esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To address this problem, we extended SMS with an appropriate message validity period, failover options and redundancies so that SMS could be considered as a transmission medium for a wide variety of data. The proposed system organisation allows flexible design space for optimum trade-off (e.g., between processing power and storage capacity) as well as able to provide a wires-free environment so as not to limit movement and attract undesired attention during continuous monitoring. Based on the architecture, we propose a generic communications protocol to work inside an SMS message and provide the necessary redundancy and robustness for the transmission of non-critical medical telemetry. Such telemetry could then be transmitted from a mobile telephone or other cellular mobile engine, and this capability could be used to increase the freedom of non-critical patients requiring monitoring. The rest of the paper is organised as follows. In Section 2, the background information is discussed. A brief background in SMS and telemetric data as well as various methods of collecting them is given. The design requirements for patient monitoring system are also discussed. The medical telemetry system architecture for physiological data collection is described in Section 3. Section 4 discusses a generic communications protocol to work inside an SMS message and provide the necessary redundancy and robustness for the transmission of non-critical medical telemetry. Such telemetry could then be transmitted from a mobile telephone or other cellular mobile engine, and this capability could be used to increase the freedom of non-critical patients requiring monitoring. The conclusion and future directions is given in Section 5.
## 3 The Proposed Fuzzy Relevance-Based Cluster Head Selection Algorithm
### Cluster Head Selection
#### 3.3.2 Available Power  
The available power is the available energy capacity. In this paper, we consider the energy power level of each node while calculating the available power, in order to increase the network lifetime. Whenever a node forwards a packet, it loses some amount of energy whose amount depends on factors such as the nature of packets, their size, access frequency, and the distance between the nodes. An available power function considers all these factors and decides which one, among all the discovered paths, should be selected for an energy-efficient transmission. We have considered individual energy power in considering the path, that is, if there is a path with a node having very low energy level, then the available power function does not select that path, irrespective of whether or not that path is time efficient.  
The available power for node \(x_{i}\), \(AP(x_{i})\), depends on the number of nodes for the cluster i. The larger \(AP(x_{i})\) means the more stable power and the more energy power. Thus, the node with large \(AP(x_{i})\) is highly likely to be selected as a cluster head and able to support the network lifetime for a long time. Therefore, we consider the available power function to increase the network lifetime, and it is defined by Equation (3):  
\[AP(x_{i})=\frac{\sum\limits_{x_{j}\in Cluster_{x_{j}}}}{n_{i}} \tag{3}\]  
where \(n_{i}\) is the number of nodes in the cluster \(i\), and \(P_{x_{j}}\) is the available energy power of the node \(x_{j}\).
## 2 Methodology
### Damage Identification
#### 2.4.2 The Volume of the Convex Hull  
The volume of the convex hull is a geometric indicator that measures the total three-dimensional space occupied by the data points in the latent space. This volume is enclosed by the smallest convex polyhedron that can contain all the points. In SHM, this indicator captures the spatial dispersion of sensor data under varying load conditions, providing a quantitative metric of changes in structural integrity.  
This indicator is particularly valuable, as it accounts for both the dispersion and the spatial arrangement and alignment of the data points. Such geometric configurations can reveal subtle changes or anomalies in the inspected structure, like the initiation of cracks or the propagation of existing damage, which might not be detectable through density measurements alone. Normalizing the convex hull volume by the maximum volume across different load steps contributes to the maintenance of a consistent scale for comparison, thus enhancing interpretability and visualizations.  
The convex hull volume helps in understanding how structural responses spread out in the latent space. An increase in volume might indicate the development of structural anomalies, such as cracks or damage, that alter the beam's overall geometry. This indicator can be used as a complement of density by offering a broader perspective on how structural changes affect the latent space. It provides a new perspective to the analysis by including the total spatial extent as well as the shape of the data. Changes in volume might be associated with damage growth that does not alter density but might affect the geometric distribution of the points in the latent space.  
