esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulation & Terminology  
Let \(\Phi=\{c_{1},c_{2},c_{3},\dots,\ c_{n}\}\) be a set of active connections/traffic flows supported by the system of interest. Also, let \(\mathcal{M}=\{m_{1},m_{2},\dots,\ m_{k}\}\) be a set of malicious connections. For simplicity reasons, assume that \(\mathcal{M}\
eq\ \Phi\) and \(\mathcal{M}\subset\ \Phi\). Then, there exists a configuration that can be obtained from simply partitioning the connections in \(\Phi\) into multiple subsets \(u_{1},\ u_{2},\ \dots u_{l}\) so that there is at least one \(u=\ \mathcal{M}\). To discover \(\mathcal{M}\) our strategy may engage in an iterative process of partitioning \(\Phi\) into further subsets. Finally, we assume that each iteration of partitioning involves some cost \(\mathcal{W}\). Then, in its simplest variation, the task at hand can be defined as to discover a partitioning strategy that identifies \(\mathcal{M}\), with _arg min_\(\mathcal{W}\). By discovering such a configuration, one may achieve full isolation of malicious connections without any prior knowledge of the status of each connection (malicious, benign).  
Notice that the optimal configuration merely depends on the applied partitioning algorithm and the time needed to achieve that configuration; thus, the resources of the underlying environment. Therefore, when the decided partition strategy is applied, the infrastructure should expect to see an insignificant impact on their resource allocation, i.e., decreased replica servers' utilization, increased network traffic, and power consumption. Additionally, the effectiveness of migration techniques depends on the quality and maturity of existing technologies. Increasing the frequency of migration can help identify or mitigate the cause of an attack, but this comes with added defense costs [21]. Additionally, the configuration of migration spaces, such as the structure of the network environment can be pivotal towards improving security. In this work, we do not investigate those impacts since that requires a large-scale realistic setup, e.g., a testbed residing on a cloud data center.
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 5 Performance Evaluation
### Performance Comparison
#### 5.2.6 Influence of Data Transmission Speed  
The impact of data transmission speed on delivery ratio is shown in Figure 21. It can be seen that the delivery ratio increases as the transmission speed increases for all schemes. This is because nodes can forward more messages with larger data transmission speed during the limited contact time. The delivery ratio of CBCAIS scheme is highest, because of the effective message forwarding. The delivery ratio of the direct scheme is the lowest.  
The impact of data transmission speed on delay is shown in Figure 22. It can be seen that the delay decreases as the transmission speed increases for all schemes. This is because messages can be forwarded quickly with large data transmission speed, which leads to a reduction in the average delay. The delay in the CBCAIS scheme is slightly longer than in the SMART, Multicenter, and GTA schemes, because more messages with longer delay are delivered. The delay in the direct scheme is the smallest because each node only buffers its own message, and cannot forward messages to others with smaller delivery delay.  
Figure 21: Delivery ratio for varying transmission speeds.  
Figure 22: Delay with varying transmission speeds.  
The impact of data transmission speed on overhead ratio is shown in Figure 23. It can be seen that the overhead ratio of the direct scheme is always 1; for other schemes, the overhead ratio decreases as the transmission speed increases. This is because more messages can be successfully forwarded with larger data transmission speed. The overhead ratio of CBCAIS is lower than in the SMART, Multicent, and GTA schemes.  
The impact of data transmission speed on energy is shown in Figure 24. It can be seen that the energy increases as the transmission speed increases for all schemes. This is because more messages are successfully forwarded with larger data transmission speed, which will consume more energy. The energy consumption of CBCAIS is slightly higher than in the SMART, Multicent, and GTA schemes. The energy consumption of the direct scheme is lowest because the messages are not forwarded by other nodes.
