esa-sceva/satcom-chunk-collection huggingface dataset:
# Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
## Appendix A  
Here we provide a brief synopsis of the most well-known refractive index power spectral density models that are used here for generation of computer simulated (SIM) datasets in the atmospheric turbulence machine learning studies described.  
The seminal Kolmogorov two-thirds power law corresponds to the refractive index spatial spectrum model known as the Kolmogorov turbulence spectrum [1]  
\[\Phi_{n}^{KO}(\kappa)\ =\ 0.033\text{-}C_{n}^{2}\kappa^{-11/3}. \tag{1}\]  
Expression (1) is defined within the spatial spectrum wavenumber \(\kappa\) range \(2\pi/L_{0}<<\kappa<l_{0}\) associated with the inertial subrange bounded by the outer \(L_{0}\) and inner \(l_{0}\) turbulent scales, where the inner scale is associated with the smallest scale refractive index inhomogeneities (on the order of millimeters near the ground [41]), and the outer scale \(L_{0}\) is defined by the largest turbulent scale. For heights of the order of tens of meters above the Earth's surface the turbulent outer scale is roughly on the order of the height above ground [2]. To preserve spectrum model (1) continuity over the entire range of wavenumbers several models for the refractive index power spectral density have been suggested [2,42,43].  
The Von Karman \(\Phi_{n}^{VK}(\kappa)\) and Tatarskii \(\Phi_{n}^{TA}(\kappa)\) models are defined over the entire spectral range (\(0\leq\kappa<\infty\)) and are among the most popular refractive index power spectral density models:  
\[\Phi_{n}^{VK}(\kappa)\ =\ 0.033\text{-}C_{n}^{2}\text{-}\exp(-\kappa^{2}/ \kappa_{m}^{2})(\kappa^{2}+\kappa_{L}^{2})^{-11/6}, \tag{2}\] \[\Phi_{n}^{TA}(\kappa)\ =\ 0.033\text{-}C_{n}^{2}\kappa^{-11/3}\exp(- \kappa^{2}/\kappa_{m}^{2}),\]  
where \(\kappa_{L}\ =\ 2\pi/L_{0}\) and \(\kappa_{m}\ =\ 5.92/l_{0}\).  
Other atmospheric spectral models have been proposed that account for the \"bump\" at large wavenumbers near \(\kappa\approx 1/l_{0}\) that were first observed in measurements of temperature data [44,45]. The most commonly used model of this sort is the [PERSON] power spectral density model [43]:  
\[\Phi_{n}^{AN}(\kappa)\ =\ \Phi_{n}^{VK}(\kappa)[1+1.802(\kappa/\kappa_{m})-0.25 4(\kappa/\kappa_{m})^{7/6}]. \tag{3}\]  
There has recently been emerging interest in analysis of the so-called non-Kolmogorov turbulence power spectrum models [13,14], generated by formal replacement of the -11/3 power law in Equation (1) by a power factor \(3<\alpha<4\):  
\[\Phi_{n}^{NK}(\kappa,\alpha)\ =\ 0.25\pi^{-2}\Gamma(\alpha-1)\cos(\alpha\pi/2 )\beta\text{-}C_{n}^{2}\kappa^{-\alpha}, \tag{4}\]  
where \(\beta\) is a dimensional constant with unit \(\left[m^{11/3-\alpha}\right]\), and \(\Gamma(x)\) denotes the Gamma function. The Kolmogorov power spectrum model corresponds to \(\alpha\ =\ 11/3\approx 3.66\), in which case Equation (4) reduces to Equation (1).
