esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Modeling of Scholte Wave in Acoustic-Elastic Coupled TTI Anisotropic Media
## 2 Wave Equations
### Bond Transformation  
Introducing the matrix of elastic coefficients of the TTI media is the key to deriving the 3D TTI acoustic-elastic coupled equation. Thus, we perform coordinate rotation on VTI media to obtain the elastic coefficient matrix of TTI media.  
As shown in Figure 1, \(\theta\) is the polar angle and \(\varphi\) is the azimuth angle. The elastic coefficient matrix of TTI media can be obtained by rotating VTI twice.  
We consider both the polar angle and the azimuth angle:  
\[\mathrm{D}=\mathrm{TRCR}^{\mathrm{T}}\mathrm{T}^{\mathrm{T}}, \tag{4}\]  
where D is the elastic coefficient matrix of TTI media, and R and T are the newly defined conversion matrices.  
\[\mathrm{R}=\begin{pmatrix}\cos^{2}\theta&0&\sin^{2}\theta&0&-\sin 2\theta&0\\ 0&1&0&0&0&0\\ \sin^{2}\theta&0&\cos^{2}\theta&0&\sin 2\theta&0\\ 0&0&0&\cos\theta&0&\sin\theta\\ \frac{1}{2}\sin 2\theta&0&-\frac{1}{2}\sin 2\theta&0&\cos 2\theta&0\\ 0&0&0&-\sin\theta&0&\cos\theta\end{pmatrix}, \tag{5}\]  
\[\mathrm{T}=\begin{pmatrix}\cos^{2}\varphi&\sin^{2}\varphi&0&0&0&-\sin 2 \varphi\\ \sin^{2}\varphi&\cos^{2}\varphi&0&0&0&\sin 2\varphi\\ 0&0&1&0&0&0\\ 0&0&0&\cos\varphi&\sin\varphi&0\\ 0&0&-\sin\varphi&\cos\varphi&0\\ \frac{1}{2}\sin 2\varphi&-\frac{1}{2}\sin 2\varphi&0&0&0&\cos 2\varphi\end{pmatrix} \tag{6}\]
## 3 Throughput Performance Limits of Multimode Underwater Acoustic Networks
### The Method of Solving the Model
#### 3.2.3 Policy Iteration  
When a strategy \(\pi\) is provided, a value function \(V_{\pi}(\hat{s})\) can be derived based on the strategy. Subsequently, a greedy strategy \(\pi^{\prime}=greedy(v_{\pi})\) can be obtained based on the value function. A new value function can then be obtained according to the new strategy \(\pi^{\prime}\), leading to the generation of a new greedy strategy. This iterative cycle eventually yields the optimal value function \(\pi_{*}\) and the optimal strategy \(V_{\pi_{*}}\). The process of policy updating and improvement through cyclic iteration is referred to as policy iteration. The goal of policy iteration is to achieve convergence toward an optimal policy by iteratively calculating the value function. The specific process is outlined below:  
\[\pi_{0}\overset{E}{\rightarrow}v_{\pi_{0}}\overset{I}{\rightarrow}\pi_{1} \overset{E}{\rightarrow}v_{\pi_{1}}\overset{I}{\rightarrow}\pi_{2}\overset{E} {\rightarrow}\cdots\overset{I}{\rightarrow}\pi_{*}\overset{E}{\rightarrow}v_{ \pi_{*}} \tag{17}\]  
where \(E\) indicates policy evaluation and \(I\) indicates policy promotion. The convergence condition is that the optimal strategy obtained in the last two iterations is the same.
###### Abstract  
Separation of downgoing and upgoing P-waves and S-waves in an offset 2D/3D vertical seismic profile (VSP) record is a basic step in VSP data processing. Radon transform and median filtering methods are commonly used to separate wavefields. However, with the former, it is hard to strike a balance between the resolution and amplitude preservation, while the latter ignores the variation in the slowness (apparent velocity) of events. In order to solve these problems, we propose a method based on a modified linear Radon transform and dynamic time shifting. We derive the slowness spectrum of an input VSP record through the modified Radon transform. Based on the optimal slope values of the events derived from the slowness spectrum, we align the wavefields by dynamic time shifting. Then, the target wavefields are separated using a median filtering constrained by the linear Radon transform. Our wavefield separation process is iterated until the four kinds of wavefields are separated according to a priority ranking based on the order of the energy spectrum from strong to weak. The results were found to be better than those derived from wave separation using the traditional median filter or those derived using the high-resolution Radon transform method.  
