esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### The Major Classification of Digital Watermarking Techniques
#### 2.4.2 Human's Perception  
In this classification, watermarking techniques are categorized into invisible and visible watermark techniques [60]. In invisible watermarking techniques, information is hidden inside the object [42]. The hidden process can be performed based on three techniques, which are: robust, fragile and semi-fragile techniques [61]. The robust technique is mostly used to verify the intellectual property rights as it is resistant to many various attacks. Meanwhile, the fragile technique is used to validate owner authentication and data integrity. The semi-fragile technique combines robust and fragile techniques where fragile techniques function against malicious attacks, while robust techniques function against intentional attacks [62].
## 2 Dispersion Curve Calculation
### Wave and Finite Element Scheme
\right\}, \tag{3}\]  
with the dynamic stiffness matrix being partitioned with respect to the boundary nodes of the periodic section. Similar to \(\lambda_{y}\), the free wave propagation in a waveguide of length \(l_{x}\) has a characteristic propagation constant \(\lambda_{x}=\text{e}^{-\text{i}k_{x}l_{x}}\), which relates the displacements of the nodal degrees of freedom on the left- and right-hand sides by \(\textbf{q}_{rb}=\lambda_{x}\textbf{q}_{lb}\) and  
Figure 1: Example of a multi-layered 2D periodic structure and discretised FE model of a single periodic section. \(\mathbf{f}_{rb}=-\lambda_{x}\mathbf{f}_{lb}\). An eigenvalue problem for \(\lambda_{x}\) is formulated by substituting the above into Equation (3), that is,  
\[\lambda_{x}\bigg{\{} \begin{array}{ll}\mathbf{q}_{lb}\\ \mathbf{f}_{lb}\end{array} \bigg{\}}=\mathfrak{T}\bigg{\{} \begin{array}{ll} \mathbf{q}_{lb}\\ \mathbf{f}_{lb}\end{array} \bigg{\}}, \tag{4}\]  
where  
\[\mathfrak{T}=\bigg{[} \begin{array}{cc}-\mathbf{D}_{lr}^{-1}\mathbf{D}_{ll} &\mathbf{D}_{lr}^{-1}\\ -\mathbf{D}_{rl}+\mathbf{D}_{rr}\mathbf{D}_{lr}^{-1}\mathbf{D}_{ll}&-\mathbf{ D}_{rr}\mathbf{D}_{lr}^{-1}\end{array} \bigg{]}. \tag{5}\]  
where \(\mathfrak{T}\) is known as the transfer matrix [22]. The propagation constants \(\lambda_{x}\) are eigenvalues of \(\mathfrak{T}\). They exist in pairs of \([\lambda_{x}^{+}\emph{and}\lambda_{x}^{-}]\) for the positive and negative travelling waves, respectively. A number of formulations may be employed to mitigate this effect as described in [32]. The wavenumbers \(k_{x}\) are directly derived from the computed propagation constants. The corresponding eigenvector \(\boldsymbol{\phi}_{x}\) is also extracted and represents the mode shape of the considered wave type. The mode shape vector is internally partitioned to represent displacements and forces such that \(\boldsymbol{\phi}_{x}=\{\boldsymbol{\phi}_{q,x}^{\mathrm{T}}\boldsymbol{\phi} _{f,x}^{\mathrm{T}}\}^{\mathrm{T}}\).
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 2 Study Area and Data
### Data and Processing
#### 2.2.1 Remote Sensing Data
According to numerous studies, the proportion in the GPP of organic mass consumed by autotrophic respiration during crop growth can be regarded as a fixed [45], with a constant of proportionality of 0.42 [46; 47]. Therefore, the value 0.58 can be used as the conversion coefficient between GPP and NPP. Processing and calculating the selected MOD17A2H data products gave the NPP of winter wheat with a time resolution of 5-day, which was used in our model to estimate the yield of winter wheat.