## 3 Results  
The results obtained are summarized in Tables 2 and 3; the mean and the standard deviation of the colour changes (\(\Delta\)E) of two types of denture base resin after immersion for three periods of time (7, 14, 21 days) in four different types of staining solutions (artificial saliva, coke, red wine, coffee) were evaluated. The Bonferroni post hoc test was used to evaluate the interaction effect of the type of denture base resin, immersion time and type of solution on the colour stability. \(\Delta\)E of all denture base materials was affected by the immersion period. The staining agent, especially combined with the period of time, significantly affected the values of \(\Delta\)E for all types of specimens (Table 3). The selected interval of confidence was 95%. The results in Figure 3 represent the interrelation between the type of material and observed time period, showing the lowest values for the first week for both of the tested materials and the highest values of colour changes for Vertex for the duration of three weeks. Figure 4 represents the interaction between the staining agent and type of tested material. The optical changes for red wine, coffee and coke gradually increased for the observed period of three weeks, as they were the highest after the 21 st day. The discolouration effect of the artificial saliva between the selected periods of time was not remarkable for both groups of specimens. The mean values for NextDent were lower in comparison with Vertex BasiQ 20 for all four types of staining agents. The 3D printed resin showed better colour stability for the three observed periods of time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Material** & **Period of Time (T)** & \begin{tabular}{c} **Artificial Saliva** \\ \(\Delta\)E (Mean)** \\ \end{tabular} & \begin{tabular}{c} **Coffee** \\ \(\Delta\)E (Mean)** \\ \end{tabular} & \begin{tabular}{c} **Coca-Cola** \\ \(\Delta\)E (Mean) \\ \end{tabular} & \
\begin{tabular}{c} **Red Wine** \\ \(\Delta\)E (Mean) \\ \end{tabular} &
\begin{tabular}{c} **SD** \\ \(\Delta\)**(Standard Deviation)** \\ \end{tabular} \\ \hline NextDent Denture 3D+ & 7 days (T1) & 0.498 & 2.129 & 2.676 & 3.016 & 1.1160103 \\ Vertex BasiQ & 7 days (T1) & 0.512 & 2.328 & 2.867 & 3.268 & 1.2170435 \\ \hline NextDent Denture 3D+ & 14 days (T2) & 0.471 & 2.194 & 2.722 & 3.163 & 1.1795063 \\ Vertex BasiQ & 14 days (T2) & 0.484 & 2.576 & 2.966 & 3.508 & 1.3227450 \\ \hline NextDent Denture 3D+ & 21 days (T3) & 0.462 & 2.269 & 2.875 & 3.312 & 1.2535407 \\ Vertex BasiQ & 0.457 & 2.793 & 3.158 & 3.866 & 1.4764653 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scheme of the experiment organization—colour change values of the selected materials according to the different periods of time.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 5 Future Recommendations
### Reduction in Response Delay  
A slight delay in response is typical in IRC and FBG sensing techniques and is more prominent when the power module temperature falls. This is due to the equipment capacity limitation and the external system's effect on the measurements. For IRC, reflections from the surroundings and target distance could be the causes. To mitigate this issue, an IRC with an adjustable sampling rate and pixel resolution could be a viable solution. For the FBG, this issue may occur due to the minute air gap between the sensor portion and the targeted surface (sensor-chip gap) and the limited acquisition rate of the interrogator. An interrogator or spectrum analyzer with an auto-adjustable acquisition rate will facilitate a quick response. In addition, the sensor-chip interface could also be matched as close as possible to the specially treated thermal paste, which can be uniformly applied across the surface to avoid future heat loss and chirping failure.
