esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 7 Conclusions  
A comprehensive review is presented for in-body antennas for IBBDs (implantables, ingestibles, injectables). Designing an in-body antenna operating in a harsh bio-tissue environment is a challenging task, where several specifications are required to be considered, including the operation frequency, band selection, size, performance (gain, efficiency, radiation pattern), biocompatibility and patient safety. Widely used miniaturization techniques, such as high-permittivity substrates, lengthier current flow path on the radiator, inductive or capacitive loading, pin shorting, higher operating frequencies, ground plane modifications and use of metamaterials, are discussed, and each technique is evaluated in terms of its merits and issues. This study shows that despite having the superior potential of miniaturization, higher frequency usage suffers from significant losses, which requires additional investigations in this domain, as radio wave exposure studies and research works at these frequencies are not yet well established. Moreover, this paper reviews the antenna design and manufacturing process and illustrates the antenna testing procedures,including in vitro and in vivo testing. This paper also summarizes several existing in-body antenna designs, including planar, PIFA, wire, conformal, spiral, slotted and MIMO structures, and classifies antennas according to the IBBD applications. Although the selection of an antenna type depends on specific applications, patch-type planar structures have been more commonly adapted by researchers, as shown in Table 1. The study of patient safety is one of the primary requirements for IBBDs, which is strictly administered through the SAR and EIRP limits. Table 1 shows that most researchers ignore this step while designing in-body antennas for IBBDs, which is alarming, as it fails to demonstrate the suitability of the proposed antennas for in-body applications. In-body antennas are obligated to meet these regulations while considering the bio-tissue environment as the key testing platform and further confirming the safety profile through utilization of biocompatible materials. Finally, the article concludes with listing some of the drawbacks and limitations of existing in-body antenna technology and the forthcoming research challenges in this area.  
Conceptualization, I.N. and S.R.K.; methodology, S.R.K.; software, I.N. and S.R.K.; validation, K.A. and I.N.; resources, K.A. and S.R.K.; writing--original draft preparation, S.R.K.; writing--review and editing, K.A., I.N. and S.R.K.; supervision, S.R.K.; funding acquisition, K.A. and S.R.K. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Heriot-Watt University Kickstart Research fund granted to [PERSON].  
No new data were created in this research.  
The authors declare no conflict of interest.
# On the Lagged Diffusivity Method for the Solution of Nonlinear Finite Difference Systems
## 3 The Lagged Diffusivity Method  
We now introduce the lagged diffusivity method to solve the discretized system, referring, for instance, to Equation (9).  
Solving Equation (9) by a [PERSON]'s method would be onerous, since the Jacobian matrix of \(\mathbf{F}(\mathbf{u})\) is generally hard to compute. The lagged diffusivity method, on the other hand, linearizes the discretized system by iteratively \"lagging\" the diffusivity term. At each iteration we must thus solve a weakly nonlinear algebraic system, whose Jacobian matrix is significantly easier to compute.  
Let us study more in detail the procedure applied to Equation (9). Lagging the diffusivity means that the new iterate \(\mathbf{u}^{(v+1)}\) of the \((v+1)\)-th lagging iteration is the solution of the weakly nonlinear system  
\[\mathbf{F}_{v}(\mathbf{u})=\left[I+\tau A(\mathbf{u}^{(v)})\right]\mathbf{u}-\tau\left[\mathbf{b} (\mathbf{u}^{(v)})-\mathbf{G}(\mathbf{u})\right]-\mathbf{w}=\mathbf{0}. \tag{10}\]  
When \(A(\mathbf{u})\) is a nonsingular M-matrix, \(I+\tau A(\mathbf{u})\) is evidently nonsingular \(\forall\mathbf{u}\in\mathbb{R}^{\mu}\).  
