esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Intelligent Intruders in Wireless Sensor Networks
## 4 Demonstration, Simulation and Discussion
### Coverage, Connectivity, Connected Coverage versus Intruder Detection  
From the above discussions, it is apparent that the general network quality of service (QoS), including sensing coverage, connectivity and connected coverage, has a significant impact on the detection probability of a WSN and the escape probability of an intruder. Therefore, it is important to examine the interplay between the general network QoS and the detection probability in a random WSN in Section 2.3.  
Figure 13 illustrates the probability of coverage, connectivity, connected coverage and the detection probability of a random WSN in capturing linear, pinball and flood-fill intruders with varying node densities. In this simulation, we use the simulator to generate random topologies with different numbers of sensors ranging from five to 45 in an area of \(500\times 500\) square meters. Sensors are  
Figure 12: The escape probability (\(P_{intm}\)) of an intruder that adopts the linear, pinball and flood-fill algorithms in a WSN with varying communication range \(r_{c}\).  
identical with a sensing range of \(r_{s}=50\) m and a transmission range of \(r_{c}=100\) m. In each case, we test 500 samples, and the probabilities are computed according to the definitions in Section 2.3.  
The simulation results in Figure 13 indicate that the coverage, the connectivity and the connected coverage all increase as the node density increases, and the probability of the connected coverage is always the lowest under the same network settings, as expected. Another observation is that the behaviors of both coverage and connectivity have similar tendencies over the node density and almost overlapped each other. This is because the communication range \(r_{c}\) is set as twice the sensing range \(r_{s}\), _i.e._, \(r_{c}=2*r_{s}\) in the study, which is a critical condition that a complete coverage of a convex WSN implies connectivity among the set of working sensors [31]. Last, but most importantly, the results demonstrate the significant differences among the coverage, connectivity, connected coverage and the detection probability of a WSN. More specifically, the probability of detecting a linear intruder is always higher than the network coverage, connectivity and connected coverage, while that of detecting the intelligent pinball and flood-fill intruders is always lower than the network coverage, connectivity and connected coverage. The results support our claim that: (1) existing results on general QoS in a WSN, including coverage, connectivity and connected coverage, cannot be directly applied to the intruder detection applications; and (2) intruder's intelligence plays an important role in the detection probability of a WSN and requires a separate treatment in WSN research.
# Linear Algorithms for Radioelectric Spectrum Forecast
## 3 Case Study and Experiment Procedure
### Design of GARCH Algorithm

Table 8: Parameters estimation for low channel model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Estimated Value** & **Standard Error** & **\(t\) Value** \\ \hline \(\mu\) & \(-96.112\) & 0.0019308 & \(-49\),778.3308 \\ \(\alpha_{0}\) & 0.003516 & 0.00041447 & 8.4833 \\ GARCH(1) & 0.098255 & 0.19212 & 0.5114 \\ GARCH(2) & 0.90062 & 0.19201 & 4.6905 \\ ARCH(1) & 0.00029573 & 0.00018772 & 1.5753 \\ ARCH(2) & 0 & 0.00020886 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameters estimation for medium channel model. ARCH-GARCH model analysis is based on evaluation of standardized residuals [31]. One assumption with GARCH model is that for a good model, residuals should follow a white noise process. This is to say that it is expected that residuals be at random, independent and identically distributed, following a normal distribution. Figure 11 presents the relationship between innovations (residuals) derivate from adjusted model, the corresponding conditional standard deviations and returns. Figure 11 shows that both innovations and returns exhibit variations. In the following we intend to find out if by performing GARCH the autocorrelation of the standardized innovations disappears, which would indicate the effectiveness of GARCH model. Figure 12 corresponds to the autocorrelation of the squared standardized innovations, in which correlation was not observed. In Tables 11 and 12, results of _Ljung-Box-Pierce Q-Test_ and _Engle ARCH test_ for later analysis are presented using standardized innovations. These tests indicate no presence of correlation or ARCH effects. We have GARCH effects and also correlation between innovations that disappear after  
Figure 11: Innovations, conditional standard deviations and returns of GSM channels. Figure 12: Autocorrelation of the squared standardized innovations of GSM channels. treating the data. Therefore, the GARCH model is a proper model for explaining the variances of the three channels. Normality verification was performed by analyzing histograms of residuals and normal probability graph, as shown in Figure 13. The histograms of the three channels shows that the residuals are normally distributed. In turn, the probability graph confirms that residuals respond to a normal distribution, since most of data are spread along the straight line.
# Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure
###### Abstract  
Heart failure is a common, complex clinical syndrome with high morbidity and mortality. Hemodynamic parameter evaluation is useful for early detection, clinical outcome monitoring, timely treatment, and the overall prognosis of heart failure patients. Therefore, continuous monitoring of hemodynamic parameters helps in the evaluation of patients with suspected heart failure. The hemodynamic parameters change with respect to the contraction and expansion of the heart. Hence, in this research, two circuit-less 30 mm spherical receiver coils were implanted in both the left and right sides of the heart and an external transceiver coil was placed above the chest. The changes in the reflection coefficient of the transceiver coil at the resonant frequency changed depending on the distance between the implanted coils, allowing the contraction and expansion of the heart to be determined. This work was carried out at 13.56 MHz, considering the safety limits imposed by the FCC. The proposed reflection coefficient monitoring technique may distinguish healthy patients from heart failure and heart attack patients. The reflection coefficients at a maximum distance of 50 mm for simulation and measurement are \(-10.3\) dB and \(-10.6\) dB, respectively, at the resonant frequency.  
heart failure; hemodynamic parameters; resonant frequency; reflection coefficient; specific absorption rate +
Footnote †: journal: Medical Image Analysis
## 5 Robustness Analysis
### Comparisons with Other Algorithms
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Error Index** & **Algorithm** & **Mean** & **Std** \\ \hline Longitude (\({}^{\circ}\)) & Algorithm [9] & / & / \\ \hline Algorithm [10] & / & / & \\ \hline Algorithm [12] & / & / & \\ \hline Algorithm [16] & / & / & \\ \hline Algorithm [17] & / & / & \\ \hline Algorithm [39] & \(1.6753\times 10^{-4}\) & \(1.3200\times 10^{-5}\) \\ \hline Algorithm [40] & \(2.4564\times 10^{-4}\) & \(1.5700\times 10^{-2}\) \\ \hline Algorithm [41] & \(8.2270\times 10^{-3}\) & \(1.1150\times 10^{-3}\) \\ \hline Algorithm [42] & \(2.7791\times 10^{-5}\) & / \\ INS+LEO2 5 s/ AO & \(-0.6539\times 10^{-5}\) & \(5.6238\times 10^{-5}\) \\ \hline INS+LEO2 60 s/ AO & \(5.1428\times 10^{-5}\) & \(19.7866\times 10^{-5}\) \\ \hline Latitude (\({}^{\circ}\)) & Algorithm [9] & / & / \\ \hline Algorithm [10] & / & / & \\ \hline Algorithm [12] & / & / & \\ \hline Algorithm [16] & / & / & \\ \hline Algorithm [17] & / & / & \\ \hline Algorithm [39] & \(1.6000\times 10^{-3}\) & \(3.5382\times 10^{-4}\) \\ \hline Algorithm [40] & \(1.7986\times 10^{-4}\) & \(1.3400\times 10^{-2}\) \\ \hline Algorithm [41] & \(1.0253\times 10^{-1}\) & \(3.7700\times 10^{-4}\) \\ \hline Algorithm [42] & \(2.0918\times 10^{-5}\) & / \\ \hline INS+LEO2 5 s/ AO & \(-0.0539\times 10^{-4}\) & \(4.4523\times 10^{-5}\) \\ \hline INS+LEO2 60 s/ AO & \(-1.8018\times 10^{-4}\) & \(14.9197\times 10^{-5}\) \\ \hline Altitude (m) & Algorithm [9] & / & 4.0000 \\ \hline Algorithm [10] & / & 7.4762 \\ \hline Algorithm [12] & / & 9.8869 \\ \hline Algorithm [16] & / & 5.0000 \\ \hline Algorithm [17] & / & 4.3991 \\ \hline Algorithm [39] & 34.6000 & 6.0518 \\ \hline Algorithm [40] & 35.9300 & 14.1989 \\ \hline Algorithm [41] & \(-0.7636\) & 15.1179 \\ \hline Algorithm [42] & / & / \\ \hline INS+LEO2 5 s/ AO & \(-0.0543\) & \(0.5229\) \\ \hline INS+LEO2 60 s/ AO & \(0.8935\) & 3.9934 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: INS+LEO2-satellite adjacent orbit alternate switching ranging integrated navigation positioning algorithm and other algorithm error calculation statistics. to rely on the altimeter and also does not need continuous observation. Real-time location services can be completed only by alternate switching, which ensures real-time location requirements in challenging environments, especially real-time performance, which is particularly important in life search and rescue areas.
# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 4 Discussion
Digital Globe has a proprietary software known as GBDX ([[https://gbdxdocs.digitalglobe.com/docs/advanced-image-preprocessor](https://gbdxdocs.digitalglobe.com/docs/advanced-image-preprocessor)]([https://gbdxdocs.digitalglobe.com/docs/advanced-image-preprocessor](https://gbdxdocs.digitalglobe.com/docs/advanced-image-preprocessor))), which contains atmospheric compensation algorithms. If we use FLAASH in ENVI to generate reflectance images for Planet, the two sets of reflectance images resulting from different atmospheric compensation algorithms may still be slightly different and hence histogram matching may still be required after atmospheric compensation. Because Worldview images are not collected at nadir for most of the images, the registration and alignment with Planet images should be done carefully. In this research, we performed the alignment manually. One future direction is to develop an automated alignment program that will significantly reduce manual labor. The image compatibility issue between different satellite images together with the image alignment issue require more in depth studies in the future. The image alignment is non-trivial. Here, our goal is on image fusion algorithm assessment where we assume that the registration is done and the images (regardless whether they are radiance or reflectance) have similar histograms. Another direction is to apply the high temporal high spatial resolution images to some applications such as damage assessment due to flooding, fire, hurricane, tsunami, etc.
# Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
## 4 Solution Method
### The Solution of the Lower-Level Optimization Problem  
Solving the LLOP in Equation (10) is challenging due to its non-smooth terms. Our solution involves developing a half-quadratic splitting algorithm [60,61] that converts the problem into the following more tractable form:  
\[\min_{\mathbf{u},\mathbf{z}}\{\psi(\mathbf{u},\mathbf{z})\} \tag{13}\]  
where \(\psi(\mathbf{u},\mathbf{z})\) is defined by the following:  
\[\psi(\mathbf{u},\mathbf{z})=\frac{1}{2}\bigg{|}\bigg{|}\mathbf{A}  
Utilizing the soft threshold algorithm, we derive the update scheme for the optimization problem in Equation (17):  
\[\mathbf{z}^{k}=\text{shrink}(\mathbf{u}-\mathbf{u}_{p},\alpha_{1}/\mu) \tag{19}\]  
We use the forward-backward splitting algorithm [62; 63] to solve the sub-problem of \(\mathbf{u}\) in Equation (18), and this yields the following:  
\[\mathbf{u}^{k+1/2}=\min_{\mathbf{u}}\biggl{\{}\alpha_{2}\biggl{|}\biggl{|}\mathbf{W}\mathbf{u} \biggr{|}\biggr{|}_{1}+\frac{1}{2\delta}\biggl{|}\biggl{|}\mathbf{u}-\mathbf{q}^{k+1} \biggl{|}\biggl{|}^{2}\biggr{\}} \tag{20}\]  
where \(\mathbf{q}^{k+1}\) is an auxiliary vector calculated as follows:  
\[\mathbf{q}^{k+1}=\mathbf{u}^{k}-\delta\
abla f_{M}(\mathbf{u}^{k}) \tag{21}\]  
where \(\delta\) is a positive step size parameter; \(f_{M}(\mathbf{u})\) is as follows:  
\[f_{M}(\mathbf{u})=\frac{1}{2}\biggl{|}\biggl{|}\mathbf{A}\mathbf{u}-\mathbf{b}\biggl{|}\biggl{|} ^{2}+\frac{\mu}{2}\biggl{|}\biggl{|}\biggl{|}^{2}+1-(\mathbf{u}-\mathbf{u}_{p})\biggl{|} \biggl{|}\biggl{|}^{2} \tag{22}\]  
The intention to improve convergence results in the use of the following scheme [64]:  
\[\mathbf{u}^{k+1}=\mathbf{u}^{k+1/2}+(\eta^{k}-1)(\mathbf{u}^{k+1/2}-\mathbf{u}^{k})/\eta^{k+1} \tag{23}\]  
where \(\eta\) is updated as follows:  
\[\left\{\begin{array}{l}\eta^{1}=1\\ \eta^{k+1}=\left(1+\sqrt{1+4(\eta^{k})^{2}}\right)/2\end{array}\right. \tag{24}\]  
We summarize the above computational steps in Algorithm 1 to achieve image reconstruction. This new solver makes the computation of Equation (10) simpler and easier to use in practice.  
