esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Protocol
### Hierarchical Routing  
Routing and clustering mechanisms are interrelated in WSNs, both of them targeting to minimise energy consumption. It is desirable that packet forwarding and routing protocol overhead is distributed among all the sensor nodes according to their KPI values. This approach preserves scarce sensors recourses and, thus, extends the network lifetime.  
Energy efficiency in NEURON is achieved by hierarchical reactive routing. Nodes are organized into a hierarchy of clusters based on network proximity to the CHs. There is no proactive mechanism to build and maintain a valid routing table as the network topology continuously evolves. In addition, routing mechanism takes advantage of the routing cache entries generated during the clustering process, as presented in Section 3.2.  
The routing algorithm in NEURON presents similarities with the DSR routing protocol [40]. NEURON adopts some mechanisms from DSR for communication among CHs, while intra-cluster communication is designed independently. A _RouteRequest_ message is used for detecting a valid route  
Figure 4: Routing in NEURON.  
to a destination node, in accordance to the DSR protocol. In NEURON, however, the _RouteRequest_ message is not flooded but directly forwarded to the CH of the transmitting node. The exact path to the CH is known via the _MSolicitateCH_ messages broadcasted by CHs in regular intervals.  
When a node desires to establish communication with another node, it initially sends a _RouteRequest_ message to its CH (Figure 4). The message contains the exact path towards the CH (source routing). If a node receives a _RouteRequest_ message, it initially checks whether it operates as a CH. This is necessary because the election of new CHs is a dynamic process and thus new CHs may be present. If the node is not a CH, then it forwards the message to the next hop towards the CH according to the disseminated path from the initiator node. In case that a node along a path is unreachable, a \(RouteError\) message is generated and broadcasted within the scope of the cluster that contains the broken link. If the message is delivered to a CH during its path, the CH queries its local cache for the requested route towards the destination. If the valid entry is found, a _RouteResponse_ message is sent to the initiating node. Otherwise, the CH forwards the _RouteRequest_ message to its known CHs, exactly as routing is implemented in DSR. The CH of the destination node will directly reply to the initiator node with the correct end-to-end path (Figure 5).  
When a route to a destination is known then a _Message_Transfer_ message is initiated. This message contains the source node, the destination node, the route that must be followed in the WSN in order to reach the destination and a flag that informs the destination node whether it should respond with a confirmation (acknowledgement). The _Message_Transfer_ message is used for transferring upper layer data, e.g., overlay topology formulation.
# A Compact Monopole Wideband Antenna Based on DGS
## 3 Simulation Results and Analysis
### Parametric Analysis on G-Shaped Patch Antenna
#### 3.6.1 Effect of Variation of Lt  
A parametric analysis was conducted on the length (Lt) of the G-shaped patch antenna to improve impedance matching and bandwidth, as shown in Figure 13a. Adjusting Lt from 3 mm to 5 mm in 1 mm increments revealed that Lt significantly impacts antenna performance. The simulated S11 values indicate optimal impedance matching and bandwidth at 4 mm length, while variations above or below 4 mm lead to dual-band behavior and higher S\({}_{11}\) values.
## 4 Discussion
### Comparison with Traditional DBN Method  
To address the uncertainty in the evaluation process, the data to be assessed underwent a conversion process using the CM, as depicted in Figure 6 and Table 4. The resultant membership degrees were subsequently employed as the prior probabilities for the root node within the DBN. To compare the difference between the CM-enabled DBN and traditional DBN methods in dealing with data uncertainty, the DBN reasoning result of the cable fire risk probability under the traditional threshold method [49] is shown in Figure 16. It is evident that the trends of the reasoning results of the two methods are almost identical. However, on the one hand, the fire risk probability using the traditional DBN is consistently lower than that of the proposed method. On the other hand, the fire probability of the first phase (0-16 min) obtained by the traditional DBN is always zero, which is inconsistent with reality. This result may be due to the rough division of the data, which makes the threshold method less sensitive to uncertain data. The traditional DBN employs a frequency-based statistical method, where the occurrence frequency of assessment data in different states over a period of time is used as the prior probability input for root nodes. This represents a simplistic and rough division at the data level, consequently leading to final inference results of 0. Accordingly, compared with the traditional threshold method, CM can obtain  
Figure 15: Dynamic BM importance of the root nodes within 10 time slices.  
more information in monitoring data and expert scoring data, and the reasoning results based on CM-enabled DBN are more sensitive and reliable.
