esa-sceva/satcom-chunk-collection huggingface dataset:
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## Related Work  
### Importance of e-Health Platforms  
Because of rapid advancements in sensor and communications technologies, governments and the private sector are more intensely incorporating information and communications technology (ICT) in a wide variety of services they offer. The area of health services stands out because of its importance and direct effect on the general welfare of the population. As part of the Millennium Development Goals (MDGs), the World Health Organization (WHO) has called for action to ensure the technological development and universal delivery of health services worldwide by means of various e-Health strategies [8]. Meanwhile, recent advances in the embedded systems, intelligent sensor networks and wireless communication protocol areas enable the integration of large numbers of components combining increased computing capacity, smaller and more long-lasting power sources, expanded wireless communication capability and the miniaturization of sensors and actuators. It is the coordination of these technologies that permit the development of highly specialized applications that offer custom solutions in diverse areas such as environmental monitoring, e-health, smart transportation systems, smart cities, disaster mitigation, etc. One of the main objectives of future technological developments will be to combine mobility with ICT technology to provide a large gamut of utilities to its users, with health and social services being among the most important. To this end, new mobile devices, along with a robust wireless infrastructure that provides seamless connectivity among different fixed, mobile and satellite technologies, will trigger the development of a multitude of e-Health and Tele-Health applications that will revolutionize present-day healthcare models. Presently healthcare models are generally reactive and respond to illnesses after symptoms appear, whereas future healthcare models will be more proactive as their focus will be on prevention and early detection of illness before symptoms surface.
## 3 Detection in Receiver
### SVM Detector  
In this section, we suggest a nonlinear detection algorithm from an appealing pattern classification point of view. We detect the received signals of SIF output by using the SVM technique. The main advantage of using such a technique is that it can make full use of the characteristics of the received waveforms.
## 3 Results
### Gait Analysis
#### 3.1.3 Height of Foot Lift and Heel Strike during Walking  
The subjects' height of foot lift and heel strike during walking were evaluated during the two walking tests. None of the subjects who participated in the study showed particularly noticeable heel drag and their heel lifting was of sufficient height. In the graphs of Figure 10, it is possible to see the lifting from the ground of the right heel for one subject compared with their paired control.  
\begin{table}
\begin{tabular}{l l r r r r r r r r} \hline \hline  & **Patients** & \multicolumn{1}{c}{**P01**} & \multicolumn{1}{c}{**P02**} & \multicolumn{1}{c}{**P03**} & \multicolumn{1}{c}{**P04**} & \multicolumn{1}{c}{**P05**} \\ \hline \multicolumn{2}{c}{**Annotated MDS-UPDRS Score**} & \multicolumn{1}{c}{**1**} & \multicolumn{1}{c}{**1**} & \multicolumn{1}{c}{**1**} & \multicolumn{1}{c}{**2**} & \multicolumn{1}{c}{**1**} \\ \hline \multirow{4}{*}{**Synthetic**} & **Step 1 length (m)** & 0.48 & \(-\)42.17 & 0.41 & \(-\)32.79 & 0.50 & \(-\)9.09 & 0.41 & \(-\)33.87 & 0.51 & \(-\)10.53 \\  & **Step 2 length (m)** & 0.54 & \(-\)33.33 & 0.45 & \(-\)26.23 & 0.48 & \(-\)12.73 & 0.41 & \(-\)34.92 & 0.54 & \(-\)5.26 \\  & **Stride amplitude (m)** & 1.02 & \(-\)37.04 & 0.86 & \(-\)29.51 & 0.98 & \(-\)10.91 & 0.82 & \(-\)34.40 & 1.05 & \(-\)8.70 \\  & **Stride velocity (m/s)** & 0.90 & \(-\)37.06 & 0.79 & \(-\)28.83 & 0.79 & \(-\)13.80 & 0.88 & \(-\)27.87 & 1.05 & \(-\)0.94 \\  & **Turning (s)** & 1.29 & 84.29 & 0.77 & 2.67 & 1.04 & 44.44 & 0.87 & 77.55 & 0.97 & 94.00 \\  & **Height of foot 1 lift (m)** & 0.20 & \(-\)31.03 & 0.18 & \(-\)18.18 & 0.21 & 0.00 & 0.13 & \(-\)40.91 & 0.21 & \(-\)12.50 \\  & **Height of foot 2 lift (m)** & 0.19 & \(-\)32.14 & 0.16 & \(-\)23.81 & 0.18 & \(-\)10.00 & 0.14 & \(-\)30.00 & 0.22 & \(-\)4.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of gait features for the five PD subjects, with their annotated MDS-UPDRS scores. In red is the percentage deviation from the corresponding control subject.  