Thus, we now have to solve Equation (10) at each lagging iteration. We can do this by a [PERSON]'s method (or, in general, by an iterative method) which is stopped when the residual  
\[\mathbf{F}_{v}(\mathbf{u}^{(v+1)})=\left[I+\tau A(\mathbf{u}^{(v)})\right]\mathbf{u}^{(v+1)}- \tau\left[\mathbf{b}(\mathbf{u}^{(v)})-\mathbf{G}(\mathbf{u}^{(v+1)})\right]-\mathbf{w} \tag{11}\]  
satisfies a stopping criterion  
\[\left\|\mathbf{F}_{v}(\mathbf{u}^{(v+1)})\right\|\leq\epsilon_{v+1} \tag{12}\]  
where \(\|\cdot\|\) denotes the Euclidean norm and \(\epsilon_{v}\) is a given tolerance such that \(\epsilon_{v}\to 0\) for \(v\to\infty\). When the inner solver converges, we find \(\mathbf{u}^{(v+1)}\), which is used, in turn, to lag the diffusivity at the \((v+2)\)-th lagged iteration. Proceeding in the same way, we thus find \(\mathbf{u}^{(v+2)}\) and so on for the following iterations.
## 4 Results and Discussion
### Acoustic Characteristics of Different Configurations  
#### 4.2.1 Aerodynamic Noise Source  
This section analyzes the intensity characteristics of the aerodynamic noise dipole source using the root mean square of the time derivative of the surface fluctuating pressure.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Panhead** & **Balance** & **Upper** & **Lower** & **Base** & **Insulator 1** & **Insulator 2** & **Camera** \\ \hline Case 1 & 0.0709 & 0.0229 & 0.0287 & 0.0185 & 0.0590 & 0.0412 & 0.0132 & 0.0619 \\ Case 2 & 0.0695 & 0.0225 & 0.0292 & 0.0173 & 0.0528 & 0.0357 & 0.0149 & 0.0483 \\ Case 3 & 0.0726 & 0.0252 & 0.0286 & 0.0141 & 0.0073 & 0.0001 & \(-\)0.0037 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computed drag coefficients of each pantograph part.  
Figure 9: Iso-surface of the instantaneous normalized Q-criterion. (**a**) Case 1; (**b**) case 2; (**c**) case 3.  
After completing the instantaneous calculation, the time gradient p\({}^{\prime}\) of the fluctuating pressure on the vehicle surface is calculated by Equation (8), and then the root mean square \(p^{\prime}{}_{rms}\) is computed by Equation (9) as follows:  
\[p^{\prime}=dp/dt \tag{8}\]  
\[p^{\prime}{}_{rms}=\sqrt{\frac{\int_{t_{1}}^{t_{2}}\left(p\prime\right)^{2}dt}{t _{2}-t_{1}}} \tag{9}\]  
Figure 10 shows the \(p^{\prime}{}_{rms}\) distributions for the three pantograph installation methods. As seen in Figure 10, the pantograph sound source intensity is mainly distributed at the panhead, lower arm rod, base frame, etc. When the pantograph is installed on the train roof alone, the sound source intensity is also distributed at the balance arm. Combined with Figure 9 for analysis, the incoming airflow directly impacts the front edge of the panhead without interference, and vortex shedding occurs at the balance arm. When the sinking platform is installed on the train roof, the pressure fluctuation at the balance arm and the leeward side of the camera is weakened. When the pantograph fairing is not installed, the great sound source intensity also appears in the bottom area of the insulators. The installation of the fairing changes the position of vortex shedding when the airflow hits the pantograph. Consequently, the pressure fluctuation at the top of the insulator was stronger, and the strong pressure fluctuation area was also distributed at the front edge of the rear end of the fairing. In general, the pantograph pressure fluctuation is distributed in the area that is seriously affected by vortex shedding.
## 5 Results
### Improvement of Including MEOs  
As described in sub-Section 4.2, POD with and without MEOs are carried out using the global network (ALL). 3D RMS for GEOs and IGSOs of the two schemata are presented in Figure 10.  
Figure 10: **(a)** 3D-RMS of GEO orbits estimated with MEOs and without MEOs. **(b)** 3D-RMS of IGSO orbits estimated with MEOs and without MEOs. Unit is cm.  
Figure 9: Relationship of the orbit RMS from the Chinese regional network (CHN) and the global networks (ALL). x-axis denotes the RMS of CHN, while y-axis represents orbit RMS of ALL. The red line with slope rate 1.0 divides each figure into two parts, of which the right down stands for the improvement.  
