esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Contributions  
This formulation surpasses traditional TPC methods by leveraging a game-theoretic approach, with the utility function directly based on the SINR. This ensures that the system optimizes a key metric for wireless communication, naturally improving signal quality while minimizing interference. The model guarantees convergence to the Nash equilibrium through a potential function that aligns individual utilities with global optimization goals. Unlike traditional methods, this approach provides quantifiable performance guarantees, such as bounded PoA and Po, ensuring that the Nash equilibrium is close to the socially optimal strategy. Additionally, it incorporates SIC, enabling efficient interference management and enhancing SINR without penalizing neighboring nodes.  
The formulation's robustness is further demonstrated through Lyapunov stability analysis, which confirms the system's ability to handle perturbations and converge to equilibrium under realistic interference and noise conditions. The use of gradient descent for finding Nash equilibria ensures computational efficiency, making the model scalable and adaptable to complex, dynamic networks. Unlike traditional methods, which often rely on heuristics or lack formal stability guarantees, this approach integrates theoretical proofs with practical applicability. By addressing potential limitations transparently, the formulation provides a reliable and comprehensive framework for modern wireless networks, balancing efficiency, scalability, and stability in diverse scenarios. This formulation outperforms other game-theoretic methods for transmission power control by employing a potential game structure that guarantees convergence to a Nash equilibrium while aligning individual incentives with the system's overall performance. Moreover, general game theoretic models may be intractable when computing the Nash equilibrium. Unlike general game-theoretic approaches, where individual utility optimization may lead to inefficiencies or unpredictable outcomes, this model uses a potential function that ensures the Nash equilibrium corresponds to a near-optimal global solution.
# Geotechnical Behaviour of Fly Ash-Bentonite Used in Layers
## 4 Results and Discussion
### Shear Strength Parameters  
The total axial stress results of the series of consolidated undrained triaxial tests at increasing confining pressure were plotted on a Mohr's circle diagram to evaluate effective shear strength parameters (c and \(\phi\)). A straight line drawn as a tangent to the circles, with the equation \(\tau=\) c + \(\sigma\) tan \(\phi\), is used to simulate the condition of the sample's failure. The angle of shearing resistance (\(\phi\)) is the angle between the tangent and a line parallel to the shear stress, and the value of cohesion (c) is read from the shear stress axis, where it is cut by the tangent to the Mohr's circles. The values of c and \(\phi\) are summarized in Table 4.  
As shown in Figure 10, for a particular ratio of fly ash to bentonite, the value of cohesion increased with the increase in the number of interfaces. When the number of interfaces is equal to one, it was noticed that the value of cohesion continuously decreased as the ratio of fly ash to bentonite was increased. At number of interfaces equal to 2, the value of cohesion decreased when the ratio F:B changed from 1:1 to 2:1. However, when the ratio of F:B became 3:1, the value of cohesion increased before reducing again at F:B = 4:1. At N = 3, it was found that, with increasing ratio of F:B, an increase in value of c was recorded until the ratio was 2:1. At a 3:1 ratio of F:B, the value of cohesion decreased and then fell drastically at F:B ratio = 4:1.  
As shown in Figure 11, it was also found that the value of the angle of shear resistance is largest for fly ash. For the same ratio of fly ash to bentonite, it decreases gradually as the number of interfaces was increased. However, the values of \(\phi\) are quite close to each other, i.e., not much drastic change is observed in their response to changing ratios and varying interfaces.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**F:B**} & \multicolumn{3}{c}{**Cohesion (c), kPa**} & \multicolumn{3}{c}{**Angle of Internal Friction (b), \({}^{\circ}\)**} \\ \cline{2-7}  & **N = 1** & **N = 2** & **N = 3** & **N = 1** & **N = 2** & **N = 3** \\ \hline
1:1 & 76.8 & 83.4 & 107 & 22.5 & 22.2 & 20 \\
2:1 & 75 & 76.1 & 110 & 21 & 20 & 17 \\
3:1 & 66.6 & 86 & 105 & 21.5 & 20 & 21.5 \\
4:1 & 55 & 59 & 78.2 & 22.8 & 22.8 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Shear strength parameters of fly ash bentonite in different ratios with layers.  
Figure 10: Effective cohesion values against fly ash–bentonite ratios.
