esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
## 4 Case Study
### Cascading Failure Simulation and Analysis
#### 4.2.1 Different Initial Attack Strategies  
To evaluate the impact of attacking different numbers of edges on network performance, we conducted multiple experiments with varying numbers of edge attacks. The relationship between the number of attacked edges and the resulting failures is complex, depending on the specific network structure and the failure propagation mechanism. In general, the greater the number of initially attacked edges, the more extensive and severe the resulting failures are. If a large number of edges are initially attacked or removed, this directly reduces the network's connectivity. In this study, we adjusted the value of the number of attacked edges, \(k\), from 25 to 500 to explore the impact of edge attacks on the freeway network.  
As the results in Figure 6 suggest, the number of attacked edges significantly impacts network performance. As the number of attacked edges increases, the network performance declines more rapidly. The results for the network connectivity indicator are shown in Figure 6a. The value of the SCC decreases from 1, and when the number of attacked edges exceeds 275, the network is already destroyed after the initial attack, with no subsequent cascading failure process. Network robustness is less affected by the number of attacked edges. As shown in Figure 6b, this is because the indicator is calculated as the proportion of ultimately failed edges. The initial values for network efficiency and connectivity reliability are about 0.4 and 0.25, respectively, indicating that the freeway network's efficiency and connectivity reliability are relatively poor, as shown in Figure 6c,d. During the cascading failure process, these values continue to decrease, and when \(k=275\), both the network efficiency and connectivity reliability approach 0, indicating network collapse.
# Advances in the Neural Network Quantization: A Comprehensive Review
## 3 Quantization Techniques
### Quantization of LLMs  
The LLM (Large Language Model) is a state-of-the-art deep learning model designed for natural language processing tasks [61]. By integrating the latest quantization techniques, these models achieve significant reductions in computational and memory costs while maintaining high accuracy. However, existing solutions may still face challenges in preserving performance for highly complex tasks and ensuring generalizability across diverse datasets.  
Existing quantization methods for large language models (LLMs) can be mainly divided into two categories: weight-only quantization and joint weight and activation quantization. The former compresses a large number of weights into lower bit-widths [62], effectively reducing the memory footprint of the models. The latter quantizes both weights and activations into mixed bit-widths [63], which accelerates matrix multiplications and significantly enhances computational speed.  
However, when dealing with significant activation outliers, existing methods often exhibit limited improvements or result in unstable gradients. This challenge highlights the need for more robust quantization techniques capable of effectively managing outliers without compromising stability. Future advancements in this field may focus on adaptive quantization strategies that dynamically adjust bit-widths based on activation distributions, ensuring both efficiency and accuracy. Additionally, exploring novel quantization-aware training methods could further enhance the robustness and generalizability of quantized LLMs, paving the way for their broader application in resource-constrained environments.
## 2 Numerical Analysis
### Simulation Cases  
The engine in this study is a generator engine. It supplies mechanical energy for a 2740 kW/60 Hz generator. Due to it being an alternative current (AC) generator engine, it always operates with a fixed speed of 720 rpm and produces a power of around 2880 kW. It does not run at other speeds. Therefore, this study investigated the effect of the injector SA on the combustion and emission characteristics of the engine only at this full load. Eight simulation cases were carried out for both the diesel and DF modes. In each mode, the SA of the pilot fuel injector was changed from 145 to 160\({}^{\circ}\) with an angle step of 5\({}^{\circ}\). The SA of the fuel injectors affects the combustion and emission formations of the ICEs because it determines the targeting point of the fuel injection. The degree of consumption of \(\mathrm{O_{2}}\) in the combustion process also strongly depends on the targeting injection point [27; 28; 29]. If the SA of the injectors is too narrow, the targeting injection point is in danger of approaching the opposite wall of the combustion chamber of the engine, i.e., the piston surface [30; 31]. This causes the formation of liquid fuel films on the piston surface, due to the wall-wetting phenomena. In contrast, if the fuel injector SA becomes too wide, the outer edge of the injection region is in danger of colliding with the cylinder liner, causing the formation of the wall-wetting liquid fuel films on the cylinder liner [20; 21]. Liquid fuel films, due to the wall-wetting of the injected fuels on the combustion chamber surfaces reduce the quality of the fuel combustion. Especially, this significantly increases the amount of soot and carbon monoxide (CO) emissions owing to the incomplete combustion. For this reason, the SA of the injector of the engine in this study has been limited to the range of 145 to 160\({}^{\circ}\). A schematic of the injector spray angle and interaction between injected fuel droplets and the piston surface is presented in Figure 3.  
