esa-sceva/satcom-chunk-collection huggingface dataset:
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 4 Modeling of the Rain and Fog Effect in the Virtual LiDAR Sensor
### Physical Rain Model  
The physical rain model aims to simulate the characteristics of a rain simulator inside the virtual simulation environment. In this work, we have generated the virtual rain according to the rain distribution of the NIED rain facility recorded by the real disdrometer. Rather than evenly distributing the droplets in the environment, they are \"spawned\" from rain sources, such as sprinklers or a homogenous planar rain source. Once the droplets are spawned, their movement is determined by physical equations concerning gravity, drag forces, turbulent flow, and droplet deformation according to the base concept published by [PERSON] [28]. Figure 4 shows an exemplary visualization of the rain field generated with the physical rain model.  
It is not within the scope of this paper to explain the detailed working principle of the physical rain model, nor will its mathematical model be discussed here. The physical rain model provides access to all the properties of the droplets, including position, speed, and diameter. Based on these properties and the characteristics of the sensors' beam, the backscattered \(\beta_{back,rain}\) and extinction \(\sigma_{ext,rain}\) effects can be applied to the virtual LiDAR signal.
# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 6 Conclusions
By contrast, AI-enhanced WAMS leverage sophisticated ML algorithms and analytics to offer unprecedented situational awareness and fault prediction. These systems can process vast amounts of data from PMUs at high speeds, providing operators with real-time insights and predictive analytics that enable proactive fault detection and swift corrective actions. Moreover, the adaptability of AI ensures that these systems can continuously learn from historical data and improve their predictive models, making them highly effective in anticipating and mitigating potential faults before they escalate into significant issues. This continuous learning starkly contrasts with the static nature of SCADA systems, which rely heavily on predefined rules and manual interventions. AI-driven WAMS can identify patterns and anomalies that may not be immediately apparent to human operators, thus providing grid reliability and resilience. Finally, integrating AI with WAMS facilitates advanced applications in CPPS, such as automated fault location, isolation, and system restoration, which are critical for minimizing downtime and maintaining smart grid stability. The ability to rapidly respond to and recover from disturbances reduces the economic impact of power outages and improves the power system's overall efficiency. In essence, the synergy between AI and WAMS mitigates faults more effectively than SCADA systems can. The combination ushers in a new era of intelligent, self-responsive, CPPS that far surpass the capabilities of traditional SCADA systems and WAMS individually. The future of power system management lies in this integration, promising more resilient, reliable, and efficient energy infrastructure.
# Dual-Gratings Imaging Spectrometer
## 5 Comparison with Traditional Scheme  
Here, we are still taking the ultra-spectral imaging with H\(\alpha\) line, ionized calcium H-line, and K-line of sun as an example. For the spectrum observation of the sun's surface in the three bands, the most mainstream solution for ground-based telescopes is to use filters, and some also use birefringent filters or polarization spectrometers [19]. These schemes all take images of the sun in a very narrow band. Therefore, more attention is paid to temporal  
Figure 12: Optical structure of dual-gratings spectrometer.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline System focal length & 600 mm \\ Waveband & 391.4 nm–398.8 nm, and 653.281 nm–658.281 nm \\ Grating line pairs & 1350 L/mm and 1350 L/mm \\ Slit & 16 mm \(\times\) 5 \(\upmu\)m \\ Detector & 5 \(\upmu\)m (To calculate the number of spectrum data) \\ NA(object side) & 0.05 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the experimental system.  
and spatial changes, ignoring continuous changes in the spectrum. Solar observation satellites generally choose ultraviolet or extreme ultraviolet bands, and only analyze the visible light band as a whole. In these solutions, the filters can provide a very narrow band of high-precision solar imaging, so it is most widely used. However, it cannot provide continuous spectral data unless a new filter is replaced. Grating-type imaging spectrometer is an option commonly used to provide real-time continuous spectrum data. At present, traditional planar grating imaging spectrometers are used in some solar ultra-spectral imaging projects, such as the scientific research project 'Chinese H\(\alpha\) Solar Explorer'.  
Tables 4 and 5 take the ultra-spectral imaging of these three bands as an example to compare traditional imaging spectrometer, the filters and the dual-gratings imaging spectrometer.  
Compared with the traditional imaging spectrometer and the dual-gratings imaging spectrometer, the dual-gratings imaging spectrometer can obtain high spectral resolution while the volume is much smaller than the traditional design.  
