esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Tafel Polarization  
The corrosion of the Zn anode is a critical issue in Zn-air batteries. Specifically, the HER is due to the reaction caused by the contact of Zn with water which leads to water evaporation. This process decreases discharge performance. One solution to this problem is to add a gelling agent in the electrolyte for gelation. The polarization curve indicates the degree of HER and corrosion in the anode. The anodic Tafel line is higher and the HER is increased [41]. Figure 6 shows the polarization curves of Zn-air cells with nano CuO additives. The corrosion potential (E\({}_{\text{corr}}\)) and corrosion current density (I\({}_{\text{corr}}\)) analyzed through Tafel extrapolation are summarized in Table 3. L-CuO showed poor values for E\({}_{\text{corr}}\) (\(-1.3124\) V) and I\({}_{\text{corr}}\) (0.0585 A/cm\({}^{2}\)) among the additives. In contrast, S-CuO exhibited an E\({}_{\text{corr}}\) of \(-1.2795\) V and an I\({}_{\text{corr}}\) of 0.053 A/cm\({}^{2}\). These results verified the corrosion behavior of each nano CuO and were consistent with the results of the electrochemical discharge test correspond as well. S-CuO showed the best performance compared to the characterized material. In other words, our results indicate that a sphere-like morphology is more effective for a nano CuO additive. This advantage provided a positive effect for the suppression of the self-discharge in the electrolyte.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 7 Conclusions  
This study detected DDoS attacks using ML-based multiclass classification in a 5G mobile network environment. Subsequently, the effect of the feature selection method on the detection performance and time complexity was studied. For this purpose, an experimental 5G environment was constructed, hypotheses were made, and the 5G dataset was collected. In the experiment, the classification results using the features selected by the filter and wrapper methods were compared with those of a base model using all extracted features. In the base model, which used all 55 features, the stacking algorithm showed the highest accuracy at 97.264%. When feature selection was applied, the number of features decreased by at least 25.45%, and the multiclass classification performance exceeded 82%. In particular, DT and RF increased the accuracy of the SFS (backward) method by 9.1% and 6.34%. In contrast, KNN increased the accuracy by 17.668% in the SFS (forward) method. The accuracy of the stacking algorithm was 97.183%, which was 0.083% lower than the accuracy when all features were used. In other words, compared to the large reduction rate of the number of features, the performance was maintained and improved. In addition, an experiment was conducted to confirm the change in detection and training time according to the increase in the size of the dataset. As the size of the dataset increased, training and detection times increased dramatically when all features were used. However, when feature selection was used, the training and detection time required for algorithms was maintained in seconds. As a result, as the size of the dataset increased, the difference in time complexity between the model using feature selection and the model using all features increased exponentially. Experiments showed that feature studies using feature selection are significant for improving DDoS detection performance and reducing time complexity. As a result, we confirmed the possibility of real-time DDoS attack detection via a feature selection process. Therefore, research related to feature engineering is required to detect large-capacity DDoS attacks with low latency in 5G mobile network environments. Although this study established a 5G environment, only one gNB was built for the experiment. Therefore, in the near future, we will build two or more gNBs and establish an experimental environment by allocating gNBs by IP bandwidth. Subsequently, we plan to conduct feature-engineering-related experiments to detect DDoS attacks through multiple
## Appendix A Trajectory Planning  
### The Lateral and Longitudinal Model of the Vehicle  
During the trajectory planning for the controlled vehicle the well-known two-wheeled lateral vehicle model is used, which is introduced by [PERSON] (2010), and includes the following equations:  
\[mv_{x}(\dot{\psi}+\dot{\beta}) =C_{1}\alpha_{1}+C_{2}\alpha_{2}, \tag{11a}\] \[I_{z}\ddot{\psi} =C_{1}\alpha_{1}I_{1}-C_{2}\alpha_{2}I_{2},\] (11b) \[\dot{v}_{y} =v_{x}(\dot{\psi}+\dot{\beta}), \tag{11c}\]  
where \(I_{z}\) is the yaw inertia, \(m\) is the mass of the vehicle \(C_{1},C_{2}\) are the cornering stiffness of the front and rear tires. Furthermore, \(l_{1},l_{2}\) is the distances of the front and rear axes measured from the center of the gravity of the vehicle. The side-slip angles can be computed using equation: \(\alpha_{1}=\delta-\beta-\psi l_{1}/v_{x}\) and \(\alpha_{2}=-\beta+\psi l_{2}/v_{x}\). Using Equation (11), the state space representation of the system can be written in the following form:  
\[\dot{x} =Ax+Bu, \tag{12a}\] \[y =C^{T}x+Du, \tag{12b}\]  
where \(u\) is the steering angle (\(\delta\)). The proposed dynamic model is used during the trajectory planning. Moreover, during the longitudinal motion prediction of the surrounding vehicles, a constant acceleration is assumed. Using this assumption the following equation gives the position to the \((k+1)^{th}\) time step:  
\[s(k+1)=s(k)+v(k)t+\frac{1}{2}a(k)t^{2}, \tag{13}\]  
where \(a(k)\) gives the longitudinal acceleration and \(v(k)\) is the velocity in the \(k^{th}\) time step.
