esa-sceva/satcom-chunk-collection huggingface dataset:
# Regional Analysis of Dust Day Duration in Central Iran
## 2 Materials and Methods
### Statistical Analyses of Dust Days  
The dust codes 06 (Suspended dust that has traveled long distances) and 07 (Dust or sand rising from the station to the atmosphere) of synoptic bulletins were obtained over the study period of 1999-2018 from Central Iran weather stations. Statistics relating to dust days in the region were calculated in Excel software. Dust phenomena that lasted only one day were discarded so that only dust events (horizontal visibility below 1000 m) that lasted for several days (more than one) were retained in each station.  
Then, the average, minimum and maximum duration of dust events per year were extracted for each station and were mapped using kriging for spatial interpolation in GIS software, and maps of the range of dust cycles in the study area were drawn. Furthermore, in order to predict the maximum expected duration of dust days at different return periods, Easyfit software was used. EasyFit allows to automatically or manually fit a large number of distributions to data and select the best model in seconds. It can be used as a stand-alone application or with Microsoft Excel. EasyFit Professional software supports statistical distribution. Such as Normal, Bernoulli, Beta, Chi-square, Erlang, Exponential, Geometric, Gumble, Inverse Gaussian, Johnson, Logarithmic, Logistic, Normal, Gamma, Inverse Gamma, Pearson, T- Student, Weibull, and so on. After fitting the data to statistical distributions based on matching tests (Kolmogorov-Smirnov, Anderson-Darling, Chi-Squared) selects the most appropriate statistical distribution that is ranked 1 st. In this study, maximum annual data were used for each station.  
Using the annual DDD maximum values calculated in the previous step, the best statistical distribution function for each station was calculated. Finally, the inverse relationship with the probable values of DDD with return periods of 5, 10, 25, and 50 years was calculated, and, using GIS software, these DDD values were mapped across the region.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 1 Introduction
In 2015, GeoGuard started a project with Autostrade per l'Italia SpA, the main Italian highway operator, to monitor three points on a bridge; it is a small network, later upgraded with three additional points, that has been continuously working since then and has provided interesting results that are analyzed in the paper.
###### Abstract
The second type is a phase mismatch in the MMI input that can cause a reflection back into the access waveguides, which can result in the input being imaged back onto itself. This problem can be resolved using silicon nitride (SiN), a material with a slightly lower refractive index than Si. Moreover, SiN has a low absorption coefficient and can operate in the C-band spectrum. Additionally, it has been shown for the first time that light coupling analysis of SiN-based MMI coupler waveguides may be implemented as a two-channel demultiplexer device. Furthermore, SiN has two key characteristics when implemented in wavelength de/multiplexers and power splitters in the C-band spectrum [33]: low thermal sensitivity, which means there is only a minor shift in wavelength as the temperature changes by a single degree Celsius [34], and low back reflection. Researchers have demonstrated that the MMI coupler based on SiN has a promising potential [35]. Previous studies have demonstrated that it is possible to reduce ILs by utilizing SiN in a demultiplexer that is based on the Mach-Zehnder interferometer and operates in the O-band [36]; however, there was somewhat significant crosstalk. Back-reflection losses have not been addressed whatsoever. When fabricating waveguide structures, surface roughness needs to be considered. However, when using SiN material, the surface roughness is smaller than 0.5 nm, which is less than 2.5% of the fabrication tolerance. Moreover, a previous study measured a low optical loss of approximately 1.27 dB/cm for a width of 3 \(\upmu\)m [37]. These results make the roughness losses in SiN neglectable. This study proposes a design for a two-channel wavelength demultiplexer that splits two channels in the C-band light spectrum using an MMI coupler in a SiN buried waveguide structure. It was discovered that the suitable wavelengths were 1540 nm and 1560 nm. Moreover, a further study was made about separating two wavelengths in the C-bandwindow for four different spacings, which are 10 nm, 20 nm, 30 nm, and 40 nm. This study can be utilized to design multi-channel MMI couplers based on SiN strip waveguides [38]. The device architecture is based on a two-channel MMI coupler alongside a single input waveguide segment and taper, two output tapers, and two s-bends. In order to achieve the self-imaging effect and identify the ideal MMI coupler parameters, the geometrical dimensions of the buried waveguide structure and the MMI couplers were investigated. The beam propagation method (BPM) [39; 40; 41; 42] and finite-difference time-domain (FDTD) simulations were run, the results were evaluated, and Python codes were used to assess and process the results.
