esa-sceva/satcom-chunk-collection huggingface dataset:
# The Correlation Factors and Mechanisms of Diffusion for P and S in the Cu Single Crystal
## 2 Materials and Methods
### Computational Details  
Density functional theory (DFT) calculations of the absorption of the P and S impurities in the fcc Cu lattice and of their vacancy-assisted diffusion, including the energy barriers for the jumps, were performed with the Vienna ab initio simulation package (VASP 5.4.4) [22] with the Perdew-Burke-Ernzerhof [23] (PBE) exchange-correlation functional with pseudopotentials of the projector augmented wave [24; 25] (PAW) type. The PBE functional has shown good accuracy for describing the physical-chemical properties of the impurities in Cu [26; 27; 28; 29]. For all calculations, a plane wave cutoff at 460 eV was employed. Each supercell employed in this work contained 108 Cu atoms whose internal coordinates were optimized with DFT. The convergence of the obtained energies with the model size has been tested in previous work [28]. The k-point meshes of \(4\times 4\times 4\) in the Monkhorst-Pack sampling scheme were adjusted to produce minimal errors while keeping good computational efficiency [30]. The absorption energy of a substitutional impurity atom at a lattice site of the perfect fcc crystal is defined as  
\[\Delta E_{\text{abs}}=E_{\text{PS,Cu}}-(E_{\text{FS}}+E_{\text{Cu}}) \tag{11}\]  
where \(E_{\text{PS,Cu}}\) is the energy of a supercell of a single crystal fcc Cu containing one atom of either P or S; \(E_{\text{FS}}\) is the energy of a P or S atom in a vacuum in the same supercell employed to model the Cu crystal; and \(E_{\text{Cu}}\) is the energy of the supercell with 108 atoms of pure fcc Cu. A more negative value for \(\Delta E_{\text{abs}}\) implies stronger bonding with the Cu lattice. A similar procedure was followed for the determination of the vacancy formation energy.  
The energy barriers for diffusion (\(\Delta E_{a}\)) were computed with the nudged elastic band (NEB) method as implemented in VASP [31; 32]. The electronic structure parameters for these computations were the same as for the geometry optimizations and 7 images were employed in the TS search with the NEB routine. The activation energies reported here have been determined as  
\[\Delta E_{a}=E_{\text{TS}}-E_{\text{initial}} \tag{12}\]  
where \(E_{\text{TS}}\) is the electronic energy of the saddle point that corresponds to the TS, and \(E_{\text{initial}}\) is the electronic energy of the initial structure from which the diffusion takes place. A more positive value for \(\Delta E_{a}\) implies a larger energy barrier for the atomic jump. A similar expression was employed in the determination of the activation enthalpies \(\Delta H^{\ddagger}\). Vibrational frequencies were calculated by numerical differentiation of the forces using a second-order finite difference with a step size of 0.015 A. The Hessian matrix was mass-weighted and diagonalized to yield the frequencies and normal modes of the system. The 0 K enthalpies were obtained by adding the zero-point energies (ZPE) to the electronic energies.
# Future of IoT Networks: A Survey
## Future IoT Network
### Connection Management  
Each device may have different a communication protocol, so the connection management object may support different standards to nodes that belong to the user. The IoT home gateway or access point (AP) may manage the different standards, but there is a problem when the nodes are out of the communication range. For example, cellular communication, which provides a wide-range connection to the device, cannot be installed in the small sensors owing to the problems of price and battery consumption. Further, communication management is required in both the static and nomadic environment because a guest node that temporarily visits the network area may exist. The concept of grouping [88; 89] can be a good candidate to manage the connectivity of the devices. For instance, grouping devices can be categorized as follows: physical grouping and logical grouping.  