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 2 Distributed Marine Monitoring System
### aMussel  
The aMussel robots (Figure 2) act as underwater sensory nodes and are a key to the stated goals of distributed underwater monitoring and data collection. As can be seen in Table 1, most of the perception capabilities of the swarm are unique to the aMussel.  
For the purpose of long-term autonomy, the aMussel was developed with low energy consumption in mind. The main \"brain\" of the aMussel is a Cypress PSoC4 microcontroller, capable of deep hibernation with minimal energy consumption. The aMussel electronics were developed and connected  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Interactions**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Intraspecies**} & \multicolumn{6}{c}{**Interspecies**} \\ \cline{2-7}  & **Information** & **Fomation** & **Underwater** & **Localisation** & **Distributed** & **Sensor Node** & **Sensor Node** \\  & **Consensus** & **Control** & **Localisation** & **Sensing** & **Recharging** & **Relocation** \\ \hline Perception & \(\bigcirc\) & \(\times\) & \(\bigotimes\) & \(\bigcirc\) & \(\bigotimes\) & \(\bigotimes\) & \(\bigotimes\) \\ Information Exchange & \(\bigcirc\) & \(\times\) & \(\bigotimes\) & \(\bigotimes\) & \(\bigotimes\) & \(\bigotimes\) \\ Energy sharing & & & & & \(\bigotimes\) & \\ Navigation and movement & & & & & \(\times\) & \(\bigotimes\) \\ \hline \hline \end{tabular} aPad: \(\times\); aMussel: \(\bigcirc\); Both: \(\bigotimes\).  
\end{table}
Table 2: Swarm interactions.  
Figure 2: The aMussel marine robot and underwater sensor node.  
in a way that gives the microcontroller the ability to disable the power supply of individual modules and sensors, thus reducing the power consumption of the robot and significantly prolonging the duration of monitoring missions and experiments.  
The downside of the used microcontroller is its lack of computational power. To counteract this, the aMussel is also equipped with a Raspberry Pi unit (RPi), capable of taking and processing images, WiFi communication, storing large amounts of data, and complex calculations. Due to its high energy consumption, the RPi is only powered in rare occasions when its superior computation and storing abilities are needed.
## 2 Experimental Program
### Test Setup and Loading Procedure  
The test loading device is illustrated in Figure 3. The specimen was anchored on the ground beam with M30 bolts, and both ends of the ground beam were fixed in the laboratory by pressure beams. The horizontal load was applied by a 1000-kN MTS actuator, and the vertical load was applied by a 1000-kN hydraulic jack. A lateral brace was provided at the end of the MTS actuator, and the outside displacement of the specimen was limited by two groups of pulleys to ensure that the specimen and actuator moved in the horizontal direction. The displacement and strain gauges were arranged in the key parts to study the seismic behavior of the SFIRACSWs, and data were collected by a TDS-630 acquisition instrument. Concrete cracking, buckling of beams and columns, and specimen failure were constantly observed during the test.  
