esa-sceva/satcom-chunk-collection huggingface dataset:
# Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
## 4 Experiments
### Benchmark Comparisons
It proves that our method can avoid much computational burden for an onboard computer and is suitable for exploring large-scale scenes. Table 1 presents a quantitative performance of three methods in two mazes. Compared with FUEL and FAEP, the average exploration time of our method is reduced by 18.76% and 20.87% in Maze-1, and 18.05% and 21.77% in Maze-2. Moreover, the average flight distance of our method is reduced by 18.93% and 17.19% in Maze-1, and 15.91% and 6.79% in Maze-2. In contrast, our proposed method outperforms other methods in exploration time, flight distance, and stability. The average computation time of each module is shown in Table 2. The proposed method conducts a one-path planning with approximately 130 ms, meeting the frequency requirements for most robots. Furthermore, only 12 ms is consumed by local path planning, additionally proving the efficiency of the proposed reward evaluation method. We deconstruct the proposed method to analyze the global and local path planning module performances. Ours\({}^{\text{Local}}\) and Ours\({}^{\text{Global}}\) correspond to implementing local and global path planning, both built upon FUEL framework. The exploration data of Ours\({}^{\text{Local}}\), Ours\({}^{\text{Global}}\) in two mazes are list in Table 1.  
Figure 11: The number of remaining frontier clusters during exploration of the three methods in two mazes. Figure 7:0 The exploration progress of three methods in Maze-1 (left) and Maze-2 (right). Comparing the simulation results of Ours\({}^{\text{Global}}\) and FUEL in two mazes, the standard deviations of Ours\({}^{\text{Global}}\) in exploration time and flight distance are identical to FUEL. However, the average exploration time of Ours\({}^{\text{Global}}\) is reduced by 18.88% and 14.01%, respectively, in Maze-1 and Maze-2. The average flight distance of Ours\({}^{\text{Global}}\) is reduced by 19.57% and 12.45%, respectively, in Maze-1 and Maze-2. The average performance of Ours\({}^{\text{Global}}\) outperforms FUEL, indicating that the primary contribution of global path planning lies in improving exploration efficiency. The same inference can also be drawn from comparing Ours and Ours\({}^{\text{Local}}\). The average exploration time and flight distance of Ours\({}^{\text{Local}}\) and FUEL in both mazes are approximate. But, Ours\({}^{\text{Local}}\) maintains a low standard deviation, indicating more stability. Similar performance is observed between Ours\({}^{\text{Global}}\) and Ours. It is demonstrated that the proposed local path planning method can effectively enhance the stability of exploration efficiency. Finally, we can conclude that the exploration efficiency is primarily attributed to global path planning, while local path planning enhances the stability of exploration efficiency.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 5 Algorithm Design and Implementation  
The TDVRPD-VRZ-NFZ studied in this paper is an NP-hard problem. To solve it, a hybrid genetic algorithm and variable neighborhood search with a learning mechanism (HGAVNS-LM) is proposed. The algorithm flow is illustrated in Figure 4.