Compared with the filter and dual-gratings scheme, the filter has high spatial resolution and can obtain high-precision solar images, but cannot output continuous spectrum data in real time. In 2008, in the TESIS experiment, two telescopes (FET) used filters with mirror coatings to obtain solar images with a bandpass of 0.6 nm [20]. However, this data is still low compared to imaging spectrometers. The dual gratings spectrometer can output thousands of continuous spectrum data in real time.This makes it possible to have more discoveries in the spectral dimension.
# Application of Genetic Algorithms for Periodicity Recognition and Finite Sequences Sorting
## 4 Genetic Algorithm
### Fitness Function  
To evaluate the fitness function at each iteration for the problem under consideration, it was necessary to make a _periodicity_ check so that the sequence _did not consist of only the value_ 0 or 1.  
The work of the function to determine the periodicity of each individual has the following pseudocode (Algorithm 1):  
```
forindex\(\gets 0\) to \((N-\text{Period})\)do ifindividual[index]\(=\) individual[index + Period] then  checker\(\leftarrow\) True else  checker\(\leftarrow\) False endif endfor ifchecker\(=\) True then return True endif
```  
**Algorithm 1** Function CheckPeriodOrNo(individual, Period)  
It is known that even sequences consisting of a single value of 0 or 1 are periodic. Therefore, we will not consider this solution. Thus, it was necessary to create two additional auxiliary functions: CheckSameOrNo and ExistOrNo. The CheckSameOrNo function just  
Figure 2: Illustration of genotypes in a population for the problem under consideration.  
checks the above, and the second ExistOrNo function checks for the identity of the found solution in the solution area.  
\[\texttt{CheckPeriodOrNo}(\text{indiv.},\text{Period})=\begin{cases}\text{True},& \frac{\forall\text{idx}\in[0,N-\text{Period}-1]:}{\text{indiv.}[\text{idx}]= \text{indiv.}[\text{idx}+\text{Period}],}\\ \text{False},&\text{otherwise}.\end{cases}\]  
\[\texttt{CheckSameOrNo}(\text{indiv.})=\begin{cases}\text{True},&\forall\text{idx }_{1},\forall\text{idx}_{2}\in[0,N-1]:\text{indiv.}[\text{idx}_{1}]=\text{ indiv.}[\text{idx}_{2}],\\ \text{False},&\text{otherwise}.\end{cases}\]  
\[\texttt{ExistOrNo}(\text{solutions},\text{indiv.})=\begin{cases}\text{True},& \text{indiv.}\in\text{solutions},\\ \text{False},&\text{otherwise}.\end{cases}\]  
\[\texttt{CalculateFitness}(\text{indiv.})=\begin{cases}\text{True},&\texttt{ CheckPeriodOrNo}(\text{indiv.},\text{Period})=\text{True}\\ &\texttt{CheckSameOrNo}(\text{indiv.})=\text{False}\\ &\texttt{ExistOrNo}(\text{solutions},\text{indiv.})=\text{False},\\ \text{False},&\text{otherwise}.\end{cases}\]  
Thus, the objective function itself has the following pseudocode (Algorithm 2):  
```
fitness \(\gets 0\) ifCheckPeriodOrNo\((\text{indivdival},\text{Period})=\text{True}\)then ifCheckSameOrNo\((\text{indivdival})=\text{False}\)then ifExistOrNo(solutions,\text{indival})=\text{False}\)then fitness \(\leftarrow\) fitness \(+\)1 endif endif endif return fitness
```  
**Algorithm 2** Function CalculateFitness(individual)
# Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy +
## 3 Our Proposed Approach
### Creating the Reference Map  
The reference of our map is created by leveraging Google Earth Pro. In early 2015, Google Earth Pro costed about $400, and now it is free to use [27]. We firstly created the center lane path and saved it into KML format. Then, we extracted the coordinates of this path and stored them as our reference map of the center lane. Figure 4 shows the reference map used in this work created by using Google Earth Pro.  
Figure 3: Illustration of Map-matching process using sliding window (\(sw_{i}\)).
