esa-sceva/satcom-chunk-collection huggingface dataset:
# Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey
## 4 Game Theory Defense Strategies Against WSN Attacks
### Cooperative and Non-Cooperative Games
#### 4.4.2 Non-Cooperative Games
In [69], building reliable and survivable networks with fault tolerance is reviewed based on bio-robustness of different biological scales, i.e., gene, molecular networks, immune systems, population, and community. In [70], dynamic hybrid fault models are proposed to achieve the reliability and fault tolerance for WSNs based on the evolutionary game. The dynamic models provide real-time prediction and fault-tolerance. The dynamics of the proposed models are involved into the time-dependent failure rate and time-dependent failure modes. The utility function of the proposed evolutionary game is formulated as reliability or survivability of the WSN in which a sensor node can be sacrificed to achieve the optimal network sustainability. The same concept of dynamic hybrid fault models has been extended [74] and extensively studied in [73]. In [72], the WSN sensors are mapped as a biological population using evolutionary game in which the payoff function is represented by reliability of players. The dragonfly adult concept has been studied in [75] to guarantee optimal channel time sharing in WSNs using evolutionary game. Consequently, selfish behavior of nodes can be treated. The utility function is called fitness function that is represented by the sensors' targets. In [71], the authors propose a defense strategy for WSNs aiming at reaching a stable state between the defender and the attacker using evolutionary game. More specifically, the sensor nodes can be active and dynamic to adjust their defense strategy. The payoff function is represented by a combination of some parameters of defender and attacker, e.g., reward of successive forwarded packets, security measuring cost, successful attack, failure attack, attacking cost, and probability of successful attack attempts.
# A Survey of Candidate Waveforms for beyond 5G Systems
###### Abstract  
The 5G and beyond future wireless networks aim to support a large variety of services with increasing demand in terms of data rate and throughput while providing a higher degree of reliability, keeping the overall system complexity affordable. One of the key aspects regarding the physical layer architecture of such systems is the definition of the waveform to be used in the air interface. Such waveforms must be studied and compared in order to choose the most suitable and capable of providing the 5G and beyond services requirements, with flexible resource allocation in time and frequency domains, while providing high spectral and power efficiencies. In this paper, several beyond 5G waveforms candidates are presented, along with their transceiver architectures. Additionally, the associated advantages and disadvantages regarding the use of these transmission techniques are discussed. They are compared in a similar downlink transmission scenario where three main key performance indicators (KPIs) are evaluated. They are the peak-to-average power ratio, the overall system spectral efficiency (wherein the out of band emissions are measured, along with the spectral confinement of the power spectral density of the transmitted signals) and the bit error rate performance. Additionally, other KPIs are discussed.  
5G; spectral Efficiency; PAPR; interference cancellation; frequency domain equalization; CP-OFDM; GFDM; time interleaved block windowed burst-OFDM; filtered-OFDM; windowing time overlapping +
Footnote †: journal: Electronics Letters  
Article
# Path Planning for Laser Cladding Robot on Artificial Joint Surface Based on Topology Reconstruction
## 3 The Research Methods  
The overall procedure of laser cladding path planning proposed in this paper is shown in Figure 3. The goal was to generate an optimal laser cladding path plan for the freeform surface parts of artificial joints. The overall procedure included mainly three steps:  
**Step 1:** Analysis of artificial joint CAD model. The topological relation of the artificial joint surface was created by contacting feature vertices, edges and facets extracted from the artificial joint CAD model.  
**Step 2:** Generation of the cladding paths according to the CAD model. The laser cladding path was automatically generated using the slicing algorithm based on a topological relation. The arch height error method was used to extract reasonable cladding points from a large number of intersections.  
Figure 1: (**a**) The laser cladding test bench. (**b**) Laser cladding coating on the acetabular cup.  
Figure 2: The architecture of the processing software.  
**Step 3:** Determination of the robot position and pose. The position and pose of the robot TCP were calculated according to the coordinates and normal vectors of the interpolation points. The robot motion paths were obtained by connecting the interpolation points sequentially, which finally were converted into the robot program.