## 3 Results and Discussion
### Volatile Profile of the Extract
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compounds** & **DES-2 without PEFs** & **DES-2 with PEFs** \\ \hline Methylene chloride & nd & 1.65 \\
2-Pentenal & 0.57 & 0.58 \\
1-Chloropentane & nd & 0.13 \\
2-Penten-1-ol & 4.25 & 4.16 \\ _cis_-3-Hexenal & 0.45 & 0.29 \\ Caproaddehyde & 1.86 & 1.77 \\
2-Hexenal & 1.69 & 2.14 \\
3-Hexen-1-ol & 3.51 & 3.39 \\
1R-\(\alpha\)-Pinene * & 21.60 & 22.23 \\ Camphene & nd & 1.02 \\ Methylene chloride & nd & 1.65 \\
2-Borrene & 0.84 & nd \\ \(\gamma\)-Terpinene & nd & 1.15 \\ \(\beta\)-Terpinene & 0.20 & nd \\ (-)-\(\beta\)-Pinene & 0.12 & nd \\ Limonene & 1.02 & 0.63 \\ _trans_-\(\beta\)-Terpineol & nd & 0.65 \\ _cis_-Sabinene hydrate & 1.39 & nd \\ Pelagnaldehyde & 0.89 & nd \\
8-Linalool & 0.47 & 0.70 \\ L-Camphor & 0.63 & 0.65 \\ _trans_-Pinocarveole & nd & 0.73 \\ Neomenthone & nd & 2.61 \\ L-Menthone & 2.41 & nd \\ \(\alpha\)-Pinocarvone & 0.73 & nd \\ Camphol & 1.17 & 1.14 \\ Isomenthon & 0.28 & 0.33 \\ L-Borneol * & 6.46 & 5.93 \\ Terpinen-4-ol & 2.39 & 2.20 \\ (-)-Myrtenal & 0.27 & 0.27 \\ \(\alpha\)-Terpinol * & 8.60 & 7.24 \\ \(\alpha\)-Cubenene * & 8.04 & 7.32 \\
2,5-Dimethylhex-5-en-3-yn-2-ol & nd & 0.76 \\ \(\alpha\)-Copene & nd & 0.22 \\ \(\beta\)-Cubebene & 3.08 & 2.83 \\ \(\beta\)-Caryophyllene & 1.49 & 2.00 \\ \(\delta\)-Cadinene & 0.79 & 0.32 \\ \(\beta\)-Copaene & 0.21 & nd \\ \(\beta\)-Selinene & nd & 0.62 \\ (+)-epi-Bicyclosesquiphellandrene & 0.55 & 0.49 \\ _cis_-Murola-3,5-diene & 2.27 & 2.52 \\ L-Calamene & 3.46 & 3.90 \\ \(\beta\)-Cadinene & 3.81 & 3.47 \\ Cadinadiene-1,4 & 2.12 & 2.34 \\ Caryophyllene oxide * & 5.73 & 5.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Volatile compounds identified in the extracts prepared with DES-2 with and without PEFs, by HS–SPME/GC–MS. Values represent the % area of each compound over the total area of the identified compounds; nd: not detected; * denote the compounds with the highest concentration.
###### Abstract  
In 5G networks and beyond, managing handovers (HOs) becomes complex because of frequent user transitions through small coverage areas. The abundance of small cells (SCs) also complicates HO decisions, potentially leading to inefficient resource utilization. To optimize this process, we propose an intelligent algorithm based on a method that utilizes a fuzzy logic controller (FLC), leveraging prior expertise to dynamically adjust the time-to-trigger (TTT), and handover margin (HOM) in a 5G ultra-dense SC heterogeneous network (HetNet). FLC refines TTT based on the user's velocity to improve the response to movement. Simultaneously, it adapts HOM by considering inputs such as the reference signal received power (RSRP), user equipment (UE) speed, and cell load. The proposed approach enhances HO decisions, thereby improving the overall system performance. Evaluation using metrics such as handover rate (HOR), handover failure (HOF), radio link failure (RLF), and handover ping-pong (HOPP) demonstrate the superiority of the proposed algorithm over existing approaches.  
fuzzy logic controller; HCP; heterogeneous networks; handover; ultra-dense small cell networks; 5G +
Footnote †: journal: Accepted: 2024
## 3 UAV Path Planning Method Based Modified Fruit Fly Optimization Algorithm
### Msfoa
#### 3.1.4 The Process of MSFOA  
The whole process of the MSFOA can be described by Algorithm 2.  
```
1/*Initialization*/ Set the number of the fruit fly population \(M_{\text{pop}}\), the max number of iterations NC, swarms'
1 initial position \([x_{\text{best}},y_{\text{best}}]\), swarm number \(g\), the threshold, genetic coefficient of hybridization \(\alpha\text{e}_{1}\) and \(\alpha\text{e}_{2}\), the parameter \(R\).