## 3 Results and Discussion
### Compressive Strength of Mortar  
Figure 10 presents the set up for the compression testing, and mortar cube failure after testing. Figure 11 depicts the compressive strength of the mortar cubes with respect to lime mortar types (LL and PL) and mixing ratios. The highest strength of the LL mortar specimens was 3.11 MPa (LL-1 mixing ratio) and the lowest strength was 2.53 MPa (LL-3). For  
Figure 8: Experimental setups for Diagonal compression.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Maximum Load** & **Compressive Strength** \\  & **(kN)** & **(MPa)** \\ \hline
1 & 1024 & 60.97 \\
2 & 766 & 44.81 \\
3 & 1001 & 62.19 \\
4 & 1063 & 65.96 \\
5 & 1128 & 58.49 \\ \hline Average & 1000 & 60.56 \\ Deviation & 137 & 7.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Brick compressive strength results.  
Figure 9: Brick compression test. (**a**) Before testing, (**b**) after testing—front view, (**c**) after testing—back side view.  
the PL mortar cubes, it was 3.08 MPa (PL-3 mixing ratio) and 2.29 MPa (PL-2), respectively. It can be inferred from the results that, without white cement, the LL mortar specimens exhibited greater compressive strength compared to their PL mortar counterparts under the same mixture ratio. The compressive strengths of LL-1, LL-2 and LL-4 were 24.4%, 16.6% and 8.6% greater, respectively, than PL-1, PL-2 and PL-3. In contrast, with the addition of white cement, the compressive strength of the PL mortar specimen was 21.7% greater than that of the LL specimen.
# Secure Beamforming in 5G-Based Cognitive Radio Network
## 4 Numerical Evaluation  
The numerical rating of the proposed method was conducted in this section and the results were compared with the existing cascaded ZFBF techniques [12]. A proposed technique has a greater performance than the existing technique. The simulation parameter, which is used in the proposed technique, is shown in Table 1.  
This article was implemented using the software named 'Matlab' (The MathWorks, Inc., Natick, MA, USA) of version R2017b in the platform of 64-bit version in the x86 instruction set.  
In Figure 5, the secrecy rate of the PU and the SU is plotted to the transmitting SNR. The proposed method was also compared with the existing method of coordinated beamforming (CoBF) technique [3] and the cascading ZFBF techniques [12], respectively. The secrecy rate of the proposed method is increased with the SNR and its performances are higher than the existing methods. The CoBF technique is implemented in both the transmitting and receiving beamformers. The interference channel matrix is aligned by the transmitting beamforming technique and nullified by the receiving beamformer. Likewise, the cascaded ZFBF technique performs better than the CoBF technique [3], but the proposed performance provides a better result compared with the cascaded ZFBF method [12]. Nevertheless, an eavesdropper obstructs the interference in which the receiving beamformer is not used. The interference alignment and the proposed method are applied at the BS. Therefore, the secrecy rate increases with the SNR.  
The secrecy outage probability for the PU and the SU estimates for the different target data rate as, \(\mathrm{D_{pu}^{J,\ A}}\) and \(\mathrm{D_{su}^{J,\ A}}\). Then, the secrecy outage probability of the SU is \(\mathrm{SOP_{su}^{J,\ A}}\), which is higher than the secrecy outage probability of the PU as, \(\mathrm{SOP_{pu}^{J,\ A}}\). The probability of the secrecy outage will be increased, as the target data rate has a larger value.  
The output results, which are shown in Figures 5-9, respectively, show the effectiveness of the proposed method.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Network Size & \(200\times 200\) m\({}^{2}\) \\ \hline Channel Bandwidth & 1 MHz \\ \hline Rayleigh fading channel, \(\sigma_{u}^{2}\) & 0.8 \\ \hline Path-loss exponent, \(\upsilon\) & 3 \\ \hline Radius of inner disc, \(\mathrm{r_{i}}\) & 10 \\ \hline Radius of outer disc, \(\mathrm{r_{o}}\) & 20 \\ \hline Data rate for PU, \(\mathrm{D_{pu}^{J,\ A}}\) & 0.25, 1 \\ \hline Data rate for SU, \(\mathrm{D_{su}^{J,\ A}}\) & 1, 4 \\ \hline Number of antennas for BS, M & 2 \\ \hline Number of antennas for user, N & 2 \\ \hline Number of clusters of each cell, L & 4 \\ \hline Number of cells, C & 2 \\ \hline Number of bits/symbols, \(\mathrm{B_{i}}\) & \(10^{6}\) bits \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 5: Secrecy rate.  
