esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network
## 4 Experimental Results of Individual Model
### Testing the Classifier  
As we described earlier, the benefit of the HRC strategy is reducing the effect of the imbalanced dataset on prediction accuracy. Since the regression model already deals with the \"Normal\" and \"Anomaly\" data, the classification model only needs to classify the attacks within the \"Anomaly\" data without concern for the \"Normal\" data. Therefore, training the classifier only on the \"Anomaly\" data will significantly reduce the error caused by imbalanced data. This is illustrated in Figure 17.  
Figure 16: Incorrect traffic behavior predictions.  
Figure 15: Correct traffic behavior predictions.  
Figure 17 shows two confusion matrices that represent the classification results of the same model but were trained on two different cases: with (left) and without (right) the normal IP packages category. The confusion matrix on the left indicates a high proportion of misclassification of the four attack types as a result of imbalanced data caused by the overwhelmingly large number of normal IP package data. In this case, the prediction accuracy is only more than 0.93. The confusion matrix on the right shows the result of training the classifier to predict only the four types of attack (the detector handled normal traffic). The classifier predicted correctly even with the very little Botnet data available. The model, in this case, did not suffer any bias from the normal traffic category, and the biases between the four attack categories were too small to affect the model.
## 2 Materials and Methods  
### Emission Test Cycle  
To make the emission results representative of real driving conditions, a new transient test procedure, named NRTC, was developed for the emission measurement. The new test, introduced in cooperation with the US EPA (United States Environmental Protection Agency, Washington, DC, USA) and European Commission, needed to be used in parallel with the prior steady-state schedule, ISO 8178 C1, referred to as the Non-Road Steady Cycle (NRSC) [10]. The ISO 8178 is an international standard designed for non-road engine applications which include a collection of different steady-state engine dynamometer test cycles (designated as C1, C2, etc.) for different classes of engines and equipment. Each of these cycles represents a sequence of several steady-state modes with different weighting factors. The C1 homologation test points are shown in terms of speed, torque and weighting factor in Table 1[10].  
The NRTC test procedure is a dynamic test for CI engines used in non-road machinery. It is characterized by the transient conditions in which speed and torque change dynamically. The test is considered one of the most stringent transient cycles since it has higher speed-load variations compared to other legislative transient cycles. The NRTC test procedure lasts about 1238 s, and the EU directive also provides the dynamic sequence of the so-called normalized speed and normalized torque to be applied to the dynamometer during the test procedure (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{3}{c}{**Rated Speed**} & \multicolumn{3}{c}{**Intermediate Speed**} & **Idle** \\ \hline Torque & 100 & 75 & 50 & 10 & 100 & 75 & 50 & 0 \\ \hline Weight & 0.15 & 0.15 & 0.15 & 0.10 & 0.10 & 0.10 & 0.10 & 0.15 \\ \hline \end{tabular}
\end{table}
Table 1: Weighting Factors of C1 ISO 8178 Test Cycle.  
It is worth underlining that the NRSC shall be used for all stages, whereas the NRTC test is not mandatory for stage IIIA. The transient procedure is obligatory for stages IIIB/IV and the upcoming Stage V. Table 2 reports the emission limits for the past, current and upcoming standards [10].  
The simulations performed in this study were carried out in consideration of the NRTC obtained after the \"denormalization procedure\" of the engine speed and torque. This procedure can be found in [24]. Once denormalized, the dynamic sequence of the engine speed and torque appears, as shown in Figure 2.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 3 Proposed Scheme
### Handover Latency in Non-Location-Based RSS Extensions
#### 3.3.1 Handover Latency in Location-Based RSS Extensions  
Compare to the other schemes, the location-aware scheme provides much better handover performance due to the effective utilization of location information for handover prediction. In addition, the MN receives the services as long as it is in the pMAG domain. Furthermore, the forwarded buffered packets to nMAG from pMAG are also delivered to MN as soon as the MN attaches to nMAG. Overall, the location-aware scheme provides efficient handover latency and can be calculated using Equation (7).  
\[\text{t}_{\text{Location-aware}}=\text{t}_{\text{WRS}}+\text{t}_{\text{L2}}+ \text{t}_{\text{RS}}\,+\text{t}_{\text{w-data}} \tag{7}\]  
LH-PMIPv6 scheme is also based on location information and is not affected by RSS error, so no additional latency is added to the scheme. Handover starts when DeReg-PBU is received by LMA from the pMAG. The handover process ends when MN is attached to nMAG. Next, MAG and LMA exchange PBU and PBA messages. RA is communicated to and buffered packets are provided to nMAG by LMA for MN. Equation (8) describes the measuring of handover latency in LH-PMIPv6.  