To clarify the impact of the fuel injector SA on the engine combustion and emission characteristics, only the injector SA was adjusted, other simulation parameters and the engine operating conditions, including the supplied fuels remained unchanged. The simulation cases in this study are listed in Table 3.  
Figure 3: Schematic of the injector spray angle.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 3 Proposed Scheme Based on Homomorphic Encryption and Classic McEliece
### Generation and Matching of Protected Biometric Features  
Our proposal for biometric protection adapts the Classic McEliece NIST proposal [32] to the context of the non-device-centric biometric authentication scheme described above. The definition of Classic McEliece, which is based on the Niederreiter cryptosystem, is composed of three main algorithms: key generation (_KeyGen_), encoding (_ENCODE_) and decoding (_DECODE_). The parameter set specifies: \(q=2^{m}\), \(n\leq q\) and \(k=n-tm\). _KeyGen_ has no inputs and generates the public and private keys. The public key \(T\) is the \((n-k)\times k\) random matrix over a finite field of order 2, \(\mathbb{F}_{2}\), such that \(I_{n-k}\mid T\) is the \((n-k)\times n\) parity-check matrix \(H\) for the Goppa code where \(I\) is a \((n-k)\times(n-k)\) identity matrix. The private key is \(\Gamma=(\text{g},\ \alpha_{1},\alpha_{2},\dots,\ \alpha_{n})\), where g is a monic irreducible polynomial in \(\mathbb{F}_{q}[x]\) of degree \(t\) and \((\alpha_{1},\alpha_{2},\dots,\ \alpha_{n})\) are distinct elements of a finite field of order \(q\), \(\mathbb{F}_{q}\), generated from a uniform random string. _ENCODE_ receives, as inputs, the public key \(T\) and a vector \(e\ \in\ \mathbb{F}_{2}^{n}\) of Hamming weight \(t\). The output of the algorithm is the vector \(c\ \in\ \mathbb{F}_{2}^{n-k}\) computed as \(c=H\cdot e\) with \(H=I_{n-k}\mid T\). _DECODE_ receives, as inputs, the private key \(\Gamma\) and a vector \(c\ \in\ \mathbb{F}_{2}^{n-k}\). The first step of _DECODE_ is to extend \(c\) to \(v=(c,0,\dots,0)\in\ \mathbb{F}_{2}^{n}\) by appending \(k\) zeros. The second step is to find the unique codeword _cod_ in the Goppa code defined by \(\Gamma\), which is at a distance \(t\) from \(v\). An advantage of Classic McEliece is that there are fast algorithms to decode these codes, for example, the Berlekamp algorithm. The third step is to set \(e=v+cod\). If the Hamming weight of \(e\) is \(t\) and \(c=H\cdot e\), then \(e\) is returned.
## 2 Materials and Methods
### Data
#### 2.2.2 GEOV1 Leaf Area Index  
Satellite observations for LAI are derived from SPOT-VGT (until May 2014) and PROBA-V (from June 2014) satellite data produced by European Copernicus Global Land Service ([[https://land.copernicus.eu/global/](https://land.copernicus.eu/global/)]([https://land.copernicus.eu/global/](https://land.copernicus.eu/global/)), accessed on 21 July 2021). The SPOT/VGT LAI Version 1 (GEOV1) is derived from the SPOT/VGT Top of Atmosphere (TOA) reflectances provided by the SPOT/VEGETATION program [33], while the PROBA-V LAI Version 1 is derived from the SPOT/VGT-like Top of Atmosphere (TOA) PROBA-V reflectances generated by the PROBA2 VGT module. This module ensures the consistency of the time series when moving from SPOT/VGT to PROBA-V data. Both products are provided at a spatial resolution of 1 km and 10 days sampling time in a regular latitude/longitude grid. The accuracy (RMSE) of GEOV1 products compared to the reference in-situ data set is 0.7 for LAI [34].  
The satellite information is extrapolated to the 8 km model grids by averaging. The filtered satellite GEOV1 and the extrapolated LAI are shown in Figure 1. Figure 1 also illustrates the studied domain.
# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 4 Experimental Analysis and Results
### Performance of Individual Branches
These models were selected based on their architectural similarity, applicability to zero-day or synthetic data scenarios, and the availability of reproducible results using comparable datasets such as CTU-13 and NSL-KDD. Our proposed ZDGAN consistently achieves the best results across all evaluated metrics, with an F1-score of 0.946, AUC of 0.982, and accuracy of 0.945, outperforming FLGAN by +3.1% in F1 and +2.9% in AUC. These results highlight ZDGAN's improved generalization capacity and  
Figure 8: Radar Chart Visualization of the Comparative Performance between the two Branches. Figure 9: Visualization of the Comparative Performance between the two Branches. its ability to model rare or unseen threat patterns more effectively. The performance gains further confirm the effectiveness of our integrated pipeline, which couples synthetic data generation with ensemble transfer learning, while stating its role as a state-of-the-art solution in the domain of tabular threat detection. To assess the effectiveness of the malware image detection branch of our proposed framework, we conducted a comparative analysis against a recent state-of-the-art GAN-based approach. Table 16 shows the results for the malware image detection branch, comparing our pipeline to PlausMal-GAN [18], which applies ensemble DCGAN variants for image augmentation on the Malimg dataset. Our framework surpasses PlausMal-GAN by a substantial margin (F1-score 0.973 vs. 0.922 and AUC 0.991 vs. 0.947), mainly due to the use of transfer learning and ensemble classification, which enhance robustness and feature discrimination. The observed performance gains validate the utility of synthetic image generation in conjunction with deep learning ensembles for effective malware detection. It is important to note that we limit this comparison to PlausMal-GAN and DPGAN models, as these were the only recent and publicly documented GAN-based approaches evaluated on malware image data (e.g., Malimg), with reproducible metrics and architectural transparency. No other directly comparable models currently meet these criteria within the literature.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 1 Introduction
### The Hybrid Aerial Underwater Robotic System  
Our proposed solution is the Hybrid Aerial Underwater Robotic System (HAUCS) [30, 31]. In this system, DO sensors and radio modules are installed on a set of GPS-navigated drones which take in situ measurements of different ponds on the farm before returning to be recharged. The drones conduct a direct, full depth measurement of DO, temperature, and pressure by submersing the sensor to a depth of up to several meters below the surface to gain a full understanding of the state of each pond. A diagram of the HAUCS concept is shown in Figure 1.
# Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures
## 3 Results and Discussion
### Rheological, Chemical, and Thermal Characterization of the Extracted Binders
The F33 binder extracted from a mixture containing both RAP and RAS, which was 9 years old, exhibited the worst performance, with a \(G^{*}\).sin\(\delta\) of 11,521.50 kPa, G-R of 5883.20 kPa, and a \(\Delta T_{c}\) of \(-9.60\)\({}^{\circ}\)C. The chemical and thermal investigations corroborate that the F33 binder exhibited the highest aging indices (\(I_{CO}+I_{SO}\) = 0.21), the lowest \(I_{CC}+I_{CH}\) value at 0.83, and the highest R% at 19.71. In contrast, the F42 binder, extracted from a 14-year-old mixture with no recycled materials, had the best performance, with a G-R of 374.90 kPa and a \(\Delta T_{c}\) of \(-0.35\)\({}^{\circ}\)C. This binder had the lowest \(I_{CO}+I_{SO}\) (0.13), the highest \(I_{CC}+I_{CH}\) (0.89), and the lowest R% (16.84). The F20 binder, including 30% ABR from RAP, exhibited intermediate behavior, aligning with its rheological and chemical characteristics. Figure 4: Fatigue and block cracking parameters of extracted binders with (**a**) \(I_{CO}+I_{SO}\), (**b**) \(I_{CC}+I_{CH}\), and (**c**) R%. The Pearson correlation and \(p\)-value analysis revealed a moderate positive correlation between \(G^{*}\)-sin\(\delta\) and \(I_{CO}+I_{SO}\) (R = 0.70), a moderate negative correlation between \(G^{*}\)-sin\(\delta\) and \(I_{CC}+I_{CH}\) (R = \(-\)0.67), and a moderate-to-strong positive correlation between \(G^{*}\)-sin\(\delta\) and R% (R = 0.76). The \(p\)-values exceeded 0.05, indicating that chemical and thermal analyses were not statistically significant in predicting the fatigue cracking parameters for the randomly selected samples F20, F33, and F42. A comparable statistical analysis was performed on G-R, thermal, and chemical properties. A strong positive correlation existed between G-R and \(I_{CO}+I_{SO}\) (R = 0.99), while a strong negative correlation was observed with \(I_{CC}+I_{CH}\) (R = \(-\)0.99). Furthermore, G-R exhibited an exceptionally robust positive correlation with R% (R = 1.00). The \(p\)-values for \(I_{CO}+I_{SO}\) and \(I_{CC}+I_{CH}\) surpassed 0.05, signifying that these chemical indices were not statistically significant predictors of block cracking resistance for the chosen samples. The correlation between G-R and R% was statistically significant (\(p\)-value < 0.05), indicating that thermal residue content (%R) significantly affects the binder's resistance to block cracking. There was a strong negative correlation between \(\Delta T_{c}\) and \(I_{CO}+I_{SO}\) (R = \(-\)0.85), a strong positive correlation between \(\Delta T_{c}\) and  
owing to the restricted statistical significance of individual chemical and thermal predictors in small sample sizes, machine learning algorithms were proposed and implemented on the complete dataset to enhance the predictive accuracy of binder performance parameters, as elaborated in Section 3.3.
