 0.155 & 11.49 & 35.97 \\ Carbohydrate, g & 312.11 (84.73) & 303.49 (86.97) & 322.06 (84.43) & 0.723 & 108.33 & 436.33 \\ Carbohydrates, \% & 39.80 (7.02) & 39.48 (6.83) & 40.17 (7.49) & 0.511 & 21.85 & 47.88 \\ Indissoluble fiber, g & 14.78 (5.72) & 15.46 (6.26) & 13.99 (5.16) & 0.530 & 7.07 & 28.03 \\ Lipid, g & 129.17 (24.65) & 124.86 (20.61) & 134.14 (28.66) & 0.099 & 88.93 & 158.00 \\ Lipids, \% & 37.60 (5.03) & 37.41 (3.72) & 37.82 (6.37) & 0.042 & 28.95 & 41.46 \\ SFA, g & 33.50 (9.70) & 31.57 (7.25) & 35.74 (11.84) & 0.098 & 17.23 & 42.30 \\ SFA, \% & 9.70 (2.33) & 9.48 (1.98) & 9.94 (2.75) & 0.128 & 5.74 & 12.13 \\ MUFA, g & 63.55 (10.94) & 62.12 (10.29) & 65.20 (11.85) & 0.586 & 43.57 & 85.77 \\ MUFA, \% & 18.57 (2.34) & 18.64 (1.86) & 18.49 (2.87) & 0.120 & 13.32 & 20.39 \\ PUFA, g & 19.62 (7.75) & 19.11 (5.58) & 20.20 (9.90) & 0.430 & 12.47 & 33.80 \\ PUFA, \% & 5.69 (1.92) & 5.68 (1.28) & 5.70 (2.52) & 0.151 & 4.50 & 9.89 \\ Cholesterol, mg & 539.65 (205.529) & 503.47 (187.22) & 581.40 (224.99) & 0.458 & 248.67 & 907.00 \\ \hline \hline \end{tabular}  
* TEE: total energy expenditure; El: energy intake; SFA: saturated fatty acids; MUFA: monounsaturated fatty acids; PUFA: polyunsaturated fatty acid.  
\end{table} 
Table 2: Macronutrients’ daily intake by training group.
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
###### Abstract  
In this work, a hybrid procedure for bearing fault identification using a machine learning and adaptive cascade observer is explained. To design an adaptive cascade observer, the normal signal approximation is the first step. Therefore, the fuzzy orthonormal regressive (FOR) technique was developed to approximate the acoustic emission (AE) and vibration (non-stationary and nonlinear) bearing signals in normal conditions. After approximating the normal signal of bearing using the FOR technique, the adaptive cascade observer is modeled in four steps. First, the linear observation technique using a FOR proportional-integral (PI) observer (FOR-PIO) is developed. In the second step, to increase the power of uncertainties rejection (robustness) of the FOR-PIO, the structure procedure is used serially. Next, the fuzzy like observer is selected to increase the accuracy of FOR structure PI observer (FOR-SPIO). Moreover, the adaptive technique is used to develop the reliability of the cascade (fuzzy-structure PI) observer. Additionally to fault identification, the machine-learning algorithm using a support vector machine (SVM) is recommended. The effectiveness of the adaptive cascade observer with the SVM fault identifier was validated by a vibration and AE datasets. Based on the results, the average vibration and AE fault diagnosis using the adaptive cascade observer with the SVM fault identifier are 97.8% and 97.65%, respectively.  
Keywords:rotating machine; bearing; adaptive cascade observer; support vector machine; proportional-integral (PI) observer; structure fault observer; fuzzy technique; orthonormal regressive function approximation; adaptive technique; fault detection; fault classification; machine learning +
Footnote †: journal: Applied Sciences
## 3 Results
### Colorimetric Detection of Refined Oil in Blended Sesame Oil  
The results obtained with commercial sesame oil samples in Section 3.3 suggested that our sensor might be able to detect refined oil in fragrant sesame oil. To evaluate the sensing performance systematically, a series of blended fragrant sesame oils was prepared. Refined canola oil was used as a sample of common refined oil because of its negligible aroma. The average color difference maps of blended samples are shown in Figure 8a. Pure refined oil, which has low aroma intensity, could only produce weak signals; as the volume percentage of sesame oil increased, the color intensity increased gradually, and the color fingerprint resembled more of the color fingerprint of typical hot-pressed fragrant sesame oil.  