## 5 Experimental Results and Analysis
### Machine Learning-Based Modeling Attacks
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Learning Model**} & \multirow{2}{*}{**Challenge Bits**} & **Prediction** & \multirow{2}{*}{**Training CRP**} & \multirow{2}{*}{**Ref.**} \\  & & **Rate** & & \\ \hline LR & 64 & 66.5\% & 40,000 & [133] \\ \hline SVM & 64 & 74.7\% & 40,000 & [133] \\ \hline LR & 64 & 97\% & 40,000 & [137] \\ \hline LR & 64 & 99\% & 6500 & [138] \\ \hline SVM & 64 & 86.31\% & 1000 & [130] \\ \hline LR & 64 & 80\% & 40,000 & [139] \\ \hline LR/SVM & 64 &
\begin{tabular}{c} 51.22\%/ \\ 52.61\% \\ \end{tabular} & \multirow{2}{*}{2,097,152} & \multirow{2}{*}{[128]} \\  & & & \\ \end{tabular}
\end{table} 
Table 6: Comparing ML accuracy of APUF models in the literature. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Learning Model**} & \multicolumn{8}{c}{**Number of CRP’s**} \\ \cline{2-10}  & **50** & **2155** & **4261** & **6366** & **8472** & **105,77** & **12,683** & **14,788** & **16,994** & **19,000** \\ \hline SVM & 54.6\% & 60.7\% & 61.6\% & 61.2\% & 61.3\% & 61.2\% & 61.1\% & 61.1\% & 61.1\% \\ \hline LR & 55.8\% & 63.5\% & 63.5\% & 63.5\% & 63.5\% & 63.5\% & 63.5\% & 63.5\% & 63.5\% & 63.5\% \\ \hline MLP & 57\% & 93.7\% & 94.3\% & 94.9\% & 94.9\% & 95.3\% & 95.4\% & 95.2\% & 96.3\% & 95.4\% \\ \hline Random forest & 49.7\% & 85\% & 91.6\% & 94\% & 93.9\% & 94.8\% & 95.9\% & 95.2\% & 95.5\% & 95.9\% \\ \hline Gradient Boosting & 56.3\% & 63\% & 63.4\% & 63.8\% & 63.1\% & 63.2\% & 63.3\% & 62.5\% & 62.7\% & 63\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ML prediction accuracy versus the CRPs of proposed APUF.
## 3 Models and Methods
### Proposed Approach for Collectively Summurizing Text Streams  
The first part is the real-time event detection algorithm, which analyzes the life cycle of events and the distribution of word weights through dynamic term weighting [47] in the instant message stream. In the early stage of the event, potential major events are captured, and then the real-time event cluster is combined into an event sequence through the self-adaptive stream clustering method. The algorithm for real-time event detection is shown in Algorithm 1. ```
1:INPUT: TS[1, 2,..., m] // Twitter Streams
2:OUTPUT: RTED[1, 2,..., \(\epsilon\)]; // Real-Time Event Detection
3:1: Begin
4:for each TS[i]
5: BursT \(\leftarrow\) Dynamic Term Weighting(i)
6: RTED \(\leftarrow\) Self-Adaptive Stream Clustering(BursT, i)
7:endfor
8:End
```  
\[BursT_{w,t}=BS_{w,t}\times TI_{w,t} \tag{1}\]  
\[BS_{w^{q}_{r},t}=max\left\{\frac{ar_{w^{q}_{r},t}-E\Big{(}ar_{w^{q}_{r},t}\Big{)} }{E\Big{(}ar_{w^{q}_{r},t}\Big{)}},0\right\} \tag{2}\]  
\[TI_{w^{q}_{r},t}=1-\frac{-1}{ln\Big{|}M^{[t_{g},\ t]}}*ln\frac{\Big{|}w^{q}_{r }\Big{|}}{\Big{|}m:\ w^{q}_{r}\in W^{[t_{h},\ t]}} \tag{3}\]  
Dynamic Term Weighting can be divided into two factor values: Burst Score, \(BS\) and Term Importance, \(TI\), as shown in Formula (1). In Formula (2), Burst Score is to calculate the burstiness of a keyword according to the rate at which the keyword arrives. In Formula (3), the key word weight is the logarithm of the ratio of the number of documents in the global time window to the number of messages containing the keyword to determine the popularity of the keyword [47]. Self-adaptive stream clustering algorithm [48] is used to represent the situation that, when a new message flows into the system, the message will be compared with the existing online group for similarity. When the similarity comparison result is greater than the threshold, the new message will be added to the most similar group in the online group. If it is less than the threshold, the system will create a new group and add the message. At the same time, the system will remove non-informative messages and aging online cluster for similarity groups based on the adjustable sliding window module and the time decay function. The proposed self-adaptive stream clustering algorithm is shown in Algorithm 2. Once the adaptive stream clustering algorithm is used to process stream messages in real time, the number and weight of features will be automatically adjusted within the thread time width tw. As shown in the Algorithm 2, when a new message flows into the system, the message will be compared with the existing online cluster for similarity evaluation. When the similarity comparison result is greater than the threshold, the new message will be added to the most similar cluster. If it is less than the threshold, the system will create a new cluster and add the message. At the same time, the system will remove non-informative messages and aging online clusters based on the adjustable sliding window.