The convex hull volume provides a measure of the spread and dispersion of data points in the latent space, which is invariant with respect to coordinate rotations, translations, and scaling. Because of this, this representation is suitable for classification. When the beam is intact, the volume is constrained. However, as damage develops, the volume increases and spreads unpredictably. This shift is invariant to the initial and final states, effectively reflecting the transition from a controlled to a damaged state.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 6 Performance Evaluation  
This section is divided into two parts. We analyze the Negotiation-free encryption algorithm and traditional IPSec first and then realize the simulation experiment using the network simulation tool GNS3.  
We summarize the characteristics of several encryption algorithms of NFEM and IPSec, to indicate the superiority of NFEM. The comparisons are shown in Table 2.  
In the experiment, we compare the two respective methods using the security encryption delay and the transmission overhead. Then, we analyze the NFEM separately using the ns-3 (version 3.26) simulator. We analyze the performance of the Negotiation-free encryption algorithm according to the vehicle density, vehicle speed and beacon transmission frequency. The main parameters are shown in Table 3.
## 1 Introduction
### A Shared Spectrum Economy  
There is a pressing problem with using the upper-mid band for wireless connectivity. The 6 GHz to 24 GHz range has already been assigned for mission-critical applications, and its licenses are owned by the Department of Defense (DoD) and other government entities. Most of the DoD mission-critical sensors, including X-band radar, communication networks, science experimental bands (e.g., radio astronomy), and weather radar sensors also exist within the FR3 band. Primary users (PUs) will likely never give up their \"precious\" spectrum permanently; however, the future will likely necessitate DSA, where secondary users (SUs) such as commercial wireless networks can make use of the FR3 bands in a way that poses little or no threat to critical applications pursued by PUs. Although DSA can bring massive benefits and economic growth to society, such great power comes with great responsibility as compliance to sharing agreements must be ensured with non-repudiation and zero trust.
## 4 Optimize Parameters of LS-SVM Employing Fruit Fly Optimization Algorithm  
Fruit Fly Optimization Algorithm (FOA) is a new approach for finding global optimization based on the food finding behavior of the fruit fly [23].  
The process of optimizing the parameters \(C\) and \(\sigma\) using FOA is divided as follows and the flow diagram is illustrated in Figure 2[25,29,30].  
Step 1. Data preprocessing, mainly including normalization processing and dividing the sample data into training data and test data.  
Step 2. Initialize the parameters and the initial position of FOA: first, set the maximum number of generations (_gen_), the initial value of _Smellbest_ (bigger enough than the actual max MSE) and population size (NP); and second, randomly initial fruit fly group location (\(X_{G}\), \(Y_{G}\)) = (\(X_{0}\), \(Y_{0}\)).  
Step 3. Give the random direction \(\eta\) and swarm radius \(R\). The coordinates of the \(n\)th fruit fly (\(X_{n}\),\(Y_{n}\) ) is:  
\[X_{n}=X_{G}+R(\eta-0.5)\qquad Y_{n}=Y_{G}+R(\eta-0.5) \tag{10}\]  
where \(n=1\), \(2\),..., NP and \(\eta\in[0,1]\) is a random number.  
Step 4. Because the food location cannot be known, the distance to the origin is estimated first (\(D_{n}\)), then the smell concentration judgment value (\(S\)) is calculated whose value is the reciprocal of distance and \(C_{n}\), \(\sigma_{n}\) can be calculated.  
\[D_{n}=\sqrt{X_{n}^{2}+Y_{n}^{2}}\,,\quad S_{n}=1/\,D_{n}\,,\,\,\,\,\,\,\,\,C_{n }=20S_{n}\,,\,\,\,\,\,\sigma_{n}=0.1S_{n} \tag{11}\]  
Figure 2: The flow diagram of fruit fly optimization algorithm (FOA).  
Step 5. Train LS-SVM model using \(\sigma_{n}\), \(C_{n}\) and then conduct the 4-fold cross-validation tests. The smell concentration judgment function (or called fitness function) is defined as:  
\[\mathit{Smell}_{n}=\frac{1}{4}\sum_{i=1}^{4}[\frac{1}{m}\sum_{j=1}^{m}\left( \hat{y}_{ij}-y_{ij}\right)^{2}] \tag{12}\]  
where \(m\) is the number of the data in every sample subset, \(\hat{y}_{ij}\) is the predicted value and \(y_{ij}\) is the actual value.  