## 4 Experiments and Results
### Nutrient Solution Rise and Fall Test
#### 4.3.2 Uncontrolled Plants  
Seedlings were also transplanted into an uncontrolled system for comparison. The differences between the two groups are presented in Table 9. The maximum leaf width of the controlled plants was 5.9 cm. Because there was no more water, nutrient solution was added on the 15 th day. Figures 9 and 10 present comparisons of the controlled and uncontrolled EC and pH values. The results for the controlled system were more favorable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **Number of Plants** & **Average Number of Leaves** & **Average Length of Plants (cm)** & **EC (m/cm)** & **pH** \\ \hline
1 Day & 5 & 2.8 & 1.4 & 1.0 & 6.6 \\
3 Day & 5 & 3.8 & 2.2 & 1.0 & 6.3 \\
5 Day & 5 & 4.0 & 2.96 & 1.0 & 6.3 \\
7 Day & 5 & 4.6 & 5.38 & 1.0 & 6.6 \\
9 Day & 5 & 5.4 & 6.32 & 1.0 & 6.4 \\
11 Day & 5 & 6.4 & 7.8 & 1.0 & 6.4 \\
13 Day & 5 & 7.0 & 8.4 & 1.0 & 6.3 \\
15 Day & 5 & 7.6 & 10.4 & 1.0 & 6.4 \\
17 Day & 5 & 8.4 & 12.2 & 1.0 & 6.4 \\
19 Day & 5 & 9.4 & 13.84 & 1.0 & 6.4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Controlled plant growth and systematic observation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **Number of Plants** & **Average Number of Leaves** & **Average Length of Plants (cm)** & **EC (m/cm)** & **pH** \\ \hline
1 Day & 5 & 3 & 1.48 & 0.9 & 6.50 \\
3 Day & 5 & 4 & 2.1 & 0.9 & 6.56 \\
5 Day & 5 & 4 & 2.82 & 1.1 & 6.52 \\
7 Day & 5 & 4.2 & 4.94 & 1.1 & 6.65 \\
9 Day & 5 & 5 & 5.88 & 1.1 & 6.68 \\
11 Day & 5 & 6 & 6.8 & 1.2 & 6.60 \\
13 Day & 5 & 6.8 & 8.58 & 1.2 & 6.62 \\
15 Day & 5 & 7.6 & 10.5 & 1.5 & 6.72 \\
17 Day & 5 & 8 & 11.9 & 1.1 & 6.82 \\
19 Day & 5 & 9 & 12.94 & 1.2 & 7.54 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Uncontrolled plant growth and observation system.  
Figure 9: Comparison of lettuce EC values over 19 days.
## 2 The Proposed Synchronous Control Method for the Dual-Motor System
### Description of the Proposed Method  
To make the dual-motor system have consistent dynamic response characteristics and realize the two motors' synchronous motion, we proposed a frequency-domain characteristic compensation-based adaptive fractional-order anti-saturation synchronous control method, which contains two modules: the FDC unification module (FDC-UM), which can adjust the dynamic response characteristics of the two motors to be consistent, and the adaptive fractional-order anti-saturation module (AFOAM), which can avoid the saturation effect of the control voltage adaptively. Figure 2 illustrates the working principle of the proposed synchronous control method. The FDC-UM performs the FDC unification through compensation in the feedback control loops. With the FDC-UM, the FDC of the dual-motor system can be unified. We have  
\[\begin{cases}A_{1}=A_{2}=A_{f}\\ \varphi_{1}=\varphi_{2}=\varphi_{f}\end{cases}, \tag{6}\]  
where \(A_{1}\), \(A_{2}\) are the amplitude characteristics of the Equation (5), and \(\varphi_{1}\), \(\varphi_{2}\) are the phase characteristics. \(A_{f}\), \(\varphi_{f}\) are the amplitude and phase characteristics unified by the FDC-UM. In addition to the FDC-UM, the AFOAM is designed to prevent the peak voltage of the motion from reaching the voltage limitation. With these modules, the amplitude and phase characteristics are determined to be \(A_{a}\) and \(\varphi_{a}\) according to the input signal spectrum and theoretical calculation of the peak voltage. With the FDC unification and the elimination of the nonlinear saturation effect, the dual-motor system will possess consistent dynamic response characteristics for the two motors and thus ensure their synchronous motion.
# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment
In our previous work, we analyzed the fingerprint similarity problem using the Fresnel model and proposed a device-free localization method based on deep representation. In this paper, we focus on addressing localization in time-varying environments [16]. We use a pair of transceivers in the home environment to collect information when the target is located at different positions, which are known as reference points. We can locate the target based on the fact that the propagation pattern of the signal will be affected when the target is located at different positions. We use the time-reversal method [15] to quantify the similarity between fingerprints corresponding to different reference points, as shown in Figure 1. The time-reversal method is a signal processing technique, which combines a particular time instance \(h(t)\) with its time-reversed and conjugated counterpart \(h^{*}(-t)\) to remove the phase distortion. The time-reversal technique can refocus the energy of signal waves at the intended location, which is known as the spatial focusing effect. The Z-axis depicts the resonance strength between different reference points, which is the maximal amplitude of the entries of the cross-correlation between two complex signals and 1 is the maximum. From Figure 1, we can find that the fingerprints corresponding to different reference locations have high similarity, where the target's fingerprint cannot be correctly matched with the corresponding location. To address this problem, we propose a DFL model based on deep representation. In the indoor environment, the environment will change due to the influence of the target to the indoor environment, causing changes of propagation paths of WiFi and a degradation of localization accuracy. To address this problem, we propose an OS-ELM-based method to adapt to environmental changes and ensure the adaptability of the model. In this paper, we make the following main contributions. * We design a kind of fingerprint based on deep learning weights that can effectively tackle the fingerprint similarity problem. * We propose an online learning DFL approach that can effectively adapt to time-varying environments. * We validate our approach using data from real-world environments and demonstrate that our approach outperforms other approaches. The rest of this paper is organized as follows. Related works are discussed in Section 2. The relevant fundamentals are presented in Section 3. The proposed ML-OSELM-based DFL method is described in Section 4. Performance validation is presented in Section 5. Finally, conclusions and future work are concluded in Section 6.
## 4 Experimental Results and Discussions
### Real Time ECG Monitoring Module  
In the real time ECG monitoring module, a wearable health shirt is worn on a human body. The ability to provide a real time ECG signal from the wearable health shirt and the capability to capture the signal and present it on an Android(tm) mobile device is observed. Figure 12 shows the example of a patient wearing the body-fitted ECG health shirt. The figure shows that the sensor node embedded on the wearable health shirt is small and inobtrusive. The sensor nodes on the ECG health shirt and on the mobile device establish a WSN. The yellow light on the wireless dongle indicates that the wireless connection is established and that data transmission is available. When the Start button on the mobile screen is pressed, the wireless dongle attached to the mobile device is ready to receive and process the ECG data packet from the ECG device.  
Figure 13 shows a screen capture of the real time ECG monitoring screen on the mobile phone. The ECG vital signs are displayed graphically on the screen and further analysis of ECG vital signs is performed. QRS peaks are detected and the interval time between the waveforms, the QT and RR intervals, are calculated. This information is useful for doctors in the diagnosis and treatment process as information such as the heart rate can be estimated, the condition of heart can be determined, and any symptoms of heart attack can be detected. The data sampling rate in this system is 100 Hz, which means 10 data packets are sent within 1 second where 1 data packet consists of 10 ECG data, so 100 ECG data are sent in 1 second. The waveform display on this monitoring module is designed to be able to display up to 500 data in 5,000 milliseconds. The large screen (400 \(\times\) 800 pixels) of the Android(tm) mobile device provides a clear visualization graphic for the user, compared to our previous research work [9], which used the older generation of mobile phones, the bar type phone.  
Figure 12: Setup of ECG real time monitoring environment.
## 5 Tie Strength Applied to MANETs  
The idea of measuring relationship strength among users in a MANET scenario will allow us to select the path to forward packets from source to destination according to a forwarding algorithm designed not only based on QoS parameters, but also on a  
Figure 2: Probe message (PM) packets periodically flooded from source to destination. Probe message reply (PMR) packets gather QoS feedback about the available paths \(k\) found in iteration \(i\).  
certain level of confidence between the forwarding nodes from source to destination. Our routing proposal is designed to balance the trade-off in the forwarding path between confidence level and QoS parameters (percentage of packet losses and average end-to-end packet delay). To attain or goal, in Section 5.2 we define a new social metric named _tie strength_ to be included in the computation of the multimetric score to classify the available forwarding paths.  
It is important to notice that in social media there is no distinction between users, i.e., they are treated all the same either as trusted friend or stranger. This topic has been investigated for years and it is somehow assessed with the _tie strength_ concept. The idea of closeness between users in social media has been analyzed in different application fields, such as recommendation of goods [24], the word-of-mouth spread of information [25], or even has been explored to know how the closeness between two users can be extended to a triadic (group of three people) [26]. Our work is inspired by a previous research work developed in [10], where they introduced a predictive model that maps social media data to tie strength concept. This approach was also used in [27] to assess how the co-authorship network is related to the research performance over time. In this section, we will summarize briefly some of the basic ideas built up to be applied in this new field of study.