\[\text{t}_{\text{LH-PMIPv6}}\,=\text{t}_{\text{WRS}}+\text{t}_{\text{L2}}+ \text{t}_{\text{RS}}+\text{t}_{\text{PBA}}+\text{t}_{\text{RA}} \tag{8}\]
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 4 Simulation Results for PVCS with V2G Service
### Case 1: Sunny Day
#### 4.1.1 Scenario a: Constant Power  
Figures 5 and 6 show the power and SOC of the EVs with V2G service at constant power with the \"Sim w/o opti\" and \"Sim w/ opti\" algorithms, respectively.  
Figure 5a shows that EV1 as well EV3 are discharged at peak hours with 7 kW each for 15 min; they are then charged after providing V2G service directly until their departure time with the same constant power. As a result, EV1 and EV3 failed to achieve their desired SOC values at departure. Specifically, EV1 only reached 71% instead of the desired 85%, and EV3 reached 68.33% instead of 80%, as shown in Figure 5b. The dashed points represent the SOCs of the EVs that should be reached with respect to user preferences. On the other hand, Figure 6a shows that all the EVs are charging according to user preferences, and the desired SOC at departure is reached even when EV users opt to participate in V2G service, as shown in Figure 6b. However, discharging the EVs is not possible because it would prevent the desired SOC from being achieved, and hence meeting the requirements of EV users is given priority over discharging energy via V2G into the grid.
# Barrel Shifter Physical Unclonable Function Based Encryption
## 8 Conclusions and Future Work  
In this work, we propose an encryption protocol based on invertible and commutative PUFs and propose a circuit implementation of the required invertible and commutative PUF (BS-PUF). Spectre Monte Carlo simulations indicate only less than 1 bit delay variation when the plaintext changes. This ensures the commutativity of the system. The primary focus of this paper is to develop a PUF-based encryption protocol; to define the requirements of such a protocol--an invertible and commutative PUF; and to show that such an invertible and commutative PUF design is feasible. Simulations that establish this PUF design provide good randomness, uniqueness and reproducibility performance. These encryption PUFs have the potential to root encryption in hardware, hence increasing robustness beyond current software-only solutions.  
Much needs to be addressed to establish the practicality of invertible and commutative PUFs in real silicon implementations. An evaluation of PUFs based on more relevant permutation families such as the Keccak sponge family [20] is needed. There are many possible future directions to incorporate asymmetric encryption with these PUFs. The proposed design uses raw PUF responses; it will therefore be noisier than traditional PUFs. An error coding scheme using helper data and some form of fuzzy extraction is required.  
**Author Contributions:** Conceptualization, Y.G. and A.T.; Methodology, Y.G., T.D. and A.T.; Software, Y.G.; Validation, Y.G.; Formal Analysis, Y.G., T.D. and A.T.; Investigation, Y.G. and A.T.; Resources, A.T.; Data Curation, T.D. and Y.G.; Writing--Original Draft Preparation, Y.G., T.D. and A.T.; Writing--Review and Editing, Y.G., T.D. and A.T.; Visualization, Y.G. and A.T.; Supervision, A.T.; Project Administration, A.T.; Funding Acquisition, A.T.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ML Method** & **Bit Length** & **Prediction Rate** & **PCPs** & **Training Time** \\ \hline \multirow{3}{*}{LR} & \multirow{3}{*}{64} & 43.2\% & 800 & 0.0315 s \\  & & 52.6\% & 8000 & 0.1658 s \\  & & 79.5\% & 80,000 & 1.0104 s \\ \hline \multirow{3}{*}{LR} & \multirow{3}{*}{256} & 32.4\% & 1000 & 0.0157 s \\  & & 41.0\% & 10,000 & 0.4620 s \\ \cline{1-1}  & & 62.8\% & 100,000 & 1.6245 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: LR on the 2 nd LSB with six- and eight-stage BS-PUFs.
## 7 Attack Strategies
### Eavesdropping and Man-in-the-Middle Attacks
#### 7.5.1 Characterizing Eavesdropping in a Federated Setting  
The unauthorized interception of communications in cyber-security, especially within a federated learning setting, is known as eavesdropping. In contrast to centralized systems, FL allocates learning among local devices and only sends model updates to a central server. This design presents various communication points, all susceptible to interception. Experienced attackers might be able to deduce sensitive information from intercepted communications, thereby compromising FL's privacy objectives. Furthermore, man-in-the-middle (MitM) attacks present a live risk, as attackers are able to intercept, change, or reroute communications, leading to major threats to the reliability and safety of the FL procedure [105].  
