esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Studies
### Two-Degree-of-Freedom System
#### 3.1.1 Training Sample Number  
In this example, the PINN is used to predict the displacement response of the two-degree-of-freedom system. The convergence performance of the network is evaluated using different numbers of training samples. Input samples are randomly obtained within the interval \(t\in[0,1]\) using the Sobol sequence. The weight coefficients \(\lambda_{1}\) and \(\lambda_{2}\) of the loss function are both set to 10. The network is configured with six hidden layers, each consisting of 20 neurons. The size of the input layer is 1, and the size of the output layer is 2. The activation function tanh is used. The Adam optimization algorithm executes 50,000 iterations to minimize the loss function. This example includes 15 different numbers of training samples, with the batch size set as one-tenth of the total training sample size, and we analyze the effect of training samples on the convergence of the PINN. The results obtained from calculations using the Newmark-\(\beta\) method are used as reference solutions. Due to the numerous cases, this paper only presents the prediction results and convergence process of the loss function for 30, 100, and 500 training samples, as shown in Figure 4, where \"N\" represents the results obtained using the Newmark-\(\beta\) method and \"P\" represents the results obtained using the PINN.  
Figure 3: Two-degree-of-freedom system.  
Figure 5 shows the loss values with iterations. When the number of training samples is less than 200, the loss function decreases rapidly as the training samples increase. As the number of training samples exceeds 200, the convergence speed gradually slows. When the number of training samples reaches 500, the loss function reaches a converged state. After that, with an increase in the number of samples, the final loss value shows no significant change. Cross-validation is used to estimate the prediction error of the model. Figure 6 shows the error variation generated over time by 500 training samples. The error represents the difference between the acceleration curves calculated numerically and by PINN. It can be observed that the maximum error for \(m_{1}\) is only 0.0022, and for \(m_{2}\), it is only 0.0014 throughout the entire time record. These values are significantly smaller than the acceleration at that moment, indicating that the errors are acceptable.  
Figure 4: Partial analysis results.  
Figure 5: The convergence process of the loss function values.
## 2 Model Introduction
### BiLSTM Neural Network  
Although LSTM has achieved good results in processing long sequence information through gating mechanisms and has overcome some limitations of RNNs, it still can only process past information sequentially. In LSTM, only past information is considered, while future information is ignored. However, in practical prediction applications, it is necessary to consider information from the entire input sequence. [PERSON] et al. (2016) proposed BiLSTM by combining BRNN with LSTM units, thereby improving context-awareness. BiLSTM utilizes two independent LSTM structures that scan the entire sequence in both forward  
Figure 5: LSTM neural network.  
and backward directions. This architecture allows the model to simultaneously consider past and future information, accurately capturing feature information in the sequence. The schematic diagram of BiLSTM is shown in Figure 6.  
BiLSTM's hidden layer module is more intricate compared to traditional RNN and LSTM. It is computed as follows:  
\[h_{ft}=f(w_{f1}x_{t}+_{f2}h_{t-1}) \tag{9}\]  
\[h_{bt}=f(w_{b1}x_{t}+w_{b2}h_{t+1}) \tag{10}\]  
\[o_{t}=g(w_{o1}h_{ft}+w_{o2}h_{bt}) \tag{11}\]  
BiLSTM is divided into forward transmission and backward transmission hidden layers by which the contextual information can be obtained in a better way. In the BiLSTM model, two hidden values, forward and backward, are to be saved, and finally, forward hidden values are combined with backward hidden values to obtain the final output value. \(h_{ft}\) is the output of the forward network, \(h_{bt}\) is the output of the reverse network, and \(o_{t}\) is the output of the final hidden layer. Although BiLSTM improves the shortcomings of LSTM with poor information interaction, it has some defects in filtering noise information and fusing multi-dimensional data feature information [33,34].