## 4 Results and Discussion
### Data Pre-Processing, Correlations, and Trends
- Sun brightness: almost no correlation with PM\({}_{2.5}\). 5. **Monthly Distribution--subplot (e)**  
- March (3) shows the highest spread and outliers, indicating extreme pollution events. - February (2) and April (4) have lower medians and fewer extreme values. - Insight: this complements the monthly average analysis; March is critical for pollution control. Figure 7 presents the first dataset, which spans the entire PM\({}_{2.5}\) and PM\({}_{10}\) monitoring period and examines their relationship with SO\({}_{2}\) and meteorological variables. Figure 8 illustrates the correlation levels between these variables, excluding missing values. The results show no significant correlation between PM concentrations and the selected variables. A weak negative correlation is observed between PM\({}_{2.5}\) and temperature (\(\textbf{r}=\textbf{-0.32}\)), followed by wind velocity (\(\textbf{r}=\textbf{-0.25}\)), sun brightness (\(\textbf{r}=\textbf{-0.14}\)), and a weak positive correlation with relative humidity (\(\textbf{r}=\textbf{0.14}\)). For PM\({}_{10}\), only a low negative correlation with wind velocity (\(\textbf{r}=\textbf{-0.23}\)) is noted. These findings suggest that, at an hourly resolution over an extended period, the correlation between particulate matter concentrations and SO\({}_{2}\) and meteorological variables remains very limited. Figure 7: PMs versus SO\({}_{2}\) and meteorological variables, excluding missing values.
# Output Force Density Saturation in COMSOL Simulations of Biomimetic Artificial Muscles
## 3 Results  
The simulations were set up and run as described in Section 2, in accordance with the following considerations.  
The COMSOL models were designed to satisfy a set of practical requirements. While specific 3D printers may promise resolution around a couple tens of microns with hard resin, the requirement to use soft resin [25] would inevitably degrade the achievable resolution to some extent. Furthermore, consistent quality of printing and surface roughness, as well as the need to clear out the sacrificial material or an uncured monomer, would decrease the practical resolution further. Finally, in preparation for experimental demonstration of the proof of principle, it made sense to conduct simulations at scales that were reasonably easily achievable with current state-of-the-art printers in soft resin.  
Accordingly, while the Stratasys Object500 Connex3 3D printer has a professed resolution of \(\sim\)10 \(\upmu\)m, the manufacturer recommends at least 100 \(\upmu\)m to be used to consistently 3D-print internal channels in soft materials. This recommendation and the above considerations prompted the choice of the smallest dimension in our COMSOL simulations to be 100 \(\upmu\)m. This number was used for the thickness of the capacitor plates (plateZ), the distance between the plates (D), the thickness of the top and bottom borders of the device (c), the minimal thickness of the tendon regions between neighboring fibers (h, or u in COMSOL), and the minimal thickness (e) of the tendon regions on the lateral edges of the device. Also, in accordance with empirical evidence from PDMS microfluidics [29; 30; 31; 32; 33; 34; 35; 36], an aspect ratio of 10:1 between the lateral widths (plateX, plateY) and the longitudinal  
Figure 4: Maximal output force density vs. lateral size N of the array. The curve suggests saturation at N \(\geq\) 10, with saturation force density \(\sim\)9 kPa.  
Figure 5: Flowchart of the general procedure.  
thickness of each capacitor plate was chosen to prevent the collapse of the hollow structures that would become the fluidic electrodes.  
For every N, the tendon thickness e was swept (Figure 3) to determine its optimal value that maximized the output force density (total force per unit area). These maxima were then plotted against N to produce a saturation curve (Figure 4). This curve suggests that the force density approaches saturation at and beyond N = 10, and that the saturation level for the output force density is \(\sim\)9 kPa at the 100 \(\upmu\)m scale.