PCA was performed using the Euclidean distances of all 14 sensing units. The first two principal components accounted for 78.7% of the total variance (Figure 8b). Overlap among clusters could be observed when the volume percentage of refined oil dropped below 20%. In addition to PCA, PLS regression analysis was used to analyze the results. Here, the Euclidean distances were used as the predictors, while the volume percentages of refined oil were the responses. The PLS regression model was first constructed by selecting an appropriate number of PLS components (i.e., latent variables). It was found that when nine components were selected, r\({}^{2}\) = 0.9423, suggesting a reasonably strong correlation, and there was no significant increase in the r\({}^{2}\) value when the number of PLS components increased further. Hence, the number of PLS components was set to nine for subsequent analysis (Figure 8c). The limit of detection (LOD) of the regression model was estimated by the three-sigma method [40]. Briefly, replicates of a blank were analyzed by the analytical method, which was the PLS regression model in this case, and the responses were converted into concentration units, the standard deviation (SD) was calculated, and the LOD was estimated as LOD = 3 SD. Using the predicted refined oil concentration at 0% \(v/v\) of refined oil, the standard deviation was calculated as SD = 6.491% \(v/v\), and the LOD was thus estimated to be 19.5% \(v/v\) of refined oil. It is worth noting that the LOD of the PLS model would vary slightly if the VOC profiles of the sesame oil and refined oil changed. To further demonstrate the reproducibility, the experiment was repeated with sesame oil of different brands and batches (Supplementary Information S3), and in all cases, PLS regression models with r\({}^{2}\) > 0.9 could be obtained.  
Figure 7: **(a)** The color difference maps of selected normal hot-pressed samples (from Brands A, C, E, and F) and abnormal hot-pressed samples (from Brands B, D, G, H, and J). Stronger signals were detected from normal hot-pressed samples, especially by receptor Nos. 7, 8, and 11. **(b)** Total lignin contents of the selected commercial hot-pressed samples. Samples with abnormal color difference maps generally had lower lignan contents, except Brand B. A low lignan content suggested that refined oil might be present.
## 4 The Proposed Routing Protocol  
The current routing protocols are not smart; they find the transmission paths according to the routing table or routing discovery using the global information of the network. In the routing table, network nodes have to maintain the discovered paths. Moreover, the paths have to be recovered and updated as some of the paths can be broken. Thereupon, route discovery causes increasing network traffic, while decreasing the network lifetime. To solve these potential problems, we propose a new routing protocol called the _Static Three-Dimensional Fuzzy Routing Based on the Receiving Probability_ (SFRRP) in wireless sensor networks. The proposed protocol is an inferential routing protocol that uses a fuzzy procedure to find an appropriate path for transmitting the data packets from the sender nodes toward the base station. When a sensor node has a sensed data or a buffered data packet that should be transmitted to the base station, it chooses one of its neighbors called the _selected node_ by the proposed fuzzy system for forwarding the packet through that node. To choose the appropriate node from its neighbors, the sender node needs only the local information on its neighbors instead of the global information of the network. Therefore, SFRRP does not generate any controlling packet for route discovery. In this way, the traffic load of the network is reduced, the energy consumption of the nodes decreased, the network life and data delivery ratio considerably enhanced, and the data packets delivered to the base station in an acceptable time. Note that the proposed routing protocol can be operated in the variant states of the WSN. Furthermore, it is not needed to maintain the paths and does not involve unnecessary intermediate nodes.
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
## 3 Experimental Measurements
### Analysis of the Effects of Wavelength and Temperature on Wavefront Distortion
This means that phase fluctuations are greater at shorter wavelengths than at longer wavelengths, which is consistent with the previous conclusion and is significant for long-term observation or communication. Additionally, temperature also affects the wavefront. Generally, an increase in temperature leads to a decrease in air density and refractive index, which alters the refractive path of light waves, thereby affecting the shape of the wavefront. As shown in Figure 18, under the same wavelength conditions, all metrics increase as the temperature rises, with rapid changes in the wavefront and corresponding increases in the wavefront distortion. The degree of distortion increases with rising temperatures at the same wavelength. Figure 17: Relationship between \(C_{n}^{2}\) and \(r_{0}\) under different wavelength conditions.