## 3 Results
### The SST-SIF Link during the Onset Phase of Summer Monsoon  
The inverse ocean-land linkage observed in the correlation analyses was also found in the composite anomalies of SIF (Figure 5). The years selected to make the composite anomalies are given in Table S1. The linkage between Indian vegetation SIF anomalies and the tropical Indian Ocean SST was verified using composite anomalies of the important hydroclimatic variables (specific humidity, rainfall, soil moisture and air temperature). These auxiliary parameters depict how the SST variations in WIO, NIO and CIO are connected with the SIF anomalies through modulation of the water available to the plants for photosynthesis over India. The density contours of SIF anomalies showed that the regions over the Indian mainland with a significant impact on tropical Indian Ocean SST variability. Notable negative SIF anomalies are observed in central and northern India when the WIO, NIO and CIO are warmer-than-normal (Figure 5a-c). Similarly, the influence of colder-than-normal surface waters in the same oceanic regions enhances plants' photosynthetic rates in some ecosystems, as displayed by the density contour of positive SIF anomalies (Figure 5d-f). However, a considerable bias exists between the influence of warm and cold SST anomalies on the SIF anomalies over India. The impact of warm SST anomalies appears more profound than cold anomalies in the tropical Indian Ocean. However, apart from the results obtained in the correlation analysis, some other ACZs were also influenced by SST variations in the WIO. This predominantly includes MGP and WPH, particularly when warm SST anomalies persist in the WIO. Overall, the effect of WIO warm anomaly concentrated mainly on the SIF anomalies of WHR, TGP, UGP, MGP, EPH, WPH and CPH (Figure 5a). At the same time, the influence of negative SST anomalies in the WIO focused mainly on EPH and CPH (Figure 5d). The impact of NIO's SST is confined to the ACZs that lie in the north-central areas. As per the SST-SIF correlations, the NIO governs the SIF anomalies in the EPH, CPH, WHR, TGP, UGP and MGP. The density contour plots also reveal the same results during the years of warm anomalies in the NIO (Figure 5b). However, the oceanic influence from NIO lies predominantly over SIF anomalies in CPH during cold anomaly years (Figure 5e). The SST patterns in the western and central oceanic regions are nearly identical (Figure 1). This similarity is almost observed in the SST teleconnections to SIF anomalies over India.
### Major Contribution  
The main purpose of this paper is to investigate the nonlinearities due to the optical fiber, which limits the transmission of ultra-high data rate data through long distances, and to present a low-complexity, low-cost solution through analytical modeling and simulation analysis. The major contribution of this paper are listed, as follows.  
1. An analytical model is presented with a simple DSP receiver to compensate nonlinearities for long distance optical networks and support 8, 16, and 32 WDM channels in the presence of nonlinearities. The results validate that 32 channels supporting 100 Gbps can be easily supported up to 300 km of optical fiber transmission.
2. The performance affecting parameters such as nonlinear effective area, nonlinear refractive index, launch power, and length of fiber are used for a range of values in simulation analysis to analyze the effectiveness of the proposed technique.
3. A mathematical model for the nonlinear effects and their compensation through the proposed DSP technique is presented as the main contribution of this work. The proposed transmission model is compared for a range of nonlinearities, and the results are compared with an uncompensated case to quantify the improvement in the performance.
4. The BER and Optical Signal-to-Noise Ratio (OSNR) parameters are used as performance metrics to demonstrate the results of the proposed model. The achieved BER and OSNR are found to be 1.56 \(\times\) 10\({}^{-6}\) and 25, respectively.  
The remaining part of this paper is divided into following sections. Section 2 consists of the description of the proposed model with a block diagram. An analytical model explanation in terms of nonlinear parameters is explained in Section 3. Discussion of simulation results are presented in Section 4. Section 5 concludes the presented work.
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 2 Theory
### Delay-Doppler Domain Analysis  
The Doppler effect occurs when there is relative motion between the target and the receiver in the received acoustic signal. This motion causes a change in the frequency of the signal, which can be analyzed using the Delay-Doppler feature analysis. Therefore, the Delay-Doppler domain feature of the signal can represent the motion characteristics of the target. The velocity intervals of underwater acoustic targets with different shapes and sizes are often significantly different; so, they can show different characteristics in the Delay-Doppler feature. This research aims to analyze the Delay-Doppler features of underwater acoustic signals and use them for the purpose of distinguishing various underwater acoustic objects. The signal's Time-Frequency and Delay-Doppler domain components are interconnected and may be modified using the symplectic finite Fourier transform (SFFT) and its inverse transformation (ISFFT). The connection between the discrete signal's features in the Time-Frequency, Delay-Doppler, and time domains is shown in Figure 2.  
Figure 1: Signal Time–Frequency domain analysis process.  