## 3 Analysis of Geophysical Data
### Gravity and H/V Correlation
As in the case of Vina del Mar, there is a negative relation between period and gravity residual, although in Santiago the results are more disperse with a lower R\({}^{2}\). However, the correlation is better for soft sites. The trend's slope also increases when these soft sites are considered. Additionally, the linear trend in Vina del Mar is presented in both panels of Figure 9 as a black continuous line, while the black dashed line corresponds to the same trend translated (\(-1.5\) mGal) to better fit with the results in Santiago. From this figure, it is possible to see that the slope of Vina's trend also fits well with the results of Santiago, although it is higher than the calculated linear regression (red continuous line). Nevertheless, the black line is displaced in respect to the Santiago data. This situation is possibly related with some regional gravity effect that gives a different level to Vina del Mar (located in the Chilean coast) compared to Santiago (located in the Chilean central valley). Finally, Figure 10 shows the comparison between all the cities grouped in this work, divided into two panels as in the other figures (upper panel: all the soils categories; lower panel: only soils type D and E). In this case, the colored squares in the upper panel represent the different cities, as shown in the legend. In this figure, the data from Concepcion was added to strengthen the relationship. Concepcion is one of the biggest cities in Chile, located in the central-south portion of Chile (see location in Figure 1). Unfortunately, information about all of these same variables was not available from Concepcion; therefore, it was not possible to process and present it in the same way as for the other cities. However, given its relevance, we consider it pertinent to present the H/V predominant periods and gravity in the present analysis and compilation (extracted from [PERSON] et al. [23]). From the Figure, it is possible to see that the Mejillones results (black squares) are located in higher periods compared to other cities, indicating the high thickness of the infilling sediments in the zone. Figure 8: Correlation between gravity residuals and H/V predominant periods in Iquique. The colors represent the seismic classification of the sites according to Table 1.  
Nevertheless, the trend between gravity and period is still negative and similar to the other figures showing that, in general, the higher the period the higher the gravity residual. The linear correlation, considering all the cities, is satisfactory, and it improves when only soft sites are considered, increasing from an R\({}^{2}\) of 0.79 to an R\({}^{2}\) of 0.87. The trend line increases its slope when only soft sites are considered as in the last cases.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 2 Proposed Approach
### Hierarchical Event Correlation Model  
The hierarchical event correlation model comprises two levels of correlation. The first level performs the aggregation, clustering, and context supplementation steps of the process model, while the second level performs event interconnection and additional context supplementation. The input to the model is a stream of raw events such as network event records from a packet sniffer or live digital feeds of intelligence data such as vulnerability feeds or blacklisted IP addresses and domains. The output from the first level are clusters of events representing network sessions between pairs of communicating hosts at varying levels of correlation. These clusters become the input to the second level. The second level further clusters these events and merges them to create higher-level representations of the communication flows. The output from the second level is communication graphs which can be analyzed visually or by an IDS to uncover suspicious patterns that may indicate an attack. The high-level view of the proposed event correlation model is shown in Figure 3 below.  
Figure 3: Hierarchical event correlation model.  
Figure 2: Event correlation process.  
The details of each step of the process are covered in the subsequent sections for each level of the correlation hierarchy.
## 3 Photon Polarization
### Calculation Results
In this case, we fixed the initial electron energy to 5 GeV, and the normalized laser intensity \(a_{0}\) changes from 50 to 1000. The QED parameter \(\eta_{\rm e}\approx 2\gamma_{\rm e}|E_{\perp}|/E_{\rm s}\) and produced photon energy \(E_{\gamma}\approx 0.44\)\(\eta_{e}E_{\rm e}\) increase with increasing  
Figure 5: \(\gamma\) photon energy spectrum from theoretical QED calculation (red curves) and PIC simulations (black curves) for different electron bunch initial energies, (**a**) 1, (**b**) 2, (**c**) 5, and (**d**) 10 GeV. with a fixed laser intensity of \(a_{0}=192\). Other parameters are the same as in Figure 1.  