VSP; wave separation; Radon transform; dynamic time shifting; slowness +
Footnote †: journal: Applied Sciences
# Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization
## 1 Introduction
Gaussian process regression (GPR) is of particular interest because it enables the development of inference algorithms whose complexity does not grow with the number of observations [34; 35]. In [36; 37], situational awareness has been defined, a concept where the location information of radio nodes is collected together with information about the propagation environment. The environment model is comprised of specular multipath components (SMCs), their angle-of-arrivals (AOAs), angle-of-departures (AODs), the corresponding delays as well as points of reflections. This information can be deduced from channel measurements by means of a Simultaneous Localization And Mapping (SLAM) algorithm [38; 39], where SMCs are modeled by virtual anchors (VAs), i.e., mirror images of the anchor w.r.t. different reflecting surfaces. Using these VA positions together with a data association algorithm, multipath-assisted, single-anchor localization and tracking algorithms can be formulated. Such algorithms exploit the VA-based environment model, achieving centimeter accuracy when UWB signals are used [14; 38; 40; 41]. Crowd-sourcing of such map information has been studied in [42]. Situational awareness is of specific interest for the beam-prediction in mmWave radio systems [37]. In this paper, we combine the VA-based geometric environment model and a GPR model. The former is used to predict delay, AOA, and AOD of SMCs, while the latter is used to predict the amplitudes of SMCs, as described in [43]. Extending over our previous paper [43], the following main contributions are made:  
* We formulate a REM that predicts the SMC amplitudes, the SINR of SMCs, and the position error bound (PEB) at any position throughout the whole floor plan. * We analyze a database of UWB radio channel measurements to show to what extent the REM represents the multipath components in this set of measured channel impulse response (CIR) data. To this end, we study the number of resolvable SMCs and the energy capture (EC) of these SMCs, which is the fraction of energy carried by the SMCs in relation to the total energy of the CIR. * We show the usefulness of the REM in improving the robustness of a multipath-based single-anchor localization algorithm. The structure of the paper is as follows. The UWB indoor signal model and geometry-based channel model are described in Section 2. The parameters represented by the REM, the SMC amplitudes, the SINRs, and the PEB, are also formulated. The experimental data, as well as data pre-processing steps, are described in Section 3. Section 4 discusses the Gaussian process model (GPM) based channel predictions for the above REM. Section 5 shows how the REM is exploited for a multipath-based single-anchor localization algorithm. Section 6 shows the localization results. Section 7 concludes the paper.
## 4 Discussion  
With the rapid advancement of artificial intelligence, accurately determining the location and status of trapped individuals in search and rescue scenarios is crucial for the effective planning and execution of rescue operations. Current detection technologies primarily focus on positional information, often overlooking the status of individuals, which can lead to the inefficient allocation of rescue resources [28]. To address this issue, this study proposes a network model based on semantic segmentation technology, aimed at extracting detailed information about trapped individuals.  
This research utilizes MobileNetV2 as the backbone network to meet the requirements of mobile devices. The original ReLU6 activation function is replaced with H-Swish to reduce computational load while maintaining image integrity. To address image blurriness, the CBAM is integrated into the Pyramid Pooling Module (PPM), optimizing the PSPNet model and resulting in the MHC-PSPNet model. This model not only captures the location and entrapment status of individuals but also demonstrates superior performance compared to other semantic segmentation models.  
The results show that the MHC-PSPNet model achieves an average Intersection over Union (IoU) of 83.82% and an accuracy of 97.15%, with a parameter count of just 2.38 M, outperforming other models. The primary advantage of this model is its ability to significantly reduce the parameter count while maintaining precision, providing an effective image segmentation solution for search and rescue scenarios. Figure 13 presents segmentation results across four complex backgrounds and scenarios, demonstrating the model's broad applicability. (The following images, whether taken by the author or publicly sourced from the internet, have been pixelated and blurred for privacy protection).  
Figure 12: Visualization of five types of network segmentation results.
## 2 Theoretical Supports
### Source Identification and Source-to-Site Distribution  
To characterize the earthquake source and to predict ground shaking at a site (the location where one is interested in knowing the seismic hazard or risk), it is important to consider the geometry. The source has been classified according to the formulation of an earthquake fault. Depending on the geometry of any source like point source, line source, area source, grid source, rectangular fault, etc., the distance can be taken as epicentral distance, hypocentral distance, Joyner and Boore distance and closest distance to the fault rupture. The probability of source distribution can be done by a probability density function (PDF) or cumulative distribution function (CDF) throughout a particular source zone. Generally, three kinds of source-to-site distance distribution are illustrated by the mathematical explanation in [PERSON] [37], [PERSON] [2] and [PERSON] [1] as shown in Figure 2.  
