esa-sceva/satcom-chunk-collection huggingface dataset:
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 5 Experimental Results and Discussions
### Energy Consumption  
The current hardware design used in the prototype includes some development boards and is not yet optimized for compact design and final commercial production. Therefore, we use the Energy Trace tool to determine the analytical energy consumption of the ECG patch over time. We use the Energy trace tool in the free-run mode, where the sampling frequency is approximately 4.2 kHz. We performed analytical tests on the ECG prototype, where the device is assumed to be in the \"continuous\" operation mode. The test includes acquiring the full 12-lead ECG data and transmitting the data over BLE in real-time to a smartphone. This means the MCU is always in an active mode, and no power-saving protocols are applied other than the default settings on the MCU. Consequently, the analytical results show that the ECG patch can deliver a continuous real-time 12-lead ECG for 37 h approximately using a rechargeable lithium-ion battery with a capacity of 2000 mAh; details are shown in Table 8.
# Discrete Data Rate Adaptation for Wireless Body Area Networks
## 4 Optimization Algorithms
### Greedy Algorithm  
We adapted the greedy method to the rate assignment problem in the following way. The proposed method starts from the initial solution when no item is selected and incrementally improves the solution (see Algorithm 1 and Figure 3). It iteratively selects the node for which the ratio of the utility and the data rate is the highest among the nodes whose data rate is not larger than the available bandwidth on any of the links along the routing path, even after increase. The capacity constraints are determined in each iteration for each node. The data variations are sorted in decreasing order of the ratio of the utility and the data rate for each sensor.  
The data rate adaptation method is called periodically, and the above method can be adapted accordingly. After the first run, the method can be started from the solution of the last run, which corresponds to the data rates currently streamed in the real system. The network bandwidth can be continuously monitored in WBAN, and the data rate adaptation algorithm can run periodically with updated network bandwidths.  
At most, the while loop of the algorithm may run as many times as the number of variants: \(O(n_{v})=O(n_{s}\cdot max_{v})\). Sorting requires \(O(n_{s}\cdot max_{v}\cdot\log\ max_{v})\). The iteration through \(L_{2}\) runs \(O(n_{s})\) times in each iteration of the while loop. The capacity calculation through the route takes \(O(l_{tree})\) steps in each iteration. For this reason, the time complexity of the algorithm is \(O(n_{v}\cdot n_{s}\cdot l_{tree}+n_{s}\cdot max_{v}\cdot\log\ max_{v})=O(n_{s} \cdot max_{v}\cdot(n_{s}\cdot l_{tree}+\log\ max_{v}))\).
## 4 Discussions  
### MTVDI Captures Amazonian Seasonality  
The comparison between MTVDI and drought indicators showed that MTVDI, as a drought indicator [5], matched well with most drought indicators (Supplementary Materials Tables S2 and S3). Results also indicated that the MTVDI performed well in distinguishing different dryness patterns over the Amazon tropical forests. Additionally, MTVDI kept consistent with VPD seasonality which showed great potential in representing the Amazonian phenology [5]. In general, our analysis indicated that MTVDI captures the drought-derived phenology in Amazon rainforest.  
MTVDI showed an opposite relationship with photosynthesis proxies (EVI and SIF) on the scale of the entire study area (Figure 8), which was consistent with common sense for drought inhibited vegetation growth because increasing MTVDI cause leaf biomass loss and then decline SIF and EVI. But the correlation between MTVDI and photosynthesis proxies (EVI and SIF) show a divergent patterns between the northern and southern parts of the study area (Figure 9 and Supplementary Materials Figure S9). In southern and southwestern parts of the study area (CA, WA, SA1 and SA2), the opposite seasonal variations (Supplementary Materials Figure S9c-f) between MTVDI and EVI and SIF showed that the vegetation growth of those regions was mainly controlled by water supply [79]. Meanwhile, in the northern and northeastern part of the study area (NA1 and NA2), the consistent seasonal variations (Supplementary Materials Figure S9a,b) and positive correlation (Figure 9a,b) between MTVDI and EVI, SIF suggested the vegetation growth of those regions was restricted by the availability of radiation instead of water availability [6; 11].  
