esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 6 Design of the Flexible Wearable Heart Rate Signals Transmission and Output Module
### Low-Power Hardware Design for Flexible Wearable Heart Rate Signal Transmission and Output Module
In summary, in the low-power hardware design for the heart rate signal transmission and output module, BLE modules, Wi-Fi modules, and Zigbee modules each leverage their unique strengths. The BLE module extends system battery life while ensuring transmission quality through its low-power consumption and strong compatibility. The Wi-Fi module reduces power consumption via low-power chip features, MQTT protocol adoption, and peripheral circuit optimization, all while meeting long-distance transmission requirements. The Zigbee module, meanwhile, enables efficient and reliable transmission of heart rate signals among multiple nodes in complex monitoring networks through its self-organizing network capability. For issues such as data packet loss and transmission delay in complex scenarios, adaptive frequency hopping (AFH) technology, real-time channel evaluation mechanisms, and enhanced CSMA/CA mechanisms can also be adopted to solve the problems.
## 3 Process of Predicting Missing Logging Curves Based on BO-HKELM  
In this research, the specific steps for optimizing the parameters \((C_{0},C_{1},d,\sigma,C)\) in HKELM by the Bayesian algorithm are as follows.  
In Step 1, one must divide the input logging dataset into a training set and test set, and normalize the data to (0, 1) using the max-min normalization method. The expression of the max-min normalization method is as follows:  
\[x^{\prime}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{16}\]  
where \(x\) is the actual vector. \(x_{max}\) and \(x_{min}\) are the maximum and minimum values of the vector \(x\), respectively. \(x^{\prime}\) is the normalized vector.  
In Step 2, one must initialize the model, including the value range, the initial population, the maximum iterations, and the upper accuracy setting of the parameters \((C_{0},C_{1},d,\sigma,C)\) in HKELM.  
In Step 3, one must randomly select a group from the initial population as the initial solution.  
In Step 4, one must construct the probability distribution function of the Bayesian network through the Gaussian process, and establish or update an objective function model. The objective function is the root mean square error (RMSE), which is expressed as  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{k}-\hat{y}_{k})^{2}} \tag{17}\]  
where \(y_{k}\) is the true value, and \(\hat{y}_{k}\)is the predicted value of HKELM.  
In Step 5, one must determine the position of the next cycle parameter combination according to the acquisition function.  
In Step 6, one must calculate the probability value of the parameter combination and update the population distribution.  
In Step 7, one must determine whether the resulting combination of parameters satisfies the condition; if so, output the optimum results of \(C_{0},C_{1},d,\sigma,C\). If not, one must return to Step 4 and repeat the experiment until the condition is satisfied or the maximum number of iterations is reached.  
In Step 8, one must apply the optimum parameter combination \((C_{0},C_{1},\ d,\sigma,C)\) to the missing well logs estimation of the HKELM.  
In Step 9, one must evaluate the accuracy of the optimized prediction results using the test sample set, and compare the changes in accuracy before and after optimization.  
The optimization process is shown in Figure 2.
## 3 Validation of MANTiS-32 Sensing Performance  
### Calibration and Accuracy Assessment of the MANTiS-6050 Sensor  
MANTiS-6050 is a digital inertial sensor that outputs raw acceleration data obtained via analog-to-digital (A/D) conversion, which must be transformed into physical units of gravitational acceleration (g) for engineering applications. This section outlines the calibration procedure used to convert digital signals into calibrated acceleration values and presents an experimental evaluation of the sensor's measurement accuracy following calibration.