* Alter Model Updates: Introducing slight biases or modifications in the model updates being sent to the central server. Over time, these could skew the global model in malicious or unintended ways.
* Introduce Malicious Instructions: For federated systems that rely on dynamic model structures, attackers could potentially alter the model architectures or parameters, leading to compromised nodes.
* Data Deception: By modifying the communicated updates, attackers could deceive the central server about the nature of the data at the edge, leading to models that might be ineffectual or even counterproductive.  
In federated settings, eavesdropping and man-in-the-middle (MitM) attacks present a dual threat. On one hand, there is the ongoing risk of data inference through eavesdropping, which is passive yet persistent. On the other hand, MitM attacks pose an active and potentially catastrophic threat that could undermine the core principles of FL. Effectively safeguarding against these threats necessitates an in-depth understanding of their complexities, followed by the implementation of strong countermeasures [106].
## 2 Results
### Antimicrobial Activity Assessment
#### 2.2.3 Effects of the Compounds on Biofilm Formation  
To further evaluate the effects of the analyzed compounds against microbial biofilms, crystal violet microtiter assay was performed for compounds **1a-f**; **2a-f**; **3a**; **4a-f**; **5a,b**; **5d,e**; **5g,h**; **5j,k**; **5m,n**; **6g-k**; **6m**; and **6o,p**. The analysis showed that biofilm formation was not affected by compounds **1a-c**; **4b-f**; **5a,b**; **5d,e**; **5g,h**; **5j,k**; **5m,n**; **6g,h**; **6k**; **6m**; and **6o**. However, decreased absorbance of the stained biomass was recorded for the biofilms grown in the presence of compounds **1d-f**; **2a-f**; **3a**; **4a**; **6i,j**; and **6p**, with the minimal biofilm inhibitory assay (MBIC) ranging from 14 to 225 ug/mL (Table 2). Compounds **1e**, **2d-f**, **3a**, **4a**, and **6j** demonstrated an MBIC value of 14 ug/mL against the _P. aeruginosa_ ATCC  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Tested Compounds**} & \multicolumn{2}{c}{_S. epidermidis_} & \multicolumn{2}{c}{_B. subtilis_} & \multicolumn{2}{c}{_E. coli_} & \multicolumn{2}{c}{_P. aeruginosa_ ATCC} & \multicolumn{2}{c}{_C. albicans_} \\  & \multicolumn{2}{c}{**756**} & \multicolumn{2}{c}{**ATCC 6683**} & \multicolumn{2}{c}{**ATCC 25922**} & \multicolumn{2}{c}{**27853**} & \multicolumn{2}{c}{**128**} \\ \cline{2-11}  & **MIC** & **MBIC** & **MIC** & **MBIC** & **MIC** & **MBIC** & **MIC** & **MBIC** & **MIC** & **MBIC** \\ \hline
**1d** & \(>\)225 & \(>\)225 & \(>\)225 & \(>\)225 & 28.1 & 225 & \(>\)225 & \(>\)225 & 14 & 112.5 \\
**1e** & \(56.2\) & 56.2 & \(>\)225 & \(>\)225 & 28.1 & 56.2 & \(>\)225 & 14 & 14 & 112.5 \\
**  
27853 biofilm. Compound **1e** inhibited the biofilm-forming capacity of the Gram-positive _S. epidermidis_ 756 strain, the Gram-negative strains _E. coli_ ATCC 25922 and _P. aeruginosa_ ATCC 27853, and the fungal strain _C. albicans_ 128.  
Regarding the antimicrobial and antib biofilm activity of the standard antibiotic (ciprofloxacin) and antifungal (fluconazole) agents used as controls, the MIC values were much lower than those obtained for the tested compounds in all of the cases. This is somehow expected given the new compounds are not yet standardized in optimal formulations and their mechanisms of action could be different from those of the control drugs.