Seasonality of MTVDI showed consistent or opposite to those of photosynthesis proxies (EVI and SIF) in different subregions (Figure 8, Supplementary Materials Figure S9), which suggest MTVDI can capture the seasonality of both water-related and light-related forests, and matches well with the seasonality of VPD (Figure 6 s and Figure 7a), showing great potential in capturing well the canopy seasonality in Amazon tropical forests. MTVDI especially successfully detected the forest areas which green up during the dry season in wet regions with mean annual precipitation higher than 2000 mm per year (Figure 9c,d). It is because the MTVDI strictly adheres to the physical model, and its construction is closely related to two aspects: abiotic temperature (Ts) and biotic canopy characteristics (MPDI). MTVDI showed better performance in capturing canopy seasonality than climatic dryness indicators such as PDSI, CWD, TWS, and VPD across Amazon tropical forests.
## 4 Simulation
### Continuous Input Simulation Result  
The simulation proceeds using Figure 5. For verification of the control performance, we consider output performance, input performance, and error performance.  
Figure 8 is the previous nonsingular terminal sliding mode control output result [36; 37]. As can be seen in Figure 8, compared with other controllers, it generates based on the maximum reference input, the overshoot is 19.5% and 27%, respectively, and the convergence speed is slow. Large steady-state errors are also generated during the process. As shown in Figure 9, using a neural network can effectively improve overshoot, convergence speed, and steady state errors for continuous input. Figures 10 and 11 use additional control input as the previous robust term, and adaptive robust term. Compared with Figure 9, there is more improved convergence speed. In addition, although the control performance of the previous robust term and the adaptive robust term is similar, chattering occurs in the previous robust term.  
Figures 12 and 13 are compared for control input performance. As shown in Figures 12 and 13b-d, the controller is improved, it generates a greater control force than the NTSMC. However, as shown in Figures 12 and 13b, chattering increased more than NTSMC, and as shown in Figure 13c, the control performance was more improved, but the previous robust term caused larger chattering. As shown in Figures 12 and 13d, the proposed controller can generate a control input suitable for the situation, and the control input is removed when it is close to the steady state due to the reverse saturation function, and chattering is improved.  
Figure 7: Disturbance.  
Figure 8: NTSMC control performance for continuous input.  
Figure 10: RT-NTMSC-NN control performance for continuous input.  
Figure 9: NTMSC-NN control performance for continuous input.  
Figure 11: Proposed controller performance for continuous input.  
Figure 12: Continuous input \(\theta_{1}\) control input performance. (**a**) NTSMC input result; (**b**) NTSMC-NN input result; (**c**) NTSMC-NN-RT input result; (**d**) proposed controller input result.  
Figure 13: Continuous input control input \(\theta_{2}\) control performance. (**a**) NTSMC input result; (**b**) NTSMC-NN input result; (**c**) NTSMC-NN-RT input result; (**d**) proposed controller input result.  
Figures 14 and 15 compare errors for continuous input. It can be seen that all controllers generate a small steady-state error for continuous input. As shown in Figures 8-11, it can also be seen in Figures 14 and 15 that the performance of proposed controllers and NTSMC-NN-RT are improved compared with NTSMC-NN. NTSMC-NN-RT converges about 0.2 s faster on average than the proposed controller, though it causes the greatest chattering. The proposed controller has the least chattering among the other controllers. Chattering can be a major threat in robotic systems. Therefore, the proposed controller shows the best performance.
# Image Watermarking Scheme Using LSB and Image Gradient
## 2 Preliminaries  
### Image Gradient  
The image gradient is a core component in image processing, used to find the directional variation in the intensity of the image. The gradient magnitude and direction denote how and where variations occur. The gradient image pixel represents the change in pixel value of the original image at that point, in the relative direction. The magnitude and direction of the gradient are calculated by the equations below.  
\[\mathrm{M}=\sqrt{{g_{y}}^{2}+{g_{x}}^{2}}, \tag{1}\]  
where \(g_{y}\) is the gradient in the \(y\)-direction, and \(g_{x}\) is gradient in \(x\)-direction. \(\mathrm{M}\) is the magnitude of the gradient. The direction of the gradient is measured by \(\lambda\).  
\[\lambda=\tan^{-1}\biggl{(}\frac{g_{x}}{g_{x}}\biggr{)} \tag{2}\]  
The image gradient in this proposed process is measured by convolving the original image block with the Prewitt operator. In gradient image, the pixels with large values indicate the edges. To visualize the direction, \(x\)- and \(y\)-direction image gradients are calculated. The filter to calculate the \(x\)- and \(y\)-direction gradients is shown below  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Blind** & **Robustness** & **Extraction Type** \\ \hline
[19] & Yes & Robust to common (R2C) image processing attacks & Multi-bit \\
[20] & Yes & R2C, JPEG, and cropping & Single-bit \\
[21] & No & R2C attacks & Multi-bit \\
[22] & Yes & R2C attacks & Multi-bit \\
[23] & Yes & Robust to Rotation and Flipping attacks & Multi-bit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature review of watermarking techniques.  