The discrete sound pressure signal \(s(n)\) is acquired by fast Fourier transform (FFT), as seen in Figure 2. The symplectic finite Fourier transform (SFFT) may be used to extract the pertinent Delay-Doppler information from \(s(n)\). The formula of SFFT is as follows [24]:  
\[Y[m,n]=\frac{1}{\sqrt{MN}}\sum_{l=0}^{M-1}\sum_{k=0}^{N-1}X_{tf}[l,k]e^{-\beta \pi(\frac{m}{N}-\frac{m}{M})} \tag{2}\]  
In the above formula, the matrix \(Y[m,n]\) represents the eigenvalues of the signal in the Delay-Doppler domain, where \(m=0,\,1,\ldots,\,M-1\); \(n=0,\,1,\,\ldots,\,N-1\). \(M\) represents the quantity of grid points in the time domain, whereas \(N\) represents the number of grid points in the frequency domain. SFFT is computed by performing the discrete Fourier transform (DFT) on the \(N\)-point and then by applying the inverse discrete Fourier transform (IDFT) on the \(M\)-point.  
Figure 3 illustrates the connection between the Delay-Doppler and Time-Frequency domains. The conversion connection between the Doppler domains and time is shown by the horizontal axis. The use of the DFT enables the transformation from the time domain to the Doppler domain. The relationship between the frequency domain and the delay domain is shown by the vertical axis, and the transformation from the frequency domain to the delay domain is achieved using IDFT. The alteration in frequency resulting from the relative movement between objects is referred to as the Doppler effect. Consequently, the Doppler domain may be interpreted as the characteristic of the target's velocity, and the target's motion characteristic can be derived by analyzing the signal delay in the Doppler domain. The variation in speed among distinct targets might serve as a distinguishing characteristic for target identification.
## 3 Experiments and Analysis
### Ablation Study
#### 3.3.3 Representation Consistency: Loss Weighting  
Figure 7c,d analyze the effectiveness of loss function hyperparameters \(\lambda_{1}\) and \(\lambda_{2}\) in Equation (8). Optimal weights for the LS and GS modules need to be adjusted for different datasets. We sampled values for both weights (0 to 1), keeping other parameters fixed at 0 and adjusting either \(\lambda_{1}\) or \(\lambda_{2}\) individually. Results show incorporating the LS or GS module within a certain range improves accuracy. On S2 Looking (more pronounced global style differences), the GS module generally outperforms the LS module. Conversely, on LEVIR-CD+ (greater local content variations), the LS module performs better. Since both types of variations are typically present, fine-tuning the loss function weighting is necessary. The principle is to select the minimum weights that still yield performance improvements, avoiding negative effects from excessively large weights.  
Figure 7c,d and Table 6 reveal three key findings about loss weight selection: (1) Performance Stability: Both modules tolerate moderate weight variations while maintaining >95% of maximum gains. For S2 Looking, GS achieves peak F1 (67.57%) at \(\lambda_{2}=0.025-0.25\) (10\(\times\) range), while LEVIR-CD+ sustains >87% F1 with \(\lambda_{1}=0.01-0.075\) (7.5\(\times\) range). (2) Dataset-Specific Optimization: S2 Looking benefits more from GS (max \(\triangle\)F1 = +0.64% vs. baseline), as its wider viewpoint variations create stronger spectral discrepancies than structural changes. LEVIR-CD+ prefers LS (max \(\triangle\)F1 = +0.67%) due to its nadir perspective and 0.5 m resolution emphasizing structural details. (3) The selection principle of optimal weights follow: \(\lambda_{1}=0.05/\lambda_{2}=0.025\) for S2 Looking and \(\lambda_{1}=0.01/\lambda_{2}=0.1\) for LEVIR-CD+.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 3 Techniques and Methods
### The Adversary Model  
An adversary model for packet dropping attacks is used in this paper. In this adversary model (a type of gray-hole attack described as Attack1 in [22]), a malicious node continuously monitors the field value of received as well as overheard control packets, in order to keep track of the highest recorded value of the destination sequence number. During the routing process, the malicious node replies to an Route Request (RREQ) with the lowest hop count (which is 1) and the highest possible value of the destination sequence number. Even though the malicious node does not have a valid path, a genuine source node employing AODV protocol immediately can build a route through the adversary. The adversary then attempts to drop packets for the period of 50% of the total time. Packet dropping attacks happen at the time of routing data packets. The operations of the adversary model during route discovery phase and data transmission phase are presented in Figure 1.