Figure 6: Secrecy outage probability.  
Figure 7: Bit error rate of the ZFBF and the proposed technique.
## 4 Model Comparison  
The oil-based mud used in this paper conforms to the Bingham rheological model. Based on the experimental data, the values of plastic viscosity and static shear force at the corresponding temperature and pressure were calculated. The plastic viscosity and static shear force were predicted by the exponential model, the polynomial model, and the BP neural network, respectively.  
Based on the predicted rheological parameters, the shear stress values at various rotational speeds under experimental conditions can be calculated. Due to the model established in this article directly predicting the shear stress values at six different rotational speeds, in order to compare the advantages and disadvantages of the model, the shear stress values at six different rotational speeds were calculated based on the three models.  
The relationship between the shear stress predicted by different methods and the measured value is shown in Figure 7.  
It can be seen from the figure that there are many prediction points with errors greater than 10% in the polynomial model, and the prediction points with errors greater than 10% in the polynomial model and the neural network model are significantly fewer than those in the rheological parameter model. The prediction points with errors greater than 10% in the model established in this paper are the fewest, and the whole prediction effect of the model established in this paper is the best.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rheological Model** & **Functional Form** & **Rheological Parameters** & **Deviation, Pa** \\ \hline Bingham model & \(\tau=\tau_{0}+\mu\gamma\) & \(\tau_{0}=2.204\) & 0.986 \\  & & \(\mu=0.093\) & \\ Power-law model & \(\tau=K\gamma^{n}\) & \(K=0.153\) & 2.068 \\  & & \(n=0.93\) & \\ H-B model & \(\tau=\tau_{0}+K\gamma^{n}\) & \(\tau_{0}=3.385\) & 0.385 \\  & & \(n=1.068\) & \\ Ross model & \(\tau=A(\gamma+C)^{B}\) & \(A=0.093\) & \\  & & \(B=1\) & 0.986 \\  & & \(C=23.582\) & \\ Carson model & \(\sqrt{\tau}=\sqrt{\tau_{c}}+\sqrt{\eta_{\infty}}\sqrt{\gamma}\) & \(\tau_{c}=0.209\) & 1.752 \\  & & \(\eta_{\infty}=0.087\) & \\ Four parameter model & \(\tau=\tau_{0}+a\gamma+b\gamma^{c}\) & \(a=0.094\) & 0.985 \\  & & \(b=0.0000963\) & \\  & & \(c=0.09\) & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Rheological optimization results.  
Figure 7: Comparison between predicted values and actual values of different models.  
The bottom hole ECD values calculated based on the methods established in this article, and traditional methods are shown in Table 12 below, which also lists the bottom hole ECD values measured by PWD.  
The error of the method established in this article for calculating ECD is 0.02 g/cm\({}^{3}\), while the traditional method is 0.06 g/cm\({}^{3}\). The ECD based on this method is more accurate.
## 3 Rate Analysis of MU-PhC-MIMO VLC Systems  
This section starts by analyzing the achievable rate for \(\text{User}_{k},\forall k\), deriving the corresponding expression, and providing the expression for the weighted sum rate under the scenario of \(K\) users. Additionally, we discuss an approximate expression for system rates based on the ZF scheme.
###### Abstract  
Six fractional polysaccharides were prepared by water extraction and alcohol precipitation under controlled temperature from bletillae rhizoma, a traditional Chinese medicine. Based on this, yields of bletillae rhizome polysaccharides (RBPs) were obtained. The extracting temperature impacted the characteristics of the fractional polysaccharides. The fractional polysaccharides were characterized by glucomannan (GM) content, thermal stability, scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectroscopy, gel permeation chromatography (GPC), and X-ray diffraction (XRD). For the analysis, 2.0% \(w/v\) dispersions of the six fractional polysaccharides were prepared and their flow behaviors were evaluated using a rotational rheometer. The results showed that increased extraction temperature led to increased GM extraction yields and extraction rate, but GM content was relative stable (over 90%). The average molecular weight (Mw) of fractional polysaccharides obtained at 30, 40, 50, 60, 70, and 80 \({}^{\circ}\)C was 3.598 \(\times\) 10\({}^{4}\), 4.188 \(\times\) 10\({}^{4}\), 8.632 \(\times\) 10\({}^{4}\), 8.850 \(\times\) 10\({}^{4}\), 2.372 \(\times\) 10\({}^{5}\), and 3.081 \(\times\) 10\({}^{5}\) g/mol, respectively. SEM revealed that fractional polysaccharides had a porous structure of different sizes and densities. Thermal analysis, FTIR, and XRD results indicated that extraction temperature affects the structure and moisture content of fractional polysaccharides. All results showed that the extraction temperature has an obvious impact on the morphology, molecular weight, and polydispersity of the RBPs. This simple process is a promising method for the preparation of fractional polysaccharides.  