# DNA-PRESENT: An Improved Security and Low-Latency, Lightweight Cryptographic Solution for IoT
## 4 Security, Cost, and Performance Analysis
### Cost  
The implementation cost is directly influenced by the design of an algorithm. The area for hardware implementation is measured in Gate Equivalence (GE), reflecting both computational complexity and the chip area required (cost) for its implementation. Technically, it is the ratio between the layout area (measured in \(\upmu\)m\({}^{2}\)) and the NAND 2 gate area [33]. GE is a crucial element in lightweight cryptography and should have a small value. According to the authors [8;76], only 3000 gates are dedicated for security algorithms from a total of 1000-10,000 gates in resource-constrained devices. This implementation's GE is calculated using the base paper's fundamentals [25]. As indicated in Table 10, a significant proportion of the gates in both algorithms is accounted for by the key schedule algorithm (KSA). Notably, the recently incorporated layers only add 16 new gates, representing a negligible cost when weighed against the security improvements. The total number of gates required by PRESENT-128 is 1886.25, which increased to 2518.85 GEs for DNA-PRESENT; nonetheless, a trade-off consistently exists between achieving improved security and maintaining lower costs. Although this number is higher than the original PRESENT block cipher, the algorithm is still a lightweight solution aiming to be implemented using less than 3000 GE's [8;33;76]. It is pertinent to mention that the discussion of the DNA-based solution is primarily due to its simplicity, with the gate count required for the DNA-replication operation being only 16 GEs, which underscores its lightweight nature. However, it is important to note that this solution does not meet the criteria for ultra-lightweight algorithms, which may present potential limitations for certain IoT environments.
## 1 Introduction
It is modified by using a new signal as reference which is synthesized relying on the system impulse response at some foreknown strain level. The new reference signal is obtained by means of a post-processing filter based on the phase of either the Fourier transform or the short-time Fourier transform. The latter allows one to analyze how several signal components are affected by strain in both the time and frequency domains, being therefore suitable to cases where several wave modes arrive distributed over time and at different frequencies, such as wideband guided waves. The idea of using synthesized signals for strain monitoring through the phase of the Fourier transform was briefly proposed by [PERSON] et al. [38]. Here, it is comprehensively extended by comparing it to the short-time Fourier transform and assessing the effectiveness of the technique with regards to the foreknown strain level and on the loss of energy concentration capability. The rest of the paper is organized as follows. Section 2 explains the strain effect on ultrasonic guided waves and the original procedure for strain monitoring. Section 3 devises the modified procedure using the information of the phase of the Fourier and short-time Fourier transforms. Section 4 presents the results with experimental signals and Section 5 draws the conclusions.
# Dynamic Stability of Orthotropic Viscoelastic Rectangular Plate of an Arbitrarily Varying Thickness
## 3 Results and Discussion  
### Analytical Solution for an Orthotropic Viscoelastic Rectangular Plate of Variable Thickness  
This section presents the obtention of the analytical solution for the dynamic stability of an orthotropic viscoelastic rectangular plate of variable thickness. The solution is based on the integral relationship between stresses and strains using the weakly singular Koltunov-Rzhanitsyn kernel with weakly singular features of the Abel type. The components of the vector of forces \(\{N\}\ =\ \left(N_{x},N_{y},N_{xy}\right)\) and moments \(\{M\}=\left(M_{x},M_{y},M_{xy}\right)\) for a plate of symmetric structure in matrix form can be written as follows:  
\[\{N\}=\left\{N_{x},N_{y},N_{xy}\right\}^{T}=\left[C\right]\cdot\{\epsilon\} \tag{11}\]  
\[\{M\}=\left\{M_{x},M_{y},M_{xy}\right\}^{T}=\left[D\right]\cdot\{\chi\} \tag{12}\]  
where  
\[\{\epsilon\}=\left(\epsilon_{x},\epsilon_{y},\epsilon_{xy}\right)^{T} \tag{13}\]  
\[\{\chi\}=\left(\chi_{x},\chi_{y},\chi_{xy}\right)^{T} \tag{14}\]  
\[\epsilon_{x}=\frac{\partial u}{\partial x}+\frac{1}{2}\left[\left(\frac{ \partial w}{\partial x}\right)^{2}-\left(\frac{\partial w_{0}}{\partial x} \right)^{2}\right] \tag{15}\]  