In the vertical direction, 250 kN loads were applied to the steel columns, which was calculated by an axial compression ratio of 0.3. The axial compression ratio is the ratio of the design value of the axial load to the product of the total section area and design value of the axial compressive strength of concrete. The horizontal load was applied by the joint control method of force and displacement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Interception Position** & **Thickness** & \begin{tabular}{c} **Yield** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Ultimate** \\ **Stress** \\ **(N/mm\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Young’s** \\ **Stress** \\ **(N/mm\({}^{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Elongation at** \\ **Fracture\%** \\ \end{tabular} \\ \hline Beam flange & 8 & 270.20 & 402.30 & \(2.09\times 10^{5}\) & 31.95 \\ Beam web & 5 & 302.60 & 413.10 & \(2.64\times 10^{5}\) & 35.15 \\ Column flange & 10 & 268.30 & 447.05 & \(2.34\times 10^{5}\) & 34.40 \\ Column web & 7 & 283.75 & 452.00 & \(2.52\times 10^{5}\) & 34.00 \\ Column stiffener & 8 & 281.55 & 403.95 & \(1.80\times 10^{5}\) & 32.85 \\ \(\Phi\)6 steel bar & - & 217.30 & 345.50 & \(2.50\times 10^{5}\) & 32.70 \\ \(\Phi\)8 steel bar & - & 348.34 & 482.37 & \(2.62\times 10^{5}\) & 37.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of steel.  
Figure 3: Test setup.  
Before the yield of the specimen, the load was controlled by force and cycled once with an increase of 20 kN each time. The load-displacement curves showed a noticeable turning point as a yield sign. After the yield of the specimen, the load was controlled by displacement and cycled thrice with an increase of 0.5 \(\delta_{y}\) each time, which was the estimated yield displacement. Loading stopped when the horizontal load was down to 85% of the peak load.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 2 Materials and Methods
### Dynamic Time Warping
#### 2.3.4 Classification Accuracy  
Various studies addressed the issues of evaluating the accuracy measures of maps, with descriptions, advantages, limitations, or extensions, offering suggestions for an efficient and  
Figure 7: Computing the alignment between two sequences of TA1 (**a**) and TA2 (**b**). The vertical and horizontal values represent the date of an image from SITS, starting from 1 to 365 for TA1 and from 1 to 380 for TA2. Indices \(i\) and \(j\) are used to parse the matrix by line and by column, respectively. In these two examples, a maximum time delay, \(w\), of 45 days is depicted, meaning that only the elements of the matrix who fall within this condition (orange) are computed. With black dots is represented the main diagonal of the DTW matrix (resembling Euclidean distance). After computing the matrix from upper left to lower right, the last element of the matrix, \(m[S,T]\), is returned, as a measure of DTW dissimilarity between the two compared sequences.  
objective use within studies [67; 68]. In this study, we use the error matrix and its related accuracy measures, namely the producer's (PA) and user's accuracy (UA) and overall accuracy (OA) [69]. Furthermore, we acknowledge the fact that using reference and validation samples from CropScape may introduce additional errors in the classifications. We compared our PAs, UAs, and OAs in conjunction with those available from CropScape for individual crops, at the state level. Although we heavily altered the samples obtained from CropScape to better match the reality of our study areas, thus increasing the accuracy of the samples, we combined the errors from our analysis with the errors from CropScape by computing the square root of the sum of the two squared errors, in order to see the maximum potential errors for PAs, UAs, and OAs.
## 5 Open Issues and Challenges
### Dynamic Environment  
The mitigation scheme must work in a dynamic and mobile environment. WBASNs have the ability to suppress interference by acting independently without negotiating or exchanging their information with other WBASNs. In the case of negotiation methods, exchanging information between WBASNs may cause a long delay or slow convergence, but WBASNs perform better if they are aware of the whole network system. In the case of nonnegotiation methods, a WBASN needs to optimize its performance, converge quickly, and learn about the network system by itself.