Figure 4 and Table 6 elaborate the concept of physical and logical grouping in the IoT environment. In terms of physical grouping, devices can be grouped based on their physical proximity. Meanwhile, connection among equal IoT service task devices is needed to support the service-oriented network without establishing direct connections with one another. In this case, a new manager, which is the main agent of the service or network management-available device, must manage this group of devices, which can be classified as logical grouping. With these two categories of device grouping, any IoT service can provide effective services for users.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & & **Attributes** \\ \hline \multirow{4}{*}{Physical grouping} & \(\bullet\) & It is similar to the concept of a subnet. \\  & \(\bullet\) & Each node in the same physical group exists near the connection management object to communicate directly. \\  & \(\bullet\) & A node can be included in the physical group that can communicate the connection management object through multi-hop. \\ \hline \multirow{4}{*}{Logical grouping} & \(\bullet\) & It contains the devices that use same service or application even if they are connected to different physical networks. \\  & \(\bullet\) & The devices that belong to the same logical group are managed from each communication management object in its physical group. \\ \cline{1-1}  & \(\bullet\) & A new manager is needed to manage the network connection for all devices in the logical group. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Grouping techniques.
## 2 Saturable Absorber Fabrication and Characterization  
### Metallic-SWCNT Film Fabrication  
Purely metallic carbon nanotube thin films were fabricated using the vacuum filtration method [38; 39]. In the first step, a suspension of carbon nanotubes in a water and surfactant solution was prepared. Surfactant particles tend to adsorbate on the nanotube walls and prevent the aggregation of nanotubes in a liquid. Dry nanotubes from NanoIntegris (99% purity with tube diameters from 1.2 to 1.7 nm and lengths in the range of 0.1-4 \(\upmu\)m) were added to a 1% water solution of sodium dodecyl sulfate to achieve a 0.004 mg/mL nanotube concentration. Later, such dispersion was sonicated using horn sonication, where it was placed in ice water for 1 h with 5 min breaks after each 10 min of sonication to prevent heating of the mixture. Next, the suspension was centrifuged at 12,000 rpm for 10 min to remove the unseparated nanotubes. An appropriate amount of as-prepared dispersion was filtered by the mixed cellulose membrane (MCE filter, 25 nm pore size, 25 mm diameter from Millipore) using a vacuum filtration setup that allowed to achieve a specified thickness of the m-SWCNT film, 150 and 300 nm. The thickness can be controlled by the volume of dispersion used for filtration, and was measured with a Bruker Icon atomic force microscope. Residual surfactant was washed away from the metallic carbon nanotube film by a large amount of deionized water and isopropyl alcohol. The m-SWCNT film on the membrane was gently dried and cut into 5 \(\times\) 5 mm pieces, and then immersed in an acetone bath to dissolve the cellulose membrane. We note that the acetone was changed several times to complete the removal of the residual MCE. Then acetone was replaced by the 50% solution of isopropyl alcohol in water and each metallic carbon nanotube film was transferred to a fiber connector or a flat substrate (silicon covered by silicon dioxide or soda-time glass) and dried under a nitrogen stream.
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 5 Simulation Results and Discussion
### The Additive Noise Model  
The noise superimposed on NB-PLC signals transmitted via MV power lines must exhibit the characteristics outlined in Section 3. In this context, noise samples, drawn from the Nakagami distribution with parameters \(m=1\) and \(\Omega=2\), are fitted in the spectral domain to an exponentially decreasing PSD(\(f\)). Figure 4a shows the distribution of noise samples generated by the simulations (simulated noise) together with the Nakagami distribution used to create the relevant noise samples (theoretical noise). It is deduced that filtering the noise in order to simulate an exponentially decreasing PSD(\(f\)) only slightly modifies the samples' distribution. Figure 4b depicts the simulated noise PSD(\(f\)) in the frequency range of 0-500 KHz. It is readily verified that the simulated noise PSD possesses an exponentially decreasing mean value, at the same time preserving spectral randomness.
## 3 Numerical Validation
### Numerical Results
#### 3.3.1 Identification of the Damaged Edge  
Figures 7 and 8 show the ACBMC of each edge calculated by Equation (3). The BMAC [29] is also shown for comparison. For the first boundary mode, the ACBMC of undamaged edges-a and c in Model A and edges-a and d in Model B do not show significant changes, remaining above 0.85. Conversely, the ACBMC of damaged edges drops below 0.85. Therefore, the ACBMC of the first mode can be used to identify the damaged edges. The threshold of the ACBMC of the first mode can be set as 0.85 for damaged edge identification.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c  
In the second and third modes, the ACBMC of undamaged edges tends to decrease as damage severity increases. For example, the ACBMC of the second mode of the edge-d shows a significant decrease in cases 2-5. Additionally, the ACBMC of undamaged edges sometimes shows significant decreases or increases. Thus, the first mode's ACBMC is more reliable for damaged edge identification.  