## 2 Materials and Methods
### RNA Isolation, Reverse Transcription, and Real-Time PCR Analysis  
RNA was isolated from skin samples using a tissue homogenizer and RiboZol reagent (Amresco LLC, Solon, OH, USA) according to manufacturer's instructions. cDNA was synthesized from 500 ng RNA using the RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, Inc., Waltham, MA, USA). Real-time PCR was performed on the resultant cDNA using EvaGreen 5X qPCR mix (qARTA Bio Inc., Carson, CA, USA) and 10 pmol/uL forward and reverse primers in a LightCycler 96 (Roche Diagnostics, Mannheim, Germany). The amplification conditions consisted of 1 cycle of initial denaturation at 95 \({}^{\circ}\)C for 15 min followed by 40 cycles of denaturation at 95 \({}^{\circ}\)C for 15 s, annealing at 56 \({}^{\circ}\)C for 20 s, and extension at 72 \({}^{\circ}\)C for 20 s. The primers used to amplify Transforming growth factor beta 1 (_Tgfb1_), alpha-smooth muscle actin (_Acta2_), and hypoxanthine phosphoribosyltransferase (_Hprt_) are shown in Table 1. _Hprt_ was used as an internal control to normalize gene expression data. The relative quantities of mRNA expression were determined using the 2\({}^{-\Delta\text{ACI}}\) method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene** & **Name** & **Primer (5’-3’)** \\ \hline _Tgfb1_ & Transforming growth factor beta & Forward: CACTCCCTGGCTTCTACTGTG \\ \hline _Acta2_ & Alpha-smooth muscle actin & Reverse: GGACTGGCCGAGCCTTACGTGT \\ \hline _Hprt_ & Hypoxanthine & Forward: GTAATGACGTCAACGGGGAC \\  & phosphoribosyltransferase & Reverse: CCCAAGCCTTGCAACCTTAACCA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primer sequences for real-time PCR analysis.
## 2 Results and Discussion
### From Emulsion Droplets to Hybrid Nanospheres  
Due to the combined effect of steric and electrostatic stabilization, the prepared mini-emulsions exhibit sufficient colloidal stability under mini-emulsion polymerization. However, it is worth pointing  
Figure 5: The average diameter (\(D_{p}\)) and the polydispersity index (PDI) of the mini-emulsion droplets, at the TiO\({}_{2}\)/monomer mass ratio, 1:2, determined by DLS, as a function of the number of passes through the MC at three shear rates, (**a**) \(\dot{\gamma}=2.9\times 10^{6}\) s\({}^{-1}\) ; (**b**) \(3.4\times 10^{6}\) s\({}^{-1}\) and (**c**) \(3.9\times 10^{6}\) s\({}^{-1}\), respectively.  
out that we noticed that, after several days, there was a vertical gradient concentration along the depth in latex. This phenomenon is inevitable since TiO\({}_{2}\) has such a high density (4.23 g\(\cdot\)cm\({}^{-3}\)) that the TiO\({}_{2}\) NCs/monomer droplets tend to sediment, finally leading to phase separation. Therefore, the freshly prepared emulsion should undergo polymerization immediately after ITS treating.  
Figure 6a,b show the TEM pictures of the hybrid nanospheres obtained after the polymerization, at the TiO\(\cdot\)/monomer mass ratio equal to 1:1.5 and 1:2, respectively. It is seen that within each sphere, the TiO\({}_{2}\) primary NPs are uniformly clustered and embedded in the cross-linked polymeric matrix, as indicated by the intensive dark spots in the TEM images. The average diameter of the nanospheres estimated from Figure 6a is \(D_{p}\approx 110\) nm, consistent with the result obtained from the DLS measurement, 110 nm, as shown in Figure 3a. The sphericity of all these nanospheres is satisfactory, although the smoothness of the edges is not perfect. This may arise from the high mass and irregular shape of the TiO\({}_{2}\) primary NPs within each sphere such that slight exposure of these rigid TiO\({}_{2}\) primary NPs to the edge of the sphere would result in slightly irregular morphology. This is supported by the results in Figure 6b where, due to a smaller TiO\(\cdot\)/monomer ratio (1:2), the edge of the nanospheres is smoother. In addition, we have also prepared the hybrid nanospheres at the TiO\(\cdot\)/monomer ratio, 1:4, (pictures not shown), and found that not only the edge of the nanospheres becomes further smoother, but also their average diameter becomes smaller and the polydispersity is lower (\(D_{p}=107\) nm and PDI = 0.02, from the DLS measurements). This is understandable since as the monomer mass increases in the emulsion droplets, the distance among the TiO\({}_{2}\) NCs in the droplets increases, and it follows that the breakage of the droplets becomes easier. In fact, in the extreme case of pure monomer, the droplet diameter reduces to 41 nm under the same treating conditions. Therefore, by changing the TiO\({}_{2}\)/monomer ratio, one can tune the size and sphericity of the hybrid nanospheres. However, it should be noted that at the TiO\(\cdot\)/monomer ratio smaller than 1:4, some nanospheres containing zero or one TiO\({}_{2}\) NC appear.