## 2 Data and Methods
### Models and Calibration
#### 2.2.2 WRF-Hydro
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Chosen Option** & **Subject** & **Chosen Option** \\ \hline Driving data & 6 h FNL & Pressure & 50 hPa \\ Integration time-step & 6 s for Dom3 & Projection resolution & Lambert \\ WRF output interval & 1 h & Longwave radiation & RRTM \\ Fuping domain center & 39\({}^{\circ}\)04\({}^{\circ}\)15\({}^{\circ}\)N, 113\({}^{\circ}\)59\({}^{\circ}\)26\({}^{\circ}\)E & Shortwave radiation & Dudhia \\ Zijinguan domain center & 39\({}^{\circ}\)25\({}^{\circ}\)59\({}^{\circ}\)N, 114\({}^{\circ}\)46\({}^{\circ}\)01\({}^{\circ}\)E & Land surface & Noah \\ Horizontal grid number & 26\(\times\)28, 42\(\times\)48, 84\(\times\)96 & Microphysics & Purdue–Lin (Lin) \\ Horizontal resolution & 9 km, 3 km, 1 km & Cumulus convection & Kain–Fritsch (KF)/Explicit \\ Vertical discretization & 40 layers & Planetary boundary layer & Yonsei University (YSU) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configurations of the WRF model. it to the channel, which can easily provide additional river flow [19]. The fully coupled method is realized by recompiling and merging WRF-Hydro into WRF. As the carrier of linking WRF and hydrological modules, LSM realizes the complete interaction of model state variables and flux variables by calling the disaggregation-aggregation module. The Noah in WRF-Hydro requires forcing data including precipitation, 2m surface temperature, surface pressure, long and short wave radiation, and leaf area index. When the fully coupled option is turned on, running WRF will call the LSM module, disaggregation-aggregation module and hydrological module components of WRF-Hydro. Runoff generated in Noah before the grid disaggregation process is \"on\", and the proportion of downscaling factor is 10. This means that the variables of Noah (the maximum soil moisture content and lateral saturated hydrological conductivity of different soil type, the excess infiltration, the soil water content of each soil layer, etc.,) generated by each 1 km \(\times\) 1 km grid are disaggregated into 100m \(\times\) 100m subgrids by assigning linear weighting factors for subsurface and overland routing. Then, through a linear average of the subgrids, the fine grid variables generated by overland routing (water accumulation depth) and subsurface routing (soil moisture content of each soil layer) of the hydrological module are aggregated back to the Noah grid and the updated soil moisture and heat flux are fed back to WRF for the next iterative cycle. For details of the grid disaggregation-aggregation process, refer to the description of [PERSON] and [PERSON] [44].
# The Study of Novel Self-Rewetting Fluid Application to Loop Heat Pipe
## 2 Materials and Methods
### Wick Parameter Measurement  
The parameters of the completed wick were measured using pure water as the working fluid and a self-designed test platform with reference to ASTM E128-99 [18] (see Figure 4). This platform measures important parameters, such as the effect radius (r\({}_{\text{c}}\)), permeability (K\({}_{\text{w}}\)), and porosity (\(\epsilon\)). The effective radius is related to the size of the wick capillary force, while the permeability is related to the resistance of the working fluid to flow in the wick, and the porosity represents the number of effective pores that can generate the capillary force.  
When the air pressure exceeds the capillary pressure that can be generated by the wick itself, the air will penetrate through the wick and float out of the water in the form of air bubbles, as follows in Equation (3).  
\[\tau_{\text{c}}=\frac{2\sigma}{\Delta\text{P}_{\text{c}}} \tag{3}\]  
where \(\sigma\) is the surface tension value of the working fluid, and \(\Delta\text{P}_{\text{c}}\) is the pressure difference corresponding to the first point of air bubble generation.  
Figure 3: Wick manufacture process.  
The measured parameters are brought into Equation (4) to calculate the permeability parameters.  
\[\mathrm{K_{w}}=\frac{\mathrm{\hat{m}\mu_{v}}}{2\pi\mathrm{\rho_{v}}\Delta\mathrm{PL _{w}}}\ln(\mathrm{T_{w}}) \tag{4}\]  
\(\mathrm{L_{w}}\) is the wick length; \(\mathrm{\mu_{v}}\) is the liquid viscosity coefficient; \(\mathrm{\hat{m}}\) is the mass flow rate; \(\mathrm{\rho_{v}}\) is the liquid density; \(\Delta\mathrm{P}\) is the pressure difference through both ends of the wick; and \(\mathrm{T_{w}}\) is the wick thickness.  
Porosity measurements are mainly performed using this system (e.g., Figure 4b), where the wick is first placed in a container with a working fluid, then vacuumed to ensure the complete wetting of the wick, and the porosity is calculated as Equation (5). Water is used as the test fluid and measured using the Archimedes method.  
\[\varepsilon=\frac{\mathrm{V_{pore}}}{\mathrm{V_{total}}} \tag{5}\]  
where \(\varepsilon\) is the porosity, \(\mathrm{V_{pore}}\) is the volume of the wick absorbing the test fluid, and \(\mathrm{V_{total}}\) is the volume of the wick.  
In addition, in order to verify the pore characteristics of the wick, SEM images were taken to observe the pore structure under microscopy.