## 3 Results and Discussion
### Characterization of Dynamics
In agreement with \(R_{1}\) measurements, higher \(R_{1\rho}^{PWRA}\) values were measured for vSBR_k, which shows a higher \(T_{\rm g}\), indicating a slowdown of dynamics induced by the presence of the resin. In order to better understand the origin of \(R_{1\rho}^{PWRA}\) relaxation, the contribution of segmental dynamics (\(R_{1\rho}^{seg}\)) at different temperatures was calculated from Equation (5):  
\[R_{1\rho}^{seg}(\omega,\omega_{1})=\frac{K_{CD}}{2}[5J_{CD}(\omega)+2J_{CD}( \omega)+3J_{CD}(2\omega_{1})] \tag{5}\]  
The values of \(\tau_{\rm s}\) obtained from the analysis of \({}^{1}\)H NMRD curves and reported in Figure 4 were used for the calculation. As we can see, \(R_{1\rho}^{seg}\) shows a decreasing trend with increasing temperature, but it accounts for only a fraction, ranging from about 52% at 303 K to 14% at 343 K, of the experimental \({}^{1}\)H \(R_{1\rho}^{PWRA}\). This confirms the increasing contribution of collective polymer dynamics in the kHz frequency range as the temperature increases. The contribution of polymer dynamics was estimated as the difference \(R_{1\rho}^{pol}=R_{1\rho}^{PWRA}-R_{1\rho}^{seg}\) (Figure 5, Table S3), under the assumption of statistical independence and time-scale separation between segmental and polymer dynamics. \(R_{1\rho}^{pol}\) increases by decreasing the temperature, approaching a maximum at 303-313 K that corresponds to the condition \(\omega_{1}\tau_{\rm pol}\cong 1\). These results suggest the presence of Rouse motions with characteristic correlation times (\(\tau_{\rm pol}\)) on the order of tens of \(\mu\)s in the investigated temperature range.
# Fractional Order Processing of Satellite Images
## 3 Results
### Performance Assessment  
In order to check performance, quantification has to be made. For that, the ground truth is compared to the output of the algorithm by scanning all pixels within the image. Four instances may occur: True Positives, False Positives, True Negatives and False Negatives (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Processed Image**} \\ \hline  & & **0** & **1** \\ \hline
**Ground Truth** & 0 & TN & FP \\ \cline{2-4}  & 1 & FN & TP \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance Instances.  
Using the number of occurrences of each instance the following metrics were computed:  
\[J(A,B) = \frac{TP}{FP+TP+FN} \tag{16}\] \[DSC = \frac{2\times TP}{2\times TP+FP+FN}\] (17) \[\text{Sensitivity} = \frac{TP}{TP+FN}\] (18) \[\text{Specificity} = \frac{TN}{FP+TN} \tag{19}\]  
During the study it was concluded that the Jaccard coefficient (\(J\)) is a good overall performance metric since the results with the highest \(J\) corresponded to the ones with maximum mean performance regarding the four metrics. Thus, the Jaccard coefficient is used as overall performance indicator.  
Figure 14: Best results for image 18 processed using Roberts algorithms.  
Figure 12: Best results for image 18 processed using Canny algorithms.  
Figure 13: Best results for image 18 processed using Sobel algorithms.  
Figure 16: Best results for image 27 processed using Canny algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Metodo**} & \(\sigma\) & \multicolumn{1}{c}{**Threshold**} & \(\alpha\) & \(J\) & \(D\) & **Sensitivity** & **Specificity** \\ \hline
**Integer Canny** & 1.6 & - & - & 0.6598 & 0.7950 & 0.6767 & 0.9914 \\
**Fractional Canny** & 1.8 & - & \(-\)0.1 & 0.6600 & 0.7952 & 0.6741 & 0.9928 \\
**Integer Sobel** & - & 0.1 & - & 0.1419 & 0.2486 & 0.1445 & 0.9940 \\
**Fractional Sobel** & - & 0.9 & 0.6 & 0.6600 & 0.7952 & 0.6741 & 0.9928 \\
**Integer Roberts** & - & 0.1 & - & 0.1589 & 0.2742 & 0.1619 & 0.9934 \\
**Fractional Roberts** & - & 0.1 & \(-\)0.3 & 0.6588 & 0.7943 & 0.6771 & 0.9907 \\
**Integer LoG** & - & 0.01 & - & 0.1051 & 0.1902 & 0.1065 & 0.9957 \\
**Fractional LoG** & - & 0.3 & 3.0 & 0.6594 & 0.7948 & 0.6771 & 0.9910 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Best performance results using Grey-Scale detectors on image 18.  
Figure 15: Best results for image 18 processed using LoG algorithms.  
Figure 19: Best results for image 27 processed using LoG algorithms.  