The main concern of this approach is to classify the image into smooth and sharp portions, in order to achieve better imperceptibility of the image. For this purpose, a threshold is calculated to differentiate the local smoothness and sharpness in each block. On the basis of this threshold, a decision can be made on how many bits are to be embedded through LSB embedding.
# Geotechnical Evaluation of Diesel Contaminated Clayey Soil
## 3 Results and Discussion
### Effect of Diesel Contamination on Some Properties Related with Its Possible Remediation
#### 3.11.3 Soil's Cation Exchange Capacity  
Soil's CEC is one of the factors that influence its pH buffer capacity. After soil contamination, its CEC was reduced by 50% in comparison with its CEC at its natural condition (from 75.94 to 38.41 cmol, /kg). This indicates a drop in the pH buffering capacity of the contaminated soil that is reflected in its acidification because the higher the CEC capacity, the higher the pH buffering capacity [46].  
[PERSON] and [PERSON] [37] suggested that the decrease of the CEC was associated to the reduction of the dielectric constant of the pores' fluid, because the number of the exchangeable cations diminished when the dielectric constant of the pore fluid got reduced.  
The CEC reduction indicates that contaminated soil decreased its capacity to retain and release cations, such as Ca\({}^{2+}\), Mg\({}^{2+}\), K\({}^{+}\), and Na\({}^{+}\), by electrostatic forces, in comparison with natural soil. So, a reduction in soil fertility can be expected since Ca\({}^{2+}\), Mg\({}^{2+}\), K\({}^{+}\), Mn\({}^{2+}\), and Fe\({}^{2+}\) are considered as bioavailable metal cation species essential for green plant nutrition and soil microorganisms [47].  
It is known that organic matter has a high CEC. Thus, an increase in the CEC of contaminated soil, due to the increment of its organic matter content, can be expected. However, a decreased CEC in contaminated soil was observed. When diesel was added to the natural soil, cation exchange between diesel and calcium cations (Ca\({}^{2+}\)), contained in both clay minerals and organic matter, was promoted.  
This process released Ca\({}^{2+}\) in the soil's pores' solution that were used by clay particles to flocculate and, at the same time, to counteract the acidification effect caused by the added H\({}^{+}\). Thus, although the organic matter content increased in contaminated soil, it could not increase its CEC.
## 4 Genetic Algorithm Configuration
### GA Simulation Environment and Parameter Settings  
The GA simulation environment was configured as follows:  
* **Communication technologies**: LoRa, NB-IoT, and Mioty;
* **Orbit types:**: VLEO at 200 km and 300 km and LEO at 600 km;
* **Ground station locations**: Seoul, [37.71, 126.73]; Berlin, [52.5, 13.42]; Oslo, [59.94, 10.75]; and Houston, [29.76, \(-\)95.37];
* **Generations** = 20; **population size** = 80  
These ground stations were selected because the latitude range of 30\({}^{\circ}\) to 60\({}^{\circ}\) north represents densely populated and urbanized regions, providing a realistic scenario for the optimization of IoT-NTN satellite coverage. The population size of 80 and the number of generations set to 20 were determined based on a balance between achieving sufficient optimization convergence and managing computational complexity. Preliminary analysis considered the relationship between these parameters and the total simulation time required to explore the search space effectively within reasonable limits.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 2 Fault Feature Extraction of Rolling Bearings  
### Time-Frequency-Diagram-Based Signal Characterisation Methods  
The Continuous Wavelet Transform (CWT) [14] is an adaptive time-frequency analysis method that can automatically adjust the size of the time window according to the frequency of the original signal to obtain a higher time or frequency resolution. The continuous wavelet transform can effectively solve the problem that the window does not change with frequency in the Short-Time Fourier Transform (STFT) [15] and can also effectively improve the accuracy and efficiency of data analysis. The definition of the continuous wavelet transform is as follows:  
\[WT_{x}(b,a)=a^{-\frac{1}{2}}\int_{-\infty}^{+\infty}x(t)\psi^{*}\bigg{(}\frac{t-b }{a}\bigg{)}t \tag{1}\]  
where \(a\) is the scale factor, \(b\) is the translation factor, and \(\psi(t)\) is the wavelet basis function. The CWT analyses the resolution of the signal in time and frequency by adjusting the scale factor.  