Step 6. Find out the fruit fly with the minimum \(\mathit{Smell}\) value in the whole fly swarm and its index among the fruit fly swarm.  
\[[\mathit{bestSmell}\quad\mathit{bestIndex}]=\min(\mathit{Smell}) \tag{13}\]  
Step 7. Judge if the \(\mathit{bestSmell}\) is superior to the previous \(\mathit{Smellbest}\), if so, the fruit fly swarm will use vision to fly towards that location. The \(\mathit{Smellbest}\) and the coordinate of the fly group (\(X_{G}\), \(Y_{G}\)) are updated:  
\[\mathit{Smellbest}=\mathit{bestSmell}\,,\quad X_{G}=X(\mathit{bestIndex}), \quad Y_{G}=Y(\mathit{bestIndex}) \tag{14}\]  
Step 8. Judge if the iteration is end, if not, go to step 3 and continue to cycle. Otherwise, obtain the optimal values of \(C\) and \(\sigma\), and begin to predict using LS-SVM.  
According to the iteration steps above, there are only three parameters to be set including the population size, the initial position of population and the maximum iteration times.
## 3 Results
### Compatibility
#### 3.5.1 Setup Time Analysis  
Setup times varied based on whether the NVIDIA Container Toolkit and Docker had already been installed. The fastest setup was achieved on Windows 11, completing the installation in approximately 37 s when containerization tools were pre-installed. In contrast, the longest setup time of 3 min occurred on Ubuntu 18.04 when the NVIDIA Container Toolkit and Docker needed to be installed during the deployment process. This demonstrates that even without pre-installed containerization tools, the full installation was accomplished swiftly, highlighting the system's speed and ease of use.
## 3 Solution and Analysis of Longitudinal Deformation Curve of Surrounding Rock
### Data Comparison and Equation Optimization Analysis  
#### 3.2.1 Comparative Analysis of Practice and Theory  
Many characteristics of the surrounding rock and supporting structure in the New Austrian Tunneling Method (NATM) construction can be reflected macroscopically through deformation, that is, obtaining the convergence displacement of surrounding rock through monitoring and measurement is the most intuitive and effective information element to evaluate the bearing capacity of surrounding rock.  
According to the field monitoring data, the monitoring data of Class-III surrounding rock sections, YK30+960 and YK30+911, and Class-IV surrounding rock sections, YK31+645 and YK31+422, are compared and analyzed with the calculation data of theoretical Equation (8), as shown in Figure 4.  
Fit the curve in Figure 4 to obtain the variance and correlation coefficient of each fitting curve, as shown in Table 2:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Rock**} & \multicolumn{6}{c}{**Mechanical Parameters**} \\ \cline{2-7}  & **Density** & **Young’s Modulus** & **Poisson’s** & **Internal Friction** & **Cohesion** & **Tensile Strength** \\  & \(\rho\) **(kg/m3)** & **E (Gpa)** & **Ratio \(\
u\)** & **Angle \(\varphi\) (0)** & _c (Mpa)_ & _c\({}_{f}\)_ **(Mpa)** \\ \hline III & 2300 & 6 & 0.3 & 30 & 0.3 & 0.3 \\ \hline IV & 2000 & 2 & 0.35 & 25 & 0.2 & 0.2 \\ \hline Support & 2400 & 21 & 0.2 & & & \\ \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of surrounding rock and support.  
Figure 3: Schematic diagram of tunnel excavation: (**a**) Long step method; (**b**) Reserved core soil method. �⃝1–⃝3 is the excavation sequence of surrounding rock.  
The results show that the overall effect is good, but there are some errors. Especially between the distance ratio from the monitoring surface from 0-2, the displacement of Equation (8) is steep, the growth rate is relatively slow and there is a lag time difference with the actual construction monitoring data (the monitoring data shows that the displacement release coefficient of this section increases sharply, and the existence of the \"spatial effect\" of the tunnel face is identified), and it is also inconsistent with the law of gradually increasing the section after the steep increase. Taking this as the starting point, in order to improve the fitting accuracy, the \(x\geq 0\) segment function is optimized and improved on the basis of Equation (8).