Figure 17: Best results for image 27 processed using Sobel algorithms.  
Figure 18: Best results for image 27 processed using Roberts algorithms.
# Study on the Load-Bearing Behavior of Underground Steel Structures with Pitting Corrosion Damage
## 3 Design of Simulated Pitting Damage Tests
### Simulated Damage Test Condition  
The raw materials for the experiment were selected, employing No. 20b I-beams commonly used in underground engineering with a material grade of Q345. The designed experiments included uniaxial compression tests on intact specimens, uniaxial compression tests on specimens with preformed pitting damage, three-point bending tests on intact specimens, and three-point bending tests on specimens with preformed pitting damage. The height of the uniaxial compression specimens was 600 mm, and the specimens with preformed pitting damage are shown in Figure 3. The length of the three-point bending specimens was 1200 mm, and the specimens with preformed pitting damage are shown in Figure 4. In the uniaxial compression tests, the pitting damage was prefabricated at the center of the web of the I-beam, while in the three-point bending tests, the pitting damage was prefabricated at the center of the tensile flange at the mid-span point of the I-beam. The specific information of the tested specimens is shown in Table 1.  
In the experimental design phase, to guarantee the high repeatability of the results, three parallel samples were prepared for each operating condition in this study, and corresponding experiments were comprehensively conducted. There was no obvious crack propagation in each pit, and there was no merger or other morphological evolution between pits. In statistical analysis of the experimental outcomes, the experimental data of each parallel group exhibited a high level of consistency, which thoroughly verified the reliability and stability of the experimental approach. Furthermore, to visually compare the impacts
# New Subclass Framework and Concrete Examples of Strongly Asymmetric Public Key Agreement
## 3 Concrete Examples of SAPKA
### Noised DH (Ndh)  
The following example is a variant of the DH that does not improve its security substantially but is useful to illustrate the concept of SAPKA algorithms simply.  
The components are the same as those in Section 3.1. [PERSON] selects their secret keys \(x_{B},n_{B}\in\mathcal{S}\). The key agreement process of the NDH algorithm is described by the following steps.  
1. [PERSON] prepares the quintuple \(\mathcal{C}_{NDH}:=(x_{1},x_{2},x_{3},x_{4},N_{1})\in\mathcal{K}_{B}\) as follows: * \(x_{1}(y):=(g^{x_{B}})^{y}\) * \(x_{2}:=id\) * \(x_{3}(y)=y^{x_{B}}\) * \(x_{4}(y)=g^{y}\) * \(N_{1}(y):=y^{n_{B}^{-1}}\), where \(y\) is selected arbitrarily from \(\mathcal{S}\).
2. [PERSON] constructs their public keys \(y_{B,1},y_{B,2}\) as a map for each \[y_{B,1}(y):=N_{1}^{-1}\circ x_{4}(y)=N_{1}^{-1}(g^{y})=(g^{y})^{n_{B}}=g^{ngy}\] \[y_{B,2}(y):=x_{1}\circ x_{2}(y)=(g^{x_{B}})^{y}\] and sends \((y_{B,1},y_{B,2})\) to [PERSON]. This is equivalent to sending \(g^{n_{B}},g^{x_{B}}\in\mathcal{S}\).
3. [PERSON] selects her secret key \(x_{A}\) from \(\mathcal{S}\).
4. [PERSON] calculates her public key \(y_{A}\) as follows, \[y_{A}:=y_{B,1}(x_{A})=N_{1}^{-1}\circ x_{4}(x_{A})=g^{n_{B}x_{A}}\] and sends it to [PERSON].
5. [PERSON] calculates the SSK denoted by \(\kappa_{A}\) as in the DH case: \[\kappa_{A}:=y_{B,2}(x_{A})=x_{1}\circ x_{2}(x_{A})=(g^{x_{B}})^{x_{A}}=g^{x_{ B}x_{A}}.\]
6. [PERSON] calculates the SSK denoted by \(\kappa_{B}\) as follows: * \(x_{3}\circ N_{1}(y_{A})\) * \(x_{3}\circ N_{1}\circ N_{1}^{-1}\circ x_{4}(x_{A})\) * \(x_{3}\circ N_{1}(g^{n_{B}x_{A}})\) * \(x_{3}(g^{n_{B}^{-1}n_{B}x_{A}})\) * \(x_{3}(g^{n_{B}^{-1}n_{B}x_{A}})\) * \(x_{3}(g^{x_{A}})^{x_{B}}\) * \(g^{x_{B}x_{A}}\).