## 4 Results and Discussion
### Sensor Fault Detection  
The fault detection mechanism can be explained with the help of Figure 10, which illustrates several time steps of a bias fault sample and the corresponding prediction by the model. As discussed in the methodology section, our model takes input window \(w_{1}\) and predicts the next 20 time steps in \(\psi_{1}\), as indicated in Figure 10. This implies that with this model, we can estimate the future trends in the sensor data, and therefore, if there is an unusual change in the sensor data stream due to the possible presence of a fault, the model can predict the trend a few time steps ahead; thus, the presence of a fault can be determined. Considering input window \(w_{i}\) in the same figure, a bias fault occurred immediately after the segment. Once the model has window \(w_{i}\) as input, it will predict change due to a bias fault, and by calculating the four selected features from the predicted values, decisions can be made as to whether there is a fault in segment \(\hat{w}_{i}\) with the help of the first classifier mentioned in the proposed methodology.  
From prior information on fault locations recorded during injection of synthetic faults into the normal sensor signals, we created a labeled dataset consisting of the four selected features calculated from the prediction window, in which the corresponding time steps in the predicted signal are labeled as normal or faulty. Utilizing this dataset with some of the most widely used classifiers to identify whether the future predicted segment is faulty or normal, we exceeded 90% accuracy for all the classifiers. The commonly used classification metrics are listed in Table 4.  
Figure 8: Training and testing segments.  
Figure 9: Sensor signal prediction performance by the autoencoder.  
Fault detection performance can be observed more clearly from the confusion matrices in Figure 11. The performance metrics indicate that the boosting algorithm classifiers performed slightly better in comparison to the other classifiers. KNN had comparable performance in detecting the presence of faults in the predicted trends of the sensor signals.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Random forest & 93.39\% & 95.47\% & 91.98\% & 93.70\% \\ XGBost & 93.84\% & 95.12\% & 93.25\% & 94.18\% \\ SVM & 92.34\% & 92.00\% & 93.81\% & 92.90\% \\ KNN & 93.24\% & 95.46\% & 91.70\% & 93.54\% \\ LightGBM & 93.47\% & 94.83\% & 92.83\% & 93.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fault detection performance by the classifiers.  
Figure 11: Fault detection performance for different classifiers: (**a**) random forest, (**b**) XGBoost, (**c**) SVM, (**d**) KNN, and (**e**) LightGBM.  
Figure 10: Concept of fault detection with multi-step-ahead prediction.
## 3 Computational Modeling  
The research methodology used in the present work was based on Computational Fluid Dynamics (CFD). This method approaches the mass and momentum conservation equations differentially through a system of algebraic equations, which can be solved computationally [36]. The VOF method is also used to reproduce the interaction between the fluids, water, and air involved in the numerical simulation.  
The concept of volume fraction (\(\alpha_{q}\)) is necessary to represent the phases contained in each control volume where \(\alpha\) is a continuous variable in space and time, representing the presence of fluid inside the control volume [37]. Thus, the model consists of the two-phase mass conservation equation [38]:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot\left(\rho\overrightarrow{v} \right)=0 \tag{2}\]  
The volumetric fraction equation is given by  
\[\frac{\partial}{\partial t}\left(\rho\alpha_{q}\right)+\
abla\left(\rho\alpha _{q}\overrightarrow{v}\right)=0 \tag{3}\]A momentum equation for the mixture water/air is solved in the computational domain and given by  
\[\frac{\partial}{\partial t}\left(\rho\overrightarrow{v}\right)+\
abla\left( \rho\overrightarrow{v}\overrightarrow{v}\right)=-\
abla p+\
abla\left(\tau \right)+\rho\overrightarrow{g} \tag{4}\]  
where \(\rho\) is the density of the fluid (kg/m\({}^{3}\)), \(t\) is the time (\(s\)), \(\overrightarrow{v}\) is the flow velocity vector (m/s), \(p\) is the static pressure (Pa), \(\mu\) is the viscosity (kg/m.s), \(\tau\) is the tension tensor, and \(\overrightarrow{g}\) the acceleration of gravity (m/s\({}^{2}\)).  
