esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
###### Abstract  
Traditional firewalls and data encryption techniques can no longer match the demands of current IoT network security due to the rising amount and variety of network threats. In order to manage IoT network risks, intrusion detection solutions have been advised. Even though machine learning (ML) helps the widely used intrusion detection techniques currently in use, these algorithms struggle with low detection rates and the requirement for extensive feature engineering. The deep learning model for IoT network intrusion detection is a method for traffic anomaly detection that is suggested by this study. To extract the sequence properties of data flow through a CNN, it combines an attention mechanism with a Long Short Term Memory (LSTM) network. This method uses adaptive synthetic sampling (ADASYN) to increase the size of minority-class samples. The proposed models demonstrated acceptable precision and recall for each class when used for binary-class classification, proving their stability and capacity to identify all classes correctly. The MLP classifier's accuracy, precision, recall, and F1 value were 87%, 89%, 87%, and 89%, respectively, with an AUC score of 0.88. Overall, the proposed models performed well. The attack and all-class models exhibited good AUCs and macro metrics, the same as the proposed MLP classifier, which had an F1 score of 83% and an AUC score of 0.94. Additionally, it trained the MLP classifier and integrated the ADAM optimizer and category cross-entropy loss function for all-class classification. With an AUC value of 94%, it possessed 84% accuracy, 87% precision, 84% recall, and an 83% F1 score. A further indication of the hybrid model's ability to combine the benefits of both models to improve overall performance was that it regularly outperformed the MLP model. This model's accuracy and F1 score are better than those of earlier comparable algorithms, according to experimental results using the publicly accessible benchmark dataset for network intrusion detection (NSL-KDD).  
network intrusion detection; NSL-KDD; deep learning; adaptive synthetic sampling; network security; machine learning; classification
# Galileo: The Added Value for Integrity in
## 2 Multi-Constellation Solution
In particular, pseudorange-rates are obtained as [1]:  
\[\dot{\rho}=-\lambda\Delta f \tag{8}\]  
where \(\lambda\) is the signal wavelength. Pseudorange-rates can be modeled as:  
\[\dot{\rho}=\dot{d}+c\dot{dt}_{s}+c\dot{d}t_{r}+\epsilon_{\dot{\rho}} \tag{9}\]  
where \(\dot{d}\) is the geometric range-rate and \(c\dot{dt}_{s}\) and \(c\dot{d}t_{r}\) are the satellite and receiver clock drifts, respectively. \(\epsilon_{\dot{\rho}}\) contains all of the unmodeled errors. Correcting for the satellite clock drift and replacing \(\dot{d}\) with the dot product \((\mathbf{v_{s}}-\mathbf{v_{r}})\cdot\mathbf{e}\) yields:  
\[\dot{\rho}=(\mathbf{v_{s}}-\mathbf{v_{r}})\cdot\mathbf{e}+c\dot{ \delta t}_{r} \tag{10}\] \[\dot{\rho}-\mathbf{v_{s}}\cdot\mathbf{e}=-\mathbf{v_{r}}\cdot \mathbf{e}+c\dot{\delta t}_{r}\]  
where \(\mathbf{e}\) is the unit vector pointing along the line-of-sight from the user to the satellite. \(\mathbf{v_{s}}\) and \(\mathbf{v_{r}}\) are the satellite and receiver velocity vectors, respectively. Considering \(\delta\dot{\rho}=\dot{\rho}-\mathbf{v_{s}}\cdot\mathbf{e}\), the difference between measured and predicted pseudorange-rates is found:  
\[\delta\dot{\rho}=-\mathbf{v_{r}}\cdot\mathbf{e}+c\dot{\delta t}_{r} \tag{11}\]  
Expanding the dot product in Equation (11) and considering \(k\) measurements, a system of equations expressed in matrix form is found:  
\[\delta\dot{\rho}=H\mathbf{v}+\epsilon_{\dot{\rho}} \tag{12}\]  
where \(\mathbf{v}\) is the state vector defined as:  
\[\mathbf{x}=\mathbf{x_{0}}+\Delta\mathbf{\hat{x}} \tag{6}\]0  
\(V_{e}\), \(V_{n}\) and \(V_{u}\) are the components of the receiver velocity expressed in the east north up (ENU) frame, and \(c\delta t_{Sys}\) is the inter-system clock drift. Finally, the user velocity and receiver clock drift can be estimated as:  
\[\mathbf{x}=\mathbf{x_{0}}+\Delta\mathbf{\hat{x}} \tag{6}\]1  
where \(H\) is the design matrix detailed in Equation (4) and \(W_{\dot{\rho}}\) represents the different accuracies of the pseudorange-rates.