## 3 Developed Spar-Type Mooring System
### OC3-Hywind Spar-Type 5 MW Wind Turbine  
It is assumed that a positive \(x\) direction is consistent with the wind and wave propagation direction; positive \(z\) direction points upward; and positive \(y\) direction can then be determined using the right-hand rule, as can be seen in Figure 4. Figure 4 shows a typical OC3-Hywind spar-type 5 MW offshore wind turbine, which is moored onto the seabed using three mooring lines. The floating wind turbine can freely move in six directions (i.e., surge, sway, heave, roll, pitch, and yaw). The key structural parameters of the wind turbine are summarized in Table 1. Hub mass, nacelle mass, and tower mass are 56,780, 350,000, and 249,718 kg, respectively. Nacelle is measured to be (14.2, 2.3, and 3.5) m in (\(x\), \(y\), \(z\)) directions. Wind turbine draft is 120 m. Tower bottom and hub elevations from still sea level are 10 m and 90 m, respectively.  
Figure 4: Schematic of OC3-Hywind spar-type 5 MW offshore wind turbine.  
Figure 3: Flow chart for floating wind turbine dynamic response analysis.  
Subsequently, another developed mooring system with six mooring lines and six different fairleads is proposed, as presented in Figure 6b. Mooring lines and fairleads are correspondingly connected. They are spread uniformly around the spar-type platform with the included angle between any two neighboring mooring lines being 60\({}^{\circ}\). The developed mooring system shown in Figure 6b is labeled as T4.  
The floating wind turbine dynamic responses of T1-3 were first calculated and compared to optimize the included angle for the three-fairlead mooring system. The optimal case in T1-3 was then compared with T4 to obtain the final optimal configuration for the developed mooring system with six mooring lines.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 2 Dynamics Analysis of the Variable-Sweep Wing UAV
### Multi-Rigid-Body Dynamic Model
Linearization of the dynamic equation  
To simplify the control analysis, the longitudinal dynamic equations of the system can be linearized as follows:  
\[\dot{v}=\frac{0.5\rho v^{2}S_{\text{ref}}C_{t}\text{cos}\,\alpha}{m_{i}}- \frac{0.5\rho v^{2}S_{\text{ref}}C_{d}}{m_{i}}-g\text{sin}\,\vartheta+\frac{2 m_{1}(-\bar{l}_{1x}+\omega_{y}^{2}l_{1x})\text{cos}\,\alpha}{m_{i}} \tag{10}\]  
where \(C_{t}\) is the thrust coefficient, and \(g\) represents the acceleration due to gravity. This linearization is valid under the assumption of small angles, where the variations in angle of attack \(\alpha\) and pitch angle \(\vartheta\) are within a limited range. Such conditions are typical for steady-level flight and small perturbations around equilibrium. In practical applications, this approximation effectively simplifies the analysis and control of the system, while still capturing the essential dynamics. However, for scenarios involving large angular changes or highly dynamic maneuvers, the nonlinear effects could become significant and require a more comprehensive modeling approach.
## 2 Distribution in Nature
### Origin of STX vs. TTX  
Similar to TTX, there is an interest in the biological origin of STX. In freshwaters, this toxin is produced by prokaryotic cyanobacteria while in marine waters it is associated with eukaryotic dinoflagellates. However, several studies suggest that STX is not produced by dinoflagellates themselves, but by cocultured bacteria. These authors show that genes required for STX synthesis are encoded in the nuclear genomes of dinoflagellates and that the dinoflagellate transcripts of sxtA have the same domain structure as the cyanobacterial sxtA genes. These results show very good agreement between the presence of sxtA and STX synthesis, except in three strains of _Alexandrium tamarense_, where they did not detect the toxin. They conclude that different genes in the sxt pathway may have separate origins in dinoflagellates [58].  
Considering the biological origin of STX, a gene cluster (sxt) consisting of up to 26 genes that participate in the biosynthesis of STX, was identified. The sxt gene cluster has so far been found in five species of STX-producing cyanobacteria and in the nuclear genome of five species of dinoflagellates.  
Microbial production of STX is thus the accepted origin of STX subsequently transferred among various invertebrate and vertebrate species in the aquatic freshwater and marine food chains. In a recent report, the occurrence of the dinoflagellate _Prorocentrum minimum_, was linked to the presence of TTX in bivalves [59]. However, the dinoflagellate _A. tamarense_ is the only STX-producing species that has been reported to also produce TTX in culture [57, 60]. This suggests that biosynthetic genes for production of STX and TTX evolved largely independently in nature but leaves open the possibility that STX and TTX may share precursor molecules and some biosynthetic enzymes.  