## 5 SDN-based VANETs Security Challenges
### Bogus Information
#### 5.15.2 GPS Spoofing  
A malicious hub uses the Global Positioning System (GPS) satellite test system to deliver signals which are more grounded than the real satellite sign and have a tendency to misdirect vehicles to acknowledge the bogus position data. This kind of attack is identified with physical gadgets. Therefore, SDN-based VANETs should manage trust in such information engendering, where community oriented vehicles may identify this data and stop it [55; 96].
## 2 Materials and Methods
### Adsorption Modeling  
The kinetic data were analyzed using pseudo-first-order (Equation (1)), pseudo-second-order (Equation (2)), and pseudo-nth-order (Equation (3)) models, and the equations are as follows [24]:  
\[q_{t}=q_{e}\Big{(}1-e^{-k_{1}t}\Big{)} \tag{1}\]  
\[q_{t}=\frac{k_{2}q_{e}^{2}t}{1+k_{2}q_{e}t} \tag{2}\]  
\[q_{t}=q_{e}\Bigg{\{}1-\frac{1}{\Big{[}1+(n-1)q_{e}^{n-1}k_{n}t\Big{]}^{1/(n-1) }}\Bigg{\}} \tag{3}\]  
where \(q_{t}\) is the adsorbed amount of Sb(V) onto Fe-CMB at time \(t\) (mg/L), \(q_{e}\) is the adsorbed amount of Sb(V) onto Fe-CMB at equilibrium, \(k_{1}\) is the rate constant of pseudo-first-order model (1/h), \(k_{2}\) is the rate constant of pseudo-second-order model (g/mg/h), \(k_{n}\) is the rate constant of pseudo-nth-order model (g\({}^{n-1}\)/mg\({}^{n-1}\)/h), and \(n\) is the reaction order of pseudo-nth-order model.  
[PERSON] (Equation (4)), [PERSON] (Equation (5)), and Temkin (Equation (6)) models were employed to analyze the equilibrium data, and the equations are as follows [25]:  
\[q_{e}=\frac{Q_{m}K_{L}C_{e}}{1+K_{L}C_{e}} \tag{4}\]  
\[q_{e}=K_{F}C_{e}\lx@note{footnote}{$\frac{1}{2}$} \tag{5}\]\[q_{e}=\frac{RT}{B_{T}}\ln(K_{T}C_{e}) \tag{6}\]  
where \(C_{e}\) is the Sb(V) concentration in the aqueous solution at equilibrium (mg/L), \(K_{L}\) is the Langmuir constant related to the binding energy (L/mg), \(Q_{m}\) is the monolayer saturation capacity at a given condition, which indicates the maximum Sb(V) adsorption capacity of Fe-CMB (mg/g), \(K_{F}\) is the distribution coefficient ((mg/g)-(L/mg)\({}^{1/n}\)), and \(n\) is the Freundlich constant. \(K_{T}\) is the Temkin constant (L/g), \(B_{T}\) is related to the heat of sorption (kJ/mol), and \(R\) is the gas constant (8.314 J/mol\(\cdot\)K).  
The values of \(K_{L}\), \(Q_{m}\), \(K_{F}\), \(n\), \(K_{T}\), and \(B_{T}\) were determined by fitting the experimental data to the Langmuir, Freundlich, and D-R models. All model parameters were obtained by non-linear regression with the highest determination coefficient (\(R^{2}\)) and lowest sum of squared error (\(SSE\)) using Microsoft Excel 2016.  
The thermodynamic properties of Sb(V) adsorption by Fe-CMB were calculated using the following equation:  
\[\Delta G^{0}=\Delta H^{0}-T\Delta S^{0} \tag{7}\]  
\[\Delta G^{0}=-RT\ln K_{e} \tag{8}\]  
\[\ln K_{e}=\frac{\Delta S^{0}}{R}-\frac{\Delta H^{0}}{RT} \tag{9}\]  
\[K_{e}=\frac{\alpha q_{e}}{C_{e}} \tag{10}\]  
where \(\Delta G^{0}\) is the change in Gibbs free energy (kJ/mol), \(\Delta S^{0}\) is the change in entropy (J/mol/K), \(\Delta H^{0}\) is the change in enthalpy (kJ/mol), \(R\) is the gas constant (J/mol/K), \(K_{e}\) is the equilibrium constant (-), \(T\) is the absolute temperature (K), and \(\alpha\) is the amount of adsorbent (g/L).