## 3 Results  
### Spatiotemporal and GRF Variables  
For GRF variable (Table 1), wearing D10 orthosis led to smaller maximum loading rate than D2 (\(p\) < 0.001, \(d\) = 0.50) and control orthoses (\(p\) = 0.002, \(d\) = 0.46).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Orthosis Condition**} & \multicolumn{4}{c}{**ANOVA Results**} \\ \cline{2-7}  &
\begin{tabular}{c} **Flat** \\ **(Control)** \\ \end{tabular} & **D2** & **D6** & **D10** & **F-Value** & **\(p\)-Value** & **Partial Eta Squared (\(\eta_{p}\)\({}^{2}\))** \\ \hline Max loading rate (BW/s) & 871.4 (321.0) * & 881.7 (307.1) * & 801.7 (275.4) & 737.4 (266.4) & 11.49 & \textless{}0.001 & 0.45 \\ Contact time (ms) & 262.0 (31.5) & 263.2 (33.3) & 262.0 (30.5) & 263.6 (34.3) & 0.19 & 0.901 & 0.01 \\ Stride length (m) & 1.33 (0.12) & 1.38 (0.11) & 1.32 (0.11) & 1.36 (0.11) & 2.41 & 0.080 & 0.15 \\ Stride frequency (Hz) & 2.08 (0.45) & 1.95 (0.26) & 2.10 (0.42) & 1.99 (0.32) & 2.37 & 0.084 & 0.15 \\ \hline \hline \end{tabular} Note: Max = maximum; BW = bodyweight; * sig different from D10 (\(p\) < 0.05).  
\end{table}
Table 1: Mean (SD) values and statistical results of spatiotemporal and loading rate variables by tested conditions.
# Circular Regression Applied to GNSS-R Phase Altimetry
## 3 Linear-Circular Regression Applied to GNSS-R Altimetry  
### Circular Model  
The normal distribution \(\mathcal{N}\) is inappropriate for a physical process involving periodic angle measurements. The von Mises distribution, also known as the circular normal distribution \(\mathcal{CN}\), describes a circular random variable and can be considered as the equivalent of the normal distribution in the circular domain [30].  
The von Mises distribution \(f(\theta,\mu,\kappa)\) for a circular random variable \(\theta\), with mean value \(\mu\) and concentration parameter \(\kappa\), is given by:  
\[f(\theta;\;\mu,\;\kappa)=\frac{1}{2\pi I_{0}(\kappa)}\;\mathrm{e}^{\kappa\; \cos(\theta-\mu)} \tag{12}\]  
with:  
\[I_{0}(\kappa)=\frac{1}{2\pi}\int_{0}^{2\pi}\mathrm{e}^{\kappa\cdot\cos\;\theta} \,d\theta=\sum_{r=0}^{\infty}\left(\frac{\kappa}{2}\right)^{2r}\left(\frac{1}{ r!}\right)^{2} \tag{13}\]  
The concentration parameter \(\kappa\) takes values between zero and infinity and approximately behaves like the inverse of the Gaussian variance. The term of normalization \(I_{0}(\kappa)\) is the modified Bessel function of the first kind and order zero.  
In this paper, we propose the following model for the observation of \(\psi_{s,k}\), the phase delay associated with the satellite \(s\) at instant \(k\):  
\[y_{s,k}=\left(\alpha+\beta\;x_{s,k}^{el}+\eta_{s,k}^{y}\right)\;mod(2\pi) \tag{14}\]  
\(y_{s,k}\) is the angular observation and \(x_{s,k}^{el}\) the associated reference time frame. The parameters \(\alpha\) and \(\beta\) are respectively the ordinate at the origin and the slope of the linear evolution of the observations. The term \(\eta_{s,k}^{y}\) is a zero-mean additive von Mises noise realization with a concentration parameter \(x_{s}\). In our approach, the reference time frame corresponds to the sine of the elevation: \(x_{s,k}^{el}=\sin\left(\theta_{s,k}^{el}\right)\), and the value of \(h\) is derived from the value of \(\beta\): \(h=\frac{\lambda\beta}{4\pi}\).