## 2 Field Monitoring Project
### Tunnel Construction  
Typical drilling and blasting method was applied for the excavation of the double-arched tunnel. The cross section of the tunnel was excavated partially in several stages with minor advance being controlled at about 0.5 m in each cycle of drilling and blasting. Millisecond blasting technique was achieved by high-precision digital electronic detonator and the delay time between each interval was controlled with a range from 5 to 8 ms, making for better blasting effect and minimization of the blasting vibration strength. The diameter of each blast hole is designed to be 42 mm and the blast hole depth ranges from 0.7 to 1.9 m depending on its location. Rock emulsion explosive was adopted with density ranging from 950 to 1300 kg/m\({}^{3}\) and velocity of detonation (VOD) ranging from 3500 to 4500 m/s. To prevent overbreak in perimeter, smooth blasting was achieved by using the explosive cartridge with diameter of 25 mm in contour blast holes. While in other blast holes, the cartridge with diameter of 32 mm was adopted.  
Two different construction methods including the three heading excavation method (class IV and class V) and the central heading excavation method (class III) were applied respectively in the construction process (see Figures 2 and 3). Figure 2 demonstrates the construction sequences for constructing the double-arched tunnel in class IV and V rock masses by the three heading excavation method, also named as three-pilot drift heading method, in which the middle drift and twin-side drifts were excavated prior to excavating the main tunnels in order to reduce the disturbance of unstable surrounding rock. As shown in Figure 2, the numbers of 1-7 represents the construction steps and the red dotted-line box denotes the construction stages where field monitoring was carried out. Step 1: excavate the middle drift and construct the vertical middle wall; Steps 2, 3: excavate the twin-side drifts; Step 4: excavate upper bench and support the primary lining of the left main tunnel; Step 5: excavate core rock masses and construct the inverted arch of the left tunnel; Similarly, Step 6: excavate upper bench and support the primary lining of the right main tunnel; Step 7: excavate core rock masses and construct the inverted arch of the right tunnel; After all these steps above, both the secondary linings of the left and right tunnels are constructed. The advance footage of each construction cycle is 2.4 m. Figure 3 shows the construction procedures of the double-arched tunnel in class III rock masses by the middle heading pilot method.  
Figure 2: Schematic diagram of the three heading method in class IV and V rock masses.
## 2 Materials and Methods
### Fourier and Statistical Optics Theory
\tag{13}\]  
Now, assuming \(\tilde{S}^{\text{src}}\) is a very narrow function of \(\mathbf{s}\) (much narrower than \(T\)), Equation (13) can be approximated as  
\[ \begin{split}& W^{\text{out}}\left(\mathbf{\rho}_{1},\mathbf{\rho}_{2} \right)\approx\frac{1}{\left(\lambda^{2}f_{1}f_{2}\right)^{2}}\iint_{-\infty} ^{\infty}\left|T\left(\mathbf{t}\right)\right|^{2}\tilde{\mu}^{\text{src}}\left( \frac{\mathbf{t}}{\lambda f_{1}}\right)\exp\left[-\mathrm{j}\frac{k}{f_{2}}\left( \mathbf{\rho}_{1}-\mathbf{\rho}_{2}\right)\cdot\mathbf{t}\right]\mathrm{d}^{2}t\\ &\times\iint_{-\infty}^{\infty}\tilde{S}^{\text{src}}\left(\frac {\mathbf{s}}{\lambda f_{1}}\right)\exp\left[-\mathrm{j}\frac{k}{f_{2}}\left(\frac {\mathbf{\rho}_{1}+\mathbf{\rho}_{2}}{2}\right)\cdot\mathbf{s}\right]\mathrm{d}^{2}\mathbf{s} \end{split} . \tag{14}\]  
Note that this assumption is equivalent to assuming that the integrand of Equation (12), without the Fourier kernel, is another quasi-homogeneous source. Evaluating the above Fourier integrals produces  