bletillae rhizome; fractional polysaccharides; molecular weight; temperature Article
# Aerial Robotic Solution for Detailed Inspection of Viaducts
## 1 Introduction
### Aerial Robots Localization  
Different sensors can be used to achieve effective positioning of aerial robots. Total stations can be used to localize an UAV in motion with respect to a reference system, as in [23]. This presents several problems, such as the high cost of this tool, as well as the dependence on flying continuously in line of sight with it or having an uninterruptedwireless communication system with the aerial robot. In addition, total stations only provide position information, not orientation.  
On the other hand, the localization problem can also be solved through monocular or stereo visual cameras, as in [24; 25]. The problem with visual cameras is that they are totally dependent on external light conditions. This is especially problematic when navigating near infrastructure because of the shadows and light changes it can cause. Other localization systems use a LIDAR as the main sensor [26]. These sensors do not depend on external light conditions, as they use lasers to measure distances. While their application in autonomous driving is on the rise, their use in aerial robots is still limited because they require more available payload and a more powerful onboard computer than cameras. In [27], a comparison of several algorithms applied to aerial robots is made.
# Lactic Acid Modified Natural Rubber-Bacterial Cellulose Composites
## 3 Results
### Contact Angle Measurement  
The static contact angle was used to identify the improvement of the material hydrophilicity (Figure 4). Because of the hydroxyl groups in cellulose, it has high hydrophilic characteristics and tends to strongly interact with water. BC is considered to be a hydrophilic substance and the degree of water contact angle of BC was found to be around 47.5 \(\pm\) 2.8. However, the degree of water contact angle of NR, which is a hydrophobic polymer, was around 116.2 \(\pm\) 7.8. The dynamic water contact angle of the NRBC composites decreased as BC loading increased. According to lactic acid modification, ANRBC20 showed an increase in hydrophilicity when compared with NRBC20 film owing to a more hydrophilic polymer surface generated by acid treatment. However, there were no significant changes in hydrophilicity between ANRBC and NRBC at BC loadings \(\geq\)50 wt.%. Therefore, the acid modification played an important role in hydrophilicity of the NRBC films only for those containing a low content of BC.  
Figure 4: The static water contact angle of NR, BC, NRBC, and ANRBC films. The values are expressed as mean \(\pm\) SD (n = 3).  
Figure 3: FTIR spectra of NR, BC, NRBC, and ANRBC films.
## 3 Research Method
### Monitoring Tools for Performance Analysis  
An important aspect of our research is the performance of the solution in the Kubernetes-based environment, which was analyzed by installing and configuring monitoring tools. We considered two monitoring tools: Prometheus and IBM Cloud Monitoring (Sysdig).  
According to [29], Prometheus is a monitoring and alerting tool, with a time series database that provides a query language named PromQL. This language gives the user the ability to make expressions (queries) that can display a graph or a table in real-time. Nowadays, Prometheus is very popular in the containerization world due to its default integration with the enterprise Kubernetes platform OpenShift. As visualization software for the Prometheus tool, we use Grafana, which properly integrates with Prometheus.  
The Sysdig monitor has the role of delivering monitoring, security, and forensics for an environment based on containers. As capabilities, the Sysdig monitor has dashboards, metrics for infrastructure, and containers alongside alerting [30]. The tested solution sits under the IBM Cloud service catalog and is named IBM Cloud Monitoring.  
Table 1 presents a comparison between Prometheus and Sysdig, based on the following criteria: ease of installation, multi-cloud implementation, ease of use, resource consumption, and pricing. Based on them, we notice that the Prometheus and Grafana solution is more suitable in our case, due to the deployment capability on a Kubernetes service not  
dependable on a cloud provider, and because it is open source. The ease of configuration can be achieved by automating the deployment using helm charts.