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
###### Abstract  
Multi-Access Edge Computing (MEC) represents the central concept that enables the creation of new applications and services that bring the benefits of edge computing to networks and users. By implementing applications and services at the edge, close to users and their devices, it becomes possible to take advantage of extremely low latency, substantial bandwidth, and optimized resource usage. However, enabling this approach requires careful integration between the MEC framework and the open 5G core. This work is dedicated to designing a new service that extends the functionality of the Multi-Access Traffic Steering (MTS) API, acting as a strategic bridge between the realms of MEC and the 5G core. To accomplish this objective, we utilize free5 GC (open-source project for 5G core) as our 5G core, deployed on the Kubernetes cluster. The proposed service is validated using this framework, involving scenarios of high user density. To conclude whether the discussed solution is valid, KPIs of 5G MEC applications described in the scientific community were sought to use as a comparison parameter. The results indicate that the service effectively addresses the described issues while demonstrating its feasibility in various use cases such as e-Health, Paramedic Support, Smart Home, and Smart Farms.  
5G Core; MEC; integration; service; MTS; API +
Footnote †: journal: _et al._ Article
## 3 Methodology
### Estimate and GP Model Validation  
Error measurement is very important to determine the reliability of estimations. In this work, for estimate validation, the percentage error between the computer input (i.e., hydrocarbon depth used in the computer simulation) and the depth predicted by the GP-based inverse modeling was calculated using Equation (13). Meanwhile, for GP model validation, three error validators were used in this work, namely mean absolute deviation (MAD), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The errors of EM profiles provided by the GP model were calculated by assuming the EM responses modeled by the FE (through the CST software) as the true EM values. All equations of the MAD, MAPE, and RMSE are defined in Equations (14)-(16), respectively, as follows:  
\[Percentage\ Error=\left|\frac{h_{true}-h_{estimate}}{h_{true}}\right|\times 100\%, \tag{13}\]  
\[MAD=\frac{1}{N}\sum\lvert y_{FE}-y_{GP}\rvert, \tag{14}\]  
\[MAPE=\frac{1}{N}\sum\frac{\lvert y_{FE}-y_{GP}\rvert}{\lvert y_{FE}\rvert} \times 100\%, \tag{15}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum(y_{FE}-y_{GP})^{2}}, \tag{16}\]where \(h_{true}\) is the CST depth input, \(h_{estimate}\) is the depth estimated by the GP-based inverse modeling, \(y_{FE}\) is the EM response modeled by FE method, \(y_{GP}\) is the EM response evaluated by the forward GP modeling, and \(N\) is the total number of observations.
## 5 Process of Optimal Foil Width Determination and Results
### Results TP1
#### 5.1.3 Determination of the Optimal Foil Width for TP1
Table 16 shows the \(U_{\text{Smax}}\) values for the different depths of the protective foil, which had the width \(f\!w=6\) m.  
From Table 16, it can be seen that the \(U_{\text{Smax}}\) was lower with a deeper protective foil burial depth. The burial depth of the foil usually depends on the conditions surrounding the transmission pole. It is important to note that there are several factors in the process that can affect the accuracy of the calculation. These are as follows:  
* It is not possible to determine the parameters of the earth with absolute precision, because we can never know exactly what is hidden under the surface. * Defining material data such as concrete, rolled steel, etc., can lead to errors due to different concrete structures, corrosion affecting rolled steel, etc. * The finite element method is a numerical method dependent on input data, and its accuracy is limited. * The F4 function describes the course of Us very well, but there is still some deviation. Due to the mentioned problems, it is suggested that some reserve be added to the calculated value of the foil width to take into account the stated limitations.
# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
## 3 System Model  
### Network Model and Hypothesis  
In our scheme, there are many nodes with unique ID deployed randomly in the designated network area. Nodes are heterogeneous in terms of energy and the death of the node is only related to the energy depletion [2; 24]. The Base Station (BS) is fixed after deployment. The location of the base station is aware to all nodes and the energy of the base station is assumed unlimited. The distance between nodes is calculated by using received signal strength indicator (RSSI) and data transmission between member nodes and CHs is with multi hop communication. The nodes only know the relevant information of neighbor nodes and do not know the exact situation of global topology. The communication environment of MAC layer is ideal.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 5 Mission Process Control Strategy
### Control Strategy Solution
#### 5.2.3 Maximum Anti-Disturbance Capability
```
1: valid controllable mission \(m_{i}\) prior-missions set \(\mathcal{E}M_{m_{i}}\) of \(m_{i}\) post-missions set \(\mathcal{P}M_{m_{i}}\) of \(m_{i}\) critic path \(\hat{\mathcal{K}}^{*}(t_{c})\) Output: the maximum anti-disturbance capability \(\chi_{m_{i}^{*}}^{\max}(t_{c})\) of \(m_{i}^{*}\) at \(t_{c}\) moment
2:for\(m_{i}^{*}\) in \(\hat{\mathcal{K}}^{*}(t_{c})\)do
3:if\(c(m_{i}^{*})=1\)then
4:\(a\gets 0\), \(b\gets+\infty\)
5: create \(\hat{\mathcal{K}}_{m_{i}^{*}}^{\text{before}}\)\(\triangleright\) A mission set that includes all controllable missions before \(m_{i}^{*}\) in critical path
6: create \(\hat{\mathcal{K}}_{m_{i}^{*}}^{\text{before}}\)\(\triangleright\) A mission set that includes all controllable missions after \(m_{i}^{*}\) in critical path
7:for\(m_{i}^{*}\) in \(\hat{\mathcal{K}}_{m_{i}^{*}}^{\text{before}}\)do
8:\(a\gets a+\chi_{m_{i}^{*}}^{\text{before}}(t_{c})\)
9:endfor
10:for\(m_{i}^{*}\) in \(\hat{\mathcal{K}}_{m_{i}^{*}}^{\text{before}}\)do
11:\(a\gets a+\chi_{m_{i}^{*}}^{\text{max}}(t_{c})\)
12:if\(b<T_{PS}^{m_{i}^{*}}(t_{c})-\min(T_{PS}^{m_{i}^{*}}(t_{c}+\Delta t))\)then
13:\(b\gets T_{PS}^{m_{i}^{*}}(t_{c})-\min(T_{PS}^{m_{i}^{*}}(t_{c}+\Delta t))\)
14:endif
15:endfor
16:\(\chi_{m_{i}^{*}}^{\text{max}}(t_{c})\gets b-a\)
17:else
18:\(\chi_{m_{i}^{*}}^{\text{max}}(t_{c})\gets 0\)
19:endif
20:endfor
21:return\(\chi_{m_{i}^{*}}^{\text{max}}(t_{c})\)
```  
**Algorithm 4** Calculation of Maximum Anti-Disturbance Capability for Valid Controllable Missions
## 3 Results and Discussion
### Effect of Reaction Time on Leaching Efficiency before and after Mechanical Activation  
The effect of the reaction time on the leaching efficiency was explored further, and the results are given in Figure 7. The samples were tested at a variety of reaction temperatures, before and after mechanical activation. When comparing a with b, it was discovered that the metal yttrium in the inactivated sample can hardly be leached out at the reaction temperature of 50 \({}^{\circ}\)C, whereas the metal yttrium in the mechanically activated sample was leached in a small amount, and the leaching efficiency tends to be stable as the reaction time increases. When the leaching time was 12 h and the reaction temperature was 80 \({}^{\circ}\)C, the leaching efficiency of the mechanically activated samples reached 88.37%, but the leaching efficiency of the inactivated samples was only 48.61%. It was determined that mechanical activation can greatly improve the leaching efficiency of yttrium at higher temperatures, whereas mechanical activation has a minimal influence on the leaching efficiency of metallic yttrium at lower temperatures.  