## 3 System and Threat Models
### Threat Model Based on the Bacon Semantic Analysis
#### 3.2.1 Trajectory Prediction  
For each mix zone, the adversary classifies all inbound beacons by their pseudonyms as a trace set \(R\) and then forms all first beacons of outbound traces as a candidate set \(S\). To estimate the state of each trajectory \(r\) in \(R\) at the exit, the adversary employs a Kalman filter. The state of a vehicle \(x\) at a given time \(t\) is initially modeled as \(x_{t}=x_{t-1}+w\), while the linear relation between the state measurement \(z\) and the state \(x\) at time \(t\) can be  
Figure 2: Mix-zone deployment in VANETs using fake beacons.  
represented as \(z_{t}=Ax_{t}+v\), where \(H\) is a measurement matrix and \(v\) is the measurement noise. The noises \(w\) and \(v\), are set to obey normal distribution with zero means and a very small standard deviation. The matrices \(A\) and \(H\) are:  
\[A=\begin{bmatrix}1&0&\Delta t&0\\ 0&1&0&\Delta t\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix},\quad H=\begin{bmatrix}1&0&0&0\\ 0&1&0&0\end{bmatrix}\]  
where \(\Delta t\) represents the time interval between two estimations. For the iteration in \(r\), \(\Delta t\) equals the beacon interval. For the final estimation of arrival at the exit, \(\Delta t\) equals the difference \(\Delta t_{r_{n},s}\) between the timestamp \(t_{r_{n}}\) of the last state in \(r\) and the timestamp \(t_{s}\) of each candidate \(s\) in \(S\).  
When a new measurement is iterated at time \(t\), the Kalman filter updates the estimated state \(\hat{x}\) without correction, along with the error covariance \(\bar{P}\) affected by the disturbance noise.  
\[\hat{x}_{t}=Ax_{t-1} \tag{1}\]  
\[\bar{P}_{t}=AP_{t-1}A^{T}+Q \tag{2}\]  
where \(P_{t-1}\) is the error covariance at the previous timestamp \(t-1\), and \(Q\) is a carefully selected covariance matrix. Using \(\hat{x}\) and \(\bar{P}\), the control input matrix \(B\) can be calculated as follows:  
\[B=HP_{t}H^{T}+R \tag{3}\]  
where \(R\) is another covariance matrix that is carefully selected. The variables in the Kalman filter are then updated as follows:  
\[K=\bar{P}H^{T}R^{-1} \tag{4}\]  
\[\bar{x}=\hat{x}+K(z_{t}-H\hat{x}_{t}) \tag{5}\]  
\[P_{t}=\bar{P}-\bar{P}H^{T}(H\bar{P}H^{T}+R)^{-1}H\bar{P} \tag{6}\]  
where \(K\) represents the Kalman gain. The estimated state after correction \(x_{t}\) and the updated error covariance \(P_{k}\) are utilized in the subsequent calculation at the next timestamp \(t+1\). Lastly, the adversary leverages \(\Delta t_{r,s}\) to update the Kalman filter and acquire the estimation \(x_{r,s}\) for each \(r\) at every timestamp at each timestamp \(t_{s}\).
## 3 Results and Discussion
The absolute value is also comparable to that of common temperature sensing metal materials such as platinum (\(39.2\times 10^{-4}\) K\({}^{-1}\)), copper (\(43\times 10^{-4}\) K\({}^{-1}\)) and nickel (\(68.1\times 10^{-4}\) K\({}^{-1}\)) [47] as well as carbon nanotube networks (\(-7\times 10^{-4}\) K\({}^{-1}\)) [48] and graphene (\(15\div 80\times 10^{-4}\) K\({}^{-1}\)) [49]. The high absolute TCR value and a high nominal resistance, together with the ease of material microfabrication processing and biocompatibility of the carbon material make CNP film ideal for temperature sensing in many environments. In practical applications, it is important to calibrate the response of the sensor and to find the functional relationship between resistance and temperature. Different calibration equations are generally used for NTC thermistor [50]. The most popular equation, the basic equation, is a two-parameter exponential, while several other calibration equations employ different order polynomials. The film of carbon nanoparticles  
\[\delta=\frac{N_{p}v_{th}}{(1-\varphi)}\frac{\pi}{6}D_{p}{}^{3}t_{dep} \tag{1}\]3 Arrhenius plot of \(\ln(\text{R}/\text{R}_{0})\) versus \(1/(\text{k}_{\text{b}}\text{*}\text{T})\). \[\delta=\frac{N_{p}v_{th}}{(1-\varphi)}\frac{\pi}{6}D_{p}{}^{3}t_{dep} \tag{1}\]4 Temperature coefficient of resistance TCR measured over the interval of 300 K–400 K.  
here investigated is well fitted by a fifth order polynomial. This is clearly shown in Figure 8 that reports the fitting residual of temperature given by:  
\[\delta=\frac{N_{p}v_{th}}{(1-\varphi)}\frac{\pi}{6}D_{p}{}^{3}t_{dep} \tag{1}\]5  
where \(T_{i}\) is the temperature measured at each calibration point, and \(T_{i}^{*}\) is the temperature calculated from the calibration equation. The specified temperature point is denoted with subscript \(i\). In the whole temperature range herein investigated, the residual of the calibration fit is less than 0.1 K.