## 4 Training and Performance Evaluation of the CCBA Model  
In this Section, we describe the process of training the CCBA model and present its performance evaluations. Given the complexity of the system, the training process is divided into two stages to ensure both generalization and task-specific fine-tuning. The second stage builds upon the first one, leveraging knowledge and feature distillation to enhance the model's robustness. Following the training, we also present detailed performance evaluations, comparing the CCBA model with baseline methods across various signal-to-noise ratios (SNRs).
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Interference Mitigation in D2D  
3 GPP introduced Proximity Services in release 12 [169], enabling D2D communication with IM mechanisms designed to coexist with traditional cellular users.
## 4 The Inversion of HJC Constitutive Parameters for Hard Rock
### Multi-Objective Optimization  
Section 4.2 reveals that the influence of parameters \(N_{1}\), \(D_{1}\), and \(\varepsilon_{\max}\) on perforation depth and aperture exhibits a non-monotonic trend. Consequently, achieving convergence of these parameters in the subsequent fitting process becomes challenging. Therefore, the design variables in this study select parameters \(A_{1}\), \(B_{1}\), \(C_{7}\), and \(f_{c}\), while parameters \(N_{1}\), \(D_{1}\), and \(\varepsilon_{\max}\) are adopted based on the literature above. According to the previous literature research, a relationship between \(A_{1}\) and \(C_{7}\) can be observed when there is a change in uniaxial tensile strength, as demonstrated by Equation (12) [31], where \(c\) represents cohesion. Hence, parameters \(A_{1}\) and \(C_{7}\) are conjoined for investigation in this study.  
\[A_{1}=\frac{c}{(1+C_{7}\ln 10^{-4})f_{c}} \tag{12}\]  
On this basis, using the central composite design to simulate the combination of different parameters, the fitting surface of the target variable (\(L\), \(D\)) for the inversion variables (\(B_{1}\), \(C_{7}\), \(f_{c}\)) can be obtained. The fitting surface of perforation depth and perforation aperture under the interaction of parameters \(B_{1}\) and \(C_{7}\) is illustrated in Figure 18. The figure demonstrates a strong fit between the data points and the surface; therefore, the explicit expressions of the target variables obtained by fitting the surface for the design variables are Equations (13) and (14).  
\[\begin{array}{ll}L=&36+0.35B+20C+0.26f_{c}-0.11B^{2}-355.77C^{2}+8.23f_{c}^{ 2}\\ &-35.85 BC-2.52 Bf_{c}-59.85 Cf_{c}+155.8 BCf_{c}\\ &+13.16B^{2}C+0.98B^{2}f_{c}+676.32 BC^{2}-0.20 Bf_{c}^{2}\\ &-210.02C^{2}f_{c}-0.012B^{3}-2501.33C^{3}\\ \end{array} \tag{13}\]  
\[\begin{array}{ll}D=&1.3+0.0.016B+1.2C-0.0023f_{c}-0.0039B^{2}-27.54C^{2}+0. 39f_{c}^{2}\\ &-2.25 BC-0.11 Bf_{c}-1.95 Cf_{c}+7.58 BCf_{c}+0.83B^{2}C+0.045B^{2}f_{c}\\ &+54.43 BC^{2}-0.015 Bf_{c}^{2}-30.36C^{2}f_{c}-0.011B^{3}-176.73C^{3}\\ \end{array} \tag{14}\]  
After performing the calculations, it is evident that the multiple determination coefficients of explicit expressions (13) and (14) are \(R^{2}{}_{L}=0.941\) and \(R^{2}{}_{D}=0.952\), which are also very close to 1. The comparison between the simulated values and the predicted response values is further illustrated in Figure 19. The results demonstrate strong agreement between the predicted and simulated target variable values, providing additional evidence that the explicit expression derived from the response surface method effectively characterizes the relationship between the target variable and the three HJC constitutive parameters.  