At the same time, it can also be observed that the change in the BMAC is less than the ACBMC when the damage severity is increased. This indicates that the ACBMC is more suitable for damage identification of structural sealant than the BMAC.  
In the first stage of identification, the ACBMC of all edges is 1 in case 1; therefore, all of the edges are undamaged. In cases 2-5, the ACBMC of the first mode of edge-b are 0.82, 0.69, 0.55, and 0.45, respectively, which are less than 0.85. Therefore, the edge b can be identified as the damaged edge. The ACBMC of the first mode of edges a, c, and d are all higher than 0.85, so the three edges are undamaged. At this time, BMAC is 0.97, 0.94, 0.89, and 0.85, respectively. Compared with ACBMC, the changes in BMAC are relatively small and not sensitive to damage, so it is difficult to identify damage through changes in BMAC. In cases 6-9, the ACBMC of edges a and c are both higher than 0.85. The ACBMC of the first mode of edge-b and d are both less than 0.85. The damaged edges of model B can also be identified by the ACBMC. Therefore, using the ACBMC of the first mode to identify the damaged edge is consistent with the real damage cases.
## 5 Morse Code Decoding  
Considering that we have obtained a relatively stable Morse-encoded signal after the pretreatment stage, it is easy to identify the elements using the k-means algorithm and derive the decoded data based on the Morse code table. This stage includes dot-dash recognition, space recognition, and accuracy computation.
# Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
## 4 Numerical Simulations
### Complexity Analysis  
The signal detection is operated group by group and the implementation complexity of the mentioned detectors for each group is shown in Table 2. AMP is a low-complexity framework. For AMP-based detectors, the complexity majorly lies in the matrix multiplication operation. For each group, the complexity is \(\mathcal{O}(n^{2})\). The proposed detector has two types, i.e., the SVD form and the LMMSE form, and the results of the two versions are shown in Figure 3. In Figure 3, both forms of the proposed detector show similar performance. The LMMSE form has matrix inversion operation, and the complexity is \(\mathcal{O}(n^{3})\) per group. For the SVD form, \(\mathbf{H}\) is decomposed by SVD transformation and the complexity is \(\mathcal{O}(nR)\), where \(R\) is the rank of \(\mathbf{H}\). LLR detector works for each subcarrier with the LLR ratio calculation for each constellation point, so the complexity is \(\mathcal{O}(nM)\) per group. The optimal ML detector requires searching the space of index patterns and constellation maps, so its computation complexity exponentially increased with the index table number as well as the modulation order.
## 2 Methodology  
This chapter presents the methodology employed to assess the pointing accuracy of the ATP system, during the pointing phase, with the objective of evaluating the feasibility of facilitating ship-to-satellite optical communication. Once the satellite has been pointed, the system is able to track its position (tracking phase), so the analysis will be focused on the pointing phase. This section introduces the definition of the model along with satellite coordinates and ship motions. The kinematic definition of the model together with the control diagram to evaluate the pointing accuracy is presented, and then the last subsection introduces methodologies to reduce pointing error and evaluate the reliability of the system.
## 5 Simulation and Experimentation
### Experimental Results  
Experiments are conducted on the electromagnetic vibration isolation system to verify the effectiveness of the proposed active control methods respectively with the equivalent stiffness, with the equivalent stiffness and damping coefficient. Figure 5 shows the physical photo of the electromagnetic vibration isolator system. The digital controller is designed with the proposed active control method and realized on the STM 32 singlechip, which are integrated with data acquisition device on the development board. The C language was used for the programming and the code was compiled and then written into an STM32 chip. All parameters of the isolation system and the mass of the isolated object \(m_{0}\) are equal to parameters in simulation. Based on the control diagram in Figure 3, the experimental results with the proposed active control methods are shown in Figure 6.  
Figure 4: The simulation result with the equivalent stiffness and damping coefficient.  