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
## 3 Structural Damage Identification Methods
### Fusion Decision and Results Output of RCPN  
For a certain test sample \(A\), assume that it contains \(q\) pattern categories and the number of sensors is \(n\), and \(F_{j}\) is the total weight of the \(j\)th category. Then, the maximum value of total weight in \(q\) pattern categories is the output result after fusion:  
\[\text{out}=\underset{j=1,2,..,q}{\max}F_{j} \tag{14}\]  
\[\text{F}_{j}=\sum_{i=1}^{n}w_{i,j}\times f_{i,j} \tag{15}\]  
where, \(w_{i,j}\) is the normalized coefficients of sensor \(i\) and category \(j\), which is determined by the correct recognition rate of the network model in the training stage and,\(f_{i,j}\) is the weight of sensor \(i\) and the \(j\)th category, and its value is the product of the test sample \(A\) in sensor \(i\) and the connection weight vector of the \(j\)th category, i.e.,  
\[f_{i,j}=\sum_{m=1}^{N}a_{m}w_{i,j,m} \tag{16}\]  
where, \(N\) is the number of characteristic parameters of test samples, \(w\) is the connection weight vector corresponding to sensor \(i\) and category \(j\).  
According to this method, the RCPN decision results of the feature-level fusion model are taken as the local results of the decision-level fusion model, and the weighted average method is used to fuse the local decision result obtained from all the test samples, and the final decision results after fusion are obtained.
# Enabling Decision Making with the Modified Causal Forest: Policy Trees for Treatment Assignment
## 3 Empirical Studies
### Study 3: Interest Rates and Loan Returns
Table 3 indicates that the observed and random allocations lead to the same welfare since the observed allocations are based on a stratified random assignment. The table also shows that the policy trees increase welfare compared to the baseline policies, irrespective of the tree depth. Even implementing a policy tree of depth two demonstrates an increase in overall welfare by 31%, and deeper trees continue to yield improvements, although at a decreasing rate. The allocation pattern of the optimal policy trees shows that deeper trees tend to shift individuals away from the high-rate to a lower-risk group while increasing the average returns at the same time. The welfare estimated with the mixed trees (one or two sequentially optimal final trees) is slightly lower than those obtained by the fully optimal tree. For example, the optimal policy tree of depth four generates a welfare of ZAR 701, higher than the ZAR 686 (ZAR 685) yielded by the depth-3+1 (depth 2+2) mixed policy tree. Figure 6 shows the allocation rule for an optimal policy tree of depth 4, which combines both a large welfare improvement and policy rules that are relatively easy to interpret. As indicated in Table 3, the policy tree assigns roughly half of the sample to the high-rate treatment. Among this subgroup, the highest share (16% of the overall sample) falls between the ages of 35 and 43 and lacks higher education. Low-income individuals older than 53 also constitute a significant portion of the high-risk cohort (12% of the whole sample). The highest share of individuals assigned to the medium interest rate (14% of the sample) range from 28 to 32 years of age. The largest subgroup assigned to the low-rate group consists of high-income individuals older than 50 years (4.6% of the sample). For detailed information on the treatment allocation rules for the other policy trees, we direct the reader to Appendix A.4.3. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Policy** & \multicolumn{2}{c}{**Welfare (ZAR)**} & \multicolumn{2}{c}{**Treatment Shares (\%)**} \\  & **Average Returns** & **Low Rate** & **Medium Rate** & **High Rate** \\ \hline Observed & 497 & 39.84 & 28.40 & 31.75 \\ Random & 497 & 39.84 & 28.85 & 31.10 \\ \hline Unconstrained optimal policy tree & & & & \\ Depth-2 & 652 & 4.62 & 40.25 & 55.12 \\ Depth-3 & 678 & 4.62 & 44.62 & 50.76 \\ Depth-4 & 701 & 13.74 & 36.02 & 50.23 \\ \hline Unconstrained sequentially optimal policy trees & & & & \\ Depth-2+1 & 663 & 19.58 & 28.89 & 51.53
## 3 Hazard-Consistent Fragility Curves of Soil Liquefaction
### Procedures for Seismic Hazard Analysis  
Probabilistic seismic hazards can be assessed through four steps [19], as illustrated in Figure 1.  