Figure 18: Fitting surface of perforation depth and perforation aperture under parameters \(B_{1}\) and \(C_{7}\).
# Performance of SIMO FSO Links over Mixture Composite Irradiance Channels
## 5 Conclusions  
A SIMO FSO system, with wavelength, spatial, and time diversity, along with OOK or L-PPM modulation and OC method at the receiver side, operating over the combined effect of atmospheric turbulence and NZB-PEs, was investigated. In order to reduce the mathematical complexity of the derived expressions, a mixture composite channel model was adopted. Novel mathematical expressions of the OP and the ABER were deduced and appropriate numerical results were graphically depicted.  
Conceptualization, N.A.A., H.E.N., A.N.S., H.G.S. and G.S.T.; methodology, N.A.A., H.E.N. and H.G.S.; software, N.A.A. and A.N.S.; validation, N.A.A., H.E.N. and A.N.S.; formal analysis, N.A.A. and H.E.N.; investigation, N.A.A., H.E.N., A.N.S., H.G.S. and G.S.T.; resources, N.A.A., H.E.N., A.N.S., H.G.S. and G.S.T.; writing--original draft preparation, N.A.A., A.N.S.; writing--review and editing, H.E.N., H.G.S. and G.S.T.; supervision, H.E.N.; funding acquisition, H.E.N., G.S.T.  
N.A.A. acknowledges that this research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Program <<Human Resources Development, Education, and Lifelong Learning>> in the context of the project, \"Strengthening Human Resources Research Potential via Doctorate Research\" (MIS-5000432), implemented by the State Scholarships Foundation (IKY). HEN, A.N.S. and G.S.T. acknowledges the funding from the European Union's Horizon 2020 research and innovation program under grant agreement No: 777596.  
The authors declare no conflict of interest.
# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 2 Related Work  
### Slam  
SLAM has evolved significantly with the integration of multi-sensor fusion techniques.  
Pure visual-based SLAM (VSLAM) systems, such as Mono-SLAM [13] and PTAM [14], rely solely on cameras to estimate motion and reconstruct the surrounding environment by extracting and tracking visual features across the frame, which suffers from limitations such as scale ambiguity in monocular setups and sensitivity to lighting variations and texture-less environments.  
To address these issues, Visual-Inertial SLAM (VI-SLAM) integrates an IMU with visual data, leveraging the IMU's short-term motion constraints to enhance motion estimation. Representative VIO-SLAM systems include OKVIS [15], VINS-Mono [16], and ORB-SLAM3 [9], which achieve improved robustness in dynamic or visually degraded conditions by tightly coupling visual and inertial data.  
LIV-SLAM integrates LiDAR, IMU, and cameras, leveraging the strengths of each sensor to achieve high-precision localization and detailed scene reconstruction. State-of-the-art systems such as R\({}^{3}\)LIVE [3], R\({}^{3}\)LIVE++ [4], and Fast-LIVO [5; 6] exploit tight LiDAR-visual-inertial fusion to enhance robustness in complex environments. By combining LiDAR's depth accuracy, IMU's motion constraints, and visual data's rich feature representation,LIV-SLAM has become a leading solution for applications such as autonomous navigation, real-time mapping, and virtual reality.  