Figure 9: Distance from the origin for two paired subjects in the turning phase: (**a**) patient subject; (**b**) control subject.  
The subject belonging to the healthy control group had an average heel lift from the ground of 21 cm. On the contrary, the PD patient had an average heel lift from the ground of 13.5 cm, which was representative of the gait impairment of PD patients. The values measured for all the other subjects involved in the study are shown in Table 2.
## 4 Simulation Results  
We use the first order radio model [5] to evaluate the energy consumption of each node. According to this model, a radio dissipates \(E_{elec}=50\) nJoule/bit to run the transmitter or receiver circuitry. \(E_{elec}\) is the energy consumption of the circuit itself. Assuming \(d^{2}\) energy loss, where \(d\) is the distance between nodes, a transmission amplifier at the sender node further consumes \(E_{amp}d^{2}\), where \(E_{amp}=100\) pJoule/bit/m\({}^{2}\). \(E_{amp}\) is the energy consumed by the amplifier when transmitting packets. Thus, to transmit a \(k\)-bit message a distance \(d\) using this radio model, the radio expends:  
\[E_{Tx}(k,d)=E_{elec}\times k+E_{amp}\times k\times d^{2} \tag{3}\]  
and to receive this message, the radio expends:  
\[E_{Rx}(k)=E_{elec}\times k \tag{4}\]  
Receiving a message is not a low cost operation using these parameter values. Protocols should thus try to minimize not only the transmission distances but also the numbers of transmission and reception operations for each message. We can generalize the total transmission consumption as follows:  
\[E_{total}(k)=(E_{elec}\times k+E_{amp}\times k\times d^{2})+(E_{elec}\times k) \tag{5}\]  
In the following, we compare the performance of the proposed CBDAS with that of the Direct, PEGASIS [8], and PBDAS [20] protocols. The direct approach is simply for each node to transmit its data directly to the BS. In our simulations, the energy consumed for data aggregation is assumed to be 5 nJoule/bit/message. The simulation parameters are shown in Table 1.  
To widely evaluate the impact of some factors, we changed the value of parameters in some cases. We applied an initial energy per node of 0.25 J, 0.5 J, and 1.0 J to evaluate the impact of initial energy. We applied a grid of \(6\times 6\), \(8\times 8\), and \(10\times 10\) cells to evaluate the impact of number of cells. We also applied 100, 200, 300, 400, 500, and 600 nodes to evaluate the impact of node density.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Network area & 100 m \(\times\)100 m \\ Location of BS & (50, 150) \\ Initial energy & 0.5 J/node \\ Number of cells & 10 \(\times\)10 \\ Number of sensor nodes & 300 \\ Packet length & 2,000 bits \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for the simulation.
## 3 System Structure of USV
### Sensor System of the USV  
The USV was equipped with an inertial navigation system (INS) module, which comprised a GNSS and two L2-grade antennas for navigation. Additionally, it featured an RPM detector, a coulometer, and a reverse electromotive force-detection circuit for fault diagnosis. Furthermore, a camera sensor enabled the user to respond to emergencies (such as the sudden appearance of obstacles or collisions due to navigation algorithm errors) and survey mission areas.  
The INS module outputs the current position (latitude and longitude) and heading data of the USV. The module's data follow the National Marine Electronics Association (NMEA) 0183 format, with the position data using \"GPRMC\" and heading data using \"GPHDT\". For more precise data, the module was equipped with the capability to simultaneously acquire GPS and GNSS satellite signals, enabling the interpolation of each satellite's data.  
In this study, fault diagnosis of a USV was performed by acquiring power consumption and thruster RPM data. Equations (13) and (14) represent the thruster torque. Equation (13) calculates the torque based on the output of the rotational speed of the thruster in RPM, while (14) represents the relationship between the voltage and current input to the thruster [14].  
\[\tau=\frac{60}{2\pi}*\frac{P}{N}, \tag{13}\]  
\[\tau=\frac{pz}{2\pi\alpha}*\Phi*I_{\alpha}, \tag{14}\]  
In (13), P represents the output of the thruster, N denotes the rotational speed of the thruster in RPM. In (14), p represents the pole pairs of the thruster, z is the number of conductors in the thruster, \(\alpha\) is the parallel circuit number of the thruster, \(\Phi\) is the flux in the thruster, and \(I_{\alpha}\) represents the current input to the thrusters. According to these equations, the torque of the thruster is inversely proportional to the rotational speed and directly  
Figure 2: Schematic of the communication system between the leader and follower USVs.  
proportional to the current. Therefore, fault diagnosis for the USV can be performed by detecting and comparing the rotational speed and current values.  