```
1. Input: \(\mathbf{A}\), \(\mathbf{b}\), \(\alpha_{1}\), \(\alpha_{2}\) and \(\mu\).
2. Initialization: \(\mathbf{z}^{1}\), \(\eta^{1}\) and \(\mathbf{u}^{1}\).
3. Output: \(\mathbf{u}\).
4. For \(k=1,2,\ldots\) until convergence do
4.1 Update \(\mathbf{z}^{k+1}\) according to Equation (19).
4.2 Update \(\mathbf{q}^{k+1}\) according to Equation (21).
4.3 Update \(\mathbf{u}^{k+1/2}\) according to Equation (20).
4.4 Update \(\eta^{k+1}\) by solving Equation (24).
4.5 Update \(\mathbf{u}^{k+1}\) according to Equation (23).
5. End For
6. Return the optimal solution.
```  
**Algorithm 1**Proposed optimizer for solving Equation (10).
# Subsea Methane Hydrates: Origin and Monitoring the Impacts of Global Warming
## 4 Full Waveform Inversion for Viscoelastic Media
As a rule, the standard deviation of the discrepancy between the acquired and the synthetic data simulated for the current model is used, and thus the objective functional can be represented as a \(L_{2}\) dot product:  
\[\Phi[\lambda,\mu,\tau^{p},\tau^{S}]=\Big{(}\vec{u}(x_{r},z_{r};x_{s},z_{s}; \omega)-B[\lambda,\mu,\tau^{p},\tau^{S}],\vec{u}(x_{r},z_{r},\omega)-B[\lambda,\mu,\tau^{p},\tau^{S}]\Big{)} \tag{9}\]  
By definition, the gradient defines the linear part of the increment of the objective functional--that is:  
\[ \begin{array}{l}\Phi[\lambda+\delta\lambda,\mu+\delta\mu,\tau^{p}+\delta \tau^{p},\tau^{S}+\delta\tau^{S}]=\Phi[\lambda,\mu,\tau^{p},\tau^{S}]+\
abla \Phi[\lambda,\mu,\tau^{p},\tau^{S}]\\ \\ \Big{(}\delta\lambda,\delta\mu,\delta\tau^{p},\delta\tau^{S}\Big{)}+O\big{(} \delta\lambda^{2},\delta\mu^{2},(\delta\tau^{p})^{2},(\delta\tau^{S})^{2}) \big{)}=\Phi[\lambda,\mu,\tau^{p},\tau^{S}]\\ \\ +2\Re\bigg{\{}\vec{u}(x_{r},z_{r};x_{s},z_{s};\omega)-B[\lambda,\mu,\tau^{p}, \tau^{S}],\frac{\partial B}{\partial\lambda}\delta\lambda+\frac{\partial B}{ \partial\mu}\delta\mu+\frac{\partial B}{\partial\tau^{p}}+\frac{\partial B}{ \partial\tau^{S}}\delta\tau^{S}\bigg{\}}\end{array}  \tag{10}\]  
Thus, we obtain the following expression for representing the linear part of the increment of the objective functional--that is, for its gradient:  
\[\
abla\Phi<\delta\lambda,\delta\mu,\delta\tau^{p},\delta\tau^{S}>=\Big{(} \Big{(}\frac{\partial B}{\partial\lambda}\Big{)}^{*}<\vec{u}-B>,\delta \lambda\Big{)}+\]  
\[\Big{(}\Big{(}\frac{\partial B}{\partial\mu}\Big{)}^{*}<\vec{u}-B>,\delta \mu\Big{)}+\Big{(}\Big{(}\frac{\partial B}{\partial\tau^{p}}\Big{)}^{*}<\vec{ u}-B>,\delta\tau^{p}\Big{)}+ \tag{11}\]  
\[\Big{(}\Big{(}\frac{\partial B}{\partial\tau^{S}}\Big{)}^{*}<\vec{u}-B>,\delta \tau^{S}\Big{)}\]  
Here, \(A^{*}\) denotes the adjoint to the operator A. The obtained representation of the objective functional gradient allows us to write down the iteration process that implements the steepest descent method as follows:  
\[\lambda_{k+1}=\lambda_{k}-\alpha_{k}^{\lambda}\bigg{(}\frac{\partial B}{ \partial\lambda}\bigg{)}^{*}\langle\vec{u}-B\rangle \tag{12}\]\[\mu_{k+1}=\mu_{k}-\alpha_{k}^{\mu}\bigg{(}\frac{\partial B}{\partial\mu}\bigg{)}^{ *}\langle\vec{u}-B\rangle \tag{13}\]  