2 for\(nc=1\): NC
3 for\(g=1\): G
4 /*Multi-sucarm with multi-task searching strategy */
5 for\(i=1\): \(M_{\text{pop}}/G\)
6 if\(\text{{s}mell}_{\text{best}}^{(S)}\) is larger than threshold
7 \(X_{i}^{(g)}=x_{\text{best}}^{(g)}+\sin((\pi/2)*\text{random}(-1,1))\)
8 \(Y_{i}^{(g)}=y_{\text{best}}^{(g)}+\sin((\pi/2)*\text{random}(-1,1))\)
9 else
10 \(X_{i}^{(g)}=x_{\text{best}}^{(g)}+R*\text{random}(-1,1)\)
11 \(Y_{i}^{(g)}=y_{\text{best}}^{(g)}+R*\text{random}(-1,1)\)
12 end if /*Calculate smell concentration */
13 \(S_{\text{old},i}^{(g)}=\sqrt{(X_{i}^{(g)})^{2}+(Y_{i}^{(g)})^{2}}\)
14 \(\text{{s}mell}_{\text{old},i}^{(g)}=F(S_{\text{old},i}^{(g)})\)
15 /*Vision searching in old swarms*/
16 \(\text{{Index}}_{\text{old}}^{(g)}=\underset{i}{\text{argmin}}(\text{{s}mell}_{ \text{old},i}^{(g)})\)
17 end for
18 end for /*Competitive strategies of offspring*/
19 for\(g=1\): G
20 for\(i=1\): M\({}_{\text{pop}}/G\)
21 \(X_{\text{new},i}^{(g)}=\alpha\text{e}_{1}*x_{\text{old}}^{\text{random}(1,G)} +c\text{e}_{2}*X_{i}^{(g)}\)
22 \(Y_{\text{new},i}^{(g)}=c\text{e}_{1}*y_{\text{old}}^{\text{random}(1,G)}+c \text{e}_{2}*Y_{i}^{(g)}\)
23 /*Vision searching in new swarms */
24 \(S_{\text{new},i}^{(g)}=\sqrt{(X_{\text{new},i}^{(g)})^{2}+(Y_{\text{new},i}^{( g)})^{2}}\)
25 \(\text{{s}mell}_{\text{new},i}^{(g)}=F(S_{\text{new},i}^{(g)})\)
26 \(\text{{Index}}_{\text{new}}^{(g)}=\underset{i}{\text{argmin}}(\text{{s}mell}_{ \text{new},i}^{(g)})\)
27 end for
28 end for /*Competitive strategy of offspring */
29 for\(g=1\): G
30
```  
**Algorithm 2**MSFOA
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 4 Materials and Methods
### Synchronization Preamble Design  
In a practical communication system, a preamble is usually added before each frame of data to be sent at the transmitter. At the receiver, the received signal is correlated with the local sequences, and the exact position of the frame header is determined by detecting the correlation peak. PN sequences are often used as a preamble. CAZAC sequences have better autocorrelation and cross-correlation properties than traditional PN sequences. As can be seen from Equation (5), the correlation peak value is proportional to the length of the CAZAC sequences; therefore, it is easier to detect the frame header with a longer preamble. On the other hand, the existence of CFO will affect the performance of correlation peak detection. The longer the preamble, the greater the influence of CFO; therefore, it is better for the preamble to be shorter.  
In order to reduce the impact of CFO, while still guaranteeing the performance of the correlation peak detection, a novel preamble based on the ZC sequences is proposed. The structure of the new preamble is shown in Figure 2.  
It can be seen from Figure 2 that a data frame is composed of a preamble and \(k\) OFDM symbols (including CP). The preamble consists of \(m\) identical ZC sequences of length \(N\), and \(m\) and \(N\) satisfy \(N=N_{\text{FFT}}/m\), where \(N_{\text{FFT}}\) is the length of the OFDM symbols.