In the case of a point source, if the distance of the source-to-site distance is \(r_{S}\), the distribution probability \(F_{R}(r)\) for \(R=r_{S}\) is assumed to be one and the probability for \(R\
eq r_{S}\) is zero. For the linear source, the fault between \(L=l\) and \(L=l+dl\) is equal to the probability that occurs between the \(R=r\) and \(R=r+dr\) by the following equation:  
\[f_{L}(l)dl=f_{R_{i}}(r)dr \tag{1}\]  
where \(P_{L}(l)\) and \(P_{R}(r)\) are the CDF for the variables of \(L\) and \(R\) respectively.  
And if the earthquakes are needed to distribute uniformly over the length of the fault, \(P_{L}(l)=\frac{1}{L_{f}}\) when \(l^{2}=r^{2}-r_{min}^{2}\) the CDF for \(R\) will be:  
\[f_{R_{i}}(r)=\frac{r}{L_{f}\sqrt{r^{2}-r_{min}^{2}}} \tag{2}\]  
For the area source, the distribution is also followed similarly to the linear source. PSHRisk-tool first converts the geographic coordinates of latitude-longitude into the corresponding Cartesian coordinates (if needed) with site location. In the case of a 3D fault, triangular meshing is used for the discretization of faults (Figure 3). The triangular meshing is more advantageous for modeling a curved  
Figure 2: Examples of source-to-site distance for different source geometries and probability distribution: (a) Point source, (b) Line source, (c) Area source.  
fault as there is no overlap between adjacent elements. The triangular element size on the fault can be modified to satisfy the grid resolution requirements.
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
## 4 MATLAB Simulations
### One Ship Scenario  
Figure 1 presents the scenario using one ship. The speed of the ship is 2 m/s, and that of the AUV is 2 m/s. The AUV starts from the origin. The ship starts from (2000,0) in meters, and the trajectory of the ship at every minute is plotted with red asterisks. The starting point of the ship is marked with a diamond. The trajectory of the AUV at every minute is depicted with blue circles.  
Consider the case where the proposed localization method is utilized. In Figure 2, we plot \(meanE_{k}\), \(maxE_{k}\), and \(minE_{k}\) with respect to sample-index \(k\). The AUV maneuvers at 750 and 2250 s. At these moments, the estimation error decreases sharply. This is similar to the case where the maneuver of an observer makes the BOTMA system observable [36].  
To run one Monte-Carlo simulation takes about 1.3 s utilizing MATLAB simulations. See that the computational load of the proposed localization scheme is very low.
# CNN for User Activity Detection Using Encrypted In-App Mobile Data
## 4 Experimental Results and Discussion  
The performance evaluation of the proposed model is presented in this section. When evaluating the proposed model, the following two factors are considered to measure the performance.  
* Ability to detect previously trained in-app activities correctly;
* Ability to detect previously untrained/unknown in-app activities as noise data.  
Figure 4: Framework to detect unknown in-app activity data.  
The CNN models were constructed, trained, and tested by Keras 2.5.0 with TensorFlow 2.5.0 running at the back end. Experiments were conducted using 92 in-app activities from the Facebook, Instagram, YouTube, Viber, WhatsApp, Gmail, Skype and Messenger apps (see Table 1). Eightly percent of the total samples were dedicated for training, and remaining 20% for validation.  
Accuracy was used to evaluate the model performance, which is computed as follows:  
\[\text{Accuracy of known data}=\frac{\text{True Positives}\ +\ \text{True Negatives}}{\text{Total no. of known instances}} \tag{4}\]  
\[\text{Accuracy of unknown data}=\frac{\text{True Negatives}}{\text{Total no. of unknown instances}} \tag{5}\]
## 2 PV Model of the Power System
### The PV Output Modeling Based on GMM-HMM  
It is presumed that \(N\) unobservable scenarios exist for factors affecting PV output, such as the weather. The discrete natural numbers from 1 to \(N\) are used as state variables. Moreover, the output \(O=\{O_{1},O_{2},\ldots,O_{T}\}\) of multiple PVs is utilised as an observation variable, where \(O_{T}=[O_{T}(1),O_{T}(2),\ldots,O_{T}(m)]^{T}\) is the column vector comprised of \(m\) PV output at time \(T\), and the hidden Markov model of PV output is depicted in Figure 1.  