laser intensity. Similarly, the photon production rate and cutoff energy increase with increasing laser intensity, as shown in Figure 7a. However, the photon polarization degree decreases with increasing laser intensity, as shown in Figure 7b. For the high electron energy (5 GeV) and high laser intensity (\(a_{0}=1000\)) case, the electron Lorentz factor \(\gamma_{e}\) and QED parameter \(\eta_{e}\) are efficiently enhanced and the NCS process is triggered with high efficiency. However, at an extremely high laser intensity, the ponderomotive force \(\Gamma_{\rm P}\sim-m_{e}c^{2}\
abla a_{0}^{2}\) acting on an electron is extremely high, thereby scattering high-speed electrons. This results in a short interaction time and low polarization conversion rate. Therefore, the photon polarization degree is only approximately 45% for the extremely high laser intensity of \(a_{0}=1000\), as shown in Figure 7b. For the low laser intensity (\(a_{0}=50\)), the ponderomotive force acting on an electron is lower than that acting in the high-intensity laser case. High-energy electrons can penetrate the high field region and stay for a long time in this region (indicating a long interaction time). This may result in a high polarization conversion rate. Thus, 1 GeV level linearly polarized photons with a high polarization degree of 63% are produced in the low laser intensity configuration, as shown in Figure 7b. Notably, in the theoretical calculation of the photon polarization in Section 3, we considered the photon energy distribution only from single Compton scattering. However, in a simulation, an electron can experience multiple Compton scattering, which would change the energy of the acquired electron bunch, causing the theoretical results slightly different from our simulation result shown in Figure 5.
# Influence of Phosphogypsum Dump on the Soil Ecosystem in the Sumy region (Ukraine)
## 3 Results and Discussion
### The Study of the Soil from Different Horizons in the Territory Near the Phosphogypsum Dump
The Sr content in soil is highly controlled by parent rocks and climate, and therefore, its concentrations ranges from 50 to 1000 mg.kg\({}^{-1}\)[29]. Nevertheless, content of strontium can be associated with the influence of the phosphogypsum dump in the Sumy region, which requires further monitoring. The soil fluoride levels measured in this study were comparable to the average soil fluoride of soils globally [24]. Based on the results from XRF analysis (Table 3), as well as the diffraction pattern, the chemical compositions of soil horizons indicate the presence of alumino-silicates in the studied samples. Figure 7 shows the infrared spectra of soil horizons. The absorption peaks at wavenumbers 3620, 3363, and 1633 cm\({}^{-1}\) are assigned to H-O-H stretching vibrations of water molecules that are weakly hydrogen-bonded to the Si-O surface [30]. The spectra of the soil horizons show an intensive band at 1003 cm\({}^{-1}\) attributed to the Si-O stretching vibrations, and at 693 and 453 cm\({}^{-1}\), which could be assigned to Si-O-Al (octahedral Al) and Si-O-Si bending vibrations, respectively. Another characteristic band of amorphous silica was observed at 776 cm\({}^{-1}\)[31]. In addition, three horizons revealed the presence of an absorption band at 1434 and 875 cm\({}^{-1}\), referring to the stretching vibrations of CO\({}_{3}\) groups [32]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sample** & **MgO** & **Al\({}_{2}\)O\({}_{3}\)** & **SiO\({}_{2}\)** & **P\({}_{2}\)O\({}_{5}\)** & **SO\({}_{3}\)** & **K\({}_{2}\)O** & **CaO** & **Fe\({}_{2}\)O\({}_{3}\)** & **SrO** & **F** \\ \hline
**S1 (S1)** & 1.231 & 9.19 & 49.98 & 0.17 & 0.31 & 1.71 & 0.50 & 4.07 & 0.019 & \textless{}0.05 \\
**H1 (S1)** & 1.048 & 7.65 & 56.09 & 0.42 & 0.53 & 1.34 & 3.19 & 2.33 & 0.018 & 0.06 \\
**E1 (S1)** & 1.104 & 8.25 & 57.4 & 0.46 & 0.55 & 1.47 & 4.13 & 2.61 & 0.022 & 0.05 \\
**E2 (S1)** & 1.094 & 8.159 & 55.22 & 0.44 & 0.35 & 1.52 & 3.77 & 2.66 & 0.021 & 0.05 \\
**I1 (S1)** & 1.715 & 10.79 & 61.63 & 0.16 & 0.10 & 1.90 & 0.96 & 3.61 & 0.012 & 0.05 \\
**H2 (S2)** & 1.067 & 8.54 & 58.85 & 0.16 & 0.17 & 1.64 & 0.74 & 2.66 & 0.016 & \textless{}0.05 \\
**H3 (S3)** & 1.073 & 8.83 & 64.66 & 0.28 & 0.20 & 1.81 & 1.13 & 2.77 & 0.018 & \textless{}0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Chemical composition of soil horizons from Haplic Luvisols soil (sampling point S1) and soils around the phosphogypsum dump (in %).