## Appendix B  
Based on field investigations, the thickness of the fracture infill layer varies between approximately 18 mm and 24 mm. Therefore, a representative infill thickness of 20 mm was selected for this study.  
The infill material collected from the engineering site was divided into four subsamples according to the sampling locations, and mineralogical composition analysis was performed for each. As shown in Figure A1 and Table A3, the five sets of samples exhibit a high degree of consistency in mineral composition. Major minerals such as quartz, albite, microcline, and muscovite-1M show relatively minor variations in content, with quartz content remaining stable at approximately 40-43.5%, and the variations in other major phases also within a reasonable range. In addition, minor minerals including kaolinite, gypsum, and calcite, although present in lower amounts, were consistently detected in all samples, indicating a relatively stable distribution within the infill material. Overall, the mineralogical differences among the samples are minimal.  
Similarly, samples collected from different locations on site were grouped and subjected to particle size distribution analysis. As shown in Figure A2, all four groups of samples exhibit typical bimodal distribution characteristics. The primary peaks are concentrated in the coarse particle range (approximately 300-1000 \(\upmu\)m), while the secondary peaks are located in the fine particle range (approximately 1-10 \(\upmu\)m), indicating the presence of both coarse and fine particles within the samples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Angle (\({}^{\circ}\))** & **Maximum Element Size** & **Minimum Element Size** & **Curvature Factor** & **Domain Element** & **Degrees of Freedom** & **Peak Compressive Strength (MPa)** \\ \hline
32.44 & 0.333 & 0.00113 & 0.25 & 492,986 (39,625) & 556,953 (2,464,931) & 9.8629 \\
34.44 & 0.333 & 0.00113 & 0.25 & 489,880 (39,307) & 552,767 (2,449,401) & 9.9067 \\
36.44 & 0.333 & 0.00113 & 0.25 & 506,603 (39,949) & 575,112 (2,534,016) & 10.0755 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mesh configurations at different angles.  
Figure A1: Mineral composition of the samples: (**a**) Sample 1; (**b**) Sample 2; (**c**) Sample 3; (**d**) Sample 4.  
Although there are slight differences in the position of the main peaks, peak intensities, and volume density values among the four groups, their overall distribution patterns remain consistent, with similar curve shapes and comparable particle size spans. These results suggest that the particle size distributions are generally similar across the samples, reflecting comparable material sources or processing conditions. Therefore, the four groups can be considered as the same type of material and are suitable for unified comparative analysis in subsequent studies.
## 6 Conclusions  
In conclusion, the TFNN approach demonstrates significant advantages in enhancing the longevity and efficiency of IoT networks. By autonomously adapting to the diverse dynamics of these networks in a decentralized manner, TFNN minimizes the energy consumption associated with communication, which is a critical factor for battery-powered IoT devices. TFNN is a single-shot pattern recognition approach, and thus TFNN does not incur the communication and energy overhead associated with the multiple rounds needed to converge and recognize patterns. This study leveraged the MNIST digit dataset to evaluate the performance of TFNN in recognizing event patterns. The results illustrate that TFNN, utilizing the LEACH algorithm for hierarchical clustering, effectively reduces the energy costs associated with long-distance communications to the base-station. By allowing intra-cluster communications and limiting direct transmissions to the base-station to cluster heads and unclustered nodes, the algorithm significantly enhances operational efficiency. This approach not only prolongs the network's lifespan but also maintains a high level of accuracy in event detection. Ultimately, the findings underscore the potential of TFNN to support the sustainable operation of IoT networks, enabling them to function effectively over extended periods while maintaining acceptable levels of pattern recognition accuracy.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The author declares no conflicts of interest.