Given that the observation variable in the research is continuous, the emission probability from the state variable to the observation variable is no longer represented by the form of the observation probability matrix. Instead, it is described by the continuous probability distribution, which is specifically the joint probability distribution of the observation variable. The GMM-HMM is a common type of statistical learning model, and Ref. [13] has shown that it performs significantly better than the Gaussian HMM. The Gaussian hidden Markov model (HMM) uses the Gaussian distribution as the emission distribution in each hidden state. However, the Gaussian distribution does not adhere to the actual PV output distribution, which results in a significant amount of inaccuracy. When modelling the PV output with HMM, the mixed Gaussian distribution is employed to better fit the features of any shape distribution. This helps enhance the modelling accuracy of the PV output.  
Assuming the mixed Gaussian model is composed of K Gaussian models, the Gaussian mixture model probability density function is as follows:  
\[P(X\;|\;\theta)=\sum_{m=1}^{K}\omega_{m}\phi(X\;|\;\theta_{m}) \tag{6}\]  
Figure 1: Hidden Markov model diagram of multiple PVs.  
where \(\omega_{m}\) is the coefficient of weight, \(\omega_{m}\geqslant 0\), and \(\sum\limits_{m=1}^{K}\omega_{m}=1\), \(\theta_{m}=\left(\mu_{m},\sigma_{\kappa}^{2}\right)\), \(\phi(X\mid\theta_{m})\) is the Gaussian distribution probability density function:  
\[\phi(X\mid\theta_{m})=1/\sqrt{2\pi}\sigma_{m}*\exp\left(-\frac{(X-\mu_{m})^{2} }{2\sigma_{m}^{2}}\right) \tag{7}\]  
In this paper, the joint probability distribution of the observation variables is characterised by a multidimensional mixed Gaussian distribution. Therefore, the probability distribution of the observation variable can be explained as follows when the state at time \(t\) is \(i\):  
\[b_{i}=\sum\limits_{m=1}^{K}\omega_{m}N_{i}(\mu_{m},\sum\limits_{m})\text{, }1<i<N \tag{8}\]  
where \(N_{i}(\mu_{m},\sum_{m})\) is the m-th gaussian distribution in the mixed Gaussian model when the state is \(i\) at time \(t\). \(\mu_{m}=\{\mu_{m}^{1},\mu_{m}^{2},\ldots,\mu_{m}^{M}\}\) and \(\sum_{m}\) are the mean vector and covariance matrix, and M is the number of PV. Equation (8) shows that when the state at time \(t\) is \(i\), the observation variable \(O_{t}\) follows a mixed Gaussian distribution with a weight coefficient of \(\alpha_{k}\), a mean value of Gaussian distributions of all dimensions of \(\mu_{m}\), and a covariance matrix of \(\sum_{m}\).
## 2 Materials and Methods
### Method
#### 2.6.8 Start of Real-Time Monitoring  
Once the IoMT system setup, medical and support staff training, and patient registration on the mobile app have been completed, the real-time monitoring of vital signs can begin. Real-time tracking is carried out through the data analysis platform in the cloud, where the vital signs data of the patients registered in the mobile application are displayed. Medical and support staff can access the platform via mobile devices or computers and monitor patients' vital signs in real-time. Medical and support staff must be trained in using the cloud data analysis platform and in interpreting vital signs data to make appropriate clinical decisions [36]. Staff must also be alert and constantly monitor patient data to detect any abnormalities or risk situations. If any abnormality is seen in the patient's vital signs, the medical and support staff must take immediate measures to ensure the health and safety of the patients. This may involve a more detailed clinical evaluation or the transference of the patient to a higher care unit [37]. Therefore, it is essential to constantly evaluate the IoMT system to guarantee its effectiveness and efficiency in detecting and monitoring patients' vital signs.  
Once the real-time monitoring has started, it is essential to constantly supervise the system to guarantee its correct operation and the early detection of any anomaly or failure. Alerts can be set to notify medical and support staff if a critical situation is detected by monitoring the patient's vital signs. In addition, it is advisable to periodically monitor the data collected and its analysis to assess the evolution of the patient's health status and make the appropriate decisions regarding their treatment and care. Finally, medical and support staff must be trained to interpret the data and take action accordingly.
## 3 Modeling Program
### Evaluation Index  
For the trained model, there need to be evaluation metrics to judge the results after model testing. In this study, the most commonly used root mean square error (RMSE) and R-squared (R\({}^{2}\)) values were selected as evaluation metrics. These evaluation metrics can intuitively reflect the quality of the model, thereby indicating the accuracy of the experimental predictions for the mechanical ROP and the degree of fit with the original data.  