## 4 Discussion
### Discussion of Research Findings  
#### 4.1.1 Training and Validation Results  
Figure 4 illustrates a dashboard showing the validation values, during the training phase, in the corresponding subset of validation images (\(n=60\)). In the first validation step, \(\text{CNN}_{\text{Adam}}\) performs better than \(\text{CNN}_{\text{SCDM}}\) (\(\text{CNN}_{\text{Adam}}\): 85%, \(\text{CNN}_{\text{SCDM}}\): 78%), but in future validations, its performance is inferior to that of \(\text{CNN}_{\text{SCDM}}\), as it tended to overfit the training data. These results are in line with other published conclusions [26; 27], claiming that adaptive-based algorithms can boost the CNN computations, by using a vector of changing learning rates, one for each parameter, which is adapted as the training algorithm progresses. This is in contrast to stochastic gradient descent (SGD) optimizers, which use a constant learning rate during the training process [27]. These publications emphasize that even with a small number of mini-batches (64 image patches in the current study), [PERSON] finds no solutions whose performance matches state-of-the-art. It has been constantly shown to be related to non-generalized results and especially in this case to non-convergence. In conclusion, it is usually noted that in systems with large computational resources, the use of SGD-type optimization techniques remains the ideal solution.
# Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM
## 4 CAV Driving Risk Quantification Model
### Collision Severity  
To measure the severity of a collision, a collision severity index is introduced as a proxy for the impact of rear-end collisions. In specific situations, the potential impact of a rear-end collision is determined by the product of the following vehicle's speed and the speed difference between the following and leading vehicles.  
\[CS=\left\{\begin{array}{cc}\omega\cdot v_{q}\cdot\left(v_{q}-v_{h}\right)&,p_ {q}=1,\left|x_{h}-x_{q}\right|<l\\ -\infty&,p_{q}=0\end{array}\right\} \tag{16}\]  
where \(\omega\) is a proportional factor that includes vehicle-specific collision factors such as the vehicle's size, mass, and color; and 1 is the threshold distance, beyond which the lead and following vehicles are far enough apart that the collision severity no longer exists. The value of 1 should be sufficiently large to account for all possible accidents, and it may depend on factors such as the speed limit of the road and other conditions.  
Figure 5: Collision probability and possibility in different scenarios: (**a**) low speed and long distance; (**b**) low speed and short distance; (**c**) high speed and long distance; and (**d**) high speed and short distance.  
\(\omega=1\) is used to account for the impact of all vehicle-specific factors. \(CS_{l}\) is defined as the limiting value of \(CS\), which is used to normalize the \(CS\) to avoid large values in the calculation. \(CS_{l}\) corresponds to the scenario where the vehicle is traveling at the speed limit of the road, while the lead vehicle is stationary.  
\[CS_{l}=v_{l}^{2} \tag{17}\]  
where \(v_{l}\) is the speed limit on the road.  
The collision severity is modeled as a monotonically increasing function of \(CS\), since an increase in the following vehicle's speed and relative speed results in a greater severity, and vice versa. Based on [15], the risk of pedestrian fatalities increases monotonically with the vehicle collision speed. A positive exponential function is used to describe this monotonic growth and to calculate the collision severity:  
\[Q=\exp\left(\frac{CS}{CS_{L}}\right) \tag{18}\]  
where \(Q\) is the collision severity.  
The collision severity of all vehicles in each maneuver category observed during the time interval is aggregated to obtain a comprehensive collision severity index for the road segment:  
\[Q_{z}=\sum_{i}^{n}\sum_{t_{a}}^{t_{b}}(Q\cdot\theta) \tag{19}\]  
The resulting \(Q_{z}\) is divided by the sum of \(\theta\) to obtain the average collision severity (ACS) for each vehicle in the given traffic condition:  
\[ACS=\frac{Q_{z}}{\sum_{i}^{n}\sum_{t_{a}}^{t_{b}}(\theta)} \tag{20}\]  
Risk quantification can be calculated as the product of _ACP_ and _ACS_:  
\[R=ACP\cdot ACS \tag{21}\]
# Time-Optimal Trajectory Planning for Woodworking Manipulators Using an Improved PSO Algorithm
## 3 Experiments and Results  
### Time Optimal Trajectory Planning Process  
In this section, GoldS-PSO was used to optimize the running time of woodworking manipulator within the constraints of each joint's maximum velocity and acceleration. As described in Section 2.2, the interpolation time \(t_{i}\) of each segment is an important parameter in 3-5-3 piecewise polynomial interpolation trajectory planning. Therefore, while ensuring a smooth and controllable trajectory, minimizing the \(t_{i}\) of each segment is necessary to improve the efficiency of manipulation. The optimization problem for the interpolation time of the \(i\)th joint is as follows:  
\[f(t)=\min\ (t_{i1}+t_{i2}+t_{i3}), \tag{31}\]  
\[\max\ \Big{\{}\Big{|}v_{ij}\Big{|}\Big{\}}\leq V_{max},\]  
\[\max\ \Big{\{}\Big{|}a_{ij}\Big{|}\Big{\}}\leq A_{max}\]  
where \(t_{ij}\), \(v_{ij}\), and \(a_{ij}\) are the interpolation time, velocity, and acceleration of the \(j\)th segment of the \(i\)th joint, respectively. \(V_{max}\) and \(A_{max}\) represent the maximum velocity and acceleration of each joint. The simultaneous movement of each joint was optimized separately to ensure that they meet the velocity and acceleration limits. The maximum value of each optimized joint in each segment was considered the optimization result. Tallying the optimization results of each segment to obtain the final optimized running time. Then, we could propose a woodworking manipulator time-optimal trajectory planning method based on 3-5-3 piecewise polynomial interpolation and the GoldS-PSO algorithm. Figure 10 shows a flowchart of the process.