\[ \begin{split}& W^{\text{out}}\left(\mathbf{\rho}_{1},\mathbf{\rho}_{2} \right)=\frac{1}{M^{2}}S^{\text{src}}\left(\frac{\mathbf{\rho}_{1}+\mathbf{\rho}_{2} }{2M}\right)\mu^{\text{fit}}\left(\mathbf{\rho}_{1}-\mathbf{\rho}_{2}\right)\\ &\mu^{\text{fit}}\left(\mathbf{\rho}_{1}-\mathbf{\rho}_{2}\right)=\iint_ {-\infty}^{\infty}\left|T\left(\lambda f_{1}\mathbf{u}\right)\right|^{2}\tilde{ \mu}^{\text{src}}\left(\mathbf{u}\right)\exp\left[\mathrm{j}2\pi\left(\frac{\mathbf{ \rho}_{1}-\mathbf{\rho}_{2}}{M}\right)\cdot\mathbf{u}\right]\mathrm{d}^{2}u\end{split} . \tag{15}\]  
The output SD and SDoC are  
\[ \begin{split}& S^{\text{out}}\left(\mathbf{\rho}\right)=W^{\text{ out}}\left(\mathbf{\rho},\mathbf{\rho}\right)=\frac{1}{M^{2}}S^{\text{src}}\left(\frac{\mathbf{ \rho}}{M}\right)\mu^{\text{fit}}\left(0\right)\\ &\mu^{\text{out}}\left(\mathbf{\rho}_{1},\mathbf{\rho}_{2}\right)=\frac{W ^{\text{out}}\left(\mathbf{\rho}_{1},\mathbf{\rho}_{2}\right)}{\sqrt{S^{\text{out}} \left(\mathbf{\rho}_{1}\right)S^{\text{out}}\left(\mathbf{\rho}_{2}\right)}}\approx \frac{\mu^{\text{fit}}\left(\mathbf{\rho}_{1}-\mathbf{\rho}_{2}\right)}{\mu^{\text{fit} }\left(0\right)}.\end{split}  \tag{16}\]  
Note that the output SD \(S^{\text{out}}\) (physically, the source shape) is a magnified version of \(S^{\text{src}}\), and the output SDoC \(\mu^{\text{out}}\) is a magnified and filtered version of \(\mu^{\text{src}}\). Although expressions for \(S^{\text{out}}\) and \(\mu^{\text{out}}\) have been derived, an equation for \(T\) in terms of \(\mu^{\text{out}}\) (i.e., what \(T\) yields a given \(\mu^{\text{out}}\)) is most useful.
## 6 Conclusions  
In conclusion, we proposed integrating Federated Learning (FL) with Gini Index-based feature selection in order to enhance DDoS detection in IoV. This dual approach effectively addresses key challenges, including privacy preservation, computational efficiency, and detection accuracy. By selecting the most important features with the Gini Index, we reduced the dataset size, minimizing computational demands while ensuring high detection performance. This is especially beneficial for resource-constrained IoV systems, which enhance the model's generalization capability. Additionally, security and privacy are ensured by FL, which allows collaborative training of the global model without sharing raw data. This minimizes data transmission to central servers, promoting scalable and privacy-preserving solutions for smart vehicle networks. Our method demonstrated high accuracy in DDoS attack detection with a reduced feature set, proving to be practical and effective for protecting IoV systems from cyber threats. This system minimizes time complexity and memory usage through Federated Learning and feature selection. By decentralizing training and reducing data dimensionality, it ensures high efficiency and scalability, making it ideal for real-time applications. The results show its potential for deployment in resource-constrained IoV environments, which offers a practical solution for DDoS detection.  
In the future, we will further strive to make the process of feature selection more refined and adaptive to dynamic network conditions and various attacking patterns. We intend to harness advanced algorithms for federated learning to improve model robustness and scalability. Furthermore, we are also interested in applying our approach to other IoV security challenges, like intrusion and anomaly detection, to fully protect connected vehicle security. We hope that by continuous innovation in these regards, we can help come up with an IoV system that is much safer and more resilient.  
Conceptualization, M.H.S.; Methodology, M.D., M.H.S. and S.R.; Software, M.D.; Validation, M.D.; Investigation, S.R.; Writing--original draft, M.D.; Writing--review & editing, M.H.S. and S.R.; Supervision, M.H.S. and S.R. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Open Access Funding by TU Wien.  