Mean square error (MSE)  
The MSE is the mean of the squared differences between the practical and predicted values. Lower MSE values suggest a more accurate model fit. This is specifically shown in Equation (11).  
\[\text{MSE}=\frac{1}{n}{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})}^{2}} \tag{11}\]
2. Root mean square error (RSME)  
The RMSE is the square root of the MSE. It is relatively sensitive to outliers in the data. The specific definition is shown in Equation (12).  
\[\text{RMSE}=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{n}{(Y_{i}-f(x_{i}))}^{2}}} \tag{12}\]  
Here, \(Y_{i}\) is the predicted data, \(f(x_{i})\) is the real data, \(N\) indicates the number of data points.
3. Mean absolute error (MAE)  
The MAE is the average of the absolute differences between the practical and predicted values. A lower MAE signifies a more precise model alignment. This is specifically shown in Equation (13).  
\[\text{MAE}=\frac{1}{n}{\sum\limits_{i=1}^{n}{|y_{i}-\hat{y}_{i}|}} \tag{13}\]
4. R value (R2_SCORE)  
The R value is also called the R2_SCORE, which translates as the fitting coefficient. For a given variable, there is a series of observed values \(y_{i}\) and corresponding predicted values \(\hat{y}_{i}\). This measures how well the predicted values fit the true values. The \(R\) value is defined in Equation (14). The prediction model explains the proportion of the variance of the variables.  
\[R^{2}=1-\frac{{\sum\limits_{i}{(\hat{y}_{i}-y_{i})}^{2}}}{{\sum\limits_{i}{(y _{i}-\overline{y})}^{2}}} \tag{14}\]  
Here, \(\hat{y}_{i}\) is the predicted value, \(y_{i}\) is the true value, and \(\overline{y}\) is the average of the true values.  
Test Result Analysis  
Using the trained BP neural network model, predictions were made for the data in the test set. Comparing the results of mechanical ROP predictions for granite, limestone, and concrete blocks with other neural network algorithms, evaluations were conducted using MAE, MSE, RMSE, and R\({}^{2}\), as shown in Figure 7.  
Figure 7: \(Cont.\)  
From Figure 7, it can be seen that both the regression algorithm and the BP neural network algorithm achieved good prediction results on different experimental samples, with prediction accuracies basically above 90%. Among them, the MAE and MSE are the smallest when compared with other algorithms, and the RMSE results are close to those of KNN and Random Forest, proving that the BP neural network has better prediction performance and smaller errors in drilling speed prediction.
## Materials and Methods
### Evaluation of the Binding Parameters of 2-FAL on MIP
#### 2.7.2 Binding Parameters by Sensors  
The signal obtained from a sensor is usually directly proportional to the analyte concentration in the layer near the transductor. In this work the signal given by the electrochemical sensor is the peak current (i\({}_{p}\)) obtained by square wave voltammetry (SWV), and that by the SPR sensor is the resonance wavelength variation \(\Delta\lambda\). In both cases the relation between the concentration of the adsorbed target, [RA], and the signal S is:  
\[\text{S}=\text{k}\ [\text{RA}] \tag{5}\]  
The constant k is different for different transduction methods, i.e., k\({}_{\text{EC}}\) in the case of the electrochemical transduction and k\({}_{\text{SPR}}\) in the case of SPR transduction. According to Equation (5), the Langmuir equation (Equation (3)) can be transformed in the following way:  
\[S=\frac{k\ c_{R}K_{A}[A]}{1+K_{A}[A]} \tag{6}\]  
Equation (6) is a binding isotherm, in which a signal related to the concentration in the MIP phase is measured instead of the adsorbed concentration. The maximum signal i\({}_{\text{pmax}}\), in the case of electrochemical transduction, or \(\Delta\lambda_{\text{max}}\) in the case of SPR transduction, is obtained when [A] >> 1/K\({}_{A}\), and S\({}_{\text{max}}\) = k\({}_{\text{CR}}\). Similarly to the isotherm obtained by batch equilibration method, a common linearized form of the Equation (6) is the Scatchard plot:  
\[\frac{S}{[A]}=-K_{A}[A]+K_{A}kc_{R} \tag{7}\]  
From the binding isotherms obtained by sensors it is possible to evaluate K\({}_{A}\) but not c\({}_{R}\), at least if k (the proportionality constant of the signal) is unknown.  
For two kinds of sites with association constants K\({}_{A1}\) and K\({}_{A2}\) the following relationship holds  
\[S=\frac{k\ c_{R2}K_{A2}[A]}{1+K_{A2}[A]}+\frac{k\ c_{R1}K_{A1}[A]}{1+K_{A1}[A]} \tag{8}\]