\[\epsilon_{y}=\frac{\partial v}{\partial y}+\frac{1}{2}\left[\left(\frac{ \partial w}{\partial y}\right)^{2}-\left(\frac{\partial w_{0}}{\partial y} \right)^{2}\right] \tag{16}\]  
\[\epsilon_{xy}=\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}+ \frac{\partial w}{\partial x}\frac{\partial w}{\partial y}-\frac{\partial w_{ 0}}{\partial x}\frac{\partial w_{0}}{\partial y} \tag{17}\]  
\[\chi_{x}=-\frac{\partial^{2}w}{\partial x^{2}} \tag{18}\]  
\[\chi_{y}=-\frac{\partial^{2}w}{\partial y^{2}} \tag{19}\]  
\[\chi_{xy}=-\frac{\partial^{2}w}{\partial x\partial y} \tag{20}\]  
The stiffness matrixes \(\left[C\right]\) and \(\left[D\right]\) are given as follows [47]:  
\[C=\left( \begin{array}{ccc}C_{11}&C_{12}&C_{16}\\ C_{12}&C_{22}&C_{26}\\ C_{16}&C_{26}&C_{66}\end{array} \right) \tag{21}\]  
\[D=\left( \begin{array}{ccc}D_{11}&D_{12}&D_{16}\\ D_{12}&D_{22}&D_{26}\\ D_{16}&D_{26}&D_{66}\end{array}
## 5 Conclusions  
We evaluated eight-day and annual WUE values estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET during 2003-2008 at eight ChinaFLUX sites. MODIS products underestimated WUE in four forest ecosystems and one cropland ecosystem, while overestimating it in three grassland ecosystems. At the four forest sites, MODIS WUE was underestimated because of undervalued GPP and overvalued ET. In contrast, MODIS WUE was significantly overvalued mainly due to underestimated ET in the three grassland ecosystems. Sensitivity analysis shows that annual WUE derived from MODIS GPP and ET was most sensitive to the maximum light use efficiency (LUE\({}_{\text{max}}\)) and FPAR. The underestimation of GPP at forest and cropland sites may result from undervalued LUE\({}_{\text{max}}\). The overestimation of ET and, accordingly, underestimation of WUE at three subtropical and tropical forests were partly derived from the bias of meteorological data used in the MODIS algorithms. The bias in LAI may not influence the estimation of ET and WUE much, due to its small sensitivity. Uncertainties in eddy flux data due to gap-filling processes and the unbalanced surface energy issue also accounted for the bias of MODIS-produced WUE. Further improvements of the algorithms of the MODIS products (including LAI/FPAR, GPP and ET) and the driving meteorological data, as well as deeper investigation to identify the contributions of the main error sources to the uncertainty in estimated WUE are required in the future.  
This work was financial supported by \"Strategic Priority Research Program--Climate Change: Carbon Budget and Relevant Issues\" of the Chinese Academy of Sciences (XDA05050600). We thank all of the related staff of ChinaFLUX and CERN for their contributions from the observations to the data processing. We thank [PERSON] for writing the program to perform the sensitivity analysis and also [PERSON] for processing the relative humidity data at three flux sites.
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 3 Design and Simulation of Particle Damper
### Analysis of the Influence of Particle Damper Materials and Structural Parameters
#### 3.4.6 Damper Structure  
The energy dissipation characteristics of particle dampers also change depending on the damper structure. Particle collision dampers with two-, four-, and six-cavity damper cavity structures were tested. Their effects on the total energy dissipation of the particle system were investigated at three filling rates of 20%, 60%, and 80%.  
As shown in Figure 14, the total energy dissipated by the particle system increased and then decreased with the number of damper cavities when the particle filling rate was the same. When the damper was a two-cavity structure, the space in a single cavity of the particle damper was larger, allowing for higher free motion travel of the particles in the cavity. Therefore, the effective contact energy dissipation between particles and between particles and the container wall was reduced. As the number of damper cavities increased, the space in the individual cavities of the damper became smaller, the traveling distance of the particle system became shorter, and the energy dissipated due to collisions and friction increased. When the space in the individual cavities of a particle damper was small, the motion of the particles inside the container was limited. The ability to dissipate energy through collisions and friction between contacting bodies was reduced, and thus the total energy dissipated was reduced.