## 5 Discussion  
In the previous sections, we demonstrate surface measurement using a new developed algorithm, which is preferable to conventional methods. As we can see in Section 3 and Figures 5 and 6, the new algorithm has a strong sidelobe suppression ability and can compensate for both the bias modulation and the coupling errors. As proposed by [PERSON] (1984), the error caused by the combined effect of phase-shift miscalibration and the harmonics error is still one of the most frequently overlooked sources of error. Phase-shift miscalibration increase the sensitivity to high-order harmonics present in wavelength-tuning Fizeau interferometer. In this paper, the RMS phase error of phase shift miscalibration was also calculated with a value of 0.7 nm, which was the smallest error among all algorithms. The results demonstrated that the new algorithm can more strongly compensate for phase shift miscalibration in the presence of harmonics in comparison with other conventional algorithms, even for a phase shift miscalibration of \(-30\%\) and high reflectivity (30%) sample surface. Based on [PERSON]'s theory [PERSON] (1984), the quantization error of the CCD can be neglected with a total of 91 steps in measurement. In the experiment, since there was a nonlinearity of about 3% in the PZT response, we applied a quadratic voltage increment to the PZT and the nonlinearity decrease to 1% of the total phase shift. The standard deviation was 2.05 nm, which was caused by environmental variances in the laboratory, such as temperature variation and floor vibration. The errors can be reduced in a stable environment by using a properly performing laser source. The uncertainty of the reference surface shape was \(\mathcal{N}30=21\) nm. As a total, the uncertainty of the surface shape measurement was 23 nm.  
The standard deviation was also calculated, as shown in Table 3, in comparison with other conventional algorithms. The standard deviation of synchronous detection algorithm shows the worst value among all because it cannot suppress the phase shift error and coupling error. [PERSON]'s \(2N-1\) and [PERSON]'s \(2N-1\) can only suppress phase shift miscalibration \(\varepsilon_{0}\). [PERSON]'s \(5N-4\) algorithm has a much better repeatability. However, the novel \(10N-9\) shows the best performance because of the strong ability to suppress coupling errors.  
Even though the CCD we used in the experiments is low-noise device, the noise of the camera sensor is still a challenging work for researchers. In future work, the optimization of the exposure time and gain should still be considered. We should also focus on improving the insensitivity of the algorithm, improving the measurement speed, and using higher-quality equipment in order to improve the performance of the \(10N-9\) algorithm.
## 3 Geometry and Numerical Method  
### Geometry and Boundary Conditions  
Figure 3 presents the gap model, which comprises the impeller back gap of a sCO\({}_{2}\) compressor, such as a disk-type gap, a labyrinth seal, and a shaft-type gap. For the computation domain, the inner radius of the impeller back gap \(R_{\rm i}\) is fixed as 13.0 mm. The inner radius at inlet and outlet \(R_{\rm in}\) and \(R_{\rm out}\), as well as the length of the outer walls at inlet and outlet \(L_{\rm in}\) and \(L_{\rm out}\) are constants and remain consistent with the investigations of [PERSON] et al. [16]. The geometric parameters of the labyrinth seal remain unchanged due to the structural limitations of the rotor, with its dimensions detailed in Table 1.  