## 5 Study Results and Discussion
### Human Survival Capacity  
The mean accelerations of the human body (weighted by the masses of each body part), through directions XX (horizontal) and YY (vertical), can be compared with human tolerance curves, such as \"Eiband curves\" [40], plotted based on experimental results, in which astronauts were subjected to high accelerations when seated on a rigid seat and pressed against it by a harness. When using these curves, trapezoidal seat acceleration pulses are compared with the limits established in these curves, where not only the maximum acceleration value of the pulse is important, but also its duration. As in this study, the occupant may suffer some relative movement in relation to the chair, and the comparison pulses were traced such that the area of the diagram with the evolution of accelerations over time coincided with the area of the trapezoidal pulses.  
For the situation that leads to higher spine compression (situation 2, \"upper\" chair, Figure 8), an acceleration pulse with a duration of 63 milliseconds and a maximum vertical acceleration of 22 g is obtained (Figure 12a), reasonably below the limit of human tolerance, which is about 40 g in this situation (limit for \"moderate injury\"). The acceleration is not constant through the duration of uniform acceleration of the pulse, reaching a sudden peak of 46 g, which lasts less than 2 milliseconds above the [PERSON]'s human tolerance limit. For the horizontal accelerations, the pulse with the highest acceleration lasts 11 milliseconds and has a maximum acceleration of 43 g (Figure 12b), also below the human tolerance limit, which in this case is about 110 g (\"moderate injury\"), and it is never surpassed.  
Figure 11: Impact against UPN 100’s profiles’ bumpers (situation 2, “lower” chair).  
Figure 12: Critical acceleration pulses for the human body.
## 5 Experiments
#### Evaluation Criteria  
The performance of the proposed method was analyzed and compared using various evaluation metrics. In this context, the obtained results were evaluated in terms of accuracy, recall, and _F1-Score_.  
Accuracy: Indicates the ratio of correctly predicted to all predictions. A value close to 1 indicates that the classification is a model that is highly accurate. It is shown in Equation (5)  
\[\uparrow Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
where \(TP\) (True Positive): What is actually true (actual positive) and what is predicted correctly (predicted positive). \(TN\) (True Negative): It is not actually true (actual negative) and not predicted correctly (predicted negative). \(FP\) (False Positive): Actually incorrect (actual negative) and correctly predicted (predicted positive). \(FN\) (False Negative): What is actually correct (actual positive) and not correctly predicted (predicted negative).  
Recall: Shows the ratio of correctly predicted to true positives. A value close to 1 indicates that the classification is a model that is highly recalled. It is shown in Equation (6).  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
_F1-Score_: Recall and Precision harmonic average. A value close to 1 indicates that the classification is a model that has a high _F1-Score_. It is shown in Equation (7).  
\[\uparrow F1\text{-}Score=\frac{2\ \times\ Precision\ \times\ Recall}{(Precision+Recall)} \tag{7}\]  
Wilcoxon signed-rank test: It is a test frequently used in the literature to test whether the results obtained by the algorithms are significant or not if they are remarkably close to each other. If the obtained value is greater than the significance level, H0 (Null Hypothesis) is accepted. Accepting the H0 hypothesis indicates that there is no significant difference between the two methods. If the obtained value is less than the significance level, H1 (Alternative Hypothesis) is accepted. H1 Hypothesis is the opposite of H0 Hypothesis. In  
\begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Hyperparameters** & **Values** \\ \hline \multirow{3}{*}{BAT} & \(A\ (\mathit{Loudness})\) & 0.5 \\  & \(r\ (\mathit{Pulse}\ rate)\) & 0.5 \\  & \(\mathcal{Q}\) & [0, 2] \\ \hline \multirow{3}{*}{FA} & \(\mathit{alpha}\ (\mathit{Randomness})\) & 0.5 \\  & \(\mathit{Beta}_{min}\ (\mathit{minimum}\ value\ of\ beta)\) & 0.2 \\  & \(\mathrm{Gama}(\mathit{absorption}\ coefficient)\) & 1 \\ \hline GWO & \(\mathit{alpha}\) & 2 \\ \hline MFO & \(b\) & 1 \\ \hline MVO & \(\mathit{wep}_{max}\) & 1 \\  & \(\mathit{wep}_{min}\) & 0.2 \\ \hline \multirow{3}{*}{PSO} & \(c1\), & 2 \\  & \(c2\) & 2 \\  & \(w_{max}\) & 0.9 \\  & \(w_{min}\) & 0.2 \\ \hline WOA & \(\mathit{alpha}\) & 2 \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._other words, the H1 hypothesis states that there is a significant difference. In this study, the significance level was determined as _p_-Value 0.05 [44].