In addition, temperature is known to have a significant effect on the viscosity of DESs. At low temperatures, the viscosity of the DESs increases, and the diffusion rate of the protons decreases, which makes the reaction between the DESs and the metal yttrium difficult [24]. In contrast, as the temperature of the DESs increased, the viscosity decreased and the mobility of the protons increased. Moreover, at the optimal reaction temperature, mechanical activation increased the energy stored in the sample and the amount of energy that weakens or destroys the crystal structure within the sample. When the kinetic energy was greater than or equal to the activation energy, the number of molecules increased, which led to an increase in the number of effective collisions, thereby accelerating the rate  
Figure 6: The effect of reaction temperature on leaching efficiency before and after mechanical activation (tMA = 60 min tL= 12 h).  
of chemical reaction [25; 26]. Therefore, the yttrium leaching efficiency can be significantly increased at the same time. The result that the mechanical activation increases the reaction rates indicated that the system diffusion was controlled (either externally or through an inert product layer), and the same should be true with the temperature, so actually the region where diffusion is manifested should simultaneously be mechanical activation and high temperature.
# Enhanced Enzymatic Degradability of Livestock Blood Pretreated with Ultrasonic Technique
## 2 Materials and Methods
### Method for Ultrasonic Treatment of Livestock Blood  
Figure 1a illustrates the architecture of the ultrasonic treatment device for blood solubilization, which consists of an ultrasound generator and a reactor. The ultrasound generator consists of a converter, control module, transducer, and horn tip. The probe-shaped horn tip had a frequency of 20 kHz and a maximum power of 2000 W. The frequency of the horn tip was set to 20 kHz because previous studies have indicated that the Low-frequency range (below 40 kHz) enables the effective destruction of cells [21; 22], and subjecting cells to a 30 min treatment at 20 kHz resulted in high protein solubilization efficiency, while higher frequencies were Less efficient for the ultrasonic treatment of proteins [27]. The reactor consisted of a 2 L beaker, a water bath for temperature control, a temperature sensor, and an agitator with a maximum speed of 1000 rpm.  
The ultrasonic treatment experiments were conducted in a batch mode using 1 L of Livestock blood as the starting material, with agitation at 200 rpm to ensure proper ultrasound dispersion. Experiments were performed to assess the solubilization efficiency of blood, with the ultrasonic irradiation density set between 0.1 and 2.0 W/mL and irradiation time from 10 to 60 min at 20 kHz frequency.
## 3 Results
### Dimethoate Interaction with IG and GO Surfaces
#### 3.1.1 Hill and Scatchard Analysis
The results were analyzed in parallel by applying the Scatchard model to study the binding mode and estimate the binding parameters according to Equation (4) and Equation (5):  
\[(C_{ads}/C_{BS})/C_{eq}=NK_{ads}+(C_{ads}/C_{BS})K_{ads} \tag{4}\]  
\[\ln C_{eq}=-1/n_{H}\ln(N/(C_{ads}/C_{BS})-1)+\ln K_{ads} \tag{5}\]  
In the case of random binding, Equation (4) should be the straight line, since cooperative binding results in a humped curve, which rises to a maximum and then declines to 0 with the intercept at x-axis equal to \(N\), i.e., the total concentration of occupied BS per mol of the macromolecule surface. Equation (5) represents the modified Hill plot [36,37]. Here, \(K_{ads}\) is the ligand-macromolecule association constant. As the example, Figure 3 represents the Equations (4) and (5) for DMT adsorption on 2 g dm\({}^{-3}\) IG and 0.25 g dm\({}^{-3}\) GO surfaces. It is worth noticing that Equation (4) represents the humped curve for all experiments, which is characteristic of cooperative binding. Moreover, Equation (5) is sensitive to \(N\), and its linearity was tested for selected \(N\) values obtained from the end point of adsorption  
Figure 3: Scatchard plots (Equation (4)) for DMT interaction with (**a**) 2 g dm\({}^{-3}\) IG and (**b**) 0.25 g dm\({}^{-3}\) GO. Inset: Hill plots (Equation (5)). Figure 2: (**a**) Dependence of adsorbed on equilibrium DMT concentration on 2 g dm\({}^{-3}\) IG and GO; (**b**) dependence of adsorbed DMT concentration on IG and GO (inset) binding sites (\(C_{BS}\)) in the suspension containing 5 \(\times\) 10\({}^{-4}\) mol dm\({}^{-3}\) DMT after equilibration at 25 \({}^{\circ}\)C.  
curves, which indicate DMT concentration bound per mol of IG or GO binding sites. The mean values of the obtained parameters of reaction mechanism obtained from the performed experiments are given in Table 1.