The first step involves identifying the earthquake sources. As Figure 2 illustrates, this study models the sources in the deep seismic zone (hypocentral depth \(>35\) km) as point sources [20]. In contrast, for the shallow seismic zone (hypocentral depth \(<35\) km), the sources are represented as area sources or line sources, such as active faults [21]. For shallow seismic zones, earthquakes are likely to cause long ruptures. Hence, well-defined faults (Type I sources) will be modeled as line sources, while unknown faults (Type III sources) will be modeled as area sources [1]. As to the deep seismic zone, earthquakes occur in subduction zones that are sufficiently distant from the site, making the point source model more suitable.  
The next step is to describe how often and how strongly each source produces earthquakes, or, in other words, the relationship between the frequency and the magnitude of seismic events. A maximum possible earthquake is selected for each seismic source.  
Figure 1: Procedures for seismic hazard analysis.  
The third step is to assess the earthquake's impact. This study utilizes [PERSON]'s formula [22] to calculate the variation in peak ground acceleration (PGA) with respect to the earthquake's magnitude (\(M\)) and the distance (\(R\)) from the site to the source:  
\[PGA_{R}=f(M,R)=b_{1}e^{b_{2}M}[R+b_{4}\exp(b_{5}M)]^{-b_{3}} \tag{16}\]  
\[\sigma_{\text{InE}}=0.68 \tag{17}\]  
in which \(PGA_{R}\) denotes the horizontal PGA for hard site conditions; \(b_{1}\)-\(b_{5}\) are regression coefficients with values of \(b_{1}\) = 0.00369, \(b_{2}\) = 1.75377, \(b_{3}\) = 2.05644, \(b_{4}\) = 0.12220, and \(b_{5}\) = 0.78315, respectively [23]. \(\sigma_{\text{InE}}\) represents the standard deviation of the model error's natural logarithm.  
Figure 2: Seismic zone and epicentral distribution for events with \(M\) > 5.0. (a) Shallow seismic zone; (b) deep seismic zone.  
The last step is to estimate the site's seismic hazard, which entails calculating the probability of various levels of ground shaking occurring at the site within a given year. This seismic hazard analysis assumes that earthquakes follow a stationary Poisson process, which means that they occur randomly and independently over time.  
Following the above four steps, the annual probability of peak ground acceleration, \(\mathrm{PGA_{R}}\), exceeding a value of \(a\) can be calculated as follows [19]:  
\[P\left[PGA_{R}>a\right]_{1\ year}=1-\exp\left\{-\sum_{i=1}^{n}P\left[PGA_{R}>a \ |E_{i}|\cdot v_{i}\right\}\right\} \tag{18}\]  
where \(E_{i}\) denotes the event that the earthquake with \(M\geq M_{0}\) occurs in source \(i\); \(v_{i}\) is the annual average occurrence rate of earthquakes with \(M\geq M_{0}\) in source \(i\); \(M_{0}\) is the smallest magnitude of engineering concern; \(n\) is the number of potential seismic sources considered in the region of the site.