In this paper, the rolling bearing data set of Case Western Reserve University [16] is taken as an example for analysis. An SKF-6205 drive end bearing is selected, whose speed is 1797 r/min, and the signal sampling frequency is 12 kHz. Table 1 shows the relevant parameters of the bearings used. A continuous wavelet transform based on a Morlet wavelet is used to generate a 32 \(\times\) 32 wavelet time-frequency diagram. Figures 1 and 2 show time domain diagrams and CWT time-frequency diagrams corresponding to bearing inner ring and outer ring faults, respectively.  
From Figures 1 and 2, it is evident that under identical operational conditions conditions, the time domain waveform changes at different fault locations in the bearing exhibit remarkable similarities. As a result, the uncertainty in the fault diagnosis analysis using only time domain signals also increases substantially. However, a CWT time-frequency diagram can clearly show the energy distribution in different time-frequency regions, with brighter colors indicating that the signals in the time-frequency region have higher amplitude or higher energy. Therefore, in this paper, the CWT is used to convert the original one-dimensional vibration signals into a two-dimensional time-frequency diagram as a way to enhance the differences between different faulty bearing signals. The 32 \(\times\) 32 time-frequency diagram thus generated is input into the network for fault identification.
## 3 Numerical Analysis
### Analytical Test Functions
#### 3.3.1 Wind Farm Proxy  
A first family of test functions emulates the energy production of a wind farm. Each wind turbine yields a certain amount of energy which depends on the wake effect caused by neighboring turbines. The inputs are in the form of a cloud of points \(X=\{\mathbf{x}_{1},...,\mathbf{x}_{n}\}\), with \(\mathbf{x}_{i}=(x_{i},y_{i})\in\mathbb{R}^{2}\) (\(d=2\)). Each \(\mathbf{x}_{i}\) represents the Cartesian coordinates of a turbine. The test function given below represents the total yield considering all turbines,  
\[F_{\theta}(\{\mathbf{x}_{1},...,\mathbf{x}_{n}\})=\sum_{i=1}^{n}\biggl{(} \prod_{j,j\
eq i}f_{p\theta}(\mathbf{x}_{j},\mathbf{x}_{i})\biggr{)}f_{0}( \mathbf{x}_{i})\;.\]  
Details about the function \(f_{p\theta}\) (representing the gain factor of the turbine \(\mathbf{x}_{i}\) due to the wake effect of \(\mathbf{x}_{j}\)) and \(f_{0}\) (a constant representing the maximal yield of a given turbine) may be found in Appendix C. In the remainder of the paper, we use the following notations:  
* \(F_{\theta}\) stands for functions that account for wind coming from a single direction \(\theta\in(0,360)\).
* \(F_{nd}=\frac{1}{n}\sum_{i=1}^{n}F_{\theta_{i}}\) stands for functions modeling the average effect of \(n\) wind directions chosen in \((0,360)\).  
We consider the following test functions: \(F_{0}\), \(F_{90}\), \(F_{45}\), \(F_{4d}\). The function \(F_{4d}\) calculates the average production over four directions: 0, 60, 120 (0 is chosen two times). We provide graphical representations of \(\mathbf{x}_{i}\longrightarrow f_{p\theta}(\mathbf{x}_{j},\mathbf{x}_{i})\) with \(\mathbf{x}_{j}=(0,0)\) for the four scenarios in Figure 7. These graphics show the amplitude of the wake interaction between two turbines, depending on their relative position and the direction of the wind. For instance, in the case \(\theta=0\), we can notice that the scale of the interaction is more important in the direction of the wind and only occurs _behind_ the fixed turbine.  
\(F_{\theta}\) is obtained by summing the contribution of each point. We give two examples (\(F_{0}\) and \(F_{45}\)) of the function layouts in Figure 8, where nine points are fixed and one is allowed to vary. The values next to the points show their individual contribution to the total production. There is no unit of measure. We can see that the yield of an additional point depends on the direction of the wind and the placement of the existing points. \(F_{0}\) and \(F_{90}\) are just rotations of one another and the interest in keeping both of them is to check the invariance of the algorithms with respect to a rotation of the solution, a property that is not satisfied by all optimization algorithms, as discussed in [44].