## 4 Numerical Applications
### Sensitive Analysis
#### 4.2.3 Effect of the Failure Rate of the Transmission Line  
Figure 10 shows the effect of the transmission line reliability on the installed \(P_{dchmax}\) of energy storage under a certain system reliability. In the study, the failure rate is utilized to model the reliability of transmission lines. Therefore, the influence of the failure rate on the installed \(P_{dchmax}\) of energy storage is studied. From Figure 10, under a certain system reliability, it can be noted that the variation trend of the desired of the four different installed locations (i.e., LP40, LP30, LP45 and DG) are consistent. Moreover, the installed \(P_{dchmax}\) of energy storage increases with the failure rate of the transmission line. With the increasing of the failure rate, the system reliability decreases. Therefore, the system reliability risk brought by the increasing failure rate of transmission lines should be compensated by other factors. The energy storage system can effectively improve the system reliability by peak load shifting, and the increase in \(P_{dchmax}\) of energy storage well compensates for the system reliability risk. Thus, \(P_{dchmax}\) of energy storage will increase with an increasing failure rate to maintain the certain system reliability. When the transmission failure rate is determined, there is no larger difference between the different installed locations when the failure rate of transmission line is 0.5; the desired installed energy storage at different locations becomes different when the failure rate of the transmission line is more than 0.6. The desired installed \(P_{dchmax}\) increases at LP40, LP30, LP45 and DG, respectively. The desired value is close when the energy storage is installed at LP30 and LP40, and is lower than that when the installation occurs at LP45 and DG. The relation between the installed locations is irrelevant to the failure rate of the transmission lines.  
Figure 9: Changing of the \(P_{dchmax}\) of energy storage against the failure rate of the generators.
## 6 Experiments on SBS Mitigation and Parametric Amplification with a DOF
### Comparison of SBS Threshold  
In this section, we discuss the experiment to determine the strength of SBS associated with two nonlinear fibers, a DOF and a standard HNLF with constant dispersion. The length of the standard HNLF used is 1 km with a constant dispersion of \(\beta_{2}=-0.07\) ps\({}^{2}\)/km. The available DOF used here is of length 1.25 km and has an oscillating dispersion profile as with a wavelength \(z_{\text{mod}}=250\) m and phase \(\varphi_{\text{mod}}=0\) rad. The average dispersion \(\overline{\beta}_{2}=12\) ps\({}^{2}\)/km, dispersion modulation amplitude \(\beta_{2}=2\) ps\({}^{2}\)/km.  
Figure 16a depicts the schematic of the experimental setup that is employed to measure the SBS threshold. First, light from a narrow linewidth laser is passed through an erbium doped fiber amplifier (EDFA) and fed to the fiber under test (FUT) through a circulator. The reflected signal from the FUT obtained at port 3 of the circulator constitutes of pump and the Stokes wave if the input power to FUT is near to or higher than the SBS threshold. The Stokes wave is filtered out from this reflected signal (also containing Rayleigh back-reflections) using a flat-top band pass filter (Yenista, XTM-50) of bandwidth 10 GHz centered at Stokes frequency (10.335 GHz for DOF and 9.634 GHz for HNLF). The power of the filtered Stokes wave for different input power levels is then measured using a powermeter (PM) to obtain the reflectivity as a function of input power, where reflectivity is defined as the ratio of reflected power to input power. The reflectivity for different input power levels for these FUT, indicating a significant difference in the SBS threshold levels are shown in Figure 16. As expected, reflectivity is seen to be increasing with increase in input power in both the fibers. From Figure 16b, the SBS threshold power, which is defined as the input power for which the reflectivity is 1% (\(-20\) dB), is found to be 12.2 dBm for HNLF and 15.5 dBm for DOF. The reflectivity at a particular input power is higher for the HNLF and thus its SBS threshold is correspondingly lower than that of the DOF. In case of the DOE, the coherent build-up of back-scattered waves is prevented through periodic oscillations  
Figure 15: Brillouin frequency shift vs. length of the fiber considering different dispersion modulation wavelength: \(z_{\text{mod}}=181\) m (red), 281 m (blue) and 377 m (magenta). Parameters of the DOF are provided in the text.  
of dispersion and hence, the threshold is found to be much higher compared to that of the HNLF.