Despite these advancements, challenges remain in real-time computation, large-scale map maintenance, and drift resistance, necessitating further research into efficient multi-sensor fusion strategies.
###### Abstract  
The objective of this study was to compare the effects of applying a 50% wheat grain diet with those of a diet with 3% additional dietary fibre from various sources on the development of broiler chickens' gastrointestinal tract and its related organs and to model this phenomenon based on data obtained from 35-day-old chickens using multiple regression equations. The use of various structural components, including oat hull (OH), sunflower hull (SH), sugar beet pulp (SBP), and wheat bran (WB), in proportions of 3% of the diet not only affects digestive processes in broiler chickens' gastrointestinal tract but also causes a change in the length of their intestinal sections or the weight of related organs. These effects can be taken into account when creating an experimental model, the results of which can at least be partially applicable to human studies. The use of OH and SH (3%) in the birds' diets resulted in a significantly higher body weight (\(p<0.05\)) compared with the use of SBP and WB. OH in the diet significantly increased (\(p<0.01\)) the weight of the chicken's gizzards compared with the other dietary fibre sources, apart from SH. On the other hand, the weight of the proventriculars in chickens fed the diet containing OH was significantly lower than that of the chickens fed the diet containing SBP (\(p<0.05\)). The use of SH in the diet caused a significant decrease (\(p<0.01\)) in the weight of the chickens' heart. Compared with other additional sources of dietary fibre, OH in the diet also significantly increased (\(p<0.05\)) the lengths of the small and large intestines, as well as the total length of the intestines. A correlation analysis showed a significant, average, positive relationship (\(p<0.05\)) between the content of TDF in the diet and the weight of the gizzard and indicated a significant positive correlation between the lengths of the jejunum and the remaining sections of the intestines. Additionally, the regression equation models indicated a significant effect (\(p<0.01\)) of all the independent variables on the jejun, ileal, and caecal lengths and the liver weight. The application of the regression model confirmed significant changes in the small intestine and liver weight depending on the type of dietary fibre and other independent variables, which can also be taken into account when assessing diseases in people with thin intestines. However, further studies with separate models still need to be conducted using experiments including both soluble and insoluble fibre.  
animal model; dietary fibre; _Gallus domesticus_; multiple regression; intestines ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# UnifiedFace: A Uniform Margin Loss Function for Face Recognition
## 4 Experiments
### Experiments in Embedded Devices  
We conducted a 1:1 speed test on a computer platform (i9-12900K CPU). In addition, we also performed accuracy test experiments on the computer platform with the LFW dataset, as illustrated in Table 12. GhostFaceNet achieve the best performer in the LFW and is 15.3 ms faster than EfficientNet, MobileNetV2, and MobileFaceNet, 2.39 times, 1.67 times, and 1.55 times, respectively.  