Additionally, the gap width of the impeller back gap \(\delta\), which equals the difference between \(R_{\rm o}\) and \(R_{\rm in}\), is variable. The values of \(R_{\rm o}\) and \(\eta\) for different cases are listed in Table 2 in detail. The selection of geometric parameters is based on the compressor dimensions in the TAC unit. The main dimensions of compressors of similar power are basically within the range.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Teeth number, \(N\) & 3 & - \\ Teeth width, \(B\) & 0.20 & mm \\ Clearance width, \(C\) & 0.05 & mm \\ Teeth height at left side, \(H_{\rm i}\) & 0.45 & mm \\ Teeth height at right side, \(H_{\rm r}\) & 0.85 & mm \\ Cavity height, \(H_{\rm c}\) & 0.40 & mm \\ Cavity length of inner wall, \(L_{\rm i}\) & 2.20 & mm \\ Cavity length of outer wall, \(L_{\rm o}\) & 2.00 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric parameters of the labyrinth seal.  
Figure 3: Gap model and schematic diagram of computational domain.  
The physical properties of CO\({}_{2}\) in this study were obtained from the NIST database REFPROP (Gaithersburg, MD, USA), utilizing the equation of state proposed by Ref. [37]. At the inlet and outlet planes, pressure-type boundary conditions are specified, with the parameters listed in Table 3. Notably, the inlet pressure corresponds to that of the impeller outlet of sCO\({}_{2}\) compressors, and it can be adjusted according to the compressor operating under the off-design conditions. The inlet pressure is derived from the TAC unit and similarly powered compressors. However, the outlet pressure is 2 MPa, which depends on seal performance [30]. Additionally, the inner walls, rotating at a speed determined by the Reynolds number \(Re\) and stationary outer walls are adiabatic and non-slip. It is worth stating that for \(Re\), the actual sCO\({}_{2}\) compressor operation will not be below \(10^{5}\) except in the startup condition, and the large range of \(Re\) is chosen to make the \(C_{\text{f}}\) models more applicable.
## 4 ADRC-Based ABS Slip Ratio Tracking Control
### Design of ADRC
#### 4.2.1 Extended State Observer (ESO) Design  
The ESO is utilized to estimate both the state variables and the overall disturbance within the system. Equation (19) is recast into a state equation representation, as follows:  
\[\left\{\begin{array}{l}\dot{\theta}_{1}=\theta_{2}\\ \dot{\theta}_{2}=\theta_{3}+bu\\ \dot{\theta}_{3}=h\\ y_{1}=\theta_{1}=\lambda\end{array}\right. \tag{20}\]  
where \(\lambda\) is the true slip rate, \(\theta_{3}=f=\frac{1}{m\
u}[(\lambda-1)F_{2}\mu_{(\lambda)}+2F_{2}\mu_{( \lambda)}\dot{\lambda}-\tau F_{2}R^{2}\dot{\mu}(\lambda)]\) is the new expansion state variable, is the total perturbation, \(h\) is the differential value of the total perturbation, \(b=\tau K_{b}K_{t}R\), and \(\dot{I}_{a}\) is the differential value of the current.  
From (14), the extended state observer formula can be obtained, as follows:  
\[\left\{\begin{array}{l}\varepsilon=z_{1}-y\\ \dot{z}_{1}=z_{2}-\gamma_{1}\varepsilon\\ \dot{z}_{2}=z_{3}-\gamma_{2}f_{al}(\varepsilon_{1},0.5,\delta_{1})+bu\\ \dot{z}_{3}=-\gamma_{3}f_{al}(\varepsilon_{1},0.25,\delta_{1})\end{array}\right. \tag{21}\]  
where \(z_{1},z_{2}\), and \(z_{3}\) are the observations of \(\theta_{1}\), \(\theta_{2}\) and \(\theta_{3}\), respectively; \(\gamma_{1}\), \(\gamma_{2}\), and \(\gamma_{3}\) are the parameters of the controller; and the function \(f_{al}\) is as above.
# M-Emu: A Platform for Multicast Emulation
## 4 Validation  
In this part, we explore the effectiveness and efficiency of M-Emu. First, we take PIM-SM emulation as an example to introduce how M-Emu can effectively support the emulation of various multicast technologies. Secondly, we compare the differences between M-Emu and the original NS4 in terms of CPU usage and memory usage, etc., when deploying the PIM-SM emulation.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Orbital Angular Momentum Antennas
#### 3.4.4 Array Configurations for OAM Systems  
There are numerous antenna array configurations used to propagate OAM vortex electromagnetic waves (refs. [85; 86; 88; 89], with the circular design being the prevailing one (due to its inherent circularly polarized (CP) response). Nevertheless, apart from their circular form, one can observe noticeable differences between the designs that stem from the fact that different desired excitations may be needed (ref. [84]). A broad spectrum of technologies and materials, especially dielectric ones, are utilized in the construction process. The main goal is to obtain more patch antenna cells in one array to achieve a reduced profile.