Moreover, the strong conservation in the sxt cluster, under radical changes in environmental conditions and organism diversity, shows that it has continued to play an important adaptive role in some cyanobacteria [57, 61].  
In conclusion, more than one biosynthetic mechanism for TTX production could have evolved as convergent in nature, taking into account the lack of an obvious biological source of TTX common to marine _vs._ terrestrial animal species. A likely scenario is that synthetic genes for TTX have transferred across species in evolution. A recent study proposed that origin of TTX may be due to any of the following combinations: exogenous, endogenous or by the symbiotic association among the animals acquiring it and the microorganisms that are reported produce it [46]. These questions emphasize the importance for future research of identifying natural biosynthetic genes for TTX. Despite all of these assumptions, the exact origin and pathway for the synthesis and bio-transfer of TTX is not yet fully known and requires further investigation. It is of high importance and an open question to gain more insights into the spread of TTX in both prokaryotic and eukaryotic systems as well as to elucidate the genes and enzymatic pathways responsible for the biosynthesis of TTX in bacteria.
# Communication Infrastructure Design for Reliable UAV Operations in Air Mobility Corridors
## 4 Discussion
### Future Directions of Research  
The results of this study provide a framework for designing and evaluating radio communication strategies within UAV air mobility corridors. However, to further the operational impact of these findings, several avenues for future research are proposed.  
Future work should extend beyond linear corridor models to incorporate realistic 2D and 3D geometries, including altitude layering, turns, intersections, and no-fly zones. Integration with high-resolution urban terrain and building data will enable more accurate simulations of signal propagation, shadowing effects, and radio visibility.  
While the current study considers static configurations, future research could explore adaptive strategies that respond to real-time UAV traffic load, environmental conditions, or failure states. Techniques such as dynamic power adjustment, mobile repeater deployment (e.g., via ground vehicles or drones), and AI-based network optimization can significantly improve both efficiency and resilience.  
Security threats such as jamming, spoofing, or cyberattacks targeting radio infrastructure pose serious risks to UAV operations. Future models should incorporate adversarial failure modes and analyze the robustness of different deployment strategies under intentional disruption scenarios.  
Future work should incorporate detailed cost modeling, including installation, maintenance, energy use, and lifecycle considerations. Multi-objective optimization techniques can be used to find cost-effective configurations that meet target reliability thresholds under budget, space, and regulatory constraints.  
To bridge the gap between theoretical modeling and operational deployment, future research should focus on implementing pilot systems or digital twins for corridor segments. Empirical data collection on communication performance, interference, and system behavior under failure conditions will be crucial for model calibration and refinement.
###### Abstract
What will happen when the jammer is also intelligent which can learn the communication mode of users and adjust its jamming strategy dynamically? Inspired by [25], in this paper, we investigate the performance of DRL-based anti-jamming method in face of different modes of jamming, including traditional jammers and intelligent jammer based on reinforcement learning (RL). RL [26; 27] is an intelligent algorithm that gets feedback from the environment which does not need the priori information. Compare to DRL, an RL algorithm needs less calculation and fewer iterations to converge. In the wireless network, communication devices often access the spectrum for a short time, which only provide the jammers with a small amount of data. Due to this reason, we assume that the jammer adopts RL algorithm, instead of DRL. In this paper, we design an RL-based jamming algorithm and investigate the performance of a DRL-based anti-jamming method. The implementation as well as the theoretical performance are discussed. Different from [28], we take a small possibility of random jamming as a way to against DRL-based user. We theoretically analyze the condition when the DRL-based anti-jamming algorithm does not converge and provide the process of proof. The main contributions are summarized as follows:  
* We investigate the performance of DRL-based anti-jamming method in face of different jamming modes. Without the prior information of users, we designed an RL-based jamming algorithm to against the DRL algorithm, and the simulation results verify the effectiveness of the jamming method. * We theoretically analyze the condition when the DRL-based algorithm cannot converge, and verified it by simulation. We organize this paper as follows. Section 2 discusses system model. In Section 3, we present the RL-based jamming algorithm. The analysis of DRL algorithm is provided in Section 4, followed by the simulation results in Section 5. Concluding remarks of the paper are in Section 6.