No new datasets were created. The data presented in this study are openly available at [[https://www.unb.ca/cic/datasets/ddos-2019.html](https://www.unb.ca/cic/datasets/ddos-2019.html)]([https://www.unb.ca/cic/datasets/ddos-2019.html](https://www.unb.ca/cic/datasets/ddos-2019.html)), DOI: 10.1109/CCST.2019.888 8419, accessed on 1 December 2024.  
The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Program.  
The authors declare no conflicts of interest.
## 2 Problem Description and Model Analysis
### A Configuration of PLL  
The peer-to-peer control strategy in island mode in the intelligent micro network is necessary as shown in Figure 1, which means that micro power supplies in MG have the same control state. First, each micro power supply performs local control according to the voltage and frequency of the access point. Plug and play are realized and automatized adjustment of voltage and frequency takes place, eliminating communication, improving the reliability of MG, and reducing costs.  
However, this control strategy has certain limitations that prevent it from eliminating the harmonic distortion and frequency change in voltage, current, and power characteristics of the distributed generator and power grid to some extent and ensuring the effectiveness and feasibility of model verification.  
An accurate estimation of power grid frequency was a key step, so moving the average filter (MAF) is designed to eliminate the frequency fluctuation in phase-locked loop in the synchronous reference frame as follow Figure 2. The MAF transfer function is:  
\[G_{MAF}(z)=\frac{1}{N}\bullet\frac{1-z^{-N}}{1-z^{-1}} \tag{1}\]  
where \(N=T_{w}/T_{s}\) is the number of samples within the window length of MAF, and \(T_{w}\) is the window length of MAF.  
As the uncertainty of three-phase voltage is mostly odd harmonics, and even harmonics are generated in SRF, half of the base period was selected as the window length. The number of samples is \(N=T/2T_{s}\), where \(t\) is the fundamental frequency period of grid voltage [15]. The state space equation of the integral control term is as follows:  
\[z_{0}(k+1)=A_{p}z_{0}(k)+B_{p}\epsilon(k) \tag{2}\]  
Figure 1: Microgrid peer-to-peer control.  
where \(A_{p}=I_{2\times 2}\), \(B_{p}=T_{s}\times I_{2\times 2}\), \(z_{0}(k+1)\) is the state vector of the integral control term, \(\varepsilon(k)=\begin{bmatrix}i_{2q}^{*}(k)\\ i_{2d}^{*}(k)\end{bmatrix}-C_{d}x_{s}(k)\) is the current error vector, \(i_{2}^{*}(k)\) is the reference vector.  
The improved nominal model was analyzed. First, a unit was analyzed that as shown in Figure 3, obtain the stable changes of frequency and voltage of the power grid unit.  
The parameters of connecting between the DG unit and the main grid are as follows: According to Table 1, two DG devices were used in parallel to obtain stable voltage and power as shown in Figures 4 and 5.