## 4 Performance Evaluation
### Simulation Results and Discussion  
Based on the procedures elaborated in \"Section 4\", the proposed framework comparison with benchmark techniques helped in confirming the performance of proposed framework in terms of the energy efficiency, packet delivery ratio and throughput. In addition, variability of the design parameters. Figure 8 represents energy efficiency on vertical axis for DAWPC-MAC (proposed)and existing schemes TDTSPC-MAC, DC-MAC, UW-ALOHA-QM [14; 37; 42] while time on horizontal axis for analysis. It is evident from simulation results that at start across time energy consumption remains between 1-3 J/s linearly while at later part from 4-5 J/s there is stability and approximately non-linear behavior for existing schemes. In contrast proposed scheme performs better in terms of energy consumption with 1 J/s and approximately constant (linear) behavior overall in dynamic oceanic environment.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Network Parameters** & **Purpose** & **Value** \\ \hline \(\delta\) & Time-offset step size & 5 ms \\ \hline
**Ocean Water Parameters** & & \\ \hline Salinity & Concentration of dissolved salts & [35; 37] PSU \\ \hline Temperature & & [0; 35] \\ \hline
**Learning Parameters (DQN)** & & \\ \hline \(\alpha\) & Learning rate for deep Q-learning algorithm & 0.1 \\ \hline Discount Factor (DQN) & Discount factor for Q-learning update & 0.9 \\ \hline Number of Episodes (DQN) & Total number of episodes for training & 1000 \\ \hline Batch Size (DQN) & Number of samples in each training batch & 32 \\ \hline Epsilon (DQN) & Exploration factor for epsilon-greedy policy & 0.1 \\ \hline \end{tabular}
\end{table}
Table 2: Cont.
## 4 Designed Schemes
### Data Forwarding and Cooperation
In summary, two routing schemes are proposed, the first one is non-cooperative EERD scheme, which saves the network energy. The second one is cooperative EERD scheme which increases the network stability. Both the schemes consider a certain fitness based on function residual energy, BER, and distance. The upper four sink nodes located at water surface are equipped with GPS. These sink nodes provide help for determining the coordinates of the sensor nodes for distance calculation.
## 1 Introduction
Although the simplified mathematical model can improve the computational speed, the simplified assumptions make it difficult for the model to meet the accuracy requirements or cause it to fail to take into account the NOx conversion efficiency and NH\({}_{3}\) slip at the same time [13; 21; 22]. Therefore, it is a worthwhile study to find a high-precision model to optimize the urea injection amount of SCR system to control NOx emission and NH\({}_{3}\) slip under the premise of reducing the calculation time. In order to solve the above problems, this paper proposes a new optimization method for urea injection in diesel engines based on the CPO-SVM-NSGA-II model. The method uses the CPO optimization algorithm to optimize the SVM model and, under the premise of ensuring the prediction accuracy of the SVM model for NOx conversion efficiency and NH\({}_{3}\) slip, the SVM model is used as the objective function to optimize the SCR urea injection using the NSGA-II to improve the NOx conversion efficiency of the SCR as much as possible within the NH\({}_{3}\) emission limits required by the emission regulations. Also, the optimization time of the model proposed in this study will be reduced considerably compared to the mathematical model.
# A Combined Elevation Angle and C/N0 Weighting Method for GNSS PPP on Xiaomi MI8 Smartphones
## 4 Experimental Results and Discussion
### Kinematic Experiment in an Open Sky Environment
The RMS statistics of the positioning errors are provided in Table 2 in the east, north and up components as well as the three-dimensional (3D) direction. Based on the combined weighting scenario, the RMS values of the position errors are 0.55 m, 0.73 m and 1.35 m in the east, north and up components, respectively, which improve by about 25.7%, 2.7% and 26.2% over the elevation-angle-only weighting scenario, and by about 53.8%, 5.2% and 16.7% over the C/N0-only weighting scenario. For the 3D position, the improvement can reach 22.7% and 24.2% over the elevation-angle-only and C/N0-only weighting scenarios, respectively. The obtained PPP accuracy is comparable to the existing research [12; 13]. Figure 10: Quad-constellation PPP errors using three different weighting scenarios. ELE, C/N0 and ELE/CN0 represent weighting scenarios of elevation-angle-only, C/N0-only and combined elevation angle and C/N0, respectively. Figure 9: Carrier phase observation variances for GPS G01 satellite at L5 frequency (**a**) and GLONASS R09 satellite at G1 frequency (**b**). ELE, C/N0 and ELE/CN0 represent weighting scenarios of elevation-angle-only, C/N0-only and combined elevation angle and C/N0, respectively.