From Figure 6, it can be seen that the vibration displacement \(x\) is stable at \(5.67\times 10^{-4}\) m based on the active control method with the equivalent stiffness after the system is disturbed by an external force. The rise time \(t_{r}\) is 0.05 s, the peak time \(t_{p}\) is 0.09 s, the overshoot \(\sigma\)% is 59.08%, the settling time \(t_{s}\) is 1.46 s and the number of oscillations \(N\) is 5. The vibration displacement \(x\) is stable at \(5.54\times 10^{-4}\) m based on the active control method with the equivalent stiffness and damping coefficient. The rise time \(t_{r}\) is 0.08 s, the peak time \(t_{p}\) is 0.11 s, the overshoot \(\sigma\)% is 9.83%, the settling time \(t_{s}\) is 0.39 s and the number of oscillations \(N\) is 1. Although the steady-state errors between the experimental results and the simulation results of the two active control methods are different, it is small enough to be negligible. In addition, the experimental results and the simulation results have the same variation trend. The experimental results demonstrates that the proposed active control methods can effectively control vibration. Compared with the active control approach with the equivalent stiffness, the active control approach with the equivalent stiffness and damping coefficient not only has shorter response time, but also has smaller oscillations and overshoot to ensure the stability of the electromagnetic isolation system and an isolated object.  
Figure 5: The experimental physical photo.  
Figure 6: The experimental result with the active control methods.
## 4 One-Dimensional-Two-Dimensional Drainage Overflow Modeling  
Considering the computational time for 1-D-2-D simulation, the drainage overflow modeling was only carried out in Kamping Melayu Subang, located in the northern part of the Damansara catchment (Figure 5). This area was chosen due to its frequent floods caused by drainage issues, as reported by DID [8]. In Figure 5, the study area was divided into 15 sub-catchments with 245 junctions, 244 conduits, and 10 outfall nodes. For rainfall input, Kamping Melayu Subang Station (No. 3010001) was assigned as the rain gauge for the sub-catchments. The drains were defined as open rectangular-shaped conduits, representing an open channel with a rectangular cross-section. Based on Equation (3), the initial drainage flow was computed as 0.086 m\({}^{3}\)/s. There were two scenarios in the hydraulic simulations: scenario 1 (historic drainage) and scenario 2 (current drainage). Note that the current drains in the year 2020 had a width of 0.61-1.37 m and a depth of 0.46-1.52 m, which are on average 15-30% larger than the historic drains in 2015.  
Figure 4: Comparison of water level time series modeled and observed at TTDI Jaya: (**a**) calibration and (**b**) validation.  
Figure 5: Catchment discretization for 1-D-2-D drainage overflow modeling.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multicolumn{2}{c}{**R2**} & \multicolumn{2}{c}{**NSE**} & \multicolumn{2}{c}{**RE (\%)**} \\ \cline{2-6}  & **PCSWMM** & **InfoWorks ICM** & **PCSWMM** & **InfoWorks ICM** & **PCSWMM** & **InfoWorks ICM** \\ \hline Calibration & 0.68 & 0.47 & 0.52 & 0.42 & 4.79 & \(-\)3.74 \\ Validation & 0.62 & 0.56 & 0.10 & 0.26 & 10.58 & 9.28 \\ \hline \hline \end{tabular} Note: Negative sign means the model overestimates the observed data.  
\end{table}
Table 3: Model performance for the calibration and validation periods.  
In this study, the 1-D-2-D model was constructed using the direct connection approach [45], which allows the 1-D conveyance network (i.e., drainage) to be directly connected to the 2-D floodplain. As the first step in developing a 2-D model, a bounding layer is required to define the extent of the 2-D model domain. In the model setup, a 2-D bounding layer was added with a 10 m resolution hexagonal mesh and a roughness coefficient of 0.033, whereas a point layer or so-called 2-D node layer was generated using the elevation data from the DEM to represent the floodplain topography. Based on the attributes of the layers defined previously, a 2-D mesh layer was finally created with 9416 junctions and 2-D cells. For 1-D-2-D simulation, each junction in the 1-D model was then connected to the closest 2-D junction point, using the connection tool in PCSWMM (Figure 6). This connection allows for the free transfer of flow from the 1-D drainage model to the 2-D model for flood extents in areas.