## 4 Simulation and Analysis of EKF-GAPF  
To validate the effectiveness of EKF-GAPF, this section conducts verification and comparison from three aspects: the optimization capability of AGA, particle diversity, and the accuracy of multipath parameter estimation. Before the experiment, a signal model needs to be constructed. Since only one dominant multipath is received in signal propagation studies, and considering multiple multipaths does not significantly improve multipath elimination performance [21], a detailed analysis is initially conducted for the single multipath signal. In the simulation, a satellite C/A code signal is used, with a sampling interval of \(T_{s}=T_{c}/10\). It is assumed that there are six correlators, with spacing between each branch and the correlators set to \(\pm 0.5T_{c}\), \(\pm 0.3T_{c}\), and \(\pm 0.1T_{c}\). The characteristic parameters of the direct signal and the multipath signal are set as follows: the amplitude of the direct signal is 1, the amplitude of the multipath signal is 0.7, the time delay of the direct signal is 0.1, and the time delay of the multipath signal is 0.3. Under these conditions, the real values of the estimated parameters can be expressed as \(x=[1;0.7;0.1;0.3]\). In Equation (10), the number of multipath signals is set to \(M=1\), leading to an updated measurement matrix \(H\). For all algorithms, the state transition matrix \(F\) is a fourth-order identity matrix, and the system noise \(Q\) is set to diag(0.0001, 0.0001, 0.0001, 0.0001). \(R\) is calculated from the square of the standard deviation of the normalized correlation between the received signal and the local signals. The number of iterations is 500, and the number of particles used in PF, EPF, GAPF, and EKF-GAPF is 40. Additionally, for GAPF and EKF-GAPF, the parameters CR1, CR2, and G are set to 0.9, 0.4, and 0.2, respectively.
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### System Planning of IECPS
#### 5.2.2 Cross-System Integration in Planning  
With the coordinated progress of electrification and digital transformation, the integration between energy systems and cross-domain systems, such as transportation and cyber, is continuously improving [77]. Traditional energy system planning is gradually shifting towards cross-system integration planning to address the increasing demand for defense and control in energy systems. Cross-system integration planning technology aims to establish a unified information-sharing platform, applying transportation data to energy dispatching while ensuring data security. This approach, which leverages cyber systems to coordinate and optimize energy and transportation systems, not only effectively improves energy utilization efficiency but also provides a novel technological methodology for IECPS.  
Specifically, the cross-system integration planning technology takes into account the significant aggregation and adjustment capabilities of EVs, which can act as a \"virtual power plant\" for regional energy regulation. When a deliberate attack occurs, by adjusting the charging prices or controlling the charging power of charging stations, the energy supply and demand in the region can be rapidly balanced, effectively suppressing the chain propagation of faults and preventing the further expansion of the attack's impact. Moreover, after the coordinated planning of transportation and IECPS, IECPS can utilize shared traffic flow data to adjust the repair teams' emergency routes, ensuring the fastest restoration of energy supply, and thereby significantly enhancing the defense capability of IECPS.
## 5 Results and Analysis
### The One-Block Approach
For the LS method, the time window should not be too long, since a long window may introduce unnecessary noise in the estimate. Figure 12 shows the CIR obtained by both LS and MP methods. The number of taps for the MP algorithm was preset to \(P=4\) and finds two main peaks at 12-15 ms, and another main peak at 48 ms. This observation supports the earlier findings there are two main groups of arrivals separated by approximately 35-40 ms. The MP algorithm estimates the same dominant arrivals, but the LS algorithm introduces noise-like values for the taps that should be zeros. Using both the MP and LS algorithms, performances of PPC-McDFE and PPC-DFE are compared. Figure 13 show the performance comparison at a symbol rate of 2 kilo-symbol/s. The performance of the three structures changes with time, as measured in period. Using the MP algorithm, the performance of both PPC-McDFE and PPC-DFE is improved in most periods. Even though the performance of PPC-DFE is improved by the MP algorithm, e.g., 2.1 dB in maximum (the 15 th period), it is still far less than the performance of PPC-McDFE, which overtakes that of PPC-DFE from 3.1 dB (the 11 th period) to 7.0 dB (the 3 rd period). In average, McDFE leads the performance. In this performance evaluation, it is important to consider the computational time. Based on the same personal computer, McDFE consumed about 20 times computational time than PPC-McDFE (MP) to achieve the approximate performance. Figure 12: CIR estimated by the LS method and the MP algorithm.