# Signal Detection for Ambient Backscatter Communication with OFDM Carriers
## 2 System Model and Problem Formulation
### Received Signal at the Reader  
At the reader, due to the multipath effect, two portions of the direct-link interference signal \(y_{d}(n)\) in each OFDM symbol period are identical, i.e., \(y_{d}(n)=y_{d}(n+N),n=N_{sr}-1,\cdots,N_{cp}-1\). Similarly, for the received ambient OFDM signal \(c(n)\) at the tag, \(c(n)=c(n+N),n=N_{st}-1,\cdots,N_{cp}-1\). Finally, we obtain the received backscattered signal \(y_{b}(n)\) at the reader as  
\[y_{b}(n)=\begin{cases}y_{b}(n+N),&\text{if $B=0$}\\ -y_{b}(n+N),&\text{if $B=1$}\end{cases}. \tag{4}\]  
For \(n=L-1,\cdots,N_{cp}-1\), we have  
\[z(n)\triangleq y(n)-y(n+N)=\begin{cases}v(n),&\text{if $B=0$}\\ u(n)+v(n),&\text{if $B=1$}\end{cases}. \tag{5}\]  
Here, \(v(n)\sim\mathcal{CN}(0,\sigma_{v}^{2})\) with \(\sigma_{v}^{2}=\rho\sigma^{2}\) where \(\rho\) is the noise uncertainty factor with a given upper bound noise uncertainty (in dB) \(B=\text{supp}\{10\log_{10}\rho\}\)[8], and \(u(n)\sim\mathcal{CN}(0,\sigma_{u}^{2})\) with \(\sigma_{u}^{2}=4P_{s}|\eta|^{2}|h_{tr}|^{2}\sum_{l=0}^{N_{st}-1}|h_{st}(l)|^{2}\). Our goal is to design a test statistic for the reader to recover the tag signal \(x(n)\) from the received signal \(y(n)\) without knowledge on OFDM ambient signal \(\sqrt{P_{s}}s(n)\)transmitted from RF source. We begin by exterminating the following detection problem which tries to distinguish between the hypotheses \(H_{0}\) and \(H_{1}\).  
\[\begin{split} H_{0}:\ z(n)&=v(n),\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{if}\ B=0\\ H_{1}:\ z(n)&=u(n)+v(n),\ \ \text{if}\ B=1\end{split}. \tag{6}\]  
We define the detection SNR as \(\gamma\triangleq\frac{\sigma_{v}^{2}}{\sigma_{v}^{2}}\).
In particular, the proof-of-concept includes the Artificial Neural Network (ANN) that is trained on the Arduino with 2 KBytes of RAM (Random Access Memory) to detect network attacks. Finally, the analysis of the current needs of intelligent IoT infrastructure protection is provided to highlight a particular way of achieving a trade-off between intelligent security systems and qualitative delivery of services. A potential application of advanced data analytics to combat the crimes in Smart Cities was presented in a winning idea during Interpol Thinkathon 2018 competition ([[https://www.politiforum.no/artikler/norsk-forskning-pa-framtidens-politi-ti-topps-i-interpol/449053](https://www.politiforum.no/artikler/norsk-forskning-pa-framtidens-politi-ti-topps-i-interpol/449053)]([https://www.politiforum.no/artikler/norsk-forskning-pa-framtidens-politi-ti-topps-i-interpol/449053](https://www.politiforum.no/artikler/norsk-forskning-pa-framtidens-politi-ti-topps-i-interpol/449053)), accessed on 21 September 2021) by the NTNU team, where the idea of advanced data analytics in Smart Cities has been presented. The paper is organized as follows. Section 2 gives an overview of the existing smart applications and modern community-accepted IoT components currently available on the market. Aspects of the utilization of Machine Learning on the Internet of Things end-node are explored in Section 3. A suggested use case scenario and methodology of the IoT-based Neural Network training is given in Section 4. Moreover, various sides of complexity and applicability in the resources-constrained environment have been studied with respect to ANN. Corresponding training and testing phases together with computing performance implications are evaluated in Section 5. Finally, discussions of the achieved results and conclusions are given in Section 6.
## 2 Materials and Methods
### WT Method  
GNSS, GRACE, and precipitation time series data include different periodic components, such as observational errors, noise, seasonal loading deformations, and trend signals. WT can decompose a time domain signal into several stacked wavelet functions, which are obtained by translation and scaling of basis functions. Compared with the traditional Fourier transform method, WT can conduct multi-resolution analysis, and simultaneously characterize the local signal characteristics in the time and frequency domains. WT, a time-frequency analysis method, allows changes in both time and frequency windows. We used Morlet basis functions [26] and Fourier transform to analyze the periodic components of the time series; subsequently, we used Daubechies (db) basis functions [27; 28] to extract the periodic terms, which were used for seasonal intensity analysis of different data at each station. The periodic term extracted by the seventh order db8 basis function corresponded to a daily sampling rate of 0.35-0.70 years, including semi-annual periodic components, whereas the seasonal term extracted from the eighth order corresponded to 0.70-1.40 years, including annual periodic components. The sum of the periodic components represented both annual and semi-annual cycles.