## Appendix A Joint Posterior Density for Nenomimo
The prior density for \(\mu_{j},\ j=1,\ 2,..,K\) is: \[g\big{(}\mu_{j}\big{|}\eta_{j},\varphi_{j}^{2}\big{)}=\frac{1}{\sqrt{2\pi} \varphi_{j}}\exp\left(\frac{1}{2}\left(\frac{\mu_{j}-\eta_{j}}{\varphi_{j}} \right)^{2}\right)\] where \(\eta_{j}\) and \(\varphi_{j}^{2}\) are the mean and variance from the data, respectively; \(0\leq\eta_{j}\leq 255\) and \(\varphi_{j}>0\). 2. The prior density for \(\sigma_{j},\ j=1,2,..,K\) is given by: \[g\big{(}\sigma_{j}\big{|}\alpha_{j},\beta_{j}\big{)}=\frac{\beta_{j}^{\alpha_ {j}}}{\Gamma\big{(}\alpha_{j}\big{)}}\big{(}\sigma_{j}^{-(\alpha_{j}+1)} \big{)}\exp\left(-\frac{\beta_{j}}{\alpha_{j}}\right).\]
3. The prior density for \(\gamma_{j},\ j=1,2,..,K\) is: \[g\big{(}\gamma_{j}\big{|}\tau_{j},\tau_{j},l_{j},u_{j}\big{)}=\frac{\Gamma \big{(}2\tau_{j}\big{)}}{\Gamma\big{(}\tau_{j}\big{)}^{2}}\frac{\big{(}\big{(} \gamma_{j}-l_{j}\big{)}\big{(}u_{j}-\gamma_{j}\big{)}\big{)}^{\tau_{j}-1}}{R^{ (2\tau_{j}-1)}},\] where \(R=u_{j}-l_{j}\), \(u_{j}>l_{j}\geq 0\), \(\tau_{j}\geq 1\), and \(\gamma_{j}>0\). 4. Prior density for \(p_{j},\ \ j=1,\ 2,..,K\). Assume that \(\mathbf{p}\sim\text{Dir}(p_{1},p_{2},\dots,p_{K},\delta_{1},\delta_{2},\dots, \delta_{K})\), with \(\delta_{j}=1,\forall j=1,\ 2,\dots,\ K\); then, we can get the prior density as: \[ \begin{array}{l}g(p_{1},p_{2},\dots,p_{K}[\delta_{1},\delta_{2},\dots, \delta_{K})\\ \end{array}  =\frac{1}{\text{B}(\delta)}\prod_{j=1}^{K}p_{j}^{\delta_{j}}=\frac{ \Gamma\big{(}\sum_{j=1}^{K}1\big{)}}{\prod_{j=1}^{K}\Gamma(1)}\prod_{j=1}^{K}p_ {j}^{0}\] \[=\frac{\Gamma(K)}{\prod_{j=1}^{K}\Gamma(1)}=\frac{(K-1)!}{\prod_{j= 1}^{K}(1)}=(K-1)!,\] where the normalized constant \(\text{B}(\delta)\) is a multinomial beta function that could be expressed as the following gamma function: \[\text{B}(\delta)=\frac{\prod_{j=1}^{K}\Gamma\big{(}\delta_{j}\big{)}}{\Gamma \Big{(}\sum_{j=1}^{K}\delta_{j}\big{)}},\ \delta=(\delta_{1},\delta_{2},\dots,\delta_{K}).\]
## 5 Performance Evaluation  
This section involves thorough performance analyses and evaluation of the proposed CDCS in simulation methodology. We first analyze and evaluate the compression efficiency of DCS by reconstruction error rate and PSNR. We then provide reconstructed images to compare performance differences visually. Finally, we test the performance of the proposed routing algorithm that works together with CDCS. The parameter settings mainly refer to Refs. [25; 41]. The default parameters are summarized in Table 1.  
We evaluate the performance of CDCS under varying traffic load and QoS requirements. For performance comparison, we choose two baselines, i.e., multi-channel MMSPEED (referred to as M3 SPEED) and CDCS without correlation-aware design (referred to as DCSR). M3 SPEED supports service differentiation and probabilistic QoS guarantees using probabilistic multipath forwarding. Considering that it fails to support CR, we extend its capabilities concerning spectrum sensing and channel selection to make it comparable.  
Three experiments are designed to study compression efficiency, energy efficiency, and QoS provisioning. All the data presented include the average of 100 random experiments to improve the accuracy of experimental results. The performance metrics to be examined are as follows:  
1. Reconstruction error rate: the ratio of image reconstruction error to the original image.
2. Peak-signal-to-noise ratio (PSNR) in the unit of decibel (dB) of reconstructed images, calculated by \[10 log_{10}\frac{255^{2}}{\frac{1-P_{D}(X)}{256\cdot 256}}.\]
3. Reconstructed images achieved by CDCS and DCSR.
4. Reliability: the proportion of packets received at the sink out of the total number of packets that satisfy different QoS requirements.