## 2 Chirp Rate Estimation
### Analysis of the Estimation Accuracy  
The accuracy of the estimators has been compared in this section to the CRLB for the considered signal model. The analyzed complex chirp signal is given by:  
\[x[n]=A_{x}\exp\left(j\kappa\frac{n^{2}}{2}\right), \tag{13}\]  
where \(A_{x}=1\), \(n\in[0,N-1]\). This signal is merged in a white Gaussian noise \(w[n]\) with a variance \(\sigma_{w}^{2}\). Thus, the observation vector \(\mathbf{x}=[x[0],x[1],...,x[N-2],x[N-1]]^{T}\) is normally distributed:  
\[x\sim\mathcal{N}(\boldsymbol{\mu}(\boldsymbol{\alpha}),\boldsymbol{C}( \boldsymbol{\alpha})), \tag{14}\]  
Figure 2: CR estimation in the TF domain—an interpretation.  
As can be seen, for signals with signal-noise ratio (SNR) \(\geq\) 0 dB the \(\mathfrak{D}\) and \(\mathfrak{F}\) estimator give very similar results. The signals from this range will be considered later in the paper, so choosing one of the two more accurate tools was right. Additionally, according to the authors' experience, this estimator is less sensitive to the changes of the analysis window width. Because of the uncertainty problem in the \(\mathfrak{K}\) estimator, the CR was verified in the vicinity of the \(f_{\text{ref}}\). For this reason, the variance of this tool is clearly greater than in the case of the other two estimators. Although all of the tools used are characterized by an error in relation to the CRLB, it should be kept in mind that for SNR \(\geq\) 0 dB the variance of the \(\mathfrak{D}\) and \(\mathfrak{F}\) estimators is satisfactorily low, and in many practical cases is sufficient to estimate CR.
## 3 Results and Discussion
### Wind Directionality Effects of Extreme Wind Loads
#### 3.3.1 Wind Directionality of Known Building Orientation  
Selecting the windward side of the high-rise building standard model, as the north side corresponds to the wind direction N given by [PERSON] et al. (2019), it should be noted that the wind tunnel test has a wind direction interval of 10\({}^{\circ}\), and the wind direction data interval is 22.5\({}^{\circ}\). In this study, an interval equal to 22.5\({}^{\circ}\) is used to consider the wind direction effects. The corresponding wind tunnel test data are analyzed with the wind direction and the minimum wind direction angle of the building orientation.  
Ten typical measuring taps on the building's facade were selected. The number of measuring points on the east elevation is 16, 19, and 22; the number of measuring points on the north elevation is 87, 106, and 110; and the number of measuring points on the west elevation is 177 and 197. The survey points on the south elevation are numbered 256 and 279. Table 3 shows the wind directionality factors of 10 typical measuring points. As can be seen from Table 3, the wind directionality factors of different measuring points are inconsistent and are lower than the standard value of 0.90.  
Figure 16 shows the histogram of the wind directionality factors of all the measuring taps for given building orientation. It can be observed that the wind directionality factors of each measuring tap vary within the range of 0.65-0.97, and The \(K_{\rm d}\) value is concentrated in the range of 0.8-0.9, with an average value of 0.85 and a coefficient of variation of 0.04.  
Figure 17 shows the probability distribution fitting results of the \(K_{\rm d}\) for the Gumbel and GEV probability distribution. The Gumbel distribution has a good fitting effect, and the GEV distribution has an obvious separation in some areas. Using the AIC method for testing the fitting goodness, it can be obtained that the wind directionality factor \(K_{\rm d}\) value is more suitable for the Gumbel distribution, and the 85% quantile value of the Gumbel distribution is about 0.90. Therefore, it can be concluded that the 0.90 wind directionality factor considered by GB50009 (2012) has a guaranteed rate of 85%.  
Figure 16: Wind directionality factors at different taps.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Measuring** & **16** & **19** & **22** & **87** & **106** & **110** & **177** & **197** & **256** & **279** \\
**Taps Number** & **16** & **19** & **22** & **87** & **106** & **110** & **177** & **197** & **256** & **279** \\ \hline \(K_{\rm d}\) & 0.79 & 0.88 & 0.84 & 0.88 & 0.9 & 0.78 & 0.86 & 0.8 & 0.82 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Directionality factor \(K_{\rm d}\) of different measuring taps for known building orientation.