\[\tau_{k+1}^{P}=\tau_{k}^{P}-\alpha_{k}^{P}\left(\frac{\partial B}{\partial\tau^{ P}}\right)^{*}\langle\vec{u}-B\rangle \tag{14}\]  
\[\tau_{k+1}^{S}=\tau_{k}^{S}-\alpha_{k}^{S}\bigg{(}\frac{\partial B}{\partial \tau^{S}}\bigg{)}^{*}\langle\vec{u}-B\rangle \tag{15}\]
# 6G to Take the Digital Divide by Storm: Key Technologies and Trends to Bridge the Gap
## 3 The Technologies for Bridging the Service-Delivery Divide
### Device-to-Device  
Although technologies such as Software-Defined Networking (SDN) have emerged as enablers in the evolution process of 5G networks [17], the trend to evaluate distributed networking approaches to extend the network coverage and scalability has gained great momentum. On the eve of 6G, this still represents a key solution to boost the access to connectivity, therefore, also bridging the digital divide. Particularly, Device-to-Device (D2D) communications could be harnessed to master the problem of the limited distance that the higher frequency waves exploited in future 6G networks can cover [18]. Actually, relay nodes could be exploited to forward the signal through the establishment of direct communications, thus extending network coverage and facilitating access to services even to devices outside the antenna's LoS. D2D communications are characterized by high speed and low latency thanks to the proximity between end-devices [19], hence they are entirely in line with the requirements of the upcoming 6G networks.
# An Optically Transparent Metasurface for Microwave Amplitude-Phase Manipulation
## 2 Results
### Design of the Meta-Atom
In terms of the phase modulation, it can be easily realized by changing the arm length \(l\) of the metal-frame-based H-shaped resonator, and an additional \(\pi\) shift can be accomplished by mirroring the meta-atom along the \(y\)-axis, as shown in Figure 3c,d. It should be noted that eight-level phase gradient with a \(\pi/4\) interval is adopted in this work, and the selected parameters marked by asterisks are \(l=2.5\), \(3.4\), \(4.5\), and \(6.3\) mm. Meanwhile, their cross-polarized reflection efficiencies are all above 0.9, which overcomes the large EM loss disadvantage of ITO-based meta-atoms [22]. To further examine the intrinsic coupling between amplitude and phase modulations, the simulated amplitude and phase of \(r_{yx}\) as a function of both \(l\) and \(\beta\) are presented in Figure 3e and Figure 3f, respectively. Intuitively, the amplitude depends only on the rotation angle, whereas a full \(2\pi\) phase coverage can be realized by changing the arm length and mirroring the structure. Such remarkable characteristics indicate that the amplitude-phase manipulation realized by our proposed meta-atom is independent and has low crosstalk, facilitating the implementation of complex wavefront control functionalities. Figure 2: Simulated cross-polarized reflection amplitude with different (**a**) bottom metallic mesh periods (\(p_{m}\)), (**b**) bottom metallic mesh linewidths (\(w_{m1}\)), and (**c**) top metal frame linewidths (\(w_{m2}\)). (**d**) Calculated optical transmittance of a typical metallic mesh.
# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
## 3 Proposed Method
### CSBlock
\(x\) is the input feature map, and \(s\) is the channel attention feature map obtained from Equation (11). The high-frequency features included in the feature map were emphasized and feature map channels containing unnecessary features were suppressed. The channel attention map is output between 0 and 1 through the sigmoid activation function, necessary channels are passed through multiplication with the input feature map, and unnecessary channels are suppressed through a number close to 0. Figure 10 is the spatial attention structure of the proposed method. Unlike the existing CBAM, the output of convolution was composed of one channel without pooling process. This reduces spatial information loss that may occur during the pooling process and emphasizes spatial features. \[SA(x)=H_{1}(x)\times x, \tag{10}\]  
Equation (10) generates an accentuated feature map by multiplying the spatial attention feature map with the input feature map. \(H_{1}(*)\) is a one-channel convolution operation, and \(x\) is the input feature map. Convolution is a process of calculating surrounding pixels into a kernel, and can use spatial information. \(H_{1}(*)\) calculates the relation between pixels in the feature map. Through channel attention, feature maps with high frequencies are emphasized, and features that lack high frequencies or are unnecessary are suppressed. In the highlighted feature map, the part with a large difference in neighboring pixel values was effectively calculated through spatial attention. Figure 10: Spatial Attention Structure. Figure 9: Channel Attention Structure. \[CSBlock(x)=SA(CA(x))\times residual\ scale, \tag{11}\]  
Equation (11) is the formula of the final \(CSBlock\) to which channel attention and spatial attention are applied. \(SA(\)*\()\) is the spatial attention process of Equation (10), and \(CA(\)*\()\) is the channel attention process of Equation (9). Stable learning was conducted using the residual scale. This structure effectively solves the problem of inability to learn or performance degradation that occurs in the process of learning a deep network by limiting the value of the gradient.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis on hop composition was performed with SPSS 25.0 software (SPSS, Inc., Chicago, IL, USA). Data were reported as mean \(\pm\) standard deviation (SD) of three independent experiments with three replicates. Prior to chemometric applications, all variables, used for hop quality analysis, were auto scaled (transformation into z-scores) to standardize the statistical importance of all responses. Significant mean differences were established using the Mann-Whitney test for independent and nonparametric procedures (\(p<0.0167\) for Bonferroni's correction, where not specified differently).  
The chemometric analysis of IR data, acquired from hop samples from farms under investigation during three consecutive years (2016-2018 harvest), was preceded by several pre-processing steps for each data set. The data obtained were then subjected to principal component analysis (PCA). This multivariate technique is usually the first step in data exploration: PCA defines new variables, which consist of linear combinations of the original variables, so that the first axis is in the direction that contains most of the variations. The Savitzky-Golay method with third-order smoothing polynomial through eleven points was used to calculate the second derivative of the IR spectra of hop samples used in the brewing trials (2018 vintage) to obtain a more detailed information on their molecular fingerprinting.  
Statgraphics software ver. 5.1 (Manugistics, Rockville, MD, USA) was used to perform the ANOVA (LSD test, \(p\leq 0.05\)) on GC-O OE areas, while the quantitative measurements from sensory analysis were subjected to ANOVA and the Tukey test (95%), using XLSTAT software version 2016 (Addinsoft, Paris, France).