## 3 Methodology
### Fixed Scenario  
The iFogSim2 toolkit was used to run the simulation of the fixed scenario, where a fixed number of CCTV cameras, CO\({}_{2}\) devices, and lighting sensors were inputted into the number of devices in the iFogSim2 algorithm code. The floor plan of the smart office building included fixed devices numbering 7, 17, and 22 for the CCTV, CO\({}_{2}\), and light sensors, respectively. The fixed scenario was designed to reflect a predefined set of smart IoT devices placed across various locations around the real-world floor plan in the smart office building. The parameter values were sourced from real-world data derived from the datasheets of the smart IoT devices used in this study. The number and types of the smart IoT devices was predetermined by the real-world building infrastructure and did not vary outside these fixed numbers for the simulation. The parameter values such as MIPS, RAM, Uplink, and Downlink bandwidth were set to specific real-world values and remained constant during the simulation experiment. The performance metrics for the energy consumption, latency, and network bandwidth usage data were collected while simulating with the fixed conditions, representing the fixed scenario method.
# Evaluation and Improvement of Lidar Performance Based on Temporal and Spatial Variance Calculation
## 6 Conclusions  
The equivalence between the variance and the mean is the characteristic of the Poisson distribution of this data set. In this paper, calculation models for temporal variance and spatial variance were constructed to evaluate the variance-mean distribution characteristics of lidar analog data and PC data. The calculation results show that both the spatial variance and the temporal variance of the PC data distributions in far field were basically equal to their mean, while the PC data in the near field do not conform to the Poisson distribution due to the pile up effect. The variances of the analog data were not equal to their mean, meaning that the analog data do not conform to the Poisson distribution, but the data in near field had the potential to be converted to PC-like data using linear transformation.  
To make the spatial variance equal to their corresponding mean in the variance-mean plots, the dead time of the Licel TR40-160 in ultraviolet elastic scanning lidar was estimated to be 3.488 ns. The optimal threshold voltage of the PC system and the linear working range of the PMT were evaluated using the number of non-zero PC data, which was counted using the variance distribution. The experimental results show that the optimal threshold voltage of the PC system in Licel TR40-160 is 1.20 mV, and the linear working range of the high voltages of the PMT is between \(-\)500 V and \(-\)1000 V.  
A novel method for data gluing between analog data and PC data is proposed based on variance calculation. The analog data can be transformed to PC-like data by a linear transformation, in which the transfer coefficients are investigated by minimizing the differences between the variances and their corresponding means. The experimental results show that the normalized lidar data trace improved the lidar detection capability and expressed the atmospheric condition uniformly.  
All authors made great contributions to the presented work. [PERSON], [PERSON] ([PERSON]) and [PERSON] made the variance calculation model and discussed its application on lidar performance. [PERSON] and [PERSON] provided the technical guidance and contributed to the investigation of lidar performance. [PERSON], [PERSON] and [PERSON]. ([PERSON]) helped with the LiDAR measurements and data processing.  
This research was funded by the National Natural Science Foundation of China, grant number 41775035, 41627807, the China Postdoctoral Science Foundation, grant number 2014M560799, and the Natural Science Basic Research Plan in Shaanxi Province of China, grant number 2019 JM-416. The Intergovernmental Scientific and Technological Cooperation Project between China and Slovenia (2016-105-11-8) supported the academic exchange between the University of Nova Gorica and Xi'an University of Technology.  
We acknowledge the support of academic exchange by Slovenian Research Agency and Ministry of Science and Technology of the People's Republic of China.  
The authors declare no conflict of interest.