## 3 Results
### Percolation Analysis  
To assess the robustness of the networks, we used a network percolation analysis procedure [33]. We took the weighted adjacency matrix of the previously constructed pareidolic responses and applied these percolation analysis steps to compare the resiliency of the semantic networks of the different conditions (virtual cave vs. open space). During this process, network links were progressively removed based on their strength, and the size of the largest connected component (LCCS) was measured at each step. The percolation integral (\(\varphi\)), which represents the area under the curve of the LCCS across percolation steps, was used as an indicator of network robustness. We found a higher percolation integral in the virtual cave network (\(\varphi\) = 53.76) compared to the open space network (\(\varphi\) = 49.99, see Figure 4).  
We used four different statistical approaches to assess the difference in our results: LONO, LOSO, link shuffling analysis (LS), and Effect of Noise (EN). We found the difference statistically significant in all four conditions (see Table 2). These consistent results indicate that the semantic network in the virtual cave condition is more robust and flexible compared to the open space semantic network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Cave** & **Open** & \(P\) & **d** \\ \hline LONO & 53.2 (0.18) & 49.48 (0.13) & \(<\)0.00001 & 23.508 \\ LOSO & 52.59 (1.19) & 49.03 (1.05) & \(<\)0.00001 & 3.126 \\ LS & 56.19 (0.66) & 51.6 (0.45) & \(<\)0.00001 & 8.115 \\ EN & 53.85 (0.16) & 50.02 (0.16) & \(<\)0.00001 & 24.181 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the comparison between the networks of cave and open conditions from the two-tailed paired-sample permutation \(t\)-test on percolation integral. Mean and standard deviation in parenthesis. LONO: leave-one-node-out; LOSO: leave-one-subject-out; LS: link shuffling analysis; and EN: Effect of Noise analysis. \(p\) = \(p\)-value, d = Cohen’s d effect size: 0.2, small; 0.5, moderate; 0.8, large, 1.1 very large.  
Figure 4: Network Percolation and Statistical Results. (**A**) Graphical representation of the line plot of the percolation process of the open space (in blue) and cave (in yellow) networks. The y-axis represents the number of nodes that survived the percolation process, and the \(x\)-axis represents the increasing weight threshold of the nodes removed during the percolation steps, from 0.0 to 1.0; (**B**) Visualization of semantic networks in open space and cave conditions displayed at different weight thresholds along the percolation process; and (**C**) Line plots of the LONO, LOSO, LS, and EN procedures (on the left); bar plots showing the \(\varphi\) between conditions for the four different procedures (on the right).
## 2 Materials and Methods
### Gated Recurrent Unit  
The gated recurrent unit (GRU) is an artificial neural network cell introduced by [PERSON] et al. [44]. The GRU was developed to more effectively model long-term dependencies in sequential data processing tasks. A GRU contains two main gate mechanisms: the update and reset gates. The update gate determines how much the model updates between existing information and new incoming information. This gate controls how much the model adapts to the new information and regulates the information transfer. The reset gate determines how much the previous hidden state is merged with the current knowledge. A high reset gate value reduces the influence of prior knowledge, allowing the model to focus more on current knowledge. GRU has a more straightforward structure compared to Long Short-Term Memory (LSTM) cells; it is computationally efficient by eliminating the separate cell state in LSTMs. This simplicity makes the GRU an alternative that offers faster training and prediction processes. GRUs have been used effectively in language modeling, machine translation, and time series analysis.