## 3 Results
### Correlations between GPP and Driving Factors
#### 3.4.3 Dominant Factor for GPP Variation  
The above-mentioned five factors (daily sunshine duration of summer, daily mean temperature of summer, daily precipitation of summer, GSL, and FPAR) all had a direct or indirect impact on GPP. In this section, we determined the regression between AGG and these five factors with an SMLR model at the pixel level, and thus found the dominant factor for the GPP variation.  
The result of the regression was displayed in Figure 10. It showed that, except for some areas in the northern GKM, most parts of the GKM were highly correlated with daily sunshine duration and mean temperature in summer, so these two factors acted as dominant or subdominant factors in  
Figure 9: Correlation coefficient between Fraction of Photosynthetically Active Radiation (FPAR) and AGG in the GKM during 1982–2014. (Yellow and red indicate significant positive correlation (r \(>\) 0, \(p\) \(<\) 0.05), blue indicates significant negative correlation (r \(<\) 0, \(p\) \(<\) 0.05), and gray indicates non-significant correlation (\(p\) \(>\) 0.05)).  
our study area. Observations in an area called Mohe County in the northern GKM differed where most parts were closely related to FPAR instead of daily sunshine duration and mean temperature of summer. Furthermore, GSL and daily precipitation did not exert key effects on GPP when compared with the other three factors.  
According to the coefficients before each independent variable derived from the SMLR model at the pixel level, we calculated the zonal mean value of coefficients for each dominant factor and the results are shown in Table 2. Taking sunshine duration as an example, when sunshine duration is a dominant factor in our study area, each unit increase in sunshine duration causes a 0.519 unit increase in AGG.
## 4 Discussion  
This study evaluated (i) the between-session reliability and (ii) the concurrent validity of quadriceps and hamstring maximum strength and the associated ratio (H/Q-ratio) on a novel, portable, and easy to use alternative to the golden standard IDs. The FysioMeter H-station exhibited good to excellent relative between-session reliability for the quadriceps, hamstring, and H/Q-ratio measures. Furthermore, we found an acceptable absolute reliability for all the above measures. Lastly, compared to the ID, the quadriceps test showed a high correlation, while the hamstring and the H/Q showed a moderate correlation.  
Figure 4: Bland–Altman plot (**A**) concurrent validity of H-station compared to the isokinetic dynamometer measuring maximum quadriceps strength. (**B**) Concurrent validity of H-station compared to the isokinetic dynamometer measuring maximum hamstring strength. (**C**) Concurrent validity of H-station compared to the isokinetic dynamometer measuring hamstring/quadriceps ratio. Dotted gray lines represent the limits of agreement and solid black lines represent the mean difference between devices.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**H-Station**} & \multicolumn{2}{c}{**ID**} & \multicolumn{2}{c}{**Diff**} & \multicolumn{1}{c}{**Pearson r**} & \multicolumn{1}{c}{**95\% LOA**} & \multicolumn{1}{c}{**LOA (\%)**} \\ \cline{2-9}  & **Mean** & **SD** & **Mean** & **SD** & & & **(lb–ub)** & \\ \hline Quad (Nm) & 249.5 & \(\pm\)81.3 & 281.9 & \(\pm\)54.2 & 32.4 * & 0.69 & (\(-\)147.6–82.7) & 43.3 \\ Ham (Nm) & 138.3 & \(\pm\)43.2 & 148.1 & \(\pm\)41.8 & 9.9 & 0.79 & (\(-\)64.2–44.5) & 37.9 \\ H/Q ratio & 0.58 & \(\pm\)0.17 & 0.53 & \(\pm\)0.12 & 0.05 & 0.37 & (\(-\)0.28–0.38) & 59.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics and concurrent validity of the H-station compared with the Humac NORM ID (N = 19) from the first session.