According to the 3D-RMS shown in Figure 10, the two MEOs bring almost no improvement on GEOs. On the contrary, a 10% improvement is found for IGSOs on average. Although the improvement is not as much as that of network geometry, it does further increase the orbit quality of the global network. Further investigations should be carried out if more simultaneously observed MEOs can be involved to provide stronger constraints on receiver clocks and tropospheric delay parameters.
## 3 Results and Discussion
### Human Reliability Analysis
#### 3.4.1 Research Status and Discussion  
HRA is an alternative expression of HEP. A state-of-the-art HRA survey has been published [61]. Table 6 shows the number of collected articles. Furthermore, detailed information on these articles is presented in Appendix D. Only one review article was found in this group [61]. Hence, the studies of the collected articles in Table 6 may pro-vide some human factors, human errors, or human element information but not in-volve their boundaries. This section could be a supplement to the contents of Sections 3.1-3.3.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Category** & **Number of Documents** \\ \hline Journal article & 24 \\ Conference paper & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Collected articles on HRA.  
[PERSON] et al. [62] developed a performance-influencing factor (PIF) hierarchy to address the problems in HRA. PIF, also known as PSF, is a common expression used to describe the causes affecting human performance in different systems. Classified PIFs can be split into five groups: organizational-based factors, team-based factors, person-based factors, situation/stressor-based factors, and machine-based factors. The first three groups can be found in the scope of the human factor in Section 3.2.3. Machine-based factors can be found in the scope of the human element (ship factors). Furthermore, situation/stressor-based factors or situation-based factors are actually within the scope of the human factor (preconditions for personal/group/organizational factors). [PERSON] et al. [63] proposed an FBN-CREAM model to study HRA. The recognized common performance conditions were organized into three groups: management-related factors, environment-related factors, and work-related factors. Compared with the analyses in Sections 3.1-3.3 management-related factors fall within the scope of the human factor (organizational factors), while the other two fall under the human element description. [PERSON] et al. [64] used a SOHRA approach to calculate the HEP in a crankshaft overhauling operation. The most common errors were unreliable instruments, poor feedback, inadequate checking, and insufficient crew knowledge. Unreliable instruments fall within the range of the human element, and the other three are in the scope of human error, according to Section 3.3. Similar research can be found in [PERSON] et al. [65] and [PERSON] et al. [66]. Furthermore, other papers have reported HRA research with different models, such as the HEART [67], AHP-CREAM [68], and HENT [17] models.  
Figure 5: Summary of “human error” [16; 56; 57; 58; 59; 60].
## 3 Methodology and Proposed Innovation
### Employed PA Formulation  
The variation of the studied network topology depicted in Figure 3 is designed as a directed graph \(G=(V,E)\), which, respectively, consists of the set of substrate nodes denoted by \(V=\{n_{1},n_{2},\ldots,n_{z}\}\), where \(|V|=z\), and the set of physical links notated by \(E=\{l_{1},l_{2},\ldots,l_{k}\}\), where \(|E|=k\).  
In this work, the ordered septet \((V,P,M,\oplus,\omega,\preceq,\varphi)\) is introduced as the algebraic blueprint for all metric formulations within the proposed framework. \(P=\{i_{1},i_{2},\ldots,i_{x}\}\), where \(|P|=x\), specifies the set of the explored paths constituted by the elements of links belonging to \(E\), as \(i_{j}\subset E\,\forall j\), where \(1\leq j\leq x\). \(M\) is the set of the examined metrics, while the \(\omega(l)\) notation correlates with the mapping function that returns the metric values of the processed link \(l\in E\). Furthermore, the \(\oplus\) symbol denotes the binary operation that provides the path concatenation, while the order relation \(\preceq\) notation indicates the preference between two compared paths. Finally, \(\varphi\) corresponds to the special set that holds all paths that are unsuitable for selection, because their properti  
Figure 3: A graph representation of a variant of the studied SN (\(V=\{A,B,C,D,E,F,G,H\}\)).  
fundamental principles of the framework or have insufficient resources for the examined embedding. Table 1 illustrates the utilization of the adopted septet formulation, regarding the studied SN of Figure 3.