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 3 Results
### Experiment Results  
We make full use of the open-source vehicle city data set. The data set used in the experiment is captured by the vehicle platform of the Hong Kong Polytechnic University, China. The vehicle navigation system data includes INS data and GNSS original pseudo-range data, together with accurate ground truth from the SPAN-CPT system. The detailed sensors setup, noise analysis and data format of this open-source dataset are in [26]. The path is shown in Figure 11.  
The on-board strapdown inertial navigation system is composed of a gyroscope and an accelerometer. The vehicle position is calculated by angular velocity and acceleration. The raw GNSS RINEX information includes pseudo-range measurements, Doppler shift and ephemeris. The INS data is provided by Xsens Mti-10, and raw GNSS data is collected by u-blox M8T. Due to the urban canyon environment in Hong Kong, the GNSS signal is easily disturbed, resulting in non-line-of-sight (NLOS) receptions, multipath effect or breakdown.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sources** & \multicolumn{2}{c}{**Fault Information**} & \multicolumn{1}{c}{**Fault Duration**} \\ \hline \multirow{3}{*}{Position (m)} & longitude & 4.7 & 1.2 \\  & latitude & 3.1 & 1.0 \\  & height & 5.5 & 1.9 \\ \hline \multirow{3}{*}{Velocity (m/s)} & east velocity & 0.18 & 0.11 \\  & north velocity & 0.08 & 0.06 \\ \cline{1-1}  & up velocity & 0.14 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: RMS of navigation errors obtained by simulation under the single-satellite ramp fault.  
Figure 10: The box diagram of fused position errors under the single-satellite ramp fault.  
Figure 9: The fusion states’ errors under the single-satellite ramp fault: (**a**) position errors; (**b**) velocity errors.  
As a high-frequency sensor, the preprocessed IMU measurement value is regarded as an equivalent IMU factor. When the GNSS detects the pseudo-range fault due to the proposed FDI algorithm, the data isolation and fusion system isolates the fault pseudo-range measurements and adds the GNSS factor calculated from the remaining normal data.  
The dataset UrbanNav-HK 2020 is obtained in the urban community environment of residences. Since the experimental environment is residential, no fewer than four satellites are observed during the experimental vehicle's trajectory. The performance is analyzed by the dataset UrbanNav-HK 2020 in the following figures. In Figure 12, the number of satellites in the low urbanization environment is greater than 4. If the detection value in the figure is greater than the threshold (132-150 s), it is the case that the pseudo-range errors are large, due to the surrounding buildings.  
By analyzing the detection value of sub-filters during 132-150 s, the fault detection value of a satellite with a PRN of 8 exceeds the threshold. After the isolation of satellite  
Figure 11: The path of the vehicle platform in an urban environment.  
Figure 12: The test statistic of fault detection obtained by the dataset UrbanNav-HK 2020.
# Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
## 2 System Model with FTN Signaling  
In FTN signaling, a transmission signal can be described as follows:  
\[z(t)=\sum_{k}w_{k}\cdot\ f(t-k\tau\ T) \tag{1}\]  
where \(f(t)\) is a baseband transmit pulse, whose bandwidth is \((1+\alpha)/(2T)\) for a roll-off factor \(\alpha\); \(w_{k}\) is the \(k\)-th transmit symbol, which is one of \(M\)-QAM symbols \(\mathbf{c}=[c(1),\ \cdots,\ c(m),\ \cdots,\ c(M)]\); and \(\tau\) is the FTN factor which controls the symbol interval. \(\tau T\) is the symbol interval \((0<\tau\leq 1)\); the symbol interval \(\tau T\) is shorter than or equal to the Nyquist pulse interval, \(T\), and the pulses are not orthogonal for \(\tau<1\). In the case of \(\tau=1\), Equation (1) becomes the conventional Nyquist signaling. In addition, \(f(t)\) has the unit energy. \(\int_{-\infty}^{\infty}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 3 Results and Discussion
### The Hyperparameter Setting of the Model  
The hyperparameters of the MultiDilatedFormer model are set as shown in Table 1. The proposed model adds a Dropout layer with \(p=0.2\) in each module, which is mainly used to reduce the over-fitting problem in the neural network and make the network more robust. The cross-entropy loss is selected as the loss function of fault diagnosis.