A camera was installed to allow the user to monitor the real-time mission status of the USV. To enable the user to identify unexpected obstacles (such as reefs and other vessels) that may suddenly appear along the course of the USV, a wide-angle camera with a broad field of view was selected. Additionally, to ensure visibility during nighttime or in dark environments while conducting missions, a camera with infrared capabilities was mounted. This setup enabled the user to confirm and take actions, such as emergency stops or course correction, in response to unforeseen obstacles in the USV mission path.
## 1 Introduction
As mentioned, AgNWs are a frequently used electrically conductive material for manufacturing sensor substrates. This is due to, among other things, their excellent electrical and thermal conductivity, as well as their relatively low susceptibility to external influences. The synthesis routes for their manufacture are relatively well understood, allowing control over the size of the final product. It is also essential that the synthesis does not require the use of environmentally hazardous reagents or sophisticated apparatus. Therefore, this makes the material easy to fabricate under laboratory conditions. Silver nanowires are manufactured using the polyol process, in which polyols [36] act as both a reducing agent for the nanomaterial precursor and as a solvent for the reaction substrates and synthesized products. Fabrication of the nanowires in question is possible using a stabilizing substance, polyvinylpyrrolidone (PVP), with different molecular weights. By selective adsorption on surfaces with the highest surface energy (1 0 0), PVP enables the preferential expansion of the surfaces (1 1 1) that constitute the main component of the terminal parts of the nanowire [37]. Additionally, the anisotropic growth of AgNWs can be intensified by the addition of so-called mediating substances. These are usually represented by chlorides of monovalent metals or those occurring in two oxidation levels. The addition of these substances provides the possibility of technological control on the formation of nanowires at the nucleation stage. Moreover, it enables the elimination of oxygen adsorbed on the expanding (1 1 1) planes of the nanowires. A literature review indicated the need for more information on stress sensors based on substrates using a mixture of conductive material and a filler of renewable material for their construction. This observation opened the field for research into this issue to fill the observed gap. The research allowed an experimental assessment of the feasibility of using a hybrid paper made from a mixture of silver nanowires, cellulose pulp, and PDMS resin to manufacture sensors that respond to external forces. The results showed that the proposed solution can be successfully applied to indicate the dynamic interactions occurring in engineering issues. In addition, it was demonstrated that the developed solution has the potential to analyze effects related to human life activity, which may find application in healthcare.
## 2 Materials and Methods
The first series occurred after 66-70 h from the end of irradiation and the measurement time ranged between 3600 s (1 h) and 18,000 s (5 h), each irradiated sample was placed at 4 cm from the detector; the second series after three weeks from the end of irradiation and the measurement time was 24-48 h long; each sample was placed in contact with the detector. Table 1 reports all the nuclear data and limits of detection (LODs) for the elements studied in this work. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & **Product Nucleide** & **Half Life** & \(\gamma\)**-ray Used** & **LOD** & **Radionuclide** \\  & & & **(keV)** & **(ug g\({}^{-1}\))** & **Interfering (keV)** \\ \hline Ag & \({}^{110}\)\({}^{-}\)Ang & 250.4 d & 657.7 & 0.4 & \\ As & \({}^{76}\)As & 26.3 h & 559.2 & 0.008 & \({}^{122}\)Sb (564.0 keV) \\ Au & \({}^{198}\)Au & 2.70 d & 411.8 & 0.001 & \({}^{152}\)Eu (411.0 keV) \\ Ba & \({}^{131}\)Ba & 11.5 d & 496.3 & 10 & \\ Br & \({}^{82}\)Br & 1.47 d & 776.5 & 0.02 & \({}^{152}\)Eu (778.6 keV) \\ Cd & \({}^{115}\)Cd & 2.2 d & 527.7 & 2 & \\ Ce & \({}^{141}\)Ce & 32.38 d & 145.4 & 58 b & \\ Co & \({}^{60}\)Co & 5.272 y & 1332.5 & 0.86 b & \\ Cr & \({}^{51}\)Cr & 27.7 d & 320.0 & 88 b & \\ Cs & \({}^{134}\)Cs & 2.062 y & 795.7 & 1.2 b & \\ Dy & \({}^{165}\)Dy & 2.35 h & 361.7 & 0.01 b & \\ Eu & \({}^{152}\)Eu & 12.7 y & 1408.0 & 0.3 b & \\ Fe & \({}^{59}\)Fe & 45.1 d & 1099.2 & 6.3 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Nuclear data and limit of detection (LOD) of each element determined by instrumental neutron activation analysis (INAA).