5. Energy consumption: the average energy utilization for a received frame at the sink.
6. Delay: the average end-to-end delay for delivering packets to the sink.
# mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm
## 4 Simulation Testbed
Smart City scenarios present different traffic conditions, which makes it necessary to define different traffic workloads for the present work. As it was carried out in our previous work, the node density (ND) is also considered for the present simulations, using 5, 10 and 15. These different ND factors result in the use of different area sizes such as \(145\times 145\) m or \(700\times 700\) m area. The traffic is not only defined by the ND, but the time interval (TI) between sending is also a critical factor in terms of traffic analysis. For this purpose, two statistically independent values have been considered: 1 and 30 s. Finally, it is necessary to mention that this work considers two different propagation models (PM) in order to develop a fair comparison. As it is shown in [26], the use of a deterministic and a probabilistic propagation model is recommended, so, for this work, the _Free Space Model_ (FS) is selected as a deterministic model and the _Log-Normal Shadowing_ (LNS) as a probabilistic model, resulting in a large comparison. The different scenarios simulated are shown in Table 2. Additionally, every scenario has been simulated 10 times, using different randomly generated scenarios. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier Frequency & 2.4 GHz \\ Carrier Sense Sensitivity & \(-85\) dBm \\ Transmit Power & 1.0 mW \\ \(T_{link}\) & 10.0 s \\ \(T_{alive}\) & 600.0 s \\ \(T_{down}\) & 5.0 s \\ \(T_{recomect}\) & 2.0 s \\ \(T_{ack}\) & 1.5 s \\ \(L_{nodes}\) & 50 \\ \(TH_{baselevel}\) & 45 \\ \(TH_{role}\) & 130 \\ \(rssi\)_PctLst_ & 0.3 \\ \(ratSnsngAckRd\) & 0.3 \\ \(channel\,AliveTime\) & 250.0 s \\ \(numChannels\) & 10 \\ Payload Size & 70 Bytes \\ \(MAX_{SimTime}\) & 3600 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main configuration parameters for the simulation.
## 9 Results and Discussion
### Improvement in Cold Start  
We analyzed our trace data to examine the execution characteristics of incoming requests. The results, shown in Figure 11, indicate that the hybrid model achieves the highest percentage of warm starts and the lowest cold start overhead, reflecting an aggressive reuse of available containers. Specifically, 97% of all requests are assigned to warm containers.  
Figure 10: Distribution of P99 tail latency components. (**Right**) P99 latency breakdown for short-lived function workloads. (**Left**) P99 latency breakdown for resource-intensive function workloads.  
Figure 9: Overall tail latency comparison across four methods. The top horizontal line represents the 99 th percentile latency. The box illustrates the interquartile range, showing the 25 th, 50 th, and 75 th percentile latencies. Outliers have been omitted.  
Although the cold start overhead increases for resource-intensive applications (reaching 8% in the MM case), the hybrid model still outperforms all baseline methods. In contrast, OpenWhisk exhibits the highest cold start overhead across all scenarios, with 75% of requests triggering cold starts. HotC delivers generally strong performance but maintains a lower warm start percentage in the MM and PN workloads. The cold-to-warm start ratio plays a crucial role in determining overall latency. In optimal scenarios, where all requests are handled by warm containers, latency consists only of execution time and queuing delay, both of which are minimal due to the lightweight nature of serverless functions. However, as the cold start ratio increases, a fixed initialization penalty is introduced. This overhead significantly inflates latency, and its cumulative effect becomes the dominant factor in average response time. Therefore, reducing the cold start ratio is essential for maintaining low latency, as confirmed by the results shown in Figures 8 and 11.  
Although the number of cold and warm starts does not necessarily reflect the overall system performance--as previously noted, LC achieves the lowest latency, and HotC performs slightly better in certain scenarios--a complete performance picture emerges when we also consider invocation execution and SLA violations. This analysis aligns with the core principle of the buffer-aware scheduler, which prioritizes reusing warm containers over minimizing latency, provided that SLA constraints are not violated.