The VOF method does not explicitly calculate the position of the free surface between fluids. Thus, it is necessary to discretize the volumetric fraction in the interface region between the two fluids. In this way, cells with \(\alpha_{water}\) between 0 and 1 contain the interface between water and air (\(\alpha_{air}=1-\alpha_{water}\)). If \(\alpha_{water}=0\), the cell is without water and full of air (\(\alpha_{air}=1\)), and the same goes for the complete water cell and without air.  
The zone's physical properties between the two fluids are calculated as the weighted averages based on the volumetric fraction. In this way, density and viscosity are written, respectively, as below [39], and Table 1 presents the properties of the fluids [40]:  
\[\rho =\alpha_{water}\rho_{water}+\alpha_{air}\rho_{air} \tag{5}\] \[\mu =\alpha_{water}\mu_{water}+\alpha_{air}\mu_{air} \tag{6}\]  
The solution of Equations (2) and (4) is achieved with the FVM. The FVM is a way to obtain a discrete version of a partial differential equation (PDE). The method's main advantage is that spatial discretization is done directly in physical space, so there are no problems with the coordinate systems' transformations, as in the finite difference method (FDM) [41; 42].
# _photonics_
###### Abstract  
Quantum key distribution (QKD) has gained a lot of attention over the past few years, but the implementation of quantum security applications is still challenging to accomplish with the current technology. Towards a global-scale quantum-secured network, satellite communications seem to be a promising candidate to successfully support the quantum communication infrastructure (QCI) by delivering quantum keys to optical ground terminals. In this research, we examined the feasibility of satellite-to-ground QKD under daylight and nighttime conditions using the decoy-state BB84 QKD protocol. We evaluated its performance on a hypothetical constellation with 10 satellites in sun-synchronous Low Earth Orbit (LEO) that are assumed to communicate over a period of one year with three optical ground stations (OGSs) located in Greece. By taking into account the atmospheric effects of turbulence as well as the background solar radiance, we showed that positive normalized secure key rates (SKRs) up to \(3.9\times 10^{-4}\) (bps/pulse) can be obtained, which implies that satellite-to-ground QKD can be feasible for various conditions, under realistic assumptions in an existing infrastructure.  
Keywords:quantum key distribution (QKD); satellite constellation; free-space optics (FSO); turbulence; Low Earth Orbit (LEO); decoy-state BB84 QKD +
Footnote †: journal: _Photonics_
# Stationary Target Identification in a Traffic Monitoring Radar System
## 1 Introduction
Thus, we propose a method for discriminating stationary road structures and moving vehicles using the velocity  
Figure 1: Traffic monitoring radar installed on the overpass. characteristics of the detected targets, which makes the detection performance of the traffic monitoring radar reliable. The authors in [12] proposed a method to classify moving targets and stationary targets in the time-frequency domain. However, in this method, it is impossible to immediately classify the target because the data accumulated in the time axis are used. In other words, the method in [12] cannot be applied in real time. In our work, the target characteristics in the angle-velocity domain are used to classify stationary structures and moving vehicles. Unlike moving targets, stationary targets have a certain tendency in this domain, which means stationary targets appear as sample points on a line. Thus, if the straight line can be estimated, we can only extract stationary targets. To estimate the trend line, we use the M-estimator sample and consensus (MSAC) algorithm [13], which is a more advanced algorithm from random sample consensus (RANSAC) [14; 15]. The RANSAC and MSAC algorithms can extract points with a certain trend from multiple sample points. Using the data measured on the highway, we evaluate the performance of the proposed classification method and compare it with the conventional method such as linear regression. Through the proposed method, it is possible to classify meaningful targets among a number of detected targets at every signal processing cycle, unlike the method in [12]. In addition, our proposed method shows stable and reliable performance because it does not classify the targets using a simple threshold value, but classifies them by finding a trend in the measured data. The remainder of this paper is organized as follows. In Section 2, we introduce our traffic monitoring radar and basic signal processing used for target detection in the radar system. Then, the signal measurement environment and target detection results using our radar sensor are described in Section 3. Next, in Section 4, the proposed stationary target identification method and its classification performance are presented. Finally, we conclude this paper in Section 5.