## 4 Experiments
### Application in Low Textured Scenes
#### 4.4.2 Implementation Details  
The experiment uses the BiResNeRF architecture, and the experimental details are basically the same as those in Section 4.3, only requiring modification of the learning rate parameters. For pose parameters, the learning rate decays from \(1\times 10^{-2}\) to \(1\times 10^{-5}\), with a decay coefficient of 0.999827. In the first stage, the learning rate of the low-resolution module is set from \(5\times 10^{-3}\) to \(1\times 10^{-4}\), with a decay coefficient of 0.999902. The learning rate of the high-resolution module remains low at \(5\times 10^{-5}\). In the second stage, the learning rate of the neural radiance field parameters of the high-resolution module increases to \(0.005\times 0.3\), and then decreases to 0.0001. Since there is no initial pose, all camera initial poses are aligned with the object, i.e., the coordinate origin, to ensure that the neural network can be trained normally. The training process was carried out for 40,000 iterations.
## 3 Results  
### Obtaining the Orthophoto Plan and the Digital Elevation Model (DEM)  
The first concrete result needed for the inundation study was the orthoimage of the study area (Figure 7). To have a better view of the orthophoto plane in the studied area, the option \"Show Base Map\" was used, which more clearly highlights the surroundings adjacent to the area of interest.  
Figure 5: The cloud of points after classifications.  
Figure 6: Building and georeferencing the DEM.  
Therefore, all the stages necessary for the generation of the orthophoto plane were completed, with each stage having its own characteristics. At the beginning of the process, 185 aerial photographs were aligned, generating 232,347 points, representing the totality of drone position points and image intersections. The ultra-high-quality point cloud was then built, generating a volume of 561,249,646 points, which then produced 154,314,192 faces of the mesh. The mesh was used to build the tilted model with a diversity of nine levels and a resolution of 3.16 cm/pixel. From this model, the DEM was obtained with a plane resolution of 57,438 \(\times\) 51,180 and a terrestrial resolution of 3.16 cm/pixel.  
The next step to construct the digital model of the ground was to obtain the characteristic level curves (Figure 8). These contour lines were created in the Global Mapper 23 program (Blue Marble Geographics, Hallowell, ME, USA). To create the level curves, the authors decided to use the DEM exported from the AgiSoft Metashape 1.8.5. program, saved with the \".tiff\" extension.  
Figure 8: Level curves.  
Figure 7: The orthophoto plane resulting from the processing of the information taken with the UAV.  
The next step was to export the contour lines from the Global Mapper program. The curves were exported as a \".dxf\" type file, which was necessary for compatibility with the ArcGIS 10.5 program (ESRI-United States of America). The contour lines represent the basis on which the digital terrain model will be created later.  
Since \".dxf\" files do not allow us to add elevation values, there is a crucial step in determining the Raster Model, namely, the transformation of the contour lines into a \".shp\" file (shape file). For the subsequent processing of the digital model, the extension of the ArcGIS program, namely, ArcScene, was used.  
The digital model of the ground can be observed in the figure below (Figure 9).
## 5 Conclusions  
This research employs a high-precision positioning module and a real-time perception module to acquire road environment information in specific scenarios. Considering spatiotemporal safety constraints and obstacle avoidance curvature constraints, it determines control points for the local obstacle avoidance path, which is discretely interpolated using B-spline curves. Comparative analysis with paths generated using the Bezier curve algorithm, conducted through simulation and real vehicle experiments, illustrates that in each road scenario, the local obstacle avoidance algorithm produces smoother obstacle avoidance paths. Minimal variations in the front-wheel steering angle and yaw angle of the intelligent driving sightseeing vehicle indicate enhanced vehicle stability.  
When integrated with geographic information fusion, the algorithm demonstrates the potential to elevate path planning precision, strengthen environmental perception capabilities, optimize the route selection, and enhance the real-time responsiveness of path updates. It is noteworthy, however, that due to constraints within the real-world testing environment for vehicular experiments, the local obstacle avoidance algorithm has yet to undergo testing in high-speed scenarios. Future research endeavors will encompass testing the algorithm in high-speed contexts and its application across diverse environments, further validating its performance.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app132011183/s1](https://www.mdpi.com/article/10.3390/app132011183/s1)]([https://www.mdpi.com/article/10.3390/app132011183/s1](https://www.mdpi.com/article/10.3390/app132011183/s1)), Table S1: Vehicle Parameter Table.  
Methodology, D.Y.; Software, R.L.; Writing--original draft, X.Y. and F.W.; Writing--review & editing, L.M. and A.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (2021 YFB3901300).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.