# Outlier Detection and Prediction in Evolving Communities
## 2 Related Work
### Community Detection  
The majority of methods for community detection are exclusively structure- or content-oriented. We consider that leveraging both yields the best results in terms of cohesive community structure, and as COTILES is just such a hybrid approach, we discuss works that exploit both structural and content information. The survey in [7] proceeded via an exhaustive search and categorization of such works, dividing them based on the timing when the structure and the content of the network were examined: early fusion methods, in which connections and attributes are fused before the community detection process; simultaneous fusion methods, where this happens at the same time; and late fusion methods, where community detection takes place initially. The authors concluded with the idea that analyzing attributed networks can boost community detection in sparsely connected networks while handling missing or noisy attributes, and is in most cases more effective than analyzing networks by taking into account only one dimension. While the survey included both static and dynamic methods, it did not focus on the differences between them.  
Traditional community detection takes place in static graphs, where both the nodes and the edges between them are known in advance. It is obvious that having additional information about the time at which each interaction occurs can add useful granularity to the analysis. For example, the author of a scientific paper is much more likely to be a member of a community in which they share recent co-authorship than other communities whose members they collaborated years ago with. This additional information is received from graphs, referred to as temporal graphs, which in addition to structural information (and content, in our case) include a timestamp for each interaction specifying the exact time when it occurred.
###### Abstract  
In recent years, GIS (Geographical Information System) vector maps are widely used in everyday life, science, and the military. However, the production process of vector maps is expensive, and a large volume of vector map data is easily stolen and illegally distributed. Therefore, original providers desire an encryption solution to encrypt GIS vector map data before being stored and transmitted in order to prevent pirate attacks and to ensure secure transmission. In this paper, we propose an encryption algorithm for GIS vector map data for preventing illegal copying, and ensuring secured storage and transmission. Polyline/polygon data of GIS vector maps are extracted to compute a backbone object. The backbone object is then selectively simplified by the multi-scale simplification algorithm in order to determine the feature vertices of the backbone object. The feature vertices of the backbone object are encrypted by the advanced encryption standard and the secret key. Finally, all vertices of the backbone object are randomized by the random Gaussian distribution algorithm to obtain the encrypted GIS vector map. Experimental results show that the entire map is altered completely after the encryption process. The proposed method is responsive to the various GIS vector map data formats, and also provides better security than previous methods. The computation time of the proposed method is also significantly shorter than that of previous methods.  
G +
Footnote †: journal: Applied Sciences  
_Article_  
Vector Map Random Encryption Algorithm Based on Multi-Scale Simplification and Gaussian Distribution
# Evaluation of the Acceleration Vibration Signal for Aggregates of the Horizontal Drilling Stand
## 4 Experimental Results and Analysis
### Vibration Signal Processing in the Time-Frequency Domain  
The dynamics of the vibration signal of the acceleration of the operating modes and the process of disintegration of the andesite rock can be observed mainly during the construction of the spectrograms.  
The spectrograms are the result of time-frequency analysis. It can be stated that they present the presence of frequencies or frequency bands as a function of time. The presented spectrograms are constructed with the time of experimental measurement of the vibration signal \(t=6\) s at the sampling frequency \(f_{s}=18\) kHz and \(T_{s}=55.5\)\(\upmu\)s. The spectrograms were obtained from the operating modes I.-IX. In the figures (see Figures 13-15), the spectrograms are shown in 2D and 3D. Dominant frequencies and frequency bands are marked in 2D and 3D views. Recognition of dominant frequencies from spectrograms is more difficult if they are not individually separated. Such a frequency is \(f=4000\) Hz. From the spectrograms (see Figure 15e,f), it is clear that the range of the andesite disintegrating frequency is 6000-9000 Hz.  
The spectrograms confirm a broad-spectrum vibrational signal, especially when all-important aggregates are in operation and the andesite drilling process.  
The processing and presentation of vibrational signals of an acceleration in the frequency domain presented by the spectra and in the time-frequency spectrograms are adequate, consistent, and document the suitability of the algorithms and methods used. Therefore, it can be stated that the analyzes of the measured vibration signals are correct.  
Figure 12: Power spectra by FFT algorithm (**(a)** frequency power spectrum of vibration signal of three aggregates without drill bit, **(b)** frequency power spectrum of vibration signal of three aggregates with drill bit, **(c)** frequency power spectrum of the andesite rock disintegrating vibration signal).  