## 2 Materials and Methods
### Growth Characteristics and Biomass Productivity  
The microalgal growth rate was estimated by measuring the optical density (OD) and dry weight (DW) at 2-day intervals over an 8-day period. Culture samples were collected, and the OD at 680 nm was measured using a spectrophotometer (Optimizer 2120 UV spectrophotometer, Mecasys, Daejeon, Republic of Korea). For determining DW, a sheet of GF/C Whatman(tm) filter paper (0.7 \(\upmu\)m pore size, Thermo Fisher Scientific, Leicestershire, UK) was initially weighed, and 5 mL of the culture was filtered through it. The filter papers were then dried in an oven at 105 \({}^{\circ}\)C for 24 h, allowed to cool to room temperature, and reweighed. The microalgal DW was calculated by subtracting the initial weight of the filter paper from its final weight after drying.  
Chlorophyll \(a\) (Chl-_a_) and chlorophyll \(b\) (Chl-_b_) were quantified on days 4 and 8 of microalgal cultivation in varying concentrations of DSW. Microalgal cultures were collected in 2 mL tubes and centrifuged at 16,022\(\times\)\(g\) at 4 \({}^{\circ}\)C for 5 min. After discarding the supernatant, the centrifugation step was repeated to ensure complete collection of the microalgae pellets from the 2 mL of culture. The pellets were then resuspended in 1 mL of methanol (MeOH) and disrupted by ultrasonication (550 Sonic Dismemberator, Fisher Scientific, Hampton, NH, USA) at 40 Hz for 1 min to extract pigments. The extracted pigment solution was incubated at 4 \({}^{\circ}\)C for 24 h in the dark to stabilize it and then it was centrifuged at 16,022\(\times\)\(g\) at 4 \({}^{\circ}\)C for 20 min to separate the supernatant. The absorbances of the supernatant at 649 nm and 664 nm were measured using a spectrophotometer to quantify the Chl-_a_ and Chl-_b_ concentrations, respectively. The concentrations were calculated using appropriate equations [33].  
\[\text{Chl-}a=13.36\text{A}_{664}-5.19\text{A}_{649} \tag{1}\]  
\[\text{Chl-}b=27.43\text{A}_{649}-8.12\text{A}_{664} \tag{2}\]
## 3 Polyphenols Effects on VOCs Release
### Effects on Terpenoids  
Terpenoids are varietal compounds essentially coming from grapes as enzymatically produced secondary metabolites of the terpenoid pathway and existing as saturated/unsaturated and cyclic/acyclic hydrocarbons that can contain alcohol, aldehyde, ketone, ester, ether, and acetal functionalities. These compounds are also present as terpen glycosides that can be hydrolyzed to free volatile aglycones in different phases of wine production and life, mainly by yeast glycosylates during fermentation, and by the acidic conditions during wine storage. From a sensory point of view, terpen are largely responsible for citric, floral, and balsamic aromas [61]. The effect of polyphenols on monoterpenoids volatility has been mostly observed in deodorized/reconstituted real wines [62], and linalool in model wine solutions [63]. According to data reported in the literature and summarized in Table 1, a common trend can be noted. Indeed, independently from the VOC hydrophobicity within the tested range (2.67 \(\leq\) log_P_ \(\leq\) 3.47) and fromthe type of matrix representative of different PPhs compositions, the release of the tested terpenoids decreases at increased tannin concentrations [62; 63]. Interestingly, considering the real wine matrices, it can be noted that while the aged-red wine has significantly retained terpinen-4-ol, \(\beta\)-citronellol, and nerol, in the young-red wine, a significant retention effect has been observed for all the terpene compounds, including \(\alpha\)-terpineol andlinalool [62]. Based on results by [PERSON] and [PERSON] [51] suggesting that monomeric or oligomeric/polymeric polyphenols can differently impact aromas volatility, this behavior could be linked to the lower concentration of polymeric polyphenols [64] of young-red wines compared to aged ones. Considering the sensory importance of these odorants and that they act in combination with each other [39], it could be hypothesized that the PPhs composition of wine could impact the olfactory perception of monoterrenes.  
The results of a recent study [65] referred to a possible sensory impact on terpen perception as an effect of the presence of gallic and \(p\)-coumaric acids. Independent of phenolic acids composition and concentration, both acids tended to decrease the production and volatilization of free terpen during fermentation, with \(p\)-coumaric acid showing a greater restraining effect. Studies of linalool and its terpene glycosides have shown that the main driving forces in their interactions with these phenolic acids are dispersive interactions and hydrogen bonding. Sensory analyses confirmed a decrease in the perception of some aromatic notes related to the presence of free terpen (e.g., tropical, and sweet fruit aromas), albeit not enough to be statistically significant. The authors concluded that the matrix effect of phenolic acids can effectively control the release and modulate the global feature of wine aromas.