Meanwhile, we conduct running speed comparison experiments in resource-constrained embedded device to further illustrate the advantages of GhostFaceNet in embedded devices. The embedded platform of choice is the ZYNO 7020, which uses ARM+FPGA SOC technology to integrate ARM Cortex-A9 and FPGA programmable logic on a single chip. We use MTCNN [56] for face images detection and crop it to \(112\times 112\). We use NCNN for PyTorch model transformation and implement the final runtime test. We do not apply quantization to the model, so there is no degradation in the model recognition capability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**m**} & \multicolumn{3}{c}{**IJB-B**} & \multicolumn{3}{c}{**IJB-C**} \\  & & \(\mathbf{1\times e^{-6}}\) & \(\mathbf{1\times e^{-5}}\) & \(\mathbf{1\times e^{-4}}\) & \(\mathbf{1\times e^{-6}}\) & \(\mathbf{1\times e^{-5}}\) & \(\mathbf{1\times e^{-4}}\) \\ \hline SphereFace & 3 & 42.00 & 85.84 & 93.90 & 78.77 & 92.18 & 95.68 \\ SphereFaceR v1 & 1.5 & 34.56 & 88.88 & 94.47 & 88.39 & 93.69 & 95.97 \\ SphereFaceR v2 & 1.5 & 37.22 & 88.45 & 94.51 & 87.79 & 93.34 & 95.99 \\ CosFace & 0.45 & 41.29 & 90.21 & 94.63 & 89.61 & 94.02 & 96.11 \\ ArcFace & 0.45 & 37.41 & 89.82 & 94.20 & 89.42 & 93.38 & 95.85 \\ CurricularFace & 0.45 & 36.38 & 90.06 & 94.37 & 89.58 & 93.83 & 95.85 \\ UnifiedFace(MS1M V3) & 0.4 & 42.51 & 88.52 & 94.03 & 88.50 & 93.12 & 95.62 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Evaluation on IJB-B and IJB-C. We use ResNet-100 as the backbone architecture and MS1 MV3 as the training dataset for all the compared methods. Results are in % and higher number indicates better performance.  
Figure 8: Comparison of the ROC curves of UnifiedFace and the popular loss function. (**a**) ROC for IJBB; (**b**) ROC for IJBC.  
The runtime results of GhostFaceNet and other methods for single-face image recognition in embedded devices are shown in Table 13. GhostFaceNet is 11.04 times, 8.57 times, 2.75 times, and 2.82 times faster than ResNet50, EfficientNet, MobileNetV2, and MobileFaceNet, respectively. In general, GhostFaceNet outperforms other lightweight models in embedded devices in terms of speed and is suitable for deployment in resource-limited embedded devices. For example, in practical scenarios where system resources are limited, such as time and attendance card punching, community access control, and surveillance devices, it becomes very difficult to deploy complex models, and our proposed GhostFaceNet can perform the relevant recognition tasks very easily.
## 4 Results and Discussions
### Phase-Resolved Partial Discharge  
The comparison of the average apparent charge and pulse count at different voltages in SF\({}_{6}\) and g3 in the POC is shown in Figure 6. Data in Figure 6 are average values extracted from the 10 PRPD patterns for each case at a gas pressure of 0.5 MPa. It was revealed that the average apparent charge and pulse count in two types of gases increased with the applied voltage. Both the average apparent charge and the pulse count in g3 were higher than those in SF\({}_{6}\), although PD occurred at a lower voltage level. This result can also be indicated from the PRPD patterns shown in Figure 7. Take the voltage of 17 kV as an example, where the average apparent charge and the pulse count in SF\({}_{6}\) were 7.15 pC and 323 N/s, respectively. However, in g3, the average apparent charge and the pulse count increased rapidly to 42.16 pC and 4378 N/s. Similar results were obtained in the POE, as shown in the figures in the Appendix A. Therefore, to achieve the same insulation ability with SF\({}_{6}\) for g3 from the perspective of PD, it is necessary to increase the gas pressure or increase the mixing ratio of NOVECTM 4710. From the PRPD patterns in Figures 7 and A2, the phase angle at which the discharge pulse occurred was 48\({}^{\circ}\)-140\({}^{\circ}\) in the POC and 230\({}^{\circ}\)-322\({}^{\circ}\) in the POE, regardless of gas type.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Electrode System** & **Gas** & **Rising Time [ns]** & **Falling Time [ns]** & **Pulse Width [ns]** \\ \hline \multirow{2}{*}{POC} & SF\({}_{6}\) & 7.81 & 6.46 & 10.24 \\  & g3 & 10.50 & 6.04 & 11.21 \\ \hline \multirow{2}{*}{POE} & SF\({}_{6}\) & 7.04 & 6.06 & 9.10 \\  & g3 & 11.90 & 6.66 & 14.12 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of pulse parameters.  
Figure 5: PD pulse and its FFT in SF\({}_{6}\) and g3 in the POE. (**a**) In time domain; (**b**) In frequency domain.