## 4 Influence of Magnetic Load and Current Density on Temperature  
### Influence of Magnetic Load on Motor Temperature  
This section studies the effect of magnetic load on temperature, and the stator core loss is used to reflect the change in magnetic load. The relationship between stator core loss and stator magnetic flux density can be obtained by the following equations [19; 20]:  
\[P_{Fe\_yoke}=C_{Fej}f^{1.3}B_{j}{}^{2}G_{j} \tag{1}\]  
\[P_{Fe\_tetheth}=C_{Fej}f^{1.3}B_{i}{}^{2}G_{i} \tag{2}\]  
where \(P_{Fe\_yoke}\) is the core loss of stator yoke, \(C_{Fej}\) is the core loss coefficient of stator yoke, \(G_{j}\) is the weight of the stator yoke, \(B_{j}\) is the magnetic flux density of the stator yoke, \(P_{Fe\_tetheth}\) is the core loss of stator teeth, \(C_{Fei}\) is the core loss coefficient of stator teeth,\(G_{i}\) is the weight of the stator teeth, \(B_{i}\) is the magnetic flux density of the stator teeth, \(f\) is the frequency of the magnetic field.  
From Equations (1) and (2), the stator core losses under different stator magnetic flux densities are obtained, and the results are shown in Figure 8. The stator core loss increases from 2724 W to 4843 W, and the stator magnetic flux density increases from 1.2 T to 1.6 T.  
The temperature distribution and comparison of the motor under different stator magnetic flux densities are shown in Figures 9 and 10. As shown in Figure 9, the highest temperature still occurs at the winding, and the lowest temperature appears at the rotor yoke. When the stator magnetic flux density increases from 1.2 T to 1.6 T, the temperature of each part increases. Among them, the temperature rise at the stator winding is the highest, which is only 9 \({}^{\circ}\)C. The temperature rise at PM is the smallest, which is 2.7 \({}^{\circ}\)C. Therefore, changes in magnetic flux density have a mild effect on temperature.  
Figure 8: Stator core loss under different stator magnetic flux density.
## 1 Introduction
However, these techniques have drawbacks such as background noise control, unable to fulfill the current requirements for selectivity [9], detection limits of CV [12], insolubility of metals and the multiple peaks of ASV [13], complicated interferences and complex matrices in SWASV [14], EIS's inability to identify different ions [15], frequent fouling of electrodes in the case of ECL [16], and only few enzymes are sensitive to heavy metals for the case of biosensors [17]. Hence, the use of portable x-ray fluorescence spectrometry (pXRF) technique in onsite metals detection and analysis is appropriate in rugged condition yet provides user of accurate and rapid analysis. This is in contrast of laboratory-based methods with significant limitations such as expensive instrumentation, complex sample preparation and applicability in field conditions [18]. Therefore, non-destructive analytical technique, such as relatively simple spectra line void of many interferences and rapid multi-element analyses [19], contributes significantly to the successful implementation of this study. Concentration maps were frequently used tool for spatial monitoring. Spatial information in water resources are limited, and GW quality data can be obtained only through spot sampling. However, this procedure often requires extensive manpower and resources [10]. The issue in this practice and the determination of sample locations density influences the accuracy of the generated spatial maps [19]. The integration of in-situ measurements and GIS--based spatial interpolation techniques offer an improvement in the presentation and display of the status of GW quality in an area. The use of this integrated approach provides a clear and intelligent base--maps which can be utilized by researchers, policy makers, implementors for planning proposals [20] and creation of strategic programs. Several studies on GW monitoring and assessment implemented using GIS--based approaches focused on different Southeast Asian countries such as the Philippines [1; 21], Thailand [22], Malaysia [23], Singapore [24], Indonesia [25], Cambodia [26], Laos [27], and Vietnam [28]. This article illustrates the quantification and mapping of the concentrations of heavy metals such as Ba, Cu, Fe, Mn, and Zn in GW and presents the utilization of in-situ GW quality monitoring that uses a hybrid machine learning-geospatial interpolation technique and pXRF. This type of technique and analyses give prompt, accurate data and information on the current GW quality. This is to address the challenges encountered on-ground during sampling activities, the scarcity of instruments in the Philippines due to its price, and the complex samples preparation required by some laboratory-based instruments. Analyzing heavy metal concentrations in a faster, accurate and convenient method can help the researchers, authorities, water utility companies and local government units in making prompt decision, guidelines and strategic programs.
# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 3 Results
### Supervised ML (Physical Fatigue)  
A novel approach for the objective and automatic classification of physical exhaustion based on HRV was presented by [PERSON] et al. (2022) [PERSON] et al. (2022). Their 80 healthy subjects were asked to perform a preset treadmill exercise, during which their ECG data were collected. From the ECG, 24 HRV features were computed. The 10 selected HRV features identified for physical fatigue detection are reported in Appendix C, as explained in [PERSON] et al. (2022). The classification model adopted four supervised ML algorithms, namely DT, SVM, K-nearest neighbor (KNN) and a light gradient boosting machine (LightGBM). The LightGBM achieved the best performance and had an accuracy of 85.5% and an F1 score of 0.801. The results verify the feasibility of using HRV to evaluate physical fatigue. The development of psychosocial strategies that address mood, anxiety, pain and fatigue in SCI rehabilitation programs must consider these cumulative risks.
# Electrospinning of Essential Oils
## 3 Electrospun Fibres Containing EOs and Their Applications
### Oregono Essential Oil
In addition, the fibres with 5.0% and 7.5% v/v of oregano EO dramatically decreased the proliferation of 4T1 mammary carcinoma cells, achieving a complete antiproliferative activity at 72 h. Other studies have reported on the cytotoxicity of oregano extracts on tumour cell lines, and attributed it to some of its chemical constituents, such as rosmarinic acid and 4-terpineol, and their synergistic interaction [80]. The current literature on the electrospinningning of oregano EO demonstrates that a sustained release of the bioactive agent can be achieved from polymeric nanofibres, and systems with a prolonged antibacterial, antioxidant, and antitumor activity can be developed.
## 2 Driving Principle and Structure
### Driving Principle and Structure of the EPM  
The EPM composes of two magnetic materials, NdFeB magnet and another Alnico5 magnet; a coil is wrapped around these, as shown in Figure 2. The basic principle driving of the EPM was showed using the MagNet 7.0 electromagnetic analysis software from Mentor. If the magnetization directions of the two magnets are oppositely combined, the magnetic flux only circulates inside connected iron as shown in Figure 2a. After a switching current pulse of the opposite polarity applied to Alinico5, the magnetization directions of the two magnets were the same, and the EPM generated an external magnetic flux as shown in Figure 2b. The magnetization direction of the NdFeB material is unchanged because it has high coercivity. In ferromagnetic materials, the phenomenon of magnetic hysteresis occurs. [PERSON] coined the term \"hysteresis\" to describe his observation that the magnetic induction in a metal behind the applied current, and he also demonstrated that the molecules of a ferromagnetic material acted as reversible permanent magnets [16]. The normal coercivity of Alnico 5 is approximately 50 kA/m, and the remnant flux density is approximately 1.2 T. The normal coercivity of NdFeB is approximately 1000 kA/m, and the remnant flux density is approximately 1.2-1.3 T [17].  
When a switching current is applied to the coil wound, the magnetization direction of the Alnico 5 magnet changes, and the external magnetic force generates attractive force.  
Figure 1: Driving principle of a wobble motor: (**a**) Schematic of a wobble motor; (**b**) Driving principle.
## 4 Results
### 3D Full Head/Face Scanning and Parametric Head Model
#### 4.1.3 Laser Scanning  
The laser-based scanning provided better results in terms of resolution, as shown in Figure 12. However, small movements such as blinking eyes can still cause some minor distortion in the final 3D model. Additionally, the scanning efficiency was influenced by different skin colour and type of skin (e.g., oily skin). One of the main drawbacks this technology presents is its high cost, the large amount of scanning data points, and the need of a specialized software for post-processing.