# Mesh Twisting Technique for Swirl Induced Laminar Flow Used to Determine a Desired Blade Shape
###### Abstract  
Swirling flow has been shown to increase heat transfer in heat exchangers. However, producing swirl while not presenting a severe pressure drop can be a challenge. In this paper, a desired shape of guidance blades for laminar swirl flow is determined by numerical simulation in OpenFOAM. Emphasis is on the mesh technique, where a predefined blade shape is formed by mesh twisting, or morphing. The validity of numerical simulations on a twisted mesh is shown by comparing it to the theoretical solution of laminar flow in a pipe without swirl and guidance blades. A sensitivity study shows that a cell size ratio of 0.025 of diameter is sufficient and affects the solution minimally. To determine the desired shape of guidance blades previously found optimal swirl decay and velocity profile for laminar swirling flow are utilized. Three blade shapes are explored: (I) with a twist angle that varies with axial location only; (II) having a deviation angle matching the theoretical deviation angle for laminar swirling flow; (III) same as II but with a hollow center. Simulations are performed for \(\mathrm{Re}=100\) and swirl number \(S=0.2\). Case II is able to sustain swirl longest while maintaining a low pressure drop and is therefore a desired swirler shape profile as predicted theoretically.  
swirling flow; laminar flow; mesh morphing; guidance blades/vanes; OpenFOAM
## 2 LDPC Convolutional Codes  
An LDPC-CC of rate \(R=b/n\) can be defined by the \(n\times p\) polynomial syndrome former matrix (the polynomial-domain transposed parity-check matrix)\[\textbf{H}^{T}(D)=\begin{bmatrix}h_{0,0}(D)&h_{0,1}(D)&\ldots&h_{0,p-1}(D)\\ h_{1,0}(D)&h_{1,1}(D)&\ldots&h_{1,p-1}(D)\\ \vdots&\vdots&\ddots&\vdots\\ h_{n-1,0}(D)&h_{n-1,1}(D)&\ldots&h_{n-1,p-1}(D)\end{bmatrix} \tag{1}\]  
where \(p=n-b\). The n-tuple of a codeword is given by  
\[\textbf{V}(D)\triangleq[\textbf{v}^{(0)}(D),\textbf{v}^{(1)}(D),...,\textbf{v }^{(n-1)}(D)] \tag{2}\]  
and we have  
\[\textbf{V}(D)\textbf{H}^{T}(D)=\textbf{0}(D). \tag{3}\]  
After multiplexing, the codeword in Equation (2) can also be expressed as  
\[\textbf{v}(D)\triangleq D\cdot\textbf{v}^{(0)}(D^{n})+D^{2}\cdot\textbf{v}^{ (1)}(D^{n})...+D^{n}\cdot\textbf{v}^{(n-1)}(D^{n}). \tag{4}\]  
A periodically shifted codeword \(D^{l}\textbf{V}(D)\) or (\(D^{n\cdot l}\textbf{v}(D)\)), \(l\)\(\in\)\(\mathbb{Z}\), is also a codeword, and, consequently, it satisfies the constraint imposed by the polynomial syndrome former matrix, _i.e._,  
\[D^{l}\textbf{V}(D)\textbf{H}^{T}(D)=\textbf{0}(D) \tag{5}\]  
Given \(\textbf{H}^{T}(D)\) in Equation (1), its weight matrix is defined as  
\[\textbf{B}=\begin{bmatrix}W(h_{0,0}(D))&W(h_{0,1}(D))&\cdots&W(h_{0,p-1}(D)) \\ W(h_{1,0}(D))&W(h_{1,1}(D))&\cdots&W(h_{1,p-1}(D))\\ \vdots&\vdots&\ddots&\vdots\\ W(h_{n-1,0}(D))&W(h_{n-1,1}(D))&\cdots&W(h_{n-1,p-1}(D))\end{bmatrix} \tag{6}\]  
where \(W(h_{i,j}(D))\) indicates the number of additive items (monomials) of the polynomial entry \(h_{i,j}(D)\). Note that, if \(h_{i,j}(D)\) is an empty entry, then \(W(h_{i,j}(D))\)=0. The maximum power of the monomials in Equation (1) is defined as the syndrome former memory \(m_{s}\). If there are \(J\) and \(K\) monomials in each row and each column of Equation (1) respectively, then the LDPC-CC is regular and denoted by \((m_{s},J,K)\).
# An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks
## 3 The Proposed Algorithm
### Network Model
However, in the light of Definition 2, only neighbor \(3\), neighbor \(4\) and neighbor \(6\) in the candidate intermediate region (the red area) are selected as the intermediate nodes to forward messages. **Definition 3**. : _We define the intermediate node degree as the number of intermediate nodes in the candidate set whose distances and predicted distances to the next hop nodes are less than the current nodes and corresponding intermediate nodes, denoted by \(\eta_{rel}\)._  
For instance, Figure 2 shows that the neighbor degree of node \(S\) is \(6\); however, the intermediate node degree is \(3\), according to Definition 3.