## 3 Ultra-Low-Frequency Broadband Characteristics
### Influence of the Material Parameters on the Ultra-Low-Frequency Broadband Characteristics  
In this subsection, the effect of the structural material parameters on the band gap properties is investigated. Generally, the elastic modulus \(E\) and the mass density \(\rho\) of the epoxy resins are found to be within \(1-10\) GPa and \(800-2300\) kg/m\({}^{3}\), respectively. In this study, the elastic modulus \(E\) and the mass density \(\rho\) are changed while keeping the other material and geometric parameters, and the results are presented in Figure 10. The results show that as the elastic modulus of the structure increases from 1 GPa to 10 GPa, its band gap gradually increases and moves to a higher frequency direction. This is due to the increase in the structural elastic modulus, which leads to an increase in the structural stiffness. As a result, the cutoff frequency of its band gap is shifted to higher frequencies. As the mass density \(\rho\) increases, the band gap narrows gradually and its center frequency goes down slowly. With the change in the mass density \(\rho\) of the structure, the coupled resonance mode of the structure and the Lamb wave mode changes accordingly, which causes the resonance frequency of the structure to shift to lower frequencies.
## 4 Discussion
### Comparison of Model Performance  
To predict multiple soil properties in the study area, we executed RF and XGBoost models using 40 environmental covariates and the optimal covariate combination. The RFE-optimized machine learning models exhibited significant superiority to the all-variable models in terms of prediction performance (Table 4). We additionally observed similar overall trends in the spatial distribution of each soil property predicted by the two models with variable selection (Figure 5). This consistency verifies the effectiveness and accuracy of the machine learning models developed in this study. The range of mean predicted  
Figure 7: Spatial distribution of uncertainty in model predictions of soil properties.  
values for the 100 iterations of the RF and XGBoost models was close to the statistical range of the soil properties in soil samples (Table 2), indicating that the selection of machine learning models was reliable. From a global distribution perspective, the prediction of soil properties by integrating machine learning and variable selection solves the problem of multicollinearity that may exist among variables in the all-variable prediction. From a local distribution perspective, the models developed in this study are able to capture rich, detailed information on multiple soil properties.  
The XGBoost model with RFE-based variable selection showed a slightly higher accuracy than the RF model, despite no considerable difference between the two models. Specifically, in the SOC prediction, the RF model yielded a 13.02% higher RMSE and a 5.37% lower R\({}^{2}\) than the XGBoost model, similar to the results of the STP and SAP predictions (Table 4). However, the RF model performed better than the XGBoost model with regard to the spatial distribution and uncertainty of the predicted soil properties. For example, the uncertainty estimates of the RF model were generally lower than those of the XGBoost model for the SOC prediction (Figure 7). Uncertainty mainly arises from heterogeneity in the spatial distribution of soil properties. Due to the highly variable environmental characteristics across regions, the correlations between soil properties and environmental covariates are complicated and unquantifiable. As a result, the lack of information for model fitness leads to a decrease in the model stability [49]. Additionally, biases in the resampling process may propagate into the prediction [50].  
In summary, the RF model has the best prediction performance and can be considered as the optimal model for predicting the soil properties in the study area [51; 52; 53]. The maps of SOC, STP, and SAP based on the RF_sel model are provided as the final prediction results (Figure 5). This study presents useful information that could help select the method and optimize the accuracy of predicting the spatial distribution of soil properties at small scales.