## 2 The Principle of Leak Location Method Based on the Generalized Cross-Correlation Function
### Wave Speed Estimation Based on Modal Analysis of Gas-Leakage-Induced Guided Waves  
Since the gas solid coupling between the in-pipe gas and the pipe wall can be ignored due to the acoustic impedance mismatch between them, the gas-filled pipes can be approximated as hollow cylinders [9]. The dispersive behaviors of the guided waves in a gas pipe can be analyzed by the guided wave theory of the hollow cylinders [29]. The guided waves in a pipe are grouped into three families: longitudinal mode, torsional mode, and flexural mode. These modes are denoted by \(L(0,m)\), \(T(0,m)\) and \(F(n,m)\), respectively, where \(n\) and \(m\) represent the circumferential and radial modal parameters, respectively. The theoretical analysis shows that the radial vibration signal collected by AE sensors is dominated by the dispersive fundamental flexural mode \(F(1,1)\)[30].  
If the material of the pipe wall is changed, the group speed dispersive curve needs to be calculated again based on some parameters before determining the group speed of the leak-induced signal, those parameters are external diameter, thickness, elastic modulus, Poisson's ratio and density of the pipe wall. The material and geometric parameters of the given pipe are shown in Table 1. According to the parameters of the given pipe, the group speed dispersive curves of the flexural modes shown in Figure 3 can be obtained using the guided wave theory of hollow cylinders in MATLAB R2018b. The speed of the AE signal can be online estimated by the peak frequency of the AE signal in combination with the known group speed dispersive curve of the fundamental flexural mode [9].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(a\) (mm) & \(b\) (mm) & \(\mu\) & \(\rho\) (kg/m\({}^{3}\)) & \(G\) (GPa) & \(E\) (GPa) \\ \hline
13.9 & 16.7 & 0.35 & 8000 & 76 & 205 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material and geometric parameters of the given pipe.
## 4 Sub-Array Demonstrator  
### Full-Wave Results  
The radiating element analyzed in the previous section is here used on a \(2\times 8\) array to realize a demonstrator. This analysis is particularly useful to understand if the unit-cell simulation results (that emulate an infinite-array environment) are consistent with a finite-array simulation setup. The simulation setup is similar to the one described in Section 3, but now the finite \(2\times 8\) array is simulated replacing the periodic boundary conditions with the radiation boundary conditions.  
In Figure 9, the \(F\)-parameters obtained from the \(2\times 8\) array simulation are shown. The \(F\)-parameters are used to calculate antenna coupling coefficient sources in the case of simultaneous excitation, when is not possible to apply the general S-parameter definition anymore, and they can therefore be considered as active scattering parameters. In particular,  
Figure 8: (**a–c**) Detailed antenna structure. (**a**) Front view and cross sections at different heights. (**b**) Back view from the ground plane and feeding structure. (**c**) Back view from SMA connectors.  
\(F_{\rm hh(vv)}\)-parameters refers to the reflection coefficient of a port V(H) when all the other ports H(V) are excited, while the \(F_{\rm hv(vh)}\)-parameters refers to the isolation between the ports H(V) when all the others V(H) are excited. In particular, in the \(f_{\rm min}\)-\(f_{\rm max}\) band the \(F_{\rm hh}\)- and \(F_{\rm vv}\)-parameters are well below \(-10\) dB, and have a similar behavior to the reflection coefficients obtained from the unit cell simulation for both H and V ports. The same holds for the \(F_{\rm hv}\)- and \(F_{\rm vh}\)-parameters, that are similar to the transmission coefficients values obtained from the unit-cell simulation. The good agreement between the