# Constraint Preserving Mixers for the Quantum Approximate Optimization Algorithm
## 5 Constrained Mixers
### \"One-Hot\" Aka \"XY\"-Mixer
#### 5.1.4 Trotterizations  
Not all Pauli-strings of the mixer in Equation (51) commute. This necessitates a suitable and efficient Trotterization. We will use Theorems 2 and 3 to identify valid Trotterized  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**n** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **15** \\ \hline \hline \(\mathbf{H_{M,B}}\) & & & & & & & & & & & \\ \(T_{A}\) & \(12\cdot 2\) & \(32\cdot 3\) & \(80\cdot 4\) & \(192\cdot 5\) & \(448\cdot 6\) & \(1024\cdot 7\) & \(2304\cdot 8\) & \(5120\cdot 9\) & \(245760\cdot 14\) \\ \(T_{A,C}\) & \(12\cdot 3\) & \(32\cdot 4\) & \(80\cdot 5\) & \(192\cdot 6\) & \(448\cdot 7\) & \(1024\cdot 8\) & \(2304\cdot 9\) & \(5120\cdot 10\) & \(245760\cdot 15\) \\ \(T_{A}\) & \(12\cdot 3\) & \(32\cdot 6\) & \(80\cdot 10\) & \(192\cdot 15\) & \(448\cdot 21\) & \(1024\cdot 28\) & \(2304\cdot 36\) & \(5120\cdot 45\) & \(245760\cdot 105\) \\ \(\mathbf{H_{M,B}}=\mathbb{L}_{H_{M,C_{i}}}\) & \(4\cdot 2\) & \(4\cdot 3\) & \(4\cdot 4\) & \(4\cdot 5\) & \(4\cdot 6\) & \(4\cdot 7\) & \(4\cdot 8\) & \(4\cdot 9\) & \(4\cdot 10\) \\ \(T_{A,C}\) & \(4\cdot 3\) & \(4\cdot 4\) & \(4\cdot 5\) & \(4\cdot 6\) & \(4\cdot 7\) & \(4\cdot 8\) & \(4\cdot 9\) & \(4\cdot 10\) & \(4\cdot 15\) \\ \(T_{A}\) & \(4\cdot 3\) & \(4\cdot 6\) & \(4\cdot 10\) & \(4\cdot 15\) & \(4\cdot 21\) & \(4\cdot 28\) & \(4\cdot 36\) & \(4\cdot 45\) & \(4\cdot 105\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the cost \(\#\text{CX}=\text{Cost}(H_{M})\) of mixers constrained to “one-hot” states. The Trotterized versions we define \(T_{1}=T_{\text{O(1)},c}\) and \(T_{2}=T_{E(1)}\). All Hamiltonians need to be Trotterized.  
mixers. As pointed out in [19], when \(n\)_is a power of two_, one can realize a Trotterization, which is exact in the feasible subspace \(B\). Termed _simultaneous complete-graph_ mixer, this involves all possible pairs \((i,j)\) corresponding to a certain Trotterization of mixer for \(T_{A}\). We will see that there are more efficient mixers that provide transitions between all pairs of feasible states.  
Another possibility is to Trotterize \(T_{\Delta,c}\) or \(T_{\Delta}\) according to odd and even entries as described in Section 3.2.4. This is what is termed a _parity-partitioned_ mixer in [19]. However, fewer and fewer feasible states can be reached as \(n\) increases, as we have seen in Figure 6. Repeated applications (\(r>0\) in Equation (6)) are necessary, and \(r\) increases with increasing \(n\). Figure 7 shows a comparison of different Trotterizations. As the cost of the mixer is dictated by the number of nonzero entries of the transition matrix, it is more efficient to _add_ mixers for off-diagonals according to \(\sum_{i\in I}(T_{O(i)}+T_{E(i)})\) for some suitable index set \(I\).