# State Analysis of the Water Quality in Rivers in Consideration of Diffusion Phenomenon
## 1 Introduction
The equation being considered contains diffusion and convection coefficients which are treated as control variables in the process of minimising the error evaluation function between the observed specific gravity and the solution of the control equation. In the study, discussed in the literature item [13], the one-dimensional advection-diffusion equation with variable coefficients, was solved for three dispersion problems: (a) dissolved substance dispersion in steady flow through a heterogeneous medium, (b) time-dependent dissolved substance dispersion in homogeneous flow, through a homogeneous medium, and (c) dissolved substance dispersion along time-dependent flow, through a heterogeneous medium. The new independent variables are introduced through separate transformations, in terms of which the advection-diffusion equation in each problem is reduced to an equation with constant coefficients. By considering contaminants that are chemically inert and flow passively with the carrier fluid undergoing diffusion, the authors investigated the diffusion behaviour of a passive contaminant in a progressive wave field with strong turbulence using analytical methods [14]. The focus is on nonlinear interactions between stochastic diffusion and deterministic wave-induced oscillatory advection. The scope of the study was limited to cases  
Figure 1: Intelligent river water quality control and monitoring system. where there is a small parameter \(\delta\) between advective and diffusive displacements, which allowed the disturbance analysis to be conducted. The study [15; 16] evaluated different definitions of the Peclet number for their ability to determine the relative importance of transport by advection and transport by diffusion, in low-permeability environments. It was identified that when deciding not to consider advection in low-permeability environments, the Peclet number, including the porosity available for diffusion, should be used instead of the effective porosity value. In this paper, the authors present considerations on the influence of diffusion, in the process of modelling flow phenomena, which constitutes another intelligent module in the developed system for monitoring and controlling water quality status. Many elements introduced into the aquatic environment are passive and conservative; therefore, the most important processes influencing the spread and transport of these pollutants, i.e., advection and diffusion, are considered in the presented numerical experiments. This allows one to separate the dynamics of water flow and the dynamics of the transport of the dissolved substance mass.
## Appendix C  
In this appendix we present the problem arising in SVD based watermarking schemes based on the watermarking of singular values only. Such schemes are vulnerable to the SVs substitution attack, i.e., the procedure of substituting SVs of an image with similar SVs derived from another image.  
To better clarify this fact, let us consider two images represented by means of matrices \(A\) and \(B\). Applying a _whole SVD transform_ on both matrices by means of (2), we obtain:  
\[\begin{split} SVD(A)&=\left[U_{{}_{A}}S_{{}_{A}}V_{{ }_{A}}\right]\\ SVD(B)&=\left[U_{{}_{B}}S_{{}_{B}}V_{{}_{B}}\right] \end{split} \tag{23}\]  
where \(U_{{}_{A}}\), \(U_{{}_{B}}\) are the left singular vector matrices, \(V_{{}_{A}}\), \(V_{{}_{B}}\) are the right singular vectors matrices and \(S_{{}_{A}}\), \(S_{{}_{B}}\) are matrices containing on their diagonals the SVs of the \(A\) and \(B\) matrices. The \(S_{{}_{A}}\) and \(S_{{}_{B}}\) matrices have an interesting property: they are very similar, i.e., the SVs of matrix \(A\) are highly correlated with those of matrix \(B\).  
Then, exchanging \(S_{{}_{A}}\) with \(S_{{}_{B}}\) in (23), we obtain:  
\[D=U_{{}_{A}}\cdot S_{{}_{B}}\cdot V_{{}_{A}}^{{}^{T}} \tag{24}\]  
Finally, comparing matrix \(A\) with matrix \(D\), we can notice that they are highly correlated and that the image \(D\) has a good visual quality. Analogously, we could repeat the reconstruction procedure in (24) with any image with similar SVs and we would obtain always the same result.  
For example, let's take as the matrix \(A\) the well-known \"Lena\" image and as matrix \(B\) a randomly chosen image from a common image dataset [58]. They are both gray-scale images of size 512\(\times\)512\(\times\)8 bpp (Figure 5).  
Next, we substitute the SVs of the \(A\) image with those of the \(B\) image. The resulting image \(D\) is presented in Figure 6. We can observe from this figure that the quality of the image \(D\) is high. Note that the same procedure may be applied to block based SVD producing the same results.  
Given the above presented behavior of the SVD transform, we can identify two possible attack scenarios concerning SVs based watermarking schemes:* when both images \(A\) and \(B\) are watermarked by different owners, the watermark in \(A\) may be completely substituted by the one in \(B\), therefore changing the ownership of the image;
* when only image \(A\) is marked, the substitution of its SVs with those of an unmarked image \(B\) causes the watermark removal from \(A\), and by consequence, a failure in proving the rightful ownership of \(A\).  
Many watermarking schemes which embed the watermark signal by modifying the SVs of the cover image have this problem, as has been demonstrated in various recent articles [15, 16, 31-35].