Figure 13: Spectrograms of vibration signals of acceleration (**(a)** 2D view of motor signal spectra, **(b)** 3D view of motor signal spectra, **(c)** 2D view of water pump signal spectra, **(d)** 3D view of water pump signal spectra, **(e)** 2D view of hydro-generator signal spectra, **(f)** 3D view of hydro-generator signal spectra).  
Figure 14: Spectrograms of vibration signals of acceleration ((**a**) motor and pump in 2D view, (**b**) motor and pump in 3D view, (**c**) motor and hydro-generator in 2D view, (**d**) motor and hydro-generator in 3D view, (**e**) pump and hydro-generator in 2D view, (**f**) pump and hydro-generator in 3D view).
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 2 Preliminaries  
### Stochastic Bit-Sequence Generation  
A stochastic approach to LDPC decoding was introduced in [21]. This decoding process converts the received channel probability values into a stochastic bit-sequence equivalent. In general, the LLR of the \(s\)-th symbol \(x_{s}\) of a code word is found from the \(s\)-th received channel symbol \(y_{s}\) at \(SVN_{s}\), which is represented as \(L_{ch,p_{s}}\). By considering the binary phase-shift keying (BPSK) modulation over an additive white Gaussian noise (AWGN) channel with a zero mean and variance \(\sigma^{2}\), the channel LLR computed as \(L_{ch,p_{s}}=2\times y_{s}/\sigma^{2}\). The channel probability is also known as the intrinsic message and is calculated as  
\[P_{ch,p_{s}}=P(x_{s}=1/y_{s})=\left[\frac{exp(L_{ch,p_{s}})}{exp(L_{ch,p_{s}})+ 1}\right] \tag{2}\]  
Figure 2: Schematic of comparator.  
The term \(exp()\) is an exponential function. The \(P_{ch,v_{5}}\) value is represented with \(W\) binary symbols and compared with \(W\)-bit pseudo-random number (PRN) \(U\in[0,1]\) of the comparator [22], which varies with every clock cycle to generate an equivalent stochastic bit sequence of length \(f=8\), as shown in Figure 2. For example, \(P_{ch,v_{5}}=0.3125\) is represented as \(W=4\)-bit fractional binary symbols \(P=0101\). At each clock cycle, a PRN generator based on a linear feedback shift register (LFSR) [23] produces a new \(W=4\)-bit PRN \(U\). Suppose \(P>U\): the comparator output at that clock cycle is 1 and otherwise is 0. As shown in Table 1, after eight clock cycles, the comparator output is 00100101, which is the stochastic bit sequence belonging to \(P_{ch,v_{5}}=0.3125\). This sequence has a mean value of 3/8, which is close to \(P_{ch,v_{5}}=0.3125\)[24].
# Research on the Water Entry of the Fuselage Cylindrical Structure Based on the Improved SPH Model
## 3 Damage Model  
Research on metal structures' deformation and failure response during water entry is necessary. Therefore, this section introduces the damage model in the SPH method. In the TL-SFPM method, the Lagrangian kernel function is based on the initial configuration of the particles. However, when the solid undergoes damage or fracture, losing its continuity, some particles in the supported domain of particle \(i\) no longer influence particle \(i\). Therefore, the Lagrangian kernel function is no longer suitable for the current physical model, and an improved algorithm is needed to accurately describe the damage model.  
Fortunately, it is known that the Eulerian kernel function in traditional SPH methods describes the interaction between particles based on the current particle configuration. Hence, the following improved algorithm is proposed. When the deformation of solid particle \(i\) or its neighbor particles exceeds the damage limitation strain, in the discretized governing equations, the Lagrangian kernel function based on the initial particle configuration is transformed into the Eulerian kernel function based on the current particle configuration. This can capture the distribution of damage around particle \(i\) at the present  
Figure 3: Contact algorithm for fluids and solids (the red particles represent the solid particle, the blue particles represent the fluid particles).  
time. The approach fully utilizes the characteristics of the Eulerian kernel function to adaptively adjust the interaction between particles. At this time, the stress term in the governing equations should also be transformed from the first Piola-Kirchhoff stress tensor, \(\mathbf{P}\), to the Cauchy stress, \(\mathbf{\sigma}\).