## 1 Introduction
Utilizing both datasets allowed us to achieve precise validation where radiosonde data were available, while ensuring broader spatial and temporal analysis with ERA5, offering a more comprehensive assessment of GNSS-derived ZTD and PWV. The investigation, utilizing widely distributed stations and data spanning one year, provides valuable insights into the service's performance and stability across diverse regions over an extended period.
# Effect of P+ Source Pattern in 4H-SiC Trench-Gate MOSFETs on Low Specific On-Resistance
## 1 Introduction  
Compared with silicon, the high critical electric field of silicon carbide (SiC) allows a low on-resistance to fabricate a thin epitaxial layer with a high doping concentration for a specified breakdown voltage. SiC MOSFETs with low on-resistance are useful in high-power and high-switching-speed applications such as DC-to-DC converters, motor drives, and electric vehicles. Researchers have attempted to minimize specific on-resistance (R\({}_{\text{on,sp}}\)). With regard to device structure, trench-gate MOSFETs (TMOSFETs) have great potential for low on-resistance due to the absence of a JFET region and a small cell-pitch compared with planar gate MOSFETs [1, 2, 3, 4]. Advanced process technologies including post-oxidation annealing in nitric oxide (NO) [5, 6, 7, 8], self-aligned ohmic contact with two-step rapid thermal annealing (RTA) [9, 10, 11], and ion implantation at high temperatures [12, 13] provide high-quality SiO\({}_{2}\)/4H-SiC interfaces with low contact resistance. A prevalent channel self-alignment method increases current density [14, 15, 16]. However, in existing photolithography areas, critical limitations of smaller cell pitches result from critical dimension control [17, 18]. The pattern mask for the P+ source diode uses square and stripe patterns. The square pattern formed by etching all edge directions must be enlarged more than the stripe pattern to prevent a distorted pattern [19]. As the specific on-resistance is proportional to the cell pitch, a P+ source with a square pattern produces a high on-resistance.  
In this study, a novel device architecture with a grid pattern is proposed to minimize the cell pitch. Unlike a conventional P+ source with stripe and square patterns, the proposed P+ source pattern architecture was fabricated beside the trench gate. Although the reduced channel area increases on-resistance, the proposed device provides a small cell pitch. Thus, the P+ source grid TMOSFETs provide lower on-resistance than both prevalent devices. In addition, this structure can increase the breakdown voltage about 11% more than the conventional structures due to a reduced 3-D effect at the P+/N+ junction and enhance the third-quadrant characteristics by increasing the P+ source areas.
## 7 Defenses against Privacy Attacks
### Secure Multi-Party Computation  
Secure Multi-party Computation (SMPC) allows multiple parties to collaboratively compute a function on their individual inputs while ensuring the confidentiality of personal  
Figure 16: Visual representation of knowledge distillation.  
information [154; 155]. This technique plays a critical role in maintaining data privacy across applications such as financial transactions, healthcare analytics, and decentralized decision-making processes [156]. By enabling computations without disclosing sensitive data, SMPC ensures that information remains protected throughout distributed networks. A graphical representation of the process is illustrated in Figure 17.  
Augmented Multi-party Computation (AMPC), introduced by [PERSON] et al. [157], enhances privacy in FL. AMPC aims to safeguard local training data and models from other participants and the central server, assuming the server is \"honest-but-curious\". Utilizing MPC, AMPC distributes local models into secret shares, with public shares for the server and private shares locally, thereby preventing gradient leakage and enhancing the overall security.  
Additionally, [PERSON] et al. [158] explored privacy in FL by combining secure MPC with differential privacy. Their protocol allows clients to securely compute the weighted averages of local model updates while keeping their data and model weights confidential from the server. To counter collusion risks among clients, the protocol integrates differential privacy mechanisms that mask the individual weights and maintain the privacy of the aggregated results through noise addition.  
[PERSON] et al. used a similar approach of combining differential privacy and MPC to protect participant privacy in FL [159]. Differential privacy adds noise to model weights to protect against information disclosure, while MPC encrypts weights using shared values between client pairs. This dual-layered defense ensures that even colluding clients receive only noisy approximations, strengthening the privacy defenses in FL scenarios. These methodologies collectively advance the privacy protections necessary for secure collaborative computations in distributed environments.